From f9fd4a84d0a162347a57397a196e5b21ea624583 Mon Sep 17 00:00:00 2001
From: GitHub Actions <actions@github.com>
Date: Fri, 18 Feb 2022 11:59:51 +0000
Subject: [PATCH] Built site for torch: 0.7.0@7d284ef

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-      <h1>Contributing to torch</h1>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Contributing to torch</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/.github/CONTRIBUTING.md" class="external-link"><code>.github/CONTRIBUTING.md</code></a></small>
     </div>
 
 <div id="contributing-to-torch" class="section level1">
 
-<p>This outlines how to propose a change to torch. For more detailed info about contributing to this, and other tidyverse packages, please see the <a href="https://rstd.io/tidy-contrib"><strong>development contributing guide</strong></a>.</p>
-<div id="fixing-typos" class="section level2">
-<h2 class="hasAnchor">
-<a href="#fixing-typos" class="anchor"></a>Fixing typos</h2>
-<p>You can fix typos, spelling mistakes, or grammatical errors in the documentation directly using the GitHub web interface, as long as the changes are made in the <em>source</em> file. This generally means you’ll need to edit <a href="https://roxygen2.r-lib.org/articles/roxygen2.html">roxygen2 comments</a> in an <code>.R</code>, not a <code>.Rd</code> file. You can find the <code>.R</code> file that generates the <code>.Rd</code> by reading the comment in the first line.</p>
-<p>See also the [Documentation] section.</p>
+<p>This outlines how to propose a change to torch. For more detailed info about contributing to this, and other tidyverse packages, please see the <a href="https://rstd.io/tidy-contrib" class="external-link"><strong>development contributing guide</strong></a>.</p>
+<div class="section level2">
+<h2 id="fixing-typos">Fixing typos<a class="anchor" aria-label="anchor" href="#fixing-typos"></a></h2>
+<p>You can fix typos, spelling mistakes, or grammatical errors in the documentation directly using the GitHub web interface, as long as the changes are made in the <em>source</em> file. This generally means you’ll need to edit <a href="https://roxygen2.r-lib.org/articles/roxygen2.html" class="external-link">roxygen2 comments</a> in an <code>.R</code>, not a <code>.Rd</code> file. You can find the <code>.R</code> file that generates the <code>.Rd</code> by reading the comment in the first line.</p>
+<p>See also the <a href="#documentation">Documentation</a> section.</p>
 </div>
-<div id="filing-bugs" class="section level2">
-<h2 class="hasAnchor">
-<a href="#filing-bugs" class="anchor"></a>Filing bugs</h2>
-<p>If you find a bug in <code>torch</code> please open an issue <a href="https://github.com/mlverse/torch/issues">here</a>. Please, provide detailed information on how to reproduce the bug. It would be great to also provide a <a href="https://reprex.tidyverse.org/"><code>reprex</code></a>.</p>
+<div class="section level2">
+<h2 id="filing-bugs">Filing bugs<a class="anchor" aria-label="anchor" href="#filing-bugs"></a></h2>
+<p>If you find a bug in <code>torch</code> please open an issue <a href="https://github.com/mlverse/torch/issues" class="external-link">here</a>. Please, provide detailed information on how to reproduce the bug. It would be great to also provide a <a href="https://reprex.tidyverse.org/" class="external-link"><code>reprex</code></a>.</p>
 </div>
-<div id="feature-requests" class="section level2">
-<h2 class="hasAnchor">
-<a href="#feature-requests" class="anchor"></a>Feature requests</h2>
-<p>Feel free to open issues <a href="https://github.com/mlverse/torch/issues">here</a> and add the <code>feature-request</code> tag. Try searching if there’s already an open issue for your <code>feature-request</code>, in this case it’s better to comment or upvote it intead of opening a new one.</p>
+<div class="section level2">
+<h2 id="feature-requests">Feature requests<a class="anchor" aria-label="anchor" href="#feature-requests"></a></h2>
+<p>Feel free to open issues <a href="https://github.com/mlverse/torch/issues" class="external-link">here</a> and add the <code>feature-request</code> tag. Try searching if there’s already an open issue for your <code>feature-request</code>, in this case it’s better to comment or upvote it intead of opening a new one.</p>
 </div>
-<div id="examples" class="section level2">
-<h2 class="hasAnchor">
-<a href="#examples" class="anchor"></a>Examples</h2>
+<div class="section level2">
+<h2 id="examples">Examples<a class="anchor" aria-label="anchor" href="#examples"></a></h2>
 <p>We welcome contributed examples. feel free to open a PR with new examples. The examples should be placed in the <code>vignettes/examples</code> folder.</p>
 <p>The examples should be an .R file and a .Rmd file with the same name that just renders the code.</p>
-<p>See <a href="https://github.com/mlverse/torch/blob/master/vignettes/examples/mnist-mlp.R">mnist-mlp.R</a> and <a href="https://github.com/mlverse/torch/blob/master/vignettes/examples/mnist-mlp.Rmd">mnist-mlp.Rmd</a></p>
-<p>One must be able to run the example without manually downloading any dataset/file. You should also add an entry to the <a href="https://github.com/mlverse/torch/blob/master/_pkgdown.yml#L24-L25"><code>_pkgdown.yaml</code> file</a>.</p>
+<p>See <a href="https://github.com/mlverse/torch/blob/master/vignettes/examples/mnist-mlp.R" class="external-link">mnist-mlp.R</a> and <a href="https://github.com/mlverse/torch/blob/master/vignettes/examples/mnist-mlp.Rmd" class="external-link">mnist-mlp.Rmd</a></p>
+<p>One must be able to run the example without manually downloading any dataset/file. You should also add an entry to the <a href="https://github.com/mlverse/torch/blob/master/_pkgdown.yml#L24-L25" class="external-link"><code>_pkgdown.yaml</code> file</a>.</p>
 </div>
-<div id="code-contributions" class="section level2">
-<h2 class="hasAnchor">
-<a href="#code-contributions" class="anchor"></a>Code contributions</h2>
-<p>We have many open issues in the <a href="https://github.com/mlverse/torch/issues">github repo</a> if there’s one item that you want to work on, you can comment on it and ask for directions.</p>
-<div id="requirements" class="section level3">
-<h3 class="hasAnchor">
-<a href="#requirements" class="anchor"></a>Requirements</h3>
-<ul>
-<li>R installation</li>
+<div class="section level2">
+<h2 id="code-contributions">Code contributions<a class="anchor" aria-label="anchor" href="#code-contributions"></a></h2>
+<p>We have many open issues in the <a href="https://github.com/mlverse/torch/issues" class="external-link">github repo</a> if there’s one item that you want to work on, you can comment on it and ask for directions.</p>
+<div class="section level3">
+<h3 id="code-style">Code style<a class="anchor" aria-label="anchor" href="#code-style"></a></h3>
+<ul><li><p>New R code should follow the <a href="https://style.tidyverse.org" class="external-link"><code>tidyverse</code> style guide</a>. You can use the <a href="https://CRAN.R-project.org/package=styler" class="external-link">styler</a> package to apply these styles, or simply run <code>tools/style.sh</code> script, which also formats the code and removes whitespaces. Please don’t restyle code that has nothing to do with your PR.</p></li>
+<li><p>New C/C++ code should follow <a href="https://google.github.io/styleguide/cppguide.html" class="external-link"><code>Google</code> style guide</a>. You can use the <a href="https://clang.llvm.org/docs/ClangFormat.html" class="external-link"><code>clang-format</code></a> to apply these styles, or simply run <code>tools/style.sh</code> script, which also formats the code and removes whitespaces. Please don’t restyle code that has nothing to do with your PR.</p></li>
+<li><p>We use <a href="https://cran.r-project.org/package=roxygen2" class="external-link">roxygen2</a>, with <a href="https://cran.r-project.org/web/packages/roxygen2/vignettes/rd-formatting.html" class="external-link">Markdown syntax</a>, to build all documentation for the package.</p></li>
+<li><p>We use <a href="https://cran.r-project.org/package=testthat" class="external-link">testthat</a> for unit tests. Contributions with test cases included are easier to accept.</p></li>
+</ul></div>
+<div class="section level3">
+<h3 id="requirements">Requirements<a class="anchor" aria-label="anchor" href="#requirements"></a></h3>
+<ul><li>R installation</li>
 <li>R Tools for compilation (only on Windows)</li>
 <li>The <code>devtools</code> package</li>
 <li>CMake to compile lantern binaries</li>
-</ul>
-</div>
-<div id="workflow" class="section level3">
-<h3 class="hasAnchor">
-<a href="#workflow" class="anchor"></a>Workflow</h3>
+</ul></div>
+<div class="section level3">
+<h3 id="workflow">Workflow<a class="anchor" aria-label="anchor" href="#workflow"></a></h3>
 <p>We use <code>devtools</code> as the toolchain for development, but a few steps must be done before setiing up.</p>
 <p>The first time you clone the repository, you must run:</p>
 <div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/source.html">source</a></span><span class="op">(</span><span class="st">"tools/buildlantern.R"</span><span class="op">)</span></code></pre></div>
+<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/source.html" class="external-link">source</a></span><span class="op">(</span><span class="st">"tools/buildlantern.R"</span><span class="op">)</span></code></pre></div>
 <p>This will compile Lantern binaries and download LibTorch and copy the binaries to <code>deps</code> folder in the working directory.</p>
 <p>This command must be run everytime you modify lantern code. ie. code that lives in <code>lantern/src</code>.</p>
 <p>You can the run</p>
@@ -238,41 +132,29 @@
 <p>To run the test suite.</p>
 </div>
 </div>
-<div id="documentation" class="section level2">
-<h2 class="hasAnchor">
-<a href="#documentation" class="anchor"></a>Documentation</h2>
+<div class="section level2">
+<h2 id="documentation">Documentation<a class="anchor" aria-label="anchor" href="#documentation"></a></h2>
 <p>We use roxygen2 to generate the documentation. IN order to update the docs, edit the file in the <code>R</code> directory. To regenerate and preview the docs, use the custom <code>tools/document.R</code> script, as we need to patch roxygen2 to avoid running the examples on CRAN.</p>
 </div>
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-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
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+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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diff --git a/LICENSE-text.html b/LICENSE-text.html
index dfc578a82..b4fb5b505 100644
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-<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://torch.mlverse.org/docs">torch</a></span><span class="op">)</span>
+<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://torch.mlverse.org/docs">torch</a></span><span class="op">)</span>
 <span class="fu"><a href="../reference/torch_manual_seed.html">torch_manual_seed</a></span><span class="op">(</span><span class="fl">1</span><span class="op">)</span> <span class="co"># setting seed for reproducibility</span></code></pre></div>
-<p>This vignette showcases the basic functionality of distributions in torch. Currently the distributions modules are considered ‘work in progress’ and are still experimental features in the torch package. You can see the progress in this <a href="https://github.com/mlverse/torch/issues/479">link</a>.</p>
-<p>The distributions modules in torch are modelled after PyTorch’s <a href="https://pytorch.org/docs/stable/distributions.html#">distributions module</a> which in turn is based on the TensorFlow <a href="https://arxiv.org/abs/1711.10604">Distributions package</a>.</p>
-<p>This vignette is based in the TensorFlow’s distributions <a href="https://www.tensorflow.org/probability/examples/TensorFlow_Distributions_Tutorial#basic_univariate_distributions">tutorial</a>.</p>
-<div id="basic-univariate-distributions" class="section level2">
-<h2 class="hasAnchor">
-<a href="#basic-univariate-distributions" class="anchor"></a>Basic univariate distributions</h2>
+<p>This vignette showcases the basic functionality of distributions in torch. Currently the distributions modules are considered ‘work in progress’ and are still experimental features in the torch package. You can see the progress in this <a href="https://github.com/mlverse/torch/issues/479" class="external-link">link</a>.</p>
+<p>The distributions modules in torch are modelled after PyTorch’s <a href="https://pytorch.org/docs/stable/distributions.html#" class="external-link">distributions module</a> which in turn is based on the TensorFlow <a href="https://arxiv.org/abs/1711.10604" class="external-link">Distributions package</a>.</p>
+<p>This vignette is based in the TensorFlow’s distributions <a href="https://www.tensorflow.org/probability/examples/TensorFlow_Distributions_Tutorial#basic_univariate_distributions" class="external-link">tutorial</a>.</p>
+<div class="section level2">
+<h2 id="basic-univariate-distributions">Basic univariate distributions<a class="anchor" aria-label="anchor" href="#basic-univariate-distributions"></a>
+</h2>
 <p>Let’s start and create a new instance of a normal distribution:</p>
 <div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span class="va">n</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/distr_normal.html">distr_normal</a></span><span class="op">(</span>loc <span class="op">=</span> <span class="fl">0</span>, scale <span class="op">=</span> <span class="fl">1</span><span class="op">)</span>
@@ -162,15 +123,15 @@
 <div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span class="va">n</span><span class="op">$</span><span class="fu">sample</span><span class="op">(</span><span class="op">)</span>
 <span class="co">#&gt; torch_tensor</span>
-<span class="co">#&gt; -0.6378</span>
+<span class="co">#&gt;  0.3550</span>
 <span class="co">#&gt; [ CPUFloatType{1} ]</span></code></pre></div>
 <p>or, draw multiple samples:</p>
 <div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span class="va">n</span><span class="op">$</span><span class="fu">sample</span><span class="op">(</span><span class="fl">3</span><span class="op">)</span>
 <span class="co">#&gt; torch_tensor</span>
-<span class="co">#&gt; -1.4832</span>
-<span class="co">#&gt; -0.0547</span>
-<span class="co">#&gt; -0.4866</span>
+<span class="co">#&gt; -0.7894</span>
+<span class="co">#&gt;  0.3833</span>
+<span class="co">#&gt; -0.6563</span>
 <span class="co">#&gt; [ CPUFloatType{3,1} ]</span></code></pre></div>
 <p>We can evaluate the log probability of values:</p>
 <div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
@@ -178,23 +139,23 @@
 <span class="co">#&gt; torch_tensor</span>
 <span class="co">#&gt; -0.9189</span>
 <span class="co">#&gt; [ CPUFloatType{1} ]</span>
-<span class="fu"><a href="https://rdrr.io/r/base/Log.html">log</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/stats/Normal.html">dnorm</a></span><span class="op">(</span><span class="fl">0</span><span class="op">)</span><span class="op">)</span> <span class="co"># equivalent R code</span>
+<span class="fu"><a href="https://rdrr.io/r/base/Log.html" class="external-link">log</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/stats/Normal.html" class="external-link">dnorm</a></span><span class="op">(</span><span class="fl">0</span><span class="op">)</span><span class="op">)</span> <span class="co"># equivalent R code</span>
 <span class="co">#&gt; [1] -0.9189385</span></code></pre></div>
 <p>or, evaluate multiple log probabilities:</p>
 <div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="va">n</span><span class="op">$</span><span class="fu">log_prob</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">2</span>, <span class="fl">4</span><span class="op">)</span><span class="op">)</span>
+<code class="sourceCode R"><span class="va">n</span><span class="op">$</span><span class="fu">log_prob</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">2</span>, <span class="fl">4</span><span class="op">)</span><span class="op">)</span>
 <span class="co">#&gt; torch_tensor</span>
 <span class="co">#&gt; -0.9189</span>
 <span class="co">#&gt; -2.9189</span>
 <span class="co">#&gt; -8.9189</span>
 <span class="co">#&gt; [ CPUFloatType{3} ]</span></code></pre></div>
 </div>
-<div id="multiple-distributions" class="section level2">
-<h2 class="hasAnchor">
-<a href="#multiple-distributions" class="anchor"></a>Multiple distributions</h2>
+<div class="section level2">
+<h2 id="multiple-distributions">Multiple distributions<a class="anchor" aria-label="anchor" href="#multiple-distributions"></a>
+</h2>
 <p>A distribution can take a tensor as it’s parameters:</p>
 <div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="va">b</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/distr_bernoulli.html">distr_bernoulli</a></span><span class="op">(</span>probs <span class="op">=</span> <span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0.25</span>, <span class="fl">0.5</span>, <span class="fl">0.75</span><span class="op">)</span><span class="op">)</span><span class="op">)</span>
+<code class="sourceCode R"><span class="va">b</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/distr_bernoulli.html">distr_bernoulli</a></span><span class="op">(</span>probs <span class="op">=</span> <span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.25</span>, <span class="fl">0.5</span>, <span class="fl">0.75</span><span class="op">)</span><span class="op">)</span><span class="op">)</span>
 <span class="va">b</span>
 <span class="co">#&gt; torch_Bernoulli ()</span></code></pre></div>
 <p>This object represents 3 independent Bernoulli distributions, one for each element of the tensor.</p>
@@ -218,9 +179,9 @@
 <span class="co">#&gt;  0  0  1</span>
 <span class="co">#&gt; [ CPUFloatType{6,3} ]</span></code></pre></div>
 </div>
-<div id="using-distributions-within-models" class="section level2">
-<h2 class="hasAnchor">
-<a href="#using-distributions-within-models" class="anchor"></a>Using distributions within models</h2>
+<div class="section level2">
+<h2 id="using-distributions-within-models">Using distributions within models<a class="anchor" aria-label="anchor" href="#using-distributions-within-models"></a>
+</h2>
 <p>The <code>log_prob</code> method of distributions can be differentiated, thus, distributions can be used to train models in torch.</p>
 <p>Let’s implement a Gaussian linear model, but first let’s simulate some data</p>
 <div class="sourceCode" id="cb10"><pre class="downlit sourceCode r">
@@ -228,7 +189,7 @@
 <span class="va">y</span> <span class="op">&lt;-</span> <span class="fl">2</span><span class="op">*</span><span class="va">x</span> <span class="op">+</span> <span class="fl">1</span> <span class="op">+</span> <span class="fu"><a href="../reference/torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">100</span>, <span class="fl">1</span><span class="op">)</span></code></pre></div>
 <p>and plot:</p>
 <div class="sourceCode" id="cb11"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html">plot</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/numeric.html">as.numeric</a></span><span class="op">(</span><span class="va">x</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/numeric.html">as.numeric</a></span><span class="op">(</span><span class="va">y</span><span class="op">)</span><span class="op">)</span></code></pre></div>
+<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/numeric.html" class="external-link">as.numeric</a></span><span class="op">(</span><span class="va">x</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/numeric.html" class="external-link">as.numeric</a></span><span class="op">(</span><span class="va">y</span><span class="op">)</span><span class="op">)</span></code></pre></div>
 <p><img src="distributions_files/figure-html/unnamed-chunk-11-1.png" width="700"></p>
 <p>We can now define our model:</p>
 <div class="sourceCode" id="cb12"><pre class="downlit sourceCode r">
@@ -258,8 +219,8 @@
   <span class="va">loss</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/torch_mean.html">torch_mean</a></span><span class="op">(</span><span class="op">-</span><span class="va">d</span><span class="op">$</span><span class="fu">log_prob</span><span class="op">(</span><span class="va">y</span><span class="op">)</span><span class="op">)</span>
   <span class="va">loss</span><span class="op">$</span><span class="fu">backward</span><span class="op">(</span><span class="op">)</span>
   <span class="va">opt</span><span class="op">$</span><span class="fu">step</span><span class="op">(</span><span class="op">)</span>
-  <span class="kw">if</span> <span class="op">(</span><span class="va">i</span> <span class="op"><a href="https://rdrr.io/r/base/Arithmetic.html">%%</a></span> <span class="fl">10</span> <span class="op">==</span> <span class="fl">0</span><span class="op">)</span>
-    <span class="fu"><a href="https://rdrr.io/r/base/cat.html">cat</a></span><span class="op">(</span><span class="st">"iter: "</span>, <span class="va">i</span>, <span class="st">" loss: "</span>, <span class="va">loss</span><span class="op">$</span><span class="fu">item</span><span class="op">(</span><span class="op">)</span>, <span class="st">"\n"</span><span class="op">)</span>
+  <span class="kw">if</span> <span class="op">(</span><span class="va">i</span> <span class="op"><a href="https://rdrr.io/r/base/Arithmetic.html" class="external-link">%%</a></span> <span class="fl">10</span> <span class="op">==</span> <span class="fl">0</span><span class="op">)</span>
+    <span class="fu"><a href="https://rdrr.io/r/base/cat.html" class="external-link">cat</a></span><span class="op">(</span><span class="st">"iter: "</span>, <span class="va">i</span>, <span class="st">" loss: "</span>, <span class="va">loss</span><span class="op">$</span><span class="fu">item</span><span class="op">(</span><span class="op">)</span>, <span class="st">"\n"</span><span class="op">)</span>
 <span class="op">}</span>
 <span class="co">#&gt; iter:  10  loss:  1.809854 </span>
 <span class="co">#&gt; iter:  20  loss:  1.606322 </span>
@@ -288,9 +249,9 @@
 <span class="co">#&gt; torch_tensor</span>
 <span class="co">#&gt;  0.9716</span>
 <span class="co">#&gt; [ CPUFloatType{1} ][ requires_grad = TRUE ]</span></code></pre></div>
-<p>and quickly compare with the <code><a href="https://rdrr.io/r/stats/glm.html">glm()</a></code> function:</p>
+<p>and quickly compare with the <code><a href="https://rdrr.io/r/stats/glm.html" class="external-link">glm()</a></code> function:</p>
 <div class="sourceCode" id="cb15"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/base/summary.html">summary</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/stats/glm.html">glm</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/numeric.html">as.numeric</a></span><span class="op">(</span><span class="va">y</span><span class="op">)</span> <span class="op">~</span> <span class="fu"><a href="https://rdrr.io/r/base/numeric.html">as.numeric</a></span><span class="op">(</span><span class="va">x</span><span class="op">)</span><span class="op">)</span><span class="op">)</span>
+<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/stats/glm.html" class="external-link">glm</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/numeric.html" class="external-link">as.numeric</a></span><span class="op">(</span><span class="va">y</span><span class="op">)</span> <span class="op">~</span> <span class="fu"><a href="https://rdrr.io/r/base/numeric.html" class="external-link">as.numeric</a></span><span class="op">(</span><span class="va">x</span><span class="op">)</span><span class="op">)</span><span class="op">)</span>
 <span class="co">#&gt; </span>
 <span class="co">#&gt; Call:</span>
 <span class="co">#&gt; glm(formula = as.numeric(y) ~ as.numeric(x))</span>
@@ -314,31 +275,28 @@
 <span class="co">#&gt; </span>
 <span class="co">#&gt; Number of Fisher Scoring iterations: 2</span></code></pre></div>
 </div>
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diff --git a/articles/examples/basic-autograd.html b/articles/examples/basic-autograd.html
index 986766368..37f5b0382 100644
--- a/articles/examples/basic-autograd.html
+++ b/articles/examples/basic-autograd.html
@@ -4,14 +4,14 @@
 <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
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+<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
+<meta name="description" content="torch">
 <title>basic-autograd • torch</title>
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 <meta property="og:title" content="basic-autograd">
 <meta property="og:description" content="torch">
 <!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-      <h1 data-toc-skip>basic-autograd</h1>
+<script src="basic-autograd_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row">
+  <main id="main"><div class="page-header">
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-      <div class="hidden name"><code>basic-autograd.Rmd</code></div>
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+      <div class="d-none name"><code>basic-autograd.Rmd</code></div>
     </div>
 
     
     
 <div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://torch.mlverse.org/docs">torch</a></span><span class="op">)</span>
+<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://torch.mlverse.org/docs">torch</a></span><span class="op">)</span>
 
-<span class="co"># creates example tensors. x requires_grad = TRUE tells that </span>
+<span class="co"># creates example tensors. x requires_grad = TRUE tells that</span>
 <span class="co"># we are going to take derivatives over it.</span>
 <span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fl">3</span>, requires_grad <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span>
 <span class="va">y</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fl">2</span><span class="op">)</span>
@@ -161,32 +122,30 @@
 
 <span class="co"># get do/dx = 2 * x (at x = 3)</span>
 <span class="va">x</span><span class="op">$</span><span class="va">grad</span></code></pre></div>
-<pre><code>## torch_tensor
-##  6
-## [ CPUFloatType{1} ]</code></pre>
-  </div>
-
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-
-      </div>
-
+<pre><code><span class="co">## torch_tensor</span>
+<span class="co">##  6</span>
+<span class="co">## [ CPUFloatType{1} ]</span></code></pre>
+  </main>
 </div>
 
 
 
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diff --git a/articles/examples/basic-autograd_files/anchor-sections-1.0/anchor-sections.css b/articles/examples/basic-autograd_files/anchor-sections-1.0/anchor-sections.css
deleted file mode 100644
index 07aee5fcb..000000000
--- a/articles/examples/basic-autograd_files/anchor-sections-1.0/anchor-sections.css
+++ /dev/null
@@ -1,4 +0,0 @@
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diff --git a/articles/examples/basic-autograd_files/anchor-sections-1.0/anchor-sections.js b/articles/examples/basic-autograd_files/anchor-sections-1.0/anchor-sections.js
deleted file mode 100644
index 570f99a0a..000000000
--- a/articles/examples/basic-autograd_files/anchor-sections-1.0/anchor-sections.js
+++ /dev/null
@@ -1,33 +0,0 @@
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-
-  // Use section id when pandoc runs with --section-divs
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-    return ((x.classList.contains('section') || (x.tagName === 'SECTION'))
-            ? x.id : '');
-  };
-
-  // Add anchors
-  h.forEach(function(x) {
-    const id = x.id || section_id(x.parentElement);
-    if (id === '') {
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-    anchor.href = '#' + id;
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index 5e00ff06e..402563bb0 100644
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-  <div class="col-md-9 contents">
-    <div class="page-header toc-ignore">
-      <h1 data-toc-skip>basic-nn-module</h1>
+<script src="basic-nn-module_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row">
+  <main id="main"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>basic-nn-module</h1>
             
       
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/master/vignettes/examples/basic-nn-module.Rmd"><code>vignettes/examples/basic-nn-module.Rmd</code></a></small>
-      <div class="hidden name"><code>basic-nn-module.Rmd</code></div>
-
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/vignettes/examples/basic-nn-module.Rmd" class="external-link"><code>vignettes/examples/basic-nn-module.Rmd</code></a></small>
+      <div class="d-none name"><code>basic-nn-module.Rmd</code></div>
     </div>
 
     
     
 <div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://torch.mlverse.org/docs">torch</a></span><span class="op">)</span>
+<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://torch.mlverse.org/docs">torch</a></span><span class="op">)</span>
 
-<span class="co"># creates example tensors. x requires_grad = TRUE tells that </span>
+<span class="co"># creates example tensors. x requires_grad = TRUE tells that</span>
 <span class="co"># we are going to take derivatives over it.</span>
 <span class="va">dense</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/nn_module.html">nn_module</a></span><span class="op">(</span>
   clasname <span class="op">=</span> <span class="st">"dense"</span>,
   <span class="co"># the initialize function tuns whenever we instantiate the model</span>
   initialize <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="va">in_features</span>, <span class="va">out_features</span><span class="op">)</span> <span class="op">{</span>
-    
+
     <span class="co"># just for you to see when this function is called</span>
-    <span class="fu"><a href="https://rdrr.io/r/base/cat.html">cat</a></span><span class="op">(</span><span class="st">"Calling initialize!"</span><span class="op">)</span> 
-    
+    <span class="fu"><a href="https://rdrr.io/r/base/cat.html" class="external-link">cat</a></span><span class="op">(</span><span class="st">"Calling initialize!"</span><span class="op">)</span>
+
     <span class="co"># we use nn_parameter to indicate that those tensors are special</span>
     <span class="co"># and should be treated as parameters by `nn_module`.</span>
     <span class="va">self</span><span class="op">$</span><span class="va">w</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/nn_parameter.html">nn_parameter</a></span><span class="op">(</span><span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="va">in_features</span>, <span class="va">out_features</span><span class="op">)</span><span class="op">)</span>
     <span class="va">self</span><span class="op">$</span><span class="va">b</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/nn_parameter.html">nn_parameter</a></span><span class="op">(</span><span class="fu"><a href="../../reference/torch_zeros.html">torch_zeros</a></span><span class="op">(</span><span class="va">out_features</span><span class="op">)</span><span class="op">)</span>
-    
+
   <span class="op">}</span>,
   <span class="co"># this function is called whenever we call our model on input.</span>
   forward <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="op">{</span>
-    <span class="fu"><a href="https://rdrr.io/r/base/cat.html">cat</a></span><span class="op">(</span><span class="st">"Calling forward!"</span><span class="op">)</span>
+    <span class="fu"><a href="https://rdrr.io/r/base/cat.html" class="external-link">cat</a></span><span class="op">(</span><span class="st">"Calling forward!"</span><span class="op">)</span>
     <span class="fu"><a href="../../reference/torch_mm.html">torch_mm</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">self</span><span class="op">$</span><span class="va">w</span><span class="op">)</span> <span class="op">+</span> <span class="va">self</span><span class="op">$</span><span class="va">b</span>
   <span class="op">}</span>
 <span class="op">)</span>
 
 <span class="va">model</span> <span class="op">&lt;-</span> <span class="fu">dense</span><span class="op">(</span><span class="fl">3</span>, <span class="fl">1</span><span class="op">)</span></code></pre></div>
-<pre><code>## Calling initialize!</code></pre>
+<pre><code><span class="co">## Calling initialize!</span></code></pre>
 <div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="co"># you can get all parameters </span>
+<code class="sourceCode R"><span class="co"># you can get all parameters</span>
 <span class="va">model</span><span class="op">$</span><span class="va">parameters</span></code></pre></div>
-<pre><code>## $w
-## torch_tensor
-## -0.9114
-## -0.6847
-##  0.8039
-## [ CPUFloatType{3,1} ][ requires_grad = TRUE ]
-## 
-## $b
-## torch_tensor
-##  0
-## [ CPUFloatType{1} ][ requires_grad = TRUE ]</code></pre>
+<pre><code><span class="co">## $w</span>
+<span class="co">## torch_tensor</span>
+<span class="co">## -0.6168</span>
+<span class="co">## -1.6195</span>
+<span class="co">##  0.5953</span>
+<span class="co">## [ CPUFloatType{3,1} ][ requires_grad = TRUE ]</span>
+<span class="co">## </span>
+<span class="co">## $b</span>
+<span class="co">## torch_tensor</span>
+<span class="co">##  0</span>
+<span class="co">## [ CPUFloatType{1} ][ requires_grad = TRUE ]</span></code></pre>
 <div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span class="co"># or individually</span>
 <span class="va">model</span><span class="op">$</span><span class="va">w</span></code></pre></div>
-<pre><code>## torch_tensor
-## -0.9114
-## -0.6847
-##  0.8039
-## [ CPUFloatType{3,1} ][ requires_grad = TRUE ]</code></pre>
+<pre><code><span class="co">## torch_tensor</span>
+<span class="co">## -0.6168</span>
+<span class="co">## -1.6195</span>
+<span class="co">##  0.5953</span>
+<span class="co">## [ CPUFloatType{3,1} ][ requires_grad = TRUE ]</span></code></pre>
 <div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span class="va">model</span><span class="op">$</span><span class="va">b</span></code></pre></div>
-<pre><code>## torch_tensor
-##  0
-## [ CPUFloatType{1} ][ requires_grad = TRUE ]</code></pre>
+<pre><code><span class="co">## torch_tensor</span>
+<span class="co">##  0</span>
+<span class="co">## [ CPUFloatType{1} ][ requires_grad = TRUE ]</span></code></pre>
 <div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span class="co"># create an input tensor</span>
 <span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">10</span>, <span class="fl">3</span><span class="op">)</span>
 <span class="va">y_pred</span> <span class="op">&lt;-</span> <span class="fu">model</span><span class="op">(</span><span class="va">x</span><span class="op">)</span></code></pre></div>
-<pre><code>## Calling forward!</code></pre>
+<pre><code><span class="co">## Calling forward!</span></code></pre>
 <div class="sourceCode" id="cb11"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span class="va">y_pred</span></code></pre></div>
-<pre><code>## torch_tensor
-## -0.4937
-## -0.4409
-##  0.3153
-## -1.7081
-## -0.3058
-## -0.3550
-## -0.6447
-## -1.4729
-##  0.7792
-##  0.9236
-## [ CPUFloatType{10,1} ][ grad_fn = &lt;AddBackward0&gt; ]</code></pre>
-  </div>
-
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-
-      </div>
-
+<pre><code><span class="co">## torch_tensor</span>
+<span class="co">## -3.2110</span>
+<span class="co">## -1.7189</span>
+<span class="co">##  1.8856</span>
+<span class="co">##  0.1495</span>
+<span class="co">##  2.2306</span>
+<span class="co">##  1.3621</span>
+<span class="co">## -1.1145</span>
+<span class="co">##  0.3061</span>
+<span class="co">## -0.7151</span>
+<span class="co">## -0.9466</span>
+<span class="co">## [ CPUFloatType{10,1} ][ grad_fn = &lt;AddBackward0&gt; ]</span></code></pre>
+  </main>
 </div>
 
 
 
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deleted file mode 100644
index 07aee5fcb..000000000
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diff --git a/articles/examples/basic-nn-module_files/anchor-sections-1.0/anchor-sections.js b/articles/examples/basic-nn-module_files/anchor-sections-1.0/anchor-sections.js
deleted file mode 100644
index 570f99a0a..000000000
--- a/articles/examples/basic-nn-module_files/anchor-sections-1.0/anchor-sections.js
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diff --git a/articles/examples/dataset.html b/articles/examples/dataset.html
index 4d1dc1f16..85a6e85ff 100644
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+    <a class="dropdown-item" href="../../articles/tensor-creation.html">Creating tensors</a>
+    <a class="dropdown-item" href="../../articles/indexing.html">Indexing</a>
+    <a class="dropdown-item" href="../../articles/tensor/index.html">Tensor class</a>
+    <a class="dropdown-item" href="../../articles/serialization.html">Serialization</a>
+    <h6 class="dropdown-header" data-toc-skip>Datasets</h6>
+    <a class="dropdown-item" href="../../articles/loading-data.html">Loading Data</a>
+    <h6 class="dropdown-header" data-toc-skip>Autograd</h6>
+    <a class="dropdown-item" href="../../articles/using-autograd.html">Using autograd</a>
+    <a class="dropdown-item" href="../../articles/extending-autograd.html">Extending autograd</a>
+    <a class="dropdown-item" href="../../articles/python-to-r.html">Python models</a>
+  </div>
 </li>
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+<li class="active nav-item dropdown">
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+<li class="nav-item">
+  <a class="nav-link" href="../../news/index.html">Changelog</a>
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-<ul class="nav navbar-nav navbar-right">
-<li>
-  <a href="https://github.com/mlverse/torch/">
-    <span class="fab fa-github fa-lg"></span>
+<form class="form-inline my-2 my-lg-0" role="search">
+        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../../search.json" id="search-input" placeholder="Search for" autocomplete="off">
+</form>
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-      
 
-      </header><script src="dataset_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header toc-ignore">
-      <h1 data-toc-skip>dataset</h1>
+<script src="dataset_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row">
+  <main id="main"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>dataset</h1>
             
       
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/master/vignettes/examples/dataset.Rmd"><code>vignettes/examples/dataset.Rmd</code></a></small>
-      <div class="hidden name"><code>dataset.Rmd</code></div>
-
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/vignettes/examples/dataset.Rmd" class="external-link"><code>vignettes/examples/dataset.Rmd</code></a></small>
+      <div class="d-none name"><code>dataset.Rmd</code></div>
     </div>
 
     
     
 <div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://torch.mlverse.org/docs">torch</a></span><span class="op">)</span>
+<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://torch.mlverse.org/docs">torch</a></span><span class="op">)</span>
 
 <span class="co"># In deep learning models you don't usually have all your data in RAM</span>
 <span class="co"># because you are usually training using mini-batch gradient descent</span>
@@ -164,15 +125,15 @@
 
 <span class="va">df_dataset</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/dataset.html">dataset</a></span><span class="op">(</span>
   <span class="st">"mydataset"</span>,
-  
+
   <span class="co"># the input data to your dataset goes in the initialize function.</span>
   <span class="co"># our dataset will take a dataframe and the name of the response</span>
   <span class="co"># variable.</span>
   initialize <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="va">df</span>, <span class="va">response_variable</span><span class="op">)</span> <span class="op">{</span>
-    <span class="va">self</span><span class="op">$</span><span class="va">df</span> <span class="op">&lt;-</span> <span class="va">df</span><span class="op">[</span>,<span class="op">-</span><span class="fu"><a href="https://rdrr.io/r/base/which.html">which</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/names.html">names</a></span><span class="op">(</span><span class="va">df</span><span class="op">)</span> <span class="op">==</span> <span class="va">response_variable</span><span class="op">)</span><span class="op">]</span>
+    <span class="va">self</span><span class="op">$</span><span class="va">df</span> <span class="op">&lt;-</span> <span class="va">df</span><span class="op">[</span>,<span class="op">-</span><span class="fu"><a href="https://rdrr.io/r/base/which.html" class="external-link">which</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">df</span><span class="op">)</span> <span class="op">==</span> <span class="va">response_variable</span><span class="op">)</span><span class="op">]</span>
     <span class="va">self</span><span class="op">$</span><span class="va">response_variable</span> <span class="op">&lt;-</span> <span class="va">df</span><span class="op">[[</span><span class="va">response_variable</span><span class="op">]</span><span class="op">]</span>
   <span class="op">}</span>,
-  
+
   <span class="co"># the .getitem method takes an index as input and returns the</span>
   <span class="co"># corresponding item from the dataset.</span>
   <span class="co"># the index could be anything. the dataframe could have many</span>
@@ -181,20 +142,20 @@
   <span class="co"># in our case the index will be a row of the data.frame,</span>
   .getitem <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="va">index</span><span class="op">)</span> <span class="op">{</span>
     <span class="va">response</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="va">self</span><span class="op">$</span><span class="va">response_variable</span><span class="op">[</span><span class="va">index</span><span class="op">]</span><span class="op">)</span>
-    <span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/numeric.html">as.numeric</a></span><span class="op">(</span><span class="va">self</span><span class="op">$</span><span class="va">df</span><span class="op">[</span><span class="va">index</span>,<span class="op">]</span><span class="op">)</span><span class="op">)</span>
-    
+    <span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/numeric.html" class="external-link">as.numeric</a></span><span class="op">(</span><span class="va">self</span><span class="op">$</span><span class="va">df</span><span class="op">[</span><span class="va">index</span>,<span class="op">]</span><span class="op">)</span><span class="op">)</span>
+
     <span class="co"># note that the dataloaders will automatically stack tensors</span>
     <span class="co"># creating a new dimension</span>
-    <span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span><span class="op">(</span>x <span class="op">=</span> <span class="va">x</span>, y <span class="op">=</span> <span class="va">response</span><span class="op">)</span>
+    <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>x <span class="op">=</span> <span class="va">x</span>, y <span class="op">=</span> <span class="va">response</span><span class="op">)</span>
   <span class="op">}</span>,
-  
-  <span class="co"># It's optional, but helpful to define the .length method returning </span>
-  <span class="co"># the number of elements in the dataset. This is needed if you want </span>
+
+  <span class="co"># It's optional, but helpful to define the .length method returning</span>
+  <span class="co"># the number of elements in the dataset. This is needed if you want</span>
   <span class="co"># to shuffle your dataset.</span>
   .length <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="op">)</span> <span class="op">{</span>
-    <span class="fu"><a href="https://rdrr.io/r/base/length.html">length</a></span><span class="op">(</span><span class="va">self</span><span class="op">$</span><span class="va">response_variable</span><span class="op">)</span>
+    <span class="fu"><a href="https://rdrr.io/r/base/length.html" class="external-link">length</a></span><span class="op">(</span><span class="va">self</span><span class="op">$</span><span class="va">response_variable</span><span class="op">)</span>
   <span class="op">}</span>
-  
+
 <span class="op">)</span>
 
 
@@ -204,64 +165,62 @@
 
 <span class="co"># now we can get an item with</span>
 <span class="va">mtcars_dataset</span><span class="op">$</span><span class="fu">.getitem</span><span class="op">(</span><span class="fl">1</span><span class="op">)</span></code></pre></div>
-<pre><code>## $x
-## torch_tensor
-##    6.0000
-##  160.0000
-##  110.0000
-##    3.9000
-##    2.6200
-##   16.4600
-##    0.0000
-##    1.0000
-##    4.0000
-##    4.0000
-## [ CPUFloatType{10} ]
-## 
-## $y
-## torch_tensor
-##  21
-## [ CPUFloatType{1} ]</code></pre>
+<pre><code><span class="co">## $x</span>
+<span class="co">## torch_tensor</span>
+<span class="co">##    6.0000</span>
+<span class="co">##  160.0000</span>
+<span class="co">##  110.0000</span>
+<span class="co">##    3.9000</span>
+<span class="co">##    2.6200</span>
+<span class="co">##   16.4600</span>
+<span class="co">##    0.0000</span>
+<span class="co">##    1.0000</span>
+<span class="co">##    4.0000</span>
+<span class="co">##    4.0000</span>
+<span class="co">## [ CPUFloatType{10} ]</span>
+<span class="co">## </span>
+<span class="co">## $y</span>
+<span class="co">## torch_tensor</span>
+<span class="co">##  21</span>
+<span class="co">## [ CPUFloatType{1} ]</span></code></pre>
 <div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span class="co"># Given a dataset you can create a dataloader with</span>
 <span class="va">dl</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/dataloader.html">dataloader</a></span><span class="op">(</span><span class="va">mtcars_dataset</span>, batch_size <span class="op">=</span> <span class="fl">15</span>, shuffle <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span>
 
 <span class="co"># we can then loop trough the elements of the dataloader with</span>
-<span class="fu">coro</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/pkg/coro/man/collect.html">loop</a></span><span class="op">(</span><span class="kw">for</span><span class="op">(</span><span class="va">batch</span> <span class="kw">in</span> <span class="va">dl</span><span class="op">)</span> <span class="op">{</span>
-  <span class="fu"><a href="https://rdrr.io/r/base/cat.html">cat</a></span><span class="op">(</span><span class="st">"X size:  "</span><span class="op">)</span>
-  <span class="fu"><a href="https://rdrr.io/r/base/print.html">print</a></span><span class="op">(</span><span class="va">batch</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="fu">size</span><span class="op">(</span><span class="op">)</span><span class="op">)</span>
-  <span class="fu"><a href="https://rdrr.io/r/base/cat.html">cat</a></span><span class="op">(</span><span class="st">"Y size:  "</span><span class="op">)</span>
-  <span class="fu"><a href="https://rdrr.io/r/base/print.html">print</a></span><span class="op">(</span><span class="va">batch</span><span class="op">[[</span><span class="fl">2</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="fu">size</span><span class="op">(</span><span class="op">)</span><span class="op">)</span>
+<span class="fu">coro</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/pkg/coro/man/collect.html" class="external-link">loop</a></span><span class="op">(</span><span class="kw">for</span><span class="op">(</span><span class="va">batch</span> <span class="kw">in</span> <span class="va">dl</span><span class="op">)</span> <span class="op">{</span>
+  <span class="fu"><a href="https://rdrr.io/r/base/cat.html" class="external-link">cat</a></span><span class="op">(</span><span class="st">"X size:  "</span><span class="op">)</span>
+  <span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">batch</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="fu">size</span><span class="op">(</span><span class="op">)</span><span class="op">)</span>
+  <span class="fu"><a href="https://rdrr.io/r/base/cat.html" class="external-link">cat</a></span><span class="op">(</span><span class="st">"Y size:  "</span><span class="op">)</span>
+  <span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">batch</span><span class="op">[[</span><span class="fl">2</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="fu">size</span><span class="op">(</span><span class="op">)</span><span class="op">)</span>
 <span class="op">}</span><span class="op">)</span></code></pre></div>
-<pre><code>## X size:  [1] 15 10
-## Y size:  [1] 15  1
-## X size:  [1] 15 10
-## Y size:  [1] 15  1
-## X size:  [1]  2 10
-## Y size:  [1] 2 1</code></pre>
-  </div>
-
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-
-      </div>
-
+<pre><code><span class="co">## X size:  [1] 15 10</span>
+<span class="co">## Y size:  [1] 15  1</span>
+<span class="co">## X size:  [1] 15 10</span>
+<span class="co">## Y size:  [1] 15  1</span>
+<span class="co">## X size:  [1]  2 10</span>
+<span class="co">## Y size:  [1] 2 1</span></code></pre>
+  </main>
 </div>
 
 
 
-      <footer><div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p></p>
+<p>Developed by Daniel Falbel, Javier Luraschi.</p>
 </div>
 
-<div class="pkgdown">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p>
+<div class="pkgdown-footer-right">
+  <p></p>
+<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.2.</p>
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diff --git a/articles/examples/dataset_files/anchor-sections-1.0/anchor-sections.css b/articles/examples/dataset_files/anchor-sections-1.0/anchor-sections.css
deleted file mode 100644
index 07aee5fcb..000000000
--- a/articles/examples/dataset_files/anchor-sections-1.0/anchor-sections.css
+++ /dev/null
@@ -1,4 +0,0 @@
-/* Styles for section anchors */
-a.anchor-section {margin-left: 10px; visibility: hidden; color: inherit;}
-a.anchor-section::before {content: '#';}
-.hasAnchor:hover a.anchor-section {visibility: visible;}
diff --git a/articles/examples/dataset_files/anchor-sections-1.0/anchor-sections.js b/articles/examples/dataset_files/anchor-sections-1.0/anchor-sections.js
deleted file mode 100644
index 570f99a0a..000000000
--- a/articles/examples/dataset_files/anchor-sections-1.0/anchor-sections.js
+++ /dev/null
@@ -1,33 +0,0 @@
-// Anchor sections v1.0 written by Atsushi Yasumoto on Oct 3rd, 2020.
-document.addEventListener('DOMContentLoaded', function() {
-  // Do nothing if AnchorJS is used
-  if (typeof window.anchors === 'object' && anchors.hasOwnProperty('hasAnchorJSLink')) {
-    return;
-  }
-
-  const h = document.querySelectorAll('h1, h2, h3, h4, h5, h6');
-
-  // Do nothing if sections are already anchored
-  if (Array.from(h).some(x => x.classList.contains('hasAnchor'))) {
-    return null;
-  }
-
-  // Use section id when pandoc runs with --section-divs
-  const section_id = function(x) {
-    return ((x.classList.contains('section') || (x.tagName === 'SECTION'))
-            ? x.id : '');
-  };
-
-  // Add anchors
-  h.forEach(function(x) {
-    const id = x.id || section_id(x.parentElement);
-    if (id === '') {
-      return null;
-    }
-    let anchor = document.createElement('a');
-    anchor.href = '#' + id;
-    anchor.classList = ['anchor-section'];
-    x.classList.add('hasAnchor');
-    x.appendChild(anchor);
-  });
-});
diff --git a/articles/examples/index.html b/articles/examples/index.html
index 26ce1f2f4..6b8bb3e77 100644
--- a/articles/examples/index.html
+++ b/articles/examples/index.html
@@ -4,14 +4,14 @@
 <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
 <meta charset="utf-8">
 <meta http-equiv="X-UA-Compatible" content="IE=edge">
-<meta name="viewport" content="width=device-width, initial-scale=1.0">
+<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
+<meta name="description" content="torch">
 <title>Examples • torch</title>
-<!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link href="https://cdnjs.cloudflare.com/ajax/libs/bootswatch/3.4.0/united/bootstrap.min.css" rel="stylesheet" crossorigin="anonymous">
-<script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../../bootstrap-toc.css">
-<script src="../../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous">
+<script src="../../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
+<link href="../../deps/bootstrap-5.1.0/bootstrap.min.css" rel="stylesheet">
+<script src="../../deps/bootstrap-5.1.0/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous">
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-      <span class="fu"><a href="../../reference/nn_batch_norm2d.html">nn_batch_norm2d</a></span>(<span class="fl">256</span>),
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-      <span class="fu"><a href="../../reference/nn_conv_transpose2d.html">nn_conv_transpose2d</a></span>(<span class="fl">256</span>, <span class="fl">128</span>, <span class="kw">kernel_size</span> <span class="kw">=</span> <span class="fl">4</span>,
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-      <span class="fu"><a href="../../reference/nn_batch_norm2d.html">nn_batch_norm2d</a></span>(<span class="fl">128</span>),
-      <span class="fu"><a href="../../reference/nn_relu.html">nn_relu</a></span>(),
-      <span class="fu"><a href="../../reference/nn_conv_transpose2d.html">nn_conv_transpose2d</a></span>(<span class="fl">128</span>, <span class="no">out_channels</span>, <span class="kw">kernel_size</span> <span class="kw">=</span> <span class="fl">4</span>,
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-      <span class="fu"><a href="../../reference/nn_leaky_relu.html">nn_leaky_relu</a></span>(<span class="fl">0.2</span>, <span class="kw">inplace</span> <span class="kw">=</span> <span class="fl">TRUE</span>),
-      <span class="fu"><a href="../../reference/nn_conv2d.html">nn_conv2d</a></span>(<span class="fl">16</span>, <span class="fl">32</span>, <span class="kw">kernel_size</span> <span class="kw">=</span> <span class="fl">4</span>, <span class="kw">stride</span> <span class="kw">=</span> <span class="fl">2</span>, <span class="kw">padding</span> <span class="kw">=</span> <span class="fl">1</span>, <span class="kw">bias</span> <span class="kw">=</span> <span class="fl">FALSE</span>),
-      <span class="fu"><a href="../../reference/nn_batch_norm2d.html">nn_batch_norm2d</a></span>(<span class="fl">32</span>),
-      <span class="fu"><a href="../../reference/nn_leaky_relu.html">nn_leaky_relu</a></span>(<span class="fl">0.2</span>, <span class="kw">inplace</span> <span class="kw">=</span> <span class="fl">TRUE</span>),
-      <span class="fu"><a href="../../reference/nn_conv2d.html">nn_conv2d</a></span>(<span class="fl">32</span>, <span class="fl">64</span>, <span class="kw">kernel_size</span> <span class="kw">=</span> <span class="fl">4</span>, <span class="kw">stride</span> <span class="kw">=</span> <span class="fl">2</span>, <span class="kw">padding</span> <span class="kw">=</span> <span class="fl">1</span>, <span class="kw">bias</span> <span class="kw">=</span> <span class="fl">FALSE</span>),
-      <span class="fu"><a href="../../reference/nn_batch_norm2d.html">nn_batch_norm2d</a></span>(<span class="fl">64</span>),
-      <span class="fu"><a href="../../reference/nn_leaky_relu.html">nn_leaky_relu</a></span>(<span class="fl">0.2</span>, <span class="kw">inplace</span> <span class="kw">=</span> <span class="fl">TRUE</span>),
-      <span class="fu"><a href="../../reference/nn_conv2d.html">nn_conv2d</a></span>(<span class="fl">64</span>, <span class="fl">128</span>, <span class="kw">kernel_size</span> <span class="kw">=</span> <span class="fl">4</span>, <span class="kw">stride</span> <span class="kw">=</span> <span class="fl">2</span>, <span class="kw">padding</span> <span class="kw">=</span> <span class="fl">1</span>, <span class="kw">bias</span> <span class="kw">=</span> <span class="fl">FALSE</span>),
-      <span class="fu"><a href="../../reference/nn_leaky_relu.html">nn_leaky_relu</a></span>(<span class="fl">0.2</span>, <span class="kw">inplace</span> <span class="kw">=</span> <span class="fl">TRUE</span>)
-    )
-    <span class="no">self</span>$<span class="no">linear</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/nn_linear.html">nn_linear</a></span>(<span class="fl">128</span>, <span class="fl">1</span>)
-    <span class="no">self</span>$<span class="no">sigmoid</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/nn_sigmoid.html">nn_sigmoid</a></span>()
-  },
-  <span class="kw">forward</span> <span class="kw">=</span> <span class="kw">function</span>(<span class="no">input</span>) {
-    <span class="no">x</span> <span class="kw">&lt;-</span> <span class="no">self</span>$<span class="fu">main</span>(<span class="no">input</span>)
-    <span class="no">x</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/torch_flatten.html">torch_flatten</a></span>(<span class="no">x</span>, <span class="kw">start_dim</span> <span class="kw">=</span> <span class="fl">2</span>)
-    <span class="no">x</span> <span class="kw">&lt;-</span> <span class="no">self</span>$<span class="fu">linear</span>(<span class="no">x</span>)
-    <span class="no">self</span>$<span class="fu">sigmoid</span>(<span class="no">x</span>)
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-)
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-  <span class="no">img</span> <span class="kw">&lt;-</span> <span class="fu">G</span>(<span class="no">noise</span>)
-  <span class="no">img</span> <span class="kw">&lt;-</span> <span class="no">img</span>$<span class="fu">cpu</span>()
-  <span class="no">img</span> <span class="kw">&lt;-</span> <span class="no">img</span>[<span class="fl">1</span>,<span class="fl">1</span>,,,<span class="no">newaxis</span>]/<span class="fl">2</span> + <span class="fl">0.5</span>
-  <span class="no">img</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/torch_stack.html">torch_stack</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span>(<span class="no">img</span>, <span class="no">img</span>, <span class="no">img</span>), <span class="kw">dim</span> <span class="kw">=</span> <span class="fl">2</span>)[<span class="no">..</span>,<span class="fl">1</span>]
-  <span class="no">img</span> <span class="kw">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/grDevices/as.raster.html">as.raster</a></span>(<span class="fu"><a href="../../reference/as_array.html">as_array</a></span>(<span class="no">img</span>))
-  <span class="fu"><a href="https://rdrr.io/r/base/plot.html">plot</a></span>(<span class="no">img</span>)
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-<span class="no">device</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/torch_device.html">torch_device</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/ifelse.html">ifelse</a></span>(<span class="fu"><a href="../../reference/cuda_is_available.html">cuda_is_available</a></span>(),  <span class="st">"cuda"</span>, <span class="st">"cpu"</span>))
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-<span class="no">G</span> <span class="kw">&lt;-</span> <span class="fu">generator</span>(<span class="kw">latent_dim</span> <span class="kw">=</span> <span class="fl">100</span>, <span class="kw">out_channels</span> <span class="kw">=</span> <span class="fl">1</span>)
-<span class="no">D</span> <span class="kw">&lt;-</span> <span class="fu">discriminator</span>(<span class="kw">in_channels</span> <span class="kw">=</span> <span class="fl">1</span>)
-
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-  <span class="kw">if</span> (<span class="fu"><a href="https://rdrr.io/r/base/grep.html">grepl</a></span>(<span class="st">"conv"</span>, <span class="no">m</span>$<span class="no">.classes</span><span class="kw">[[</span><span class="fl">1</span>]])) {
-    <span class="fu"><a href="../../reference/nn_init_normal_.html">nn_init_normal_</a></span>(<span class="no">m</span>$<span class="no">weight</span>$<span class="fu"><a href="https://rdrr.io/r/utils/data.html">data</a></span>(), <span class="fl">0.0</span>, <span class="fl">0.02</span>)
-  } <span class="kw">else</span> <span class="kw">if</span> (<span class="fu"><a href="https://rdrr.io/r/base/grep.html">grepl</a></span>(<span class="st">"batch_norm"</span>, <span class="no">m</span>$<span class="no">.classes</span><span class="kw">[[</span><span class="fl">1</span>]])) {
-    <span class="fu"><a href="../../reference/nn_init_normal_.html">nn_init_normal_</a></span>(<span class="no">m</span>$<span class="no">weight</span>$<span class="fu"><a href="https://rdrr.io/r/utils/data.html">data</a></span>(), <span class="fl">1.0</span>, <span class="fl">0.02</span>)
-    <span class="fu"><a href="../../reference/nn_init_constant_.html">nn_init_constant_</a></span>(<span class="no">m</span>$<span class="no">bias</span>$<span class="fu"><a href="https://rdrr.io/r/utils/data.html">data</a></span>(), <span class="fl">0</span>)
-  }
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-<span class="no">G</span><span class="kw">[[</span><span class="fl">1</span>]]$<span class="fu"><a href="https://rdrr.io/r/base/apply.html">apply</a></span>(<span class="no">init_weights</span>)
-<span class="no">D</span><span class="kw">[[</span><span class="fl">1</span>]]$<span class="fu"><a href="https://rdrr.io/r/base/apply.html">apply</a></span>(<span class="no">init_weights</span>)
-
-<span class="no">G</span>$<span class="fu">to</span>(<span class="kw">device</span> <span class="kw">=</span> <span class="no">device</span>)
-<span class="no">D</span>$<span class="fu">to</span>(<span class="kw">device</span> <span class="kw">=</span> <span class="no">device</span>)
-
-<span class="no">G_optimizer</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/optim_adam.html">optim_adam</a></span>(<span class="no">G</span>$<span class="no">parameters</span>, <span class="kw">lr</span> <span class="kw">=</span> <span class="fl">2</span> * <span class="fl">1e-4</span>, <span class="kw">betas</span> <span class="kw">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">0.5</span>, <span class="fl">0.999</span>))
-<span class="no">D_optimizer</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/optim_adam.html">optim_adam</a></span>(<span class="no">D</span>$<span class="no">parameters</span>, <span class="kw">lr</span> <span class="kw">=</span> <span class="fl">2</span> * <span class="fl">1e-4</span>, <span class="kw">betas</span> <span class="kw">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">0.5</span>, <span class="fl">0.999</span>))
-
-<span class="no">fixed_noise</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="fl">1</span>, <span class="fl">100</span>, <span class="fl">1</span>, <span class="fl">1</span>, <span class="kw">device</span> <span class="kw">=</span> <span class="no">device</span>)
-
-<span class="no">loss</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/nn_bce_loss.html">nn_bce_loss</a></span>()
-
-<span class="kw">for</span> (<span class="no">epoch</span> <span class="kw">in</span> <span class="fl">1</span>:<span class="fl">10</span>) {
-
-  <span class="no">pb</span> <span class="kw">&lt;-</span> <span class="kw pkg">progress</span><span class="kw ns">::</span><span class="no">progress_bar</span>$<span class="fu">new</span>(
-    <span class="kw">total</span> <span class="kw">=</span> <span class="fu"><a href="https://rdrr.io/r/base/length.html">length</a></span>(<span class="no">dl</span>),
-    <span class="kw">format</span> <span class="kw">=</span> <span class="st">"[:bar] :eta Loss D: :lossd Loss G: :lossg"</span>
-  )
-  <span class="no">lossg</span> <span class="kw">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>()
-  <span class="no">lossd</span> <span class="kw">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>()
-
-  <span class="kw">for</span> (<span class="no">b</span> <span class="kw">in</span> <span class="fu"><a href="../../reference/enumerate.html">enumerate</a></span>(<span class="no">dl</span>)) {
-
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-    <span class="no">y_fake</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/torch_zeros.html">torch_zeros</a></span>(<span class="fl">32</span>, <span class="kw">device</span> <span class="kw">=</span> <span class="no">device</span>)
-
-    <span class="no">noise</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="fl">32</span>, <span class="fl">100</span>, <span class="fl">1</span>, <span class="fl">1</span>, <span class="kw">device</span> <span class="kw">=</span> <span class="no">device</span>)
-    <span class="no">fake</span> <span class="kw">&lt;-</span> <span class="fu">G</span>(<span class="no">noise</span>)
-
-    <span class="no">img</span> <span class="kw">&lt;-</span> <span class="no">b</span><span class="kw">[[</span><span class="fl">1</span>]]$<span class="fu">to</span>(<span class="kw">device</span> <span class="kw">=</span> <span class="no">device</span>)
-
-    <span class="co"># train the discriminator ---</span>
-    <span class="no">D_loss</span> <span class="kw">&lt;-</span> <span class="fu">loss</span>(<span class="fu"><a href="https://rdrr.io/r/stats/deriv.html">D</a></span>(<span class="no">img</span>), <span class="no">y_real</span>) + <span class="fu">loss</span>(<span class="fu"><a href="https://rdrr.io/r/stats/deriv.html">D</a></span>(<span class="no">fake</span>$<span class="fu"><a href="https://rdrr.io/r/base/detach.html">detach</a></span>()), <span class="no">y_fake</span>)
-
-    <span class="no">D_optimizer</span>$<span class="fu">zero_grad</span>()
-    <span class="no">D_loss</span>$<span class="fu">backward</span>()
-    <span class="no">D_optimizer</span>$<span class="fu"><a href="https://rdrr.io/r/stats/step.html">step</a></span>()
-
-    <span class="co"># train the generator ---</span>
-
-    <span class="no">G_loss</span> <span class="kw">&lt;-</span> <span class="fu">loss</span>(<span class="fu"><a href="https://rdrr.io/r/stats/deriv.html">D</a></span>(<span class="no">fake</span>), <span class="no">y_real</span>)
-
-    <span class="no">G_optimizer</span>$<span class="fu">zero_grad</span>()
-    <span class="no">G_loss</span>$<span class="fu">backward</span>()
-    <span class="no">G_optimizer</span>$<span class="fu"><a href="https://rdrr.io/r/stats/step.html">step</a></span>()
-
-    <span class="no">lossd</span> <span class="kw">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="no">lossd</span>, <span class="no">D_loss</span>$<span class="fu">item</span>())
-    <span class="no">lossg</span> <span class="kw">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="no">lossg</span>, <span class="no">G_loss</span>$<span class="fu">item</span>())
-    <span class="no">pb</span>$<span class="fu">tick</span>(<span class="kw">tokens</span> <span class="kw">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span>(<span class="kw">lossd</span> <span class="kw">=</span> <span class="fu"><a href="https://rdrr.io/r/base/mean.html">mean</a></span>(<span class="no">lossd</span>), <span class="kw">lossg</span> <span class="kw">=</span> <span class="fu"><a href="https://rdrr.io/r/base/mean.html">mean</a></span>(<span class="no">lossg</span>)))
-  }
-  <span class="fu">plot_gen</span>(<span class="no">fixed_noise</span>)
-
-  <span class="fu"><a href="https://rdrr.io/r/base/cat.html">cat</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/sprintf.html">sprintf</a></span>(<span class="st">"Epoch %d - Loss D: %3f Loss G: %3f\n"</span>, <span class="no">epoch</span>, <span class="fu"><a href="https://rdrr.io/r/base/mean.html">mean</a></span>(<span class="no">lossd</span>), <span class="fu"><a href="https://rdrr.io/r/base/mean.html">mean</a></span>(<span class="no">lossg</span>)))
-}</pre></body></html></div>
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-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
-</div>
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-<div class="pkgdown">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.5.1.</p>
-</div>
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diff --git a/articles/examples/mnist-dcgan_files/accessible-code-block-0.0.1/empty-anchor.js b/articles/examples/mnist-dcgan_files/accessible-code-block-0.0.1/empty-anchor.js
deleted file mode 100644
index ca349fd6a..000000000
--- a/articles/examples/mnist-dcgan_files/accessible-code-block-0.0.1/empty-anchor.js
+++ /dev/null
@@ -1,15 +0,0 @@
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-  for (var i = 0; i < codeList.length; i++) {
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diff --git a/articles/examples/mnist-mlp.html b/articles/examples/mnist-mlp.html
deleted file mode 100644
index df53285e9..000000000
--- a/articles/examples/mnist-mlp.html
+++ /dev/null
@@ -1,247 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en">
-<head>
-<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
-<meta charset="utf-8">
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-  <div class="col-md-9 contents">
-    <div class="page-header toc-ignore">
-      <h1 data-toc-skip>Extending Autograd</h1>
+<script src="extending-autograd_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row">
+  <main id="main"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Extending Autograd</h1>
             
       
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/master/vignettes/extending-autograd.Rmd"><code>vignettes/extending-autograd.Rmd</code></a></small>
-      <div class="hidden name"><code>extending-autograd.Rmd</code></div>
-
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/vignettes/extending-autograd.Rmd" class="external-link"><code>vignettes/extending-autograd.Rmd</code></a></small>
+      <div class="d-none name"><code>extending-autograd.Rmd</code></div>
     </div>
 
     
     
 <div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://torch.mlverse.org/docs">torch</a></span><span class="op">)</span></code></pre></div>
+<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://torch.mlverse.org/docs">torch</a></span><span class="op">)</span></code></pre></div>
 <p>Adding operations to autograd requires implementing a new <code>autograd_function</code> for each operation. Recall that <code>autograd_functions</code>s are what <code>autograd</code> uses to compute the results and gradients, and encode the operation history. Every new function requires you to implement 2 methods:</p>
 <ul>
 <li><p><code>forward()</code> - the code that performs the operation. It can take as many arguments as you want, with some of them being optional, if you specify the default values. All kinds of R objects are accepted here. Tensor arguments that track history (i.e., with <code>requires_grad=TRUE</code>) will be converted to ones that don’t track history before the call, and their use will be registered in the graph. Note that this logic won’t traverse lists or any other data structures and will only consider Tensor’s that are direct arguments to the call. You can return either a single Tensor output, or a list of <code>Tensor</code>s if there are multiple outputs. Also, please refer to the docs of <code>autograd_function</code> to find descriptions of useful methods that can be called only from <code>forward()</code>.</p></li>
 <li><p><code>backward()</code> - gradient formula. It will be given as many Tensor arguments as there were outputs, with each of them representing gradient w.r.t. that output. It should return as many <code>Tensor</code>s as there were <code>Tensor's</code> that required gradients in <code>forward</code>, with each of them containing the gradient w.r.t. its corresponding input.</p></li>
 </ul>
-<div id="note" class="section level2">
-<h2 class="hasAnchor">
-<a href="#note" class="anchor"></a>Note</h2>
+<div class="section level2">
+<h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a>
+</h2>
 <p>It’s the user’s responsibility to use the special functions in the forward’s <code>ctx</code> properly in order to ensure that the new <code>autograd_function</code> works properly with the autograd engine.</p>
 <ul>
 <li><p><code>save_for_backward()</code> must be used when saving input or ouput of the forward to be used later in the backward.</p></li>
@@ -161,16 +122,16 @@
 <li><p><code>mark_non_differentiable()</code> must be used to tell the engine if an output is not differentiable.</p></li>
 </ul>
 </div>
-<div id="examples" class="section level2">
-<h2 class="hasAnchor">
-<a href="#examples" class="anchor"></a>Examples</h2>
+<div class="section level2">
+<h2 id="examples">Examples<a class="anchor" aria-label="anchor" href="#examples"></a>
+</h2>
 <p>Below you can find code for a linear function:</p>
 <div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span class="va">linear</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/autograd_function.html">autograd_function</a></span><span class="op">(</span>
   forward <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="va">ctx</span>, <span class="va">input</span>, <span class="va">weight</span>, <span class="va">bias</span> <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span> <span class="op">{</span>
     <span class="va">ctx</span><span class="op">$</span><span class="fu">save_for_backward</span><span class="op">(</span>input <span class="op">=</span> <span class="va">input</span>, weight <span class="op">=</span> <span class="va">weight</span>, bias <span class="op">=</span> <span class="va">bias</span><span class="op">)</span>
     <span class="va">output</span> <span class="op">&lt;-</span> <span class="va">input</span><span class="op">$</span><span class="fu">mm</span><span class="op">(</span><span class="va">weight</span><span class="op">$</span><span class="fu">t</span><span class="op">(</span><span class="op">)</span><span class="op">)</span>
-    <span class="kw">if</span> <span class="op">(</span><span class="op">!</span><span class="fu"><a href="https://rdrr.io/r/base/NULL.html">is.null</a></span><span class="op">(</span><span class="va">bias</span><span class="op">)</span><span class="op">)</span>
+    <span class="kw">if</span> <span class="op">(</span><span class="op">!</span><span class="fu"><a href="https://rdrr.io/r/base/NULL.html" class="external-link">is.null</a></span><span class="op">(</span><span class="va">bias</span><span class="op">)</span><span class="op">)</span>
       <span class="va">output</span> <span class="op">&lt;-</span> <span class="va">output</span> <span class="op">+</span> <span class="va">bias</span><span class="op">$</span><span class="fu">unsqueeze</span><span class="op">(</span><span class="fl">0</span><span class="op">)</span><span class="op">$</span><span class="fu">expand_as</span><span class="op">(</span><span class="va">output</span><span class="op">)</span>
     
     <span class="va">output</span>
@@ -179,7 +140,7 @@
     
     <span class="va">s</span> <span class="op">&lt;-</span> <span class="va">ctx</span><span class="op">$</span><span class="va">saved_variables</span>
     
-    <span class="va">grads</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span><span class="op">(</span>
+    <span class="va">grads</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>
       input <span class="op">=</span> <span class="cn">NULL</span>,
       weight <span class="op">=</span> <span class="cn">NULL</span>,
       bias <span class="op">=</span> <span class="cn">NULL</span>
@@ -191,7 +152,7 @@
     <span class="kw">if</span> <span class="op">(</span><span class="va">ctx</span><span class="op">$</span><span class="va">needs_input_grad</span><span class="op">$</span><span class="va">weight</span><span class="op">)</span>
       <span class="va">grads</span><span class="op">$</span><span class="va">weight</span> <span class="op">&lt;-</span> <span class="va">grad_output</span><span class="op">$</span><span class="fu">t</span><span class="op">(</span><span class="op">)</span><span class="op">$</span><span class="fu">mm</span><span class="op">(</span><span class="va">s</span><span class="op">$</span><span class="va">input</span><span class="op">)</span>
     
-    <span class="kw">if</span> <span class="op">(</span><span class="op">!</span><span class="fu"><a href="https://rdrr.io/r/base/NULL.html">is.null</a></span><span class="op">(</span><span class="va">s</span><span class="op">$</span><span class="va">bias</span><span class="op">)</span> <span class="op">&amp;&amp;</span> <span class="va">ctx</span><span class="op">$</span><span class="va">needs_input_grad</span><span class="op">$</span><span class="va">bias</span><span class="op">)</span>
+    <span class="kw">if</span> <span class="op">(</span><span class="op">!</span><span class="fu"><a href="https://rdrr.io/r/base/NULL.html" class="external-link">is.null</a></span><span class="op">(</span><span class="va">s</span><span class="op">$</span><span class="va">bias</span><span class="op">)</span> <span class="op">&amp;&amp;</span> <span class="va">ctx</span><span class="op">$</span><span class="va">needs_input_grad</span><span class="op">$</span><span class="va">bias</span><span class="op">)</span>
       <span class="va">grads</span><span class="op">$</span><span class="va">bias</span> <span class="op">&lt;-</span> <span class="va">grad_output</span><span class="op">$</span><span class="fu">sum</span><span class="op">(</span>dim <span class="op">=</span> <span class="fl">0</span><span class="op">)</span>
     
     <span class="va">grads</span>
@@ -206,7 +167,7 @@
   <span class="op">}</span>,
   backward <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="va">ctx</span>, <span class="va">grad_output</span><span class="op">)</span> <span class="op">{</span>
     <span class="va">v</span> <span class="op">&lt;-</span> <span class="va">ctx</span><span class="op">$</span><span class="va">saved_variables</span>
-    <span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span><span class="op">(</span>
+    <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>
       tensor <span class="op">=</span> <span class="va">grad_output</span> <span class="op">*</span> <span class="va">v</span><span class="op">$</span><span class="va">constant</span>
     <span class="op">)</span>
   <span class="op">}</span>
@@ -220,31 +181,28 @@
 <span class="co">#&gt;  2</span>
 <span class="co">#&gt; [ CPUFloatType{1} ]</span></code></pre></div>
 </div>
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diff --git a/articles/extending-autograd_files/anchor-sections-1.0/anchor-sections.css b/articles/extending-autograd_files/anchor-sections-1.0/anchor-sections.css
deleted file mode 100644
index 07aee5fcb..000000000
--- a/articles/extending-autograd_files/anchor-sections-1.0/anchor-sections.css
+++ /dev/null
@@ -1,4 +0,0 @@
-/* Styles for section anchors */
-a.anchor-section {margin-left: 10px; visibility: hidden; color: inherit;}
-a.anchor-section::before {content: '#';}
-.hasAnchor:hover a.anchor-section {visibility: visible;}
diff --git a/articles/extending-autograd_files/anchor-sections-1.0/anchor-sections.js b/articles/extending-autograd_files/anchor-sections-1.0/anchor-sections.js
deleted file mode 100644
index 570f99a0a..000000000
--- a/articles/extending-autograd_files/anchor-sections-1.0/anchor-sections.js
+++ /dev/null
@@ -1,33 +0,0 @@
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-  // Do nothing if AnchorJS is used
-  if (typeof window.anchors === 'object' && anchors.hasOwnProperty('hasAnchorJSLink')) {
-    return;
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-
-  const h = document.querySelectorAll('h1, h2, h3, h4, h5, h6');
-
-  // Do nothing if sections are already anchored
-  if (Array.from(h).some(x => x.classList.contains('hasAnchor'))) {
-    return null;
-  }
-
-  // Use section id when pandoc runs with --section-divs
-  const section_id = function(x) {
-    return ((x.classList.contains('section') || (x.tagName === 'SECTION'))
-            ? x.id : '');
-  };
-
-  // Add anchors
-  h.forEach(function(x) {
-    const id = x.id || section_id(x.parentElement);
-    if (id === '') {
-      return null;
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-    let anchor = document.createElement('a');
-    anchor.href = '#' + id;
-    anchor.classList = ['anchor-section'];
-    x.classList.add('hasAnchor');
-    x.appendChild(anchor);
-  });
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-<script src="autograd_files/anchor-sections-1.0/anchor-sections.js"></script><div class="row">
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-      <h1 data-toc-skip>Autograd: automatic differentiation</h1>
-            
-      
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/master/vignettes/getting-started/autograd.Rmd"><code>vignettes/getting-started/autograd.Rmd</code></a></small>
-      <div class="hidden name"><code>autograd.Rmd</code></div>
-
-    </div>
-
-    
-    
-<blockquote>
-<p>Note: This is an R port of the official tutorial available <a href="https://pytorch.org/tutorials/beginner/blitz/tensor_tutorial.html#sphx-glr-beginner-blitz-tensor-tutorial-py">here</a>. All credits goes to <a href="http://soumith.ch/">Soumith Chintala</a>.</p>
-</blockquote>
-<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://torch.mlverse.org/docs">torch</a></span><span class="op">)</span></code></pre></div>
-<p>Central to all neural networks in torch is the autograd functionality. Let’s first briefly visit this, and we will then go to training our first neural network.</p>
-<p>Autograd provides automatic differentiation for all operations on Tensors. It is a define-by-run framework, which means that your backprop is defined by how your code is run, and that every single iteration can be different.</p>
-<p>Let us see this in more simple terms with some examples.</p>
-<div id="tensor" class="section level2">
-<h2 class="hasAnchor">
-<a href="#tensor" class="anchor"></a>Tensor</h2>
-<p><code>torch_tensor</code> is the central class of the package. If you set its attribute <code>$requires_grad</code> as <code>TRUE</code>, it starts to track all operations on it. When you finish your computation you can call <code>$backward()</code> and have all the gradients computed automatically. The gradient for this tensor will be accumulated into <code>$grad</code> attribute.</p>
-<p>To stop a tensor from tracking history, you can call <code>$detach()</code> to detach it from the computation history, and to prevent future computation from being tracked.</p>
-<p>To prevent tracking history (and using memory), you can also wrap the code block in <code>with_no_grad({&lt;code&gt;})</code>. This can be particularly helpful when evaluating a model because the model may have trainable parameters with <code>requires_grad=TRUE</code>, but for which we don’t need the gradients.</p>
-<p>There’s one more class which is very important for autograd implementation - a <code>autograd_function</code>.</p>
-<p>Tensor and Function are interconnected and build up an acyclic graph, that encodes a complete history of computation. Each tensor has a <code>$grad_fn</code> attribute that references an <code>autograd_function</code> that has created the Tensor (except for Tensors created by the user - their <code>grad_fn</code> is <code>NULL</code>).</p>
-<p>If you want to compute the derivatives, you can call <code>$backward()</code> on a Tensor. If Tensor is a scalar (i.e. it holds a one element data), you don’t need to specify any arguments to <code>backward()</code>, however if it has more elements, you need to specify a gradient argument that is a tensor of matching shape.</p>
-<p>Create a tensor and set <code>requires_grad=TRUE</code> to track computation with it:</p>
-<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_ones.html">torch_ones</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">2</span>, requires_grad <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span>
-<span class="va">x</span>
-<span class="co">#&gt; torch_tensor</span>
-<span class="co">#&gt;  1  1</span>
-<span class="co">#&gt;  1  1</span>
-<span class="co">#&gt; [ CPUFloatType{2,2} ]</span></code></pre></div>
-<p>Do a tensor operation:</p>
-<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="va">y</span> <span class="op">&lt;-</span> <span class="va">x</span> <span class="op">+</span> <span class="fl">2</span>
-<span class="va">y</span>
-<span class="co">#&gt; torch_tensor</span>
-<span class="co">#&gt;  3  3</span>
-<span class="co">#&gt;  3  3</span>
-<span class="co">#&gt; [ CPUFloatType{2,2} ]</span></code></pre></div>
-<p><code>y</code> was created as a result of an operation, so it has a <code>grad_fn</code>.</p>
-<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="va">y</span><span class="op">$</span><span class="va">grad_fn</span>
-<span class="co">#&gt; AddBackward1</span></code></pre></div>
-<p>Do more operations on <code>y</code></p>
-<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="va">z</span> <span class="op">&lt;-</span> <span class="va">y</span> <span class="op">*</span> <span class="va">y</span> <span class="op">*</span> <span class="fl">3</span>
-<span class="va">z</span>
-<span class="co">#&gt; torch_tensor</span>
-<span class="co">#&gt;  27  27</span>
-<span class="co">#&gt;  27  27</span>
-<span class="co">#&gt; [ CPUFloatType{2,2} ]</span>
-<span class="va">out</span> <span class="op">&lt;-</span> <span class="va">z</span><span class="op">$</span><span class="fu">mean</span><span class="op">(</span><span class="op">)</span>
-<span class="va">out</span>
-<span class="co">#&gt; torch_tensor</span>
-<span class="co">#&gt; 27</span>
-<span class="co">#&gt; [ CPUFloatType{} ]</span></code></pre></div>
-<p><code>$requires_grad_( ... )</code> changes an existing Tensor’s <code>requires_grad</code> flag in-place. The input flag defaults to <code>FALSE</code> if not given.</p>
-<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="va">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">2</span><span class="op">)</span>
-<span class="va">a</span> <span class="op">&lt;-</span> <span class="op">(</span><span class="va">a</span> <span class="op">*</span> <span class="fl">3</span><span class="op">)</span> <span class="op">/</span> <span class="op">(</span><span class="va">a</span> <span class="op">-</span> <span class="fl">1</span><span class="op">)</span>
-<span class="va">a</span><span class="op">$</span><span class="va">requires_grad</span>
-<span class="co">#&gt; [1] FALSE</span>
-<span class="va">a</span><span class="op">$</span><span class="fu">requires_grad_</span><span class="op">(</span><span class="cn">TRUE</span><span class="op">)</span>
-<span class="co">#&gt; torch_tensor</span>
-<span class="co">#&gt;  1.6068  1.0066</span>
-<span class="co">#&gt; -0.8837  1.9715</span>
-<span class="co">#&gt; [ CPUFloatType{2,2} ]</span>
-<span class="va">a</span><span class="op">$</span><span class="va">requires_grad</span>
-<span class="co">#&gt; [1] TRUE</span>
-<span class="va">b</span> <span class="op">&lt;-</span> <span class="op">(</span><span class="va">a</span> <span class="op">*</span> <span class="va">a</span><span class="op">)</span><span class="op">$</span><span class="fu">sum</span><span class="op">(</span><span class="op">)</span>
-<span class="va">b</span><span class="op">$</span><span class="va">grad_fn</span>
-<span class="co">#&gt; SumBackward0</span></code></pre></div>
-</div>
-<div id="gradients" class="section level2">
-<h2 class="hasAnchor">
-<a href="#gradients" class="anchor"></a>Gradients</h2>
-<p>Let’s backprop now. Because out contains a single scalar, <code>out$backward()</code> is equivalent to <code>out$backward(torch.tensor(1.))</code>.</p>
-<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="va">out</span><span class="op">$</span><span class="fu">backward</span><span class="op">(</span><span class="op">)</span></code></pre></div>
-<p>Print gradients d(out)/dx</p>
-<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="va">x</span><span class="op">$</span><span class="va">grad</span>
-<span class="co">#&gt; torch_tensor</span>
-<span class="co">#&gt;  4.5000  4.5000</span>
-<span class="co">#&gt;  4.5000  4.5000</span>
-<span class="co">#&gt; [ CPUFloatType{2,2} ]</span></code></pre></div>
-<p>You should have got a matrix of <code>4.5</code>. Let’s call the <code>out</code> <em>Tensor</em> <span class="math inline">\(o\)</span>.</p>
-<p>We have that <span class="math inline">\(o = \frac{1}{4}\sum_i z_i\)</span>, <span class="math inline">\(z_i = 3(x_i+2)^2\)</span> and <span class="math inline">\(z_i\bigr\rvert_{x_i=1} = 27\)</span>. Therefore, <span class="math inline">\(\frac{\partial o}{\partial x_i} = \frac{3}{2}(x_i+2)\)</span>, hence <span class="math inline">\(\frac{\partial o}{\partial x_i}\bigr\rvert_{x_i=1} = \frac{9}{2} = 4.5\)</span>.</p>
-<p>Mathematically, if you have a vector valued function <span class="math inline">\(\vec{y}=f(\vec{x})\)</span>, then the gradient of <span class="math inline">\(\vec{y}\)</span> with respect to <span class="math inline">\(\vec{x}\)</span> is a Jacobian matrix:</p>
-<p><span class="math display">\[
-  J=\left(\begin{array}{ccc}
-   \frac{\partial y_{1}}{\partial x_{1}} &amp; \cdots &amp; \frac{\partial y_{1}}{\partial x_{n}}\\
-   \vdots &amp; \ddots &amp; \vdots\\
-   \frac{\partial y_{m}}{\partial x_{1}} &amp; \cdots &amp; \frac{\partial y_{m}}{\partial x_{n}}
-   \end{array}\right)
-\]</span></p>
-<p>Generally speaking, <code>autograd</code> is an engine for computing vector-Jacobian product. That is, given any vector <span class="math inline">\(v=\left(\begin{array}{cccc} v_{1} &amp; v_{2} &amp; \cdots &amp; v_{m}\end{array}\right)^{T}\)</span>, compute the product <span class="math inline">\(v^{T}\cdot J\)</span>. If <span class="math inline">\(v\)</span> happens to be the gradient of a scalar function <span class="math inline">\(l=g\left(\vec{y}\right)\)</span>, that is, <span class="math inline">\(v=\left(\begin{array}{ccc}\frac{\partial l}{\partial y_{1}} &amp; \cdots &amp; \frac{\partial l}{\partial y_{m}}\end{array}\right)^{T}\)</span>, then by the chain rule, the vector-Jacobian product would be the gradient of <span class="math inline">\(l\)</span> with respect to <span class="math inline">\(\vec{x}\)</span>:</p>
-<p><span class="math display">\[
-  J^{T}\cdot v=\left(\begin{array}{ccc}
-   \frac{\partial y_{1}}{\partial x_{1}} &amp; \cdots &amp; \frac{\partial y_{m}}{\partial x_{1}}\\
-   \vdots &amp; \ddots &amp; \vdots\\
-   \frac{\partial y_{1}}{\partial x_{n}} &amp; \cdots &amp; \frac{\partial y_{m}}{\partial x_{n}}
-   \end{array}\right)\left(\begin{array}{c}
-   \frac{\partial l}{\partial y_{1}}\\
-   \vdots\\
-   \frac{\partial l}{\partial y_{m}}
-   \end{array}\right)=\left(\begin{array}{c}
-   \frac{\partial l}{\partial x_{1}}\\
-   \vdots\\
-   \frac{\partial l}{\partial x_{n}}
-   \end{array}\right)
-\]</span></p>
-<p>(Note that <span class="math inline">\(v^{T}\cdot J\)</span> gives a row vector which can be treated as a column vector by taking <span class="math inline">\(J^{T}\cdot v\)</span>.)</p>
-<p>This characteristic of vector-Jacobian product makes it very convenient to feed external gradients into a model that has non-scalar output.</p>
-<p>Now let’s take a look at an example of vector-Jacobian product:</p>
-<div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">3</span>, requires_grad<span class="op">=</span><span class="cn">TRUE</span><span class="op">)</span>
-<span class="va">y</span> <span class="op">&lt;-</span> <span class="fl">100</span> <span class="op">*</span> <span class="va">x</span>
-<span class="va">y</span>
-<span class="co">#&gt; torch_tensor</span>
-<span class="co">#&gt;   -5.4368</span>
-<span class="co">#&gt;  105.8399</span>
-<span class="co">#&gt;  118.4054</span>
-<span class="co">#&gt; [ CPUFloatType{3} ]</span></code></pre></div>
-<p>Now in this case y is no longer a scalar. <code>autograd</code> could not compute the full Jacobian directly, but if we just want the vector-Jacobian product, simply pass the vector to backward as argument:</p>
-<div class="sourceCode" id="cb10"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="va">v</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0.1</span>, <span class="fl">1.0</span>, <span class="fl">0.0001</span><span class="op">)</span><span class="op">)</span>
-<span class="va">y</span><span class="op">$</span><span class="fu">backward</span><span class="op">(</span><span class="va">v</span><span class="op">)</span>
-
-<span class="va">x</span><span class="op">$</span><span class="va">grad</span>
-<span class="co">#&gt; torch_tensor</span>
-<span class="co">#&gt;  1.0000e+01</span>
-<span class="co">#&gt;  1.0000e+02</span>
-<span class="co">#&gt;  1.0000e-02</span>
-<span class="co">#&gt; [ CPUFloatType{3} ]</span></code></pre></div>
-<p>You can also stop autograd from tracking history on Tensors with <code>$requires_grad=TRUE</code> either by wrapping the code block in with <code><a href="../../reference/with_no_grad.html">with_no_grad()</a></code>:</p>
-<div class="sourceCode" id="cb11"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="va">x</span><span class="op">$</span><span class="va">requires_grad</span>
-<span class="co">#&gt; [1] TRUE</span>
-<span class="op">(</span><span class="va">x</span> <span class="op">**</span> <span class="fl">2</span><span class="op">)</span><span class="op">$</span><span class="va">requires_grad</span>
-<span class="co">#&gt; [1] TRUE</span>
-
-<span class="fu"><a href="../../reference/with_no_grad.html">with_no_grad</a></span><span class="op">(</span><span class="op">{</span>
-  <span class="fu"><a href="https://rdrr.io/r/base/print.html">print</a></span><span class="op">(</span><span class="op">(</span><span class="va">x</span> <span class="op">**</span> <span class="fl">2</span><span class="op">)</span><span class="op">$</span><span class="va">requires_grad</span><span class="op">)</span>
-<span class="op">}</span><span class="op">)</span>
-<span class="co">#&gt; [1] FALSE</span></code></pre></div>
-<div class="sourceCode" id="cb12"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="va">x</span><span class="op">$</span><span class="va">requires_grad</span>
-<span class="co">#&gt; [1] TRUE</span>
-<span class="va">y</span> <span class="op">&lt;-</span> <span class="va">x</span><span class="op">$</span><span class="kw">detach</span><span class="op">(</span><span class="op">)</span>
-<span class="va">y</span><span class="op">$</span><span class="va">requires_grad</span>
-<span class="co">#&gt; [1] FALSE</span>
-<span class="va">x</span><span class="op">$</span><span class="fu">eq</span><span class="op">(</span><span class="va">y</span><span class="op">)</span><span class="op">$</span><span class="fu">all</span><span class="op">(</span><span class="op">)</span>
-<span class="co">#&gt; torch_tensor</span>
-<span class="co">#&gt; 1</span>
-<span class="co">#&gt; [ CPUBoolType{} ]</span></code></pre></div>
-<p>Read Later:</p>
-<p>Document about <code><a href="../../reference/autograd_function.html">help(autograd_function)</a></code>, <code><a href="../../articles/using-autograd.html">vignette("using-autograd")</a></code>, <code><a href="../../articles/extending-autograd.html">vignette("extending-autograd")</a></code>.</p>
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diff --git a/articles/getting-started/autograd_files/accessible-code-block-0.0.1/empty-anchor.js b/articles/getting-started/autograd_files/accessible-code-block-0.0.1/empty-anchor.js
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-// v0.0.1
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diff --git a/articles/getting-started/control-flow-and-weight-sharing.html b/articles/getting-started/control-flow-and-weight-sharing.html
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-    <div class="page-header toc-ignore">
-      <h1 data-toc-skip>Control flow &amp; Weight sharing</h1>
-            
-      
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/master/vignettes/getting-started/control-flow-and-weight-sharing.Rmd"><code>vignettes/getting-started/control-flow-and-weight-sharing.Rmd</code></a></small>
-      <div class="hidden name"><code>control-flow-and-weight-sharing.Rmd</code></div>
-
-    </div>
-
-    
-    
-<blockquote>
-<p>Note: This is an R port of the official tutorial available <a href="https://pytorch.org/tutorials/beginner/examples_tensor/two_layer_net_numpy.html#sphx-glr-beginner-examples-tensor-two-layer-net-numpy-py">here</a>. All credits goes to <a href="https://github.com/jcjohnson/pytorch-examples">Justin Johnson</a>.</p>
-</blockquote>
-<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://torch.mlverse.org/docs">torch</a></span><span class="op">)</span></code></pre></div>
-<p>As an example of dynamic graphs and weight sharing, we implement a very strange model: a fully-connected ReLU network that on each forward pass chooses a random number between 1 and 4 and uses that many hidden layers, reusing the same weights multiple times to compute the innermost hidden layers.</p>
-<p>For this model we can use normal R flow control to implement the loop, and we can implement weight sharing among the innermost layers by simply reusing the same Module multiple times when defining the forward pass.</p>
-<p>We can easily implement this model using <code>nn_module</code>:</p>
-<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="va">dynamic_net</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/nn_module.html">nn_module</a></span><span class="op">(</span>
-   <span class="st">"dynamic_net"</span>,
-   <span class="co"># In the constructor we construct three nn_linear instances that we will use</span>
-   <span class="co"># in the forward pass.</span>
-   initialize <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="va">D_in</span>, <span class="va">H</span>, <span class="va">D_out</span><span class="op">)</span> <span class="op">{</span>
-      <span class="va">self</span><span class="op">$</span><span class="va">input_linear</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/nn_linear.html">nn_linear</a></span><span class="op">(</span><span class="va">D_in</span>, <span class="va">H</span><span class="op">)</span>
-      <span class="va">self</span><span class="op">$</span><span class="va">middle_linear</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/nn_linear.html">nn_linear</a></span><span class="op">(</span><span class="va">H</span>, <span class="va">H</span><span class="op">)</span>
-      <span class="va">self</span><span class="op">$</span><span class="va">output_linear</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/nn_linear.html">nn_linear</a></span><span class="op">(</span><span class="va">H</span>, <span class="va">D_out</span><span class="op">)</span>
-   <span class="op">}</span>,
-   <span class="co"># For the forward pass of the model, we randomly choose either 0, 1, 2, or 3</span>
-   <span class="co"># and reuse the middle_linear Module that many times to compute hidden layer</span>
-   <span class="co"># representations.</span>
-   <span class="co"># </span>
-   <span class="co"># Since each forward pass builds a dynamic computation graph, we can use normal</span>
-   <span class="co"># R control-flow operators like loops or conditional statements when</span>
-   <span class="co"># defining the forward pass of the model.</span>
-   <span class="co"># </span>
-   <span class="co"># Here we also see that it is perfectly safe to reuse the same Module many</span>
-   <span class="co"># times when defining a computational graph. This is a big improvement from Lua</span>
-   <span class="co"># Torch, where each Module could be used only once.</span>
-   forward <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="op">{</span>
-      <span class="va">h_relu</span> <span class="op">&lt;-</span> <span class="va">self</span><span class="op">$</span><span class="fu">input_linear</span><span class="op">(</span><span class="va">x</span><span class="op">)</span><span class="op">$</span><span class="fu">clamp</span><span class="op">(</span>min <span class="op">=</span> <span class="fl">0</span><span class="op">)</span>
-      <span class="kw">for</span> <span class="op">(</span><span class="va">i</span> <span class="kw">in</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq_len</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/sample.html">sample.int</a></span><span class="op">(</span><span class="fl">4</span>, size <span class="op">=</span> <span class="fl">1</span><span class="op">)</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span>
-         <span class="va">h_relu</span> <span class="op">&lt;-</span> <span class="va">self</span><span class="op">$</span><span class="fu">middle_linear</span><span class="op">(</span><span class="va">h_relu</span><span class="op">)</span><span class="op">$</span><span class="fu">clamp</span><span class="op">(</span>min<span class="op">=</span><span class="fl">0</span><span class="op">)</span>
-      <span class="op">}</span>
-      <span class="va">y_pred</span> <span class="op">&lt;-</span> <span class="va">self</span><span class="op">$</span><span class="fu">output_linear</span><span class="op">(</span><span class="va">h_relu</span><span class="op">)</span>
-      <span class="va">y_pred</span>
-   <span class="op">}</span>
-<span class="op">)</span>
-
-
-<span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="../../reference/cuda_is_available.html">cuda_is_available</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span>
-   <span class="va">device</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_device.html">torch_device</a></span><span class="op">(</span><span class="st">"cuda"</span><span class="op">)</span>
-<span class="op">}</span> <span class="kw">else</span> <span class="op">{</span>
-   <span class="va">device</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_device.html">torch_device</a></span><span class="op">(</span><span class="st">"cpu"</span><span class="op">)</span>
-<span class="op">}</span>
-   
-<span class="co"># N is batch size; D_in is input dimension;</span>
-<span class="co"># H is hidden dimension; D_out is output dimension.</span>
-<span class="va">N</span> <span class="op">&lt;-</span> <span class="fl">64</span>
-<span class="va">D_in</span> <span class="op">&lt;-</span> <span class="fl">1000</span>
-<span class="va">H</span> <span class="op">&lt;-</span> <span class="fl">100</span>
-<span class="va">D_out</span> <span class="op">&lt;-</span> <span class="fl">10</span>
-
-<span class="co"># Create random input and output data</span>
-<span class="co"># Setting requires_grad=FALSE (the default) indicates that we do not need to </span>
-<span class="co"># compute gradients with respect to these Tensors during the backward pass.</span>
-<span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="va">N</span>, <span class="va">D_in</span>, device<span class="op">=</span><span class="va">device</span><span class="op">)</span>
-<span class="va">y</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="va">N</span>, <span class="va">D_out</span>, device<span class="op">=</span><span class="va">device</span><span class="op">)</span>
-
-<span class="co"># Construct our model by instantiating the class defined above</span>
-<span class="va">model</span> <span class="op">&lt;-</span> <span class="fu">dynamic_net</span><span class="op">(</span><span class="va">D_in</span>, <span class="va">H</span>, <span class="va">D_out</span><span class="op">)</span>
-
-<span class="co"># The nn package also contains definitions of popular loss functions; in this</span>
-<span class="co"># case we will use Mean Squared Error (MSE) as our loss function.</span>
-<span class="va">loss_fn</span> <span class="op">&lt;-</span> <span class="va">nnf_mse_loss</span>
-
-<span class="co"># Use the optim package to define an Optimizer that will update the weights of</span>
-<span class="co"># the model for us. Here we will use Adam; the optim package contains many other</span>
-<span class="co"># optimization algorithms. The first argument to the Adam constructor tells the</span>
-<span class="co"># optimizer which Tensors it should update.</span>
-<span class="va">learning_rate</span> <span class="op">&lt;-</span> <span class="fl">1e-4</span>
-<span class="va">optimizer</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/optim_adam.html">optim_adam</a></span><span class="op">(</span><span class="va">model</span><span class="op">$</span><span class="va">parameters</span>, lr<span class="op">=</span><span class="va">learning_rate</span><span class="op">)</span>
-
-<span class="kw">for</span> <span class="op">(</span><span class="va">t</span> <span class="kw">in</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq_len</a></span><span class="op">(</span><span class="fl">500</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span>
-   <span class="co"># Forward pass: compute predicted y by passing x to the model. Module objects</span>
-   <span class="co"># can be called like functions. When doing so you pass a Tensor of input</span>
-   <span class="co"># data to the Module and it produces a Tensor of output data.</span>
-   <span class="va">y_pred</span> <span class="op">&lt;-</span> <span class="fu">model</span><span class="op">(</span><span class="va">x</span><span class="op">)</span>
-   
-   <span class="co"># Compute and print loss. We pass Tensors containing the predicted and true</span>
-   <span class="co"># values of y, and the loss function returns a Tensor containing the</span>
-   <span class="co"># loss.</span>
-   <span class="va">loss</span> <span class="op">&lt;-</span> <span class="fu">loss_fn</span><span class="op">(</span><span class="va">y_pred</span>, <span class="va">y</span><span class="op">)</span>
-   <span class="kw">if</span> <span class="op">(</span><span class="va">t</span> <span class="op">%%</span> <span class="fl">100</span> <span class="op">==</span> <span class="fl">0</span> <span class="op">||</span> <span class="va">t</span> <span class="op">==</span> <span class="fl">1</span><span class="op">)</span>
-      <span class="fu"><a href="https://rdrr.io/r/base/cat.html">cat</a></span><span class="op">(</span><span class="st">"Step:"</span>, <span class="va">t</span>, <span class="st">":"</span>, <span class="fu"><a href="https://rdrr.io/r/base/numeric.html">as.numeric</a></span><span class="op">(</span><span class="va">loss</span><span class="op">)</span>, <span class="st">"\n"</span><span class="op">)</span>
-   
-   <span class="co"># Before the backward pass, use the optimizer object to zero all of the</span>
-   <span class="co"># gradients for the variables it will update (which are the learnable</span>
-   <span class="co"># weights of the model). This is because by default, gradients are</span>
-   <span class="co"># accumulated in buffers( i.e, not overwritten) whenever $backward()</span>
-   <span class="co"># is called. Checkout docs of `autograd_backward` for more details.</span>
-   <span class="va">optimizer</span><span class="op">$</span><span class="fu">zero_grad</span><span class="op">(</span><span class="op">)</span>
-
-   <span class="co"># Backward pass: compute gradient of the loss with respect to model</span>
-   <span class="co"># parameters</span>
-   <span class="va">loss</span><span class="op">$</span><span class="fu">backward</span><span class="op">(</span><span class="op">)</span>
-
-   <span class="co"># Calling the step function on an Optimizer makes an update to its</span>
-   <span class="co"># parameters</span>
-   <span class="va">optimizer</span><span class="op">$</span><span class="fu">step</span><span class="op">(</span><span class="op">)</span>
-<span class="op">}</span>
-<span class="co">#&gt; Step: 1 : 1.013146 </span>
-<span class="co">#&gt; Step: 100 : 0.9793269 </span>
-<span class="co">#&gt; Step: 200 : 0.4408751 </span>
-<span class="co">#&gt; Step: 300 : 0.1257476 </span>
-<span class="co">#&gt; Step: 400 : 0.05437518 </span>
-<span class="co">#&gt; Step: 500 : 0.01907376</span></code></pre></div>
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deleted file mode 100644
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diff --git a/articles/getting-started/custom-nn.html b/articles/getting-started/custom-nn.html
deleted file mode 100644
index b0fae72be..000000000
--- a/articles/getting-started/custom-nn.html
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-<!DOCTYPE html>
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-    <div class="page-header toc-ignore">
-      <h1 data-toc-skip>Custom nn modules</h1>
-            
-      
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/master/vignettes/getting-started/custom-nn.Rmd"><code>vignettes/getting-started/custom-nn.Rmd</code></a></small>
-      <div class="hidden name"><code>custom-nn.Rmd</code></div>
-
-    </div>
-
-    
-    
-<blockquote>
-<p>Note: This is an R port of the official tutorial available <a href="https://pytorch.org/tutorials/beginner/examples_tensor/two_layer_net_numpy.html#sphx-glr-beginner-examples-tensor-two-layer-net-numpy-py">here</a>. All credits goes to <a href="https://github.com/jcjohnson/pytorch-examples">Justin Johnson</a>.</p>
-</blockquote>
-<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://torch.mlverse.org/docs">torch</a></span><span class="op">)</span></code></pre></div>
-<p>Sometimes you will want to specify models that are more complex than a sequence of existing Modules; for these cases you can define your own Modules by using <code>nn_module</code> function and defining a forward which receives input Tensors and produces output Tensors using other modules or other autograd operations on Tensors.</p>
-<p>In this example we implement our two-layer network as a custom Module subclass:</p>
-<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="va">two_layer_net</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/nn_module.html">nn_module</a></span><span class="op">(</span>
-   <span class="st">"two_layer_net"</span>,
-   initialize <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="va">D_in</span>, <span class="va">H</span>, <span class="va">D_out</span><span class="op">)</span> <span class="op">{</span>
-      <span class="va">self</span><span class="op">$</span><span class="va">linear1</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/nn_linear.html">nn_linear</a></span><span class="op">(</span><span class="va">D_in</span>, <span class="va">H</span><span class="op">)</span>
-      <span class="va">self</span><span class="op">$</span><span class="va">linear2</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/nn_linear.html">nn_linear</a></span><span class="op">(</span><span class="va">H</span>, <span class="va">D_out</span><span class="op">)</span>
-   <span class="op">}</span>,
-   forward <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="op">{</span>
-      <span class="va">x</span> <span class="op">%&gt;%</span> 
-         <span class="va">self</span><span class="op">$</span><span class="fu">linear1</span><span class="op">(</span><span class="op">)</span> <span class="op">%&gt;%</span> 
-         <span class="fu"><a href="../../reference/nnf_relu.html">nnf_relu</a></span><span class="op">(</span><span class="op">)</span> <span class="op">%&gt;%</span> 
-         <span class="va">self</span><span class="op">$</span><span class="fu">linear2</span><span class="op">(</span><span class="op">)</span>
-   <span class="op">}</span>
-<span class="op">)</span>
-
-
-<span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="../../reference/cuda_is_available.html">cuda_is_available</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span>
-   <span class="va">device</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_device.html">torch_device</a></span><span class="op">(</span><span class="st">"cuda"</span><span class="op">)</span>
-<span class="op">}</span> <span class="kw">else</span> <span class="op">{</span>
-   <span class="va">device</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_device.html">torch_device</a></span><span class="op">(</span><span class="st">"cpu"</span><span class="op">)</span>
-<span class="op">}</span>
-   
-<span class="co"># N is batch size; D_in is input dimension;</span>
-<span class="co"># H is hidden dimension; D_out is output dimension.</span>
-<span class="va">N</span> <span class="op">&lt;-</span> <span class="fl">64</span>
-<span class="va">D_in</span> <span class="op">&lt;-</span> <span class="fl">1000</span>
-<span class="va">H</span> <span class="op">&lt;-</span> <span class="fl">100</span>
-<span class="va">D_out</span> <span class="op">&lt;-</span> <span class="fl">10</span>
-
-<span class="co"># Create random input and output data</span>
-<span class="co"># Setting requires_grad=FALSE (the default) indicates that we do not need to </span>
-<span class="co"># compute gradients with respect to these Tensors during the backward pass.</span>
-<span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="va">N</span>, <span class="va">D_in</span>, device<span class="op">=</span><span class="va">device</span><span class="op">)</span>
-<span class="va">y</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="va">N</span>, <span class="va">D_out</span>, device<span class="op">=</span><span class="va">device</span><span class="op">)</span>
-
-<span class="co"># Construct our model by instantiating the class defined above</span>
-<span class="va">model</span> <span class="op">&lt;-</span> <span class="fu">two_layer_net</span><span class="op">(</span><span class="va">D_in</span>, <span class="va">H</span>, <span class="va">D_out</span><span class="op">)</span>
-
-<span class="co"># The nn package also contains definitions of popular loss functions; in this</span>
-<span class="co"># case we will use Mean Squared Error (MSE) as our loss function.</span>
-<span class="va">loss_fn</span> <span class="op">&lt;-</span> <span class="va">nnf_mse_loss</span>
-
-<span class="co"># Use the optim package to define an Optimizer that will update the weights of</span>
-<span class="co"># the model for us. Here we will use Adam; the optim package contains many other</span>
-<span class="co"># optimization algorithms. The first argument to the Adam constructor tells the</span>
-<span class="co"># optimizer which Tensors it should update.</span>
-<span class="va">learning_rate</span> <span class="op">&lt;-</span> <span class="fl">1e-4</span>
-<span class="va">optimizer</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/optim_sgd.html">optim_sgd</a></span><span class="op">(</span><span class="va">model</span><span class="op">$</span><span class="va">parameters</span>, lr<span class="op">=</span><span class="va">learning_rate</span><span class="op">)</span>
-
-<span class="kw">for</span> <span class="op">(</span><span class="va">t</span> <span class="kw">in</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq_len</a></span><span class="op">(</span><span class="fl">500</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span>
-   <span class="co"># Forward pass: compute predicted y by passing x to the model. Module objects</span>
-   <span class="co"># can be called like functions. When doing so you pass a Tensor of input</span>
-   <span class="co"># data to the Module and it produces a Tensor of output data.</span>
-   <span class="va">y_pred</span> <span class="op">&lt;-</span> <span class="fu">model</span><span class="op">(</span><span class="va">x</span><span class="op">)</span>
-   
-   <span class="co"># Compute and print loss. We pass Tensors containing the predicted and true</span>
-   <span class="co"># values of y, and the loss function returns a Tensor containing the</span>
-   <span class="co"># loss.</span>
-   <span class="va">loss</span> <span class="op">&lt;-</span> <span class="fu">loss_fn</span><span class="op">(</span><span class="va">y_pred</span>, <span class="va">y</span><span class="op">)</span>
-   <span class="kw">if</span> <span class="op">(</span><span class="va">t</span> <span class="op">%%</span> <span class="fl">100</span> <span class="op">==</span> <span class="fl">0</span> <span class="op">||</span> <span class="va">t</span> <span class="op">==</span> <span class="fl">1</span><span class="op">)</span>
-      <span class="fu"><a href="https://rdrr.io/r/base/cat.html">cat</a></span><span class="op">(</span><span class="st">"Step:"</span>, <span class="va">t</span>, <span class="st">":"</span>, <span class="fu"><a href="https://rdrr.io/r/base/numeric.html">as.numeric</a></span><span class="op">(</span><span class="va">loss</span><span class="op">)</span>, <span class="st">"\n"</span><span class="op">)</span>
-   
-   <span class="co"># Before the backward pass, use the optimizer object to zero all of the</span>
-   <span class="co"># gradients for the variables it will update (which are the learnable</span>
-   <span class="co"># weights of the model). This is because by default, gradients are</span>
-   <span class="co"># accumulated in buffers( i.e, not overwritten) whenever $backward()</span>
-   <span class="co"># is called. Checkout docs of `autograd_backward` for more details.</span>
-   <span class="va">optimizer</span><span class="op">$</span><span class="fu">zero_grad</span><span class="op">(</span><span class="op">)</span>
-
-   <span class="co"># Backward pass: compute gradient of the loss with respect to model</span>
-   <span class="co"># parameters</span>
-   <span class="va">loss</span><span class="op">$</span><span class="fu">backward</span><span class="op">(</span><span class="op">)</span>
-
-   <span class="co"># Calling the step function on an Optimizer makes an update to its</span>
-   <span class="co"># parameters</span>
-   <span class="va">optimizer</span><span class="op">$</span><span class="fu">step</span><span class="op">(</span><span class="op">)</span>
-<span class="op">}</span>
-<span class="co">#&gt; Step: 1 : 1.04628 </span>
-<span class="co">#&gt; Step: 100 : 1.033974 </span>
-<span class="co">#&gt; Step: 200 : 1.021832 </span>
-<span class="co">#&gt; Step: 300 : 1.009992 </span>
-<span class="co">#&gt; Step: 400 : 0.9984027 </span>
-<span class="co">#&gt; Step: 500 : 0.9870082</span></code></pre></div>
-<p>In the <a href="control-flow-and-weight-sharing.html">next example</a> we will about dynamic graphs in torch.</p>
-  </div>
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diff --git a/articles/getting-started/neural-networks.html b/articles/getting-started/neural-networks.html
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-      <h1 data-toc-skip>Neural networks</h1>
-            
-      
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/master/vignettes/getting-started/neural-networks.Rmd"><code>vignettes/getting-started/neural-networks.Rmd</code></a></small>
-      <div class="hidden name"><code>neural-networks.Rmd</code></div>
-
-    </div>
-
-    
-    
-<blockquote>
-<p>Note: This is an R port of the official tutorial available <a href="https://pytorch.org/tutorials/beginner/blitz/tensor_tutorial.html#sphx-glr-beginner-blitz-tensor-tutorial-py">here</a>. All credits goes to <a href="http://soumith.ch/">Soumith Chintala</a>.</p>
-</blockquote>
-<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://torch.mlverse.org/docs">torch</a></span><span class="op">)</span></code></pre></div>
-<p>Neural networks can be constructed using the <code>nn</code> functionality.</p>
-<p>Now that you had a glimpse of <code>autograd</code>, <code>nn</code> depends on <code>autograd</code> to define models and differentiate them. An nn.Module contains layers, and a method <code>forward(input)</code> that returns the output.</p>
-<p>For example, look at this network that classifies digit images:</p>
-<div class="figure">
-<img src="assets/mnist.png" alt=""><p class="caption">Convnet for mnist classification</p>
-</div>
-<p>It is a simple feed-forward network. It takes the input, feeds it through several layers one after the other, and then finally gives the output.</p>
-<p>A typical training procedure for a neural network is as follows:</p>
-<ul>
-<li>Define the neural network that has some learnable parameters (or weights)</li>
-<li>Iterate over a dataset of inputs</li>
-<li>Process input through the network</li>
-<li>Compute the loss (how far is the output from being correct)</li>
-<li>Propagate gradients back into the network’s parameters</li>
-<li>Update the weights of the network, typically using a simple update rule: <code>weight = weight - learning_rate * gradient</code>.</li>
-</ul>
-<div id="define-the-network" class="section level2">
-<h2 class="hasAnchor">
-<a href="#define-the-network" class="anchor"></a>Define the network</h2>
-<p>Let’s define this network:</p>
-<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="va">Net</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/nn_module.html">nn_module</a></span><span class="op">(</span>
-  initialize <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="op">)</span> <span class="op">{</span>
-    <span class="va">self</span><span class="op">$</span><span class="va">conv1</span> <span class="op">=</span> <span class="fu"><a href="../../reference/nn_conv2d.html">nn_conv2d</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">6</span>, <span class="fl">3</span><span class="op">)</span>
-    <span class="va">self</span><span class="op">$</span><span class="va">conv2</span> <span class="op">=</span> <span class="fu"><a href="../../reference/nn_conv2d.html">nn_conv2d</a></span><span class="op">(</span><span class="fl">6</span>, <span class="fl">16</span>, <span class="fl">3</span><span class="op">)</span>
-    <span class="co"># an affine operation: y = Wx + b</span>
-    <span class="va">self</span><span class="op">$</span><span class="va">fc1</span> <span class="op">=</span> <span class="fu"><a href="../../reference/nn_linear.html">nn_linear</a></span><span class="op">(</span><span class="fl">16</span> <span class="op">*</span> <span class="fl">6</span> <span class="op">*</span> <span class="fl">6</span>, <span class="fl">120</span><span class="op">)</span>  <span class="co"># 6*6 from image dimension</span>
-    <span class="va">self</span><span class="op">$</span><span class="va">fc2</span> <span class="op">=</span> <span class="fu"><a href="../../reference/nn_linear.html">nn_linear</a></span><span class="op">(</span><span class="fl">120</span>, <span class="fl">84</span><span class="op">)</span>
-    <span class="va">self</span><span class="op">$</span><span class="va">fc3</span> <span class="op">=</span> <span class="fu"><a href="../../reference/nn_linear.html">nn_linear</a></span><span class="op">(</span><span class="fl">84</span>, <span class="fl">10</span><span class="op">)</span>
-  <span class="op">}</span>,
-  forward <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="op">{</span>
-    <span class="va">x</span> <span class="op">%&gt;%</span> 
-      
-      <span class="va">self</span><span class="op">$</span><span class="fu">conv1</span><span class="op">(</span><span class="op">)</span> <span class="op">%&gt;%</span> 
-      <span class="fu"><a href="../../reference/nnf_relu.html">nnf_relu</a></span><span class="op">(</span><span class="op">)</span> <span class="op">%&gt;%</span> 
-      <span class="fu"><a href="../../reference/nnf_max_pool2d.html">nnf_max_pool2d</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">2</span>,<span class="fl">2</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span> 
-      
-      <span class="va">self</span><span class="op">$</span><span class="fu">conv2</span><span class="op">(</span><span class="op">)</span> <span class="op">%&gt;%</span> 
-      <span class="fu"><a href="../../reference/nnf_relu.html">nnf_relu</a></span><span class="op">(</span><span class="op">)</span> <span class="op">%&gt;%</span> 
-      <span class="fu"><a href="../../reference/nnf_max_pool2d.html">nnf_max_pool2d</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">2</span>,<span class="fl">2</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span> 
-      
-      <span class="fu"><a href="../../reference/torch_flatten.html">torch_flatten</a></span><span class="op">(</span>start_dim <span class="op">=</span> <span class="fl">2</span><span class="op">)</span> <span class="op">%&gt;%</span> 
-      
-      <span class="va">self</span><span class="op">$</span><span class="fu">fc1</span><span class="op">(</span><span class="op">)</span> <span class="op">%&gt;%</span> 
-      <span class="fu"><a href="../../reference/nnf_relu.html">nnf_relu</a></span><span class="op">(</span><span class="op">)</span> <span class="op">%&gt;%</span> 
-      
-      <span class="va">self</span><span class="op">$</span><span class="fu">fc2</span><span class="op">(</span><span class="op">)</span> <span class="op">%&gt;%</span> 
-      <span class="fu"><a href="../../reference/nnf_relu.html">nnf_relu</a></span><span class="op">(</span><span class="op">)</span> <span class="op">%&gt;%</span> 
-      
-      <span class="va">self</span><span class="op">$</span><span class="fu">fc3</span><span class="op">(</span><span class="op">)</span>
-  <span class="op">}</span>
-<span class="op">)</span>
-
-<span class="va">net</span> <span class="op">&lt;-</span> <span class="fu">Net</span><span class="op">(</span><span class="op">)</span></code></pre></div>
-<p>You just have to define the <code>forward</code> function, and the <code>backward</code> function (where gradients are computed) is automatically defined for you using <code>autograd.</code> You can use any of the Tensor operations in the <code>forward</code> function.</p>
-<p>The learnable parameters of a model are returned by <code>net$parameters</code>.</p>
-<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/utils/str.html">str</a></span><span class="op">(</span><span class="va">net</span><span class="op">$</span><span class="va">parameters</span><span class="op">)</span>
-<span class="co">#&gt; List of 10</span>
-<span class="co">#&gt;  $ conv1.weight:Float [1:6, 1:1, 1:3, 1:3]</span>
-<span class="co">#&gt;  $ conv1.bias  :Float [1:6]</span>
-<span class="co">#&gt;  $ conv2.weight:Float [1:16, 1:6, 1:3, 1:3]</span>
-<span class="co">#&gt;  $ conv2.bias  :Float [1:16]</span>
-<span class="co">#&gt;  $ fc1.weight  :Float [1:120, 1:576]</span>
-<span class="co">#&gt;  $ fc1.bias    :Float [1:120]</span>
-<span class="co">#&gt;  $ fc2.weight  :Float [1:84, 1:120]</span>
-<span class="co">#&gt;  $ fc2.bias    :Float [1:84]</span>
-<span class="co">#&gt;  $ fc3.weight  :Float [1:10, 1:84]</span>
-<span class="co">#&gt;  $ fc3.bias    :Float [1:10]</span></code></pre></div>
-<p>Let’s try a random 32x32 input. Note: expected input size of this net (LeNet) is 32x32. To use this net on the MNIST dataset, please resize the images from the dataset to 32x32.</p>
-<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">1</span>, <span class="fl">32</span>, <span class="fl">32</span><span class="op">)</span>
-<span class="va">out</span> <span class="op">&lt;-</span> <span class="fu">net</span><span class="op">(</span><span class="va">input</span><span class="op">)</span>
-<span class="va">out</span>
-<span class="co">#&gt; torch_tensor</span>
-<span class="co">#&gt;  0.0673 -0.0700  0.0210  0.1113  0.1069  0.0171  0.0774  0.0898 -0.0002  0.0961</span>
-<span class="co">#&gt; [ CPUFloatType{1,10} ]</span></code></pre></div>
-<p>Zero the gradient buffers of all parameters and backprops with random gradients:</p>
-<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="va">net</span><span class="op">$</span><span class="fu">zero_grad</span><span class="op">(</span><span class="op">)</span>
-<span class="va">out</span><span class="op">$</span><span class="fu">backward</span><span class="op">(</span><span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">10</span><span class="op">)</span><span class="op">)</span></code></pre></div>
-<blockquote>
-<p><strong>Note</strong>: <code>nn</code> only supports mini-batches. The entire torch.nn package only supports inputs that are a mini-batch of samples, and not a single sample. For example, <code>nn_conv2d</code> will take in a 4D Tensor of nSamples x nChannels x Height x Width. If you have a single sample, just use <code>input$unsqueeze(1)</code> to add a fake batch dimension.</p>
-</blockquote>
-<p>Before proceeding further, let’s recap all the classes you’ve seen so far.</p>
-<div id="recap" class="section level3">
-<h3 class="hasAnchor">
-<a href="#recap" class="anchor"></a>Recap</h3>
-<ul>
-<li><p><code>torch_tensor</code> - A multi-dimensional array with support for autograd operations like <code>backward()</code>. Also holds the gradient w.r.t. the tensor.</p></li>
-<li><p><code>nn_module</code> - Neural network module. Convenient way of encapsulating parameters, with helpers for moving them to GPU, exporting, loading, etc.</p></li>
-<li><p><code>nn_parameter</code> - A kind of Tensor, that is automatically registered as a parameter when assigned as an attribute to a Module.</p></li>
-<li><p><code>autograd_function</code> - Implements forward and backward definitions of an autograd operation. Every Tensor operation creates at least a single Function node that connects to functions that created a Tensor and encodes its history.</p></li>
-</ul>
-</div>
-<div id="at-this-point-we-covered" class="section level3">
-<h3 class="hasAnchor">
-<a href="#at-this-point-we-covered" class="anchor"></a>At this point, we covered</h3>
-<ul>
-<li>Defining a neural network</li>
-<li>Processing inputs and calling backward</li>
-</ul>
-</div>
-<div id="still-left" class="section level3">
-<h3 class="hasAnchor">
-<a href="#still-left" class="anchor"></a>Still left</h3>
-<ul>
-<li>Computing the loss</li>
-<li>Updating the weights of the network</li>
-</ul>
-</div>
-</div>
-<div id="loss-function" class="section level2">
-<h2 class="hasAnchor">
-<a href="#loss-function" class="anchor"></a>Loss function</h2>
-<p>A loss function takes the (output, target) pair of inputs, and computes a value that estimates how far away the output is from the target.</p>
-<p>There are several different loss functions under the nn package . A simple loss is: <code>nnf_mse_loss</code> which computes the mean-squared error between the input and the target.</p>
-<p>For example:</p>
-<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="va">output</span> <span class="op">&lt;-</span> <span class="fu">net</span><span class="op">(</span><span class="va">input</span><span class="op">)</span>
-<span class="va">target</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">10</span><span class="op">)</span>  <span class="co"># a dummy target, for example</span>
-<span class="va">target</span> <span class="op">&lt;-</span> <span class="va">target</span><span class="op">$</span><span class="fu">view</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="op">-</span><span class="fl">1</span><span class="op">)</span><span class="op">)</span>  <span class="co"># make it the same shape as output</span>
-
-<span class="va">loss</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/nnf_mse_loss.html">nnf_mse_loss</a></span><span class="op">(</span><span class="va">output</span>, <span class="va">target</span><span class="op">)</span>
-<span class="va">loss</span>
-<span class="co">#&gt; torch_tensor</span>
-<span class="co">#&gt; 1.33572</span>
-<span class="co">#&gt; [ CPUFloatType{} ]</span></code></pre></div>
-<p>Now, if you follow loss in the backward direction, using its <code>$grad_fn</code> attribute, you will see a graph of computations that looks like this:</p>
-<pre><code>input -&gt; conv2d -&gt; relu -&gt; maxpool2d -&gt; conv2d -&gt; relu -&gt; maxpool2d
-      -&gt; view -&gt; linear -&gt; relu -&gt; linear -&gt; relu -&gt; linear
-      -&gt; MSELoss
-      -&gt; loss</code></pre>
-<p>So, when we call <code>loss$backward()</code>, the whole graph is differentiated w.r.t. the loss, and all Tensors in the graph that has requires_grad=True will have their <code>#grad</code> Tensor accumulated with the gradient.</p>
-<p>For illustration, let us follow a few steps backward:</p>
-<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="va">loss</span><span class="op">$</span><span class="va">grad_fn</span>
-<span class="co">#&gt; MseLossBackward</span>
-<span class="va">loss</span><span class="op">$</span><span class="va">grad_fn</span><span class="op">$</span><span class="va">next_functions</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span>
-<span class="co">#&gt; AddmmBackward</span>
-<span class="va">loss</span><span class="op">$</span><span class="va">grad_fn</span><span class="op">$</span><span class="va">next_functions</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">next_functions</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span>
-<span class="co">#&gt; torch::autograd::AccumulateGrad</span></code></pre></div>
-</div>
-<div id="backprop" class="section level2">
-<h2 class="hasAnchor">
-<a href="#backprop" class="anchor"></a>Backprop</h2>
-<p>To backpropagate the error all we have to do is to <code>loss$backward()</code>. You need to clear the existing gradients though, else gradients will be accumulated to existing gradients.</p>
-<p>Now we shall call <code>loss$backward()</code>, and have a look at conv1’s bias gradients before and after the backward.</p>
-<div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="va">net</span><span class="op">$</span><span class="fu">zero_grad</span><span class="op">(</span><span class="op">)</span>     <span class="co"># zeroes the gradient buffers of all parameters</span>
-
-<span class="co"># conv1.bias.grad before backward</span>
-<span class="va">net</span><span class="op">$</span><span class="va">conv1</span><span class="op">$</span><span class="va">bias</span><span class="op">$</span><span class="va">grad</span>
-<span class="co">#&gt; torch_tensor</span>
-<span class="co">#&gt;  0</span>
-<span class="co">#&gt;  0</span>
-<span class="co">#&gt;  0</span>
-<span class="co">#&gt;  0</span>
-<span class="co">#&gt;  0</span>
-<span class="co">#&gt;  0</span>
-<span class="co">#&gt; [ CPUFloatType{6} ]</span>
-
-<span class="va">loss</span><span class="op">$</span><span class="fu">backward</span><span class="op">(</span><span class="op">)</span>
-
-<span class="co"># conv1.bias.grad after backward</span>
-<span class="va">net</span><span class="op">$</span><span class="va">conv1</span><span class="op">$</span><span class="va">bias</span><span class="op">$</span><span class="va">grad</span>
-<span class="co">#&gt; torch_tensor</span>
-<span class="co">#&gt; 0.01 *</span>
-<span class="co">#&gt; -0.4329</span>
-<span class="co">#&gt; -1.0576</span>
-<span class="co">#&gt;  0.9670</span>
-<span class="co">#&gt;  0.7630</span>
-<span class="co">#&gt; -0.6947</span>
-<span class="co">#&gt;  2.2084</span>
-<span class="co">#&gt; [ CPUFloatType{6} ]</span></code></pre></div>
-<p>Now, we have seen how to use loss functions.</p>
-</div>
-<div id="update-the-weights" class="section level2">
-<h2 class="hasAnchor">
-<a href="#update-the-weights" class="anchor"></a>Update the weights</h2>
-<p>The simplest update rule used in practice is the Stochastic Gradient Descent (SGD):</p>
-<p><span class="math display">\[weight = weight - learning_rate * gradient\]</span></p>
-<p>We can implement this using simple R code:</p>
-<div class="sourceCode" id="cb10"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="va">learning_rate</span> <span class="op">&lt;-</span> <span class="fl">0.01</span>
-<span class="kw">for</span> <span class="op">(</span><span class="va">f</span> <span class="kw">in</span> <span class="va">net</span><span class="op">$</span><span class="va">parameters</span><span class="op">)</span> <span class="op">{</span>
-  <span class="fu"><a href="../../reference/with_no_grad.html">with_no_grad</a></span><span class="op">(</span><span class="op">{</span>
-    <span class="va">f</span><span class="op">$</span><span class="fu">sub_</span><span class="op">(</span><span class="va">f</span><span class="op">$</span><span class="va">grad</span> <span class="op">*</span> <span class="va">learning_rate</span><span class="op">)</span>
-  <span class="op">}</span><span class="op">)</span>
-<span class="op">}</span></code></pre></div>
-<blockquote>
-<p><strong>Note:</strong> Weight updates here is wraped around <code>with_no_grad</code> as we don’t the updates to be tracked by the autograd engine.</p>
-</blockquote>
-<p>However, as you use neural networks, you want to use various different update rules such as SGD, Nesterov-SGD, Adam, RMSProp, etc.</p>
-<div class="sourceCode" id="cb11"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="co"># create your optimizer</span>
-<span class="va">optimizer</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/optim_sgd.html">optim_sgd</a></span><span class="op">(</span><span class="va">net</span><span class="op">$</span><span class="va">parameters</span>, lr <span class="op">=</span> <span class="fl">0.01</span><span class="op">)</span>
-
-<span class="co"># in your training loop:</span>
-<span class="va">optimizer</span><span class="op">$</span><span class="fu">zero_grad</span><span class="op">(</span><span class="op">)</span>   <span class="co"># zero the gradient buffers</span>
-<span class="va">output</span> <span class="op">&lt;-</span> <span class="fu">net</span><span class="op">(</span><span class="va">input</span><span class="op">)</span>
-<span class="va">loss</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/nnf_mse_loss.html">nnf_mse_loss</a></span><span class="op">(</span><span class="va">output</span>, <span class="va">target</span><span class="op">)</span>
-<span class="va">loss</span><span class="op">$</span><span class="fu">backward</span><span class="op">(</span><span class="op">)</span>
-<span class="va">optimizer</span><span class="op">$</span><span class="fu">step</span><span class="op">(</span><span class="op">)</span>    <span class="co"># Does the update</span>
-<span class="co">#&gt; NULL</span></code></pre></div>
-<blockquote>
-<p><strong>Note:</strong> Observe how gradient buffers had to be manually set to zero using <code>optimizer$zero_grad()</code>. This is because gradients are accumulated as explained in the Backprop section.</p>
-</blockquote>
-</div>
-  </div>
-
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-
-        <nav id="toc" data-toggle="toc"><h2 data-toc-skip>Contents</h2>
-    </nav>
-</div>
-
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-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
-</div>
-
-<div class="pkgdown">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p>
-</div>
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deleted file mode 100644
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diff --git a/articles/getting-started/new-autograd-functions.html b/articles/getting-started/new-autograd-functions.html
deleted file mode 100644
index 2a87b30b2..000000000
--- a/articles/getting-started/new-autograd-functions.html
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-<!DOCTYPE html>
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-<script src="new-autograd-functions_files/anchor-sections-1.0/anchor-sections.js"></script><div class="row">
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-      <h1 data-toc-skip>Defining new autograd functions</h1>
-            
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-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/master/vignettes/getting-started/new-autograd-functions.Rmd"><code>vignettes/getting-started/new-autograd-functions.Rmd</code></a></small>
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-<blockquote>
-<p>Note: This is an R port of the official tutorial available <a href="https://pytorch.org/tutorials/beginner/examples_tensor/two_layer_net_numpy.html#sphx-glr-beginner-examples-tensor-two-layer-net-numpy-py">here</a>. All credits goes to <a href="https://github.com/jcjohnson/pytorch-examples">Justin Johnson</a>.</p>
-</blockquote>
-<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://torch.mlverse.org/docs">torch</a></span><span class="op">)</span></code></pre></div>
-<p>Under the hood, each primitive autograd operator is really two functions that operate on Tensors. The forward function computes output Tensors from input Tensors. The backward function receives the gradient of the output Tensors with respect to some scalar value, and computes the gradient of the input Tensors with respect to that same scalar value.</p>
-<p>In torch we can easily define our own autograd operator by defining a subclass of <code>autograd_function</code> and implementing the forward and backward functions. We can then use our new autograd operator by constructing an instance and calling it like a function, passing Tensors containing input data.</p>
-<p>In this example we define our own custom autograd function for performing the ReLU nonlinearity, and use it to implement our two-layer network:</p>
-<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="co"># We can implement our own custom autograd Functions by subclassing</span>
-<span class="co"># autograd_function and implementing the forward and backward passes</span>
-<span class="co"># which operate on Tensors.</span>
-<span class="va">my_relu</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/autograd_function.html">autograd_function</a></span><span class="op">(</span>
-   <span class="co"># In the forward pass we receive a Tensor containing the input and return</span>
-   <span class="co"># a Tensor containing the output. ctx is a context object that can be used</span>
-   <span class="co"># to stash information for backward computation. You can cache arbitrary</span>
-   <span class="co"># objects for use in the backward pass using the ctx$save_for_backward method.</span>
-   forward <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="va">ctx</span>, <span class="va">input</span><span class="op">)</span> <span class="op">{</span>
-      <span class="va">ctx</span><span class="op">$</span><span class="fu">save_for_backward</span><span class="op">(</span>input <span class="op">=</span> <span class="va">input</span><span class="op">)</span>
-      <span class="va">input</span><span class="op">$</span><span class="fu">clamp</span><span class="op">(</span>min <span class="op">=</span> <span class="fl">0</span><span class="op">)</span>
-   <span class="op">}</span>,
-   <span class="co"># In the backward pass we receive a Tensor containing the gradient of the loss</span>
-   <span class="co"># with respect to the output, and we need to compute the gradient of the loss</span>
-   <span class="co"># with respect to the input.</span>
-   backward <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="va">ctx</span>, <span class="va">grad_output</span><span class="op">)</span> <span class="op">{</span>
-      <span class="va">v</span> <span class="op">&lt;-</span> <span class="va">ctx</span><span class="op">$</span><span class="va">saved_variables</span>
-      <span class="va">grad_input</span> <span class="op">&lt;-</span> <span class="va">grad_output</span><span class="op">$</span><span class="fu">clone</span><span class="op">(</span><span class="op">)</span>
-      <span class="va">grad_input</span><span class="op">[</span><span class="va">v</span><span class="op">$</span><span class="va">input</span> <span class="op">&lt;</span> <span class="fl">0</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="fl">0</span>
-      <span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span><span class="op">(</span>input <span class="op">=</span> <span class="va">grad_input</span><span class="op">)</span>
-   <span class="op">}</span>
-<span class="op">)</span>
-
-<span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="../../reference/cuda_is_available.html">cuda_is_available</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span>
-   <span class="va">device</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_device.html">torch_device</a></span><span class="op">(</span><span class="st">"cuda"</span><span class="op">)</span>
-<span class="op">}</span> <span class="kw">else</span> <span class="op">{</span>
-   <span class="va">device</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_device.html">torch_device</a></span><span class="op">(</span><span class="st">"cpu"</span><span class="op">)</span>
-<span class="op">}</span>
-   
-<span class="co"># N is batch size; D_in is input dimension;</span>
-<span class="co"># H is hidden dimension; D_out is output dimension.</span>
-<span class="va">N</span> <span class="op">&lt;-</span> <span class="fl">64</span>
-<span class="va">D_in</span> <span class="op">&lt;-</span> <span class="fl">1000</span>
-<span class="va">H</span> <span class="op">&lt;-</span> <span class="fl">100</span>
-<span class="va">D_out</span> <span class="op">&lt;-</span> <span class="fl">10</span>
-
-<span class="co"># Create random input and output data</span>
-<span class="co"># Setting requires_grad=FALSE (the default) indicates that we do not need to </span>
-<span class="co"># compute gradients with respect to these Tensors during the backward pass.</span>
-<span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="va">N</span>, <span class="va">D_in</span>, device<span class="op">=</span><span class="va">device</span><span class="op">)</span>
-<span class="va">y</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="va">N</span>, <span class="va">D_out</span>, device<span class="op">=</span><span class="va">device</span><span class="op">)</span>
-
-<span class="co"># Randomly initialize weights</span>
-<span class="co"># Setting requires_grad=TRUE indicates that we want to compute gradients with</span>
-<span class="co"># respect to these Tensors during the backward pass.</span>
-<span class="va">w1</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="va">D_in</span>, <span class="va">H</span>, device<span class="op">=</span><span class="va">device</span>, requires_grad <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span>
-<span class="va">w2</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="va">H</span>, <span class="va">D_out</span>, device<span class="op">=</span><span class="va">device</span>, requires_grad <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span>
-
-<span class="va">learning_rate</span> <span class="op">&lt;-</span> <span class="fl">1e-6</span>
-<span class="kw">for</span> <span class="op">(</span><span class="va">t</span> <span class="kw">in</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq_len</a></span><span class="op">(</span><span class="fl">500</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span>
-   <span class="co"># Forward pass: compute predicted y using operations on Tensors; these</span>
-   <span class="co"># are exactly the same operations we used to compute the forward pass using</span>
-   <span class="co"># Tensors, but we do not need to keep references to intermediate values since</span>
-   <span class="co"># we are not implementing the backward pass by hand.</span>
-   <span class="va">y_pred</span> <span class="op">&lt;-</span> <span class="fu">my_relu</span><span class="op">(</span><span class="va">x</span><span class="op">$</span><span class="fu">mm</span><span class="op">(</span><span class="va">w1</span><span class="op">)</span><span class="op">)</span><span class="op">$</span><span class="fu">mm</span><span class="op">(</span><span class="va">w2</span><span class="op">)</span>
-   
-   <span class="co"># Compute and print loss using operations on Tensors.</span>
-   <span class="co"># Now loss is a Tensor of shape (1,)</span>
-   <span class="va">loss</span> <span class="op">&lt;-</span> <span class="op">(</span><span class="va">y_pred</span> <span class="op">-</span> <span class="va">y</span><span class="op">)</span><span class="op">$</span><span class="fu">pow</span><span class="op">(</span><span class="fl">2</span><span class="op">)</span><span class="op">$</span><span class="fu">sum</span><span class="op">(</span><span class="op">)</span>
-   <span class="kw">if</span> <span class="op">(</span><span class="va">t</span> <span class="op">%%</span> <span class="fl">100</span> <span class="op">==</span> <span class="fl">0</span> <span class="op">||</span> <span class="va">t</span> <span class="op">==</span> <span class="fl">1</span><span class="op">)</span>
-      <span class="fu"><a href="https://rdrr.io/r/base/cat.html">cat</a></span><span class="op">(</span><span class="st">"Step:"</span>, <span class="va">t</span>, <span class="st">":"</span>, <span class="fu"><a href="https://rdrr.io/r/base/numeric.html">as.numeric</a></span><span class="op">(</span><span class="va">loss</span><span class="op">)</span>, <span class="st">"\n"</span><span class="op">)</span>
-   
-   <span class="co"># Use autograd to compute the backward pass. This call will compute the</span>
-   <span class="co"># gradient of loss with respect to all Tensors with requires_grad=True.</span>
-   <span class="co"># After this call w1$grad and w2$grad will be Tensors holding the gradient</span>
-   <span class="co"># of the loss with respect to w1 and w2 respectively.</span>
-   <span class="va">loss</span><span class="op">$</span><span class="fu">backward</span><span class="op">(</span><span class="op">)</span>
-   
-   <span class="co"># Manually update weights using gradient descent. Wrap in `with_no_grad`</span>
-   <span class="co"># because weights have requires_grad=TRUE, but we don't need to track this</span>
-   <span class="co"># in autograd.</span>
-   <span class="co"># You can also use optim_sgd to achieve this.</span>
-   <span class="fu"><a href="../../reference/with_no_grad.html">with_no_grad</a></span><span class="op">(</span><span class="op">{</span>
-      
-      <span class="co"># operations suffixed with an `_` operates on in-place on the tensor.</span>
-      <span class="va">w1</span><span class="op">$</span><span class="fu">sub_</span><span class="op">(</span><span class="va">learning_rate</span> <span class="op">*</span> <span class="va">w1</span><span class="op">$</span><span class="va">grad</span><span class="op">)</span>
-      <span class="va">w2</span><span class="op">$</span><span class="fu">sub_</span><span class="op">(</span><span class="va">learning_rate</span> <span class="op">*</span> <span class="va">w2</span><span class="op">$</span><span class="va">grad</span><span class="op">)</span>
-      
-      <span class="co"># Manually zero the gradients after updating weights</span>
-      <span class="va">w1</span><span class="op">$</span><span class="va">grad</span><span class="op">$</span><span class="fu">zero_</span><span class="op">(</span><span class="op">)</span>
-      <span class="va">w2</span><span class="op">$</span><span class="va">grad</span><span class="op">$</span><span class="fu">zero_</span><span class="op">(</span><span class="op">)</span>
-   <span class="op">}</span><span class="op">)</span>
-<span class="op">}</span>
-<span class="co">#&gt; Step: 1 : 24166310 </span>
-<span class="co">#&gt; Step: 100 : 714.7551 </span>
-<span class="co">#&gt; Step: 200 : 6.447984 </span>
-<span class="co">#&gt; Step: 300 : 0.08702322 </span>
-<span class="co">#&gt; Step: 400 : 0.00166944 </span>
-<span class="co">#&gt; Step: 500 : 0.000139622</span></code></pre></div>
-<p>In the <a href="nn.html">next example</a> we will learn how to use the neural networks abstractions in torch.</p>
-  </div>
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diff --git a/articles/getting-started/nn.html b/articles/getting-started/nn.html
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-      <h1 data-toc-skip>nn: neural networks with torch</h1>
-            
-      
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/master/vignettes/getting-started/nn.Rmd"><code>vignettes/getting-started/nn.Rmd</code></a></small>
-      <div class="hidden name"><code>nn.Rmd</code></div>
-
-    </div>
-
-    
-    
-<blockquote>
-<p>Note: This is an R port of the official tutorial available <a href="https://pytorch.org/tutorials/beginner/examples_tensor/two_layer_net_numpy.html#sphx-glr-beginner-examples-tensor-two-layer-net-numpy-py">here</a>. All credits goes to <a href="https://github.com/jcjohnson/pytorch-examples">Justin Johnson</a>.</p>
-</blockquote>
-<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://torch.mlverse.org/docs">torch</a></span><span class="op">)</span></code></pre></div>
-<p>Computational graphs and autograd are a very powerful paradigm for defining complex operators and automatically taking derivatives; however for large neural networks raw autograd can be a bit too low-level.</p>
-<p>When building neural networks we frequently think of arranging the computation into layers, some of which have learnable parameters which will be optimized during learning.</p>
-<p>In TensorFlow, packages like Keras, TensorFlow-Slim, and TFLearn provide higher-level abstractions over raw computational graphs that are useful for building neural networks.</p>
-<p>In torch, the nn functionality serves this same purpose. The nn feature defines a set of Modules, which are roughly equivalent to neural network layers. A Module receives input Tensors and computes output Tensors, but may also hold internal state such as Tensors containing learnable parameters. The nn collection also defines a set of useful loss functions that are commonly used when training neural networks.</p>
-<p>In this example we use nn to implement our two-layer network:</p>
-<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="../../reference/cuda_is_available.html">cuda_is_available</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span>
-   <span class="va">device</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_device.html">torch_device</a></span><span class="op">(</span><span class="st">"cuda"</span><span class="op">)</span>
-<span class="op">}</span> <span class="kw">else</span> <span class="op">{</span>
-   <span class="va">device</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_device.html">torch_device</a></span><span class="op">(</span><span class="st">"cpu"</span><span class="op">)</span>
-<span class="op">}</span>
-   
-<span class="co"># N is batch size; D_in is input dimension;</span>
-<span class="co"># H is hidden dimension; D_out is output dimension.</span>
-<span class="va">N</span> <span class="op">&lt;-</span> <span class="fl">64</span>
-<span class="va">D_in</span> <span class="op">&lt;-</span> <span class="fl">1000</span>
-<span class="va">H</span> <span class="op">&lt;-</span> <span class="fl">100</span>
-<span class="va">D_out</span> <span class="op">&lt;-</span> <span class="fl">10</span>
-
-<span class="co"># Create random input and output data</span>
-<span class="co"># Setting requires_grad=FALSE (the default) indicates that we do not need to </span>
-<span class="co"># compute gradients with respect to these Tensors during the backward pass.</span>
-<span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="va">N</span>, <span class="va">D_in</span>, device<span class="op">=</span><span class="va">device</span><span class="op">)</span>
-<span class="va">y</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="va">N</span>, <span class="va">D_out</span>, device<span class="op">=</span><span class="va">device</span><span class="op">)</span>
-
-<span class="co"># Use the nn package to define our model as a sequence of layers. nn_sequential</span>
-<span class="co"># is a Module which contains other Modules, and applies them in sequence to</span>
-<span class="co"># produce its output. Each Linear Module computes output from input using a</span>
-<span class="co"># linear function, and holds internal Tensors for its weight and bias.</span>
-<span class="va">model</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/nn_sequential.html">nn_sequential</a></span><span class="op">(</span>
-    <span class="fu"><a href="../../reference/nn_linear.html">nn_linear</a></span><span class="op">(</span><span class="va">D_in</span>, <span class="va">H</span><span class="op">)</span>,
-    <span class="fu"><a href="../../reference/nn_relu.html">nn_relu</a></span><span class="op">(</span><span class="op">)</span>,
-    <span class="fu"><a href="../../reference/nn_linear.html">nn_linear</a></span><span class="op">(</span><span class="va">H</span>, <span class="va">D_out</span><span class="op">)</span>
-<span class="op">)</span>
-
-<span class="co"># The nn package also contains definitions of popular loss functions; in this</span>
-<span class="co"># case we will use Mean Squared Error (MSE) as our loss function.</span>
-<span class="va">loss_fn</span> <span class="op">&lt;-</span> <span class="va">nnf_mse_loss</span>
-
-<span class="va">learning_rate</span> <span class="op">&lt;-</span> <span class="fl">1e-6</span>
-<span class="kw">for</span> <span class="op">(</span><span class="va">t</span> <span class="kw">in</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq_len</a></span><span class="op">(</span><span class="fl">500</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span>
-   <span class="co"># Forward pass: compute predicted y by passing x to the model. Module objects</span>
-   <span class="co"># can be called like functions. When doing so you pass a Tensor of input</span>
-   <span class="co"># data to the Module and it produces a Tensor of output data.</span>
-   <span class="va">y_pred</span> <span class="op">&lt;-</span> <span class="fu">model</span><span class="op">(</span><span class="va">x</span><span class="op">)</span>
-   
-   <span class="co"># Compute and print loss. We pass Tensors containing the predicted and true</span>
-   <span class="co"># values of y, and the loss function returns a Tensor containing the</span>
-   <span class="co"># loss.</span>
-   <span class="va">loss</span> <span class="op">&lt;-</span> <span class="fu">loss_fn</span><span class="op">(</span><span class="va">y_pred</span>, <span class="va">y</span><span class="op">)</span>
-   <span class="kw">if</span> <span class="op">(</span><span class="va">t</span> <span class="op">%%</span> <span class="fl">100</span> <span class="op">==</span> <span class="fl">0</span> <span class="op">||</span> <span class="va">t</span> <span class="op">==</span> <span class="fl">1</span><span class="op">)</span>
-      <span class="fu"><a href="https://rdrr.io/r/base/cat.html">cat</a></span><span class="op">(</span><span class="st">"Step:"</span>, <span class="va">t</span>, <span class="st">":"</span>, <span class="fu"><a href="https://rdrr.io/r/base/numeric.html">as.numeric</a></span><span class="op">(</span><span class="va">loss</span><span class="op">)</span>, <span class="st">"\n"</span><span class="op">)</span>
-   
-   <span class="co"># Zero the gradients before running the backward pass.</span>
-   <span class="va">model</span><span class="op">$</span><span class="fu">zero_grad</span><span class="op">(</span><span class="op">)</span>
-
-   <span class="co"># Backward pass: compute gradient of the loss with respect to all the learnable</span>
-   <span class="co"># parameters of the model. Internally, the parameters of each Module are stored</span>
-   <span class="co"># in Tensors with requires_grad=TRUE, so this call will compute gradients for</span>
-   <span class="co"># all learnable parameters in the model.</span>
-   <span class="va">loss</span><span class="op">$</span><span class="fu">backward</span><span class="op">(</span><span class="op">)</span>
-   
-   <span class="co"># Update the weights using gradient descent. Each parameter is a Tensor, so</span>
-   <span class="co"># we can access its gradients like we did before.</span>
-   <span class="fu"><a href="../../reference/with_no_grad.html">with_no_grad</a></span><span class="op">(</span><span class="op">{</span>
-      <span class="kw">for</span> <span class="op">(</span><span class="va">param</span> <span class="kw">in</span> <span class="va">model</span><span class="op">$</span><span class="va">parameters</span><span class="op">)</span> <span class="op">{</span>
-         <span class="va">param</span><span class="op">$</span><span class="fu">sub_</span><span class="op">(</span><span class="va">learning_rate</span> <span class="op">*</span> <span class="va">param</span><span class="op">$</span><span class="va">grad</span><span class="op">)</span>
-      <span class="op">}</span>
-   <span class="op">}</span><span class="op">)</span>
-<span class="op">}</span>
-<span class="co">#&gt; Step: 1 : 1.018742 </span>
-<span class="co">#&gt; Step: 100 : 1.018609 </span>
-<span class="co">#&gt; Step: 200 : 1.018475 </span>
-<span class="co">#&gt; Step: 300 : 1.018341 </span>
-<span class="co">#&gt; Step: 400 : 1.018206 </span>
-<span class="co">#&gt; Step: 500 : 1.018072</span></code></pre></div>
-<p>In the <a href="optim.html">next example</a> we will learn how to use optimizers implemented in torch.</p>
-  </div>
-
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-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
-</div>
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-  <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p>
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deleted file mode 100644
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-/* Styles for section anchors */
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-      <h1 data-toc-skip>optim: optimizers in torch</h1>
-            
-      
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/master/vignettes/getting-started/optim.Rmd"><code>vignettes/getting-started/optim.Rmd</code></a></small>
-      <div class="hidden name"><code>optim.Rmd</code></div>
-
-    </div>
-
-    
-    
-<blockquote>
-<p>Note: This is an R port of the official tutorial available <a href="https://pytorch.org/tutorials/beginner/examples_tensor/two_layer_net_numpy.html#sphx-glr-beginner-examples-tensor-two-layer-net-numpy-py">here</a>. All credits goes to <a href="https://github.com/jcjohnson/pytorch-examples">Justin Johnson</a>.</p>
-</blockquote>
-<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://torch.mlverse.org/docs">torch</a></span><span class="op">)</span></code></pre></div>
-<p>Up to this point we have updated the weights of our models by manually mutating the Tensors holding learnable parameters (with <code>with_no_grad</code> to avoid tracking history in autograd). This is not a huge burden for simple optimization algorithms like stochastic gradient descent, but in practice we often train neural networks using more sophisticated optimizers like AdaGrad, RMSProp, Adam, etc.</p>
-<p>The optim package in torch abstracts the idea of an optimization algorithm and provides implementations of commonly used optimization algorithms.</p>
-<p>In this example we will use the nn package to define our model as before, but we will optimize the model using the Adam algorithm provided by <code>optim</code>:</p>
-<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="../../reference/cuda_is_available.html">cuda_is_available</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span>
-   <span class="va">device</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_device.html">torch_device</a></span><span class="op">(</span><span class="st">"cuda"</span><span class="op">)</span>
-<span class="op">}</span> <span class="kw">else</span> <span class="op">{</span>
-   <span class="va">device</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_device.html">torch_device</a></span><span class="op">(</span><span class="st">"cpu"</span><span class="op">)</span>
-<span class="op">}</span>
-   
-<span class="co"># N is batch size; D_in is input dimension;</span>
-<span class="co"># H is hidden dimension; D_out is output dimension.</span>
-<span class="va">N</span> <span class="op">&lt;-</span> <span class="fl">64</span>
-<span class="va">D_in</span> <span class="op">&lt;-</span> <span class="fl">1000</span>
-<span class="va">H</span> <span class="op">&lt;-</span> <span class="fl">100</span>
-<span class="va">D_out</span> <span class="op">&lt;-</span> <span class="fl">10</span>
-
-<span class="co"># Create random input and output data</span>
-<span class="co"># Setting requires_grad=FALSE (the default) indicates that we do not need to </span>
-<span class="co"># compute gradients with respect to these Tensors during the backward pass.</span>
-<span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="va">N</span>, <span class="va">D_in</span>, device<span class="op">=</span><span class="va">device</span><span class="op">)</span>
-<span class="va">y</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="va">N</span>, <span class="va">D_out</span>, device<span class="op">=</span><span class="va">device</span><span class="op">)</span>
-
-<span class="co"># Use the nn package to define our model as a sequence of layers. nn_sequential</span>
-<span class="co"># is a Module which contains other Modules, and applies them in sequence to</span>
-<span class="co"># produce its output. Each Linear Module computes output from input using a</span>
-<span class="co"># linear function, and holds internal Tensors for its weight and bias.</span>
-<span class="va">model</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/nn_sequential.html">nn_sequential</a></span><span class="op">(</span>
-    <span class="fu"><a href="../../reference/nn_linear.html">nn_linear</a></span><span class="op">(</span><span class="va">D_in</span>, <span class="va">H</span><span class="op">)</span>,
-    <span class="fu"><a href="../../reference/nn_relu.html">nn_relu</a></span><span class="op">(</span><span class="op">)</span>,
-    <span class="fu"><a href="../../reference/nn_linear.html">nn_linear</a></span><span class="op">(</span><span class="va">H</span>, <span class="va">D_out</span><span class="op">)</span>
-<span class="op">)</span>
-
-<span class="co"># The nn package also contains definitions of popular loss functions; in this</span>
-<span class="co"># case we will use Mean Squared Error (MSE) as our loss function.</span>
-<span class="va">loss_fn</span> <span class="op">&lt;-</span> <span class="va">nnf_mse_loss</span>
-
-<span class="co"># Use the optim package to define an Optimizer that will update the weights of</span>
-<span class="co"># the model for us. Here we will use Adam; the optim package contains many other</span>
-<span class="co"># optimization algorithms. The first argument to the Adam constructor tells the</span>
-<span class="co"># optimizer which Tensors it should update.</span>
-<span class="va">learning_rate</span> <span class="op">&lt;-</span> <span class="fl">1e-4</span>
-<span class="va">optimizer</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/optim_adam.html">optim_adam</a></span><span class="op">(</span><span class="va">model</span><span class="op">$</span><span class="va">parameters</span>, lr<span class="op">=</span><span class="va">learning_rate</span><span class="op">)</span>
-
-<span class="kw">for</span> <span class="op">(</span><span class="va">t</span> <span class="kw">in</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq_len</a></span><span class="op">(</span><span class="fl">500</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span>
-   <span class="co"># Forward pass: compute predicted y by passing x to the model. Module objects</span>
-   <span class="co"># can be called like functions. When doing so you pass a Tensor of input</span>
-   <span class="co"># data to the Module and it produces a Tensor of output data.</span>
-   <span class="va">y_pred</span> <span class="op">&lt;-</span> <span class="fu">model</span><span class="op">(</span><span class="va">x</span><span class="op">)</span>
-   
-   <span class="co"># Compute and print loss. We pass Tensors containing the predicted and true</span>
-   <span class="co"># values of y, and the loss function returns a Tensor containing the</span>
-   <span class="co"># loss.</span>
-   <span class="va">loss</span> <span class="op">&lt;-</span> <span class="fu">loss_fn</span><span class="op">(</span><span class="va">y_pred</span>, <span class="va">y</span><span class="op">)</span>
-   <span class="kw">if</span> <span class="op">(</span><span class="va">t</span> <span class="op">%%</span> <span class="fl">100</span> <span class="op">==</span> <span class="fl">0</span> <span class="op">||</span> <span class="va">t</span> <span class="op">==</span> <span class="fl">1</span><span class="op">)</span>
-      <span class="fu"><a href="https://rdrr.io/r/base/cat.html">cat</a></span><span class="op">(</span><span class="st">"Step:"</span>, <span class="va">t</span>, <span class="st">":"</span>, <span class="fu"><a href="https://rdrr.io/r/base/numeric.html">as.numeric</a></span><span class="op">(</span><span class="va">loss</span><span class="op">)</span>, <span class="st">"\n"</span><span class="op">)</span>
-   
-   <span class="co"># Before the backward pass, use the optimizer object to zero all of the</span>
-   <span class="co"># gradients for the variables it will update (which are the learnable</span>
-   <span class="co"># weights of the model). This is because by default, gradients are</span>
-   <span class="co"># accumulated in buffers( i.e, not overwritten) whenever $backward()</span>
-   <span class="co"># is called. Checkout docs of `autograd_backward` for more details.</span>
-   <span class="va">optimizer</span><span class="op">$</span><span class="fu">zero_grad</span><span class="op">(</span><span class="op">)</span>
-
-   <span class="co"># Backward pass: compute gradient of the loss with respect to model</span>
-   <span class="co"># parameters</span>
-   <span class="va">loss</span><span class="op">$</span><span class="fu">backward</span><span class="op">(</span><span class="op">)</span>
-
-   <span class="co"># Calling the step function on an Optimizer makes an update to its</span>
-   <span class="co"># parameters</span>
-   <span class="va">optimizer</span><span class="op">$</span><span class="fu">step</span><span class="op">(</span><span class="op">)</span>
-<span class="op">}</span>
-<span class="co">#&gt; Step: 1 : 0.9679546 </span>
-<span class="co">#&gt; Step: 100 : 0.06467929 </span>
-<span class="co">#&gt; Step: 200 : 0.0009209086 </span>
-<span class="co">#&gt; Step: 300 : 9.68739e-06 </span>
-<span class="co">#&gt; Step: 400 : 3.821675e-07 </span>
-<span class="co">#&gt; Step: 500 : 1.036825e-08</span></code></pre></div>
-<p>In the <a href="custom-nn.html">next example</a> we will learn how to create custom <code>nn_modules</code>.</p>
-  </div>
-
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-  <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p>
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diff --git a/articles/getting-started/tensors-and-autograd.html b/articles/getting-started/tensors-and-autograd.html
deleted file mode 100644
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-<script src="tensors-and-autograd_files/anchor-sections-1.0/anchor-sections.js"></script><div class="row">
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-      <h1 data-toc-skip>Tensors and autograd</h1>
-            
-      
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/master/vignettes/getting-started/tensors-and-autograd.Rmd"><code>vignettes/getting-started/tensors-and-autograd.Rmd</code></a></small>
-      <div class="hidden name"><code>tensors-and-autograd.Rmd</code></div>
-
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-<blockquote>
-<p>Note: This is an R port of the official tutorial available <a href="https://pytorch.org/tutorials/beginner/examples_tensor/two_layer_net_numpy.html#sphx-glr-beginner-examples-tensor-two-layer-net-numpy-py">here</a>. All credits goes to <a href="https://github.com/jcjohnson/pytorch-examples">Justin Johnson</a>.</p>
-</blockquote>
-<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://torch.mlverse.org/docs">torch</a></span><span class="op">)</span></code></pre></div>
-<p>In the previous examples, we had to manually implement both the forward and backward passes of our neural network. Manually implementing the backward pass is not a big deal for a small two-layer network, but can quickly get very hairy for large complex networks.</p>
-<p>Thankfully, we can use automatic differentiation to automate the computation of backward passes in neural networks. The autograd feature in torch provides exactly this functionality. When using autograd, the forward pass of your network will define a computational graph; nodes in the graph will be Tensors, and edges will be functions that produce output Tensors from input Tensors. Backpropagating through this graph then allows you to easily compute gradients.</p>
-<p>This sounds complicated, it’s pretty simple to use in practice. Each Tensor represents a node in a computational graph. If x is a Tensor that has <code>x$requires_grad=TRUE</code> then <code>x$grad</code> is another Tensor holding the gradient of x with respect to some scalar value.</p>
-<p>Here we use torch Tensors and autograd to implement our two-layer network; now we no longer need to manually implement the backward pass through the network:</p>
-<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="../../reference/cuda_is_available.html">cuda_is_available</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span>
-   <span class="va">device</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_device.html">torch_device</a></span><span class="op">(</span><span class="st">"cuda"</span><span class="op">)</span>
-<span class="op">}</span> <span class="kw">else</span> <span class="op">{</span>
-   <span class="va">device</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_device.html">torch_device</a></span><span class="op">(</span><span class="st">"cpu"</span><span class="op">)</span>
-<span class="op">}</span>
-   
-<span class="co"># N is batch size; D_in is input dimension;</span>
-<span class="co"># H is hidden dimension; D_out is output dimension.</span>
-<span class="va">N</span> <span class="op">&lt;-</span> <span class="fl">64</span>
-<span class="va">D_in</span> <span class="op">&lt;-</span> <span class="fl">1000</span>
-<span class="va">H</span> <span class="op">&lt;-</span> <span class="fl">100</span>
-<span class="va">D_out</span> <span class="op">&lt;-</span> <span class="fl">10</span>
-
-<span class="co"># Create random input and output data</span>
-<span class="co"># Setting requires_grad=FALSE (the default) indicates that we do not need to </span>
-<span class="co"># compute gradients with respect to these Tensors during the backward pass.</span>
-<span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="va">N</span>, <span class="va">D_in</span>, device<span class="op">=</span><span class="va">device</span><span class="op">)</span>
-<span class="va">y</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="va">N</span>, <span class="va">D_out</span>, device<span class="op">=</span><span class="va">device</span><span class="op">)</span>
-
-<span class="co"># Randomly initialize weights</span>
-<span class="co"># Setting requires_grad=TRUE indicates that we want to compute gradients with</span>
-<span class="co"># respect to these Tensors during the backward pass.</span>
-<span class="va">w1</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="va">D_in</span>, <span class="va">H</span>, device<span class="op">=</span><span class="va">device</span>, requires_grad <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span>
-<span class="va">w2</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="va">H</span>, <span class="va">D_out</span>, device<span class="op">=</span><span class="va">device</span>, requires_grad <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span>
-
-<span class="va">learning_rate</span> <span class="op">&lt;-</span> <span class="fl">1e-6</span>
-<span class="kw">for</span> <span class="op">(</span><span class="va">t</span> <span class="kw">in</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq_len</a></span><span class="op">(</span><span class="fl">500</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span>
-   <span class="co"># Forward pass: compute predicted y using operations on Tensors; these</span>
-   <span class="co"># are exactly the same operations we used to compute the forward pass using</span>
-   <span class="co"># Tensors, but we do not need to keep references to intermediate values since</span>
-   <span class="co"># we are not implementing the backward pass by hand.</span>
-   <span class="va">y_pred</span> <span class="op">&lt;-</span> <span class="va">x</span><span class="op">$</span><span class="fu">mm</span><span class="op">(</span><span class="va">w1</span><span class="op">)</span><span class="op">$</span><span class="fu">clamp</span><span class="op">(</span>min<span class="op">=</span><span class="fl">0</span><span class="op">)</span><span class="op">$</span><span class="fu">mm</span><span class="op">(</span><span class="va">w2</span><span class="op">)</span>
-   
-   <span class="co"># Compute and print loss using operations on Tensors.</span>
-   <span class="co"># Now loss is a Tensor of shape (1,)</span>
-   <span class="va">loss</span> <span class="op">&lt;-</span> <span class="op">(</span><span class="va">y_pred</span> <span class="op">-</span> <span class="va">y</span><span class="op">)</span><span class="op">$</span><span class="fu">pow</span><span class="op">(</span><span class="fl">2</span><span class="op">)</span><span class="op">$</span><span class="fu">sum</span><span class="op">(</span><span class="op">)</span>
-   <span class="kw">if</span> <span class="op">(</span><span class="va">t</span> <span class="op">%%</span> <span class="fl">100</span> <span class="op">==</span> <span class="fl">0</span> <span class="op">||</span> <span class="va">t</span> <span class="op">==</span> <span class="fl">1</span><span class="op">)</span>
-      <span class="fu"><a href="https://rdrr.io/r/base/cat.html">cat</a></span><span class="op">(</span><span class="st">"Step:"</span>, <span class="va">t</span>, <span class="st">":"</span>, <span class="fu"><a href="https://rdrr.io/r/base/numeric.html">as.numeric</a></span><span class="op">(</span><span class="va">loss</span><span class="op">)</span>, <span class="st">"\n"</span><span class="op">)</span>
-   
-   <span class="co"># Use autograd to compute the backward pass. This call will compute the</span>
-   <span class="co"># gradient of loss with respect to all Tensors with requires_grad=True.</span>
-   <span class="co"># After this call w1$grad and w2$grad will be Tensors holding the gradient</span>
-   <span class="co"># of the loss with respect to w1 and w2 respectively.</span>
-   <span class="va">loss</span><span class="op">$</span><span class="fu">backward</span><span class="op">(</span><span class="op">)</span>
-   
-   <span class="co"># Manually update weights using gradient descent. Wrap in `with_no_grad`</span>
-   <span class="co"># because weights have requires_grad=TRUE, but we don't need to track this</span>
-   <span class="co"># in autograd.</span>
-   <span class="co"># You can also use optim_sgd to achieve this.</span>
-   <span class="fu"><a href="../../reference/with_no_grad.html">with_no_grad</a></span><span class="op">(</span><span class="op">{</span>
-      
-      <span class="co"># operations suffixed with an `_` operates on in-place on the tensor.</span>
-      <span class="va">w1</span><span class="op">$</span><span class="fu">sub_</span><span class="op">(</span><span class="va">learning_rate</span> <span class="op">*</span> <span class="va">w1</span><span class="op">$</span><span class="va">grad</span><span class="op">)</span>
-      <span class="va">w2</span><span class="op">$</span><span class="fu">sub_</span><span class="op">(</span><span class="va">learning_rate</span> <span class="op">*</span> <span class="va">w2</span><span class="op">$</span><span class="va">grad</span><span class="op">)</span>
-      
-      <span class="co"># Manually zero the gradients after updating weights</span>
-      <span class="va">w1</span><span class="op">$</span><span class="va">grad</span><span class="op">$</span><span class="fu">zero_</span><span class="op">(</span><span class="op">)</span>
-      <span class="va">w2</span><span class="op">$</span><span class="va">grad</span><span class="op">$</span><span class="fu">zero_</span><span class="op">(</span><span class="op">)</span>
-   <span class="op">}</span><span class="op">)</span>
-<span class="op">}</span>
-<span class="co">#&gt; Step: 1 : 24573142 </span>
-<span class="co">#&gt; Step: 100 : 423.7524 </span>
-<span class="co">#&gt; Step: 200 : 1.833858 </span>
-<span class="co">#&gt; Step: 300 : 0.01267943 </span>
-<span class="co">#&gt; Step: 400 : 0.0002803126 </span>
-<span class="co">#&gt; Step: 500 : 4.453462e-05</span></code></pre></div>
-<p>In the <a href="new-autograd-functions.html">next example</a> we will learn how to create new autograd functions.</p>
-  </div>
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deleted file mode 100644
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diff --git a/articles/getting-started/tensors.html b/articles/getting-started/tensors.html
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-      <h1 data-toc-skip>torch Tensors</h1>
-            
-      
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/master/vignettes/getting-started/tensors.Rmd"><code>vignettes/getting-started/tensors.Rmd</code></a></small>
-      <div class="hidden name"><code>tensors.Rmd</code></div>
-
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-    
-<blockquote>
-<p>Note: This is an R port of the official tutorial available <a href="https://pytorch.org/tutorials/beginner/examples_tensor/two_layer_net_numpy.html#sphx-glr-beginner-examples-tensor-two-layer-net-numpy-py">here</a>. All credits goes to <a href="https://github.com/jcjohnson/pytorch-examples">Justin Johnson</a>.</p>
-</blockquote>
-<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://torch.mlverse.org/docs">torch</a></span><span class="op">)</span></code></pre></div>
-<p>R arrays are great, but they cannot utilize GPUs to accelerate its numerical computations. For modern deep neural networks, GPUs often provide speedups of 50x or greater, so unfortunately pure R won’t be enough for modern deep learning.</p>
-<p>Here we introduce the most fundamental torch concept: the Tensor. A torch Tensor is conceptually similar to an R array: a Tensor is an n-dimensional array, and torch provides many functions for operating on these Tensors. Behind the scenes, Tensors can keep track of a computational graph and gradients, but they’re also useful as a generic tool for scientific computing.</p>
-<p>Also unlike R, torch Tensors can utilize GPUs to accelerate their numeric computations. To run a torch Tensor on GPU, you simply need to cast it to a new datatype.</p>
-<p>Here we use torch Tensors to fit a two-layer network to random data. Like the R before we need to manually implement the forward and backward passes through the network:</p>
-<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="../../reference/cuda_is_available.html">cuda_is_available</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span>
-   <span class="va">device</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_device.html">torch_device</a></span><span class="op">(</span><span class="st">"cuda"</span><span class="op">)</span>
-<span class="op">}</span> <span class="kw">else</span> <span class="op">{</span>
-   <span class="va">device</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_device.html">torch_device</a></span><span class="op">(</span><span class="st">"cpu"</span><span class="op">)</span>
-<span class="op">}</span>
-   
-<span class="co"># N is batch size; D_in is input dimension;</span>
-<span class="co"># H is hidden dimension; D_out is output dimension.</span>
-<span class="va">N</span> <span class="op">&lt;-</span> <span class="fl">64</span>
-<span class="va">D_in</span> <span class="op">&lt;-</span> <span class="fl">1000</span>
-<span class="va">H</span> <span class="op">&lt;-</span> <span class="fl">100</span>
-<span class="va">D_out</span> <span class="op">&lt;-</span> <span class="fl">10</span>
-
-<span class="co"># Create random input and output data</span>
-<span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="va">N</span>, <span class="va">D_in</span>, device<span class="op">=</span><span class="va">device</span><span class="op">)</span>
-<span class="va">y</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="va">N</span>, <span class="va">D_out</span>, device<span class="op">=</span><span class="va">device</span><span class="op">)</span>
-
-<span class="co"># Randomly initialize weights</span>
-<span class="va">w1</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="va">D_in</span>, <span class="va">H</span>, device<span class="op">=</span><span class="va">device</span><span class="op">)</span>
-<span class="va">w2</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="va">H</span>, <span class="va">D_out</span>, device<span class="op">=</span><span class="va">device</span><span class="op">)</span>
-
-<span class="va">learning_rate</span> <span class="op">&lt;-</span> <span class="fl">1e-6</span>
-<span class="kw">for</span> <span class="op">(</span><span class="va">t</span> <span class="kw">in</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq_len</a></span><span class="op">(</span><span class="fl">500</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span>
-   <span class="co"># Forward pass: compute predicted y</span>
-   <span class="va">h</span> <span class="op">&lt;-</span> <span class="va">x</span><span class="op">$</span><span class="fu">mm</span><span class="op">(</span><span class="va">w1</span><span class="op">)</span>
-   <span class="va">h_relu</span> <span class="op">&lt;-</span> <span class="va">h</span><span class="op">$</span><span class="fu">clamp</span><span class="op">(</span>min<span class="op">=</span><span class="fl">0</span><span class="op">)</span>
-   <span class="va">y_pred</span> <span class="op">&lt;-</span> <span class="va">h_relu</span><span class="op">$</span><span class="fu">mm</span><span class="op">(</span><span class="va">w2</span><span class="op">)</span>
-   
-   <span class="co"># Compute and print loss</span>
-   <span class="va">loss</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/numeric.html">as.numeric</a></span><span class="op">(</span><span class="op">(</span><span class="va">y_pred</span> <span class="op">-</span> <span class="va">y</span><span class="op">)</span><span class="op">$</span><span class="fu">pow</span><span class="op">(</span><span class="fl">2</span><span class="op">)</span><span class="op">$</span><span class="fu">sum</span><span class="op">(</span><span class="op">)</span><span class="op">)</span>
-   <span class="kw">if</span> <span class="op">(</span><span class="va">t</span> <span class="op">%%</span> <span class="fl">100</span> <span class="op">==</span> <span class="fl">0</span> <span class="op">||</span> <span class="va">t</span> <span class="op">==</span> <span class="fl">1</span><span class="op">)</span>
-      <span class="fu"><a href="https://rdrr.io/r/base/cat.html">cat</a></span><span class="op">(</span><span class="st">"Step:"</span>, <span class="va">t</span>, <span class="st">":"</span>, <span class="va">loss</span>, <span class="st">"\n"</span><span class="op">)</span>
-   
-   <span class="co"># Backprop to compute gradients of w1 and w2 with respect to loss</span>
-   <span class="va">grad_y_pred</span> <span class="op">&lt;-</span> <span class="fl">2.0</span> <span class="op">*</span> <span class="op">(</span><span class="va">y_pred</span> <span class="op">-</span> <span class="va">y</span><span class="op">)</span>
-   <span class="va">grad_w2</span> <span class="op">&lt;-</span> <span class="va">h_relu</span><span class="op">$</span><span class="fu">t</span><span class="op">(</span><span class="op">)</span><span class="op">$</span><span class="fu">mm</span><span class="op">(</span><span class="va">grad_y_pred</span><span class="op">)</span>
-   <span class="va">grad_h_relu</span> <span class="op">&lt;-</span> <span class="va">grad_y_pred</span><span class="op">$</span><span class="fu">mm</span><span class="op">(</span><span class="va">w2</span><span class="op">$</span><span class="fu">t</span><span class="op">(</span><span class="op">)</span><span class="op">)</span>
-   <span class="va">grad_h</span> <span class="op">&lt;-</span> <span class="va">grad_h_relu</span><span class="op">$</span><span class="fu">clone</span><span class="op">(</span><span class="op">)</span>
-   <span class="va">grad_h</span><span class="op">[</span><span class="va">h</span> <span class="op">&lt;</span> <span class="fl">0</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="fl">0</span>
-   <span class="va">grad_w1</span> <span class="op">&lt;-</span> <span class="va">x</span><span class="op">$</span><span class="fu">t</span><span class="op">(</span><span class="op">)</span><span class="op">$</span><span class="fu">mm</span><span class="op">(</span><span class="va">grad_h</span><span class="op">)</span>
-   
-   <span class="co"># Update weights using gradient descent</span>
-   <span class="va">w1</span> <span class="op">&lt;-</span> <span class="va">w1</span> <span class="op">-</span> <span class="va">learning_rate</span> <span class="op">*</span> <span class="va">grad_w1</span>
-   <span class="va">w2</span> <span class="op">&lt;-</span> <span class="va">w2</span> <span class="op">-</span> <span class="va">learning_rate</span> <span class="op">*</span> <span class="va">grad_w2</span>
-<span class="op">}</span>
-<span class="co">#&gt; Step: 1 : 28228804 </span>
-<span class="co">#&gt; Step: 100 : 330.6301 </span>
-<span class="co">#&gt; Step: 200 : 1.205863 </span>
-<span class="co">#&gt; Step: 300 : 0.006815678 </span>
-<span class="co">#&gt; Step: 400 : 0.0001808387 </span>
-<span class="co">#&gt; Step: 500 : 3.467693e-05</span></code></pre></div>
-<p>In the <a href="tensors-and-autograd.html">next example</a> we will use autograd instead of computing the gradients manually.</p>
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deleted file mode 100644
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index 07aee5fcb..000000000
--- a/articles/getting-started/tensors_files/anchor-sections-1.0/anchor-sections.css
+++ /dev/null
@@ -1,4 +0,0 @@
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diff --git a/articles/getting-started/tensors_files/anchor-sections-1.0/anchor-sections.js b/articles/getting-started/tensors_files/anchor-sections-1.0/anchor-sections.js
deleted file mode 100644
index 570f99a0a..000000000
--- a/articles/getting-started/tensors_files/anchor-sections-1.0/anchor-sections.js
+++ /dev/null
@@ -1,33 +0,0 @@
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-  // Do nothing if AnchorJS is used
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-  if (Array.from(h).some(x => x.classList.contains('hasAnchor'))) {
-    return null;
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-            ? x.id : '');
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-  // Add anchors
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diff --git a/articles/getting-started/warmup.html b/articles/getting-started/warmup.html
deleted file mode 100644
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-<script src="warmup_files/anchor-sections-1.0/anchor-sections.js"></script><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header toc-ignore">
-      <h1 data-toc-skip>Warm-up</h1>
-            
-      
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/master/vignettes/getting-started/warmup.Rmd"><code>vignettes/getting-started/warmup.Rmd</code></a></small>
-      <div class="hidden name"><code>warmup.Rmd</code></div>
-
-    </div>
-
-    
-    
-<blockquote>
-<p>Note: This is an R port of the official tutorial available <a href="https://pytorch.org/tutorials/beginner/examples_tensor/two_layer_net_numpy.html#sphx-glr-beginner-examples-tensor-two-layer-net-numpy-py">here</a>. All credits goes to <a href="https://github.com/jcjohnson/pytorch-examples">Justin Johnson</a>.</p>
-</blockquote>
-<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://torch.mlverse.org/docs">torch</a></span><span class="op">)</span></code></pre></div>
-<p>A fully-connected ReLU network with one hidden layer and no biases, trained to predict y from x using Euclidean error.</p>
-<p>This implementation uses pure R to manually compute the forward pass, loss, and backward pass.</p>
-<p>An R array is a generic n-dimensional array; it does not know anything about deep learning or gradients or computational graphs, and is just a way to perform generic numeric computations.</p>
-<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="co"># N is batch size; D_in is input dimension;</span>
-<span class="co"># H is hidden dimension; D_out is output dimension.</span>
-<span class="va">N</span> <span class="op">&lt;-</span> <span class="fl">64</span>
-<span class="va">D_in</span> <span class="op">&lt;-</span> <span class="fl">1000</span>
-<span class="va">H</span> <span class="op">&lt;-</span> <span class="fl">100</span>
-<span class="va">D_out</span> <span class="op">&lt;-</span> <span class="fl">10</span>
-
-<span class="co"># Create random input and output data</span>
-<span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/array.html">array</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/stats/Normal.html">rnorm</a></span><span class="op">(</span><span class="va">N</span><span class="op">*</span><span class="va">D_in</span><span class="op">)</span>, dim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="va">N</span>, <span class="va">D_in</span><span class="op">)</span><span class="op">)</span>
-<span class="va">y</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/array.html">array</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/stats/Normal.html">rnorm</a></span><span class="op">(</span><span class="va">N</span><span class="op">*</span><span class="va">D_out</span><span class="op">)</span>, dim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="va">N</span>, <span class="va">D_out</span><span class="op">)</span><span class="op">)</span>
-
-<span class="co"># Randomly initialize weights</span>
-<span class="va">w1</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/array.html">array</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/stats/Normal.html">rnorm</a></span><span class="op">(</span><span class="va">D_in</span><span class="op">*</span><span class="va">H</span><span class="op">)</span>, dim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="va">D_in</span>, <span class="va">H</span><span class="op">)</span><span class="op">)</span>
-<span class="va">w2</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/array.html">array</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/stats/Normal.html">rnorm</a></span><span class="op">(</span><span class="va">H</span><span class="op">*</span><span class="va">D_out</span><span class="op">)</span>, dim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="va">H</span>, <span class="va">D_out</span><span class="op">)</span><span class="op">)</span>
-
-<span class="va">learning_rate</span> <span class="op">&lt;-</span> <span class="fl">1e-6</span>
-<span class="kw">for</span> <span class="op">(</span><span class="va">t</span> <span class="kw">in</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq_len</a></span><span class="op">(</span><span class="fl">500</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span>
-   <span class="co"># Forward pass: compute predicted y</span>
-   <span class="va">h</span> <span class="op">&lt;-</span> <span class="va">x</span> <span class="op">%*%</span> <span class="va">w1</span>
-   <span class="va">h_relu</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html">ifelse</a></span><span class="op">(</span><span class="va">h</span> <span class="op">&lt;</span> <span class="fl">0</span>, <span class="fl">0</span>, <span class="va">h</span><span class="op">)</span>
-   <span class="va">y_pred</span> <span class="op">&lt;-</span> <span class="va">h_relu</span> <span class="op">%*%</span> <span class="va">w2</span>
-   
-   <span class="co"># Compute and print loss</span>
-   <span class="va">loss</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="op">(</span><span class="va">y_pred</span> <span class="op">-</span> <span class="va">y</span><span class="op">)</span><span class="op">^</span><span class="fl">2</span><span class="op">)</span>
-   <span class="kw">if</span> <span class="op">(</span><span class="va">t</span> <span class="op">%%</span> <span class="fl">100</span> <span class="op">==</span> <span class="fl">0</span> <span class="op">||</span> <span class="va">t</span> <span class="op">==</span> <span class="fl">1</span><span class="op">)</span>
-      <span class="fu"><a href="https://rdrr.io/r/base/cat.html">cat</a></span><span class="op">(</span><span class="st">"Step:"</span>, <span class="va">t</span>, <span class="st">":"</span>, <span class="va">loss</span>, <span class="st">"\n"</span><span class="op">)</span>
-   
-   <span class="co"># Backprop to compute gradients of w1 and w2 with respect to loss</span>
-   <span class="va">grad_y_pred</span> <span class="op">&lt;-</span> <span class="fl">2</span> <span class="op">*</span> <span class="op">(</span><span class="va">y_pred</span> <span class="op">-</span> <span class="va">y</span><span class="op">)</span>
-   <span class="va">grad_w2</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/t.html">t</a></span><span class="op">(</span><span class="va">h_relu</span><span class="op">)</span> <span class="op">%*%</span> <span class="va">grad_y_pred</span>
-   <span class="va">grad_h_relu</span> <span class="op">&lt;-</span> <span class="va">grad_y_pred</span> <span class="op">%*%</span> <span class="fu"><a href="https://rdrr.io/r/base/t.html">t</a></span><span class="op">(</span><span class="va">w2</span><span class="op">)</span>
-   <span class="va">grad_h</span> <span class="op">&lt;-</span> <span class="va">grad_h_relu</span>
-   <span class="va">grad_h</span><span class="op">[</span><span class="va">h</span> <span class="op">&lt;</span> <span class="fl">0</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="fl">0</span>
-   <span class="va">grad_w1</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/t.html">t</a></span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="op">%*%</span> <span class="va">grad_h</span>
-   
-   <span class="co"># Update weights</span>
-   <span class="va">w1</span> <span class="op">&lt;-</span> <span class="va">w1</span> <span class="op">-</span> <span class="va">learning_rate</span> <span class="op">*</span> <span class="va">grad_w1</span>
-   <span class="va">w2</span> <span class="op">&lt;-</span> <span class="va">w2</span> <span class="op">-</span> <span class="va">learning_rate</span> <span class="op">*</span> <span class="va">grad_w2</span>
-<span class="op">}</span>
-<span class="co">#&gt; Step: 1 : 24029709 </span>
-<span class="co">#&gt; Step: 100 : 553.7803 </span>
-<span class="co">#&gt; Step: 200 : 1.694745 </span>
-<span class="co">#&gt; Step: 300 : 0.0077663 </span>
-<span class="co">#&gt; Step: 400 : 5.43958e-05 </span>
-<span class="co">#&gt; Step: 500 : 6.158879e-07</span></code></pre></div>
-<p>In the <a href="tensors.html">next example</a> we will replace the R array for a torch Tensor.</p>
-  </div>
-
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-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
-</div>
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deleted file mode 100644
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--- a/articles/getting-started/warmup_files/accessible-code-block-0.0.1/empty-anchor.js
+++ /dev/null
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-      <h1 data-toc-skip>What is torch?</h1>
-            
-      
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/master/vignettes/getting-started/what-is-torch.Rmd"><code>vignettes/getting-started/what-is-torch.Rmd</code></a></small>
-      <div class="hidden name"><code>what-is-torch.Rmd</code></div>
-
-    </div>
-
-    
-    
-<blockquote>
-<p>Note: This is an R port of the official tutorial available <a href="https://pytorch.org/tutorials/beginner/blitz/tensor_tutorial.html#sphx-glr-beginner-blitz-tensor-tutorial-py">here</a>. All credits goes to <a href="http://soumith.ch/">Soumith Chintala</a>.</p>
-</blockquote>
-<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://torch.mlverse.org/docs">torch</a></span><span class="op">)</span></code></pre></div>
-<p>It’s a scientific computing package targeted at two sets of audiences:</p>
-<ul>
-<li>An array library to use the power of GPUs</li>
-<li>a deep learning research platform that provides maximum flexibility and speed</li>
-</ul>
-<div id="getting-started" class="section level2">
-<h2 class="hasAnchor">
-<a href="#getting-started" class="anchor"></a>Getting started</h2>
-<div id="tensors" class="section level3">
-<h3 class="hasAnchor">
-<a href="#tensors" class="anchor"></a>Tensors</h3>
-<p>Tensors are similar to R arrays, with the addition being that Tensors can also be used on a GPU to accelerate computing.</p>
-<blockquote>
-<p>Note: An uninitialized matrix is declared, but does not contain definite known values before it is used. When an uninitialized matrix is created, whatever values were in the allocated memory at the time will appear as the initial values.</p>
-</blockquote>
-<p>Construct a 5x3 matrix, uninitialized:</p>
-<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_empty.html">torch_empty</a></span><span class="op">(</span><span class="fl">5</span>, <span class="fl">3</span><span class="op">)</span>
-<span class="va">x</span>
-<span class="co">#&gt; torch_tensor</span>
-<span class="co">#&gt;  0.0000e+00  8.5899e+09 -2.5192e-13</span>
-<span class="co">#&gt;  1.0845e-19  1.1210e-44  1.6816e-43</span>
-<span class="co">#&gt;  7.1466e-44  4.4842e-44  1.5274e-43</span>
-<span class="co">#&gt;  1.3593e-43  1.6255e-43  1.5975e-43</span>
-<span class="co">#&gt;  1.4714e-43  1.6816e-43  6.1657e-44</span>
-<span class="co">#&gt; [ CPUFloatType{5,3} ]</span></code></pre></div>
-<p>Construct a randomly initialized matrix:</p>
-<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_rand.html">torch_rand</a></span><span class="op">(</span><span class="fl">5</span>, <span class="fl">3</span><span class="op">)</span>
-<span class="va">x</span>
-<span class="co">#&gt; torch_tensor</span>
-<span class="co">#&gt;  0.8047  0.9970  0.3180</span>
-<span class="co">#&gt;  0.2996  0.6715  0.8695</span>
-<span class="co">#&gt;  0.9981  0.0952  0.8207</span>
-<span class="co">#&gt;  0.7128  0.9619  0.6479</span>
-<span class="co">#&gt;  0.4912  0.9580  0.4217</span>
-<span class="co">#&gt; [ CPUFloatType{5,3} ]</span></code></pre></div>
-<p>Construct a matrix filled zeros and of dtype long:</p>
-<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_zeros.html">torch_zeros</a></span><span class="op">(</span><span class="fl">5</span>, <span class="fl">3</span>, dtype <span class="op">=</span> <span class="fu"><a href="../../reference/torch_dtype.html">torch_long</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span>
-<span class="va">x</span>
-<span class="co">#&gt; torch_tensor</span>
-<span class="co">#&gt;  0  0  0</span>
-<span class="co">#&gt;  0  0  0</span>
-<span class="co">#&gt;  0  0  0</span>
-<span class="co">#&gt;  0  0  0</span>
-<span class="co">#&gt;  0  0  0</span>
-<span class="co">#&gt; [ CPULongType{5,3} ]</span></code></pre></div>
-<p>Construct a tensor directly from data:</p>
-<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">5.5</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span>
-<span class="va">x</span>
-<span class="co">#&gt; torch_tensor</span>
-<span class="co">#&gt;  5.5000</span>
-<span class="co">#&gt;  3.0000</span>
-<span class="co">#&gt; [ CPUFloatType{2} ]</span></code></pre></div>
-<p>or create a tensor based on an existing tensor. These methods will reuse properties of the input tensor, e.g. dtype, unless new values are provided by user</p>
-<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn_like.html">torch_randn_like</a></span><span class="op">(</span><span class="va">x</span>, dtype <span class="op">=</span> <span class="fu"><a href="../../reference/torch_dtype.html">torch_float</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="co"># override dtype!</span>
-<span class="va">x</span>                                               <span class="co"># result has the same size</span>
-<span class="co">#&gt; torch_tensor</span>
-<span class="co">#&gt; -1.2922</span>
-<span class="co">#&gt; -0.6301</span>
-<span class="co">#&gt; [ CPUFloatType{2} ]</span></code></pre></div>
-<p>Get its size:</p>
-<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="va">x</span><span class="op">$</span><span class="fu">size</span><span class="op">(</span><span class="op">)</span>
-<span class="co">#&gt; [1] 2</span></code></pre></div>
-</div>
-<div id="operations" class="section level3">
-<h3 class="hasAnchor">
-<a href="#operations" class="anchor"></a>Operations</h3>
-<p>There are multiple syntaxes for operations. In the following example, we will take a look at the addition operation.</p>
-<p>Addition: syntax 1</p>
-<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_rand.html">torch_rand</a></span><span class="op">(</span><span class="fl">5</span>, <span class="fl">3</span><span class="op">)</span>
-<span class="va">y</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_rand.html">torch_rand</a></span><span class="op">(</span><span class="fl">5</span>, <span class="fl">3</span><span class="op">)</span>
-<span class="va">x</span> <span class="op">+</span> <span class="va">y</span>
-<span class="co">#&gt; torch_tensor</span>
-<span class="co">#&gt;  0.1534  0.5196  1.1073</span>
-<span class="co">#&gt;  1.1957  0.8504  1.1173</span>
-<span class="co">#&gt;  1.0868  1.1021  1.8485</span>
-<span class="co">#&gt;  0.4676  1.0156  0.2103</span>
-<span class="co">#&gt;  1.0188  1.1065  0.8460</span>
-<span class="co">#&gt; [ CPUFloatType{5,3} ]</span></code></pre></div>
-<p>Addition: syntax 2</p>
-<div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="fu"><a href="../../reference/torch_add.html">torch_add</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">y</span><span class="op">)</span>
-<span class="co">#&gt; torch_tensor</span>
-<span class="co">#&gt;  0.1534  0.5196  1.1073</span>
-<span class="co">#&gt;  1.1957  0.8504  1.1173</span>
-<span class="co">#&gt;  1.0868  1.1021  1.8485</span>
-<span class="co">#&gt;  0.4676  1.0156  0.2103</span>
-<span class="co">#&gt;  1.0188  1.1065  0.8460</span>
-<span class="co">#&gt; [ CPUFloatType{5,3} ]</span></code></pre></div>
-<p>Addition: in-place</p>
-<div class="sourceCode" id="cb10"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="va">y</span><span class="op">$</span><span class="fu">add_</span><span class="op">(</span><span class="va">x</span><span class="op">)</span>
-<span class="co">#&gt; torch_tensor</span>
-<span class="co">#&gt;  0.1534  0.5196  1.1073</span>
-<span class="co">#&gt;  1.1957  0.8504  1.1173</span>
-<span class="co">#&gt;  1.0868  1.1021  1.8485</span>
-<span class="co">#&gt;  0.4676  1.0156  0.2103</span>
-<span class="co">#&gt;  1.0188  1.1065  0.8460</span>
-<span class="co">#&gt; [ CPUFloatType{5,3} ]</span>
-<span class="va">y</span>
-<span class="co">#&gt; torch_tensor</span>
-<span class="co">#&gt;  0.1534  0.5196  1.1073</span>
-<span class="co">#&gt;  1.1957  0.8504  1.1173</span>
-<span class="co">#&gt;  1.0868  1.1021  1.8485</span>
-<span class="co">#&gt;  0.4676  1.0156  0.2103</span>
-<span class="co">#&gt;  1.0188  1.1065  0.8460</span>
-<span class="co">#&gt; [ CPUFloatType{5,3} ]</span></code></pre></div>
-<blockquote>
-<p>Note: Any operation that mutates a tensor in-place is post-fixed with an <code>_</code>. For example: <code>x$copy_(y)</code>, <code>x$t_()</code>, will change x.</p>
-</blockquote>
-<p>You can use standard R-like indexing with all bells and whistles! See more about indexing with <code><a href="../../articles/indexing.html">vignette("indexing")</a></code>.</p>
-<div class="sourceCode" id="cb11"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="va">x</span><span class="op">[</span>, <span class="fl">1</span><span class="op">]</span>
-<span class="co">#&gt; torch_tensor</span>
-<span class="co">#&gt;  0.0319</span>
-<span class="co">#&gt;  0.2944</span>
-<span class="co">#&gt;  0.7091</span>
-<span class="co">#&gt;  0.2032</span>
-<span class="co">#&gt;  0.4828</span>
-<span class="co">#&gt; [ CPUFloatType{5} ]</span></code></pre></div>
-<p>Resizing: If you want to resize/reshape tensor, you can use <code>torch_view</code>:</p>
-<div class="sourceCode" id="cb12"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">4</span>, <span class="fl">4</span><span class="op">)</span>
-<span class="va">y</span> <span class="op">&lt;-</span> <span class="va">x</span><span class="op">$</span><span class="fu">view</span><span class="op">(</span><span class="fl">16</span><span class="op">)</span>
-<span class="va">z</span> <span class="op">&lt;-</span> <span class="va">x</span><span class="op">$</span><span class="fu">view</span><span class="op">(</span>size <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="op">-</span><span class="fl">1</span>, <span class="fl">8</span><span class="op">)</span><span class="op">)</span>  <span class="co"># the size -1 is inferred from other dimensions</span>
-<span class="va">x</span><span class="op">$</span><span class="fu">size</span><span class="op">(</span><span class="op">)</span>
-<span class="co">#&gt; [1] 4 4</span>
-<span class="va">y</span><span class="op">$</span><span class="fu">size</span><span class="op">(</span><span class="op">)</span>
-<span class="co">#&gt; [1] 16</span>
-<span class="va">z</span><span class="op">$</span><span class="fu">size</span><span class="op">(</span><span class="op">)</span>
-<span class="co">#&gt; [1] 2 8</span></code></pre></div>
-<p>If you have a one element tensor, use <code>$item()</code> to get the value as an R number</p>
-<div class="sourceCode" id="cb13"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">1</span><span class="op">)</span>
-<span class="va">x</span>
-<span class="co">#&gt; torch_tensor</span>
-<span class="co">#&gt; -0.4257</span>
-<span class="co">#&gt; [ CPUFloatType{1} ]</span>
-<span class="va">x</span><span class="op">$</span><span class="fu">item</span><span class="op">(</span><span class="op">)</span>
-<span class="co">#&gt; [1] -0.4256831</span></code></pre></div>
-<p>You can find a complete list of operations in the reference page.</p>
-</div>
-</div>
-<div id="r-bridge" class="section level2">
-<h2 class="hasAnchor">
-<a href="#r-bridge" class="anchor"></a>R bridge</h2>
-<p>Converting a Torch Tensor to an R array and vice versa is a breeze.</p>
-<div id="converting-a-torch-tensor-into-an-r-array" class="section level3">
-<h3 class="hasAnchor">
-<a href="#converting-a-torch-tensor-into-an-r-array" class="anchor"></a>Converting a torch tensor into an R array</h3>
-<div class="sourceCode" id="cb14"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="va">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_ones.html">torch_ones</a></span><span class="op">(</span><span class="fl">5</span><span class="op">)</span>
-<span class="va">a</span>
-<span class="co">#&gt; torch_tensor</span>
-<span class="co">#&gt;  1</span>
-<span class="co">#&gt;  1</span>
-<span class="co">#&gt;  1</span>
-<span class="co">#&gt;  1</span>
-<span class="co">#&gt;  1</span>
-<span class="co">#&gt; [ CPUFloatType{5} ]</span></code></pre></div>
-<div class="sourceCode" id="cb15"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="va">b</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/as_array.html">as_array</a></span><span class="op">(</span><span class="va">a</span><span class="op">)</span>
-<span class="va">b</span>
-<span class="co">#&gt; [1] 1 1 1 1 1</span></code></pre></div>
-</div>
-<div id="converting-r-arrays-to-torch-tensors" class="section level3">
-<h3 class="hasAnchor">
-<a href="#converting-r-arrays-to-torch-tensors" class="anchor"></a>Converting R arrays to torch tensors</h3>
-<div class="sourceCode" id="cb16"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="va">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/rep.html">rep</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">5</span><span class="op">)</span>
-<span class="va">a</span>
-<span class="co">#&gt; [1] 1 1 1 1 1</span>
-<span class="va">b</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="va">a</span><span class="op">)</span>
-<span class="va">b</span>
-<span class="co">#&gt; torch_tensor</span>
-<span class="co">#&gt;  1</span>
-<span class="co">#&gt;  1</span>
-<span class="co">#&gt;  1</span>
-<span class="co">#&gt;  1</span>
-<span class="co">#&gt;  1</span>
-<span class="co">#&gt; [ CPUFloatType{5} ]</span></code></pre></div>
-<p>Currently supported types are numerics and boolean types.</p>
-</div>
-</div>
-<div id="cuda-tensors" class="section level2">
-<h2 class="hasAnchor">
-<a href="#cuda-tensors" class="anchor"></a>CUDA tensors</h2>
-<p>Tensors can be moved onto any device using the <code>$to</code> method.</p>
-<div class="sourceCode" id="cb17"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="../../reference/cuda_is_available.html">cuda_is_available</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span>
-  <span class="va">device</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_device.html">torch_device</a></span><span class="op">(</span><span class="st">"cuda"</span><span class="op">)</span>
-  <span class="va">y</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_ones_like.html">torch_ones_like</a></span><span class="op">(</span><span class="va">x</span>, device <span class="op">=</span> <span class="va">device</span><span class="op">)</span>  <span class="co"># directly create a tensor on GPU</span>
-  <span class="va">x</span> <span class="op">&lt;-</span> <span class="va">x</span><span class="op">$</span><span class="fu">to</span><span class="op">(</span><span class="va">device</span><span class="op">)</span>                       <span class="co"># or just use strings ``.to("cuda")``</span>
-  <span class="va">z</span> <span class="op">&lt;-</span> <span class="va">x</span> <span class="op">+</span> <span class="va">y</span>
-  <span class="fu"><a href="https://rdrr.io/r/base/print.html">print</a></span><span class="op">(</span><span class="va">z</span><span class="op">)</span>
-  <span class="fu"><a href="https://rdrr.io/r/base/print.html">print</a></span><span class="op">(</span><span class="va">z</span><span class="op">$</span><span class="fu">to</span><span class="op">(</span>device <span class="op">=</span> <span class="st">"cpu"</span>, <span class="fu"><a href="../../reference/torch_dtype.html">torch_double</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span><span class="op">)</span> <span class="co"># `$to` can also change dtype together!</span>
-<span class="op">}</span></code></pre></div>
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 <div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://torch.mlverse.org/docs">torch</a></span><span class="op">)</span></code></pre></div>
+<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://torch.mlverse.org/docs">torch</a></span><span class="op">)</span></code></pre></div>
 <p>In this article we describe the indexing operator for torch tensors and how it compares to the R indexing operator for arrays.</p>
-<p>Torch’s indexing semantics are closer to numpy’s semantics than R’s. You will find a lot of similarities between this article and the <code>numpy</code> indexing article available <a href="https://docs.scipy.org/doc/numpy-1.10.0/user/basics.indexing.html">here</a>.</p>
-<div id="single-element-indexing" class="section level2">
-<h2 class="hasAnchor">
-<a href="#single-element-indexing" class="anchor"></a>Single element indexing</h2>
+<p>Torch’s indexing semantics are closer to numpy’s semantics than R’s. You will find a lot of similarities between this article and the <code>numpy</code> indexing article available <a href="https://docs.scipy.org/doc/numpy-1.10.0/user/basics.indexing.html" class="external-link">here</a>.</p>
+<div class="section level2">
+<h2 id="single-element-indexing">Single element indexing<a class="anchor" aria-label="anchor" href="#single-element-indexing"></a>
+</h2>
 <p>Single element indexing for a 1-D tensors works mostly as expected. Like R, it is 1-based. Unlike R though, it accepts negative indices for indexing from the end of the array. (In R, negative indices are used to remove elements.)</p>
 <div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="fl">10</span><span class="op">)</span>
@@ -164,7 +125,7 @@
 <span class="co">#&gt; [ CPULongType{} ]</span></code></pre></div>
 <p>You can also subset matrices and higher dimensions arrays using the same syntax:</p>
 <div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="va">x</span> <span class="op">&lt;-</span> <span class="va">x</span><span class="op">$</span><span class="fu">reshape</span><span class="op">(</span>shape <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">2</span>,<span class="fl">5</span><span class="op">)</span><span class="op">)</span>
+<code class="sourceCode R"><span class="va">x</span> <span class="op">&lt;-</span> <span class="va">x</span><span class="op">$</span><span class="fu">reshape</span><span class="op">(</span>shape <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>,<span class="fl">5</span><span class="op">)</span><span class="op">)</span>
 <span class="va">x</span>
 <span class="co">#&gt; torch_tensor</span>
 <span class="co">#&gt;   1   2   3   4   5</span>
@@ -189,9 +150,9 @@
 <span class="co">#&gt;  5</span>
 <span class="co">#&gt; [ CPULongType{5} ]</span></code></pre></div>
 </div>
-<div id="slicing-and-striding" class="section level2">
-<h2 class="hasAnchor">
-<a href="#slicing-and-striding" class="anchor"></a>Slicing and striding</h2>
+<div class="section level2">
+<h2 id="slicing-and-striding">Slicing and striding<a class="anchor" aria-label="anchor" href="#slicing-and-striding"></a>
+</h2>
 <p>It is possible to slice and stride arrays to extract sub-arrays of the same number of dimensions, but of different sizes than the original. This is best illustrated by a few examples:</p>
 <div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="fl">10</span><span class="op">)</span>
@@ -242,7 +203,7 @@
 <span class="co">#&gt;  10</span>
 <span class="co">#&gt; [ CPULongType{6} ]</span></code></pre></div>
 <blockquote>
-<p>Note: the slicing behavior relies on <a href="https://adv-r.hadley.nz/evaluation.html#evaluation">Non Standard Evaluation</a>. It requires that the expression is passed to the <code>[</code> not exactly the resulting R vector.</p>
+<p>Note: the slicing behavior relies on <a href="https://adv-r.hadley.nz/evaluation.html#evaluation" class="external-link">Non Standard Evaluation</a>. It requires that the expression is passed to the <code>[</code> not exactly the resulting R vector.</p>
 </blockquote>
 <p>To allow dynamic dynamic indices, you can create a new slice using the <code>slc</code> function. For example:</p>
 <div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
@@ -261,37 +222,37 @@
 <span class="co">#&gt;  5</span>
 <span class="co">#&gt; [ CPULongType{3} ]</span></code></pre></div>
 </div>
-<div id="getting-the-complete-dimension" class="section level2">
-<h2 class="hasAnchor">
-<a href="#getting-the-complete-dimension" class="anchor"></a>Getting the complete dimension</h2>
+<div class="section level2">
+<h2 id="getting-the-complete-dimension">Getting the complete dimension<a class="anchor" aria-label="anchor" href="#getting-the-complete-dimension"></a>
+</h2>
 <p>Like in R, you can take all elements in a dimension by leaving an index empty.</p>
 <p>Consider a matrix:</p>
 <div class="sourceCode" id="cb10"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">3</span><span class="op">)</span>
 <span class="va">x</span>
 <span class="co">#&gt; torch_tensor</span>
-<span class="co">#&gt; -0.1247  1.0235 -0.0020</span>
-<span class="co">#&gt; -0.5360 -0.5432  1.1372</span>
+<span class="co">#&gt;  0.4345  2.2531 -0.8595</span>
+<span class="co">#&gt; -1.8210 -0.9156 -1.1016</span>
 <span class="co">#&gt; [ CPUFloatType{2,3} ]</span></code></pre></div>
 <p>The following syntax will give you the first row:</p>
 <div class="sourceCode" id="cb11"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span class="va">x</span><span class="op">[</span><span class="fl">1</span>,<span class="op">]</span>
 <span class="co">#&gt; torch_tensor</span>
-<span class="co">#&gt; -0.1247</span>
-<span class="co">#&gt;  1.0235</span>
-<span class="co">#&gt; -0.0020</span>
+<span class="co">#&gt;  0.4345</span>
+<span class="co">#&gt;  2.2531</span>
+<span class="co">#&gt; -0.8595</span>
 <span class="co">#&gt; [ CPUFloatType{3} ]</span></code></pre></div>
 <p>And this would give you the first 2 columns:</p>
 <div class="sourceCode" id="cb12"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span class="va">x</span><span class="op">[</span>,<span class="fl">1</span><span class="op">:</span><span class="fl">2</span><span class="op">]</span>
 <span class="co">#&gt; torch_tensor</span>
-<span class="co">#&gt; -0.1247  1.0235</span>
-<span class="co">#&gt; -0.5360 -0.5432</span>
+<span class="co">#&gt;  0.4345  2.2531</span>
+<span class="co">#&gt; -1.8210 -0.9156</span>
 <span class="co">#&gt; [ CPUFloatType{2,2} ]</span></code></pre></div>
 </div>
-<div id="dropping-dimensions" class="section level2">
-<h2 class="hasAnchor">
-<a href="#dropping-dimensions" class="anchor"></a>Dropping dimensions</h2>
+<div class="section level2">
+<h2 id="dropping-dimensions">Dropping dimensions<a class="anchor" aria-label="anchor" href="#dropping-dimensions"></a>
+</h2>
 <p>By default, when indexing by a single integer, this dimension will be dropped to avoid the singleton dimension:</p>
 <div class="sourceCode" id="cb13"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">3</span><span class="op">)</span>
@@ -302,12 +263,12 @@
 <code class="sourceCode R"><span class="va">x</span><span class="op">[</span><span class="fl">1</span>,,drop <span class="op">=</span> <span class="cn">FALSE</span><span class="op">]</span><span class="op">$</span><span class="va">shape</span>
 <span class="co">#&gt; [1] 1 3</span></code></pre></div>
 </div>
-<div id="adding-a-new-dimension" class="section level2">
-<h2 class="hasAnchor">
-<a href="#adding-a-new-dimension" class="anchor"></a>Adding a new dimension</h2>
+<div class="section level2">
+<h2 id="adding-a-new-dimension">Adding a new dimension<a class="anchor" aria-label="anchor" href="#adding-a-new-dimension"></a>
+</h2>
 <p>It’s possible to add a new dimension to a tensor using index-like syntax:</p>
 <div class="sourceCode" id="cb15"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">10</span><span class="op">)</span><span class="op">)</span>
+<code class="sourceCode R"><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">10</span><span class="op">)</span><span class="op">)</span>
 <span class="va">x</span><span class="op">$</span><span class="va">shape</span>
 <span class="co">#&gt; [1] 1</span>
 <span class="va">x</span><span class="op">[</span>, <span class="va">newaxis</span><span class="op">]</span><span class="op">$</span><span class="va">shape</span>
@@ -319,12 +280,12 @@
 <code class="sourceCode R"><span class="va">x</span><span class="op">[</span>,<span class="cn">NULL</span><span class="op">]</span><span class="op">$</span><span class="va">shape</span>
 <span class="co">#&gt; [1] 1 1</span></code></pre></div>
 </div>
-<div id="dealing-with-variable-number-of-indices" class="section level2">
-<h2 class="hasAnchor">
-<a href="#dealing-with-variable-number-of-indices" class="anchor"></a>Dealing with variable number of indices</h2>
+<div class="section level2">
+<h2 id="dealing-with-variable-number-of-indices">Dealing with variable number of indices<a class="anchor" aria-label="anchor" href="#dealing-with-variable-number-of-indices"></a>
+</h2>
 <p>Sometimes we don’t know how many dimensions a tensor has, but we do know what to do with the last available dimension, or the first one. To subsume all others, we can use <code>..</code>:</p>
 <div class="sourceCode" id="cb17"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="va">z</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="fl">125</span><span class="op">)</span><span class="op">$</span><span class="fu">reshape</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">5</span>,<span class="fl">5</span>,<span class="fl">5</span><span class="op">)</span><span class="op">)</span>
+<code class="sourceCode R"><span class="va">z</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="fl">125</span><span class="op">)</span><span class="op">$</span><span class="fu">reshape</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">5</span>,<span class="fl">5</span>,<span class="fl">5</span><span class="op">)</span><span class="op">)</span>
 <span class="va">z</span><span class="op">[</span><span class="fl">1</span>,<span class="va">..</span><span class="op">]</span>
 <span class="co">#&gt; torch_tensor</span>
 <span class="co">#&gt;   1   2   3   4   5</span>
@@ -342,32 +303,32 @@
 <span class="co">#&gt;  101  106  111  116  121</span>
 <span class="co">#&gt; [ CPULongType{5,5} ]</span></code></pre></div>
 </div>
-<div id="indexing-with-vectors" class="section level2">
-<h2 class="hasAnchor">
-<a href="#indexing-with-vectors" class="anchor"></a>Indexing with vectors</h2>
+<div class="section level2">
+<h2 id="indexing-with-vectors">Indexing with vectors<a class="anchor" aria-label="anchor" href="#indexing-with-vectors"></a>
+</h2>
 <p>Vector indexing is also supported but care must be taken regarding performance as, in general its much less performant than slice based indexing.</p>
 <blockquote>
-<p>Note: Starting from version 0.5.0, vector indexing in torch follows R semantics, prior to that the behavior was similar to <a href="https://numpy.org/doc/stable/reference/arrays.indexing.html#advanced-indexing">numpy’s advanced indexing</a>. To use the old behavior, consider using <code><a href="../reference/torch_index.html">?torch_index</a></code>, <code><a href="../reference/torch_index_put.html">?torch_index_put</a></code> or <code>torch_index_put_</code>.</p>
+<p>Note: Starting from version 0.5.0, vector indexing in torch follows R semantics, prior to that the behavior was similar to <a href="https://numpy.org/doc/stable/reference/arrays.indexing.html#advanced-indexing" class="external-link">numpy’s advanced indexing</a>. To use the old behavior, consider using <code><a href="../reference/torch_index.html">?torch_index</a></code>, <code><a href="../reference/torch_index_put.html">?torch_index_put</a></code> or <code>torch_index_put_</code>.</p>
 </blockquote>
 <div class="sourceCode" id="cb18"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">4</span>,<span class="fl">4</span><span class="op">)</span>
-<span class="va">x</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">1</span>,<span class="fl">3</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">1</span>,<span class="fl">3</span><span class="op">)</span><span class="op">]</span>
+<span class="va">x</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>,<span class="fl">3</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>,<span class="fl">3</span><span class="op">)</span><span class="op">]</span>
 <span class="co">#&gt; torch_tensor</span>
-<span class="co">#&gt; -0.8025  0.2212</span>
-<span class="co">#&gt;  0.5788  0.3573</span>
+<span class="co">#&gt;  0.3352  0.8809</span>
+<span class="co">#&gt; -0.0952 -0.3883</span>
 <span class="co">#&gt; [ CPUFloatType{2,2} ]</span></code></pre></div>
 <p>You can also use boolean vectors, for example:</p>
 <div class="sourceCode" id="cb19"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="va">x</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="cn">TRUE</span>, <span class="cn">FALSE</span>, <span class="cn">TRUE</span>, <span class="cn">FALSE</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="cn">TRUE</span>, <span class="cn">FALSE</span>, <span class="cn">TRUE</span>, <span class="cn">FALSE</span><span class="op">)</span><span class="op">]</span>
+<code class="sourceCode R"><span class="va">x</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="cn">TRUE</span>, <span class="cn">FALSE</span>, <span class="cn">TRUE</span>, <span class="cn">FALSE</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="cn">TRUE</span>, <span class="cn">FALSE</span>, <span class="cn">TRUE</span>, <span class="cn">FALSE</span><span class="op">)</span><span class="op">]</span>
 <span class="co">#&gt; torch_tensor</span>
-<span class="co">#&gt; -0.8025  0.2212</span>
-<span class="co">#&gt;  0.5788  0.3573</span>
+<span class="co">#&gt;  0.3352  0.8809</span>
+<span class="co">#&gt; -0.0952 -0.3883</span>
 <span class="co">#&gt; [ CPUFloatType{2,2} ]</span></code></pre></div>
 <p>The above examples also work if the index were long or boolean tensors, instead of R vectors. It’s also possible to index with multi-dimensional boolean tensors:</p>
 <div class="sourceCode" id="cb20"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/cbind.html">rbind</a></span><span class="op">(</span>
-  <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">1</span>,<span class="fl">2</span>,<span class="fl">3</span><span class="op">)</span>,
-  <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">4</span>,<span class="fl">5</span>,<span class="fl">6</span><span class="op">)</span>
+<code class="sourceCode R"><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/cbind.html" class="external-link">rbind</a></span><span class="op">(</span>
+  <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>,<span class="fl">2</span>,<span class="fl">3</span><span class="op">)</span>,
+  <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span>,<span class="fl">5</span>,<span class="fl">6</span><span class="op">)</span>
 <span class="op">)</span><span class="op">)</span>
 <span class="va">x</span><span class="op">[</span><span class="va">x</span><span class="op">&gt;</span><span class="fl">3</span><span class="op">]</span>
 <span class="co">#&gt; torch_tensor</span>
@@ -376,31 +337,28 @@
 <span class="co">#&gt;  6</span>
 <span class="co">#&gt; [ CPUFloatType{3} ]</span></code></pre></div>
 </div>
-  </div>
-
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-        <nav id="toc" data-toggle="toc"><h2 data-toc-skip>Contents</h2>
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+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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 </div>
 
 
 
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-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <footer><div class="pkgdown-footer-left">
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+<p>Developed by Daniel Falbel, Javier Luraschi.</p>
 </div>
 
-<div class="pkgdown">
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+<div class="pkgdown-footer-right">
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diff --git a/articles/indexing_files/anchor-sections-1.0/anchor-sections.css b/articles/indexing_files/anchor-sections-1.0/anchor-sections.css
deleted file mode 100644
index 07aee5fcb..000000000
--- a/articles/indexing_files/anchor-sections-1.0/anchor-sections.css
+++ /dev/null
@@ -1,4 +0,0 @@
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-.hasAnchor:hover a.anchor-section {visibility: visible;}
diff --git a/articles/indexing_files/anchor-sections-1.0/anchor-sections.js b/articles/indexing_files/anchor-sections-1.0/anchor-sections.js
deleted file mode 100644
index 570f99a0a..000000000
--- a/articles/indexing_files/anchor-sections-1.0/anchor-sections.js
+++ /dev/null
@@ -1,33 +0,0 @@
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-  // Do nothing if AnchorJS is used
-  if (typeof window.anchors === 'object' && anchors.hasOwnProperty('hasAnchorJSLink')) {
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-  const h = document.querySelectorAll('h1, h2, h3, h4, h5, h6');
-
-  // Do nothing if sections are already anchored
-  if (Array.from(h).some(x => x.classList.contains('hasAnchor'))) {
-    return null;
-  }
-
-  // Use section id when pandoc runs with --section-divs
-  const section_id = function(x) {
-    return ((x.classList.contains('section') || (x.tagName === 'SECTION'))
-            ? x.id : '');
-  };
-
-  // Add anchors
-  h.forEach(function(x) {
-    const id = x.id || section_id(x.parentElement);
-    if (id === '') {
-      return null;
-    }
-    let anchor = document.createElement('a');
-    anchor.href = '#' + id;
-    anchor.classList = ['anchor-section'];
-    x.classList.add('hasAnchor');
-    x.appendChild(anchor);
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-});
diff --git a/articles/installation.html b/articles/installation.html
index 413ddf5cd..3e85f2402 100644
--- a/articles/installation.html
+++ b/articles/installation.html
@@ -4,14 +4,14 @@
 <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
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-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/master/vignettes/installation.Rmd"><code>vignettes/installation.Rmd</code></a></small>
-      <div class="hidden name"><code>installation.Rmd</code></div>
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+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/vignettes/installation.Rmd" class="external-link"><code>vignettes/installation.Rmd</code></a></small>
+      <div class="d-none name"><code>installation.Rmd</code></div>
     </div>
 
     
     
 <p>After the usual R package installation, <code>torch</code> requires installing other 2 libraries: LibTorch and LibLantern. They are automatically installed by detecting information about you OS if you are using <code>torch</code> in interactive mode. If you are running <code>torch</code> in non-interactive environments you need to set the <code>TORCH_INSTALL</code> env var to 1, so it’s automatically installed or manually call <code><a href="../reference/install_torch.html">torch::install_torch()</a></code>.</p>
 <p>We have provide pre-compiled binaries for all major platforms and you can find specific installation instructions below.</p>
-<div id="windows" class="section level2">
-<h2 class="hasAnchor">
-<a href="#windows" class="anchor"></a>Windows</h2>
-<div id="cpu" class="section level3">
-<h3 class="hasAnchor">
-<a href="#cpu" class="anchor"></a>CPU</h3>
+<div class="section level2">
+<h2 id="windows">Windows<a class="anchor" aria-label="anchor" href="#windows"></a>
+</h2>
+<div class="section level3">
+<h3 id="cpu">CPU<a class="anchor" aria-label="anchor" href="#cpu"></a>
+</h3>
 <p>If you don’t have a GPU or want to install the CPU version of <code>torch</code>, you can install with:</p>
 <div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/utils/install.packages.html">install.packages</a></span><span class="op">(</span><span class="st">"torch"</span><span class="op">)</span></code></pre></div>
+<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/utils/install.packages.html" class="external-link">install.packages</a></span><span class="op">(</span><span class="st">"torch"</span><span class="op">)</span></code></pre></div>
 <p>Some Windows distributions don’t have the Visual Studio runtime pre-installed and you will observe an error like:</p>
 <pre><code>Error in cpp_lantern_init(normalizePath(install_path())): C:\Users\User\Documents\R\R-4.0.2\library\torch\deps\lantern.dll - The specified module could not be found.</code></pre>
-<p>See <a href="https://github.com/mlverse/torch/issues/246#issuecomment-695097121">here</a> for instructions on how to install it.</p>
+<p>See <a href="https://github.com/mlverse/torch/issues/246#issuecomment-695097121" class="external-link">here</a> for instructions on how to install it.</p>
 </div>
-<div id="gpu" class="section level3">
-<h3 class="hasAnchor">
-<a href="#gpu" class="anchor"></a>GPU</h3>
+<div class="section level3">
+<h3 id="gpu">GPU<a class="anchor" aria-label="anchor" href="#gpu"></a>
+</h3>
 <p>Since version 0.1.1 torch supports GPU installation on Windows. In order to use GPU’s with torch you need to:</p>
 <ul>
-<li><p>Have a CUDA compatible NVIDIA GPU. You can find if you have a CUDA compatible GPU <a href="https://developer.nvidia.com/cuda-gpus#compute">here</a>.</p></li>
-<li><p>Have properly installed the NVIDIA CUDA toolkit version 10.2 or 11.1. For CUDA v11.1, follow the installation instructions <a href="https://docs.nvidia.com/cuda/archive/11.1.1/index.html">here</a>. <strong>Note</strong>: We currently do <strong>not</strong> support the latest CUDA version 11.</p></li>
-<li><p>Have installed cuDNN version 7.6 (if using CUDA v10.2) or cuDNN 8.0 (if using CUDA 11.1). Follow the installation instructions available <a href="https://docs.nvidia.com/deeplearning/cudnn/archives/cudnn-805/install-guide/index.html">here</a>.</p></li>
+<li><p>Have a CUDA compatible NVIDIA GPU. You can find if you have a CUDA compatible GPU <a href="https://developer.nvidia.com/cuda-gpus#compute" class="external-link">here</a>.</p></li>
+<li><p>Have properly installed the NVIDIA CUDA toolkit version 10.2 or 11.1. For CUDA v11.1, follow the installation instructions <a href="https://docs.nvidia.com/cuda/archive/11.1.1/index.html" class="external-link">here</a>. <strong>Note</strong>: We currently do <strong>not</strong> support the latest CUDA version 11.</p></li>
+<li><p>Have installed cuDNN version 7.6 (if using CUDA v10.2) or cuDNN 8.0 (if using CUDA 11.1). Follow the installation instructions available <a href="https://docs.nvidia.com/deeplearning/cudnn/archives/cudnn-805/install-guide/index.html" class="external-link">here</a>.</p></li>
 </ul>
 <p>Once you have installed all pre-requisites you can install <code>torch</code> with:</p>
 <div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/utils/install.packages.html">install.packages</a></span><span class="op">(</span><span class="st">"torch"</span><span class="op">)</span></code></pre></div>
+<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/utils/install.packages.html" class="external-link">install.packages</a></span><span class="op">(</span><span class="st">"torch"</span><span class="op">)</span></code></pre></div>
 <p>If you have followed default installation locations we will detect that you have CUDA software installed and automatically download the GPU enabled Lantern binaries. You can also specify the <code>CUDA</code> env var with something like <code>Sys.setenv(CUDA="11.1")</code> if you want to force an specific version of the CUDA toolkit.</p>
 </div>
 </div>
-<div id="macos" class="section level2">
-<h2 class="hasAnchor">
-<a href="#macos" class="anchor"></a>MacOS</h2>
-<div id="cpu-1" class="section level3">
-<h3 class="hasAnchor">
-<a href="#cpu-1" class="anchor"></a>CPU</h3>
+<div class="section level2">
+<h2 id="macos">MacOS<a class="anchor" aria-label="anchor" href="#macos"></a>
+</h2>
+<div class="section level3">
+<h3 id="cpu-1">CPU<a class="anchor" aria-label="anchor" href="#cpu-1"></a>
+</h3>
 <p>We only support CPU builds of torch on MacOS. On MacOS you can install torch with:</p>
 <div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/utils/install.packages.html">install.packages</a></span><span class="op">(</span><span class="st">"torch"</span><span class="op">)</span></code></pre></div>
+<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/utils/install.packages.html" class="external-link">install.packages</a></span><span class="op">(</span><span class="st">"torch"</span><span class="op">)</span></code></pre></div>
 </div>
 </div>
-<div id="linux" class="section level2">
-<h2 class="hasAnchor">
-<a href="#linux" class="anchor"></a>Linux</h2>
-<div id="cpu-2" class="section level3">
-<h3 class="hasAnchor">
-<a href="#cpu-2" class="anchor"></a>CPU</h3>
+<div class="section level2">
+<h2 id="linux">Linux<a class="anchor" aria-label="anchor" href="#linux"></a>
+</h2>
+<div class="section level3">
+<h3 id="cpu-2">CPU<a class="anchor" aria-label="anchor" href="#cpu-2"></a>
+</h3>
 <p>To install the cpu version of <code>torch</code> you can run:</p>
 <div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/utils/install.packages.html">install.packages</a></span><span class="op">(</span><span class="st">"torch"</span><span class="op">)</span></code></pre></div>
+<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/utils/install.packages.html" class="external-link">install.packages</a></span><span class="op">(</span><span class="st">"torch"</span><span class="op">)</span></code></pre></div>
 </div>
-<div id="gpu-1" class="section level3">
-<h3 class="hasAnchor">
-<a href="#gpu-1" class="anchor"></a>GPU</h3>
+<div class="section level3">
+<h3 id="gpu-1">GPU<a class="anchor" aria-label="anchor" href="#gpu-1"></a>
+</h3>
 <p>To install the GPU version of <code>torch</code> on linux you must verify that:</p>
 <ul>
-<li><p>You have a NVIDIA CUDA compatible GPU. You can find if you have a CUDA compatible GPU <a href="https://developer.nvidia.com/cuda-gpus#compute">here</a>.</p></li>
-<li><p>You have correctly installed the NVIDIA CUDA Toolkit versions 10.2 or 11.1, follow the instructions <a href="https://docs.nvidia.com/cuda/archive/11.1.1/index.html">here</a>.</p></li>
-<li><p>You have installed cuDNN version 7.6 - (for CUDA 10.2) and 8.0 for CUDA 11.1. Follow the installation instructions available <a href="https://docs.nvidia.com/deeplearning/cudnn/archives/cudnn-805/install-guide/index.html">here</a>.</p></li>
+<li><p>You have a NVIDIA CUDA compatible GPU. You can find if you have a CUDA compatible GPU <a href="https://developer.nvidia.com/cuda-gpus#compute" class="external-link">here</a>.</p></li>
+<li><p>You have correctly installed the NVIDIA CUDA Toolkit versions 10.2 or 11.1, follow the instructions <a href="https://docs.nvidia.com/cuda/archive/11.1.1/index.html" class="external-link">here</a>.</p></li>
+<li><p>You have installed cuDNN version 7.6 - (for CUDA 10.2) and 8.0 for CUDA 11.1. Follow the installation instructions available <a href="https://docs.nvidia.com/deeplearning/cudnn/archives/cudnn-805/install-guide/index.html" class="external-link">here</a>.</p></li>
 </ul>
 <p>Once you have installed all pre-requisites you can install <code>torch</code> with:</p>
 <div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/utils/install.packages.html">install.packages</a></span><span class="op">(</span><span class="st">"torch"</span><span class="op">)</span></code></pre></div>
+<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/utils/install.packages.html" class="external-link">install.packages</a></span><span class="op">(</span><span class="st">"torch"</span><span class="op">)</span></code></pre></div>
 <p>If you have followed default installation locations we will detect that you have CUDA software installed and automatically download the GPU enabled Lantern binaries. You can also specify the <code>CUDA</code> env var with something like <code>Sys.setenv(CUDA="10.2")</code> if you want to force an specific version of the CUDA toolkit.</p>
 </div>
 </div>
-<div id="troubleshooting" class="section level2">
-<h2 class="hasAnchor">
-<a href="#troubleshooting" class="anchor"></a>Troubleshooting</h2>
-<div id="large-file-download-timeout" class="section level3">
-<h3 class="hasAnchor">
-<a href="#large-file-download-timeout" class="anchor"></a>Large file download timeout</h3>
+<div class="section level2">
+<h2 id="troubleshooting">Troubleshooting<a class="anchor" aria-label="anchor" href="#troubleshooting"></a>
+</h2>
+<div class="section level3">
+<h3 id="large-file-download-timeout">Large file download timeout<a class="anchor" aria-label="anchor" href="#large-file-download-timeout"></a>
+</h3>
 <p>If you encounter timeout during library download, or if after a while, downloads end-up with a warning such as:</p>
 <pre><code>Warning messages:
 1: In utils::download.file(library_url, temp_file) :
@@ -227,9 +188,9 @@
 <div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span class="fu"><a href="../reference/install_torch.html">install_torch</a></span><span class="op">(</span>timeout <span class="op">=</span> <span class="fl">600</span><span class="op">)</span></code></pre></div>
 </div>
-<div id="file-based-download" class="section level3">
-<h3 class="hasAnchor">
-<a href="#file-based-download" class="anchor"></a>File based download</h3>
+<div class="section level3">
+<h3 id="file-based-download">File based download<a class="anchor" aria-label="anchor" href="#file-based-download"></a>
+</h3>
 <p>In cases where you cannot reach download servers from the machine you intend to install torch on, last resort is to install Torch and Lantern library from files. This is done in 3 steps :</p>
 <p>1- get the download URLs of the files.</p>
 <div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
@@ -241,31 +202,28 @@
                         liblantern <span class="op">=</span> <span class="st">"file:///tmp/Linux-gpu-101.zip"</span><span class="op">)</span></code></pre></div>
 </div>
 </div>
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+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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+    <footer><div class="pkgdown-footer-left">
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+<p>Developed by Daniel Falbel, Javier Luraschi.</p>
 </div>
 
-<div class="pkgdown">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p>
+<div class="pkgdown-footer-right">
+  <p></p>
+<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.2.</p>
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diff --git a/articles/installation_files/anchor-sections-1.0/anchor-sections.css b/articles/installation_files/anchor-sections-1.0/anchor-sections.css
deleted file mode 100644
index 07aee5fcb..000000000
--- a/articles/installation_files/anchor-sections-1.0/anchor-sections.css
+++ /dev/null
@@ -1,4 +0,0 @@
-/* Styles for section anchors */
-a.anchor-section {margin-left: 10px; visibility: hidden; color: inherit;}
-a.anchor-section::before {content: '#';}
-.hasAnchor:hover a.anchor-section {visibility: visible;}
diff --git a/articles/installation_files/anchor-sections-1.0/anchor-sections.js b/articles/installation_files/anchor-sections-1.0/anchor-sections.js
deleted file mode 100644
index 570f99a0a..000000000
--- a/articles/installation_files/anchor-sections-1.0/anchor-sections.js
+++ /dev/null
@@ -1,33 +0,0 @@
-// Anchor sections v1.0 written by Atsushi Yasumoto on Oct 3rd, 2020.
-document.addEventListener('DOMContentLoaded', function() {
-  // Do nothing if AnchorJS is used
-  if (typeof window.anchors === 'object' && anchors.hasOwnProperty('hasAnchorJSLink')) {
-    return;
-  }
-
-  const h = document.querySelectorAll('h1, h2, h3, h4, h5, h6');
-
-  // Do nothing if sections are already anchored
-  if (Array.from(h).some(x => x.classList.contains('hasAnchor'))) {
-    return null;
-  }
-
-  // Use section id when pandoc runs with --section-divs
-  const section_id = function(x) {
-    return ((x.classList.contains('section') || (x.tagName === 'SECTION'))
-            ? x.id : '');
-  };
-
-  // Add anchors
-  h.forEach(function(x) {
-    const id = x.id || section_id(x.parentElement);
-    if (id === '') {
-      return null;
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-    let anchor = document.createElement('a');
-    anchor.href = '#' + id;
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-    x.classList.add('hasAnchor');
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diff --git a/articles/loading-data.html b/articles/loading-data.html
index da7d6df9f..8e62d0a28 100644
--- a/articles/loading-data.html
+++ b/articles/loading-data.html
@@ -4,14 +4,14 @@
 <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
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+<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
+<meta name="description" content="torch">
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+<script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
+<link href="../deps/bootstrap-5.1.0/bootstrap.min.css" rel="stylesheet">
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-<script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet">
+<!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet">
 <meta property="og:title" content="Loading data">
 <meta property="og:description" content="torch">
 <!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
@@ -25,133 +25,95 @@
   gtag('config', 'UA-178883486-1');
 </script>
 </head>
-<body data-spy="scroll" data-target="#toc">
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-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
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+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
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+    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary"><div class="container">
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-    <span class="fab fa-github fa-lg"></span>
+<form class="form-inline my-2 my-lg-0" role="search">
+        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off">
+</form>
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-      
 
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-  <div class="col-md-9 contents">
-    <div class="page-header toc-ignore">
-      <h1 data-toc-skip>Loading data</h1>
+
+<script src="loading-data_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row">
+  <main id="main"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Loading data</h1>
             
       
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/master/vignettes/loading-data.Rmd"><code>vignettes/loading-data.Rmd</code></a></small>
-      <div class="hidden name"><code>loading-data.Rmd</code></div>
-
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/vignettes/loading-data.Rmd" class="external-link"><code>vignettes/loading-data.Rmd</code></a></small>
+      <div class="d-none name"><code>loading-data.Rmd</code></div>
     </div>
 
     
     
 <div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://torch.mlverse.org/docs">torch</a></span><span class="op">)</span></code></pre></div>
-<div id="datasets-and-data-loaders" class="section level2">
-<h2 class="hasAnchor">
-<a href="#datasets-and-data-loaders" class="anchor"></a>Datasets and data loaders</h2>
-<p>Central to data ingestion and preprocessing are datasets and data loaders.</p>
-<p><code>torch</code> comes equipped with a bag of datasets related to, mostly, image recognition and natural language processing (e.g., <code>mnist_dataset()</code>), which can be iterated over by means of <code>dataloader</code>s:</p>
-<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1"></a><span class="co"># ...</span></span>
+<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://torch.mlverse.org/docs">torch</a></span><span class="op">)</span></code></pre></div>
+<div class="section level2">
+<h2 id="datasets-and-data-loaders">Datasets and data loaders<a class="anchor" aria-label="anchor" href="#datasets-and-data-loaders"></a>
+</h2>
+<p>Central to data ingestion and preprocessing are datasets and data loaders. A dataset is an object that holds the data to use, while a data loader is an object that will load the data from a dataset providing a way to access subsets of the data. By using datasets and data loaders you will have a process for clearly organizing your data and passing it to other components of the torch package, such as model training.</p>
+<p>Built into <code>torch</code> are premade datasets that are commonly used in machine learning, such as the MNIST handwriting dataset (<code>mnist_dataset()</code>). Most of the prebuilt datasets relate to image recognition and natural language processing.</p>
+<p>Below is an example of how you would use the MNIST dataset with a dataloader. First, the <code>minst_dataset()</code> function is used to create <code>ds</code> which is a <code>Dataset</code> object. Then a dataloader <code>dl</code> is created to query that data. Finally, that dataloader is used in a <code><a href="https://rdrr.io/pkg/coro/man/collect.html" class="external-link">coro::loop()</a></code> to iterate over batches of that data:</p>
+<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1"></a><span class="co"># Create a dataset from included data</span></span>
 <span id="cb2-2"><a href="#cb2-2"></a>ds &lt;-<span class="st"> </span><span class="kw">mnist_dataset</span>(</span>
 <span id="cb2-3"><a href="#cb2-3"></a>  dir, </span>
 <span id="cb2-4"><a href="#cb2-4"></a>  <span class="dt">download =</span> <span class="ot">TRUE</span>, </span>
@@ -161,39 +123,85 @@
 <span id="cb2-8"><a href="#cb2-8"></a>  }</span>
 <span id="cb2-9"><a href="#cb2-9"></a>)</span>
 <span id="cb2-10"><a href="#cb2-10"></a></span>
-<span id="cb2-11"><a href="#cb2-11"></a>dl &lt;-<span class="st"> </span><span class="kw">dataloader</span>(ds, <span class="dt">batch_size =</span> <span class="dv">32</span>, <span class="dt">shuffle =</span> <span class="ot">TRUE</span>)</span>
-<span id="cb2-12"><a href="#cb2-12"></a></span>
-<span id="cb2-13"><a href="#cb2-13"></a>coro<span class="op">::</span><span class="kw">loop</span>(<span class="cf">for</span> (b <span class="cf">in</span> dl)) {</span>
-<span id="cb2-14"><a href="#cb2-14"></a><span class="co"># ...</span></span>
-<span id="cb2-15"><a href="#cb2-15"></a>}<span class="er">)</span></span></code></pre></div>
-<p>Cf. <code>vignettes/examples/mnist-cnn.R</code> for a complete example.</p>
-<p>What if you want to train on a different dataset? In these cases, you subclass <code>Dataset</code>, an abstract container that needs to know how to iterate over the given data. To that purpose, your subclass needs to implement <code>.getitem()</code>, and say what should be returned when the data loader is asking for the next batch.</p>
-<p>In <code>.getitem()</code>, you can implement whatever preprocessing you require. Additionally, you should implement <code>.length()</code>, so users can find out how many items there are in the dataset.</p>
-<p>While this may sound complicated, it is not at all. The base logic is straightforward – complexity will, naturally, correlate with how involved your preprocessing is. To provide you with a simple but functional prototype, here we show how to create your own <code>dataset</code> to train on <a href="https://github.com/allisonhorst/palmerpenguins">Allison Horst's penguins</a>.</p>
+<span id="cb2-11"><a href="#cb2-11"></a><span class="co"># Create the loader to query the data in batches</span></span>
+<span id="cb2-12"><a href="#cb2-12"></a>dl &lt;-<span class="st"> </span><span class="kw">dataloader</span>(ds, <span class="dt">batch_size =</span> <span class="dv">32</span>, <span class="dt">shuffle =</span> <span class="ot">TRUE</span>)</span>
+<span id="cb2-13"><a href="#cb2-13"></a></span>
+<span id="cb2-14"><a href="#cb2-14"></a>coro<span class="op">::</span><span class="kw">loop</span>(<span class="cf">for</span> (b <span class="cf">in</span> dl)) {</span>
+<span id="cb2-15"><a href="#cb2-15"></a><span class="co"># use the data from each batch `b` here</span></span>
+<span id="cb2-16"><a href="#cb2-16"></a><span class="co"># ...</span></span>
+<span id="cb2-17"><a href="#cb2-17"></a>}<span class="er">)</span></span></code></pre></div>
+<p>See <code>vignettes/examples/mnist-cnn.R</code> for a complete example.</p>
+<p>In the more common situation where you have a unique set of data that isn’t included with the package you’ll need to make a custom <code>Dataset</code> subclass by using the <code><a href="../reference/dataset.html">dataset()</a></code> function. The custom <code>Dataset</code> subclass is an abstract R6 container for the data. It will need to know some information about the particular dataset, such as how to iterate over it.</p>
+<p>At a minimum, when using <code><a href="../reference/dataset.html">dataset()</a></code> to create a custom <code>Dataset</code> class you’ll want to define the following:</p>
+<ul>
+<li>
+<code>name</code> - for convenience, keep track of what type of data it is</li>
+<li>
+<code>initialize</code> - a member function defining how to create a object with that class. It could have no parameters, for when all objects of that class will be the same, or you can give it specific parameters usually for if different objects should have different data.</li>
+<li>
+<code>.getitem</code> - this member function is called when the dataloader goes to pull a new batch of data. You can include preprocessing in this function if needed. Note that the function will be called extremely frequently, so it’s advantageous to make it fast.</li>
+<li>
+<code>.length</code> - this will return the amount of data in the dataset, which is helpful for users.</li>
+</ul>
 </div>
-<div id="a-custom-dataset" class="section level2">
-<h2 class="hasAnchor">
-<a href="#a-custom-dataset" class="anchor"></a>A custom dataset</h2>
+<div class="section level2">
+<h2 id="example-of-using-a-custom-dataset">Example of using a custom Dataset<a class="anchor" aria-label="anchor" href="#example-of-using-a-custom-dataset"></a>
+</h2>
+<p>While this may sound complicated the base logic is only a few steps–the complexity often comes from the data itself and how involved your preprocessing is. Here we show how to create your own <code>Dataset</code> class to train on <a href="https://github.com/allisonhorst/palmerpenguins" class="external-link">Allison Horst's penguins</a>.</p>
+<table style="width:100%;" class="table">
+<colgroup>
+<col width="16%">
+<col width="35%">
+<col width="16%">
+<col width="16%">
+<col width="16%">
+</colgroup>
+<thead><tr class="header">
+<th align="left">Component</th>
+<th align="center">
+<code>Dataset</code> R6 class</th>
+<th align="center">
+<code>Dataset</code> object</th>
+<th align="center">
+<code>DataLoader</code> object</th>
+<th align="center">batch</th>
+</tr></thead>
+<tbody>
+<tr class="odd">
+<td align="left">Description</td>
+<td align="center">Output of <code><a href="../reference/dataset.html">dataset()</a></code>. When calling <code><a href="../reference/dataset.html">dataset()</a></code> it should have at least a <code>name</code>, <code>initialize</code>, <code>.getitem</code>, and <code>.length</code>. Output is a <code>Dataset</code> generator.</td>
+<td align="center">Object created by using the custom <code>Dataset</code> generator. Actually stores the data</td>
+<td align="center">Object that queries the <code>Dataset</code> object to pull batches of data</td>
+<td align="center">The subsample of data used for things like model training</td>
+</tr>
+<tr class="even">
+<td align="left">Penguin example</td>
+<td align="center"><code>penguins_dataset</code></td>
+<td align="center"><code>tuxes</code></td>
+<td align="center"><code>dl</code></td>
+<td align="center"><code>b</code></td>
+</tr>
+</tbody>
+</table>
 <div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://allisonhorst.github.io/palmerpenguins/">palmerpenguins</a></span><span class="op">)</span>
-<span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://magrittr.tidyverse.org">magrittr</a></span><span class="op">)</span>
+<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://allisonhorst.github.io/palmerpenguins/" class="external-link">palmerpenguins</a></span><span class="op">)</span>
+<span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://magrittr.tidyverse.org" class="external-link">magrittr</a></span><span class="op">)</span>
 
 <span class="va">penguins</span>
-<span class="co">#&gt; # A tibble: 344 × 8</span>
+<span class="co">#&gt; <span style="color: #949494;"># A tibble: 344 × 8</span></span>
 <span class="co">#&gt;    species island    bill_length_mm bill_depth_mm flipper_length_mm body_mass_g</span>
-<span class="co">#&gt;    &lt;fct&gt;   &lt;fct&gt;              &lt;dbl&gt;         &lt;dbl&gt;             &lt;int&gt;       &lt;int&gt;</span>
-<span class="co">#&gt;  1 Adelie  Torgersen           39.1          18.7               181        3750</span>
-<span class="co">#&gt;  2 Adelie  Torgersen           39.5          17.4               186        3800</span>
-<span class="co">#&gt;  3 Adelie  Torgersen           40.3          18                 195        3250</span>
-<span class="co">#&gt;  4 Adelie  Torgersen           NA            NA                  NA          NA</span>
-<span class="co">#&gt;  5 Adelie  Torgersen           36.7          19.3               193        3450</span>
-<span class="co">#&gt;  6 Adelie  Torgersen           39.3          20.6               190        3650</span>
-<span class="co">#&gt;  7 Adelie  Torgersen           38.9          17.8               181        3625</span>
-<span class="co">#&gt;  8 Adelie  Torgersen           39.2          19.6               195        4675</span>
-<span class="co">#&gt;  9 Adelie  Torgersen           34.1          18.1               193        3475</span>
-<span class="co">#&gt; 10 Adelie  Torgersen           42            20.2               190        4250</span>
-<span class="co">#&gt; # … with 334 more rows, and 2 more variables: sex &lt;fct&gt;, year &lt;int&gt;</span></code></pre></div>
-<p><code>Dataset</code>s are R6 classes created using the <code><a href="../reference/dataset.html">dataset()</a></code> constructor. You can pass a <code>name</code> and various member functions. Among those should be <code>initialize()</code>, to create instance variables, <code>.getitem()</code>, to indicate how the data should be returned, and <code>.length()</code>, to say how many items we have.</p>
+<span class="co">#&gt;    <span style="color: #949494; font-style: italic;">&lt;fct&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;fct&gt;</span>              <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>         <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>             <span style="color: #949494; font-style: italic;">&lt;int&gt;</span>       <span style="color: #949494; font-style: italic;">&lt;int&gt;</span></span>
+<span class="co">#&gt; <span style="color: #BCBCBC;"> 1</span> Adelie  Torgersen           39.1          18.7               181        <span style="text-decoration: underline;">3</span>750</span>
+<span class="co">#&gt; <span style="color: #BCBCBC;"> 2</span> Adelie  Torgersen           39.5          17.4               186        <span style="text-decoration: underline;">3</span>800</span>
+<span class="co">#&gt; <span style="color: #BCBCBC;"> 3</span> Adelie  Torgersen           40.3          18                 195        <span style="text-decoration: underline;">3</span>250</span>
+<span class="co">#&gt; <span style="color: #BCBCBC;"> 4</span> Adelie  Torgersen           <span style="color: #BB0000;">NA</span>            <span style="color: #BB0000;">NA</span>                  <span style="color: #BB0000;">NA</span>          <span style="color: #BB0000;">NA</span></span>
+<span class="co">#&gt; <span style="color: #BCBCBC;"> 5</span> Adelie  Torgersen           36.7          19.3               193        <span style="text-decoration: underline;">3</span>450</span>
+<span class="co">#&gt; <span style="color: #BCBCBC;"> 6</span> Adelie  Torgersen           39.3          20.6               190        <span style="text-decoration: underline;">3</span>650</span>
+<span class="co">#&gt; <span style="color: #BCBCBC;"> 7</span> Adelie  Torgersen           38.9          17.8               181        <span style="text-decoration: underline;">3</span>625</span>
+<span class="co">#&gt; <span style="color: #BCBCBC;"> 8</span> Adelie  Torgersen           39.2          19.6               195        <span style="text-decoration: underline;">4</span>675</span>
+<span class="co">#&gt; <span style="color: #BCBCBC;"> 9</span> Adelie  Torgersen           34.1          18.1               193        <span style="text-decoration: underline;">3</span>475</span>
+<span class="co">#&gt; <span style="color: #BCBCBC;">10</span> Adelie  Torgersen           42            20.2               190        <span style="text-decoration: underline;">4</span>250</span>
+<span class="co">#&gt; <span style="color: #949494;"># … with 334 more rows, and 2 more variables: sex &lt;fct&gt;, year &lt;int&gt;</span></span></code></pre></div>
 <p>In addition, any number of helper functions can be defined.</p>
 <p>Here, we assume the <code>penguins</code> have already been loaded, and all preprocessing consists in removing lines with <code>NA</code> values, transforming <code>factor</code>s to numbers starting from 0, and converting from R data types to <code>torch</code> tensors.</p>
 <p>In <code>.getitem</code>, we essentially decide how this data is going to be used: All variables besides <code>species</code> go into <code>x</code>, the predictor, and <code>species</code> will constitute <code>y</code>, the target. Predictor and target are returned in a list, to be accessed as <code>batch[[1]]</code> and <code>batch[[2]]</code> during training.</p>
@@ -211,7 +219,7 @@
     <span class="va">x</span> <span class="op">&lt;-</span> <span class="va">self</span><span class="op">$</span><span class="va">data</span><span class="op">[</span><span class="va">index</span>, <span class="fl">2</span><span class="op">:</span><span class="op">-</span><span class="fl">1</span><span class="op">]</span>
     <span class="va">y</span> <span class="op">&lt;-</span> <span class="va">self</span><span class="op">$</span><span class="va">data</span><span class="op">[</span><span class="va">index</span>, <span class="fl">1</span><span class="op">]</span><span class="op">$</span><span class="fu">to</span><span class="op">(</span><span class="fu"><a href="../reference/torch_dtype.html">torch_long</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span>
     
-    <span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">y</span><span class="op">)</span>
+    <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">y</span><span class="op">)</span>
   <span class="op">}</span>,
   
   .length <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="op">)</span> <span class="op">{</span>
@@ -220,13 +228,13 @@
   
   prepare_penguin_data <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="op">)</span> <span class="op">{</span>
     
-    <span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/na.fail.html">na.omit</a></span><span class="op">(</span><span class="va">penguins</span><span class="op">)</span> 
+    <span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/na.fail.html" class="external-link">na.omit</a></span><span class="op">(</span><span class="va">penguins</span><span class="op">)</span> 
     <span class="co"># conveniently, the categorical data are already factors</span>
-    <span class="va">input</span><span class="op">$</span><span class="va">species</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/numeric.html">as.numeric</a></span><span class="op">(</span><span class="va">input</span><span class="op">$</span><span class="va">species</span><span class="op">)</span>
-    <span class="va">input</span><span class="op">$</span><span class="va">island</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/numeric.html">as.numeric</a></span><span class="op">(</span><span class="va">input</span><span class="op">$</span><span class="va">island</span><span class="op">)</span>
-    <span class="va">input</span><span class="op">$</span><span class="va">sex</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/numeric.html">as.numeric</a></span><span class="op">(</span><span class="va">input</span><span class="op">$</span><span class="va">sex</span><span class="op">)</span>
+    <span class="va">input</span><span class="op">$</span><span class="va">species</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/numeric.html" class="external-link">as.numeric</a></span><span class="op">(</span><span class="va">input</span><span class="op">$</span><span class="va">species</span><span class="op">)</span>
+    <span class="va">input</span><span class="op">$</span><span class="va">island</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/numeric.html" class="external-link">as.numeric</a></span><span class="op">(</span><span class="va">input</span><span class="op">$</span><span class="va">island</span><span class="op">)</span>
+    <span class="va">input</span><span class="op">$</span><span class="va">sex</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/numeric.html" class="external-link">as.numeric</a></span><span class="op">(</span><span class="va">input</span><span class="op">$</span><span class="va">sex</span><span class="op">)</span>
     
-    <span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/matrix.html">as.matrix</a></span><span class="op">(</span><span class="va">input</span><span class="op">)</span>
+    <span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/matrix.html" class="external-link">as.matrix</a></span><span class="op">(</span><span class="va">input</span><span class="op">)</span>
     <span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="va">input</span><span class="op">)</span>
   <span class="op">}</span>
 <span class="op">)</span></code></pre></div>
@@ -253,7 +261,7 @@
 <span class="co">#&gt; [ CPULongType{} ]</span></code></pre></div>
 <p>To be able to iterate over <code>tuxes</code>, we need a data loader (we override the default batch size of <code>1</code>):</p>
 <div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="va">dl</span> <span class="op">&lt;-</span><span class="va">tuxes</span> <span class="op"><a href="../reference/%&gt;%.html">%&gt;%</a></span> <span class="fu"><a href="../reference/dataloader.html">dataloader</a></span><span class="op">(</span>batch_size <span class="op">=</span> <span class="fl">8</span><span class="op">)</span></code></pre></div>
+<code class="sourceCode R"><span class="va">dl</span> <span class="op">&lt;-</span> <span class="va">tuxes</span> <span class="op"><a href="../reference/pipe.html">%&gt;%</a></span> <span class="fu"><a href="../reference/dataloader.html">dataloader</a></span><span class="op">(</span>batch_size <span class="op">=</span> <span class="fl">8</span><span class="op">)</span></code></pre></div>
 <p>Calling <code>.length()</code> on a data loader (as opposed to a dataset) will return the number of <code>batches</code> we have:</p>
 <div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span class="va">dl</span><span class="op">$</span><span class="fu">.length</span><span class="op">(</span><span class="op">)</span>
@@ -286,11 +294,10 @@
 <span class="co">#&gt;  1</span>
 <span class="co">#&gt;  1</span>
 <span class="co">#&gt; [ CPULongType{8} ]</span></code></pre></div>
-<p>To train a network, we can use <code><a href="https://rdrr.io/pkg/coro/man/collect.html">coro::loop()</a></code> to iterate over batches.</p>
-</div>
-<div id="training-with-data-loaders" class="section level2">
-<h2 class="hasAnchor">
-<a href="#training-with-data-loaders" class="anchor"></a>Training with data loaders</h2>
+<p>To train a network, we can use <code><a href="https://rdrr.io/pkg/coro/man/collect.html" class="external-link">coro::loop()</a></code> to iterate over batches.</p>
+<div class="section level3">
+<h3 id="training-with-data-loaders">Training with data loaders<a class="anchor" aria-label="anchor" href="#training-with-data-loaders"></a>
+</h3>
 <p>Our example network is very simple. (In reality, we would want to treat <code>island</code> as the categorical variable it is, and either one-hot-encode or embed it.)</p>
 <div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span class="va">net</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/nn_module.html">nn_module</a></span><span class="op">(</span>
@@ -300,10 +307,10 @@
     <span class="va">self</span><span class="op">$</span><span class="va">fc2</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/nn_linear.html">nn_linear</a></span><span class="op">(</span><span class="fl">32</span>, <span class="fl">3</span><span class="op">)</span>
   <span class="op">}</span>,
   forward <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="op">{</span>
-    <span class="va">x</span> <span class="op"><a href="../reference/%&gt;%.html">%&gt;%</a></span> 
-      <span class="va">self</span><span class="op">$</span><span class="fu">fc1</span><span class="op">(</span><span class="op">)</span> <span class="op"><a href="../reference/%&gt;%.html">%&gt;%</a></span> 
-      <span class="fu"><a href="../reference/nnf_relu.html">nnf_relu</a></span><span class="op">(</span><span class="op">)</span> <span class="op"><a href="../reference/%&gt;%.html">%&gt;%</a></span> 
-      <span class="va">self</span><span class="op">$</span><span class="fu">fc2</span><span class="op">(</span><span class="op">)</span> <span class="op"><a href="../reference/%&gt;%.html">%&gt;%</a></span> 
+    <span class="va">x</span> <span class="op"><a href="../reference/pipe.html">%&gt;%</a></span> 
+      <span class="va">self</span><span class="op">$</span><span class="fu">fc1</span><span class="op">(</span><span class="op">)</span> <span class="op"><a href="../reference/pipe.html">%&gt;%</a></span> 
+      <span class="fu"><a href="../reference/nnf_relu.html">nnf_relu</a></span><span class="op">(</span><span class="op">)</span> <span class="op"><a href="../reference/pipe.html">%&gt;%</a></span> 
+      <span class="va">self</span><span class="op">$</span><span class="fu">fc2</span><span class="op">(</span><span class="op">)</span> <span class="op"><a href="../reference/pipe.html">%&gt;%</a></span> 
       <span class="fu"><a href="../reference/nnf_log_softmax.html">nnf_log_softmax</a></span><span class="op">(</span>dim <span class="op">=</span> <span class="fl">1</span><span class="op">)</span>
   <span class="op">}</span>
 <span class="op">)</span>
@@ -316,20 +323,20 @@
 <div class="sourceCode" id="cb11"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span class="kw">for</span> <span class="op">(</span><span class="va">epoch</span> <span class="kw">in</span> <span class="fl">1</span><span class="op">:</span><span class="fl">10</span><span class="op">)</span> <span class="op">{</span>
   
-  <span class="va">l</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="op">)</span>
+  <span class="va">l</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="op">)</span>
   
-  <span class="fu">coro</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/pkg/coro/man/collect.html">loop</a></span><span class="op">(</span><span class="kw">for</span> <span class="op">(</span><span class="va">b</span> <span class="kw">in</span> <span class="va">dl</span><span class="op">)</span> <span class="op">{</span>
+  <span class="fu">coro</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/pkg/coro/man/collect.html" class="external-link">loop</a></span><span class="op">(</span><span class="kw">for</span> <span class="op">(</span><span class="va">b</span> <span class="kw">in</span> <span class="va">dl</span><span class="op">)</span> <span class="op">{</span>
     <span class="va">optimizer</span><span class="op">$</span><span class="fu">zero_grad</span><span class="op">(</span><span class="op">)</span>
     <span class="va">output</span> <span class="op">&lt;-</span> <span class="fu">model</span><span class="op">(</span><span class="va">b</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span><span class="op">)</span>
     <span class="va">loss</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/nnf_nll_loss.html">nnf_nll_loss</a></span><span class="op">(</span><span class="va">output</span>, <span class="va">b</span><span class="op">[[</span><span class="fl">2</span><span class="op">]</span><span class="op">]</span><span class="op">)</span>
     <span class="va">loss</span><span class="op">$</span><span class="fu">backward</span><span class="op">(</span><span class="op">)</span>
     <span class="va">optimizer</span><span class="op">$</span><span class="fu">step</span><span class="op">(</span><span class="op">)</span>
-    <span class="va">l</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="va">l</span>, <span class="va">loss</span><span class="op">$</span><span class="fu">item</span><span class="op">(</span><span class="op">)</span><span class="op">)</span>
+    <span class="va">l</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="va">l</span>, <span class="va">loss</span><span class="op">$</span><span class="fu">item</span><span class="op">(</span><span class="op">)</span><span class="op">)</span>
   <span class="op">}</span><span class="op">)</span>
   
-  <span class="fu"><a href="https://rdrr.io/r/base/cat.html">cat</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/sprintf.html">sprintf</a></span><span class="op">(</span><span class="st">"Loss at epoch %d: %3f\n"</span>, <span class="va">epoch</span>, <span class="fu"><a href="https://rdrr.io/r/base/mean.html">mean</a></span><span class="op">(</span><span class="va">l</span><span class="op">)</span><span class="op">)</span><span class="op">)</span>
+  <span class="fu"><a href="https://rdrr.io/r/base/cat.html" class="external-link">cat</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/sprintf.html" class="external-link">sprintf</a></span><span class="op">(</span><span class="st">"Loss at epoch %d: %3f\n"</span>, <span class="va">epoch</span>, <span class="fu"><a href="https://rdrr.io/r/base/mean.html" class="external-link">mean</a></span><span class="op">(</span><span class="va">l</span><span class="op">)</span><span class="op">)</span><span class="op">)</span>
 <span class="op">}</span>
-<span class="co">#&gt; Loss at epoch 1: 493.409939</span>
+<span class="co">#&gt; Loss at epoch 1: 306.894899</span>
 <span class="co">#&gt; Loss at epoch 2: 2.068251</span>
 <span class="co">#&gt; Loss at epoch 3: 2.068251</span>
 <span class="co">#&gt; Loss at epoch 4: 2.068251</span>
@@ -339,32 +346,124 @@
 <span class="co">#&gt; Loss at epoch 8: 2.068251</span>
 <span class="co">#&gt; Loss at epoch 9: 2.068251</span>
 <span class="co">#&gt; Loss at epoch 10: 2.068251</span></code></pre></div>
+<p>Through this example we have trained a deep learning model using <code><a href="../reference/dataset.html">dataset()</a></code> to define a custom class and then loaded it in batches with a data loader. By using the dataset and data loader we were able to write code that split the data preprocessing and setup from the model training itself.</p>
 </div>
-  </div>
+</div>
+<div class="section level2">
+<h2 id="notes-on-efficiency">Notes on efficiency<a class="anchor" aria-label="anchor" href="#notes-on-efficiency"></a>
+</h2>
+<p>When using datasets and data loaders you may find that under certain conditions your code is running more slowly than you’d expect. In some situations the overhead of using dataloaders and datasets can impact overall performance. This may change in time as the R/C++ integration of Torch improves, but for now there are some workarounds:</p>
+<div class="section level3">
+<h3 id="use--getbatch-instead-of--getitem">Use <code>.getbatch()</code> instead of <code>.getitem()</code><a class="anchor" aria-label="anchor" href="#use--getbatch-instead-of--getitem"></a>
+</h3>
+<p>By default a dataloader will use the <code>.getitem()</code> member function to pull each single datapoint individually. You can speed this up by switching to using <code>.getbatch()</code> which will pull all the datapoints in a batch at once:</p>
+<div class="sourceCode" id="cb12"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span class="va">penguins_dataset_batching</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/dataset.html">dataset</a></span><span class="op">(</span>
+  
+  name <span class="op">=</span> <span class="st">"penguins_dataset_batching"</span>,
+  
+  initialize <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="op">)</span> <span class="op">{</span>
+    <span class="va">self</span><span class="op">$</span><span class="va">data</span> <span class="op">&lt;-</span> <span class="va">self</span><span class="op">$</span><span class="fu">prepare_penguin_data</span><span class="op">(</span><span class="op">)</span>
+  <span class="op">}</span>,
+  
+  <span class="co"># the only change is that this went from .getitem to .getbatch</span>
+  .getbatch <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="va">index</span><span class="op">)</span> <span class="op">{</span>
+    
+    <span class="va">x</span> <span class="op">&lt;-</span> <span class="va">self</span><span class="op">$</span><span class="va">data</span><span class="op">[</span><span class="va">index</span>, <span class="fl">2</span><span class="op">:</span><span class="op">-</span><span class="fl">1</span><span class="op">]</span>
+    <span class="va">y</span> <span class="op">&lt;-</span> <span class="va">self</span><span class="op">$</span><span class="va">data</span><span class="op">[</span><span class="va">index</span>, <span class="fl">1</span><span class="op">]</span><span class="op">$</span><span class="fu">to</span><span class="op">(</span><span class="fu"><a href="../reference/torch_dtype.html">torch_long</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span>
+    
+    <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">y</span><span class="op">)</span>
+  <span class="op">}</span>,
+  
+  .length <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="op">)</span> <span class="op">{</span>
+    <span class="va">self</span><span class="op">$</span><span class="va">data</span><span class="op">$</span><span class="fu">size</span><span class="op">(</span><span class="op">)</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span>
+  <span class="op">}</span>,
+  
+  prepare_penguin_data <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="op">)</span> <span class="op">{</span>
+    
+    <span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/na.fail.html" class="external-link">na.omit</a></span><span class="op">(</span><span class="va">penguins</span><span class="op">)</span> 
+    <span class="co"># conveniently, the categorical data are already factors</span>
+    <span class="va">input</span><span class="op">$</span><span class="va">species</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/numeric.html" class="external-link">as.numeric</a></span><span class="op">(</span><span class="va">input</span><span class="op">$</span><span class="va">species</span><span class="op">)</span>
+    <span class="va">input</span><span class="op">$</span><span class="va">island</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/numeric.html" class="external-link">as.numeric</a></span><span class="op">(</span><span class="va">input</span><span class="op">$</span><span class="va">island</span><span class="op">)</span>
+    <span class="va">input</span><span class="op">$</span><span class="va">sex</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/numeric.html" class="external-link">as.numeric</a></span><span class="op">(</span><span class="va">input</span><span class="op">$</span><span class="va">sex</span><span class="op">)</span>
+    
+    <span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/matrix.html" class="external-link">as.matrix</a></span><span class="op">(</span><span class="va">input</span><span class="op">)</span>
+    <span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="va">input</span><span class="op">)</span>
+  <span class="op">}</span>
+<span class="op">)</span></code></pre></div>
+<p>In many instances the only change is to exactly replace just <code>.getitem</code> with <code>.getbatch</code> since often the <code>.getitem</code> function is written to handle vectors of indices. In this penguins example the <code>.getitem</code> function used the index to select the rows, which will work fine with a vector instead</p>
+</div>
+<div class="section level3">
+<h3 id="remove-dataset-dataloader-and-manually-define-the-function-calls">Remove dataset dataloader and manually define the function calls<a class="anchor" aria-label="anchor" href="#remove-dataset-dataloader-and-manually-define-the-function-calls"></a>
+</h3>
+<p>If switching to <code>.getbatch</code> does not provide the benefit you were expecting you could also remove the <code>dataset</code> entirely and manually pass the data. At this point you are trading readability of your code and convenience for speed.</p>
+<div class="sourceCode" id="cb13"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/na.fail.html" class="external-link">na.omit</a></span><span class="op">(</span><span class="va">penguins</span><span class="op">)</span> 
+<span class="co"># conveniently, the categorical data are already factors</span>
+<span class="va">input</span><span class="op">$</span><span class="va">species</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/numeric.html" class="external-link">as.numeric</a></span><span class="op">(</span><span class="va">input</span><span class="op">$</span><span class="va">species</span><span class="op">)</span>
+<span class="va">input</span><span class="op">$</span><span class="va">island</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/numeric.html" class="external-link">as.numeric</a></span><span class="op">(</span><span class="va">input</span><span class="op">$</span><span class="va">island</span><span class="op">)</span>
+<span class="va">input</span><span class="op">$</span><span class="va">sex</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/numeric.html" class="external-link">as.numeric</a></span><span class="op">(</span><span class="va">input</span><span class="op">$</span><span class="va">sex</span><span class="op">)</span>
 
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+<span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/matrix.html" class="external-link">as.matrix</a></span><span class="op">(</span><span class="va">input</span><span class="op">)</span>
+<span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="va">input</span><span class="op">)</span>
 
-        <nav id="toc" data-toggle="toc"><h2 data-toc-skip>Contents</h2>
-    </nav>
-</div>
+<span class="va">data_x</span> <span class="op">&lt;-</span> <span class="va">input</span><span class="op">[</span>, <span class="fl">2</span><span class="op">:</span><span class="op">-</span><span class="fl">1</span><span class="op">]</span>
+<span class="va">data_y</span> <span class="op">&lt;-</span> <span class="va">input</span><span class="op">[</span>, <span class="fl">1</span><span class="op">]</span><span class="op">$</span><span class="fu">to</span><span class="op">(</span><span class="fu"><a href="../reference/torch_dtype.html">torch_long</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span>
+
+<span class="va">batch_size</span> <span class="op">&lt;-</span> <span class="fl">8</span>
+<span class="va">num_data_points</span> <span class="op">&lt;-</span> <span class="va">data_y</span><span class="op">$</span><span class="fu">size</span><span class="op">(</span><span class="fl">1</span><span class="op">)</span>
+<span class="va">num_batches</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/Round.html" class="external-link">floor</a></span><span class="op">(</span><span class="va">num_data_points</span><span class="op">/</span><span class="va">batch_size</span><span class="op">)</span>
+
+<span class="kw">for</span><span class="op">(</span><span class="va">epoch</span> <span class="kw">in</span> <span class="fl">1</span><span class="op">:</span><span class="fl">10</span><span class="op">)</span><span class="op">{</span>
+
+  <span class="co"># rearrange the data each epoch</span>
+  <span class="va">permute</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/torch_randperm.html">torch_randperm</a></span><span class="op">(</span><span class="va">num_data_points</span><span class="op">)</span> <span class="op">+</span> <span class="fl">1L</span>
+  <span class="va">data_x</span> <span class="op">&lt;-</span> <span class="va">data_x</span><span class="op">[</span><span class="va">permute</span><span class="op">]</span>
+  <span class="va">data_y</span> <span class="op">&lt;-</span> <span class="va">data_y</span><span class="op">[</span><span class="va">permute</span><span class="op">]</span>
+  
+  <span class="co"># manually loop through the batches</span>
+  <span class="kw">for</span><span class="op">(</span><span class="va">batch_idx</span> <span class="kw">in</span> <span class="fl">1</span><span class="op">:</span><span class="va">num_batches</span><span class="op">)</span><span class="op">{</span>
+
+    <span class="co"># here index is a vector of the indices in the batch</span>
+    <span class="va">index</span> <span class="op">&lt;-</span> <span class="op">(</span><span class="va">batch_size</span><span class="op">*</span><span class="op">(</span><span class="va">batch_idx</span><span class="op">-</span><span class="fl">1</span><span class="op">)</span> <span class="op">+</span> <span class="fl">1</span><span class="op">)</span><span class="op">:</span><span class="op">(</span><span class="va">batch_idx</span><span class="op">*</span><span class="va">batch_size</span><span class="op">)</span>
+    
+    <span class="va">x</span> <span class="op">&lt;-</span> <span class="va">data_x</span><span class="op">[</span><span class="va">index</span><span class="op">]</span>
+    <span class="va">y</span> <span class="op">&lt;-</span> <span class="va">data_y</span><span class="op">[</span><span class="va">index</span><span class="op">]</span><span class="op">$</span><span class="fu">to</span><span class="op">(</span><span class="fu"><a href="../reference/torch_dtype.html">torch_long</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span>
 
+    <span class="va">optimizer</span><span class="op">$</span><span class="fu">zero_grad</span><span class="op">(</span><span class="op">)</span>
+    <span class="va">output</span> <span class="op">&lt;-</span> <span class="fu">model</span><span class="op">(</span><span class="va">x</span><span class="op">)</span>
+    <span class="va">loss</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/nnf_nll_loss.html">nnf_nll_loss</a></span><span class="op">(</span><span class="va">output</span>, <span class="va">y</span><span class="op">)</span>
+    <span class="va">loss</span><span class="op">$</span><span class="fu">backward</span><span class="op">(</span><span class="op">)</span>
+    <span class="va">optimizer</span><span class="op">$</span><span class="fu">step</span><span class="op">(</span><span class="op">)</span>
+    <span class="va">l</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="va">l</span>, <span class="va">loss</span><span class="op">$</span><span class="fu">item</span><span class="op">(</span><span class="op">)</span><span class="op">)</span>
+  <span class="op">}</span>
+  
+  <span class="fu"><a href="https://rdrr.io/r/base/cat.html" class="external-link">cat</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/sprintf.html" class="external-link">sprintf</a></span><span class="op">(</span><span class="st">"Loss at epoch %d: %3f\n"</span>, <span class="va">epoch</span>, <span class="fu"><a href="https://rdrr.io/r/base/mean.html" class="external-link">mean</a></span><span class="op">(</span><span class="va">l</span><span class="op">)</span><span class="op">)</span><span class="op">)</span>
+<span class="op">}</span></code></pre></div>
+</div>
+</div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside>
 </div>
 
 
 
-      <footer><div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <footer><div class="pkgdown-footer-left">
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+<p>Developed by Daniel Falbel, Javier Luraschi.</p>
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+<div class="pkgdown-footer-right">
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diff --git a/articles/loading-data_files/anchor-sections-1.0/anchor-sections.css b/articles/loading-data_files/anchor-sections-1.0/anchor-sections.css
deleted file mode 100644
index 07aee5fcb..000000000
--- a/articles/loading-data_files/anchor-sections-1.0/anchor-sections.css
+++ /dev/null
@@ -1,4 +0,0 @@
-/* Styles for section anchors */
-a.anchor-section {margin-left: 10px; visibility: hidden; color: inherit;}
-a.anchor-section::before {content: '#';}
-.hasAnchor:hover a.anchor-section {visibility: visible;}
diff --git a/articles/loading-data_files/anchor-sections-1.0/anchor-sections.js b/articles/loading-data_files/anchor-sections-1.0/anchor-sections.js
deleted file mode 100644
index 570f99a0a..000000000
--- a/articles/loading-data_files/anchor-sections-1.0/anchor-sections.js
+++ /dev/null
@@ -1,33 +0,0 @@
-// Anchor sections v1.0 written by Atsushi Yasumoto on Oct 3rd, 2020.
-document.addEventListener('DOMContentLoaded', function() {
-  // Do nothing if AnchorJS is used
-  if (typeof window.anchors === 'object' && anchors.hasOwnProperty('hasAnchorJSLink')) {
-    return;
-  }
-
-  const h = document.querySelectorAll('h1, h2, h3, h4, h5, h6');
-
-  // Do nothing if sections are already anchored
-  if (Array.from(h).some(x => x.classList.contains('hasAnchor'))) {
-    return null;
-  }
-
-  // Use section id when pandoc runs with --section-divs
-  const section_id = function(x) {
-    return ((x.classList.contains('section') || (x.tagName === 'SECTION'))
-            ? x.id : '');
-  };
-
-  // Add anchors
-  h.forEach(function(x) {
-    const id = x.id || section_id(x.parentElement);
-    if (id === '') {
-      return null;
-    }
-    let anchor = document.createElement('a');
-    anchor.href = '#' + id;
-    anchor.classList = ['anchor-section'];
-    x.classList.add('hasAnchor');
-    x.appendChild(anchor);
-  });
-});
diff --git a/articles/python-to-r.html b/articles/python-to-r.html
index b75ddbebe..50902d672 100644
--- a/articles/python-to-r.html
+++ b/articles/python-to-r.html
@@ -4,14 +4,14 @@
 <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
 <meta charset="utf-8">
 <meta http-equiv="X-UA-Compatible" content="IE=edge">
-<meta name="viewport" content="width=device-width, initial-scale=1.0">
+<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
+<meta name="description" content="torch">
 <title>Python to R • torch</title>
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+<h2 id="loading-models-saved-in-python">Loading models saved in python<a class="anchor" aria-label="anchor" href="#loading-models-saved-in-python"></a>
+</h2>
 <p>Currently the only way to load models from python is to rewrite the model architecture in R. All the parameter names must be identical. A complete example from Python to R is shown below. This is an extension of the Serialization vignette.</p>
 <p>An artificial neural net is implemented below in Python. Note the final line which uses torch.save().</p>
 <p>```{python eval=FALSE] import torch import numpy as np</p>
@@ -219,31 +180,27 @@ newData = torch_tensor(array(rnorm(n=1000),dim=c(10,100)),dtype=torch_float32())
 
 predictMe &lt;- model(newData)</code></pre>
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-  <div class="col-md-9 contents">
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-      <h1 data-toc-skip>Serialization</h1>
+
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+  <main id="main"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Serialization</h1>
             
       
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/master/vignettes/serialization.Rmd"><code>vignettes/serialization.Rmd</code></a></small>
-      <div class="hidden name"><code>serialization.Rmd</code></div>
-
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/vignettes/serialization.Rmd" class="external-link"><code>vignettes/serialization.Rmd</code></a></small>
+      <div class="d-none name"><code>serialization.Rmd</code></div>
     </div>
 
     
     
 <div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://torch.mlverse.org/docs">torch</a></span><span class="op">)</span></code></pre></div>
+<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://torch.mlverse.org/docs">torch</a></span><span class="op">)</span></code></pre></div>
 <p>Torch tensors in R are pointers to Tensors allocated by LibTorch. This has one major consequence for serialization. One cannot simply use <code>saveRDS</code> for serializing tensors, as you would save the pointer but not the data itself. When reloading a tensor saved with <code>saveRDS</code> the pointer might have been deleted in LibTorch and you would get wrong results.</p>
 <p>To solve this problem, <code>torch</code> implements specialized functions for serializing tensors to the disk:</p>
 <ul>
@@ -155,9 +116,9 @@
 <code><a href="../reference/torch_load.html">torch_load()</a></code>: to load the models or tensors back to the session.</li>
 </ul>
 <p>Please note that this format is still experimental and you shouldn’t use it for long term storage.</p>
-<div id="saving-tensors" class="section level2">
-<h2 class="hasAnchor">
-<a href="#saving-tensors" class="anchor"></a>Saving tensors</h2>
+<div class="section level2">
+<h2 id="saving-tensors">Saving tensors<a class="anchor" aria-label="anchor" href="#saving-tensors"></a>
+</h2>
 <p>You can save any object of type <code>torch_tensor</code> to the disk using:</p>
 <div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">10</span>, <span class="fl">10</span><span class="op">)</span>
@@ -167,9 +128,9 @@
 <span class="fu"><a href="../reference/torch_allclose.html">torch_allclose</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">x_</span><span class="op">)</span>
 <span class="co">#&gt; [1] TRUE</span></code></pre></div>
 </div>
-<div id="saving-modules" class="section level2">
-<h2 class="hasAnchor">
-<a href="#saving-modules" class="anchor"></a>Saving modules</h2>
+<div class="section level2">
+<h2 id="saving-modules">Saving modules<a class="anchor" aria-label="anchor" href="#saving-modules"></a>
+</h2>
 <p>The <code>torch_save</code> and <code>torch_load</code> functions also work for <code>nn_modules</code> objects.</p>
 <p>When saving an <code>nn_module</code>, all the object is serialized including the model structure and it’s state.</p>
 <div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
@@ -180,8 +141,8 @@
     <span class="va">self</span><span class="op">$</span><span class="va">fc2</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/nn_linear.html">nn_linear</a></span><span class="op">(</span><span class="fl">10</span>, <span class="fl">1</span><span class="op">)</span>
   <span class="op">}</span>,
   forward <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="op">{</span>
-    <span class="va">x</span> <span class="op"><a href="../reference/%&gt;%.html">%&gt;%</a></span> 
-      <span class="va">self</span><span class="op">$</span><span class="fu">fc1</span><span class="op">(</span><span class="op">)</span> <span class="op"><a href="../reference/%&gt;%.html">%&gt;%</a></span> 
+    <span class="va">x</span> <span class="op"><a href="../reference/pipe.html">%&gt;%</a></span> 
+      <span class="va">self</span><span class="op">$</span><span class="fu">fc1</span><span class="op">(</span><span class="op">)</span> <span class="op"><a href="../reference/pipe.html">%&gt;%</a></span> 
       <span class="va">self</span><span class="op">$</span><span class="fu">fc2</span><span class="op">(</span><span class="op">)</span>
   <span class="op">}</span>
 <span class="op">)</span>
@@ -195,9 +156,9 @@
 <span class="fu"><a href="../reference/torch_allclose.html">torch_allclose</a></span><span class="op">(</span><span class="fu">model</span><span class="op">(</span><span class="va">x</span><span class="op">)</span>, <span class="fu">model_</span><span class="op">(</span><span class="va">x</span><span class="op">)</span><span class="op">)</span>
 <span class="co">#&gt; [1] TRUE</span></code></pre></div>
 </div>
-<div id="loading-models-saved-in-python" class="section level2">
-<h2 class="hasAnchor">
-<a href="#loading-models-saved-in-python" class="anchor"></a>Loading models saved in python</h2>
+<div class="section level2">
+<h2 id="loading-models-saved-in-python">Loading models saved in python<a class="anchor" aria-label="anchor" href="#loading-models-saved-in-python"></a>
+</h2>
 <p>Currently the only way to load models from python is to rewrite the model architecture in R. All the parameter names must be identical.</p>
 <p>You can then save the PyTorch model state_dict using:</p>
 <pre><code>torch.save(model, fpath, _use_new_zipfile_serialization=True)</code></pre>
@@ -206,11 +167,11 @@
 <code class="sourceCode R"><span class="va">state_dict</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/load_state_dict.html">load_state_dict</a></span><span class="op">(</span><span class="va">fpath</span><span class="op">)</span>
 <span class="va">model</span> <span class="op">&lt;-</span> <span class="fu">Model</span><span class="op">(</span><span class="op">)</span>
 <span class="va">model</span><span class="op">$</span><span class="fu">load_state_dict</span><span class="op">(</span><span class="va">state_dict</span><span class="op">)</span></code></pre></div>
-<p>You can find working examples in <code>torchvision</code>. For example <a href="https://github.com/mlverse/torchvision/blob/main/R/models-alexnet.R#L2-L63">this</a> is what we do for the AlexNet model.</p>
+<p>You can find working examples in <code>torchvision</code>. For example <a href="https://github.com/mlverse/torchvision/blob/main/R/models-alexnet.R#L2-L63" class="external-link">this</a> is what we do for the AlexNet model.</p>
 </div>
-<div id="saving-optimizer-state" class="section level2">
-<h2 class="hasAnchor">
-<a href="#saving-optimizer-state" class="anchor"></a>Saving optimizer state</h2>
+<div class="section level2">
+<h2 id="saving-optimizer-state">Saving optimizer state<a class="anchor" aria-label="anchor" href="#saving-optimizer-state"></a>
+</h2>
 <p>You can save the state of optimizers so you can continue training from the exact same position.</p>
 <p>In order to this we use the <code>state_dict()</code> and <code><a href="../reference/load_state_dict.html">load_state_dict()</a></code> methods from the optimizer combined with <code>torch_save</code>:</p>
 <div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
@@ -226,38 +187,35 @@
 <span class="co">#&gt; NULL</span>
 
 <span class="co"># Now let's save the optimizer state</span>
-<span class="va">tmp</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/tempfile.html">tempfile</a></span><span class="op">(</span><span class="op">)</span>
+<span class="va">tmp</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/tempfile.html" class="external-link">tempfile</a></span><span class="op">(</span><span class="op">)</span>
 <span class="fu"><a href="../reference/torch_save.html">torch_save</a></span><span class="op">(</span><span class="va">opt</span><span class="op">$</span><span class="fu">state_dict</span><span class="op">(</span><span class="op">)</span>, <span class="va">tmp</span><span class="op">)</span>
 
 <span class="co"># And now let's create a new optimizer and load back</span>
 <span class="va">opt2</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/optim_adam.html">optim_adam</a></span><span class="op">(</span><span class="va">model</span><span class="op">$</span><span class="va">parameters</span><span class="op">)</span>
 <span class="va">opt2</span><span class="op">$</span><span class="fu">load_state_dict</span><span class="op">(</span><span class="fu"><a href="../reference/torch_load.html">torch_load</a></span><span class="op">(</span><span class="va">tmp</span><span class="op">)</span><span class="op">)</span></code></pre></div>
 </div>
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diff --git a/articles/serialization_files/anchor-sections-1.0/anchor-sections.css b/articles/serialization_files/anchor-sections-1.0/anchor-sections.css
deleted file mode 100644
index 07aee5fcb..000000000
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+++ /dev/null
@@ -1,4 +0,0 @@
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diff --git a/articles/serialization_files/anchor-sections-1.0/anchor-sections.js b/articles/serialization_files/anchor-sections-1.0/anchor-sections.js
deleted file mode 100644
index 570f99a0a..000000000
--- a/articles/serialization_files/anchor-sections-1.0/anchor-sections.js
+++ /dev/null
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-  const h = document.querySelectorAll('h1, h2, h3, h4, h5, h6');
-
-  // Do nothing if sections are already anchored
-  if (Array.from(h).some(x => x.classList.contains('hasAnchor'))) {
-    return null;
-  }
-
-  // Use section id when pandoc runs with --section-divs
-  const section_id = function(x) {
-    return ((x.classList.contains('section') || (x.tagName === 'SECTION'))
-            ? x.id : '');
-  };
-
-  // Add anchors
-  h.forEach(function(x) {
-    const id = x.id || section_id(x.parentElement);
-    if (id === '') {
-      return null;
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-    let anchor = document.createElement('a');
-    anchor.href = '#' + id;
-    anchor.classList = ['anchor-section'];
-    x.classList.add('hasAnchor');
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diff --git a/articles/tensor-creation.html b/articles/tensor-creation.html
index 38a1ed7b7..8b37168cb 100644
--- a/articles/tensor-creation.html
+++ b/articles/tensor-creation.html
@@ -4,14 +4,14 @@
 <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
 <meta charset="utf-8">
 <meta http-equiv="X-UA-Compatible" content="IE=edge">
-<meta name="viewport" content="width=device-width, initial-scale=1.0">
+<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
+<meta name="description" content="torch">
 <title>Creating tensors • torch</title>
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 <meta property="og:title" content="Creating tensors">
 <meta property="og:description" content="torch">
 <!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-      <div class="hidden name"><code>tensor-creation.Rmd</code></div>
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+      <div class="d-none name"><code>tensor-creation.Rmd</code></div>
     </div>
 
     
     
 <div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://torch.mlverse.org/docs">torch</a></span><span class="op">)</span></code></pre></div>
+<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://torch.mlverse.org/docs">torch</a></span><span class="op">)</span></code></pre></div>
 <p>In this article we describe various ways of creating <code>torch</code> tensors in R.</p>
-<div id="from-r-objects" class="section level2">
-<h2 class="hasAnchor">
-<a href="#from-r-objects" class="anchor"></a>From R objects</h2>
+<div class="section level2">
+<h2 id="from-r-objects">From R objects<a class="anchor" aria-label="anchor" href="#from-r-objects"></a>
+</h2>
 <p>You can create tensors from R objects using the <code>torch_tensor</code> function. The <code>torch_tensor</code> function takes an R vector, matrix or array and creates an equivalent <code>torch_tensor</code>.</p>
 <p>You can see a few examples below:</p>
 <div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">1</span>,<span class="fl">2</span>,<span class="fl">3</span><span class="op">)</span><span class="op">)</span>
+<code class="sourceCode R"><span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>,<span class="fl">2</span>,<span class="fl">3</span><span class="op">)</span><span class="op">)</span>
 <span class="co">#&gt; torch_tensor</span>
 <span class="co">#&gt;  1</span>
 <span class="co">#&gt;  2</span>
@@ -161,20 +122,20 @@
 <span class="co">#&gt; [ CPUFloatType{3} ]</span>
 
 <span class="co"># conform to row-major indexing used in torch</span>
-<span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/matrix.html">matrix</a></span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="fl">10</span>, ncol <span class="op">=</span> <span class="fl">5</span>, nrow <span class="op">=</span> <span class="fl">2</span>, byrow <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span><span class="op">)</span>
+<span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/matrix.html" class="external-link">matrix</a></span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="fl">10</span>, ncol <span class="op">=</span> <span class="fl">5</span>, nrow <span class="op">=</span> <span class="fl">2</span>, byrow <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span><span class="op">)</span>
 <span class="co">#&gt; torch_tensor</span>
 <span class="co">#&gt;   1   2   3   4   5</span>
 <span class="co">#&gt;   6   7   8   9  10</span>
 <span class="co">#&gt; [ CPULongType{2,5} ]</span>
-<span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/array.html">array</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/stats/Uniform.html">runif</a></span><span class="op">(</span><span class="fl">12</span><span class="op">)</span>, dim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">2</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span><span class="op">)</span>
+<span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/array.html" class="external-link">array</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/stats/Uniform.html" class="external-link">runif</a></span><span class="op">(</span><span class="fl">12</span><span class="op">)</span>, dim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">2</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span><span class="op">)</span>
 <span class="co">#&gt; torch_tensor</span>
 <span class="co">#&gt; (1,.,.) = </span>
-<span class="co">#&gt;   0.9896  0.2151  0.5776</span>
-<span class="co">#&gt;   0.4114  0.0860  0.0425</span>
+<span class="co">#&gt;   0.9997  0.0685  0.2189</span>
+<span class="co">#&gt;   0.0015  0.2691  0.5970</span>
 <span class="co">#&gt; </span>
 <span class="co">#&gt; (2,.,.) = </span>
-<span class="co">#&gt;   0.7145  0.7958  0.5771</span>
-<span class="co">#&gt;   0.4278  0.4387  0.2531</span>
+<span class="co">#&gt;   0.4444  0.7189  0.2738</span>
+<span class="co">#&gt;   0.0837  0.3057  0.9979</span>
 <span class="co">#&gt; [ CPUFloatType{2,2,3} ]</span></code></pre></div>
 <p>By default, we will create tensors in the <code>cpu</code> device, converting their R datatype to the corresponding torch <code>dtype</code>.</p>
 <blockquote>
@@ -199,20 +160,20 @@
 </ul>
 <p>These options are available for all functions that can be used to create new tensors, including the factory functions listed in the next section.</p>
 </div>
-<div id="using-creation-functions" class="section level2">
-<h2 class="hasAnchor">
-<a href="#using-creation-functions" class="anchor"></a>Using creation functions</h2>
+<div class="section level2">
+<h2 id="using-creation-functions">Using creation functions<a class="anchor" aria-label="anchor" href="#using-creation-functions"></a>
+</h2>
 <p>You can also use the <code>torch_*</code> functions listed below to create torch tensors using some algorithm.</p>
 <p>For example, the <code>torch_randn</code> function will create tensors using the normal distribution with mean 0 and standard deviation 1. You can use the <code>...</code> argument to pass the size of the dimensions. For example, the code below will create a normally distributed tensor with shape 5x3.</p>
 <div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">5</span>, <span class="fl">3</span><span class="op">)</span>
 <span class="va">x</span>
 <span class="co">#&gt; torch_tensor</span>
-<span class="co">#&gt;  0.0569  0.9298 -0.0773</span>
-<span class="co">#&gt; -0.9757 -0.6644  1.1595</span>
-<span class="co">#&gt; -1.6948 -1.3728 -0.6792</span>
-<span class="co">#&gt; -0.7060 -0.1746  0.2670</span>
-<span class="co">#&gt;  0.1342 -0.2037 -0.6476</span>
+<span class="co">#&gt; -2.1127  1.5563 -1.4043</span>
+<span class="co">#&gt; -0.5640 -2.7906 -0.4972</span>
+<span class="co">#&gt;  1.1102 -1.4511 -0.7686</span>
+<span class="co">#&gt;  0.2785 -1.1359 -1.4382</span>
+<span class="co">#&gt;  0.9929 -0.5821  0.2325</span>
 <span class="co">#&gt; [ CPUFloatType{5,3} ]</span></code></pre></div>
 <p>Another example is <code>torch_ones</code>, which creates a tensor filled with ones.</p>
 <div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
@@ -250,9 +211,9 @@
 <code>torch_zeros</code>: Returns a tensor filled with all zeros.</li>
 </ul>
 </div>
-<div id="conversion" class="section level2">
-<h2 class="hasAnchor">
-<a href="#conversion" class="anchor"></a>Conversion</h2>
+<div class="section level2">
+<h2 id="conversion">Conversion<a class="anchor" aria-label="anchor" href="#conversion"></a>
+</h2>
 <p>Once a tensor exists you can convert between <code>dtype</code>s and move to a different device with <code>to</code> method. For example:</p>
 <div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fl">1</span><span class="op">)</span>
@@ -269,31 +230,28 @@
 <div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1"></a>x &lt;-<span class="st"> </span><span class="kw">torch_tensor</span>(<span class="dv">1</span>)</span>
 <span id="cb7-2"><a href="#cb7-2"></a>y &lt;-<span class="st"> </span>x<span class="op">$</span><span class="kw">cuda</span>()<span class="er">)</span></span></code></pre></div>
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diff --git a/articles/tensor-creation_files/anchor-sections-1.0/anchor-sections.css b/articles/tensor-creation_files/anchor-sections-1.0/anchor-sections.css
deleted file mode 100644
index 07aee5fcb..000000000
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+++ /dev/null
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diff --git a/articles/tensor-creation_files/anchor-sections-1.0/anchor-sections.js b/articles/tensor-creation_files/anchor-sections-1.0/anchor-sections.js
deleted file mode 100644
index 570f99a0a..000000000
--- a/articles/tensor-creation_files/anchor-sections-1.0/anchor-sections.js
+++ /dev/null
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-    return ((x.classList.contains('section') || (x.tagName === 'SECTION'))
-            ? x.id : '');
-  };
-
-  // Add anchors
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-    const id = x.id || section_id(x.parentElement);
-    if (id === '') {
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diff --git a/articles/tensor/index.html b/articles/tensor/index.html
index bbe870c34..97b07cb45 100644
--- a/articles/tensor/index.html
+++ b/articles/tensor/index.html
@@ -4,14 +4,14 @@
 <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
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+<script src="index_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row">
+  <main id="main"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Tensor objects</h1>
             
       
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+      <div class="d-none name"><code>index.Rmd</code></div>
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 <div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://torch.mlverse.org/docs">torch</a></span><span class="op">)</span></code></pre></div>
+<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://torch.mlverse.org/docs">torch</a></span><span class="op">)</span></code></pre></div>
 <p>Central to torch is the <code>torch_tensor</code> objects. <code>torch_tensor</code>’s are R objects very similar to R6 instances. Tensors have a large amount of methods that can be called using the <code>$</code> operator.</p>
-<p>Following is a list of all methods that can be called by tensor objects and their documentation. You can also look at <a href="https://pytorch.org/docs/stable/tensors.html">PyTorch’s documentation</a> for additional details.</p>
-<div id="numpy_t" class="section level2">
-<h2 class="hasAnchor">
-<a href="#numpy_t" class="anchor"></a>numpy_T</h2>
+<p>Following is a list of all methods that can be called by tensor objects and their documentation. You can also look at <a href="https://pytorch.org/docs/stable/tensors.html" class="external-link">PyTorch’s documentation</a> for additional details.</p>
+<div class="section level2">
+<h2 id="numpy_t">numpy_T<a class="anchor" aria-label="anchor" href="#numpy_t"></a>
+</h2>
 <p>Is this Tensor with its dimensions reversed.</p>
 <p>If <code>n</code> is the number of dimensions in <code>x</code>, <code>x$numpy_T()</code> is equivalent to <code>x$permute(n, n-1, ..., 1)</code>.</p>
 </div>
-<div id="abs" class="section level2">
-<h2 class="hasAnchor">
-<a href="#abs" class="anchor"></a>abs</h2>
+<div class="section level2">
+<h2 id="abs">abs<a class="anchor" aria-label="anchor" href="#abs"></a>
+</h2>
 <p>abs() -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_abs.html">?torch_abs</a></code></p>
 </div>
-<div id="abs_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#abs_" class="anchor"></a>abs_</h2>
+<div class="section level2">
+<h2 id="abs_">abs_<a class="anchor" aria-label="anchor" href="#abs_"></a>
+</h2>
 <p>abs_() -&gt; Tensor</p>
 <p>In-place version of <code>$abs</code></p>
 </div>
-<div id="absolute" class="section level2">
-<h2 class="hasAnchor">
-<a href="#absolute" class="anchor"></a>absolute</h2>
+<div class="section level2">
+<h2 id="absolute">absolute<a class="anchor" aria-label="anchor" href="#absolute"></a>
+</h2>
 <p>absolute() -&gt; Tensor</p>
 <p>Alias for [$abs()]</p>
 </div>
-<div id="absolute_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#absolute_" class="anchor"></a>absolute_</h2>
+<div class="section level2">
+<h2 id="absolute_">absolute_<a class="anchor" aria-label="anchor" href="#absolute_"></a>
+</h2>
 <p>absolute_() -&gt; Tensor</p>
 <p>In-place version of <code>$absolute</code> Alias for [$abs_()]</p>
 </div>
-<div id="acos" class="section level2">
-<h2 class="hasAnchor">
-<a href="#acos" class="anchor"></a>acos</h2>
+<div class="section level2">
+<h2 id="acos">acos<a class="anchor" aria-label="anchor" href="#acos"></a>
+</h2>
 <p>acos() -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_acos.html">?torch_acos</a></code></p>
 </div>
-<div id="acos_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#acos_" class="anchor"></a>acos_</h2>
+<div class="section level2">
+<h2 id="acos_">acos_<a class="anchor" aria-label="anchor" href="#acos_"></a>
+</h2>
 <p>acos_() -&gt; Tensor</p>
 <p>In-place version of <code>$acos</code></p>
 </div>
-<div id="acosh" class="section level2">
-<h2 class="hasAnchor">
-<a href="#acosh" class="anchor"></a>acosh</h2>
+<div class="section level2">
+<h2 id="acosh">acosh<a class="anchor" aria-label="anchor" href="#acosh"></a>
+</h2>
 <p>acosh() -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_acosh.html">?torch_acosh</a></code></p>
 </div>
-<div id="acosh_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#acosh_" class="anchor"></a>acosh_</h2>
+<div class="section level2">
+<h2 id="acosh_">acosh_<a class="anchor" aria-label="anchor" href="#acosh_"></a>
+</h2>
 <p>acosh_() -&gt; Tensor</p>
 <p>In-place version of <code>$acosh</code></p>
 </div>
-<div id="add" class="section level2">
-<h2 class="hasAnchor">
-<a href="#add" class="anchor"></a>add</h2>
+<div class="section level2">
+<h2 id="add">add<a class="anchor" aria-label="anchor" href="#add"></a>
+</h2>
 <p>add(other, *, alpha=1) -&gt; Tensor</p>
 <p>Add a scalar or tensor to <code>self</code> tensor. If both <code>alpha</code> and <code>other</code> are specified, each element of <code>other</code> is scaled by <code>alpha</code> before being used.</p>
 <p>When <code>other</code> is a tensor, the shape of <code>other</code> must be broadcastable with the shape of the underlying tensor</p>
 <p>See <code><a href="../../reference/torch_add.html">?torch_add</a></code></p>
 </div>
-<div id="add_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#add_" class="anchor"></a>add_</h2>
+<div class="section level2">
+<h2 id="add_">add_<a class="anchor" aria-label="anchor" href="#add_"></a>
+</h2>
 <p>add_(other, *, alpha=1) -&gt; Tensor</p>
 <p>In-place version of <code>$add</code></p>
 </div>
-<div id="addbmm" class="section level2">
-<h2 class="hasAnchor">
-<a href="#addbmm" class="anchor"></a>addbmm</h2>
+<div class="section level2">
+<h2 id="addbmm">addbmm<a class="anchor" aria-label="anchor" href="#addbmm"></a>
+</h2>
 <p>addbmm(batch1, batch2, *, beta=1, alpha=1) -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_addbmm.html">?torch_addbmm</a></code></p>
 </div>
-<div id="addbmm_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#addbmm_" class="anchor"></a>addbmm_</h2>
+<div class="section level2">
+<h2 id="addbmm_">addbmm_<a class="anchor" aria-label="anchor" href="#addbmm_"></a>
+</h2>
 <p>addbmm_(batch1, batch2, *, beta=1, alpha=1) -&gt; Tensor</p>
 <p>In-place version of <code>$addbmm</code></p>
 </div>
-<div id="addcdiv" class="section level2">
-<h2 class="hasAnchor">
-<a href="#addcdiv" class="anchor"></a>addcdiv</h2>
+<div class="section level2">
+<h2 id="addcdiv">addcdiv<a class="anchor" aria-label="anchor" href="#addcdiv"></a>
+</h2>
 <p>addcdiv(tensor1, tensor2, *, value=1) -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_addcdiv.html">?torch_addcdiv</a></code></p>
 </div>
-<div id="addcdiv_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#addcdiv_" class="anchor"></a>addcdiv_</h2>
+<div class="section level2">
+<h2 id="addcdiv_">addcdiv_<a class="anchor" aria-label="anchor" href="#addcdiv_"></a>
+</h2>
 <p>addcdiv_(tensor1, tensor2, *, value=1) -&gt; Tensor</p>
 <p>In-place version of <code>$addcdiv</code></p>
 </div>
-<div id="addcmul" class="section level2">
-<h2 class="hasAnchor">
-<a href="#addcmul" class="anchor"></a>addcmul</h2>
+<div class="section level2">
+<h2 id="addcmul">addcmul<a class="anchor" aria-label="anchor" href="#addcmul"></a>
+</h2>
 <p>addcmul(tensor1, tensor2, *, value=1) -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_addcmul.html">?torch_addcmul</a></code></p>
 </div>
-<div id="addcmul_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#addcmul_" class="anchor"></a>addcmul_</h2>
+<div class="section level2">
+<h2 id="addcmul_">addcmul_<a class="anchor" aria-label="anchor" href="#addcmul_"></a>
+</h2>
 <p>addcmul_(tensor1, tensor2, *, value=1) -&gt; Tensor</p>
 <p>In-place version of <code>$addcmul</code></p>
 </div>
-<div id="addmm" class="section level2">
-<h2 class="hasAnchor">
-<a href="#addmm" class="anchor"></a>addmm</h2>
+<div class="section level2">
+<h2 id="addmm">addmm<a class="anchor" aria-label="anchor" href="#addmm"></a>
+</h2>
 <p>addmm(mat1, mat2, *, beta=1, alpha=1) -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_addmm.html">?torch_addmm</a></code></p>
 </div>
-<div id="addmm_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#addmm_" class="anchor"></a>addmm_</h2>
+<div class="section level2">
+<h2 id="addmm_">addmm_<a class="anchor" aria-label="anchor" href="#addmm_"></a>
+</h2>
 <p>addmm_(mat1, mat2, *, beta=1, alpha=1) -&gt; Tensor</p>
 <p>In-place version of <code>$addmm</code></p>
 </div>
-<div id="addmv" class="section level2">
-<h2 class="hasAnchor">
-<a href="#addmv" class="anchor"></a>addmv</h2>
+<div class="section level2">
+<h2 id="addmv">addmv<a class="anchor" aria-label="anchor" href="#addmv"></a>
+</h2>
 <p>addmv(mat, vec, *, beta=1, alpha=1) -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_addmv.html">?torch_addmv</a></code></p>
 </div>
-<div id="addmv_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#addmv_" class="anchor"></a>addmv_</h2>
+<div class="section level2">
+<h2 id="addmv_">addmv_<a class="anchor" aria-label="anchor" href="#addmv_"></a>
+</h2>
 <p>addmv_(mat, vec, *, beta=1, alpha=1) -&gt; Tensor</p>
 <p>In-place version of <code>$addmv</code></p>
 </div>
-<div id="addr" class="section level2">
-<h2 class="hasAnchor">
-<a href="#addr" class="anchor"></a>addr</h2>
+<div class="section level2">
+<h2 id="addr">addr<a class="anchor" aria-label="anchor" href="#addr"></a>
+</h2>
 <p>addr(vec1, vec2, *, beta=1, alpha=1) -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_addr.html">?torch_addr</a></code></p>
 </div>
-<div id="addr_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#addr_" class="anchor"></a>addr_</h2>
+<div class="section level2">
+<h2 id="addr_">addr_<a class="anchor" aria-label="anchor" href="#addr_"></a>
+</h2>
 <p>addr_(vec1, vec2, *, beta=1, alpha=1) -&gt; Tensor</p>
 <p>In-place version of <code>$addr</code></p>
 </div>
-<div id="align_as" class="section level2">
-<h2 class="hasAnchor">
-<a href="#align_as" class="anchor"></a>align_as</h2>
+<div class="section level2">
+<h2 id="align_as">align_as<a class="anchor" aria-label="anchor" href="#align_as"></a>
+</h2>
 <p>align_as(other) -&gt; Tensor</p>
 <p>Permutes the dimensions of the <code>self</code> tensor to match the dimension order in the <code>other</code> tensor, adding size-one dims for any new names.</p>
 <p>This operation is useful for explicit broadcasting by names (see examples).</p>
 <p>All of the dims of <code>self</code> must be named in order to use this method. The resulting tensor is a view on the original tensor.</p>
 <p>All dimension names of <code>self</code> must be present in <code>other$names</code>. <code>other</code> may contain named dimensions that are not in <code>self$names</code>; the output tensor has a size-one dimension for each of those new names.</p>
 <p>To align a tensor to a specific order, use <code>$align_to</code>.</p>
-<div id="examples" class="section level3">
-<h3 class="hasAnchor">
-<a href="#examples" class="anchor"></a>Examples:</h3>
+<div class="section level3">
+<h3 id="examples">Examples:<a class="anchor" aria-label="anchor" href="#examples"></a>
+</h3>
 <div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span class="co"># Example 1: Applying a mask</span>
-<span class="va">mask</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randint.html">torch_randint</a></span><span class="op">(</span>low <span class="op">=</span> <span class="fl">0</span>, high <span class="op">=</span> <span class="fl">2</span>, size <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">127</span>, <span class="fl">128</span><span class="op">)</span>, dtype<span class="op">=</span><span class="fu"><a href="../../reference/torch_dtype.html">torch_bool</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span><span class="op">$</span><span class="fu">refine_names</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="st">'W'</span>, <span class="st">'H'</span><span class="op">)</span><span class="op">)</span>
-<span class="va">imgs</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">32</span>, <span class="fl">128</span>, <span class="fl">127</span>, <span class="fl">3</span>, names<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="st">'N'</span>, <span class="st">'H'</span>, <span class="st">'W'</span>, <span class="st">'C'</span><span class="op">)</span><span class="op">)</span>
+<span class="va">mask</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randint.html">torch_randint</a></span><span class="op">(</span>low <span class="op">=</span> <span class="fl">0</span>, high <span class="op">=</span> <span class="fl">2</span>, size <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">127</span>, <span class="fl">128</span><span class="op">)</span>, dtype<span class="op">=</span><span class="fu"><a href="../../reference/torch_dtype.html">torch_bool</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span><span class="op">$</span><span class="fu">refine_names</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">'W'</span>, <span class="st">'H'</span><span class="op">)</span><span class="op">)</span>
+<span class="va">imgs</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">32</span>, <span class="fl">128</span>, <span class="fl">127</span>, <span class="fl">3</span>, names<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">'N'</span>, <span class="st">'H'</span>, <span class="st">'W'</span>, <span class="st">'C'</span><span class="op">)</span><span class="op">)</span>
 <span class="va">imgs</span><span class="op">$</span><span class="fu">masked_fill_</span><span class="op">(</span><span class="va">mask</span><span class="op">$</span><span class="fu">align_as</span><span class="op">(</span><span class="va">imgs</span><span class="op">)</span>, <span class="fl">0</span><span class="op">)</span>
 
 <span class="co"># Example 2: Applying a per-channel-scale</span>
@@ -314,52 +275,52 @@
 
 <span class="va">num_channels</span> <span class="op">&lt;-</span> <span class="fl">3</span>
 <span class="va">scale</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="va">num_channels</span>, names<span class="op">=</span><span class="st">'C'</span><span class="op">)</span>
-<span class="va">imgs</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_rand.html">torch_rand</a></span><span class="op">(</span><span class="fl">32</span>, <span class="fl">128</span>, <span class="fl">128</span>, <span class="va">num_channels</span>, names<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="st">'N'</span>, <span class="st">'H'</span>, <span class="st">'W'</span>, <span class="st">'C'</span><span class="op">)</span><span class="op">)</span>
-<span class="va">more_imgs</span> <span class="op">=</span> <span class="fu"><a href="../../reference/torch_rand.html">torch_rand</a></span><span class="op">(</span><span class="fl">32</span>, <span class="va">num_channels</span>, <span class="fl">128</span>, <span class="fl">128</span>, names<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="st">'N'</span>, <span class="st">'C'</span>, <span class="st">'H'</span>, <span class="st">'W'</span><span class="op">)</span><span class="op">)</span>
-<span class="va">videos</span> <span class="op">=</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">3</span>, <span class="va">num_channels</span>, <span class="fl">128</span>, <span class="fl">128</span>, <span class="fl">128</span>, names<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="st">'N'</span>, <span class="st">'C'</span>, <span class="st">'H'</span>, <span class="st">'W'</span>, <span class="st">'D'</span><span class="op">)</span><span class="op">)</span>
+<span class="va">imgs</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_rand.html">torch_rand</a></span><span class="op">(</span><span class="fl">32</span>, <span class="fl">128</span>, <span class="fl">128</span>, <span class="va">num_channels</span>, names<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">'N'</span>, <span class="st">'H'</span>, <span class="st">'W'</span>, <span class="st">'C'</span><span class="op">)</span><span class="op">)</span>
+<span class="va">more_imgs</span> <span class="op">=</span> <span class="fu"><a href="../../reference/torch_rand.html">torch_rand</a></span><span class="op">(</span><span class="fl">32</span>, <span class="va">num_channels</span>, <span class="fl">128</span>, <span class="fl">128</span>, names<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">'N'</span>, <span class="st">'C'</span>, <span class="st">'H'</span>, <span class="st">'W'</span><span class="op">)</span><span class="op">)</span>
+<span class="va">videos</span> <span class="op">=</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">3</span>, <span class="va">num_channels</span>, <span class="fl">128</span>, <span class="fl">128</span>, <span class="fl">128</span>, names<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">'N'</span>, <span class="st">'C'</span>, <span class="st">'H'</span>, <span class="st">'W'</span>, <span class="st">'D'</span><span class="op">)</span><span class="op">)</span>
 
 <span class="co"># scale_channels is agnostic to the dimension order of the input</span>
 <span class="fu">scale_channels</span><span class="op">(</span><span class="va">imgs</span>, <span class="va">scale</span><span class="op">)</span>
 <span class="fu">scale_channels</span><span class="op">(</span><span class="va">more_imgs</span>, <span class="va">scale</span><span class="op">)</span>
 <span class="fu">scale_channels</span><span class="op">(</span><span class="va">videos</span>, <span class="va">scale</span><span class="op">)</span></code></pre></div>
 </div>
-<div id="warning" class="section level3">
-<h3 class="hasAnchor">
-<a href="#warning" class="anchor"></a>Warning:</h3>
+<div class="section level3">
+<h3 id="warning">Warning:<a class="anchor" aria-label="anchor" href="#warning"></a>
+</h3>
 <p>The named tensor API is experimental and subject to change.</p>
 </div>
 </div>
-<div id="align_to" class="section level2">
-<h2 class="hasAnchor">
-<a href="#align_to" class="anchor"></a>align_to</h2>
+<div class="section level2">
+<h2 id="align_to">align_to<a class="anchor" aria-label="anchor" href="#align_to"></a>
+</h2>
 <p>Permutes the dimensions of the <code>self</code> tensor to match the order specified in <code>names</code>, adding size-one dims for any new names.</p>
 <p>All of the dims of <code>self</code> must be named in order to use this method. The resulting tensor is a view on the original tensor.</p>
 <p>All dimension names of <code>self</code> must be present in <code>names</code>. <code>names</code> may contain additional names that are not in <code>self$names</code>; the output tensor has a size-one dimension for each of those new names.</p>
-<div id="arguments" class="section level3">
-<h3 class="hasAnchor">
-<a href="#arguments" class="anchor"></a>Arguments:</h3>
+<div class="section level3">
+<h3 id="arguments">Arguments:<a class="anchor" aria-label="anchor" href="#arguments"></a>
+</h3>
 <ul>
 <li>names (iterable of str): The desired dimension ordering of the output tensor. May contain up to one Ellipsis that is expanded to all unmentioned dim names of <code>self</code>.</li>
 </ul>
 </div>
-<div id="examples-1" class="section level3">
-<h3 class="hasAnchor">
-<a href="#examples-1" class="anchor"></a>Examples:</h3>
-<div id="warning-1" class="section level4">
-<h4 class="hasAnchor">
-<a href="#warning-1" class="anchor"></a>Warning:</h4>
+<div class="section level3">
+<h3 id="examples-1">Examples:<a class="anchor" aria-label="anchor" href="#examples-1"></a>
+</h3>
+<div class="section level4">
+<h4 id="warning-1">Warning:<a class="anchor" aria-label="anchor" href="#warning-1"></a>
+</h4>
 <p>The named tensor API is experimental and subject to change.</p>
 </div>
 </div>
 </div>
-<div id="all" class="section level2">
-<h2 class="hasAnchor">
-<a href="#all" class="anchor"></a>all</h2>
+<div class="section level2">
+<h2 id="all">all<a class="anchor" aria-label="anchor" href="#all"></a>
+</h2>
 <p>all() -&gt; bool</p>
 <p>Returns TRUE if all elements in the tensor are TRUE, FALSE otherwise.</p>
-<div id="examples-2" class="section level4">
-<h4 class="hasAnchor">
-<a href="#examples-2" class="anchor"></a>Examples:</h4>
+<div class="section level4">
+<h4 id="examples-2">Examples:<a class="anchor" aria-label="anchor" href="#examples-2"></a>
+</h4>
 <div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span class="va">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_rand.html">torch_rand</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">2</span><span class="op">)</span><span class="op">$</span><span class="fu">to</span><span class="op">(</span>dtype <span class="op">=</span> <span class="fu"><a href="../../reference/torch_dtype.html">torch_bool</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span>
 <span class="va">a</span>
@@ -368,18 +329,18 @@
 <p>Returns TRUE if all elements in each row of the tensor in the given dimension <code>dim</code> are TRUE, FALSE otherwise.</p>
 <p>If <code>keepdim</code> is <code>TRUE</code>, the output tensor is of the same size as <code>input</code> except in the dimension <code>dim</code> where it is of size 1. Otherwise, <code>dim</code> is squeezed (see <code>?torch_squeeze()),</code> resulting in the output tensor having 1 fewer dimension than <code>input</code>.</p>
 </div>
-<div id="arguments-1" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-1" class="anchor"></a>Arguments:</h4>
+<div class="section level4">
+<h4 id="arguments-1">Arguments:<a class="anchor" aria-label="anchor" href="#arguments-1"></a>
+</h4>
 <ul>
 <li>dim (int): the dimension to reduce</li>
 <li>keepdim (bool): whether the output tensor has <code>dim</code> retained or not</li>
 <li>out (Tensor, optional): the output tensor</li>
 </ul>
 </div>
-<div id="examples-3" class="section level4">
-<h4 class="hasAnchor">
-<a href="#examples-3" class="anchor"></a>Examples:</h4>
+<div class="section level4">
+<h4 id="examples-3">Examples:<a class="anchor" aria-label="anchor" href="#examples-3"></a>
+</h4>
 <div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span class="va">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_rand.html">torch_rand</a></span><span class="op">(</span><span class="fl">4</span>, <span class="fl">2</span><span class="op">)</span><span class="op">$</span><span class="fu">to</span><span class="op">(</span>dtype <span class="op">=</span> <span class="fu"><a href="../../reference/torch_dtype.html">torch_bool</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span>
 <span class="va">a</span>
@@ -387,26 +348,26 @@
 <span class="va">a</span><span class="op">$</span><span class="fu">all</span><span class="op">(</span>dim<span class="op">=</span><span class="fl">1</span><span class="op">)</span></code></pre></div>
 </div>
 </div>
-<div id="allclose" class="section level2">
-<h2 class="hasAnchor">
-<a href="#allclose" class="anchor"></a>allclose</h2>
+<div class="section level2">
+<h2 id="allclose">allclose<a class="anchor" aria-label="anchor" href="#allclose"></a>
+</h2>
 <p>allclose(other, rtol=1e-05, atol=1e-08, equal_nan=FALSE) -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_allclose.html">?torch_allclose</a></code></p>
 </div>
-<div id="angle" class="section level2">
-<h2 class="hasAnchor">
-<a href="#angle" class="anchor"></a>angle</h2>
+<div class="section level2">
+<h2 id="angle">angle<a class="anchor" aria-label="anchor" href="#angle"></a>
+</h2>
 <p>angle() -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_angle.html">?torch_angle</a></code></p>
 </div>
-<div id="any" class="section level2">
-<h2 class="hasAnchor">
-<a href="#any" class="anchor"></a>any</h2>
+<div class="section level2">
+<h2 id="any">any<a class="anchor" aria-label="anchor" href="#any"></a>
+</h2>
 <p>any() -&gt; bool</p>
 <p>Returns TRUE if any elements in the tensor are TRUE, FALSE otherwise.</p>
-<div id="examples-4" class="section level4">
-<h4 class="hasAnchor">
-<a href="#examples-4" class="anchor"></a>Examples:</h4>
+<div class="section level4">
+<h4 id="examples-4">Examples:<a class="anchor" aria-label="anchor" href="#examples-4"></a>
+</h4>
 <div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span class="va">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_rand.html">torch_rand</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">2</span><span class="op">)</span><span class="op">$</span><span class="fu">to</span><span class="op">(</span>dtype <span class="op">=</span> <span class="fu"><a href="../../reference/torch_dtype.html">torch_bool</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span>
 <span class="va">a</span>
@@ -415,18 +376,18 @@
 <p>Returns TRUE if any elements in each row of the tensor in the given dimension <code>dim</code> are TRUE, FALSE otherwise.</p>
 <p>If <code>keepdim</code> is <code>TRUE</code>, the output tensor is of the same size as <code>input</code> except in the dimension <code>dim</code> where it is of size 1. Otherwise, <code>dim</code> is squeezed (see <code>?torch_squeeze()),</code> resulting in the output tensor having 1 fewer dimension than <code>input</code>.</p>
 </div>
-<div id="arguments-2" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-2" class="anchor"></a>Arguments:</h4>
+<div class="section level4">
+<h4 id="arguments-2">Arguments:<a class="anchor" aria-label="anchor" href="#arguments-2"></a>
+</h4>
 <ul>
 <li>dim (int): the dimension to reduce</li>
 <li>keepdim (bool): whether the output tensor has <code>dim</code> retained or not</li>
 <li>out (Tensor, optional): the output tensor</li>
 </ul>
 </div>
-<div id="examples-5" class="section level4">
-<h4 class="hasAnchor">
-<a href="#examples-5" class="anchor"></a>Examples:</h4>
+<div class="section level4">
+<h4 id="examples-5">Examples:<a class="anchor" aria-label="anchor" href="#examples-5"></a>
+</h4>
 <div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span class="va">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">4</span>, <span class="fl">2</span><span class="op">)</span> <span class="op">&lt;</span> <span class="fl">0</span>
 <span class="va">a</span>
@@ -434,115 +395,115 @@
 <span class="va">a</span><span class="op">$</span><span class="fu">any</span><span class="op">(</span><span class="fl">1</span><span class="op">)</span></code></pre></div>
 </div>
 </div>
-<div id="apply_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#apply_" class="anchor"></a>apply_</h2>
+<div class="section level2">
+<h2 id="apply_">apply_<a class="anchor" aria-label="anchor" href="#apply_"></a>
+</h2>
 <p>apply_(callable) -&gt; Tensor</p>
 <p>Applies the function <code>callable</code> to each element in the tensor, replacing each element with the value returned by <code>callable</code>.</p>
-<div id="note" class="section level4">
-<h4 class="hasAnchor">
-<a href="#note" class="anchor"></a>Note:</h4>
+<div class="section level4">
+<h4 id="note">Note:<a class="anchor" aria-label="anchor" href="#note"></a>
+</h4>
 <p>This function only works with CPU tensors and should not be used in code sections that require high performance.</p>
 </div>
 </div>
-<div id="argmax" class="section level2">
-<h2 class="hasAnchor">
-<a href="#argmax" class="anchor"></a>argmax</h2>
+<div class="section level2">
+<h2 id="argmax">argmax<a class="anchor" aria-label="anchor" href="#argmax"></a>
+</h2>
 <p>argmax(dim=NULL, keepdim=FALSE) -&gt; LongTensor</p>
 <p>See <code><a href="../../reference/torch_argmax.html">?torch_argmax</a></code></p>
 </div>
-<div id="argmin" class="section level2">
-<h2 class="hasAnchor">
-<a href="#argmin" class="anchor"></a>argmin</h2>
+<div class="section level2">
+<h2 id="argmin">argmin<a class="anchor" aria-label="anchor" href="#argmin"></a>
+</h2>
 <p>argmin(dim=NULL, keepdim=FALSE) -&gt; LongTensor</p>
 <p>See <code><a href="../../reference/torch_argmin.html">?torch_argmin</a></code></p>
 </div>
-<div id="argsort" class="section level2">
-<h2 class="hasAnchor">
-<a href="#argsort" class="anchor"></a>argsort</h2>
+<div class="section level2">
+<h2 id="argsort">argsort<a class="anchor" aria-label="anchor" href="#argsort"></a>
+</h2>
 <p>argsort(dim=-1, descending=FALSE) -&gt; LongTensor</p>
 <p>See <code><a href="../../reference/torch_argsort.html">?torch_argsort</a></code></p>
 </div>
-<div id="as_strided" class="section level2">
-<h2 class="hasAnchor">
-<a href="#as_strided" class="anchor"></a>as_strided</h2>
+<div class="section level2">
+<h2 id="as_strided">as_strided<a class="anchor" aria-label="anchor" href="#as_strided"></a>
+</h2>
 <p>as_strided(size, stride, storage_offset=0) -&gt; Tensor</p>
 <p>See [torch_as_strided()]</p>
 </div>
-<div id="as_subclass" class="section level2">
-<h2 class="hasAnchor">
-<a href="#as_subclass" class="anchor"></a>as_subclass</h2>
+<div class="section level2">
+<h2 id="as_subclass">as_subclass<a class="anchor" aria-label="anchor" href="#as_subclass"></a>
+</h2>
 <p>as_subclass(cls) -&gt; Tensor</p>
 <p>Makes a <code>cls</code> instance with the same data pointer as <code>self</code>. Changes in the output mirror changes in <code>self</code>, and the output stays attached to the autograd graph. <code>cls</code> must be a subclass of <code>Tensor</code>.</p>
 </div>
-<div id="asin" class="section level2">
-<h2 class="hasAnchor">
-<a href="#asin" class="anchor"></a>asin</h2>
+<div class="section level2">
+<h2 id="asin">asin<a class="anchor" aria-label="anchor" href="#asin"></a>
+</h2>
 <p>asin() -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_asin.html">?torch_asin</a></code></p>
 </div>
-<div id="asin_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#asin_" class="anchor"></a>asin_</h2>
+<div class="section level2">
+<h2 id="asin_">asin_<a class="anchor" aria-label="anchor" href="#asin_"></a>
+</h2>
 <p>asin_() -&gt; Tensor</p>
 <p>In-place version of <code>$asin</code></p>
 </div>
-<div id="asinh" class="section level2">
-<h2 class="hasAnchor">
-<a href="#asinh" class="anchor"></a>asinh</h2>
+<div class="section level2">
+<h2 id="asinh">asinh<a class="anchor" aria-label="anchor" href="#asinh"></a>
+</h2>
 <p>asinh() -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_asinh.html">?torch_asinh</a></code></p>
 </div>
-<div id="asinh_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#asinh_" class="anchor"></a>asinh_</h2>
+<div class="section level2">
+<h2 id="asinh_">asinh_<a class="anchor" aria-label="anchor" href="#asinh_"></a>
+</h2>
 <p>asinh_() -&gt; Tensor</p>
 <p>In-place version of <code>$asinh</code></p>
 </div>
-<div id="atan" class="section level2">
-<h2 class="hasAnchor">
-<a href="#atan" class="anchor"></a>atan</h2>
+<div class="section level2">
+<h2 id="atan">atan<a class="anchor" aria-label="anchor" href="#atan"></a>
+</h2>
 <p>atan() -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_atan.html">?torch_atan</a></code></p>
 </div>
-<div id="atan2" class="section level2">
-<h2 class="hasAnchor">
-<a href="#atan2" class="anchor"></a>atan2</h2>
+<div class="section level2">
+<h2 id="atan2">atan2<a class="anchor" aria-label="anchor" href="#atan2"></a>
+</h2>
 <p>atan2(other) -&gt; Tensor</p>
 <p>See [torch_atan2()]</p>
 </div>
-<div id="atan2_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#atan2_" class="anchor"></a>atan2_</h2>
+<div class="section level2">
+<h2 id="atan2_">atan2_<a class="anchor" aria-label="anchor" href="#atan2_"></a>
+</h2>
 <p>atan2_(other) -&gt; Tensor</p>
 <p>In-place version of <code>$atan2</code></p>
 </div>
-<div id="atan_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#atan_" class="anchor"></a>atan_</h2>
+<div class="section level2">
+<h2 id="atan_">atan_<a class="anchor" aria-label="anchor" href="#atan_"></a>
+</h2>
 <p>atan_() -&gt; Tensor</p>
 <p>In-place version of <code>$atan</code></p>
 </div>
-<div id="atanh" class="section level2">
-<h2 class="hasAnchor">
-<a href="#atanh" class="anchor"></a>atanh</h2>
+<div class="section level2">
+<h2 id="atanh">atanh<a class="anchor" aria-label="anchor" href="#atanh"></a>
+</h2>
 <p>atanh() -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_atanh.html">?torch_atanh</a></code></p>
 </div>
-<div id="atanh_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#atanh_" class="anchor"></a>atanh_</h2>
+<div class="section level2">
+<h2 id="atanh_">atanh_<a class="anchor" aria-label="anchor" href="#atanh_"></a>
+</h2>
 <p>In-place version of <code>$atanh</code></p>
 </div>
-<div id="backward" class="section level2">
-<h2 class="hasAnchor">
-<a href="#backward" class="anchor"></a>backward</h2>
+<div class="section level2">
+<h2 id="backward">backward<a class="anchor" aria-label="anchor" href="#backward"></a>
+</h2>
 <p>Computes the gradient of current tensor w.r.t. graph leaves.</p>
 <p>The graph is differentiated using the chain rule. If the tensor is non-scalar (i.e. its data has more than one element) and requires gradient, the function additionally requires specifying <code>gradient</code>. It should be a tensor of matching type and location, that contains the gradient of the differentiated function w.r.t. <code>self</code>.</p>
 <p>This function accumulates gradients in the leaves - you might need to zero <code>$grad</code> attributes or set them to <code>NULL</code> before calling it. See <code>Default gradient layouts&lt;default-grad-layouts&gt;</code> for details on the memory layout of accumulated gradients.</p>
-<div id="arguments-3" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-3" class="anchor"></a>Arguments:</h4>
+<div class="section level4">
+<h4 id="arguments-3">Arguments:<a class="anchor" aria-label="anchor" href="#arguments-3"></a>
+</h4>
 <ul>
 <li>gradient (Tensor or NULL): Gradient w.r.t. the tensor. If it is a tensor, it will be automatically converted to a Tensor that does not require grad unless <code>create_graph</code> is TRUE. NULL values can be specified for scalar Tensors or ones that don’t require grad. If a NULL value would be acceptable then this argument is optional.</li>
 <li>retain_graph (bool, optional): If <code>FALSE</code>, the graph used to compute the grads will be freed. Note that in nearly all cases setting this option to TRUE is not needed and often can be worked around in a much more efficient way. Defaults to the value of <code>create_graph</code>.</li>
@@ -550,28 +511,28 @@
 </ul>
 </div>
 </div>
-<div id="baddbmm" class="section level2">
-<h2 class="hasAnchor">
-<a href="#baddbmm" class="anchor"></a>baddbmm</h2>
+<div class="section level2">
+<h2 id="baddbmm">baddbmm<a class="anchor" aria-label="anchor" href="#baddbmm"></a>
+</h2>
 <p>baddbmm(batch1, batch2, *, beta=1, alpha=1) -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_baddbmm.html">?torch_baddbmm</a></code></p>
 </div>
-<div id="baddbmm_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#baddbmm_" class="anchor"></a>baddbmm_</h2>
+<div class="section level2">
+<h2 id="baddbmm_">baddbmm_<a class="anchor" aria-label="anchor" href="#baddbmm_"></a>
+</h2>
 <p>baddbmm_(batch1, batch2, *, beta=1, alpha=1) -&gt; Tensor</p>
 <p>In-place version of <code>$baddbmm</code></p>
 </div>
-<div id="bernoulli" class="section level2">
-<h2 class="hasAnchor">
-<a href="#bernoulli" class="anchor"></a>bernoulli</h2>
+<div class="section level2">
+<h2 id="bernoulli">bernoulli<a class="anchor" aria-label="anchor" href="#bernoulli"></a>
+</h2>
 <p>bernoulli(*, generator=NULL) -&gt; Tensor</p>
 <p>Returns a result tensor where each <span class="math inline">\(\texttt{result[i]}\)</span> is independently sampled from <span class="math inline">\(\text{Bernoulli}(\texttt{self[i]})\)</span>. <code>self</code> must have floating point <code>dtype</code>, and the result will have the same <code>dtype</code>.</p>
 <p>See <code><a href="../../reference/torch_bernoulli.html">?torch_bernoulli</a></code></p>
 </div>
-<div id="bernoulli_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#bernoulli_" class="anchor"></a>bernoulli_</h2>
+<div class="section level2">
+<h2 id="bernoulli_">bernoulli_<a class="anchor" aria-label="anchor" href="#bernoulli_"></a>
+</h2>
 <p>bernoulli_(p=0.5, *, generator=NULL) -&gt; Tensor</p>
 <p>Fills each location of <code>self</code> with an independent sample from <span class="math inline">\(\text{Bernoulli}(\texttt{p})\)</span>. <code>self</code> can have integral <code>dtype</code>.</p>
 <p>bernoulli_(p_tensor, *, generator=NULL) -&gt; Tensor</p>
@@ -580,181 +541,181 @@
 <p><code>self</code> can have integral <code>dtype</code>, but <code>p_tensor</code> must have floating point <code>dtype</code>.</p>
 <p>See also <code>$bernoulli</code> and <code><a href="../../reference/torch_bernoulli.html">?torch_bernoulli</a></code></p>
 </div>
-<div id="bfloat16" class="section level2">
-<h2 class="hasAnchor">
-<a href="#bfloat16" class="anchor"></a>bfloat16</h2>
+<div class="section level2">
+<h2 id="bfloat16">bfloat16<a class="anchor" aria-label="anchor" href="#bfloat16"></a>
+</h2>
 <p>bfloat16(memory_format=torch_preserve_format) -&gt; Tensor <code>self$bfloat16()</code> is equivalent to <code>self$to(torch_bfloat16)</code>. See [to()].</p>
-<div id="arguments-4" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-4" class="anchor"></a>Arguments:</h4>
+<div class="section level4">
+<h4 id="arguments-4">Arguments:<a class="anchor" aria-label="anchor" href="#arguments-4"></a>
+</h4>
 <ul>
 <li>memory_format (<code>torch_memory_format</code>, optional): the desired memory format of</li>
 <li>returned Tensor. Default: <code>torch_preserve_format</code>.</li>
 </ul>
 </div>
 </div>
-<div id="bincount" class="section level2">
-<h2 class="hasAnchor">
-<a href="#bincount" class="anchor"></a>bincount</h2>
+<div class="section level2">
+<h2 id="bincount">bincount<a class="anchor" aria-label="anchor" href="#bincount"></a>
+</h2>
 <p>bincount(weights=NULL, minlength=0) -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_bincount.html">?torch_bincount</a></code></p>
 </div>
-<div id="bitwise_and" class="section level2">
-<h2 class="hasAnchor">
-<a href="#bitwise_and" class="anchor"></a>bitwise_and</h2>
+<div class="section level2">
+<h2 id="bitwise_and">bitwise_and<a class="anchor" aria-label="anchor" href="#bitwise_and"></a>
+</h2>
 <p>bitwise_and() -&gt; Tensor</p>
 <p>See [torch_bitwise_and()]</p>
 </div>
-<div id="bitwise_and_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#bitwise_and_" class="anchor"></a>bitwise_and_</h2>
+<div class="section level2">
+<h2 id="bitwise_and_">bitwise_and_<a class="anchor" aria-label="anchor" href="#bitwise_and_"></a>
+</h2>
 <p>bitwise_and_() -&gt; Tensor</p>
 <p>In-place version of <code>$bitwise_and</code></p>
 </div>
-<div id="bitwise_not" class="section level2">
-<h2 class="hasAnchor">
-<a href="#bitwise_not" class="anchor"></a>bitwise_not</h2>
+<div class="section level2">
+<h2 id="bitwise_not">bitwise_not<a class="anchor" aria-label="anchor" href="#bitwise_not"></a>
+</h2>
 <p>bitwise_not() -&gt; Tensor</p>
 <p>See [torch_bitwise_not()]</p>
 </div>
-<div id="bitwise_not_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#bitwise_not_" class="anchor"></a>bitwise_not_</h2>
+<div class="section level2">
+<h2 id="bitwise_not_">bitwise_not_<a class="anchor" aria-label="anchor" href="#bitwise_not_"></a>
+</h2>
 <p>bitwise_not_() -&gt; Tensor</p>
 <p>In-place version of <code>$bitwise_not</code></p>
 </div>
-<div id="bitwise_or" class="section level2">
-<h2 class="hasAnchor">
-<a href="#bitwise_or" class="anchor"></a>bitwise_or</h2>
+<div class="section level2">
+<h2 id="bitwise_or">bitwise_or<a class="anchor" aria-label="anchor" href="#bitwise_or"></a>
+</h2>
 <p>bitwise_or() -&gt; Tensor</p>
 <p>See [torch_bitwise_or()]</p>
 </div>
-<div id="bitwise_or_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#bitwise_or_" class="anchor"></a>bitwise_or_</h2>
+<div class="section level2">
+<h2 id="bitwise_or_">bitwise_or_<a class="anchor" aria-label="anchor" href="#bitwise_or_"></a>
+</h2>
 <p>bitwise_or_() -&gt; Tensor</p>
 <p>In-place version of <code>$bitwise_or</code></p>
 </div>
-<div id="bitwise_xor" class="section level2">
-<h2 class="hasAnchor">
-<a href="#bitwise_xor" class="anchor"></a>bitwise_xor</h2>
+<div class="section level2">
+<h2 id="bitwise_xor">bitwise_xor<a class="anchor" aria-label="anchor" href="#bitwise_xor"></a>
+</h2>
 <p>bitwise_xor() -&gt; Tensor</p>
 <p>See [torch_bitwise_xor()]</p>
 </div>
-<div id="bitwise_xor_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#bitwise_xor_" class="anchor"></a>bitwise_xor_</h2>
+<div class="section level2">
+<h2 id="bitwise_xor_">bitwise_xor_<a class="anchor" aria-label="anchor" href="#bitwise_xor_"></a>
+</h2>
 <p>bitwise_xor_() -&gt; Tensor</p>
 <p>In-place version of <code>$bitwise_xor</code></p>
 </div>
-<div id="bmm" class="section level2">
-<h2 class="hasAnchor">
-<a href="#bmm" class="anchor"></a>bmm</h2>
+<div class="section level2">
+<h2 id="bmm">bmm<a class="anchor" aria-label="anchor" href="#bmm"></a>
+</h2>
 <p>bmm(batch2) -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_bmm.html">?torch_bmm</a></code></p>
 </div>
-<div id="bool" class="section level2">
-<h2 class="hasAnchor">
-<a href="#bool" class="anchor"></a>bool</h2>
+<div class="section level2">
+<h2 id="bool">bool<a class="anchor" aria-label="anchor" href="#bool"></a>
+</h2>
 <p>bool(memory_format=torch_preserve_format) -&gt; Tensor</p>
 <p><code>self$bool()</code> is equivalent to <code>self$to(torch_bool)</code>. See [to()].</p>
-<div id="arguments-5" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-5" class="anchor"></a>Arguments:</h4>
+<div class="section level4">
+<h4 id="arguments-5">Arguments:<a class="anchor" aria-label="anchor" href="#arguments-5"></a>
+</h4>
 <ul>
 <li>memory_format (<code>torch_memory_format</code>, optional): the desired memory format of</li>
 <li>returned Tensor. Default: <code>torch_preserve_format</code>.</li>
 </ul>
 </div>
 </div>
-<div id="byte" class="section level2">
-<h2 class="hasAnchor">
-<a href="#byte" class="anchor"></a>byte</h2>
+<div class="section level2">
+<h2 id="byte">byte<a class="anchor" aria-label="anchor" href="#byte"></a>
+</h2>
 <p>byte(memory_format=torch_preserve_format) -&gt; Tensor</p>
 <p><code>self$byte()</code> is equivalent to <code>self$to(torch_uint8)</code>. See [to()].</p>
-<div id="arguments-6" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-6" class="anchor"></a>Arguments:</h4>
+<div class="section level4">
+<h4 id="arguments-6">Arguments:<a class="anchor" aria-label="anchor" href="#arguments-6"></a>
+</h4>
 <ul>
 <li>memory_format (<code>torch_memory_format</code>, optional): the desired memory format of</li>
 <li>returned Tensor. Default: <code>torch_preserve_format</code>.</li>
 </ul>
 </div>
 </div>
-<div id="cauchy_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#cauchy_" class="anchor"></a>cauchy_</h2>
+<div class="section level2">
+<h2 id="cauchy_">cauchy_<a class="anchor" aria-label="anchor" href="#cauchy_"></a>
+</h2>
 <p>cauchy_(median=0, sigma=1, *, generator=NULL) -&gt; Tensor</p>
 <p>Fills the tensor with numbers drawn from the Cauchy distribution:</p>
 <p><span class="math display">\[
 f(x) = \dfrac{1}{\pi} \dfrac{\sigma}{(x - \text{median})^2 + \sigma^2}
 \]</span></p>
 </div>
-<div id="ceil" class="section level2">
-<h2 class="hasAnchor">
-<a href="#ceil" class="anchor"></a>ceil</h2>
+<div class="section level2">
+<h2 id="ceil">ceil<a class="anchor" aria-label="anchor" href="#ceil"></a>
+</h2>
 <p>ceil() -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_ceil.html">?torch_ceil</a></code></p>
 </div>
-<div id="ceil_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#ceil_" class="anchor"></a>ceil_</h2>
+<div class="section level2">
+<h2 id="ceil_">ceil_<a class="anchor" aria-label="anchor" href="#ceil_"></a>
+</h2>
 <p>ceil_() -&gt; Tensor</p>
 <p>In-place version of <code>$ceil</code></p>
 </div>
-<div id="char" class="section level2">
-<h2 class="hasAnchor">
-<a href="#char" class="anchor"></a>char</h2>
+<div class="section level2">
+<h2 id="char">char<a class="anchor" aria-label="anchor" href="#char"></a>
+</h2>
 <p>char(memory_format=torch_preserve_format) -&gt; Tensor</p>
 <p><code>self$char()</code> is equivalent to <code>self$to(torch_int8)</code>. See [to()].</p>
-<div id="arguments-7" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-7" class="anchor"></a>Arguments:</h4>
+<div class="section level4">
+<h4 id="arguments-7">Arguments:<a class="anchor" aria-label="anchor" href="#arguments-7"></a>
+</h4>
 <ul>
 <li>memory_format (<code>torch_memory_format</code>, optional): the desired memory format of</li>
 <li>returned Tensor. Default: <code>torch_preserve_format</code>.</li>
 </ul>
 </div>
 </div>
-<div id="cholesky" class="section level2">
-<h2 class="hasAnchor">
-<a href="#cholesky" class="anchor"></a>cholesky</h2>
+<div class="section level2">
+<h2 id="cholesky">cholesky<a class="anchor" aria-label="anchor" href="#cholesky"></a>
+</h2>
 <p>cholesky(upper=FALSE) -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_cholesky.html">?torch_cholesky</a></code></p>
 </div>
-<div id="cholesky_inverse" class="section level2">
-<h2 class="hasAnchor">
-<a href="#cholesky_inverse" class="anchor"></a>cholesky_inverse</h2>
+<div class="section level2">
+<h2 id="cholesky_inverse">cholesky_inverse<a class="anchor" aria-label="anchor" href="#cholesky_inverse"></a>
+</h2>
 <p>cholesky_inverse(upper=FALSE) -&gt; Tensor</p>
 <p>See [torch_cholesky_inverse()]</p>
 </div>
-<div id="cholesky_solve" class="section level2">
-<h2 class="hasAnchor">
-<a href="#cholesky_solve" class="anchor"></a>cholesky_solve</h2>
+<div class="section level2">
+<h2 id="cholesky_solve">cholesky_solve<a class="anchor" aria-label="anchor" href="#cholesky_solve"></a>
+</h2>
 <p>cholesky_solve(input2, upper=FALSE) -&gt; Tensor</p>
 <p>See [torch_cholesky_solve()]</p>
 </div>
-<div id="chunk" class="section level2">
-<h2 class="hasAnchor">
-<a href="#chunk" class="anchor"></a>chunk</h2>
+<div class="section level2">
+<h2 id="chunk">chunk<a class="anchor" aria-label="anchor" href="#chunk"></a>
+</h2>
 <p>chunk(chunks, dim=0) -&gt; List of Tensors</p>
 <p>See <code><a href="../../reference/torch_chunk.html">?torch_chunk</a></code></p>
 </div>
-<div id="clamp" class="section level2">
-<h2 class="hasAnchor">
-<a href="#clamp" class="anchor"></a>clamp</h2>
+<div class="section level2">
+<h2 id="clamp">clamp<a class="anchor" aria-label="anchor" href="#clamp"></a>
+</h2>
 <p>clamp(min, max) -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_clamp.html">?torch_clamp</a></code></p>
 </div>
-<div id="clamp_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#clamp_" class="anchor"></a>clamp_</h2>
+<div class="section level2">
+<h2 id="clamp_">clamp_<a class="anchor" aria-label="anchor" href="#clamp_"></a>
+</h2>
 <p>clamp_(min, max) -&gt; Tensor</p>
 <p>In-place version of <code>$clamp</code></p>
 </div>
-<div id="clone" class="section level2">
-<h2 class="hasAnchor">
-<a href="#clone" class="anchor"></a>clone</h2>
+<div class="section level2">
+<h2 id="clone">clone<a class="anchor" aria-label="anchor" href="#clone"></a>
+</h2>
 <p>clone(memory_format=torch_preserve_format()) -&gt; Tensor</p>
 <p>Returns a copy of the <code>self</code> tensor. The copy has the same size and data type as <code>self</code>.</p>
 <div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
@@ -763,107 +724,107 @@ f(x) = \dfrac{1}{\pi} \dfrac{\sigma}{(x - \text{median})^2 + \sigma^2}
 
 <span class="va">x</span><span class="op">$</span><span class="fu">add_</span><span class="op">(</span><span class="fl">1</span><span class="op">)</span>
 <span class="va">y</span></code></pre></div>
-<div id="note-1" class="section level4">
-<h4 class="hasAnchor">
-<a href="#note-1" class="anchor"></a>Note:</h4>
+<div class="section level4">
+<h4 id="note-1">Note:<a class="anchor" aria-label="anchor" href="#note-1"></a>
+</h4>
 <p>Unlike <code>copy_()</code>, this function is recorded in the computation graph. Gradients propagating to the cloned tensor will propagate to the original tensor.</p>
 </div>
-<div id="arguments-8" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-8" class="anchor"></a>Arguments:</h4>
+<div class="section level4">
+<h4 id="arguments-8">Arguments:<a class="anchor" aria-label="anchor" href="#arguments-8"></a>
+</h4>
 <ul>
 <li>memory_format (<code>torch_memory_format</code>, optional): the desired memory format of the returned Tensor. Default: <code>torch_preserve_format</code>.</li>
 </ul>
 </div>
 </div>
-<div id="conj" class="section level2">
-<h2 class="hasAnchor">
-<a href="#conj" class="anchor"></a>conj</h2>
+<div class="section level2">
+<h2 id="conj">conj<a class="anchor" aria-label="anchor" href="#conj"></a>
+</h2>
 <p>conj() -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_conj.html">?torch_conj</a></code></p>
 </div>
-<div id="contiguous" class="section level2">
-<h2 class="hasAnchor">
-<a href="#contiguous" class="anchor"></a>contiguous</h2>
+<div class="section level2">
+<h2 id="contiguous">contiguous<a class="anchor" aria-label="anchor" href="#contiguous"></a>
+</h2>
 <p>contiguous(memory_format=torch_contiguous_format) -&gt; Tensor</p>
 <p>Returns a contiguous in memory tensor containing the same data as <code>self</code> tensor. If <code>self</code> tensor is already in the specified memory format, this function returns the <code>self</code> tensor.</p>
-<div id="arguments-9" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-9" class="anchor"></a>Arguments:</h4>
+<div class="section level4">
+<h4 id="arguments-9">Arguments:<a class="anchor" aria-label="anchor" href="#arguments-9"></a>
+</h4>
 <ul>
 <li>memory_format (<code>torch_memory_format</code>, optional): the desired memory format of the returned Tensor. Default: <code>torch_contiguous_format</code>.</li>
 </ul>
 </div>
 </div>
-<div id="copy_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#copy_" class="anchor"></a>copy_</h2>
+<div class="section level2">
+<h2 id="copy_">copy_<a class="anchor" aria-label="anchor" href="#copy_"></a>
+</h2>
 <p>copy_(src, non_blocking=FALSE) -&gt; Tensor</p>
 <p>Copies the elements from <code>src</code> into <code>self</code> tensor and returns <code>self</code>.</p>
 <p>The <code>src</code> tensor must be broadcastable with the <code>self</code> tensor. It may be of a different data type or reside on a different device.</p>
-<div id="arguments-10" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-10" class="anchor"></a>Arguments:</h4>
+<div class="section level4">
+<h4 id="arguments-10">Arguments:<a class="anchor" aria-label="anchor" href="#arguments-10"></a>
+</h4>
 <ul>
 <li>src (Tensor): the source tensor to copy from</li>
 <li>non_blocking (bool): if <code>TRUE</code> and this copy is between CPU and GPU, the copy may occur asynchronously with respect to the host. For other cases, this argument has no effect.</li>
 </ul>
 </div>
 </div>
-<div id="cos" class="section level2">
-<h2 class="hasAnchor">
-<a href="#cos" class="anchor"></a>cos</h2>
+<div class="section level2">
+<h2 id="cos">cos<a class="anchor" aria-label="anchor" href="#cos"></a>
+</h2>
 <p>cos() -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_cos.html">?torch_cos</a></code></p>
 </div>
-<div id="cos_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#cos_" class="anchor"></a>cos_</h2>
+<div class="section level2">
+<h2 id="cos_">cos_<a class="anchor" aria-label="anchor" href="#cos_"></a>
+</h2>
 <p>cos_() -&gt; Tensor</p>
 <p>In-place version of <code>$cos</code></p>
 </div>
-<div id="cosh" class="section level2">
-<h2 class="hasAnchor">
-<a href="#cosh" class="anchor"></a>cosh</h2>
+<div class="section level2">
+<h2 id="cosh">cosh<a class="anchor" aria-label="anchor" href="#cosh"></a>
+</h2>
 <p>cosh() -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_cosh.html">?torch_cosh</a></code></p>
 </div>
-<div id="cosh_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#cosh_" class="anchor"></a>cosh_</h2>
+<div class="section level2">
+<h2 id="cosh_">cosh_<a class="anchor" aria-label="anchor" href="#cosh_"></a>
+</h2>
 <p>cosh_() -&gt; Tensor</p>
 <p>In-place version of <code>$cosh</code></p>
 </div>
-<div id="cpu" class="section level2">
-<h2 class="hasAnchor">
-<a href="#cpu" class="anchor"></a>cpu</h2>
+<div class="section level2">
+<h2 id="cpu">cpu<a class="anchor" aria-label="anchor" href="#cpu"></a>
+</h2>
 <p>cpu(memory_format=torch_preserve_format) -&gt; Tensor</p>
 <p>Returns a copy of this object in CPU memory.</p>
 <p>If this object is already in CPU memory and on the correct device, then no copy is performed and the original object is returned.</p>
-<div id="arguments-11" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-11" class="anchor"></a>Arguments:</h4>
+<div class="section level4">
+<h4 id="arguments-11">Arguments:<a class="anchor" aria-label="anchor" href="#arguments-11"></a>
+</h4>
 <ul>
 <li>memory_format (<code>torch_memory_format</code>, optional): the desired memory format of</li>
 <li>returned Tensor. Default: <code>torch_preserve_format</code>.</li>
 </ul>
 </div>
 </div>
-<div id="cross" class="section level2">
-<h2 class="hasAnchor">
-<a href="#cross" class="anchor"></a>cross</h2>
+<div class="section level2">
+<h2 id="cross">cross<a class="anchor" aria-label="anchor" href="#cross"></a>
+</h2>
 <p>cross(other, dim=-1) -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_cross.html">?torch_cross</a></code></p>
 </div>
-<div id="cuda" class="section level2">
-<h2 class="hasAnchor">
-<a href="#cuda" class="anchor"></a>cuda</h2>
+<div class="section level2">
+<h2 id="cuda">cuda<a class="anchor" aria-label="anchor" href="#cuda"></a>
+</h2>
 <p>cuda(device=NULL, non_blocking=FALSE, memory_format=torch_preserve_format) -&gt; Tensor</p>
 <p>Returns a copy of this object in CUDA memory.</p>
 <p>If this object is already in CUDA memory and on the correct device, then no copy is performed and the original object is returned.</p>
-<div id="arguments-12" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-12" class="anchor"></a>Arguments:</h4>
+<div class="section level4">
+<h4 id="arguments-12">Arguments:<a class="anchor" aria-label="anchor" href="#arguments-12"></a>
+</h4>
 <ul>
 <li>device (<code>torch_device</code>): The destination GPU device. Defaults to the current CUDA device.</li>
 <li>non_blocking (bool): If <code>TRUE</code> and the source is in pinned memory, the copy will be asynchronous with respect to the host. Otherwise, the argument has no effect. Default: <code>FALSE</code>.</li>
@@ -872,349 +833,349 @@ f(x) = \dfrac{1}{\pi} \dfrac{\sigma}{(x - \text{median})^2 + \sigma^2}
 </ul>
 </div>
 </div>
-<div id="cummax" class="section level2">
-<h2 class="hasAnchor">
-<a href="#cummax" class="anchor"></a>cummax</h2>
+<div class="section level2">
+<h2 id="cummax">cummax<a class="anchor" aria-label="anchor" href="#cummax"></a>
+</h2>
 <p>cummax(dim) -&gt; (Tensor, Tensor)</p>
 <p>See <code><a href="../../reference/torch_cummax.html">?torch_cummax</a></code></p>
 </div>
-<div id="cummin" class="section level2">
-<h2 class="hasAnchor">
-<a href="#cummin" class="anchor"></a>cummin</h2>
+<div class="section level2">
+<h2 id="cummin">cummin<a class="anchor" aria-label="anchor" href="#cummin"></a>
+</h2>
 <p>cummin(dim) -&gt; (Tensor, Tensor)</p>
 <p>See <code><a href="../../reference/torch_cummin.html">?torch_cummin</a></code></p>
 </div>
-<div id="cumprod" class="section level2">
-<h2 class="hasAnchor">
-<a href="#cumprod" class="anchor"></a>cumprod</h2>
+<div class="section level2">
+<h2 id="cumprod">cumprod<a class="anchor" aria-label="anchor" href="#cumprod"></a>
+</h2>
 <p>cumprod(dim, dtype=NULL) -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_cumprod.html">?torch_cumprod</a></code></p>
 </div>
-<div id="cumsum" class="section level2">
-<h2 class="hasAnchor">
-<a href="#cumsum" class="anchor"></a>cumsum</h2>
+<div class="section level2">
+<h2 id="cumsum">cumsum<a class="anchor" aria-label="anchor" href="#cumsum"></a>
+</h2>
 <p>cumsum(dim, dtype=NULL) -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_cumsum.html">?torch_cumsum</a></code></p>
 </div>
-<div id="data_ptr" class="section level2">
-<h2 class="hasAnchor">
-<a href="#data_ptr" class="anchor"></a>data_ptr</h2>
+<div class="section level2">
+<h2 id="data_ptr">data_ptr<a class="anchor" aria-label="anchor" href="#data_ptr"></a>
+</h2>
 <p>data_ptr() -&gt; int</p>
 <p>Returns the address of the first element of <code>self</code> tensor.</p>
 </div>
-<div id="deg2rad" class="section level2">
-<h2 class="hasAnchor">
-<a href="#deg2rad" class="anchor"></a>deg2rad</h2>
+<div class="section level2">
+<h2 id="deg2rad">deg2rad<a class="anchor" aria-label="anchor" href="#deg2rad"></a>
+</h2>
 <p>deg2rad() -&gt; Tensor</p>
 <p>See [torch_deg2rad()]</p>
 </div>
-<div id="deg2rad_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#deg2rad_" class="anchor"></a>deg2rad_</h2>
+<div class="section level2">
+<h2 id="deg2rad_">deg2rad_<a class="anchor" aria-label="anchor" href="#deg2rad_"></a>
+</h2>
 <p>deg2rad_() -&gt; Tensor</p>
 <p>In-place version of <code>$deg2rad</code></p>
 </div>
-<div id="dense_dim" class="section level2">
-<h2 class="hasAnchor">
-<a href="#dense_dim" class="anchor"></a>dense_dim</h2>
+<div class="section level2">
+<h2 id="dense_dim">dense_dim<a class="anchor" aria-label="anchor" href="#dense_dim"></a>
+</h2>
 <p>dense_dim() -&gt; int</p>
 <p>If <code>self</code> is a sparse COO tensor (i.e., with <code>torch_sparse_coo</code> layout), this returns the number of dense dimensions. Otherwise, this throws an error.</p>
 <p>See also <code>$sparse_dim</code>.</p>
 </div>
-<div id="dequantize" class="section level2">
-<h2 class="hasAnchor">
-<a href="#dequantize" class="anchor"></a>dequantize</h2>
+<div class="section level2">
+<h2 id="dequantize">dequantize<a class="anchor" aria-label="anchor" href="#dequantize"></a>
+</h2>
 <p>dequantize() -&gt; Tensor</p>
 <p>Given a quantized Tensor, dequantize it and return the dequantized float Tensor.</p>
 </div>
-<div id="det" class="section level2">
-<h2 class="hasAnchor">
-<a href="#det" class="anchor"></a>det</h2>
+<div class="section level2">
+<h2 id="det">det<a class="anchor" aria-label="anchor" href="#det"></a>
+</h2>
 <p>det() -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_det.html">?torch_det</a></code></p>
 </div>
-<div id="detach" class="section level2">
-<h2 class="hasAnchor">
-<a href="#detach" class="anchor"></a>detach</h2>
+<div class="section level2">
+<h2 id="detach">detach<a class="anchor" aria-label="anchor" href="#detach"></a>
+</h2>
 <p>Returns a new Tensor, detached from the current graph.</p>
 <p>The result will never require gradient.</p>
-<div id="note-2" class="section level4">
-<h4 class="hasAnchor">
-<a href="#note-2" class="anchor"></a>Note:</h4>
+<div class="section level4">
+<h4 id="note-2">Note:<a class="anchor" aria-label="anchor" href="#note-2"></a>
+</h4>
 <p>Returned Tensor shares the same storage with the original one.</p>
 <p>In-place modifications on either of them will be seen, and may trigger errors in correctness checks.</p>
 <p><strong>IMPORTANT NOTE</strong>: Previously, in-place size / stride / storage changes (such as <code>resize_</code> / <code>resize_as_</code> / <code>set_</code> / <code>transpose_</code>) to the returned tensor also update the original tensor. Now, these in-place changes will not update the original tensor anymore, and will instead trigger an error.</p>
 <p>For sparse tensors: In-place indices / values changes (such as <code>zero_</code> / <code>copy_</code> / <code>add_</code>) to the returned tensor will not update the original tensor anymore, and will instead trigger an error.</p>
 </div>
 </div>
-<div id="detach_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#detach_" class="anchor"></a>detach_</h2>
+<div class="section level2">
+<h2 id="detach_">detach_<a class="anchor" aria-label="anchor" href="#detach_"></a>
+</h2>
 <p>Detaches the Tensor from the graph that created it, making it a leaf. Views cannot be detached in-place.</p>
 </div>
-<div id="device" class="section level2">
-<h2 class="hasAnchor">
-<a href="#device" class="anchor"></a>device</h2>
+<div class="section level2">
+<h2 id="device">device<a class="anchor" aria-label="anchor" href="#device"></a>
+</h2>
 <p>Is the <code>torch_device</code> where this Tensor is.</p>
 </div>
-<div id="diag" class="section level2">
-<h2 class="hasAnchor">
-<a href="#diag" class="anchor"></a>diag</h2>
+<div class="section level2">
+<h2 id="diag">diag<a class="anchor" aria-label="anchor" href="#diag"></a>
+</h2>
 <p>diag(diagonal=0) -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_diag.html">?torch_diag</a></code></p>
 </div>
-<div id="diag_embed" class="section level2">
-<h2 class="hasAnchor">
-<a href="#diag_embed" class="anchor"></a>diag_embed</h2>
+<div class="section level2">
+<h2 id="diag_embed">diag_embed<a class="anchor" aria-label="anchor" href="#diag_embed"></a>
+</h2>
 <p>diag_embed(offset=0, dim1=-2, dim2=-1) -&gt; Tensor</p>
 <p>See [torch_diag_embed()]</p>
 </div>
-<div id="diagflat" class="section level2">
-<h2 class="hasAnchor">
-<a href="#diagflat" class="anchor"></a>diagflat</h2>
+<div class="section level2">
+<h2 id="diagflat">diagflat<a class="anchor" aria-label="anchor" href="#diagflat"></a>
+</h2>
 <p>diagflat(offset=0) -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_diagflat.html">?torch_diagflat</a></code></p>
 </div>
-<div id="diagonal" class="section level2">
-<h2 class="hasAnchor">
-<a href="#diagonal" class="anchor"></a>diagonal</h2>
+<div class="section level2">
+<h2 id="diagonal">diagonal<a class="anchor" aria-label="anchor" href="#diagonal"></a>
+</h2>
 <p>diagonal(offset=0, dim1=0, dim2=1) -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_diagonal.html">?torch_diagonal</a></code></p>
 </div>
-<div id="digamma" class="section level2">
-<h2 class="hasAnchor">
-<a href="#digamma" class="anchor"></a>digamma</h2>
+<div class="section level2">
+<h2 id="digamma">digamma<a class="anchor" aria-label="anchor" href="#digamma"></a>
+</h2>
 <p>digamma() -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_digamma.html">?torch_digamma</a></code></p>
 </div>
-<div id="digamma_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#digamma_" class="anchor"></a>digamma_</h2>
+<div class="section level2">
+<h2 id="digamma_">digamma_<a class="anchor" aria-label="anchor" href="#digamma_"></a>
+</h2>
 <p>digamma_() -&gt; Tensor</p>
 <p>In-place version of <code>$digamma</code></p>
 </div>
-<div id="dim" class="section level2">
-<h2 class="hasAnchor">
-<a href="#dim" class="anchor"></a>dim</h2>
+<div class="section level2">
+<h2 id="dim">dim<a class="anchor" aria-label="anchor" href="#dim"></a>
+</h2>
 <p>dim() -&gt; int</p>
 <p>Returns the number of dimensions of <code>self</code> tensor.</p>
 </div>
-<div id="dist" class="section level2">
-<h2 class="hasAnchor">
-<a href="#dist" class="anchor"></a>dist</h2>
+<div class="section level2">
+<h2 id="dist">dist<a class="anchor" aria-label="anchor" href="#dist"></a>
+</h2>
 <p>dist(other, p=2) -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_dist.html">?torch_dist</a></code></p>
 </div>
-<div id="div" class="section level2">
-<h2 class="hasAnchor">
-<a href="#div" class="anchor"></a>div</h2>
+<div class="section level2">
+<h2 id="div">div<a class="anchor" aria-label="anchor" href="#div"></a>
+</h2>
 <p>div(value) -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_div.html">?torch_div</a></code></p>
 </div>
-<div id="div_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#div_" class="anchor"></a>div_</h2>
+<div class="section level2">
+<h2 id="div_">div_<a class="anchor" aria-label="anchor" href="#div_"></a>
+</h2>
 <p>div_(value) -&gt; Tensor</p>
 <p>In-place version of <code>$div</code></p>
 </div>
-<div id="dot" class="section level2">
-<h2 class="hasAnchor">
-<a href="#dot" class="anchor"></a>dot</h2>
+<div class="section level2">
+<h2 id="dot">dot<a class="anchor" aria-label="anchor" href="#dot"></a>
+</h2>
 <p>dot(tensor2) -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_dot.html">?torch_dot</a></code></p>
 </div>
-<div id="double" class="section level2">
-<h2 class="hasAnchor">
-<a href="#double" class="anchor"></a>double</h2>
+<div class="section level2">
+<h2 id="double">double<a class="anchor" aria-label="anchor" href="#double"></a>
+</h2>
 <p>double(memory_format=torch_preserve_format) -&gt; Tensor</p>
 <p><code>self$double()</code> is equivalent to <code>self$to(torch_float64)</code>. See [to()].</p>
-<div id="arguments-13" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-13" class="anchor"></a>Arguments:</h4>
+<div class="section level4">
+<h4 id="arguments-13">Arguments:<a class="anchor" aria-label="anchor" href="#arguments-13"></a>
+</h4>
 <ul>
 <li>memory_format (<code>torch_memory_format</code>, optional): the desired memory format of</li>
 <li>returned Tensor. Default: <code>torch_preserve_format</code>.</li>
 </ul>
 </div>
 </div>
-<div id="eig" class="section level2">
-<h2 class="hasAnchor">
-<a href="#eig" class="anchor"></a>eig</h2>
+<div class="section level2">
+<h2 id="eig">eig<a class="anchor" aria-label="anchor" href="#eig"></a>
+</h2>
 <p>eig(eigenvectors=FALSE) -&gt; (Tensor, Tensor)</p>
 <p>See <code><a href="../../reference/torch_eig.html">?torch_eig</a></code></p>
 </div>
-<div id="element_size" class="section level2">
-<h2 class="hasAnchor">
-<a href="#element_size" class="anchor"></a>element_size</h2>
+<div class="section level2">
+<h2 id="element_size">element_size<a class="anchor" aria-label="anchor" href="#element_size"></a>
+</h2>
 <p>element_size() -&gt; int</p>
 <p>Returns the size in bytes of an individual element.</p>
-<div id="examples-6" class="section level4">
-<h4 class="hasAnchor">
-<a href="#examples-6" class="anchor"></a>Examples:</h4>
+<div class="section level4">
+<h4 id="examples-6">Examples:<a class="anchor" aria-label="anchor" href="#examples-6"></a>
+</h4>
 <div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
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+<code class="sourceCode R"><span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span><span class="op">)</span><span class="op">)</span><span class="op">$</span><span class="fu">element_size</span><span class="op">(</span><span class="op">)</span></code></pre></div>
 </div>
 </div>
-<div id="eq" class="section level2">
-<h2 class="hasAnchor">
-<a href="#eq" class="anchor"></a>eq</h2>
+<div class="section level2">
+<h2 id="eq">eq<a class="anchor" aria-label="anchor" href="#eq"></a>
+</h2>
 <p>eq(other) -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_eq.html">?torch_eq</a></code></p>
 </div>
-<div id="eq_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#eq_" class="anchor"></a>eq_</h2>
+<div class="section level2">
+<h2 id="eq_">eq_<a class="anchor" aria-label="anchor" href="#eq_"></a>
+</h2>
 <p>eq_(other) -&gt; Tensor</p>
 <p>In-place version of <code>$eq</code></p>
 </div>
-<div id="equal" class="section level2">
-<h2 class="hasAnchor">
-<a href="#equal" class="anchor"></a>equal</h2>
+<div class="section level2">
+<h2 id="equal">equal<a class="anchor" aria-label="anchor" href="#equal"></a>
+</h2>
 <p>equal(other) -&gt; bool</p>
 <p>See <code><a href="../../reference/torch_equal.html">?torch_equal</a></code></p>
 </div>
-<div id="erf" class="section level2">
-<h2 class="hasAnchor">
-<a href="#erf" class="anchor"></a>erf</h2>
+<div class="section level2">
+<h2 id="erf">erf<a class="anchor" aria-label="anchor" href="#erf"></a>
+</h2>
 <p>erf() -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_erf.html">?torch_erf</a></code></p>
 </div>
-<div id="erf_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#erf_" class="anchor"></a>erf_</h2>
+<div class="section level2">
+<h2 id="erf_">erf_<a class="anchor" aria-label="anchor" href="#erf_"></a>
+</h2>
 <p>erf_() -&gt; Tensor</p>
 <p>In-place version of <code>$erf</code></p>
 </div>
-<div id="erfc" class="section level2">
-<h2 class="hasAnchor">
-<a href="#erfc" class="anchor"></a>erfc</h2>
+<div class="section level2">
+<h2 id="erfc">erfc<a class="anchor" aria-label="anchor" href="#erfc"></a>
+</h2>
 <p>erfc() -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_erfc.html">?torch_erfc</a></code></p>
 </div>
-<div id="erfc_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#erfc_" class="anchor"></a>erfc_</h2>
+<div class="section level2">
+<h2 id="erfc_">erfc_<a class="anchor" aria-label="anchor" href="#erfc_"></a>
+</h2>
 <p>erfc_() -&gt; Tensor</p>
 <p>In-place version of <code>$erfc</code></p>
 </div>
-<div id="erfinv" class="section level2">
-<h2 class="hasAnchor">
-<a href="#erfinv" class="anchor"></a>erfinv</h2>
+<div class="section level2">
+<h2 id="erfinv">erfinv<a class="anchor" aria-label="anchor" href="#erfinv"></a>
+</h2>
 <p>erfinv() -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_erfinv.html">?torch_erfinv</a></code></p>
 </div>
-<div id="erfinv_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#erfinv_" class="anchor"></a>erfinv_</h2>
+<div class="section level2">
+<h2 id="erfinv_">erfinv_<a class="anchor" aria-label="anchor" href="#erfinv_"></a>
+</h2>
 <p>erfinv_() -&gt; Tensor</p>
 <p>In-place version of <code>$erfinv</code></p>
 </div>
-<div id="exp" class="section level2">
-<h2 class="hasAnchor">
-<a href="#exp" class="anchor"></a>exp</h2>
+<div class="section level2">
+<h2 id="exp">exp<a class="anchor" aria-label="anchor" href="#exp"></a>
+</h2>
 <p>exp() -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_exp.html">?torch_exp</a></code></p>
 </div>
-<div id="exp_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#exp_" class="anchor"></a>exp_</h2>
+<div class="section level2">
+<h2 id="exp_">exp_<a class="anchor" aria-label="anchor" href="#exp_"></a>
+</h2>
 <p>exp_() -&gt; Tensor</p>
 <p>In-place version of <code>$exp</code></p>
 </div>
-<div id="expand" class="section level2">
-<h2 class="hasAnchor">
-<a href="#expand" class="anchor"></a>expand</h2>
+<div class="section level2">
+<h2 id="expand">expand<a class="anchor" aria-label="anchor" href="#expand"></a>
+</h2>
 <p>expand(*sizes) -&gt; Tensor</p>
 <p>Returns a new view of the <code>self</code> tensor with singleton dimensions expanded to a larger size.</p>
 <p>Passing -1 as the size for a dimension means not changing the size of that dimension.</p>
 <p>Tensor can be also expanded to a larger number of dimensions, and the new ones will be appended at the front. For the new dimensions, the size cannot be set to -1.</p>
 <p>Expanding a tensor does not allocate new memory, but only creates a new view on the existing tensor where a dimension of size one is expanded to a larger size by setting the <code>stride</code> to 0. Any dimension of size 1 can be expanded to an arbitrary value without allocating new memory.</p>
-<div id="arguments-14" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-14" class="anchor"></a>Arguments:</h4>
+<div class="section level4">
+<h4 id="arguments-14">Arguments:<a class="anchor" aria-label="anchor" href="#arguments-14"></a>
+</h4>
 <ul>
 <li>sizes (torch_Size or int…): the desired expanded size</li>
 </ul>
 </div>
-<div id="warning-2" class="section level4">
-<h4 class="hasAnchor">
-<a href="#warning-2" class="anchor"></a>Warning:</h4>
+<div class="section level4">
+<h4 id="warning-2">Warning:<a class="anchor" aria-label="anchor" href="#warning-2"></a>
+</h4>
 <p>More than one element of an expanded tensor may refer to a single memory location. As a result, in-place operations (especially ones that are vectorized) may result in incorrect behavior. If you need to write to the tensors, please clone them first.</p>
 </div>
-<div id="examples-7" class="section level4">
-<h4 class="hasAnchor">
-<a href="#examples-7" class="anchor"></a>Examples:</h4>
+<div class="section level4">
+<h4 id="examples-7">Examples:<a class="anchor" aria-label="anchor" href="#examples-7"></a>
+</h4>
 <div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/matrix.html">matrix</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">1</span>,<span class="fl">2</span>,<span class="fl">3</span><span class="op">)</span>, ncol <span class="op">=</span> <span class="fl">1</span><span class="op">)</span><span class="op">)</span>
+<code class="sourceCode R"><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/matrix.html" class="external-link">matrix</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>,<span class="fl">2</span>,<span class="fl">3</span><span class="op">)</span>, ncol <span class="op">=</span> <span class="fl">1</span><span class="op">)</span><span class="op">)</span>
 <span class="va">x</span><span class="op">$</span><span class="fu">size</span><span class="op">(</span><span class="op">)</span>
-<span class="va">x</span><span class="op">$</span><span class="fu">expand</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">4</span><span class="op">)</span><span class="op">)</span>
-<span class="va">x</span><span class="op">$</span><span class="fu">expand</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="op">-</span><span class="fl">1</span>, <span class="fl">4</span><span class="op">)</span><span class="op">)</span>  <span class="co"># -1 means not changing the size of that dimension</span></code></pre></div>
+<span class="va">x</span><span class="op">$</span><span class="fu">expand</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">4</span><span class="op">)</span><span class="op">)</span>
+<span class="va">x</span><span class="op">$</span><span class="fu">expand</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="op">-</span><span class="fl">1</span>, <span class="fl">4</span><span class="op">)</span><span class="op">)</span>  <span class="co"># -1 means not changing the size of that dimension</span></code></pre></div>
 </div>
 </div>
-<div id="expand_as" class="section level2">
-<h2 class="hasAnchor">
-<a href="#expand_as" class="anchor"></a>expand_as</h2>
+<div class="section level2">
+<h2 id="expand_as">expand_as<a class="anchor" aria-label="anchor" href="#expand_as"></a>
+</h2>
 <p>expand_as(other) -&gt; Tensor</p>
 <p>Expand this tensor to the same size as <code>other</code>. <code>self$expand_as(other)</code> is equivalent to <code>self$expand(other.size())</code>.</p>
 <p>Please see <code>$expand</code> for more information about <code>expand</code>.</p>
-<div id="arguments-15" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-15" class="anchor"></a>Arguments:</h4>
+<div class="section level4">
+<h4 id="arguments-15">Arguments:<a class="anchor" aria-label="anchor" href="#arguments-15"></a>
+</h4>
 <ul>
 <li>other (`$): The result tensor has the same size</li>
 <li>as <code>other</code>.</li>
 </ul>
 </div>
 </div>
-<div id="expm1" class="section level2">
-<h2 class="hasAnchor">
-<a href="#expm1" class="anchor"></a>expm1</h2>
+<div class="section level2">
+<h2 id="expm1">expm1<a class="anchor" aria-label="anchor" href="#expm1"></a>
+</h2>
 <p>expm1() -&gt; Tensor</p>
 <p>See [torch_expm1()]</p>
 </div>
-<div id="expm1_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#expm1_" class="anchor"></a>expm1_</h2>
+<div class="section level2">
+<h2 id="expm1_">expm1_<a class="anchor" aria-label="anchor" href="#expm1_"></a>
+</h2>
 <p>expm1_() -&gt; Tensor</p>
 <p>In-place version of <code>$expm1</code></p>
 </div>
-<div id="exponential_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#exponential_" class="anchor"></a>exponential_</h2>
+<div class="section level2">
+<h2 id="exponential_">exponential_<a class="anchor" aria-label="anchor" href="#exponential_"></a>
+</h2>
 <p>exponential_(lambd=1, *, generator=NULL) -&gt; Tensor</p>
 <p>Fills <code>self</code> tensor with elements drawn from the exponential distribution:</p>
 <p><span class="math display">\[
 f(x) = \lambda e^{-\lambda x}
 \]</span></p>
 </div>
-<div id="fft" class="section level2">
-<h2 class="hasAnchor">
-<a href="#fft" class="anchor"></a>fft</h2>
+<div class="section level2">
+<h2 id="fft">fft<a class="anchor" aria-label="anchor" href="#fft"></a>
+</h2>
 <p>fft(signal_ndim, normalized=FALSE) -&gt; Tensor</p>
 <p>See <code>?torch_fft</code></p>
 </div>
-<div id="fill_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#fill_" class="anchor"></a>fill_</h2>
+<div class="section level2">
+<h2 id="fill_">fill_<a class="anchor" aria-label="anchor" href="#fill_"></a>
+</h2>
 <p>fill_(value) -&gt; Tensor</p>
 <p>Fills <code>self</code> tensor with the specified value.</p>
 </div>
-<div id="fill_diagonal_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#fill_diagonal_" class="anchor"></a>fill_diagonal_</h2>
+<div class="section level2">
+<h2 id="fill_diagonal_">fill_diagonal_<a class="anchor" aria-label="anchor" href="#fill_diagonal_"></a>
+</h2>
 <p>fill_diagonal_(fill_value, wrap=FALSE) -&gt; Tensor</p>
 <p>Fill the main diagonal of a tensor that has at least 2-dimensions. When dims&gt;2, all dimensions of input must be of equal length. This function modifies the input tensor in-place, and returns the input tensor.</p>
-<div id="arguments-16" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-16" class="anchor"></a>Arguments:</h4>
+<div class="section level4">
+<h4 id="arguments-16">Arguments:<a class="anchor" aria-label="anchor" href="#arguments-16"></a>
+</h4>
 <ul>
 <li>fill_value (Scalar): the fill value</li>
 <li>wrap (bool): the diagonal ‘wrapped’ after N columns for tall matrices.</li>
 </ul>
 </div>
-<div id="examples-8" class="section level4">
-<h4 class="hasAnchor">
-<a href="#examples-8" class="anchor"></a>Examples:</h4>
+<div class="section level4">
+<h4 id="examples-8">Examples:<a class="anchor" aria-label="anchor" href="#examples-8"></a>
+</h4>
 <div class="sourceCode" id="cb10"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span class="va">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_zeros.html">torch_zeros</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">3</span><span class="op">)</span>
 <span class="va">a</span><span class="op">$</span><span class="fu">fill_diagonal_</span><span class="op">(</span><span class="fl">5</span><span class="op">)</span>
@@ -1224,328 +1185,328 @@ f(x) = \lambda e^{-\lambda x}
 <span class="va">c</span><span class="op">$</span><span class="fu">fill_diagonal_</span><span class="op">(</span><span class="fl">5</span>, wrap<span class="op">=</span><span class="cn">TRUE</span><span class="op">)</span></code></pre></div>
 </div>
 </div>
-<div id="flatten" class="section level2">
-<h2 class="hasAnchor">
-<a href="#flatten" class="anchor"></a>flatten</h2>
+<div class="section level2">
+<h2 id="flatten">flatten<a class="anchor" aria-label="anchor" href="#flatten"></a>
+</h2>
 <p>flatten(input, start_dim=0, end_dim=-1) -&gt; Tensor</p>
 <p>see <code><a href="../../reference/torch_flatten.html">?torch_flatten</a></code></p>
 </div>
-<div id="flip" class="section level2">
-<h2 class="hasAnchor">
-<a href="#flip" class="anchor"></a>flip</h2>
+<div class="section level2">
+<h2 id="flip">flip<a class="anchor" aria-label="anchor" href="#flip"></a>
+</h2>
 <p>flip(dims) -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_flip.html">?torch_flip</a></code></p>
 </div>
-<div id="fliplr" class="section level2">
-<h2 class="hasAnchor">
-<a href="#fliplr" class="anchor"></a>fliplr</h2>
+<div class="section level2">
+<h2 id="fliplr">fliplr<a class="anchor" aria-label="anchor" href="#fliplr"></a>
+</h2>
 <p>fliplr() -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_fliplr.html">?torch_fliplr</a></code></p>
 </div>
-<div id="flipud" class="section level2">
-<h2 class="hasAnchor">
-<a href="#flipud" class="anchor"></a>flipud</h2>
+<div class="section level2">
+<h2 id="flipud">flipud<a class="anchor" aria-label="anchor" href="#flipud"></a>
+</h2>
 <p>flipud() -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_flipud.html">?torch_flipud</a></code></p>
 </div>
-<div id="float" class="section level2">
-<h2 class="hasAnchor">
-<a href="#float" class="anchor"></a>float</h2>
+<div class="section level2">
+<h2 id="float">float<a class="anchor" aria-label="anchor" href="#float"></a>
+</h2>
 <p>float(memory_format=torch_preserve_format) -&gt; Tensor</p>
 <p><code>self$float()</code> is equivalent to <code>self$to(torch_float32)</code>. See [to()].</p>
-<div id="arguments-17" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-17" class="anchor"></a>Arguments:</h4>
+<div class="section level4">
+<h4 id="arguments-17">Arguments:<a class="anchor" aria-label="anchor" href="#arguments-17"></a>
+</h4>
 <ul>
 <li>memory_format (<code>torch_memory_format</code>, optional): the desired memory format of</li>
 <li>returned Tensor. Default: <code>torch_preserve_format</code>.</li>
 </ul>
 </div>
 </div>
-<div id="floor" class="section level2">
-<h2 class="hasAnchor">
-<a href="#floor" class="anchor"></a>floor</h2>
+<div class="section level2">
+<h2 id="floor">floor<a class="anchor" aria-label="anchor" href="#floor"></a>
+</h2>
 <p>floor() -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_floor.html">?torch_floor</a></code></p>
 </div>
-<div id="floor_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#floor_" class="anchor"></a>floor_</h2>
+<div class="section level2">
+<h2 id="floor_">floor_<a class="anchor" aria-label="anchor" href="#floor_"></a>
+</h2>
 <p>floor_() -&gt; Tensor</p>
 <p>In-place version of <code>$floor</code></p>
 </div>
-<div id="floor_divide" class="section level2">
-<h2 class="hasAnchor">
-<a href="#floor_divide" class="anchor"></a>floor_divide</h2>
+<div class="section level2">
+<h2 id="floor_divide">floor_divide<a class="anchor" aria-label="anchor" href="#floor_divide"></a>
+</h2>
 <p>floor_divide(value) -&gt; Tensor</p>
 <p>See [torch_floor_divide()]</p>
 </div>
-<div id="floor_divide_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#floor_divide_" class="anchor"></a>floor_divide_</h2>
+<div class="section level2">
+<h2 id="floor_divide_">floor_divide_<a class="anchor" aria-label="anchor" href="#floor_divide_"></a>
+</h2>
 <p>floor_divide_(value) -&gt; Tensor</p>
 <p>In-place version of <code>$floor_divide</code></p>
 </div>
-<div id="fmod" class="section level2">
-<h2 class="hasAnchor">
-<a href="#fmod" class="anchor"></a>fmod</h2>
+<div class="section level2">
+<h2 id="fmod">fmod<a class="anchor" aria-label="anchor" href="#fmod"></a>
+</h2>
 <p>fmod(divisor) -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_fmod.html">?torch_fmod</a></code></p>
 </div>
-<div id="fmod_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#fmod_" class="anchor"></a>fmod_</h2>
+<div class="section level2">
+<h2 id="fmod_">fmod_<a class="anchor" aria-label="anchor" href="#fmod_"></a>
+</h2>
 <p>fmod_(divisor) -&gt; Tensor</p>
 <p>In-place version of <code>$fmod</code></p>
 </div>
-<div id="frac" class="section level2">
-<h2 class="hasAnchor">
-<a href="#frac" class="anchor"></a>frac</h2>
+<div class="section level2">
+<h2 id="frac">frac<a class="anchor" aria-label="anchor" href="#frac"></a>
+</h2>
 <p>frac() -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_frac.html">?torch_frac</a></code></p>
 </div>
-<div id="frac_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#frac_" class="anchor"></a>frac_</h2>
+<div class="section level2">
+<h2 id="frac_">frac_<a class="anchor" aria-label="anchor" href="#frac_"></a>
+</h2>
 <p>frac_() -&gt; Tensor</p>
 <p>In-place version of <code>$frac</code></p>
 </div>
-<div id="gather" class="section level2">
-<h2 class="hasAnchor">
-<a href="#gather" class="anchor"></a>gather</h2>
+<div class="section level2">
+<h2 id="gather">gather<a class="anchor" aria-label="anchor" href="#gather"></a>
+</h2>
 <p>gather(dim, index) -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_gather.html">?torch_gather</a></code></p>
 </div>
-<div id="ge" class="section level2">
-<h2 class="hasAnchor">
-<a href="#ge" class="anchor"></a>ge</h2>
+<div class="section level2">
+<h2 id="ge">ge<a class="anchor" aria-label="anchor" href="#ge"></a>
+</h2>
 <p>ge(other) -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_ge.html">?torch_ge</a></code></p>
 </div>
-<div id="ge_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#ge_" class="anchor"></a>ge_</h2>
+<div class="section level2">
+<h2 id="ge_">ge_<a class="anchor" aria-label="anchor" href="#ge_"></a>
+</h2>
 <p>ge_(other) -&gt; Tensor</p>
 <p>In-place version of <code>$ge</code></p>
 </div>
-<div id="geometric_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#geometric_" class="anchor"></a>geometric_</h2>
+<div class="section level2">
+<h2 id="geometric_">geometric_<a class="anchor" aria-label="anchor" href="#geometric_"></a>
+</h2>
 <p>geometric_(p, *, generator=NULL) -&gt; Tensor</p>
 <p>Fills <code>self</code> tensor with elements drawn from the geometric distribution:</p>
 <p><span class="math display">\[
 f(X=k) = p^{k - 1} (1 - p)
 \]</span></p>
 </div>
-<div id="geqrf" class="section level2">
-<h2 class="hasAnchor">
-<a href="#geqrf" class="anchor"></a>geqrf</h2>
+<div class="section level2">
+<h2 id="geqrf">geqrf<a class="anchor" aria-label="anchor" href="#geqrf"></a>
+</h2>
 <p>geqrf() -&gt; (Tensor, Tensor)</p>
 <p>See <code><a href="../../reference/torch_geqrf.html">?torch_geqrf</a></code></p>
 </div>
-<div id="ger" class="section level2">
-<h2 class="hasAnchor">
-<a href="#ger" class="anchor"></a>ger</h2>
+<div class="section level2">
+<h2 id="ger">ger<a class="anchor" aria-label="anchor" href="#ger"></a>
+</h2>
 <p>ger(vec2) -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_ger.html">?torch_ger</a></code></p>
 </div>
-<div id="get_device" class="section level2">
-<h2 class="hasAnchor">
-<a href="#get_device" class="anchor"></a>get_device</h2>
+<div class="section level2">
+<h2 id="get_device">get_device<a class="anchor" aria-label="anchor" href="#get_device"></a>
+</h2>
 <p>get_device() -&gt; Device ordinal (Integer)</p>
 <p>For CUDA tensors, this function returns the device ordinal of the GPU on which the tensor resides. For CPU tensors, an error is thrown.</p>
-<div id="examples-9" class="section level4">
-<h4 class="hasAnchor">
-<a href="#examples-9" class="anchor"></a>Examples:</h4>
+<div class="section level4">
+<h4 id="examples-9">Examples:<a class="anchor" aria-label="anchor" href="#examples-9"></a>
+</h4>
 <div class="sourceCode" id="cb11"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">4</span>, <span class="fl">5</span>, device<span class="op">=</span><span class="st">'cuda:0'</span><span class="op">)</span>
 <span class="va">x</span><span class="op">$</span><span class="fu">get_device</span><span class="op">(</span><span class="op">)</span>
 <span class="va">x</span><span class="op">$</span><span class="fu">cpu</span><span class="op">(</span><span class="op">)</span><span class="op">$</span><span class="fu">get_device</span><span class="op">(</span><span class="op">)</span>  <span class="co"># RuntimeError: get_device is not implemented for type torch_FloatTensor</span></code></pre></div>
 </div>
 </div>
-<div id="grad" class="section level2">
-<h2 class="hasAnchor">
-<a href="#grad" class="anchor"></a>grad</h2>
+<div class="section level2">
+<h2 id="grad">grad<a class="anchor" aria-label="anchor" href="#grad"></a>
+</h2>
 <p>This attribute is <code>NULL</code> by default and becomes a Tensor the first time a call to <code>backward</code> computes gradients for <code>self</code>. The attribute will then contain the gradients computed and future calls to [backward()] will accumulate (add) gradients into it.</p>
 </div>
-<div id="gt" class="section level2">
-<h2 class="hasAnchor">
-<a href="#gt" class="anchor"></a>gt</h2>
+<div class="section level2">
+<h2 id="gt">gt<a class="anchor" aria-label="anchor" href="#gt"></a>
+</h2>
 <p>gt(other) -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_gt.html">?torch_gt</a></code></p>
 </div>
-<div id="gt_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#gt_" class="anchor"></a>gt_</h2>
+<div class="section level2">
+<h2 id="gt_">gt_<a class="anchor" aria-label="anchor" href="#gt_"></a>
+</h2>
 <p>gt_(other) -&gt; Tensor</p>
 <p>In-place version of <code>$gt</code></p>
 </div>
-<div id="half" class="section level2">
-<h2 class="hasAnchor">
-<a href="#half" class="anchor"></a>half</h2>
+<div class="section level2">
+<h2 id="half">half<a class="anchor" aria-label="anchor" href="#half"></a>
+</h2>
 <p>half(memory_format=torch_preserve_format) -&gt; Tensor</p>
 <p><code>self$half()</code> is equivalent to <code>self$to(torch_float16)</code>. See [to()].</p>
-<div id="arguments-18" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-18" class="anchor"></a>Arguments:</h4>
+<div class="section level4">
+<h4 id="arguments-18">Arguments:<a class="anchor" aria-label="anchor" href="#arguments-18"></a>
+</h4>
 <ul>
 <li>memory_format (<code>torch_memory_format</code>, optional): the desired memory format of</li>
 <li>returned Tensor. Default: <code>torch_preserve_format</code>.</li>
 </ul>
 </div>
 </div>
-<div id="hardshrink" class="section level2">
-<h2 class="hasAnchor">
-<a href="#hardshrink" class="anchor"></a>hardshrink</h2>
+<div class="section level2">
+<h2 id="hardshrink">hardshrink<a class="anchor" aria-label="anchor" href="#hardshrink"></a>
+</h2>
 <p>hardshrink(lambd=0.5) -&gt; Tensor</p>
 <p>See [torch_nn.functional.hardshrink()]</p>
 </div>
-<div id="has_names" class="section level2">
-<h2 class="hasAnchor">
-<a href="#has_names" class="anchor"></a>has_names</h2>
+<div class="section level2">
+<h2 id="has_names">has_names<a class="anchor" aria-label="anchor" href="#has_names"></a>
+</h2>
 <p>Is <code>TRUE</code> if any of this tensor’s dimensions are named. Otherwise, is <code>FALSE</code>.</p>
 </div>
-<div id="histc" class="section level2">
-<h2 class="hasAnchor">
-<a href="#histc" class="anchor"></a>histc</h2>
+<div class="section level2">
+<h2 id="histc">histc<a class="anchor" aria-label="anchor" href="#histc"></a>
+</h2>
 <p>histc(bins=100, min=0, max=0) -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_histc.html">?torch_histc</a></code></p>
 </div>
-<div id="ifft" class="section level2">
-<h2 class="hasAnchor">
-<a href="#ifft" class="anchor"></a>ifft</h2>
+<div class="section level2">
+<h2 id="ifft">ifft<a class="anchor" aria-label="anchor" href="#ifft"></a>
+</h2>
 <p>ifft(signal_ndim, normalized=FALSE) -&gt; Tensor</p>
 <p>See <code>?torch_ifft</code></p>
 </div>
-<div id="imag" class="section level2">
-<h2 class="hasAnchor">
-<a href="#imag" class="anchor"></a>imag</h2>
+<div class="section level2">
+<h2 id="imag">imag<a class="anchor" aria-label="anchor" href="#imag"></a>
+</h2>
 <p>Returns a new tensor containing imaginary values of the <code>self</code> tensor. The returned tensor and <code>self</code> share the same underlying storage.</p>
-<div id="warning-3" class="section level4">
-<h4 class="hasAnchor">
-<a href="#warning-3" class="anchor"></a>Warning:</h4>
+<div class="section level4">
+<h4 id="warning-3">Warning:<a class="anchor" aria-label="anchor" href="#warning-3"></a>
+</h4>
 <p>[imag()] is only supported for tensors with complex dtypes.</p>
 </div>
-<div id="examples-10" class="section level4">
-<h4 class="hasAnchor">
-<a href="#examples-10" class="anchor"></a>Examples:</h4>
+<div class="section level4">
+<h4 id="examples-10">Examples:<a class="anchor" aria-label="anchor" href="#examples-10"></a>
+</h4>
 <div class="sourceCode" id="cb12"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">4</span>, dtype<span class="op">=</span><span class="fu">torch_cfloat</span><span class="op">(</span><span class="op">)</span><span class="op">)</span>
 <span class="va">x</span>
 <span class="va">x</span><span class="op">$</span><span class="va">imag</span></code></pre></div>
 </div>
 </div>
-<div id="index_add" class="section level2">
-<h2 class="hasAnchor">
-<a href="#index_add" class="anchor"></a>index_add</h2>
+<div class="section level2">
+<h2 id="index_add">index_add<a class="anchor" aria-label="anchor" href="#index_add"></a>
+</h2>
 <p>index_add(tensor1, dim, index, tensor2) -&gt; Tensor</p>
 <p>Out-of-place version of <code>$index_add_</code>. <code>tensor1</code> corresponds to <code>self</code> in <code>$index_add_</code>.</p>
 </div>
-<div id="index_add_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#index_add_" class="anchor"></a>index_add_</h2>
+<div class="section level2">
+<h2 id="index_add_">index_add_<a class="anchor" aria-label="anchor" href="#index_add_"></a>
+</h2>
 <p>index_add_(dim, index, tensor) -&gt; Tensor</p>
 <p>Accumulate the elements of <code>tensor</code> into the <code>self</code> tensor by adding to the indices in the order given in <code>index</code>. For example, if <code>dim == 0</code> and <code>index[i] == j</code>, then the <code>i</code> th row of <code>tensor</code> is added to the <code>j</code> th row of <code>self</code>.</p>
 <p>The <code>dim</code> th dimension of <code>tensor</code> must have the same size as the length of <code>index</code> (which must be a vector), and all other dimensions must match <code>self</code>, or an error will be raised.</p>
-<div id="note-3" class="section level4">
-<h4 class="hasAnchor">
-<a href="#note-3" class="anchor"></a>Note:</h4>
+<div class="section level4">
+<h4 id="note-3">Note:<a class="anchor" aria-label="anchor" href="#note-3"></a>
+</h4>
 <p>In some circumstances when using the CUDA backend with CuDNN, this operator may select a nondeterministic algorithm to increase performance. If this is undesirable, you can try to make the operation deterministic (potentially at a performance cost) by setting <code>torch_backends.cudnn.deterministic = TRUE</code>.</p>
 </div>
-<div id="arguments-19" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-19" class="anchor"></a>Arguments:</h4>
+<div class="section level4">
+<h4 id="arguments-19">Arguments:<a class="anchor" aria-label="anchor" href="#arguments-19"></a>
+</h4>
 <ul>
 <li>dim (int): dimension along which to index</li>
 <li>index (LongTensor): indices of <code>tensor</code> to select from</li>
 <li>tensor (Tensor): the tensor containing values to add</li>
 </ul>
 </div>
-<div id="examples-11" class="section level4">
-<h4 class="hasAnchor">
-<a href="#examples-11" class="anchor"></a>Examples:</h4>
+<div class="section level4">
+<h4 id="examples-11">Examples:<a class="anchor" aria-label="anchor" href="#examples-11"></a>
+</h4>
 <div class="sourceCode" id="cb13"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_ones.html">torch_ones</a></span><span class="op">(</span><span class="fl">5</span>, <span class="fl">3</span><span class="op">)</span>
-<span class="va">t</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/matrix.html">matrix</a></span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="fl">9</span>, ncol <span class="op">=</span> <span class="fl">3</span><span class="op">)</span>, dtype<span class="op">=</span><span class="fu"><a href="../../reference/torch_dtype.html">torch_float</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span>
-<span class="va">index</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">1L</span>, <span class="fl">4L</span>, <span class="fl">3L</span><span class="op">)</span><span class="op">)</span>
+<span class="va">t</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/matrix.html" class="external-link">matrix</a></span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="fl">9</span>, ncol <span class="op">=</span> <span class="fl">3</span><span class="op">)</span>, dtype<span class="op">=</span><span class="fu"><a href="../../reference/torch_dtype.html">torch_float</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span>
+<span class="va">index</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1L</span>, <span class="fl">4L</span>, <span class="fl">3L</span><span class="op">)</span><span class="op">)</span>
 <span class="va">x</span><span class="op">$</span><span class="fu">index_add_</span><span class="op">(</span><span class="fl">1</span>, <span class="va">index</span>, <span class="va">t</span><span class="op">)</span></code></pre></div>
 </div>
 </div>
-<div id="index_copy" class="section level2">
-<h2 class="hasAnchor">
-<a href="#index_copy" class="anchor"></a>index_copy</h2>
+<div class="section level2">
+<h2 id="index_copy">index_copy<a class="anchor" aria-label="anchor" href="#index_copy"></a>
+</h2>
 <p>index_copy(tensor1, dim, index, tensor2) -&gt; Tensor</p>
 <p>Out-of-place version of <code>$index_copy_</code>. <code>tensor1</code> corresponds to <code>self</code> in <code>$index_copy_</code>.</p>
 </div>
-<div id="index_copy_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#index_copy_" class="anchor"></a>index_copy_</h2>
+<div class="section level2">
+<h2 id="index_copy_">index_copy_<a class="anchor" aria-label="anchor" href="#index_copy_"></a>
+</h2>
 <p>index_copy_(dim, index, tensor) -&gt; Tensor</p>
 <p>Copies the elements of <code>tensor</code> into the <code>self</code> tensor by selecting the indices in the order given in <code>index</code>. For example, if <code>dim == 0</code> and <code>index[i] == j</code>, then the <code>i</code> th row of <code>tensor</code> is copied to the <code>j</code> th row of <code>self</code>.</p>
 <p>The <code>dim</code> th dimension of <code>tensor</code> must have the same size as the length of <code>index</code> (which must be a vector), and all other dimensions must match <code>self</code>, or an error will be raised.</p>
-<div id="arguments-20" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-20" class="anchor"></a>Arguments:</h4>
+<div class="section level4">
+<h4 id="arguments-20">Arguments:<a class="anchor" aria-label="anchor" href="#arguments-20"></a>
+</h4>
 <ul>
 <li>dim (int): dimension along which to index</li>
 <li>index (LongTensor): indices of <code>tensor</code> to select from</li>
 <li>tensor (Tensor): the tensor containing values to copy</li>
 </ul>
 </div>
-<div id="examples-12" class="section level4">
-<h4 class="hasAnchor">
-<a href="#examples-12" class="anchor"></a>Examples:</h4>
+<div class="section level4">
+<h4 id="examples-12">Examples:<a class="anchor" aria-label="anchor" href="#examples-12"></a>
+</h4>
 <div class="sourceCode" id="cb14"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_zeros.html">torch_zeros</a></span><span class="op">(</span><span class="fl">5</span>, <span class="fl">3</span><span class="op">)</span>
-<span class="va">t</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/matrix.html">matrix</a></span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="fl">9</span>, ncol <span class="op">=</span> <span class="fl">3</span><span class="op">)</span>, dtype<span class="op">=</span><span class="fu"><a href="../../reference/torch_dtype.html">torch_float</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span>
-<span class="va">index</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">5</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span>
+<span class="va">t</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/matrix.html" class="external-link">matrix</a></span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="fl">9</span>, ncol <span class="op">=</span> <span class="fl">3</span><span class="op">)</span>, dtype<span class="op">=</span><span class="fu"><a href="../../reference/torch_dtype.html">torch_float</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span>
+<span class="va">index</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">5</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span>
 <span class="va">x</span><span class="op">$</span><span class="fu">index_copy_</span><span class="op">(</span><span class="fl">1</span>, <span class="va">index</span>, <span class="va">t</span><span class="op">)</span></code></pre></div>
 </div>
 </div>
-<div id="index_fill" class="section level2">
-<h2 class="hasAnchor">
-<a href="#index_fill" class="anchor"></a>index_fill</h2>
+<div class="section level2">
+<h2 id="index_fill">index_fill<a class="anchor" aria-label="anchor" href="#index_fill"></a>
+</h2>
 <p>index_fill(tensor1, dim, index, value) -&gt; Tensor</p>
 <p>Out-of-place version of <code>$index_fill_</code>. <code>tensor1</code> corresponds to <code>self</code> in <code>$index_fill_</code>.</p>
 </div>
-<div id="index_fill_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#index_fill_" class="anchor"></a>index_fill_</h2>
+<div class="section level2">
+<h2 id="index_fill_">index_fill_<a class="anchor" aria-label="anchor" href="#index_fill_"></a>
+</h2>
 <p>index_fill_(dim, index, val) -&gt; Tensor</p>
 <p>Fills the elements of the <code>self</code> tensor with value <code>val</code> by selecting the indices in the order given in <code>index</code>.</p>
-<div id="arguments-21" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-21" class="anchor"></a>Arguments:</h4>
+<div class="section level4">
+<h4 id="arguments-21">Arguments:<a class="anchor" aria-label="anchor" href="#arguments-21"></a>
+</h4>
 <ul>
 <li>dim (int): dimension along which to index</li>
 <li>index (LongTensor): indices of <code>self</code> tensor to fill in</li>
 <li>val (float): the value to fill with</li>
 </ul>
 </div>
-<div id="examples-13" class="section level4">
-<h4 class="hasAnchor">
-<a href="#examples-13" class="anchor"></a>Examples:</h4>
+<div class="section level4">
+<h4 id="examples-13">Examples:<a class="anchor" aria-label="anchor" href="#examples-13"></a>
+</h4>
 <div class="sourceCode" id="cb15"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/matrix.html">matrix</a></span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="fl">9</span>, ncol <span class="op">=</span> <span class="fl">3</span><span class="op">)</span>, dtype<span class="op">=</span><span class="fu"><a href="../../reference/torch_dtype.html">torch_float</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span>
-<span class="va">index</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">3</span><span class="op">)</span>, dtype <span class="op">=</span> <span class="fu"><a href="../../reference/torch_dtype.html">torch_long</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span>
+<code class="sourceCode R"><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/matrix.html" class="external-link">matrix</a></span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="fl">9</span>, ncol <span class="op">=</span> <span class="fl">3</span><span class="op">)</span>, dtype<span class="op">=</span><span class="fu"><a href="../../reference/torch_dtype.html">torch_float</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span>
+<span class="va">index</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">3</span><span class="op">)</span>, dtype <span class="op">=</span> <span class="fu"><a href="../../reference/torch_dtype.html">torch_long</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span>
 <span class="va">x</span><span class="op">$</span><span class="fu">index_fill_</span><span class="op">(</span><span class="fl">1</span>, <span class="va">index</span>, <span class="op">-</span><span class="fl">1</span><span class="op">)</span></code></pre></div>
 </div>
 </div>
-<div id="index_put" class="section level2">
-<h2 class="hasAnchor">
-<a href="#index_put" class="anchor"></a>index_put</h2>
+<div class="section level2">
+<h2 id="index_put">index_put<a class="anchor" aria-label="anchor" href="#index_put"></a>
+</h2>
 <p>index_put(tensor1, indices, value, accumulate=FALSE) -&gt; Tensor</p>
 <p>Out-place version of <code>$index_put_</code>. <code>tensor1</code> corresponds to <code>self</code> in <code>$index_put_</code>.</p>
 </div>
-<div id="index_put_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#index_put_" class="anchor"></a>index_put_</h2>
+<div class="section level2">
+<h2 id="index_put_">index_put_<a class="anchor" aria-label="anchor" href="#index_put_"></a>
+</h2>
 <p>index_put_(indices, value, accumulate=FALSE) -&gt; Tensor</p>
 <p>Puts values from the tensor <code>value</code> into the tensor <code>self</code> using the indices specified in <code>indices</code> (which is a tuple of Tensors). The expression <code>tensor.index_put_(indices, value)</code> is equivalent to <code>tensor[indices] = value</code>. Returns <code>self</code>.</p>
 <p>If <code>accumulate</code> is <code>TRUE</code>, the elements in <code>value</code> are added to <code>self</code>. If accumulate is <code>FALSE</code>, the behavior is undefined if indices contain duplicate elements.</p>
-<div id="arguments-22" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-22" class="anchor"></a>Arguments:</h4>
+<div class="section level4">
+<h4 id="arguments-22">Arguments:<a class="anchor" aria-label="anchor" href="#arguments-22"></a>
+</h4>
 <ul>
 <li>indices (tuple of LongTensor): tensors used to index into <code>self</code>.</li>
 <li>value (Tensor): tensor of same dtype as <code>self</code>.</li>
@@ -1553,96 +1514,96 @@ f(X=k) = p^{k - 1} (1 - p)
 </ul>
 </div>
 </div>
-<div id="index_select" class="section level2">
-<h2 class="hasAnchor">
-<a href="#index_select" class="anchor"></a>index_select</h2>
+<div class="section level2">
+<h2 id="index_select">index_select<a class="anchor" aria-label="anchor" href="#index_select"></a>
+</h2>
 <p>index_select(dim, index) -&gt; Tensor</p>
 <p>See [torch_index_select()]</p>
 </div>
-<div id="indices" class="section level2">
-<h2 class="hasAnchor">
-<a href="#indices" class="anchor"></a>indices</h2>
+<div class="section level2">
+<h2 id="indices">indices<a class="anchor" aria-label="anchor" href="#indices"></a>
+</h2>
 <p>indices() -&gt; Tensor</p>
 <p>If <code>self</code> is a sparse COO tensor (i.e., with <code>torch_sparse_coo</code> layout), this returns a view of the contained indices tensor. Otherwise, this throws an error.</p>
 <p>See also <code>Tensor.values</code>.</p>
-<div id="note-4" class="section level4">
-<h4 class="hasAnchor">
-<a href="#note-4" class="anchor"></a>Note:</h4>
+<div class="section level4">
+<h4 id="note-4">Note:<a class="anchor" aria-label="anchor" href="#note-4"></a>
+</h4>
 <p>This method can only be called on a coalesced sparse tensor. See <code>Tensor.coalesce</code> for details.</p>
 </div>
 </div>
-<div id="int" class="section level2">
-<h2 class="hasAnchor">
-<a href="#int" class="anchor"></a>int</h2>
+<div class="section level2">
+<h2 id="int">int<a class="anchor" aria-label="anchor" href="#int"></a>
+</h2>
 <p>int(memory_format=torch_preserve_format) -&gt; Tensor</p>
 <p><code>self$int()</code> is equivalent to <code>self$to(torch_int32)</code>. See [to()].</p>
-<div id="arguments-23" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-23" class="anchor"></a>Arguments:</h4>
+<div class="section level4">
+<h4 id="arguments-23">Arguments:<a class="anchor" aria-label="anchor" href="#arguments-23"></a>
+</h4>
 <ul>
 <li>memory_format (<code>torch_memory_format</code>, optional): the desired memory format of</li>
 <li>returned Tensor. Default: <code>torch_preserve_format</code>.</li>
 </ul>
 </div>
 </div>
-<div id="int_repr" class="section level2">
-<h2 class="hasAnchor">
-<a href="#int_repr" class="anchor"></a>int_repr</h2>
+<div class="section level2">
+<h2 id="int_repr">int_repr<a class="anchor" aria-label="anchor" href="#int_repr"></a>
+</h2>
 <p>int_repr() -&gt; Tensor</p>
 <p>Given a quantized Tensor, <code>self$int_repr()</code> returns a CPU Tensor with uint8_t as data type that stores the underlying uint8_t values of the given Tensor.</p>
 </div>
-<div id="inverse" class="section level2">
-<h2 class="hasAnchor">
-<a href="#inverse" class="anchor"></a>inverse</h2>
+<div class="section level2">
+<h2 id="inverse">inverse<a class="anchor" aria-label="anchor" href="#inverse"></a>
+</h2>
 <p>inverse() -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_inverse.html">?torch_inverse</a></code></p>
 </div>
-<div id="irfft" class="section level2">
-<h2 class="hasAnchor">
-<a href="#irfft" class="anchor"></a>irfft</h2>
+<div class="section level2">
+<h2 id="irfft">irfft<a class="anchor" aria-label="anchor" href="#irfft"></a>
+</h2>
 <p>irfft(signal_ndim, normalized=FALSE, onesided=TRUE, signal_sizes=NULL) -&gt; Tensor</p>
 <p>See <code>?torch_irfft</code></p>
 </div>
-<div id="is_complex" class="section level2">
-<h2 class="hasAnchor">
-<a href="#is_complex" class="anchor"></a>is_complex</h2>
+<div class="section level2">
+<h2 id="is_complex">is_complex<a class="anchor" aria-label="anchor" href="#is_complex"></a>
+</h2>
 <p>is_complex() -&gt; bool</p>
 <p>Returns TRUE if the data type of <code>self</code> is a complex data type.</p>
 </div>
-<div id="is_contiguous" class="section level2">
-<h2 class="hasAnchor">
-<a href="#is_contiguous" class="anchor"></a>is_contiguous</h2>
+<div class="section level2">
+<h2 id="is_contiguous">is_contiguous<a class="anchor" aria-label="anchor" href="#is_contiguous"></a>
+</h2>
 <p>is_contiguous(memory_format=torch_contiguous_format) -&gt; bool</p>
 <p>Returns TRUE if <code>self</code> tensor is contiguous in memory in the order specified by memory format.</p>
-<div id="arguments-24" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-24" class="anchor"></a>Arguments:</h4>
+<div class="section level4">
+<h4 id="arguments-24">Arguments:<a class="anchor" aria-label="anchor" href="#arguments-24"></a>
+</h4>
 <ul>
 <li>memory_format (<code>torch_memory_format</code>, optional): Specifies memory allocation</li>
 <li>order. Default: <code>torch_contiguous_format</code>.</li>
 </ul>
 </div>
 </div>
-<div id="is_cuda" class="section level2">
-<h2 class="hasAnchor">
-<a href="#is_cuda" class="anchor"></a>is_cuda</h2>
+<div class="section level2">
+<h2 id="is_cuda">is_cuda<a class="anchor" aria-label="anchor" href="#is_cuda"></a>
+</h2>
 <p>Is <code>TRUE</code> if the Tensor is stored on the GPU, <code>FALSE</code> otherwise.</p>
 </div>
-<div id="is_floating_point" class="section level2">
-<h2 class="hasAnchor">
-<a href="#is_floating_point" class="anchor"></a>is_floating_point</h2>
+<div class="section level2">
+<h2 id="is_floating_point">is_floating_point<a class="anchor" aria-label="anchor" href="#is_floating_point"></a>
+</h2>
 <p>is_floating_point() -&gt; bool</p>
 <p>Returns TRUE if the data type of <code>self</code> is a floating point data type.</p>
 </div>
-<div id="is_leaf" class="section level2">
-<h2 class="hasAnchor">
-<a href="#is_leaf" class="anchor"></a>is_leaf</h2>
+<div class="section level2">
+<h2 id="is_leaf">is_leaf<a class="anchor" aria-label="anchor" href="#is_leaf"></a>
+</h2>
 <p>All Tensors that have <code>requires_grad</code> which is <code>FALSE</code> will be leaf Tensors by convention.</p>
 <p>For Tensors that have <code>requires_grad</code> which is <code>TRUE</code>, they will be leaf Tensors if they were created by the user. This means that they are not the result of an operation and so <code>grad_fn</code> is NULL.</p>
 <p>Only leaf Tensors will have their <code>grad</code> populated during a call to [backward()]. To get <code>grad</code> populated for non-leaf Tensors, you can use [retain_grad()].</p>
-<div id="examples-14" class="section level4">
-<h4 class="hasAnchor">
-<a href="#examples-14" class="anchor"></a>Examples:</h4>
+<div class="section level4">
+<h4 id="examples-14">Examples:<a class="anchor" aria-label="anchor" href="#examples-14"></a>
+</h4>
 <div class="sourceCode" id="cb16"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span class="va">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_rand.html">torch_rand</a></span><span class="op">(</span><span class="fl">10</span>, requires_grad<span class="op">=</span><span class="cn">TRUE</span><span class="op">)</span>
 <span class="va">a</span><span class="op">$</span><span class="va">is_leaf</span>
@@ -1672,519 +1633,519 @@ f(X=k) = p^{k - 1} (1 - p)
 <span class="co"># f requires grad, has no operation creating it</span></code></pre></div>
 </div>
 </div>
-<div id="is_meta" class="section level2">
-<h2 class="hasAnchor">
-<a href="#is_meta" class="anchor"></a>is_meta</h2>
+<div class="section level2">
+<h2 id="is_meta">is_meta<a class="anchor" aria-label="anchor" href="#is_meta"></a>
+</h2>
 <p>Is <code>TRUE</code> if the Tensor is a meta tensor, <code>FALSE</code> otherwise. Meta tensors are like normal tensors, but they carry no data.</p>
 </div>
-<div id="is_pinned" class="section level2">
-<h2 class="hasAnchor">
-<a href="#is_pinned" class="anchor"></a>is_pinned</h2>
+<div class="section level2">
+<h2 id="is_pinned">is_pinned<a class="anchor" aria-label="anchor" href="#is_pinned"></a>
+</h2>
 <p>Returns true if this tensor resides in pinned memory.</p>
 </div>
-<div id="is_quantized" class="section level2">
-<h2 class="hasAnchor">
-<a href="#is_quantized" class="anchor"></a>is_quantized</h2>
+<div class="section level2">
+<h2 id="is_quantized">is_quantized<a class="anchor" aria-label="anchor" href="#is_quantized"></a>
+</h2>
 <p>Is <code>TRUE</code> if the Tensor is quantized, <code>FALSE</code> otherwise.</p>
 </div>
-<div id="is_set_to" class="section level2">
-<h2 class="hasAnchor">
-<a href="#is_set_to" class="anchor"></a>is_set_to</h2>
+<div class="section level2">
+<h2 id="is_set_to">is_set_to<a class="anchor" aria-label="anchor" href="#is_set_to"></a>
+</h2>
 <p>is_set_to(tensor) -&gt; bool</p>
 <p>Returns TRUE if this object refers to the same <code>THTensor</code> object from the Torch C API as the given tensor.</p>
 </div>
-<div id="is_shared" class="section level2">
-<h2 class="hasAnchor">
-<a href="#is_shared" class="anchor"></a>is_shared</h2>
+<div class="section level2">
+<h2 id="is_shared">is_shared<a class="anchor" aria-label="anchor" href="#is_shared"></a>
+</h2>
 <p>Checks if tensor is in shared memory.</p>
 <p>This is always <code>TRUE</code> for CUDA tensors.</p>
 </div>
-<div id="is_signed" class="section level2">
-<h2 class="hasAnchor">
-<a href="#is_signed" class="anchor"></a>is_signed</h2>
+<div class="section level2">
+<h2 id="is_signed">is_signed<a class="anchor" aria-label="anchor" href="#is_signed"></a>
+</h2>
 <p>is_signed() -&gt; bool</p>
 <p>Returns TRUE if the data type of <code>self</code> is a signed data type.</p>
 </div>
-<div id="isclose" class="section level2">
-<h2 class="hasAnchor">
-<a href="#isclose" class="anchor"></a>isclose</h2>
+<div class="section level2">
+<h2 id="isclose">isclose<a class="anchor" aria-label="anchor" href="#isclose"></a>
+</h2>
 <p>isclose(other, rtol=1e-05, atol=1e-08, equal_nan=FALSE) -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_isclose.html">?torch_isclose</a></code></p>
 </div>
-<div id="isfinite" class="section level2">
-<h2 class="hasAnchor">
-<a href="#isfinite" class="anchor"></a>isfinite</h2>
+<div class="section level2">
+<h2 id="isfinite">isfinite<a class="anchor" aria-label="anchor" href="#isfinite"></a>
+</h2>
 <p>isfinite() -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_isfinite.html">?torch_isfinite</a></code></p>
 </div>
-<div id="isinf" class="section level2">
-<h2 class="hasAnchor">
-<a href="#isinf" class="anchor"></a>isinf</h2>
+<div class="section level2">
+<h2 id="isinf">isinf<a class="anchor" aria-label="anchor" href="#isinf"></a>
+</h2>
 <p>isinf() -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_isinf.html">?torch_isinf</a></code></p>
 </div>
-<div id="isnan" class="section level2">
-<h2 class="hasAnchor">
-<a href="#isnan" class="anchor"></a>isnan</h2>
+<div class="section level2">
+<h2 id="isnan">isnan<a class="anchor" aria-label="anchor" href="#isnan"></a>
+</h2>
 <p>isnan() -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_isnan.html">?torch_isnan</a></code></p>
 </div>
-<div id="istft" class="section level2">
-<h2 class="hasAnchor">
-<a href="#istft" class="anchor"></a>istft</h2>
+<div class="section level2">
+<h2 id="istft">istft<a class="anchor" aria-label="anchor" href="#istft"></a>
+</h2>
 <p>See <code><a href="../../reference/torch_istft.html">?torch_istft</a></code> ## item</p>
 <p>item() -&gt; number</p>
 <p>Returns the value of this tensor as a standard Python number. This only works for tensors with one element. For other cases, see <code>$tolist</code>.</p>
 <p>This operation is not differentiable.</p>
-<div id="examples-15" class="section level4">
-<h4 class="hasAnchor">
-<a href="#examples-15" class="anchor"></a>Examples:</h4>
+<div class="section level4">
+<h4 id="examples-15">Examples:<a class="anchor" aria-label="anchor" href="#examples-15"></a>
+</h4>
 <div class="sourceCode" id="cb17"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fl">1.0</span><span class="op">)</span>
 <span class="va">x</span><span class="op">$</span><span class="fu">item</span><span class="op">(</span><span class="op">)</span></code></pre></div>
 </div>
 </div>
-<div id="kthvalue" class="section level2">
-<h2 class="hasAnchor">
-<a href="#kthvalue" class="anchor"></a>kthvalue</h2>
+<div class="section level2">
+<h2 id="kthvalue">kthvalue<a class="anchor" aria-label="anchor" href="#kthvalue"></a>
+</h2>
 <p>kthvalue(k, dim=NULL, keepdim=FALSE) -&gt; (Tensor, LongTensor)</p>
 <p>See <code><a href="../../reference/torch_kthvalue.html">?torch_kthvalue</a></code></p>
 </div>
-<div id="le" class="section level2">
-<h2 class="hasAnchor">
-<a href="#le" class="anchor"></a>le</h2>
+<div class="section level2">
+<h2 id="le">le<a class="anchor" aria-label="anchor" href="#le"></a>
+</h2>
 <p>le(other) -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_le.html">?torch_le</a></code></p>
 </div>
-<div id="le_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#le_" class="anchor"></a>le_</h2>
+<div class="section level2">
+<h2 id="le_">le_<a class="anchor" aria-label="anchor" href="#le_"></a>
+</h2>
 <p>le_(other) -&gt; Tensor</p>
 <p>In-place version of <code>$le</code></p>
 </div>
-<div id="lerp" class="section level2">
-<h2 class="hasAnchor">
-<a href="#lerp" class="anchor"></a>lerp</h2>
+<div class="section level2">
+<h2 id="lerp">lerp<a class="anchor" aria-label="anchor" href="#lerp"></a>
+</h2>
 <p>lerp(end, weight) -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_lerp.html">?torch_lerp</a></code></p>
 </div>
-<div id="lerp_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#lerp_" class="anchor"></a>lerp_</h2>
+<div class="section level2">
+<h2 id="lerp_">lerp_<a class="anchor" aria-label="anchor" href="#lerp_"></a>
+</h2>
 <p>lerp_(end, weight) -&gt; Tensor</p>
 <p>In-place version of <code>$lerp</code></p>
 </div>
-<div id="lgamma" class="section level2">
-<h2 class="hasAnchor">
-<a href="#lgamma" class="anchor"></a>lgamma</h2>
+<div class="section level2">
+<h2 id="lgamma">lgamma<a class="anchor" aria-label="anchor" href="#lgamma"></a>
+</h2>
 <p>lgamma() -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_lgamma.html">?torch_lgamma</a></code></p>
 </div>
-<div id="lgamma_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#lgamma_" class="anchor"></a>lgamma_</h2>
+<div class="section level2">
+<h2 id="lgamma_">lgamma_<a class="anchor" aria-label="anchor" href="#lgamma_"></a>
+</h2>
 <p>lgamma_() -&gt; Tensor</p>
 <p>In-place version of <code>$lgamma</code></p>
 </div>
-<div id="log" class="section level2">
-<h2 class="hasAnchor">
-<a href="#log" class="anchor"></a>log</h2>
+<div class="section level2">
+<h2 id="log">log<a class="anchor" aria-label="anchor" href="#log"></a>
+</h2>
 <p>log() -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_log.html">?torch_log</a></code></p>
 </div>
-<div id="log10" class="section level2">
-<h2 class="hasAnchor">
-<a href="#log10" class="anchor"></a>log10</h2>
+<div class="section level2">
+<h2 id="log10">log10<a class="anchor" aria-label="anchor" href="#log10"></a>
+</h2>
 <p>log10() -&gt; Tensor</p>
 <p>See [torch_log10()]</p>
 </div>
-<div id="log10_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#log10_" class="anchor"></a>log10_</h2>
+<div class="section level2">
+<h2 id="log10_">log10_<a class="anchor" aria-label="anchor" href="#log10_"></a>
+</h2>
 <p>log10_() -&gt; Tensor</p>
 <p>In-place version of <code>$log10</code></p>
 </div>
-<div id="log1p" class="section level2">
-<h2 class="hasAnchor">
-<a href="#log1p" class="anchor"></a>log1p</h2>
+<div class="section level2">
+<h2 id="log1p">log1p<a class="anchor" aria-label="anchor" href="#log1p"></a>
+</h2>
 <p>log1p() -&gt; Tensor</p>
 <p>See [torch_log1p()]</p>
 </div>
-<div id="log1p_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#log1p_" class="anchor"></a>log1p_</h2>
+<div class="section level2">
+<h2 id="log1p_">log1p_<a class="anchor" aria-label="anchor" href="#log1p_"></a>
+</h2>
 <p>log1p_() -&gt; Tensor</p>
 <p>In-place version of <code>$log1p</code></p>
 </div>
-<div id="log2" class="section level2">
-<h2 class="hasAnchor">
-<a href="#log2" class="anchor"></a>log2</h2>
+<div class="section level2">
+<h2 id="log2">log2<a class="anchor" aria-label="anchor" href="#log2"></a>
+</h2>
 <p>log2() -&gt; Tensor</p>
 <p>See [torch_log2()]</p>
 </div>
-<div id="log2_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#log2_" class="anchor"></a>log2_</h2>
+<div class="section level2">
+<h2 id="log2_">log2_<a class="anchor" aria-label="anchor" href="#log2_"></a>
+</h2>
 <p>log2_() -&gt; Tensor</p>
 <p>In-place version of <code>$log2</code></p>
 </div>
-<div id="log_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#log_" class="anchor"></a>log_</h2>
+<div class="section level2">
+<h2 id="log_">log_<a class="anchor" aria-label="anchor" href="#log_"></a>
+</h2>
 <p>log_() -&gt; Tensor</p>
 <p>In-place version of <code>$log</code></p>
 </div>
-<div id="log_normal_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#log_normal_" class="anchor"></a>log_normal_</h2>
+<div class="section level2">
+<h2 id="log_normal_">log_normal_<a class="anchor" aria-label="anchor" href="#log_normal_"></a>
+</h2>
 <p>log_normal_(mean=1, std=2, *, generator=NULL)</p>
 <p>Fills <code>self</code> tensor with numbers samples from the log-normal distribution parameterized by the given mean <code>\mu</code> and standard deviation <code>\sigma</code>. Note that <code>mean</code> and <code>std</code> are the mean and standard deviation of the underlying normal distribution, and not of the returned distribution:</p>
 <p><span class="math display">\[
 f(x) = \dfrac{1}{x \sigma \sqrt{2\pi}}\ e^{-\frac{(\ln x - \mu)^2}{2\sigma^2}}
 \]</span></p>
 </div>
-<div id="logaddexp" class="section level2">
-<h2 class="hasAnchor">
-<a href="#logaddexp" class="anchor"></a>logaddexp</h2>
+<div class="section level2">
+<h2 id="logaddexp">logaddexp<a class="anchor" aria-label="anchor" href="#logaddexp"></a>
+</h2>
 <p>logaddexp(other) -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_logaddexp.html">?torch_logaddexp</a></code></p>
 </div>
-<div id="logaddexp2" class="section level2">
-<h2 class="hasAnchor">
-<a href="#logaddexp2" class="anchor"></a>logaddexp2</h2>
+<div class="section level2">
+<h2 id="logaddexp2">logaddexp2<a class="anchor" aria-label="anchor" href="#logaddexp2"></a>
+</h2>
 <p>logaddexp2(other) -&gt; Tensor</p>
 <p>See [torch_logaddexp2()]</p>
 </div>
-<div id="logcumsumexp" class="section level2">
-<h2 class="hasAnchor">
-<a href="#logcumsumexp" class="anchor"></a>logcumsumexp</h2>
+<div class="section level2">
+<h2 id="logcumsumexp">logcumsumexp<a class="anchor" aria-label="anchor" href="#logcumsumexp"></a>
+</h2>
 <p>logcumsumexp(dim) -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_logcumsumexp.html">?torch_logcumsumexp</a></code></p>
 </div>
-<div id="logdet" class="section level2">
-<h2 class="hasAnchor">
-<a href="#logdet" class="anchor"></a>logdet</h2>
+<div class="section level2">
+<h2 id="logdet">logdet<a class="anchor" aria-label="anchor" href="#logdet"></a>
+</h2>
 <p>logdet() -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_logdet.html">?torch_logdet</a></code></p>
 </div>
-<div id="logical_and" class="section level2">
-<h2 class="hasAnchor">
-<a href="#logical_and" class="anchor"></a>logical_and</h2>
+<div class="section level2">
+<h2 id="logical_and">logical_and<a class="anchor" aria-label="anchor" href="#logical_and"></a>
+</h2>
 <p>logical_and() -&gt; Tensor</p>
 <p>See [torch_logical_and()]</p>
 </div>
-<div id="logical_and_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#logical_and_" class="anchor"></a>logical_and_</h2>
+<div class="section level2">
+<h2 id="logical_and_">logical_and_<a class="anchor" aria-label="anchor" href="#logical_and_"></a>
+</h2>
 <p>logical_and_() -&gt; Tensor</p>
 <p>In-place version of <code>$logical_and</code></p>
 </div>
-<div id="logical_not" class="section level2">
-<h2 class="hasAnchor">
-<a href="#logical_not" class="anchor"></a>logical_not</h2>
+<div class="section level2">
+<h2 id="logical_not">logical_not<a class="anchor" aria-label="anchor" href="#logical_not"></a>
+</h2>
 <p>logical_not() -&gt; Tensor</p>
 <p>See [torch_logical_not()]</p>
 </div>
-<div id="logical_not_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#logical_not_" class="anchor"></a>logical_not_</h2>
+<div class="section level2">
+<h2 id="logical_not_">logical_not_<a class="anchor" aria-label="anchor" href="#logical_not_"></a>
+</h2>
 <p>logical_not_() -&gt; Tensor</p>
 <p>In-place version of <code>$logical_not</code></p>
 </div>
-<div id="logical_or" class="section level2">
-<h2 class="hasAnchor">
-<a href="#logical_or" class="anchor"></a>logical_or</h2>
+<div class="section level2">
+<h2 id="logical_or">logical_or<a class="anchor" aria-label="anchor" href="#logical_or"></a>
+</h2>
 <p>logical_or() -&gt; Tensor</p>
 <p>See [torch_logical_or()]</p>
 </div>
-<div id="logical_or_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#logical_or_" class="anchor"></a>logical_or_</h2>
+<div class="section level2">
+<h2 id="logical_or_">logical_or_<a class="anchor" aria-label="anchor" href="#logical_or_"></a>
+</h2>
 <p>logical_or_() -&gt; Tensor</p>
 <p>In-place version of <code>$logical_or</code></p>
 </div>
-<div id="logical_xor" class="section level2">
-<h2 class="hasAnchor">
-<a href="#logical_xor" class="anchor"></a>logical_xor</h2>
+<div class="section level2">
+<h2 id="logical_xor">logical_xor<a class="anchor" aria-label="anchor" href="#logical_xor"></a>
+</h2>
 <p>logical_xor() -&gt; Tensor</p>
 <p>See [torch_logical_xor()]</p>
 </div>
-<div id="logical_xor_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#logical_xor_" class="anchor"></a>logical_xor_</h2>
+<div class="section level2">
+<h2 id="logical_xor_">logical_xor_<a class="anchor" aria-label="anchor" href="#logical_xor_"></a>
+</h2>
 <p>logical_xor_() -&gt; Tensor</p>
 <p>In-place version of <code>$logical_xor</code></p>
 </div>
-<div id="logsumexp" class="section level2">
-<h2 class="hasAnchor">
-<a href="#logsumexp" class="anchor"></a>logsumexp</h2>
+<div class="section level2">
+<h2 id="logsumexp">logsumexp<a class="anchor" aria-label="anchor" href="#logsumexp"></a>
+</h2>
 <p>logsumexp(dim, keepdim=FALSE) -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_logsumexp.html">?torch_logsumexp</a></code></p>
 </div>
-<div id="long" class="section level2">
-<h2 class="hasAnchor">
-<a href="#long" class="anchor"></a>long</h2>
+<div class="section level2">
+<h2 id="long">long<a class="anchor" aria-label="anchor" href="#long"></a>
+</h2>
 <p>long(memory_format=torch_preserve_format) -&gt; Tensor</p>
 <p><code>self$long()</code> is equivalent to <code>self$to(torch_int64)</code>. See [to()].</p>
-<div id="arguments-25" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-25" class="anchor"></a>Arguments:</h4>
+<div class="section level4">
+<h4 id="arguments-25">Arguments:<a class="anchor" aria-label="anchor" href="#arguments-25"></a>
+</h4>
 <ul>
 <li>memory_format (<code>torch_memory_format</code>, optional): the desired memory format of</li>
 <li>returned Tensor. Default: <code>torch_preserve_format</code>.</li>
 </ul>
 </div>
 </div>
-<div id="lstsq" class="section level2">
-<h2 class="hasAnchor">
-<a href="#lstsq" class="anchor"></a>lstsq</h2>
+<div class="section level2">
+<h2 id="lstsq">lstsq<a class="anchor" aria-label="anchor" href="#lstsq"></a>
+</h2>
 <p>lstsq(A) -&gt; (Tensor, Tensor)</p>
 <p>See <code><a href="../../reference/torch_lstsq.html">?torch_lstsq</a></code></p>
 </div>
-<div id="lt" class="section level2">
-<h2 class="hasAnchor">
-<a href="#lt" class="anchor"></a>lt</h2>
+<div class="section level2">
+<h2 id="lt">lt<a class="anchor" aria-label="anchor" href="#lt"></a>
+</h2>
 <p>lt(other) -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_lt.html">?torch_lt</a></code></p>
 </div>
-<div id="lt_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#lt_" class="anchor"></a>lt_</h2>
+<div class="section level2">
+<h2 id="lt_">lt_<a class="anchor" aria-label="anchor" href="#lt_"></a>
+</h2>
 <p>lt_(other) -&gt; Tensor</p>
 <p>In-place version of <code>$lt</code></p>
 </div>
-<div id="lu" class="section level2">
-<h2 class="hasAnchor">
-<a href="#lu" class="anchor"></a>lu</h2>
+<div class="section level2">
+<h2 id="lu">lu<a class="anchor" aria-label="anchor" href="#lu"></a>
+</h2>
 <p>See <code><a href="../../reference/torch_lu.html">?torch_lu</a></code> ## lu_solve</p>
 <p>lu_solve(LU_data, LU_pivots) -&gt; Tensor</p>
 <p>See [torch_lu_solve()]</p>
 </div>
-<div id="map_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#map_" class="anchor"></a>map_</h2>
+<div class="section level2">
+<h2 id="map_">map_<a class="anchor" aria-label="anchor" href="#map_"></a>
+</h2>
 <p>map_(tensor, callable)</p>
 <p>Applies <code>callable</code> for each element in <code>self</code> tensor and the given <code>tensor</code> and stores the results in <code>self</code> tensor. <code>self</code> tensor and the given <code>tensor</code> must be broadcastable.</p>
 <p>The <code>callable</code> should have the signature:</p>
 <p><code>callable(a, b) -&gt; number</code></p>
 </div>
-<div id="masked_fill" class="section level2">
-<h2 class="hasAnchor">
-<a href="#masked_fill" class="anchor"></a>masked_fill</h2>
+<div class="section level2">
+<h2 id="masked_fill">masked_fill<a class="anchor" aria-label="anchor" href="#masked_fill"></a>
+</h2>
 <p>masked_fill(mask, value) -&gt; Tensor</p>
 <p>Out-of-place version of <code>$masked_fill_</code></p>
 </div>
-<div id="masked_fill_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#masked_fill_" class="anchor"></a>masked_fill_</h2>
+<div class="section level2">
+<h2 id="masked_fill_">masked_fill_<a class="anchor" aria-label="anchor" href="#masked_fill_"></a>
+</h2>
 <p>masked_fill_(mask, value)</p>
 <p>Fills elements of <code>self</code> tensor with <code>value</code> where <code>mask</code> is TRUE. The shape of <code>mask</code> must be <code>broadcastable &lt;broadcasting-semantics&gt;</code> with the shape of the underlying tensor.</p>
-<div id="arguments-26" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-26" class="anchor"></a>Arguments:</h4>
+<div class="section level4">
+<h4 id="arguments-26">Arguments:<a class="anchor" aria-label="anchor" href="#arguments-26"></a>
+</h4>
 <ul>
 <li>mask (BoolTensor): the boolean mask</li>
 <li>value (float): the value to fill in with</li>
 </ul>
 </div>
 </div>
-<div id="masked_scatter" class="section level2">
-<h2 class="hasAnchor">
-<a href="#masked_scatter" class="anchor"></a>masked_scatter</h2>
+<div class="section level2">
+<h2 id="masked_scatter">masked_scatter<a class="anchor" aria-label="anchor" href="#masked_scatter"></a>
+</h2>
 <p>masked_scatter(mask, tensor) -&gt; Tensor</p>
 <p>Out-of-place version of <code>$masked_scatter_</code></p>
 </div>
-<div id="masked_scatter_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#masked_scatter_" class="anchor"></a>masked_scatter_</h2>
+<div class="section level2">
+<h2 id="masked_scatter_">masked_scatter_<a class="anchor" aria-label="anchor" href="#masked_scatter_"></a>
+</h2>
 <p>masked_scatter_(mask, source)</p>
 <p>Copies elements from <code>source</code> into <code>self</code> tensor at positions where the <code>mask</code> is TRUE. The shape of <code>mask</code> must be :ref:<code>broadcastable &lt;broadcasting-semantics&gt;</code> with the shape of the underlying tensor. The <code>source</code> should have at least as many elements as the number of ones in <code>mask</code></p>
-<div id="arguments-27" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-27" class="anchor"></a>Arguments:</h4>
+<div class="section level4">
+<h4 id="arguments-27">Arguments:<a class="anchor" aria-label="anchor" href="#arguments-27"></a>
+</h4>
 <ul>
 <li>mask (BoolTensor): the boolean mask</li>
 <li>source (Tensor): the tensor to copy from</li>
 </ul>
 </div>
-<div id="note-5" class="section level4">
-<h4 class="hasAnchor">
-<a href="#note-5" class="anchor"></a>Note:</h4>
+<div class="section level4">
+<h4 id="note-5">Note:<a class="anchor" aria-label="anchor" href="#note-5"></a>
+</h4>
 <p>The <code>mask</code> operates on the <code>self</code> tensor, not on the given <code>source</code> tensor.</p>
 </div>
 </div>
-<div id="masked_select" class="section level2">
-<h2 class="hasAnchor">
-<a href="#masked_select" class="anchor"></a>masked_select</h2>
+<div class="section level2">
+<h2 id="masked_select">masked_select<a class="anchor" aria-label="anchor" href="#masked_select"></a>
+</h2>
 <p>masked_select(mask) -&gt; Tensor</p>
 <p>See [torch_masked_select()]</p>
 </div>
-<div id="matmul" class="section level2">
-<h2 class="hasAnchor">
-<a href="#matmul" class="anchor"></a>matmul</h2>
+<div class="section level2">
+<h2 id="matmul">matmul<a class="anchor" aria-label="anchor" href="#matmul"></a>
+</h2>
 <p>matmul(tensor2) -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_matmul.html">?torch_matmul</a></code></p>
 </div>
-<div id="matrix_power" class="section level2">
-<h2 class="hasAnchor">
-<a href="#matrix_power" class="anchor"></a>matrix_power</h2>
+<div class="section level2">
+<h2 id="matrix_power">matrix_power<a class="anchor" aria-label="anchor" href="#matrix_power"></a>
+</h2>
 <p>matrix_power(n) -&gt; Tensor</p>
 <p>See [torch_matrix_power()]</p>
 </div>
-<div id="max" class="section level2">
-<h2 class="hasAnchor">
-<a href="#max" class="anchor"></a>max</h2>
+<div class="section level2">
+<h2 id="max">max<a class="anchor" aria-label="anchor" href="#max"></a>
+</h2>
 <p>max(dim=NULL, keepdim=FALSE) -&gt; Tensor or (Tensor, Tensor)</p>
 <p>See <code><a href="../../reference/torch_max.html">?torch_max</a></code></p>
 </div>
-<div id="mean" class="section level2">
-<h2 class="hasAnchor">
-<a href="#mean" class="anchor"></a>mean</h2>
+<div class="section level2">
+<h2 id="mean">mean<a class="anchor" aria-label="anchor" href="#mean"></a>
+</h2>
 <p>mean(dim=NULL, keepdim=FALSE) -&gt; Tensor or (Tensor, Tensor)</p>
 <p>See <code><a href="../../reference/torch_mean.html">?torch_mean</a></code></p>
 </div>
-<div id="median" class="section level2">
-<h2 class="hasAnchor">
-<a href="#median" class="anchor"></a>median</h2>
+<div class="section level2">
+<h2 id="median">median<a class="anchor" aria-label="anchor" href="#median"></a>
+</h2>
 <p>median(dim=NULL, keepdim=FALSE) -&gt; (Tensor, LongTensor)</p>
 <p>See <code><a href="../../reference/torch_median.html">?torch_median</a></code></p>
 </div>
-<div id="min" class="section level2">
-<h2 class="hasAnchor">
-<a href="#min" class="anchor"></a>min</h2>
+<div class="section level2">
+<h2 id="min">min<a class="anchor" aria-label="anchor" href="#min"></a>
+</h2>
 <p>min(dim=NULL, keepdim=FALSE) -&gt; Tensor or (Tensor, Tensor)</p>
 <p>See <code><a href="../../reference/torch_min.html">?torch_min</a></code></p>
 </div>
-<div id="mm" class="section level2">
-<h2 class="hasAnchor">
-<a href="#mm" class="anchor"></a>mm</h2>
+<div class="section level2">
+<h2 id="mm">mm<a class="anchor" aria-label="anchor" href="#mm"></a>
+</h2>
 <p>mm(mat2) -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_mm.html">?torch_mm</a></code></p>
 </div>
-<div id="mode" class="section level2">
-<h2 class="hasAnchor">
-<a href="#mode" class="anchor"></a>mode</h2>
+<div class="section level2">
+<h2 id="mode">mode<a class="anchor" aria-label="anchor" href="#mode"></a>
+</h2>
 <p>mode(dim=NULL, keepdim=FALSE) -&gt; (Tensor, LongTensor)</p>
 <p>See <code><a href="../../reference/torch_mode.html">?torch_mode</a></code></p>
 </div>
-<div id="mul" class="section level2">
-<h2 class="hasAnchor">
-<a href="#mul" class="anchor"></a>mul</h2>
+<div class="section level2">
+<h2 id="mul">mul<a class="anchor" aria-label="anchor" href="#mul"></a>
+</h2>
 <p>mul(value) -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_mul.html">?torch_mul</a></code></p>
 </div>
-<div id="mul_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#mul_" class="anchor"></a>mul_</h2>
+<div class="section level2">
+<h2 id="mul_">mul_<a class="anchor" aria-label="anchor" href="#mul_"></a>
+</h2>
 <p>mul_(value)</p>
 <p>In-place version of <code>$mul</code></p>
 </div>
-<div id="multinomial" class="section level2">
-<h2 class="hasAnchor">
-<a href="#multinomial" class="anchor"></a>multinomial</h2>
+<div class="section level2">
+<h2 id="multinomial">multinomial<a class="anchor" aria-label="anchor" href="#multinomial"></a>
+</h2>
 <p>multinomial(num_samples, replacement=FALSE, *, generator=NULL) -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_multinomial.html">?torch_multinomial</a></code></p>
 </div>
-<div id="mv" class="section level2">
-<h2 class="hasAnchor">
-<a href="#mv" class="anchor"></a>mv</h2>
+<div class="section level2">
+<h2 id="mv">mv<a class="anchor" aria-label="anchor" href="#mv"></a>
+</h2>
 <p>mv(vec) -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_mv.html">?torch_mv</a></code></p>
 </div>
-<div id="mvlgamma" class="section level2">
-<h2 class="hasAnchor">
-<a href="#mvlgamma" class="anchor"></a>mvlgamma</h2>
+<div class="section level2">
+<h2 id="mvlgamma">mvlgamma<a class="anchor" aria-label="anchor" href="#mvlgamma"></a>
+</h2>
 <p>mvlgamma(p) -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_mvlgamma.html">?torch_mvlgamma</a></code></p>
 </div>
-<div id="mvlgamma_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#mvlgamma_" class="anchor"></a>mvlgamma_</h2>
+<div class="section level2">
+<h2 id="mvlgamma_">mvlgamma_<a class="anchor" aria-label="anchor" href="#mvlgamma_"></a>
+</h2>
 <p>mvlgamma_(p) -&gt; Tensor</p>
 <p>In-place version of <code>$mvlgamma</code></p>
 </div>
-<div id="names" class="section level2">
-<h2 class="hasAnchor">
-<a href="#names" class="anchor"></a>names</h2>
+<div class="section level2">
+<h2 id="names">names<a class="anchor" aria-label="anchor" href="#names"></a>
+</h2>
 <p>Stores names for each of this tensor’s dimensions.</p>
 <p><code>names[idx]</code> corresponds to the name of tensor dimension <code>idx</code>. Names are either a string if the dimension is named or <code>NULL</code> if the dimension is unnamed.</p>
 <p>Dimension names may contain characters or underscore. Furthermore, a dimension name must be a valid Python variable name (i.e., does not start with underscore).</p>
 <p>Tensors may not have two named dimensions with the same name.</p>
-<div id="warning-4" class="section level4">
-<h4 class="hasAnchor">
-<a href="#warning-4" class="anchor"></a>Warning:</h4>
+<div class="section level4">
+<h4 id="warning-4">Warning:<a class="anchor" aria-label="anchor" href="#warning-4"></a>
+</h4>
 <p>The named tensor API is experimental and subject to change.</p>
 </div>
 </div>
-<div id="narrow" class="section level2">
-<h2 class="hasAnchor">
-<a href="#narrow" class="anchor"></a>narrow</h2>
+<div class="section level2">
+<h2 id="narrow">narrow<a class="anchor" aria-label="anchor" href="#narrow"></a>
+</h2>
 <p>narrow(dimension, start, length) -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_narrow.html">?torch_narrow</a></code></p>
-<div id="examples-16" class="section level4">
-<h4 class="hasAnchor">
-<a href="#examples-16" class="anchor"></a>Examples:</h4>
+<div class="section level4">
+<h4 id="examples-16">Examples:<a class="anchor" aria-label="anchor" href="#examples-16"></a>
+</h4>
 <div class="sourceCode" id="cb18"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/matrix.html">matrix</a></span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="fl">9</span>, ncol <span class="op">=</span> <span class="fl">3</span><span class="op">)</span><span class="op">)</span>
+<code class="sourceCode R"><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/matrix.html" class="external-link">matrix</a></span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="fl">9</span>, ncol <span class="op">=</span> <span class="fl">3</span><span class="op">)</span><span class="op">)</span>
 <span class="va">x</span><span class="op">$</span><span class="fu">narrow</span><span class="op">(</span><span class="fl">1</span>, <span class="fl">1</span>, <span class="fl">3</span><span class="op">)</span>
 <span class="va">x</span><span class="op">$</span><span class="fu">narrow</span><span class="op">(</span><span class="fl">1</span>, <span class="fl">1</span>, <span class="fl">2</span><span class="op">)</span></code></pre></div>
 </div>
 </div>
-<div id="narrow_copy" class="section level2">
-<h2 class="hasAnchor">
-<a href="#narrow_copy" class="anchor"></a>narrow_copy</h2>
+<div class="section level2">
+<h2 id="narrow_copy">narrow_copy<a class="anchor" aria-label="anchor" href="#narrow_copy"></a>
+</h2>
 <p>narrow_copy(dimension, start, length) -&gt; Tensor</p>
 <p>Same as <code>Tensor.narrow</code> except returning a copy rather than shared storage. This is primarily for sparse tensors, which do not have a shared-storage narrow method. Calling <code>narrow_copy` with</code>dimemsion &gt; self<span class="math inline">\(sparse_dim()`` will return a copy with the relevant dense dimension narrowed, and ``self\)</span>shape`` updated accordingly.</p>
 </div>
-<div id="ndim" class="section level2">
-<h2 class="hasAnchor">
-<a href="#ndim" class="anchor"></a>ndim</h2>
+<div class="section level2">
+<h2 id="ndim">ndim<a class="anchor" aria-label="anchor" href="#ndim"></a>
+</h2>
 <p>Alias for <code>$dim()</code></p>
 </div>
-<div id="ndimension" class="section level2">
-<h2 class="hasAnchor">
-<a href="#ndimension" class="anchor"></a>ndimension</h2>
+<div class="section level2">
+<h2 id="ndimension">ndimension<a class="anchor" aria-label="anchor" href="#ndimension"></a>
+</h2>
 <p>ndimension() -&gt; int</p>
 <p>Alias for <code>$dim()</code></p>
 </div>
-<div id="ne" class="section level2">
-<h2 class="hasAnchor">
-<a href="#ne" class="anchor"></a>ne</h2>
+<div class="section level2">
+<h2 id="ne">ne<a class="anchor" aria-label="anchor" href="#ne"></a>
+</h2>
 <p>ne(other) -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_ne.html">?torch_ne</a></code></p>
 </div>
-<div id="ne_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#ne_" class="anchor"></a>ne_</h2>
+<div class="section level2">
+<h2 id="ne_">ne_<a class="anchor" aria-label="anchor" href="#ne_"></a>
+</h2>
 <p>ne_(other) -&gt; Tensor</p>
 <p>In-place version of <code>$ne</code></p>
 </div>
-<div id="neg" class="section level2">
-<h2 class="hasAnchor">
-<a href="#neg" class="anchor"></a>neg</h2>
+<div class="section level2">
+<h2 id="neg">neg<a class="anchor" aria-label="anchor" href="#neg"></a>
+</h2>
 <p>neg() -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_neg.html">?torch_neg</a></code></p>
 </div>
-<div id="neg_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#neg_" class="anchor"></a>neg_</h2>
+<div class="section level2">
+<h2 id="neg_">neg_<a class="anchor" aria-label="anchor" href="#neg_"></a>
+</h2>
 <p>neg_() -&gt; Tensor</p>
 <p>In-place version of <code>$neg</code></p>
 </div>
-<div id="nelement" class="section level2">
-<h2 class="hasAnchor">
-<a href="#nelement" class="anchor"></a>nelement</h2>
+<div class="section level2">
+<h2 id="nelement">nelement<a class="anchor" aria-label="anchor" href="#nelement"></a>
+</h2>
 <p>nelement() -&gt; int</p>
 <p>Alias for <code>$numel</code></p>
 </div>
-<div id="new_empty" class="section level2">
-<h2 class="hasAnchor">
-<a href="#new_empty" class="anchor"></a>new_empty</h2>
+<div class="section level2">
+<h2 id="new_empty">new_empty<a class="anchor" aria-label="anchor" href="#new_empty"></a>
+</h2>
 <p>new_empty(size, dtype=NULL, device=NULL, requires_grad=FALSE) -&gt; Tensor</p>
 <p>Returns a Tensor of size <code>size</code> filled with uninitialized data. By default, the returned Tensor has the same <code>torch_dtype</code> and <code>torch_device</code> as this tensor.</p>
-<div id="arguments-28" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-28" class="anchor"></a>Arguments:</h4>
+<div class="section level4">
+<h4 id="arguments-28">Arguments:<a class="anchor" aria-label="anchor" href="#arguments-28"></a>
+</h4>
 <ul>
 <li>dtype (<code>torch_dtype</code>, optional): the desired type of returned tensor. Default: if NULL, same <code>torch_dtype</code> as this tensor.</li>
 <li>device (<code>torch_device</code>, optional): the desired device of returned tensor. Default: if NULL, same <code>torch_device</code> as this tensor.</li>
@@ -2192,22 +2153,22 @@ f(x) = \dfrac{1}{x \sigma \sqrt{2\pi}}\ e^{-\frac{(\ln x - \mu)^2}{2\sigma^2}}
 <li>returned tensor. Default: <code>FALSE</code>.</li>
 </ul>
 </div>
-<div id="examples-17" class="section level4">
-<h4 class="hasAnchor">
-<a href="#examples-17" class="anchor"></a>Examples:</h4>
+<div class="section level4">
+<h4 id="examples-17">Examples:<a class="anchor" aria-label="anchor" href="#examples-17"></a>
+</h4>
 <div class="sourceCode" id="cb19"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span class="va">tensor</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_ones.html">torch_ones</a></span><span class="op">(</span><span class="fl">5</span><span class="op">)</span>
-<span class="va">tensor</span><span class="op">$</span><span class="fu">new_empty</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></code></pre></div>
+<span class="va">tensor</span><span class="op">$</span><span class="fu">new_empty</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></code></pre></div>
 </div>
 </div>
-<div id="new_full" class="section level2">
-<h2 class="hasAnchor">
-<a href="#new_full" class="anchor"></a>new_full</h2>
+<div class="section level2">
+<h2 id="new_full">new_full<a class="anchor" aria-label="anchor" href="#new_full"></a>
+</h2>
 <p>new_full(size, fill_value, dtype=NULL, device=NULL, requires_grad=FALSE) -&gt; Tensor</p>
 <p>Returns a Tensor of size <code>size</code> filled with <code>fill_value</code>. By default, the returned Tensor has the same <code>torch_dtype</code> and <code>torch_device</code> as this tensor.</p>
-<div id="arguments-29" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-29" class="anchor"></a>Arguments:</h4>
+<div class="section level4">
+<h4 id="arguments-29">Arguments:<a class="anchor" aria-label="anchor" href="#arguments-29"></a>
+</h4>
 <ul>
 <li>fill_value (scalar): the number to fill the output tensor with.</li>
 <li>dtype (<code>torch_dtype</code>, optional): the desired type of returned tensor. Default: if NULL, same <code>torch_dtype</code> as this tensor.</li>
@@ -2216,22 +2177,22 @@ f(x) = \dfrac{1}{x \sigma \sqrt{2\pi}}\ e^{-\frac{(\ln x - \mu)^2}{2\sigma^2}}
 <li>returned tensor. Default: <code>FALSE</code>.</li>
 </ul>
 </div>
-<div id="examples-18" class="section level4">
-<h4 class="hasAnchor">
-<a href="#examples-18" class="anchor"></a>Examples:</h4>
+<div class="section level4">
+<h4 id="examples-18">Examples:<a class="anchor" aria-label="anchor" href="#examples-18"></a>
+</h4>
 <div class="sourceCode" id="cb20"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="va">tensor</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_ones.html">torch_ones</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">2</span><span class="op">)</span>, dtype<span class="op">=</span><span class="fu"><a href="../../reference/torch_dtype.html">torch_float64</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span>
-<span class="va">tensor</span><span class="op">$</span><span class="fu">new_full</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">4</span><span class="op">)</span>, <span class="fl">3.141592</span><span class="op">)</span></code></pre></div>
+<code class="sourceCode R"><span class="va">tensor</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_ones.html">torch_ones</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span><span class="op">)</span>, dtype<span class="op">=</span><span class="fu"><a href="../../reference/torch_dtype.html">torch_float64</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span>
+<span class="va">tensor</span><span class="op">$</span><span class="fu">new_full</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">4</span><span class="op">)</span>, <span class="fl">3.141592</span><span class="op">)</span></code></pre></div>
 </div>
 </div>
-<div id="new_ones" class="section level2">
-<h2 class="hasAnchor">
-<a href="#new_ones" class="anchor"></a>new_ones</h2>
+<div class="section level2">
+<h2 id="new_ones">new_ones<a class="anchor" aria-label="anchor" href="#new_ones"></a>
+</h2>
 <p>new_ones(size, dtype=NULL, device=NULL, requires_grad=FALSE) -&gt; Tensor</p>
 <p>Returns a Tensor of size <code>size</code> filled with <code>1</code>. By default, the returned Tensor has the same <code>torch_dtype</code> and <code>torch_device</code> as this tensor.</p>
-<div id="arguments-30" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-30" class="anchor"></a>Arguments:</h4>
+<div class="section level4">
+<h4 id="arguments-30">Arguments:<a class="anchor" aria-label="anchor" href="#arguments-30"></a>
+</h4>
 <ul>
 <li>size (int…): a list, tuple, or <code>torch_Size</code> of integers defining the</li>
 <li>shape of the output tensor.</li>
@@ -2241,28 +2202,28 @@ f(x) = \dfrac{1}{x \sigma \sqrt{2\pi}}\ e^{-\frac{(\ln x - \mu)^2}{2\sigma^2}}
 <li>returned tensor. Default: <code>FALSE</code>.</li>
 </ul>
 </div>
-<div id="examples-19" class="section level4">
-<h4 class="hasAnchor">
-<a href="#examples-19" class="anchor"></a>Examples:</h4>
+<div class="section level4">
+<h4 id="examples-19">Examples:<a class="anchor" aria-label="anchor" href="#examples-19"></a>
+</h4>
 <div class="sourceCode" id="cb21"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="va">tensor</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">2</span><span class="op">)</span>, dtype<span class="op">=</span><span class="fu"><a href="../../reference/torch_dtype.html">torch_int32</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span>
-<span class="va">tensor</span><span class="op">$</span><span class="fu">new_ones</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></code></pre></div>
+<code class="sourceCode R"><span class="va">tensor</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span><span class="op">)</span>, dtype<span class="op">=</span><span class="fu"><a href="../../reference/torch_dtype.html">torch_int32</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span>
+<span class="va">tensor</span><span class="op">$</span><span class="fu">new_ones</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></code></pre></div>
 </div>
 </div>
-<div id="new_tensor" class="section level2">
-<h2 class="hasAnchor">
-<a href="#new_tensor" class="anchor"></a>new_tensor</h2>
+<div class="section level2">
+<h2 id="new_tensor">new_tensor<a class="anchor" aria-label="anchor" href="#new_tensor"></a>
+</h2>
 <p>new_tensor(data, dtype=NULL, device=NULL, requires_grad=FALSE) -&gt; Tensor</p>
 <p>Returns a new Tensor with <code>data</code> as the tensor data. By default, the returned Tensor has the same <code>torch_dtype</code> and <code>torch_device</code> as this tensor.</p>
-<div id="warning-5" class="section level4">
-<h4 class="hasAnchor">
-<a href="#warning-5" class="anchor"></a>Warning:</h4>
+<div class="section level4">
+<h4 id="warning-5">Warning:<a class="anchor" aria-label="anchor" href="#warning-5"></a>
+</h4>
 <p><code>new_tensor</code> always copies <code>data(). If you have a Tensor</code>data` and want to avoid a copy, use [$requires_grad_()] or [$detach()]. If you have a numpy array and want to avoid a copy, use [torch_from_numpy()].</p>
-<p>When data is a tensor <code>x</code>, [new_tensor()()] reads out ‘the data’ from whatever it is passed, and constructs a leaf variable. Therefore <code>tensor$new_tensor(x)</code> is equivalent to <code>x$clone()$detach()</code> and <code>tensor$new_tensor(x, requires_grad=TRUE)</code> is equivalent to <code>x$clone()$detach()$requires_grad_(TRUE)</code>. The equivalents using <code>clone()</code> and <code><a href="https://rdrr.io/r/base/detach.html">detach()</a></code> are recommended.</p>
+<p>When data is a tensor <code>x</code>, [new_tensor()()] reads out ‘the data’ from whatever it is passed, and constructs a leaf variable. Therefore <code>tensor$new_tensor(x)</code> is equivalent to <code>x$clone()$detach()</code> and <code>tensor$new_tensor(x, requires_grad=TRUE)</code> is equivalent to <code>x$clone()$detach()$requires_grad_(TRUE)</code>. The equivalents using <code>clone()</code> and <code><a href="https://rdrr.io/r/base/detach.html" class="external-link">detach()</a></code> are recommended.</p>
 </div>
-<div id="arguments-31" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-31" class="anchor"></a>Arguments:</h4>
+<div class="section level4">
+<h4 id="arguments-31">Arguments:<a class="anchor" aria-label="anchor" href="#arguments-31"></a>
+</h4>
 <ul>
 <li>data (array_like): The returned Tensor copies <code>data</code>.</li>
 <li>dtype (<code>torch_dtype</code>, optional): the desired type of returned tensor. Default: if NULL, same <code>torch_dtype</code> as this tensor.</li>
@@ -2271,23 +2232,23 @@ f(x) = \dfrac{1}{x \sigma \sqrt{2\pi}}\ e^{-\frac{(\ln x - \mu)^2}{2\sigma^2}}
 <li>returned tensor. Default: <code>FALSE</code>.</li>
 </ul>
 </div>
-<div id="examples-20" class="section level4">
-<h4 class="hasAnchor">
-<a href="#examples-20" class="anchor"></a>Examples:</h4>
+<div class="section level4">
+<h4 id="examples-20">Examples:<a class="anchor" aria-label="anchor" href="#examples-20"></a>
+</h4>
 <div class="sourceCode" id="cb22"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="va">tensor</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_ones.html">torch_ones</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">2</span><span class="op">)</span>, dtype<span class="op">=</span><span class="va">torch_int8</span><span class="op">)</span>
-<span class="va">data</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/matrix.html">matrix</a></span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="fl">4</span>, ncol <span class="op">=</span> <span class="fl">2</span><span class="op">)</span>
+<code class="sourceCode R"><span class="va">tensor</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_ones.html">torch_ones</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span><span class="op">)</span>, dtype<span class="op">=</span><span class="va">torch_int8</span><span class="op">)</span>
+<span class="va">data</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/matrix.html" class="external-link">matrix</a></span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="fl">4</span>, ncol <span class="op">=</span> <span class="fl">2</span><span class="op">)</span>
 <span class="va">tensor</span><span class="op">$</span><span class="fu">new_tensor</span><span class="op">(</span><span class="va">data</span><span class="op">)</span></code></pre></div>
 </div>
 </div>
-<div id="new_zeros" class="section level2">
-<h2 class="hasAnchor">
-<a href="#new_zeros" class="anchor"></a>new_zeros</h2>
+<div class="section level2">
+<h2 id="new_zeros">new_zeros<a class="anchor" aria-label="anchor" href="#new_zeros"></a>
+</h2>
 <p>new_zeros(size, dtype=NULL, device=NULL, requires_grad=FALSE) -&gt; Tensor</p>
 <p>Returns a Tensor of size <code>size</code> filled with <code>0</code>. By default, the returned Tensor has the same <code>torch_dtype</code> and <code>torch_device</code> as this tensor.</p>
-<div id="arguments-32" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-32" class="anchor"></a>Arguments:</h4>
+<div class="section level4">
+<h4 id="arguments-32">Arguments:<a class="anchor" aria-label="anchor" href="#arguments-32"></a>
+</h4>
 <ul>
 <li>size (int…): a list, tuple, or <code>torch_Size</code> of integers defining the</li>
 <li>shape of the output tensor.</li>
@@ -2297,384 +2258,384 @@ f(x) = \dfrac{1}{x \sigma \sqrt{2\pi}}\ e^{-\frac{(\ln x - \mu)^2}{2\sigma^2}}
 <li>returned tensor. Default: <code>FALSE</code>.</li>
 </ul>
 </div>
-<div id="examples-21" class="section level4">
-<h4 class="hasAnchor">
-<a href="#examples-21" class="anchor"></a>Examples:</h4>
+<div class="section level4">
+<h4 id="examples-21">Examples:<a class="anchor" aria-label="anchor" href="#examples-21"></a>
+</h4>
 <div class="sourceCode" id="cb23"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="va">tensor</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">1</span><span class="op">)</span>, dtype<span class="op">=</span><span class="fu"><a href="../../reference/torch_dtype.html">torch_float64</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span>
-<span class="va">tensor</span><span class="op">$</span><span class="fu">new_zeros</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></code></pre></div>
+<code class="sourceCode R"><span class="va">tensor</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span><span class="op">)</span>, dtype<span class="op">=</span><span class="fu"><a href="../../reference/torch_dtype.html">torch_float64</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span>
+<span class="va">tensor</span><span class="op">$</span><span class="fu">new_zeros</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></code></pre></div>
 </div>
 </div>
-<div id="nonzero" class="section level2">
-<h2 class="hasAnchor">
-<a href="#nonzero" class="anchor"></a>nonzero</h2>
+<div class="section level2">
+<h2 id="nonzero">nonzero<a class="anchor" aria-label="anchor" href="#nonzero"></a>
+</h2>
 <p>nonzero() -&gt; LongTensor</p>
 <p>See <code><a href="../../reference/torch_nonzero.html">?torch_nonzero</a></code></p>
 </div>
-<div id="norm" class="section level2">
-<h2 class="hasAnchor">
-<a href="#norm" class="anchor"></a>norm</h2>
+<div class="section level2">
+<h2 id="norm">norm<a class="anchor" aria-label="anchor" href="#norm"></a>
+</h2>
 <p>See <code><a href="../../reference/torch_norm.html">?torch_norm</a></code> ## normal_</p>
 <p>normal_(mean=0, std=1, *, generator=NULL) -&gt; Tensor</p>
 <p>Fills <code>self</code> tensor with elements samples from the normal distribution parameterized by <code>mean</code> and <code>std</code>.</p>
 </div>
-<div id="numel" class="section level2">
-<h2 class="hasAnchor">
-<a href="#numel" class="anchor"></a>numel</h2>
+<div class="section level2">
+<h2 id="numel">numel<a class="anchor" aria-label="anchor" href="#numel"></a>
+</h2>
 <p>numel() -&gt; int</p>
 <p>See <code>?torch_numel</code></p>
 </div>
-<div id="numpy" class="section level2">
-<h2 class="hasAnchor">
-<a href="#numpy" class="anchor"></a>numpy</h2>
+<div class="section level2">
+<h2 id="numpy">numpy<a class="anchor" aria-label="anchor" href="#numpy"></a>
+</h2>
 <p>numpy() -&gt; numpy.ndarray</p>
 <p>Returns <code>self</code> tensor as a NumPy :class:<code>ndarray</code>. This tensor and the returned <code>ndarray</code> share the same underlying storage. Changes to <code>self</code> tensor will be reflected in the :class:<code>ndarray</code> and vice versa.</p>
 </div>
-<div id="orgqr" class="section level2">
-<h2 class="hasAnchor">
-<a href="#orgqr" class="anchor"></a>orgqr</h2>
+<div class="section level2">
+<h2 id="orgqr">orgqr<a class="anchor" aria-label="anchor" href="#orgqr"></a>
+</h2>
 <p>orgqr(input2) -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_orgqr.html">?torch_orgqr</a></code></p>
 </div>
-<div id="ormqr" class="section level2">
-<h2 class="hasAnchor">
-<a href="#ormqr" class="anchor"></a>ormqr</h2>
+<div class="section level2">
+<h2 id="ormqr">ormqr<a class="anchor" aria-label="anchor" href="#ormqr"></a>
+</h2>
 <p>ormqr(input2, input3, left=TRUE, transpose=FALSE) -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_ormqr.html">?torch_ormqr</a></code></p>
 </div>
-<div id="permute" class="section level2">
-<h2 class="hasAnchor">
-<a href="#permute" class="anchor"></a>permute</h2>
+<div class="section level2">
+<h2 id="permute">permute<a class="anchor" aria-label="anchor" href="#permute"></a>
+</h2>
 <p>permute(*dims) -&gt; Tensor</p>
 <p>Returns a view of the original tensor with its dimensions permuted.</p>
-<div id="arguments-33" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-33" class="anchor"></a>Arguments:</h4>
+<div class="section level4">
+<h4 id="arguments-33">Arguments:<a class="anchor" aria-label="anchor" href="#arguments-33"></a>
+</h4>
 <ul>
 <li>dims (int…): The desired ordering of dimensions</li>
 </ul>
 </div>
-<div id="examples-22" class="section level4">
-<h4 class="hasAnchor">
-<a href="#examples-22" class="anchor"></a>Examples:</h4>
+<div class="section level4">
+<h4 id="examples-22">Examples:<a class="anchor" aria-label="anchor" href="#examples-22"></a>
+</h4>
 <div class="sourceCode" id="cb24"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">3</span>, <span class="fl">5</span><span class="op">)</span>
 <span class="va">x</span><span class="op">$</span><span class="fu">size</span><span class="op">(</span><span class="op">)</span>
-<span class="va">x</span><span class="op">$</span><span class="fu">permute</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">1</span>, <span class="fl">2</span><span class="op">)</span><span class="op">)</span><span class="op">$</span><span class="fu">size</span><span class="op">(</span><span class="op">)</span></code></pre></div>
+<span class="va">x</span><span class="op">$</span><span class="fu">permute</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">1</span>, <span class="fl">2</span><span class="op">)</span><span class="op">)</span><span class="op">$</span><span class="fu">size</span><span class="op">(</span><span class="op">)</span></code></pre></div>
 </div>
 </div>
-<div id="pin_memory" class="section level2">
-<h2 class="hasAnchor">
-<a href="#pin_memory" class="anchor"></a>pin_memory</h2>
+<div class="section level2">
+<h2 id="pin_memory">pin_memory<a class="anchor" aria-label="anchor" href="#pin_memory"></a>
+</h2>
 <p>pin_memory() -&gt; Tensor</p>
 <p>Copies the tensor to pinned memory, if it’s not already pinned.</p>
 </div>
-<div id="pinverse" class="section level2">
-<h2 class="hasAnchor">
-<a href="#pinverse" class="anchor"></a>pinverse</h2>
+<div class="section level2">
+<h2 id="pinverse">pinverse<a class="anchor" aria-label="anchor" href="#pinverse"></a>
+</h2>
 <p>pinverse() -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_pinverse.html">?torch_pinverse</a></code></p>
 </div>
-<div id="polygamma" class="section level2">
-<h2 class="hasAnchor">
-<a href="#polygamma" class="anchor"></a>polygamma</h2>
+<div class="section level2">
+<h2 id="polygamma">polygamma<a class="anchor" aria-label="anchor" href="#polygamma"></a>
+</h2>
 <p>polygamma(n) -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_polygamma.html">?torch_polygamma</a></code></p>
 </div>
-<div id="polygamma_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#polygamma_" class="anchor"></a>polygamma_</h2>
+<div class="section level2">
+<h2 id="polygamma_">polygamma_<a class="anchor" aria-label="anchor" href="#polygamma_"></a>
+</h2>
 <p>polygamma_(n) -&gt; Tensor</p>
 <p>In-place version of <code>$polygamma</code></p>
 </div>
-<div id="pow" class="section level2">
-<h2 class="hasAnchor">
-<a href="#pow" class="anchor"></a>pow</h2>
+<div class="section level2">
+<h2 id="pow">pow<a class="anchor" aria-label="anchor" href="#pow"></a>
+</h2>
 <p>pow(exponent) -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_pow.html">?torch_pow</a></code></p>
 </div>
-<div id="pow_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#pow_" class="anchor"></a>pow_</h2>
+<div class="section level2">
+<h2 id="pow_">pow_<a class="anchor" aria-label="anchor" href="#pow_"></a>
+</h2>
 <p>pow_(exponent) -&gt; Tensor</p>
 <p>In-place version of <code>$pow</code></p>
 </div>
-<div id="prod" class="section level2">
-<h2 class="hasAnchor">
-<a href="#prod" class="anchor"></a>prod</h2>
+<div class="section level2">
+<h2 id="prod">prod<a class="anchor" aria-label="anchor" href="#prod"></a>
+</h2>
 <p>prod(dim=NULL, keepdim=FALSE, dtype=NULL) -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_prod.html">?torch_prod</a></code></p>
 </div>
-<div id="put_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#put_" class="anchor"></a>put_</h2>
+<div class="section level2">
+<h2 id="put_">put_<a class="anchor" aria-label="anchor" href="#put_"></a>
+</h2>
 <p>put_(indices, tensor, accumulate=FALSE) -&gt; Tensor</p>
 <p>Copies the elements from <code>tensor</code> into the positions specified by indices. For the purpose of indexing, the <code>self</code> tensor is treated as if it were a 1-D tensor.</p>
 <p>If <code>accumulate</code> is <code>TRUE</code>, the elements in <code>tensor</code> are added to <code>self</code>. If accumulate is <code>FALSE</code>, the behavior is undefined if indices contain duplicate elements.</p>
-<div id="arguments-34" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-34" class="anchor"></a>Arguments:</h4>
+<div class="section level4">
+<h4 id="arguments-34">Arguments:<a class="anchor" aria-label="anchor" href="#arguments-34"></a>
+</h4>
 <ul>
 <li>indices (LongTensor): the indices into self</li>
 <li>tensor (Tensor): the tensor containing values to copy from</li>
 <li>accumulate (bool): whether to accumulate into self</li>
 </ul>
 </div>
-<div id="examples-23" class="section level4">
-<h4 class="hasAnchor">
-<a href="#examples-23" class="anchor"></a>Examples:</h4>
+<div class="section level4">
+<h4 id="examples-23">Examples:<a class="anchor" aria-label="anchor" href="#examples-23"></a>
+</h4>
 <div class="sourceCode" id="cb25"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="va">src</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/matrix.html">matrix</a></span><span class="op">(</span><span class="fl">3</span><span class="op">:</span><span class="fl">8</span>, ncol <span class="op">=</span> <span class="fl">3</span><span class="op">)</span><span class="op">)</span>
+<code class="sourceCode R"><span class="va">src</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/matrix.html" class="external-link">matrix</a></span><span class="op">(</span><span class="fl">3</span><span class="op">:</span><span class="fl">8</span>, ncol <span class="op">=</span> <span class="fl">3</span><span class="op">)</span><span class="op">)</span>
 <span class="va">src</span><span class="op">$</span><span class="fu">put_</span><span class="op">(</span><span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="fl">2</span><span class="op">)</span>, <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fl">9</span><span class="op">:</span><span class="fl">10</span><span class="op">)</span><span class="op">)</span></code></pre></div>
 </div>
 </div>
-<div id="q_per_channel_axis" class="section level2">
-<h2 class="hasAnchor">
-<a href="#q_per_channel_axis" class="anchor"></a>q_per_channel_axis</h2>
+<div class="section level2">
+<h2 id="q_per_channel_axis">q_per_channel_axis<a class="anchor" aria-label="anchor" href="#q_per_channel_axis"></a>
+</h2>
 <p>q_per_channel_axis() -&gt; int</p>
 <p>Given a Tensor quantized by linear (affine) per-channel quantization, returns the index of dimension on which per-channel quantization is applied.</p>
 </div>
-<div id="q_per_channel_scales" class="section level2">
-<h2 class="hasAnchor">
-<a href="#q_per_channel_scales" class="anchor"></a>q_per_channel_scales</h2>
+<div class="section level2">
+<h2 id="q_per_channel_scales">q_per_channel_scales<a class="anchor" aria-label="anchor" href="#q_per_channel_scales"></a>
+</h2>
 <p>q_per_channel_scales() -&gt; Tensor</p>
 <p>Given a Tensor quantized by linear (affine) per-channel quantization, returns a Tensor of scales of the underlying quantizer. It has the number of elements that matches the corresponding dimensions (from q_per_channel_axis) of the tensor.</p>
 </div>
-<div id="q_per_channel_zero_points" class="section level2">
-<h2 class="hasAnchor">
-<a href="#q_per_channel_zero_points" class="anchor"></a>q_per_channel_zero_points</h2>
+<div class="section level2">
+<h2 id="q_per_channel_zero_points">q_per_channel_zero_points<a class="anchor" aria-label="anchor" href="#q_per_channel_zero_points"></a>
+</h2>
 <p>q_per_channel_zero_points() -&gt; Tensor</p>
 <p>Given a Tensor quantized by linear (affine) per-channel quantization, returns a tensor of zero_points of the underlying quantizer. It has the number of elements that matches the corresponding dimensions (from q_per_channel_axis) of the tensor.</p>
 </div>
-<div id="q_scale" class="section level2">
-<h2 class="hasAnchor">
-<a href="#q_scale" class="anchor"></a>q_scale</h2>
+<div class="section level2">
+<h2 id="q_scale">q_scale<a class="anchor" aria-label="anchor" href="#q_scale"></a>
+</h2>
 <p>q_scale() -&gt; float</p>
 <p>Given a Tensor quantized by linear(affine) quantization, returns the scale of the underlying quantizer().</p>
 </div>
-<div id="q_zero_point" class="section level2">
-<h2 class="hasAnchor">
-<a href="#q_zero_point" class="anchor"></a>q_zero_point</h2>
+<div class="section level2">
+<h2 id="q_zero_point">q_zero_point<a class="anchor" aria-label="anchor" href="#q_zero_point"></a>
+</h2>
 <p>q_zero_point() -&gt; int</p>
 <p>Given a Tensor quantized by linear(affine) quantization, returns the zero_point of the underlying quantizer().</p>
 </div>
-<div id="qr" class="section level2">
-<h2 class="hasAnchor">
-<a href="#qr" class="anchor"></a>qr</h2>
+<div class="section level2">
+<h2 id="qr">qr<a class="anchor" aria-label="anchor" href="#qr"></a>
+</h2>
 <p>qr(some=TRUE) -&gt; (Tensor, Tensor)</p>
 <p>See <code><a href="../../reference/torch_qr.html">?torch_qr</a></code></p>
 </div>
-<div id="qscheme" class="section level2">
-<h2 class="hasAnchor">
-<a href="#qscheme" class="anchor"></a>qscheme</h2>
+<div class="section level2">
+<h2 id="qscheme">qscheme<a class="anchor" aria-label="anchor" href="#qscheme"></a>
+</h2>
 <p>qscheme() -&gt; torch_qscheme</p>
 <p>Returns the quantization scheme of a given QTensor.</p>
 </div>
-<div id="rad2deg" class="section level2">
-<h2 class="hasAnchor">
-<a href="#rad2deg" class="anchor"></a>rad2deg</h2>
+<div class="section level2">
+<h2 id="rad2deg">rad2deg<a class="anchor" aria-label="anchor" href="#rad2deg"></a>
+</h2>
 <p>rad2deg() -&gt; Tensor</p>
 <p>See [torch_rad2deg()]</p>
 </div>
-<div id="rad2deg_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#rad2deg_" class="anchor"></a>rad2deg_</h2>
+<div class="section level2">
+<h2 id="rad2deg_">rad2deg_<a class="anchor" aria-label="anchor" href="#rad2deg_"></a>
+</h2>
 <p>rad2deg_() -&gt; Tensor</p>
 <p>In-place version of <code>$rad2deg</code></p>
 </div>
-<div id="random_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#random_" class="anchor"></a>random_</h2>
+<div class="section level2">
+<h2 id="random_">random_<a class="anchor" aria-label="anchor" href="#random_"></a>
+</h2>
 <p>random_(from=0, to=NULL, *, generator=NULL) -&gt; Tensor</p>
 <p>Fills <code>self</code> tensor with numbers sampled from the discrete uniform distribution over <code>[from, to - 1]</code>. If not specified, the values are usually only bounded by <code>self</code> tensor’s data type. However, for floating point types, if unspecified, range will be <code>[0, 2^mantissa]</code> to ensure that every value is representable. For example, <code>torch_tensor(1, dtype=torch_double).random_()</code> will be uniform in <code>[0, 2^53]</code>.</p>
 </div>
-<div id="real" class="section level2">
-<h2 class="hasAnchor">
-<a href="#real" class="anchor"></a>real</h2>
+<div class="section level2">
+<h2 id="real">real<a class="anchor" aria-label="anchor" href="#real"></a>
+</h2>
 <p>Returns a new tensor containing real values of the <code>self</code> tensor. The returned tensor and <code>self</code> share the same underlying storage.</p>
-<div id="warning-6" class="section level4">
-<h4 class="hasAnchor">
-<a href="#warning-6" class="anchor"></a>Warning:</h4>
+<div class="section level4">
+<h4 id="warning-6">Warning:<a class="anchor" aria-label="anchor" href="#warning-6"></a>
+</h4>
 <p>[real()] is only supported for tensors with complex dtypes.</p>
 </div>
-<div id="examples-24" class="section level4">
-<h4 class="hasAnchor">
-<a href="#examples-24" class="anchor"></a>Examples:</h4>
+<div class="section level4">
+<h4 id="examples-24">Examples:<a class="anchor" aria-label="anchor" href="#examples-24"></a>
+</h4>
 <div class="sourceCode" id="cb26"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">4</span>, dtype<span class="op">=</span><span class="fu">torch_cfloat</span><span class="op">(</span><span class="op">)</span><span class="op">)</span>
 <span class="va">x</span>
 <span class="va">x</span><span class="op">$</span><span class="va">real</span></code></pre></div>
 </div>
 </div>
-<div id="reciprocal" class="section level2">
-<h2 class="hasAnchor">
-<a href="#reciprocal" class="anchor"></a>reciprocal</h2>
+<div class="section level2">
+<h2 id="reciprocal">reciprocal<a class="anchor" aria-label="anchor" href="#reciprocal"></a>
+</h2>
 <p>reciprocal() -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_reciprocal.html">?torch_reciprocal</a></code></p>
 </div>
-<div id="reciprocal_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#reciprocal_" class="anchor"></a>reciprocal_</h2>
+<div class="section level2">
+<h2 id="reciprocal_">reciprocal_<a class="anchor" aria-label="anchor" href="#reciprocal_"></a>
+</h2>
 <p>reciprocal_() -&gt; Tensor</p>
 <p>In-place version of <code>$reciprocal</code></p>
 </div>
-<div id="record_stream" class="section level2">
-<h2 class="hasAnchor">
-<a href="#record_stream" class="anchor"></a>record_stream</h2>
+<div class="section level2">
+<h2 id="record_stream">record_stream<a class="anchor" aria-label="anchor" href="#record_stream"></a>
+</h2>
 <p>record_stream(stream)</p>
 <p>Ensures that the tensor memory is not reused for another tensor until all current work queued on <code>stream</code> are complete.</p>
-<div id="note-6" class="section level4">
-<h4 class="hasAnchor">
-<a href="#note-6" class="anchor"></a>Note:</h4>
+<div class="section level4">
+<h4 id="note-6">Note:<a class="anchor" aria-label="anchor" href="#note-6"></a>
+</h4>
 <p>The caching allocator is aware of only the stream where a tensor was allocated. Due to the awareness, it already correctly manages the life cycle of tensors on only one stream. But if a tensor is used on a stream different from the stream of origin, the allocator might reuse the memory unexpectedly. Calling this method lets the allocator know which streams have used the tensor.</p>
 </div>
 </div>
-<div id="refine_names" class="section level2">
-<h2 class="hasAnchor">
-<a href="#refine_names" class="anchor"></a>refine_names</h2>
+<div class="section level2">
+<h2 id="refine_names">refine_names<a class="anchor" aria-label="anchor" href="#refine_names"></a>
+</h2>
 <p>Refines the dimension names of <code>self</code> according to <code>names</code>.</p>
 <p>Refining is a special case of renaming that “lifts” unnamed dimensions. A <code>NULL</code> dim can be refined to have any name; a named dim can only be refined to have the same name.</p>
 <p>Because named tensors can coexist with unnamed tensors, refining names gives a nice way to write named-tensor-aware code that works with both named and unnamed tensors.</p>
 <p><code>names</code> may contain up to one Ellipsis (<code>...</code>). The Ellipsis is expanded greedily; it is expanded in-place to fill <code>names</code> to the same length as <code>self$dim()</code> using names from the corresponding indices of <code>self$names</code>.</p>
-<div id="arguments-35" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-35" class="anchor"></a>Arguments:</h4>
+<div class="section level4">
+<h4 id="arguments-35">Arguments:<a class="anchor" aria-label="anchor" href="#arguments-35"></a>
+</h4>
 <ul>
 <li>names (iterable of str): The desired names of the output tensor. May contain up to one Ellipsis.</li>
 </ul>
 </div>
-<div id="examples-25" class="section level4">
-<h4 class="hasAnchor">
-<a href="#examples-25" class="anchor"></a>Examples:</h4>
+<div class="section level4">
+<h4 id="examples-25">Examples:<a class="anchor" aria-label="anchor" href="#examples-25"></a>
+</h4>
 <div class="sourceCode" id="cb27"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span class="va">imgs</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">32</span>, <span class="fl">3</span>, <span class="fl">128</span>, <span class="fl">128</span><span class="op">)</span>
-<span class="va">named_imgs</span> <span class="op">&lt;-</span> <span class="va">imgs</span><span class="op">$</span><span class="fu">refine_names</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="st">'N'</span>, <span class="st">'C'</span>, <span class="st">'H'</span>, <span class="st">'W'</span><span class="op">)</span><span class="op">)</span>
+<span class="va">named_imgs</span> <span class="op">&lt;-</span> <span class="va">imgs</span><span class="op">$</span><span class="fu">refine_names</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">'N'</span>, <span class="st">'C'</span>, <span class="st">'H'</span>, <span class="st">'W'</span><span class="op">)</span><span class="op">)</span>
 <span class="va">named_imgs</span><span class="op">$</span><span class="va">names</span></code></pre></div>
 </div>
 </div>
-<div id="register_hook" class="section level2">
-<h2 class="hasAnchor">
-<a href="#register_hook" class="anchor"></a>register_hook</h2>
+<div class="section level2">
+<h2 id="register_hook">register_hook<a class="anchor" aria-label="anchor" href="#register_hook"></a>
+</h2>
 <p>Registers a backward hook.</p>
 <p>The hook will be called every time a gradient with respect to the Tensor is computed. The hook should have the following signature::</p>
 <p>hook(grad) -&gt; Tensor or NULL</p>
 <p>The hook should not modify its argument, but it can optionally return a new gradient which will be used in place of <code>grad</code>.</p>
 <p>This function returns a handle with a method <code>handle$remove()</code> that removes the hook from the module.</p>
-<div id="example" class="section level4">
-<h4 class="hasAnchor">
-<a href="#example" class="anchor"></a>Example</h4>
+<div class="section level4">
+<h4 id="example">Example<a class="anchor" aria-label="anchor" href="#example"></a>
+</h4>
 <div class="sourceCode" id="cb28"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="va">v</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0.</span>, <span class="fl">0.</span>, <span class="fl">0.</span><span class="op">)</span>, requires_grad<span class="op">=</span><span class="cn">TRUE</span><span class="op">)</span>
+<code class="sourceCode R"><span class="va">v</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.</span>, <span class="fl">0.</span>, <span class="fl">0.</span><span class="op">)</span>, requires_grad<span class="op">=</span><span class="cn">TRUE</span><span class="op">)</span>
 <span class="va">h</span> <span class="op">&lt;-</span> <span class="va">v</span><span class="op">$</span><span class="fu">register_hook</span><span class="op">(</span><span class="kw">function</span><span class="op">(</span><span class="va">grad</span><span class="op">)</span> <span class="va">grad</span> <span class="op">*</span> <span class="fl">2</span><span class="op">)</span>  <span class="co"># double the gradient</span>
-<span class="va">v</span><span class="op">$</span><span class="fu">backward</span><span class="op">(</span><span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">1.</span>, <span class="fl">2.</span>, <span class="fl">3.</span><span class="op">)</span><span class="op">)</span><span class="op">)</span>
+<span class="va">v</span><span class="op">$</span><span class="fu">backward</span><span class="op">(</span><span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1.</span>, <span class="fl">2.</span>, <span class="fl">3.</span><span class="op">)</span><span class="op">)</span><span class="op">)</span>
 <span class="va">v</span><span class="op">$</span><span class="va">grad</span>
 <span class="va">h</span><span class="op">$</span><span class="fu">remove</span><span class="op">(</span><span class="op">)</span></code></pre></div>
 </div>
 </div>
-<div id="remainder" class="section level2">
-<h2 class="hasAnchor">
-<a href="#remainder" class="anchor"></a>remainder</h2>
+<div class="section level2">
+<h2 id="remainder">remainder<a class="anchor" aria-label="anchor" href="#remainder"></a>
+</h2>
 <p>remainder(divisor) -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_remainder.html">?torch_remainder</a></code></p>
 </div>
-<div id="remainder_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#remainder_" class="anchor"></a>remainder_</h2>
+<div class="section level2">
+<h2 id="remainder_">remainder_<a class="anchor" aria-label="anchor" href="#remainder_"></a>
+</h2>
 <p>remainder_(divisor) -&gt; Tensor</p>
 <p>In-place version of <code>$remainder</code></p>
 </div>
-<div id="rename" class="section level2">
-<h2 class="hasAnchor">
-<a href="#rename" class="anchor"></a>rename</h2>
+<div class="section level2">
+<h2 id="rename">rename<a class="anchor" aria-label="anchor" href="#rename"></a>
+</h2>
 <p>Renames dimension names of <code>self</code>.</p>
 <p>There are two main usages:</p>
 <p><code>self$rename(**rename_map)</code> returns a view on tensor that has dims renamed as specified in the mapping <code>rename_map</code>.</p>
 <p><code>self$rename(*names)</code> returns a view on tensor, renaming all dimensions positionally using <code>names</code>. Use <code>self$rename(NULL)</code> to drop names on a tensor.</p>
 <p>One cannot specify both positional args <code>names</code> and keyword args <code>rename_map</code>.</p>
-<div id="examples-26" class="section level4">
-<h4 class="hasAnchor">
-<a href="#examples-26" class="anchor"></a>Examples:</h4>
+<div class="section level4">
+<h4 id="examples-26">Examples:<a class="anchor" aria-label="anchor" href="#examples-26"></a>
+</h4>
 <div class="sourceCode" id="cb29"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="va">imgs</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_rand.html">torch_rand</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">3</span>, <span class="fl">5</span>, <span class="fl">7</span>, names<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="st">'N'</span>, <span class="st">'C'</span>, <span class="st">'H'</span>, <span class="st">'W'</span><span class="op">)</span><span class="op">)</span>
-<span class="va">renamed_imgs</span> <span class="op">&lt;-</span> <span class="va">imgs</span><span class="op">$</span><span class="fu">rename</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="st">"Batch"</span>, <span class="st">"Channels"</span>, <span class="st">"Height"</span>, <span class="st">"Width"</span><span class="op">)</span><span class="op">)</span></code></pre></div>
+<code class="sourceCode R"><span class="va">imgs</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_rand.html">torch_rand</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">3</span>, <span class="fl">5</span>, <span class="fl">7</span>, names<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">'N'</span>, <span class="st">'C'</span>, <span class="st">'H'</span>, <span class="st">'W'</span><span class="op">)</span><span class="op">)</span>
+<span class="va">renamed_imgs</span> <span class="op">&lt;-</span> <span class="va">imgs</span><span class="op">$</span><span class="fu">rename</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"Batch"</span>, <span class="st">"Channels"</span>, <span class="st">"Height"</span>, <span class="st">"Width"</span><span class="op">)</span><span class="op">)</span></code></pre></div>
 </div>
 </div>
-<div id="rename_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#rename_" class="anchor"></a>rename_</h2>
+<div class="section level2">
+<h2 id="rename_">rename_<a class="anchor" aria-label="anchor" href="#rename_"></a>
+</h2>
 <p>In-place version of <code>$rename</code>.</p>
 </div>
-<div id="renorm" class="section level2">
-<h2 class="hasAnchor">
-<a href="#renorm" class="anchor"></a>renorm</h2>
+<div class="section level2">
+<h2 id="renorm">renorm<a class="anchor" aria-label="anchor" href="#renorm"></a>
+</h2>
 <p>renorm(p, dim, maxnorm) -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_renorm.html">?torch_renorm</a></code></p>
 </div>
-<div id="renorm_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#renorm_" class="anchor"></a>renorm_</h2>
+<div class="section level2">
+<h2 id="renorm_">renorm_<a class="anchor" aria-label="anchor" href="#renorm_"></a>
+</h2>
 <p>renorm_(p, dim, maxnorm) -&gt; Tensor</p>
 <p>In-place version of <code>$renorm</code></p>
 </div>
-<div id="repeat" class="section level2">
-<h2 class="hasAnchor">
-<a href="#repeat" class="anchor"></a>repeat</h2>
+<div class="section level2">
+<h2 id="repeat">repeat<a class="anchor" aria-label="anchor" href="#repeat"></a>
+</h2>
 <p>repeat(*sizes) -&gt; Tensor</p>
 <p>Repeats this tensor along the specified dimensions.</p>
 <p>Unlike <code>$expand</code>, this function copies the tensor’s data.</p>
-<div id="arguments-36" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-36" class="anchor"></a>Arguments:</h4>
+<div class="section level4">
+<h4 id="arguments-36">Arguments:<a class="anchor" aria-label="anchor" href="#arguments-36"></a>
+</h4>
 <ul>
 <li>sizes (torch_Size or int…): The number of times to repeat this tensor along each</li>
 <li>dimension</li>
 </ul>
 </div>
-<div id="examples-27" class="section level4">
-<h4 class="hasAnchor">
-<a href="#examples-27" class="anchor"></a>Examples:</h4>
+<div class="section level4">
+<h4 id="examples-27">Examples:<a class="anchor" aria-label="anchor" href="#examples-27"></a>
+</h4>
 <div class="sourceCode" id="cb30"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">2</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span>
-<span class="va">x</span><span class="op">$</span><span class="fu">`repeat`</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">4</span>, <span class="fl">2</span><span class="op">)</span><span class="op">)</span>
-<span class="va">x</span><span class="op">$</span><span class="fu">`repeat`</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">4</span>, <span class="fl">2</span>, <span class="fl">1</span><span class="op">)</span><span class="op">)</span><span class="op">$</span><span class="fu">size</span><span class="op">(</span><span class="op">)</span></code></pre></div>
+<code class="sourceCode R"><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">2</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span>
+<span class="va">x</span><span class="op">$</span><span class="fu">`repeat`</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span>, <span class="fl">2</span><span class="op">)</span><span class="op">)</span>
+<span class="va">x</span><span class="op">$</span><span class="fu">`repeat`</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span>, <span class="fl">2</span>, <span class="fl">1</span><span class="op">)</span><span class="op">)</span><span class="op">$</span><span class="fu">size</span><span class="op">(</span><span class="op">)</span></code></pre></div>
 </div>
 </div>
-<div id="repeat_interleave" class="section level2">
-<h2 class="hasAnchor">
-<a href="#repeat_interleave" class="anchor"></a>repeat_interleave</h2>
+<div class="section level2">
+<h2 id="repeat_interleave">repeat_interleave<a class="anchor" aria-label="anchor" href="#repeat_interleave"></a>
+</h2>
 <p>repeat_interleave(repeats, dim=NULL) -&gt; Tensor</p>
 <p>See [torch_repeat_interleave()].</p>
 </div>
-<div id="requires_grad" class="section level2">
-<h2 class="hasAnchor">
-<a href="#requires_grad" class="anchor"></a>requires_grad</h2>
+<div class="section level2">
+<h2 id="requires_grad">requires_grad<a class="anchor" aria-label="anchor" href="#requires_grad"></a>
+</h2>
 <p>Is <code>TRUE</code> if gradients need to be computed for this Tensor, <code>FALSE</code> otherwise.</p>
-<div id="note-7" class="section level4">
-<h4 class="hasAnchor">
-<a href="#note-7" class="anchor"></a>Note:</h4>
+<div class="section level4">
+<h4 id="note-7">Note:<a class="anchor" aria-label="anchor" href="#note-7"></a>
+</h4>
 <p>The fact that gradients need to be computed for a Tensor do not mean that the <code>grad</code> attribute will be populated, see <code>is_leaf</code> for more details.</p>
 </div>
 </div>
-<div id="requires_grad_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#requires_grad_" class="anchor"></a>requires_grad_</h2>
+<div class="section level2">
+<h2 id="requires_grad_">requires_grad_<a class="anchor" aria-label="anchor" href="#requires_grad_"></a>
+</h2>
 <p>requires_grad_(requires_grad=TRUE) -&gt; Tensor</p>
 <p>Change if autograd should record operations on this tensor: sets this tensor’s <code>requires_grad</code> attribute in-place. Returns this tensor.</p>
 <p>[requires_grad_()]’s main use case is to tell autograd to begin recording operations on a Tensor <code>tensor</code>. If <code>tensor</code> has <code>requires_grad=FALSE</code> (because it was obtained through a DataLoader, or required preprocessing or initialization), <code>tensor.requires_grad_()</code> makes it so that autograd will begin to record operations on <code>tensor</code>.</p>
-<div id="arguments-37" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-37" class="anchor"></a>Arguments:</h4>
+<div class="section level4">
+<h4 id="arguments-37">Arguments:<a class="anchor" aria-label="anchor" href="#arguments-37"></a>
+</h4>
 <ul>
 <li>requires_grad (bool): If autograd should record operations on this tensor. Default: <code>TRUE</code>.</li>
 </ul>
 </div>
-<div id="examples-28" class="section level4">
-<h4 class="hasAnchor">
-<a href="#examples-28" class="anchor"></a>Examples:</h4>
+<div class="section level4">
+<h4 id="examples-28">Examples:<a class="anchor" aria-label="anchor" href="#examples-28"></a>
+</h4>
 <div class="sourceCode" id="cb31"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span class="co"># Let's say we want to preprocess some saved weights and use</span>
 <span class="co"># the result as new weights.</span>
-<span class="va">saved_weights</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0.1</span>, <span class="fl">0.2</span>, <span class="fl">0.3</span>, <span class="fl">0.25</span><span class="op">)</span>
+<span class="va">saved_weights</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.1</span>, <span class="fl">0.2</span>, <span class="fl">0.3</span>, <span class="fl">0.25</span><span class="op">)</span>
 <span class="va">loaded_weights</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="va">saved_weights</span><span class="op">)</span>
 <span class="va">weights</span> <span class="op">&lt;-</span> <span class="fu">preprocess</span><span class="op">(</span><span class="va">loaded_weights</span><span class="op">)</span>  <span class="co"># some function</span>
 <span class="va">weights</span>
@@ -2686,142 +2647,142 @@ f(x) = \dfrac{1}{x \sigma \sqrt{2\pi}}\ e^{-\frac{(\ln x - \mu)^2}{2\sigma^2}}
 <span class="va">weights</span><span class="op">$</span><span class="va">grad</span></code></pre></div>
 </div>
 </div>
-<div id="reshape" class="section level2">
-<h2 class="hasAnchor">
-<a href="#reshape" class="anchor"></a>reshape</h2>
+<div class="section level2">
+<h2 id="reshape">reshape<a class="anchor" aria-label="anchor" href="#reshape"></a>
+</h2>
 <p>reshape(*shape) -&gt; Tensor</p>
 <p>Returns a tensor with the same data and number of elements as <code>self</code> but with the specified shape. This method returns a view if <code>shape</code> is compatible with the current shape. See <code>$view</code> on when it is possible to return a view.</p>
 <p>See <code><a href="../../reference/torch_reshape.html">?torch_reshape</a></code></p>
-<div id="arguments-38" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-38" class="anchor"></a>Arguments:</h4>
+<div class="section level4">
+<h4 id="arguments-38">Arguments:<a class="anchor" aria-label="anchor" href="#arguments-38"></a>
+</h4>
 <ul>
 <li>shape (tuple of ints or int…): the desired shape</li>
 </ul>
 </div>
 </div>
-<div id="reshape_as" class="section level2">
-<h2 class="hasAnchor">
-<a href="#reshape_as" class="anchor"></a>reshape_as</h2>
+<div class="section level2">
+<h2 id="reshape_as">reshape_as<a class="anchor" aria-label="anchor" href="#reshape_as"></a>
+</h2>
 <p>reshape_as(other) -&gt; Tensor</p>
 <p>Returns this tensor as the same shape as <code>other</code>. <code>self$reshape_as(other)</code> is equivalent to <code>self$reshape(other.sizes())</code>. This method returns a view if <code>other.sizes()</code> is compatible with the current shape. See <code>$view</code> on when it is possible to return a view.</p>
 <p>Please see <code>reshape</code> for more information about <code>reshape</code>.</p>
-<div id="arguments-39" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-39" class="anchor"></a>Arguments:</h4>
+<div class="section level4">
+<h4 id="arguments-39">Arguments:<a class="anchor" aria-label="anchor" href="#arguments-39"></a>
+</h4>
 <ul>
 <li>other (`$): The result tensor has the same shape</li>
 <li>as <code>other</code>.</li>
 </ul>
 </div>
 </div>
-<div id="resize_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#resize_" class="anchor"></a>resize_</h2>
+<div class="section level2">
+<h2 id="resize_">resize_<a class="anchor" aria-label="anchor" href="#resize_"></a>
+</h2>
 <p>resize_(*sizes, memory_format=torch_contiguous_format) -&gt; Tensor</p>
 <p>Resizes <code>self</code> tensor to the specified size. If the number of elements is larger than the current storage size, then the underlying storage is resized to fit the new number of elements. If the number of elements is smaller, the underlying storage is not changed. Existing elements are preserved but any new memory is uninitialized.</p>
-<div id="warning-7" class="section level4">
-<h4 class="hasAnchor">
-<a href="#warning-7" class="anchor"></a>Warning:</h4>
+<div class="section level4">
+<h4 id="warning-7">Warning:<a class="anchor" aria-label="anchor" href="#warning-7"></a>
+</h4>
 <p>This is a low-level method. The storage is reinterpreted as C-contiguous, ignoring the current strides (unless the target size equals the current size, in which case the tensor is left unchanged). For most purposes, you will instead want to use <code>$view()</code>, which checks for contiguity, or <code>$reshape()</code>, which copies data if needed. To change the size in-place with custom strides, see <code>$set_()</code>.</p>
 </div>
-<div id="arguments-40" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-40" class="anchor"></a>Arguments:</h4>
+<div class="section level4">
+<h4 id="arguments-40">Arguments:<a class="anchor" aria-label="anchor" href="#arguments-40"></a>
+</h4>
 <ul>
 <li>sizes (torch_Size or int…): the desired size</li>
 <li>memory_format (<code>torch_memory_format</code>, optional): the desired memory format of Tensor. Default: <code>torch_contiguous_format</code>. Note that memory format of <code>self</code> is going to be unaffected if <code>self$size()</code> matches <code>sizes</code>.</li>
 </ul>
 </div>
-<div id="examples-29" class="section level4">
-<h4 class="hasAnchor">
-<a href="#examples-29" class="anchor"></a>Examples:</h4>
+<div class="section level4">
+<h4 id="examples-29">Examples:<a class="anchor" aria-label="anchor" href="#examples-29"></a>
+</h4>
 <div class="sourceCode" id="cb32"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/matrix.html">matrix</a></span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="fl">6</span>, ncol <span class="op">=</span> <span class="fl">2</span><span class="op">)</span><span class="op">)</span>
-<span class="va">x</span><span class="op">$</span><span class="fu">resize_</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">2</span><span class="op">)</span><span class="op">)</span></code></pre></div>
+<code class="sourceCode R"><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/matrix.html" class="external-link">matrix</a></span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="fl">6</span>, ncol <span class="op">=</span> <span class="fl">2</span><span class="op">)</span><span class="op">)</span>
+<span class="va">x</span><span class="op">$</span><span class="fu">resize_</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">2</span><span class="op">)</span><span class="op">)</span></code></pre></div>
 </div>
 </div>
-<div id="resize_as_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#resize_as_" class="anchor"></a>resize_as_</h2>
+<div class="section level2">
+<h2 id="resize_as_">resize_as_<a class="anchor" aria-label="anchor" href="#resize_as_"></a>
+</h2>
 <p>resize_as_(tensor, memory_format=torch_contiguous_format) -&gt; Tensor</p>
 <p>Resizes the <code>self</code> tensor to be the same size as the specified <code>tensor</code>. This is equivalent to <code>self$resize_(tensor.size())</code>.</p>
-<div id="arguments-41" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-41" class="anchor"></a>Arguments:</h4>
+<div class="section level4">
+<h4 id="arguments-41">Arguments:<a class="anchor" aria-label="anchor" href="#arguments-41"></a>
+</h4>
 <ul>
 <li>memory_format (<code>torch_memory_format</code>, optional): the desired memory format of Tensor. Default: <code>torch_contiguous_format</code>. Note that memory format of <code>self</code> is going to be unaffected if <code>self$size()</code> matches <code>tensor.size()</code>.</li>
 </ul>
 </div>
 </div>
-<div id="retain_grad" class="section level2">
-<h2 class="hasAnchor">
-<a href="#retain_grad" class="anchor"></a>retain_grad</h2>
+<div class="section level2">
+<h2 id="retain_grad">retain_grad<a class="anchor" aria-label="anchor" href="#retain_grad"></a>
+</h2>
 <p>Enables <code>$grad</code> attribute for non-leaf Tensors.</p>
 </div>
-<div id="rfft" class="section level2">
-<h2 class="hasAnchor">
-<a href="#rfft" class="anchor"></a>rfft</h2>
+<div class="section level2">
+<h2 id="rfft">rfft<a class="anchor" aria-label="anchor" href="#rfft"></a>
+</h2>
 <p>rfft(signal_ndim, normalized=FALSE, onesided=TRUE) -&gt; Tensor</p>
 <p>See <code>?torch_rfft</code></p>
 </div>
-<div id="roll" class="section level2">
-<h2 class="hasAnchor">
-<a href="#roll" class="anchor"></a>roll</h2>
+<div class="section level2">
+<h2 id="roll">roll<a class="anchor" aria-label="anchor" href="#roll"></a>
+</h2>
 <p>roll(shifts, dims) -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_roll.html">?torch_roll</a></code></p>
 </div>
-<div id="rot90" class="section level2">
-<h2 class="hasAnchor">
-<a href="#rot90" class="anchor"></a>rot90</h2>
+<div class="section level2">
+<h2 id="rot90">rot90<a class="anchor" aria-label="anchor" href="#rot90"></a>
+</h2>
 <p>rot90(k, dims) -&gt; Tensor</p>
 <p>See [torch_rot90()]</p>
 </div>
-<div id="round" class="section level2">
-<h2 class="hasAnchor">
-<a href="#round" class="anchor"></a>round</h2>
+<div class="section level2">
+<h2 id="round">round<a class="anchor" aria-label="anchor" href="#round"></a>
+</h2>
 <p>round() -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_round.html">?torch_round</a></code></p>
 </div>
-<div id="round_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#round_" class="anchor"></a>round_</h2>
+<div class="section level2">
+<h2 id="round_">round_<a class="anchor" aria-label="anchor" href="#round_"></a>
+</h2>
 <p>round_() -&gt; Tensor</p>
 <p>In-place version of <code>$round</code></p>
 </div>
-<div id="rsqrt" class="section level2">
-<h2 class="hasAnchor">
-<a href="#rsqrt" class="anchor"></a>rsqrt</h2>
+<div class="section level2">
+<h2 id="rsqrt">rsqrt<a class="anchor" aria-label="anchor" href="#rsqrt"></a>
+</h2>
 <p>rsqrt() -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_rsqrt.html">?torch_rsqrt</a></code></p>
 </div>
-<div id="rsqrt_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#rsqrt_" class="anchor"></a>rsqrt_</h2>
+<div class="section level2">
+<h2 id="rsqrt_">rsqrt_<a class="anchor" aria-label="anchor" href="#rsqrt_"></a>
+</h2>
 <p>rsqrt_() -&gt; Tensor</p>
 <p>In-place version of <code>$rsqrt</code></p>
 </div>
-<div id="scatter" class="section level2">
-<h2 class="hasAnchor">
-<a href="#scatter" class="anchor"></a>scatter</h2>
+<div class="section level2">
+<h2 id="scatter">scatter<a class="anchor" aria-label="anchor" href="#scatter"></a>
+</h2>
 <p>scatter(dim, index, src) -&gt; Tensor</p>
 <p>Out-of-place version of <code>$scatter_</code></p>
 </div>
-<div id="scatter_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#scatter_" class="anchor"></a>scatter_</h2>
+<div class="section level2">
+<h2 id="scatter_">scatter_<a class="anchor" aria-label="anchor" href="#scatter_"></a>
+</h2>
 <p>scatter_(dim, index, src) -&gt; Tensor</p>
 <p>Writes all values from the tensor <code>src</code> into <code>self</code> at the indices specified in the <code>index</code> tensor. For each value in <code>src</code>, its output index is specified by its index in <code>src</code> for <code>dimension != dim</code> and by the corresponding value in <code>index</code> for <code>dimension = dim</code>.</p>
 <p>For a 3-D tensor, <code>self</code> is updated as:</p>
-<pre><code>self[index[i][j][k]][j][k] = src[i][j][k]  # if dim == 0
-self[i][index[i][j][k]][k] = src[i][j][k]  # if dim == 1
-self[i][j][index[i][j][k]] = src[i][j][k]  # if dim == 2</code></pre>
+<pre><code><span class="va">self</span><span class="op">[</span><span class="va">index</span><span class="op">[</span><span class="va">i</span><span class="op">]</span><span class="op">[</span><span class="va">j</span><span class="op">]</span><span class="op">[</span><span class="va">k</span><span class="op">]</span><span class="op">]</span><span class="op">[</span><span class="va">j</span><span class="op">]</span><span class="op">[</span><span class="va">k</span><span class="op">]</span> <span class="op">=</span> <span class="va">src</span><span class="op">[</span><span class="va">i</span><span class="op">]</span><span class="op">[</span><span class="va">j</span><span class="op">]</span><span class="op">[</span><span class="va">k</span><span class="op">]</span>  <span class="co"># if dim == 0</span>
+<span class="va">self</span><span class="op">[</span><span class="va">i</span><span class="op">]</span><span class="op">[</span><span class="va">index</span><span class="op">[</span><span class="va">i</span><span class="op">]</span><span class="op">[</span><span class="va">j</span><span class="op">]</span><span class="op">[</span><span class="va">k</span><span class="op">]</span><span class="op">]</span><span class="op">[</span><span class="va">k</span><span class="op">]</span> <span class="op">=</span> <span class="va">src</span><span class="op">[</span><span class="va">i</span><span class="op">]</span><span class="op">[</span><span class="va">j</span><span class="op">]</span><span class="op">[</span><span class="va">k</span><span class="op">]</span>  <span class="co"># if dim == 1</span>
+<span class="va">self</span><span class="op">[</span><span class="va">i</span><span class="op">]</span><span class="op">[</span><span class="va">j</span><span class="op">]</span><span class="op">[</span><span class="va">index</span><span class="op">[</span><span class="va">i</span><span class="op">]</span><span class="op">[</span><span class="va">j</span><span class="op">]</span><span class="op">[</span><span class="va">k</span><span class="op">]</span><span class="op">]</span> <span class="op">=</span> <span class="va">src</span><span class="op">[</span><span class="va">i</span><span class="op">]</span><span class="op">[</span><span class="va">j</span><span class="op">]</span><span class="op">[</span><span class="va">k</span><span class="op">]</span>  <span class="co"># if dim == 2</span></code></pre>
 <p>This is the reverse operation of the manner described in <code>$gather</code>.</p>
 <p><code>self</code>, <code>index</code> and <code>src</code> (if it is a Tensor) should have same number of dimensions. It is also required that <code>index.size(d) &lt;= src.size(d)</code> for all dimensions <code>d</code>, and that <code>index.size(d) &lt;= self$size(d)</code> for all dimensions <code>d != dim</code>.</p>
 <p>Moreover, as for <code>$gather</code>, the values of <code>index</code> must be between <code>0</code> and <code>self$size(dim) - 1</code> inclusive, and all values in a row along the specified dimension <code>dim</code> must be unique.</p>
-<div id="arguments-42" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-42" class="anchor"></a>Arguments:</h4>
+<div class="section level4">
+<h4 id="arguments-42">Arguments:<a class="anchor" aria-label="anchor" href="#arguments-42"></a>
+</h4>
 <ul>
 <li>dim (int): the axis along which to index</li>
 <li>index (LongTensor): the indices of elements to scatter,</li>
@@ -2832,32 +2793,32 @@ self[i][j][index[i][j][k]] = src[i][j][k]  # if dim == 2</code></pre>
 <li>incase <code>src</code> is not specified</li>
 </ul>
 </div>
-<div id="examples-30" class="section level4">
-<h4 class="hasAnchor">
-<a href="#examples-30" class="anchor"></a>Examples:</h4>
+<div class="section level4">
+<h4 id="examples-30">Examples:<a class="anchor" aria-label="anchor" href="#examples-30"></a>
+</h4>
 <div class="sourceCode" id="cb34"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_rand.html">torch_rand</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">5</span><span class="op">)</span>
 <span class="va">x</span>
 <span class="fu"><a href="../../reference/torch_zeros.html">torch_zeros</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">5</span><span class="op">)</span><span class="op">$</span><span class="fu">scatter_</span><span class="op">(</span>
         <span class="fl">1</span>, 
-        <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/cbind.html">rbind</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">3</span>, <span class="fl">3</span>, <span class="fl">1</span>, <span class="fl">1</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">1</span>, <span class="fl">1</span>, <span class="fl">2</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span>, <span class="va">x</span><span class="op">)</span>
+        <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/cbind.html" class="external-link">rbind</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">3</span>, <span class="fl">3</span>, <span class="fl">1</span>, <span class="fl">1</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">1</span>, <span class="fl">1</span>, <span class="fl">2</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span>, <span class="va">x</span><span class="op">)</span>
 <span class="op">)</span>
 
 <span class="va">z</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_zeros.html">torch_zeros</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">4</span><span class="op">)</span><span class="op">$</span><span class="fu">scatter_</span><span class="op">(</span>
         <span class="fl">2</span>, 
-        <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/matrix.html">matrix</a></span><span class="op">(</span><span class="fl">3</span><span class="op">:</span><span class="fl">4</span>, ncol <span class="op">=</span> <span class="fl">1</span><span class="op">)</span><span class="op">)</span>, <span class="fl">1.23</span>
+        <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/matrix.html" class="external-link">matrix</a></span><span class="op">(</span><span class="fl">3</span><span class="op">:</span><span class="fl">4</span>, ncol <span class="op">=</span> <span class="fl">1</span><span class="op">)</span><span class="op">)</span>, <span class="fl">1.23</span>
 <span class="op">)</span></code></pre></div>
 </div>
 </div>
-<div id="scatter_add" class="section level2">
-<h2 class="hasAnchor">
-<a href="#scatter_add" class="anchor"></a>scatter_add</h2>
+<div class="section level2">
+<h2 id="scatter_add">scatter_add<a class="anchor" aria-label="anchor" href="#scatter_add"></a>
+</h2>
 <p>scatter_add(dim, index, src) -&gt; Tensor</p>
 <p>Out-of-place version of <code>$scatter_add_</code></p>
 </div>
-<div id="scatter_add_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#scatter_add_" class="anchor"></a>scatter_add_</h2>
+<div class="section level2">
+<h2 id="scatter_add_">scatter_add_<a class="anchor" aria-label="anchor" href="#scatter_add_"></a>
+</h2>
 <p>scatter_add_(dim, index, src) -&gt; Tensor</p>
 <p>Adds all values from the tensor <code>other</code> into <code>self</code> at the indices specified in the <code>index</code> tensor in a similar fashion as <code>~$scatter_</code>. For each value in <code>src</code>, it is added to an index in <code>self</code> which is specified by its index in <code>src</code> for <code>dimension != dim</code> and by the corresponding value in <code>index</code> for <code>dimension = dim</code>.</p>
 <p>For a 3-D tensor, <code>self</code> is updated as::</p>
@@ -2865,14 +2826,14 @@ self[i][j][index[i][j][k]] = src[i][j][k]  # if dim == 2</code></pre>
 self[i][index[i][j][k]][k] += src[i][j][k]  # if dim == 1
 self[i][j][index[i][j][k]] += src[i][j][k]  # if dim == 2</code></pre>
 <p><code>self</code>, <code>index</code> and <code>src</code> should have same number of dimensions. It is also required that <code>index.size(d) &lt;= src.size(d)</code> for all dimensions <code>d</code>, and that <code>index.size(d) &lt;= self$size(d)</code> for all dimensions <code>d != dim</code>.</p>
-<div id="note-8" class="section level4">
-<h4 class="hasAnchor">
-<a href="#note-8" class="anchor"></a>Note:</h4>
+<div class="section level4">
+<h4 id="note-8">Note:<a class="anchor" aria-label="anchor" href="#note-8"></a>
+</h4>
 <p>In some circumstances when using the CUDA backend with CuDNN, this operator may select a nondeterministic algorithm to increase performance. If this is undesirable, you can try to make the operation deterministic (potentially at a performance cost) by setting <code>torch_backends.cudnn.deterministic = TRUE</code>.</p>
 </div>
-<div id="arguments-43" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-43" class="anchor"></a>Arguments:</h4>
+<div class="section level4">
+<h4 id="arguments-43">Arguments:<a class="anchor" aria-label="anchor" href="#arguments-43"></a>
+</h4>
 <ul>
 <li>dim (int): the axis along which to index</li>
 <li>index (LongTensor): the indices of elements to scatter and add,</li>
@@ -2880,42 +2841,42 @@ self[i][j][index[i][j][k]] += src[i][j][k]  # if dim == 2</code></pre>
 <li>src (Tensor): the source elements to scatter and add</li>
 </ul>
 </div>
-<div id="examples-31" class="section level4">
-<h4 class="hasAnchor">
-<a href="#examples-31" class="anchor"></a>Examples:</h4>
+<div class="section level4">
+<h4 id="examples-31">Examples:<a class="anchor" aria-label="anchor" href="#examples-31"></a>
+</h4>
 <div class="sourceCode" id="cb36"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_rand.html">torch_rand</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">5</span><span class="op">)</span>
 <span class="va">x</span>
-<span class="fu"><a href="../../reference/torch_ones.html">torch_ones</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">5</span><span class="op">)</span><span class="op">$</span><span class="fu">scatter_add_</span><span class="op">(</span><span class="fl">1</span>, <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/cbind.html">rbind</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span>, <span class="fl">2</span>, <span class="fl">0</span>, <span class="fl">0</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">0</span>, <span class="fl">0</span>, <span class="fl">1</span>, <span class="fl">2</span><span class="op">)</span><span class="op">)</span><span class="op">)</span>, <span class="va">x</span><span class="op">)</span></code></pre></div>
+<span class="fu"><a href="../../reference/torch_ones.html">torch_ones</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">5</span><span class="op">)</span><span class="op">$</span><span class="fu">scatter_add_</span><span class="op">(</span><span class="fl">1</span>, <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/cbind.html" class="external-link">rbind</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span>, <span class="fl">2</span>, <span class="fl">0</span>, <span class="fl">0</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">0</span>, <span class="fl">0</span>, <span class="fl">1</span>, <span class="fl">2</span><span class="op">)</span><span class="op">)</span><span class="op">)</span>, <span class="va">x</span><span class="op">)</span></code></pre></div>
 </div>
 </div>
-<div id="select" class="section level2">
-<h2 class="hasAnchor">
-<a href="#select" class="anchor"></a>select</h2>
+<div class="section level2">
+<h2 id="select">select<a class="anchor" aria-label="anchor" href="#select"></a>
+</h2>
 <p>select(dim, index) -&gt; Tensor</p>
 <p>Slices the <code>self</code> tensor along the selected dimension at the given index. This function returns a view of the original tensor with the given dimension removed.</p>
-<div id="arguments-44" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-44" class="anchor"></a>Arguments:</h4>
+<div class="section level4">
+<h4 id="arguments-44">Arguments:<a class="anchor" aria-label="anchor" href="#arguments-44"></a>
+</h4>
 <ul>
 <li>dim (int): the dimension to slice</li>
 <li>index (int): the index to select with</li>
 </ul>
 </div>
-<div id="note-9" class="section level4">
-<h4 class="hasAnchor">
-<a href="#note-9" class="anchor"></a>Note:</h4>
+<div class="section level4">
+<h4 id="note-9">Note:<a class="anchor" aria-label="anchor" href="#note-9"></a>
+</h4>
 <p><code>select</code> is equivalent to slicing. For example, <code>tensor$select(0, index)</code> is equivalent to <code>tensor[index]</code> and <code>tensor$select(2, index)</code> is equivalent to <code>tensor[:,:,index]</code>.</p>
 </div>
 </div>
-<div id="set_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#set_" class="anchor"></a>set_</h2>
+<div class="section level2">
+<h2 id="set_">set_<a class="anchor" aria-label="anchor" href="#set_"></a>
+</h2>
 <p>set_(source=NULL, storage_offset=0, size=NULL, stride=NULL) -&gt; Tensor</p>
 <p>Sets the underlying storage, size, and strides. If <code>source</code> is a tensor, <code>self</code> tensor will share the same storage and have the same size and strides as <code>source</code>. Changes to elements in one tensor will be reflected in the other.</p>
-<div id="arguments-45" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-45" class="anchor"></a>Arguments:</h4>
+<div class="section level4">
+<h4 id="arguments-45">Arguments:<a class="anchor" aria-label="anchor" href="#arguments-45"></a>
+</h4>
 <ul>
 <li>source (Tensor or Storage): the tensor or storage to use</li>
 <li>storage_offset (int, optional): the offset in the storage</li>
@@ -2924,328 +2885,328 @@ self[i][j][index[i][j][k]] += src[i][j][k]  # if dim == 2</code></pre>
 </ul>
 </div>
 </div>
-<div id="share_memory_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#share_memory_" class="anchor"></a>share_memory_</h2>
+<div class="section level2">
+<h2 id="share_memory_">share_memory_<a class="anchor" aria-label="anchor" href="#share_memory_"></a>
+</h2>
 <p>Moves the underlying storage to shared memory.</p>
 <p>This is a no-op if the underlying storage is already in shared memory and for CUDA tensors. Tensors in shared memory cannot be resized.</p>
 </div>
-<div id="short" class="section level2">
-<h2 class="hasAnchor">
-<a href="#short" class="anchor"></a>short</h2>
+<div class="section level2">
+<h2 id="short">short<a class="anchor" aria-label="anchor" href="#short"></a>
+</h2>
 <p>short(memory_format=torch_preserve_format) -&gt; Tensor</p>
 <p><code>self$short()</code> is equivalent to <code>self$to(torch_int16)</code>. See [to()].</p>
-<div id="arguments-46" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-46" class="anchor"></a>Arguments:</h4>
+<div class="section level4">
+<h4 id="arguments-46">Arguments:<a class="anchor" aria-label="anchor" href="#arguments-46"></a>
+</h4>
 <ul>
 <li>memory_format (<code>torch_memory_format</code>, optional): the desired memory format of</li>
 <li>returned Tensor. Default: <code>torch_preserve_format</code>.</li>
 </ul>
 </div>
 </div>
-<div id="sigmoid" class="section level2">
-<h2 class="hasAnchor">
-<a href="#sigmoid" class="anchor"></a>sigmoid</h2>
+<div class="section level2">
+<h2 id="sigmoid">sigmoid<a class="anchor" aria-label="anchor" href="#sigmoid"></a>
+</h2>
 <p>sigmoid() -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_sigmoid.html">?torch_sigmoid</a></code></p>
 </div>
-<div id="sigmoid_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#sigmoid_" class="anchor"></a>sigmoid_</h2>
+<div class="section level2">
+<h2 id="sigmoid_">sigmoid_<a class="anchor" aria-label="anchor" href="#sigmoid_"></a>
+</h2>
 <p>sigmoid_() -&gt; Tensor</p>
 <p>In-place version of <code>$sigmoid</code></p>
 </div>
-<div id="sign" class="section level2">
-<h2 class="hasAnchor">
-<a href="#sign" class="anchor"></a>sign</h2>
+<div class="section level2">
+<h2 id="sign">sign<a class="anchor" aria-label="anchor" href="#sign"></a>
+</h2>
 <p>sign() -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_sign.html">?torch_sign</a></code></p>
 </div>
-<div id="sign_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#sign_" class="anchor"></a>sign_</h2>
+<div class="section level2">
+<h2 id="sign_">sign_<a class="anchor" aria-label="anchor" href="#sign_"></a>
+</h2>
 <p>sign_() -&gt; Tensor</p>
 <p>In-place version of <code>$sign</code></p>
 </div>
-<div id="sin" class="section level2">
-<h2 class="hasAnchor">
-<a href="#sin" class="anchor"></a>sin</h2>
+<div class="section level2">
+<h2 id="sin">sin<a class="anchor" aria-label="anchor" href="#sin"></a>
+</h2>
 <p>sin() -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_sin.html">?torch_sin</a></code></p>
 </div>
-<div id="sin_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#sin_" class="anchor"></a>sin_</h2>
+<div class="section level2">
+<h2 id="sin_">sin_<a class="anchor" aria-label="anchor" href="#sin_"></a>
+</h2>
 <p>sin_() -&gt; Tensor</p>
 <p>In-place version of <code>$sin</code></p>
 </div>
-<div id="sinh" class="section level2">
-<h2 class="hasAnchor">
-<a href="#sinh" class="anchor"></a>sinh</h2>
+<div class="section level2">
+<h2 id="sinh">sinh<a class="anchor" aria-label="anchor" href="#sinh"></a>
+</h2>
 <p>sinh() -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_sinh.html">?torch_sinh</a></code></p>
 </div>
-<div id="sinh_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#sinh_" class="anchor"></a>sinh_</h2>
+<div class="section level2">
+<h2 id="sinh_">sinh_<a class="anchor" aria-label="anchor" href="#sinh_"></a>
+</h2>
 <p>sinh_() -&gt; Tensor</p>
 <p>In-place version of <code>$sinh</code></p>
 </div>
-<div id="size" class="section level2">
-<h2 class="hasAnchor">
-<a href="#size" class="anchor"></a>size</h2>
+<div class="section level2">
+<h2 id="size">size<a class="anchor" aria-label="anchor" href="#size"></a>
+</h2>
 <p>size() -&gt; torch_Size</p>
 <p>Returns the size of the <code>self</code> tensor. The returned value is a subclass of <code>tuple</code>.</p>
-<div id="examples-32" class="section level4">
-<h4 class="hasAnchor">
-<a href="#examples-32" class="anchor"></a>Examples:</h4>
+<div class="section level4">
+<h4 id="examples-32">Examples:<a class="anchor" aria-label="anchor" href="#examples-32"></a>
+</h4>
 <div class="sourceCode" id="cb37"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span class="fu"><a href="../../reference/torch_empty.html">torch_empty</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">4</span>, <span class="fl">5</span><span class="op">)</span><span class="op">$</span><span class="fu">size</span><span class="op">(</span><span class="op">)</span></code></pre></div>
 </div>
 </div>
-<div id="slogdet" class="section level2">
-<h2 class="hasAnchor">
-<a href="#slogdet" class="anchor"></a>slogdet</h2>
+<div class="section level2">
+<h2 id="slogdet">slogdet<a class="anchor" aria-label="anchor" href="#slogdet"></a>
+</h2>
 <p>slogdet() -&gt; (Tensor, Tensor)</p>
 <p>See <code><a href="../../reference/torch_slogdet.html">?torch_slogdet</a></code></p>
 </div>
-<div id="solve" class="section level2">
-<h2 class="hasAnchor">
-<a href="#solve" class="anchor"></a>solve</h2>
+<div class="section level2">
+<h2 id="solve">solve<a class="anchor" aria-label="anchor" href="#solve"></a>
+</h2>
 <p>solve(A) -&gt; Tensor, Tensor</p>
 <p>See <code><a href="../../reference/torch_solve.html">?torch_solve</a></code></p>
 </div>
-<div id="sort" class="section level2">
-<h2 class="hasAnchor">
-<a href="#sort" class="anchor"></a>sort</h2>
+<div class="section level2">
+<h2 id="sort">sort<a class="anchor" aria-label="anchor" href="#sort"></a>
+</h2>
 <p>sort(dim=-1, descending=FALSE) -&gt; (Tensor, LongTensor)</p>
 <p>See <code><a href="../../reference/torch_sort.html">?torch_sort</a></code></p>
 </div>
-<div id="sparse_dim" class="section level2">
-<h2 class="hasAnchor">
-<a href="#sparse_dim" class="anchor"></a>sparse_dim</h2>
+<div class="section level2">
+<h2 id="sparse_dim">sparse_dim<a class="anchor" aria-label="anchor" href="#sparse_dim"></a>
+</h2>
 <p>sparse_dim() -&gt; int</p>
 <p>If <code>self</code> is a sparse COO tensor (i.e., with <code>torch_sparse_coo</code> layout), this returns the number of sparse dimensions. Otherwise, this throws an error.</p>
 <p>See also <code>Tensor.dense_dim</code>.</p>
 </div>
-<div id="sparse_mask" class="section level2">
-<h2 class="hasAnchor">
-<a href="#sparse_mask" class="anchor"></a>sparse_mask</h2>
+<div class="section level2">
+<h2 id="sparse_mask">sparse_mask<a class="anchor" aria-label="anchor" href="#sparse_mask"></a>
+</h2>
 <p>sparse_mask(input, mask) -&gt; Tensor</p>
 <p>Returns a new SparseTensor with values from Tensor <code>input</code> filtered by indices of <code>mask</code> and values are ignored. <code>input</code> and <code>mask</code> must have the same shape.</p>
-<div id="arguments-47" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-47" class="anchor"></a>Arguments:</h4>
+<div class="section level4">
+<h4 id="arguments-47">Arguments:<a class="anchor" aria-label="anchor" href="#arguments-47"></a>
+</h4>
 <ul>
 <li>input (Tensor): an input Tensor</li>
 <li>mask (SparseTensor): a SparseTensor which we filter <code>input</code> based on its indices</li>
 </ul>
 </div>
 </div>
-<div id="split" class="section level2">
-<h2 class="hasAnchor">
-<a href="#split" class="anchor"></a>split</h2>
+<div class="section level2">
+<h2 id="split">split<a class="anchor" aria-label="anchor" href="#split"></a>
+</h2>
 <p>See <code><a href="../../reference/torch_split.html">?torch_split</a></code></p>
 </div>
-<div id="sqrt" class="section level2">
-<h2 class="hasAnchor">
-<a href="#sqrt" class="anchor"></a>sqrt</h2>
+<div class="section level2">
+<h2 id="sqrt">sqrt<a class="anchor" aria-label="anchor" href="#sqrt"></a>
+</h2>
 <p>sqrt() -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_sqrt.html">?torch_sqrt</a></code></p>
 </div>
-<div id="sqrt_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#sqrt_" class="anchor"></a>sqrt_</h2>
+<div class="section level2">
+<h2 id="sqrt_">sqrt_<a class="anchor" aria-label="anchor" href="#sqrt_"></a>
+</h2>
 <p>sqrt_() -&gt; Tensor</p>
 <p>In-place version of <code>$sqrt</code></p>
 </div>
-<div id="square" class="section level2">
-<h2 class="hasAnchor">
-<a href="#square" class="anchor"></a>square</h2>
+<div class="section level2">
+<h2 id="square">square<a class="anchor" aria-label="anchor" href="#square"></a>
+</h2>
 <p>square() -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_square.html">?torch_square</a></code></p>
 </div>
-<div id="square_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#square_" class="anchor"></a>square_</h2>
+<div class="section level2">
+<h2 id="square_">square_<a class="anchor" aria-label="anchor" href="#square_"></a>
+</h2>
 <p>square_() -&gt; Tensor</p>
 <p>In-place version of <code>$square</code></p>
 </div>
-<div id="squeeze" class="section level2">
-<h2 class="hasAnchor">
-<a href="#squeeze" class="anchor"></a>squeeze</h2>
+<div class="section level2">
+<h2 id="squeeze">squeeze<a class="anchor" aria-label="anchor" href="#squeeze"></a>
+</h2>
 <p>squeeze(dim=NULL) -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_squeeze.html">?torch_squeeze</a></code></p>
 </div>
-<div id="squeeze_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#squeeze_" class="anchor"></a>squeeze_</h2>
+<div class="section level2">
+<h2 id="squeeze_">squeeze_<a class="anchor" aria-label="anchor" href="#squeeze_"></a>
+</h2>
 <p>squeeze_(dim=NULL) -&gt; Tensor</p>
 <p>In-place version of <code>$squeeze</code></p>
 </div>
-<div id="std" class="section level2">
-<h2 class="hasAnchor">
-<a href="#std" class="anchor"></a>std</h2>
+<div class="section level2">
+<h2 id="std">std<a class="anchor" aria-label="anchor" href="#std"></a>
+</h2>
 <p>std(dim=NULL, unbiased=TRUE, keepdim=FALSE) -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_std.html">?torch_std</a></code></p>
 </div>
-<div id="stft" class="section level2">
-<h2 class="hasAnchor">
-<a href="#stft" class="anchor"></a>stft</h2>
+<div class="section level2">
+<h2 id="stft">stft<a class="anchor" aria-label="anchor" href="#stft"></a>
+</h2>
 <p>See <code><a href="../../reference/torch_stft.html">?torch_stft</a></code></p>
 </div>
-<div id="storage" class="section level2">
-<h2 class="hasAnchor">
-<a href="#storage" class="anchor"></a>storage</h2>
+<div class="section level2">
+<h2 id="storage">storage<a class="anchor" aria-label="anchor" href="#storage"></a>
+</h2>
 <p>storage() -&gt; torch_Storage</p>
 <p>Returns the underlying storage.</p>
 </div>
-<div id="storage_offset" class="section level2">
-<h2 class="hasAnchor">
-<a href="#storage_offset" class="anchor"></a>storage_offset</h2>
+<div class="section level2">
+<h2 id="storage_offset">storage_offset<a class="anchor" aria-label="anchor" href="#storage_offset"></a>
+</h2>
 <p>storage_offset() -&gt; int</p>
 <p>Returns <code>self</code> tensor’s offset in the underlying storage in terms of number of storage elements (not bytes).</p>
-<div id="examples-33" class="section level4">
-<h4 class="hasAnchor">
-<a href="#examples-33" class="anchor"></a>Examples:</h4>
+<div class="section level4">
+<h4 id="examples-33">Examples:<a class="anchor" aria-label="anchor" href="#examples-33"></a>
+</h4>
 <div class="sourceCode" id="cb38"><pre class="downlit sourceCode r">
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+<code class="sourceCode R"><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">2</span>, <span class="fl">3</span>, <span class="fl">4</span>, <span class="fl">5</span><span class="op">)</span><span class="op">)</span>
 <span class="va">x</span><span class="op">$</span><span class="fu">storage_offset</span><span class="op">(</span><span class="op">)</span>
 <span class="va">x</span><span class="op">[</span><span class="fl">3</span><span class="op">:</span><span class="va">N</span><span class="op">]</span><span class="op">$</span><span class="fu">storage_offset</span><span class="op">(</span><span class="op">)</span></code></pre></div>
 </div>
 </div>
-<div id="storage_type" class="section level2">
-<h2 class="hasAnchor">
-<a href="#storage_type" class="anchor"></a>storage_type</h2>
+<div class="section level2">
+<h2 id="storage_type">storage_type<a class="anchor" aria-label="anchor" href="#storage_type"></a>
+</h2>
 <p>storage_type() -&gt; type</p>
 <p>Returns the type of the underlying storage.</p>
 </div>
-<div id="stride" class="section level2">
-<h2 class="hasAnchor">
-<a href="#stride" class="anchor"></a>stride</h2>
+<div class="section level2">
+<h2 id="stride">stride<a class="anchor" aria-label="anchor" href="#stride"></a>
+</h2>
 <p>stride(dim) -&gt; tuple or int</p>
 <p>Returns the stride of <code>self</code> tensor.</p>
 <p>Stride is the jump necessary to go from one element to the next one in the specified dimension <code>dim</code>. A tuple of all strides is returned when no argument is passed in. Otherwise, an integer value is returned as the stride in the particular dimension <code>dim</code>.</p>
-<div id="arguments-48" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-48" class="anchor"></a>Arguments:</h4>
+<div class="section level4">
+<h4 id="arguments-48">Arguments:<a class="anchor" aria-label="anchor" href="#arguments-48"></a>
+</h4>
 <ul>
 <li>dim (int, optional): the desired dimension in which stride is required</li>
 </ul>
 </div>
-<div id="examples-34" class="section level4">
-<h4 class="hasAnchor">
-<a href="#examples-34" class="anchor"></a>Examples:</h4>
+<div class="section level4">
+<h4 id="examples-34">Examples:<a class="anchor" aria-label="anchor" href="#examples-34"></a>
+</h4>
 <div class="sourceCode" id="cb39"><pre class="downlit sourceCode r">
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 <span class="va">x</span><span class="op">$</span><span class="fu">stride</span><span class="op">(</span><span class="op">)</span>
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 <span class="va">x</span><span class="op">$</span><span class="fu">stride</span><span class="op">(</span><span class="op">-</span><span class="fl">1</span><span class="op">)</span></code></pre></div>
 </div>
 </div>
-<div id="sub" class="section level2">
-<h2 class="hasAnchor">
-<a href="#sub" class="anchor"></a>sub</h2>
+<div class="section level2">
+<h2 id="sub">sub<a class="anchor" aria-label="anchor" href="#sub"></a>
+</h2>
 <p>sub(other, *, alpha=1) -&gt; Tensor</p>
 <p>Subtracts a scalar or tensor from <code>self</code> tensor. If both <code>alpha</code> and <code>other</code> are specified, each element of <code>other</code> is scaled by <code>alpha</code> before being used.</p>
 <p>When <code>other</code> is a tensor, the shape of <code>other</code> must be <code>broadcastable &lt;broadcasting-semantics&gt;</code> with the shape of the underlying tensor.</p>
 </div>
-<div id="sub_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#sub_" class="anchor"></a>sub_</h2>
+<div class="section level2">
+<h2 id="sub_">sub_<a class="anchor" aria-label="anchor" href="#sub_"></a>
+</h2>
 <p>sub_(other, *, alpha=1) -&gt; Tensor</p>
 <p>In-place version of <code>$sub</code></p>
 </div>
-<div id="sum" class="section level2">
-<h2 class="hasAnchor">
-<a href="#sum" class="anchor"></a>sum</h2>
+<div class="section level2">
+<h2 id="sum">sum<a class="anchor" aria-label="anchor" href="#sum"></a>
+</h2>
 <p>sum(dim=NULL, keepdim=FALSE, dtype=NULL) -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_sum.html">?torch_sum</a></code></p>
 </div>
-<div id="sum_to_size" class="section level2">
-<h2 class="hasAnchor">
-<a href="#sum_to_size" class="anchor"></a>sum_to_size</h2>
+<div class="section level2">
+<h2 id="sum_to_size">sum_to_size<a class="anchor" aria-label="anchor" href="#sum_to_size"></a>
+</h2>
 <p>sum_to_size(*size) -&gt; Tensor</p>
 <p>Sum <code>this</code> tensor to <code>size</code>. <code>size</code> must be broadcastable to <code>this</code> tensor size.</p>
-<div id="arguments-49" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-49" class="anchor"></a>Arguments:</h4>
+<div class="section level4">
+<h4 id="arguments-49">Arguments:<a class="anchor" aria-label="anchor" href="#arguments-49"></a>
+</h4>
 <ul>
 <li>size (int…): a sequence of integers defining the shape of the output tensor.</li>
 </ul>
 </div>
 </div>
-<div id="svd" class="section level2">
-<h2 class="hasAnchor">
-<a href="#svd" class="anchor"></a>svd</h2>
+<div class="section level2">
+<h2 id="svd">svd<a class="anchor" aria-label="anchor" href="#svd"></a>
+</h2>
 <p>svd(some=TRUE, compute_uv=TRUE) -&gt; (Tensor, Tensor, Tensor)</p>
 <p>See <code><a href="../../reference/torch_svd.html">?torch_svd</a></code></p>
 </div>
-<div id="symeig" class="section level2">
-<h2 class="hasAnchor">
-<a href="#symeig" class="anchor"></a>symeig</h2>
+<div class="section level2">
+<h2 id="symeig">symeig<a class="anchor" aria-label="anchor" href="#symeig"></a>
+</h2>
 <p>symeig(eigenvectors=FALSE, upper=TRUE) -&gt; (Tensor, Tensor)</p>
 <p>See <code><a href="../../reference/torch_symeig.html">?torch_symeig</a></code></p>
 </div>
-<div id="t" class="section level2">
-<h2 class="hasAnchor">
-<a href="#t" class="anchor"></a>t</h2>
+<div class="section level2">
+<h2 id="t">t<a class="anchor" aria-label="anchor" href="#t"></a>
+</h2>
 <p>t() -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_t.html">?torch_t</a></code></p>
 </div>
-<div id="t_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#t_" class="anchor"></a>t_</h2>
+<div class="section level2">
+<h2 id="t_">t_<a class="anchor" aria-label="anchor" href="#t_"></a>
+</h2>
 <p>t_() -&gt; Tensor</p>
 <p>In-place version of <code>$t</code></p>
 </div>
-<div id="take" class="section level2">
-<h2 class="hasAnchor">
-<a href="#take" class="anchor"></a>take</h2>
+<div class="section level2">
+<h2 id="take">take<a class="anchor" aria-label="anchor" href="#take"></a>
+</h2>
 <p>take(indices) -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_take.html">?torch_take</a></code></p>
 </div>
-<div id="tan" class="section level2">
-<h2 class="hasAnchor">
-<a href="#tan" class="anchor"></a>tan</h2>
+<div class="section level2">
+<h2 id="tan">tan<a class="anchor" aria-label="anchor" href="#tan"></a>
+</h2>
 <p>tan() -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_tan.html">?torch_tan</a></code></p>
 </div>
-<div id="tan_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#tan_" class="anchor"></a>tan_</h2>
+<div class="section level2">
+<h2 id="tan_">tan_<a class="anchor" aria-label="anchor" href="#tan_"></a>
+</h2>
 <p>tan_() -&gt; Tensor</p>
 <p>In-place version of <code>$tan</code></p>
 </div>
-<div id="tanh" class="section level2">
-<h2 class="hasAnchor">
-<a href="#tanh" class="anchor"></a>tanh</h2>
+<div class="section level2">
+<h2 id="tanh">tanh<a class="anchor" aria-label="anchor" href="#tanh"></a>
+</h2>
 <p>tanh() -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_tanh.html">?torch_tanh</a></code></p>
 </div>
-<div id="tanh_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#tanh_" class="anchor"></a>tanh_</h2>
+<div class="section level2">
+<h2 id="tanh_">tanh_<a class="anchor" aria-label="anchor" href="#tanh_"></a>
+</h2>
 <p>tanh_() -&gt; Tensor</p>
 <p>In-place version of <code>$tanh</code></p>
 </div>
-<div id="to" class="section level2">
-<h2 class="hasAnchor">
-<a href="#to" class="anchor"></a>to</h2>
+<div class="section level2">
+<h2 id="to">to<a class="anchor" aria-label="anchor" href="#to"></a>
+</h2>
 <p>to(*args, **kwargs) -&gt; Tensor</p>
 <p>Performs Tensor dtype and/or device conversion. A <code>torch_dtype</code> and :class:<code>torch_device</code> are inferred from the arguments of <code>self$to(*args, **kwargs)</code>.</p>
-<div id="note-10" class="section level4">
-<h4 class="hasAnchor">
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+<div class="section level4">
+<h4 id="note-10">Note:<a class="anchor" aria-label="anchor" href="#note-10"></a>
+</h4>
 <p>If the <code>self</code> Tensor already has the correct <code>torch_dtype</code> and :class:<code>torch_device</code>, then <code>self</code> is returned. Otherwise, the returned tensor is a copy of <code>self</code> with the desired <code>torch_dtype</code> and :class:<code>torch_device</code>.</p>
 <p>Here are the ways to call <code>to</code>:</p>
 <p>to(dtype, non_blocking=FALSE, copy=FALSE, memory_format=torch_preserve_format) -&gt; Tensor</p>
 <p>Returns a Tensor with the specified <code>dtype</code></p>
 </div>
-<div id="arguments-50" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-50" class="anchor"></a>Arguments:</h4>
+<div class="section level4">
+<h4 id="arguments-50">Arguments:<a class="anchor" aria-label="anchor" href="#arguments-50"></a>
+</h4>
 <ul>
 <li>memory_format (<code>torch_memory_format</code>, optional): the desired memory format of returned Tensor. Default: <code>torch_preserve_format</code>.</li>
 </ul>
@@ -3253,9 +3214,9 @@ self[i][j][index[i][j][k]] += src[i][j][k]  # if dim == 2</code></pre>
 <p>Returns a Tensor with the specified <code>device</code> and (optional) <code>dtype</code>. If <code>dtype</code> is <code>NULL</code> it is inferred to be <code>self$dtype</code>. When <code>non_blocking</code>, tries to convert asynchronously with respect to the host if possible, e.g., converting a CPU Tensor with pinned memory to a CUDA Tensor.</p>
 <p>When <code>copy</code> is set, a new Tensor is created even when the Tensor already matches the desired conversion.</p>
 </div>
-<div id="arguments-51" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-51" class="anchor"></a>Arguments:</h4>
+<div class="section level4">
+<h4 id="arguments-51">Arguments:<a class="anchor" aria-label="anchor" href="#arguments-51"></a>
+</h4>
 <ul>
 <li>memory_format (<code>torch_memory_format</code>, optional): the desired memory format of returned Tensor. Default: <code>torch_preserve_format</code>.</li>
 </ul>
@@ -3263,9 +3224,9 @@ self[i][j][index[i][j][k]] += src[i][j][k]  # if dim == 2</code></pre>
 <p>Returns a Tensor with same <code>torch_dtype</code> and :class:<code>torch_device</code> as the Tensor <code>other</code>. When <code>non_blocking</code>, tries to convert asynchronously with respect to the host if possible, e.g., converting a CPU Tensor with pinned memory to a CUDA Tensor.</p>
 <p>When <code>copy</code> is set, a new Tensor is created even when the Tensor already matches the desired conversion.</p>
 </div>
-<div id="examples-35" class="section level4">
-<h4 class="hasAnchor">
-<a href="#examples-35" class="anchor"></a>Examples:</h4>
+<div class="section level4">
+<h4 id="examples-35">Examples:<a class="anchor" aria-label="anchor" href="#examples-35"></a>
+</h4>
 <div class="sourceCode" id="cb40"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span class="va">tensor</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">2</span><span class="op">)</span>  <span class="co"># Initially dtype=float32, device=cpu</span>
 <span class="va">tensor</span><span class="op">$</span><span class="fu">to</span><span class="op">(</span>dtype <span class="op">=</span> <span class="fu"><a href="../../reference/torch_dtype.html">torch_float64</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span>
@@ -3274,116 +3235,116 @@ self[i][j][index[i][j][k]] += src[i][j][k]  # if dim == 2</code></pre>
 <span class="va">tensor</span><span class="op">$</span><span class="fu">to</span><span class="op">(</span>other <span class="op">=</span> <span class="va">other</span>, non_blocking<span class="op">=</span><span class="cn">TRUE</span><span class="op">)</span></code></pre></div>
 </div>
 </div>
-<div id="to_mkldnn" class="section level2">
-<h2 class="hasAnchor">
-<a href="#to_mkldnn" class="anchor"></a>to_mkldnn</h2>
+<div class="section level2">
+<h2 id="to_mkldnn">to_mkldnn<a class="anchor" aria-label="anchor" href="#to_mkldnn"></a>
+</h2>
 <p>to_mkldnn() -&gt; Tensor Returns a copy of the tensor in <code>torch_mkldnn</code> layout.</p>
 </div>
-<div id="to_sparse" class="section level2">
-<h2 class="hasAnchor">
-<a href="#to_sparse" class="anchor"></a>to_sparse</h2>
+<div class="section level2">
+<h2 id="to_sparse">to_sparse<a class="anchor" aria-label="anchor" href="#to_sparse"></a>
+</h2>
 <p>to_sparse(sparseDims) -&gt; Tensor Returns a sparse copy of the tensor. PyTorch supports sparse tensors in <code>coordinate format &lt;sparse-docs&gt;</code>.</p>
-<div id="arguments-52" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-52" class="anchor"></a>Arguments:</h4>
+<div class="section level4">
+<h4 id="arguments-52">Arguments:<a class="anchor" aria-label="anchor" href="#arguments-52"></a>
+</h4>
 <ul>
 <li>sparseDims (int, optional): the number of sparse dimensions to include in the new sparse tensor</li>
 </ul>
 </div>
 </div>
-<div id="tolist" class="section level2">
-<h2 class="hasAnchor">
-<a href="#tolist" class="anchor"></a>tolist</h2>
+<div class="section level2">
+<h2 id="tolist">tolist<a class="anchor" aria-label="anchor" href="#tolist"></a>
+</h2>
 <p>tolist() -&gt; list or number</p>
 <p>Returns the tensor as a (nested) list. For scalars, a standard Python number is returned, just like with <code>$item</code>. Tensors are automatically moved to the CPU first if necessary.</p>
 <p>This operation is not differentiable.</p>
 </div>
-<div id="topk" class="section level2">
-<h2 class="hasAnchor">
-<a href="#topk" class="anchor"></a>topk</h2>
+<div class="section level2">
+<h2 id="topk">topk<a class="anchor" aria-label="anchor" href="#topk"></a>
+</h2>
 <p>topk(k, dim=NULL, largest=TRUE, sorted=TRUE) -&gt; (Tensor, LongTensor)</p>
 <p>See <code><a href="../../reference/torch_topk.html">?torch_topk</a></code></p>
 </div>
-<div id="trace" class="section level2">
-<h2 class="hasAnchor">
-<a href="#trace" class="anchor"></a>trace</h2>
+<div class="section level2">
+<h2 id="trace">trace<a class="anchor" aria-label="anchor" href="#trace"></a>
+</h2>
 <p>trace() -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_trace.html">?torch_trace</a></code></p>
 </div>
-<div id="transpose" class="section level2">
-<h2 class="hasAnchor">
-<a href="#transpose" class="anchor"></a>transpose</h2>
+<div class="section level2">
+<h2 id="transpose">transpose<a class="anchor" aria-label="anchor" href="#transpose"></a>
+</h2>
 <p>transpose(dim0, dim1) -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_transpose.html">?torch_transpose</a></code></p>
 </div>
-<div id="transpose_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#transpose_" class="anchor"></a>transpose_</h2>
+<div class="section level2">
+<h2 id="transpose_">transpose_<a class="anchor" aria-label="anchor" href="#transpose_"></a>
+</h2>
 <p>transpose_(dim0, dim1) -&gt; Tensor</p>
 <p>In-place version of <code>$transpose</code></p>
 </div>
-<div id="triangular_solve" class="section level2">
-<h2 class="hasAnchor">
-<a href="#triangular_solve" class="anchor"></a>triangular_solve</h2>
+<div class="section level2">
+<h2 id="triangular_solve">triangular_solve<a class="anchor" aria-label="anchor" href="#triangular_solve"></a>
+</h2>
 <p>triangular_solve(A, upper=TRUE, transpose=FALSE, unitriangular=FALSE) -&gt; (Tensor, Tensor)</p>
 <p>See [torch_triangular_solve()]</p>
 </div>
-<div id="tril" class="section level2">
-<h2 class="hasAnchor">
-<a href="#tril" class="anchor"></a>tril</h2>
+<div class="section level2">
+<h2 id="tril">tril<a class="anchor" aria-label="anchor" href="#tril"></a>
+</h2>
 <p>tril(k=0) -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_tril.html">?torch_tril</a></code></p>
 </div>
-<div id="tril_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#tril_" class="anchor"></a>tril_</h2>
+<div class="section level2">
+<h2 id="tril_">tril_<a class="anchor" aria-label="anchor" href="#tril_"></a>
+</h2>
 <p>tril_(k=0) -&gt; Tensor</p>
 <p>In-place version of <code>$tril</code></p>
 </div>
-<div id="triu" class="section level2">
-<h2 class="hasAnchor">
-<a href="#triu" class="anchor"></a>triu</h2>
+<div class="section level2">
+<h2 id="triu">triu<a class="anchor" aria-label="anchor" href="#triu"></a>
+</h2>
 <p>triu(k=0) -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_triu.html">?torch_triu</a></code></p>
 </div>
-<div id="triu_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#triu_" class="anchor"></a>triu_</h2>
+<div class="section level2">
+<h2 id="triu_">triu_<a class="anchor" aria-label="anchor" href="#triu_"></a>
+</h2>
 <p>triu_(k=0) -&gt; Tensor</p>
 <p>In-place version of <code>$triu</code></p>
 </div>
-<div id="true_divide" class="section level2">
-<h2 class="hasAnchor">
-<a href="#true_divide" class="anchor"></a>true_divide</h2>
+<div class="section level2">
+<h2 id="true_divide">true_divide<a class="anchor" aria-label="anchor" href="#true_divide"></a>
+</h2>
 <p>true_divide(value) -&gt; Tensor</p>
 <p>See [torch_true_divide()]</p>
 </div>
-<div id="true_divide_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#true_divide_" class="anchor"></a>true_divide_</h2>
+<div class="section level2">
+<h2 id="true_divide_">true_divide_<a class="anchor" aria-label="anchor" href="#true_divide_"></a>
+</h2>
 <p>true_divide_(value) -&gt; Tensor</p>
 <p>In-place version of <code>$true_divide_</code></p>
 </div>
-<div id="trunc" class="section level2">
-<h2 class="hasAnchor">
-<a href="#trunc" class="anchor"></a>trunc</h2>
+<div class="section level2">
+<h2 id="trunc">trunc<a class="anchor" aria-label="anchor" href="#trunc"></a>
+</h2>
 <p>trunc() -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_trunc.html">?torch_trunc</a></code></p>
 </div>
-<div id="trunc_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#trunc_" class="anchor"></a>trunc_</h2>
+<div class="section level2">
+<h2 id="trunc_">trunc_<a class="anchor" aria-label="anchor" href="#trunc_"></a>
+</h2>
 <p>trunc_() -&gt; Tensor</p>
 <p>In-place version of <code>$trunc</code></p>
 </div>
-<div id="type" class="section level2">
-<h2 class="hasAnchor">
-<a href="#type" class="anchor"></a>type</h2>
+<div class="section level2">
+<h2 id="type">type<a class="anchor" aria-label="anchor" href="#type"></a>
+</h2>
 <p>type(dtype=NULL, non_blocking=FALSE, **kwargs) -&gt; str or Tensor Returns the type if <code>dtype</code> is not provided, else casts this object to the specified type.</p>
 <p>If this is already of the correct type, no copy is performed and the original object is returned.</p>
-<div id="arguments-53" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-53" class="anchor"></a>Arguments:</h4>
+<div class="section level4">
+<h4 id="arguments-53">Arguments:<a class="anchor" aria-label="anchor" href="#arguments-53"></a>
+</h4>
 <ul>
 <li>dtype (type or string): The desired type</li>
 <li>non_blocking (bool): If <code>TRUE</code>, and the source is in pinned memory</li>
@@ -3394,49 +3355,49 @@ self[i][j][index[i][j][k]] += src[i][j][k]  # if dim == 2</code></pre>
 </ul>
 </div>
 </div>
-<div id="type_as" class="section level2">
-<h2 class="hasAnchor">
-<a href="#type_as" class="anchor"></a>type_as</h2>
+<div class="section level2">
+<h2 id="type_as">type_as<a class="anchor" aria-label="anchor" href="#type_as"></a>
+</h2>
 <p>type_as(tensor) -&gt; Tensor</p>
 <p>Returns this tensor cast to the type of the given tensor.</p>
 <p>This is a no-op if the tensor is already of the correct type. This is equivalent to <code>self$type(tensor.type())</code></p>
-<div id="arguments-54" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-54" class="anchor"></a>Arguments:</h4>
+<div class="section level4">
+<h4 id="arguments-54">Arguments:<a class="anchor" aria-label="anchor" href="#arguments-54"></a>
+</h4>
 <ul>
 <li>tensor (Tensor): the tensor which has the desired type</li>
 </ul>
 </div>
 </div>
-<div id="unbind" class="section level2">
-<h2 class="hasAnchor">
-<a href="#unbind" class="anchor"></a>unbind</h2>
+<div class="section level2">
+<h2 id="unbind">unbind<a class="anchor" aria-label="anchor" href="#unbind"></a>
+</h2>
 <p>unbind(dim=0) -&gt; seq</p>
 <p>See <code><a href="../../reference/torch_unbind.html">?torch_unbind</a></code></p>
 </div>
-<div id="unflatten" class="section level2">
-<h2 class="hasAnchor">
-<a href="#unflatten" class="anchor"></a>unflatten</h2>
+<div class="section level2">
+<h2 id="unflatten">unflatten<a class="anchor" aria-label="anchor" href="#unflatten"></a>
+</h2>
 <p>Unflattens the named dimension <code>dim</code>, viewing it in the shape specified by <code>namedshape</code>.</p>
-<div id="arguments-55" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-55" class="anchor"></a>Arguments:</h4>
+<div class="section level4">
+<h4 id="arguments-55">Arguments:<a class="anchor" aria-label="anchor" href="#arguments-55"></a>
+</h4>
 <ul>
 <li>namedshape: (iterable of <code>(name, size)</code> tuples).</li>
 </ul>
 </div>
 </div>
-<div id="unfold" class="section level2">
-<h2 class="hasAnchor">
-<a href="#unfold" class="anchor"></a>unfold</h2>
+<div class="section level2">
+<h2 id="unfold">unfold<a class="anchor" aria-label="anchor" href="#unfold"></a>
+</h2>
 <p>unfold(dimension, size, step) -&gt; Tensor</p>
 <p>Returns a view of the original tensor which contains all slices of size <code>size</code> from <code>self</code> tensor in the dimension <code>dimension</code>.</p>
 <p>Step between two slices is given by <code>step</code>.</p>
 <p>If <code>sizedim</code> is the size of dimension <code>dimension</code> for <code>self</code>, the size of dimension <code>dimension</code> in the returned tensor will be <code>(sizedim - size) / step + 1</code>.</p>
 <p>An additional dimension of size <code>size</code> is appended in the returned tensor.</p>
-<div id="arguments-56" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-56" class="anchor"></a>Arguments:</h4>
+<div class="section level4">
+<h4 id="arguments-56">Arguments:<a class="anchor" aria-label="anchor" href="#arguments-56"></a>
+</h4>
 <ul>
 <li>dimension (int): dimension in which unfolding happens</li>
 <li>size (int): the size of each slice that is unfolded</li>
@@ -3444,59 +3405,59 @@ self[i][j][index[i][j][k]] += src[i][j][k]  # if dim == 2</code></pre>
 </ul>
 </div>
 </div>
-<div id="uniform_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#uniform_" class="anchor"></a>uniform_</h2>
+<div class="section level2">
+<h2 id="uniform_">uniform_<a class="anchor" aria-label="anchor" href="#uniform_"></a>
+</h2>
 <p>uniform_(from=0, to=1) -&gt; Tensor</p>
 <p>Fills <code>self</code> tensor with numbers sampled from the continuous uniform distribution:</p>
 <p><span class="math display">\[
 P(x) = \dfrac{1}{\text{to} - \text{from}}
 \]</span></p>
 </div>
-<div id="unique" class="section level2">
-<h2 class="hasAnchor">
-<a href="#unique" class="anchor"></a>unique</h2>
+<div class="section level2">
+<h2 id="unique">unique<a class="anchor" aria-label="anchor" href="#unique"></a>
+</h2>
 <p>Returns the unique elements of the input tensor.</p>
 <p>See <code>?torch_unique</code></p>
 </div>
-<div id="unique_consecutive" class="section level2">
-<h2 class="hasAnchor">
-<a href="#unique_consecutive" class="anchor"></a>unique_consecutive</h2>
+<div class="section level2">
+<h2 id="unique_consecutive">unique_consecutive<a class="anchor" aria-label="anchor" href="#unique_consecutive"></a>
+</h2>
 <p>Eliminates all but the first element from every consecutive group of equivalent elements.</p>
 <p>See [torch_unique_consecutive()]</p>
 </div>
-<div id="unsqueeze" class="section level2">
-<h2 class="hasAnchor">
-<a href="#unsqueeze" class="anchor"></a>unsqueeze</h2>
+<div class="section level2">
+<h2 id="unsqueeze">unsqueeze<a class="anchor" aria-label="anchor" href="#unsqueeze"></a>
+</h2>
 <p>unsqueeze(dim) -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_unsqueeze.html">?torch_unsqueeze</a></code></p>
 </div>
-<div id="unsqueeze_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#unsqueeze_" class="anchor"></a>unsqueeze_</h2>
+<div class="section level2">
+<h2 id="unsqueeze_">unsqueeze_<a class="anchor" aria-label="anchor" href="#unsqueeze_"></a>
+</h2>
 <p>unsqueeze_(dim) -&gt; Tensor</p>
 <p>In-place version of <code>$unsqueeze</code></p>
 </div>
-<div id="values" class="section level2">
-<h2 class="hasAnchor">
-<a href="#values" class="anchor"></a>values</h2>
+<div class="section level2">
+<h2 id="values">values<a class="anchor" aria-label="anchor" href="#values"></a>
+</h2>
 <p>values() -&gt; Tensor</p>
 <p>If <code>self</code> is a sparse COO tensor (i.e., with <code>torch_sparse_coo</code> layout), this returns a view of the contained values tensor. Otherwise, this throws an error.</p>
-<div id="note-11" class="section level4">
-<h4 class="hasAnchor">
-<a href="#note-11" class="anchor"></a>Note:</h4>
+<div class="section level4">
+<h4 id="note-11">Note:<a class="anchor" aria-label="anchor" href="#note-11"></a>
+</h4>
 <p>This method can only be called on a coalesced sparse tensor. See <code>Tensor$coalesce</code> for details.</p>
 </div>
 </div>
-<div id="var" class="section level2">
-<h2 class="hasAnchor">
-<a href="#var" class="anchor"></a>var</h2>
+<div class="section level2">
+<h2 id="var">var<a class="anchor" aria-label="anchor" href="#var"></a>
+</h2>
 <p>var(dim=NULL, unbiased=TRUE, keepdim=FALSE) -&gt; Tensor</p>
 <p>See <code><a href="../../reference/torch_var.html">?torch_var</a></code></p>
 </div>
-<div id="view" class="section level2">
-<h2 class="hasAnchor">
-<a href="#view" class="anchor"></a>view</h2>
+<div class="section level2">
+<h2 id="view">view<a class="anchor" aria-label="anchor" href="#view"></a>
+</h2>
 <p>view(*shape) -&gt; Tensor</p>
 <p>Returns a new tensor with the same data as the <code>self</code> tensor but of a different <code>shape</code>.</p>
 <p>The returned tensor shares the same data and must have the same number of elements, but may have a different size. For a tensor to be viewed, the new view size must be compatible with its original size and stride, i.e., each new view dimension must either be a subspace of an original dimension, or only span across original dimensions <code>d, d+1, \dots, d+k</code> that satisfy the following contiguity-like condition that <code>\forall i = d, \dots, d+k-1</code>,</p>
@@ -3504,66 +3465,63 @@ P(x) = \dfrac{1}{\text{to} - \text{from}}
 \text{stride}[i] = \text{stride}[i+1] \times \text{size}[i+1]
 \]</span></p>
 <p>Otherwise, it will not be possible to view <code>self</code> tensor as <code>shape</code> without copying it (e.g., via <code>contiguous</code>). When it is unclear whether a <code>view</code> can be performed, it is advisable to use :meth:<code>reshape</code>, which returns a view if the shapes are compatible, and copies (equivalent to calling <code>contiguous</code>) otherwise.</p>
-<div id="arguments-57" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-57" class="anchor"></a>Arguments:</h4>
+<div class="section level4">
+<h4 id="arguments-57">Arguments:<a class="anchor" aria-label="anchor" href="#arguments-57"></a>
+</h4>
 <ul>
 <li>shape (torch_Size or int…): the desired size</li>
 </ul>
 </div>
 </div>
-<div id="view_as" class="section level2">
-<h2 class="hasAnchor">
-<a href="#view_as" class="anchor"></a>view_as</h2>
+<div class="section level2">
+<h2 id="view_as">view_as<a class="anchor" aria-label="anchor" href="#view_as"></a>
+</h2>
 <p>view_as(other) -&gt; Tensor</p>
 <p>View this tensor as the same size as <code>other</code>. <code>self$view_as(other)</code> is equivalent to <code>self$view(other.size())</code>.</p>
 <p>Please see <code>$view</code> for more information about <code>view</code>.</p>
-<div id="arguments-58" class="section level4">
-<h4 class="hasAnchor">
-<a href="#arguments-58" class="anchor"></a>Arguments:</h4>
+<div class="section level4">
+<h4 id="arguments-58">Arguments:<a class="anchor" aria-label="anchor" href="#arguments-58"></a>
+</h4>
 <ul>
 <li>other (`$): The result tensor has the same size</li>
 <li>as <code>other</code>.</li>
 </ul>
 </div>
 </div>
-<div id="where" class="section level2">
-<h2 class="hasAnchor">
-<a href="#where" class="anchor"></a>where</h2>
+<div class="section level2">
+<h2 id="where">where<a class="anchor" aria-label="anchor" href="#where"></a>
+</h2>
 <p>where(condition, y) -&gt; Tensor</p>
 <p><code>self$where(condition, y)</code> is equivalent to <code>torch_where(condition, self, y)</code>. See <code><a href="../../reference/torch_where.html">?torch_where</a></code></p>
 </div>
-<div id="zero_" class="section level2">
-<h2 class="hasAnchor">
-<a href="#zero_" class="anchor"></a>zero_</h2>
+<div class="section level2">
+<h2 id="zero_">zero_<a class="anchor" aria-label="anchor" href="#zero_"></a>
+</h2>
 <p>zero_() -&gt; Tensor</p>
 <p>Fills <code>self</code> tensor with zeros.</p>
 </div>
-  </div>
-
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/articles/tensor/index_files/anchor-sections-1.0/anchor-sections.css b/articles/tensor/index_files/anchor-sections-1.0/anchor-sections.css
deleted file mode 100644
index 07aee5fcb..000000000
--- a/articles/tensor/index_files/anchor-sections-1.0/anchor-sections.css
+++ /dev/null
@@ -1,4 +0,0 @@
-/* Styles for section anchors */
-a.anchor-section {margin-left: 10px; visibility: hidden; color: inherit;}
-a.anchor-section::before {content: '#';}
-.hasAnchor:hover a.anchor-section {visibility: visible;}
diff --git a/articles/tensor/index_files/anchor-sections-1.0/anchor-sections.js b/articles/tensor/index_files/anchor-sections-1.0/anchor-sections.js
deleted file mode 100644
index 570f99a0a..000000000
--- a/articles/tensor/index_files/anchor-sections-1.0/anchor-sections.js
+++ /dev/null
@@ -1,33 +0,0 @@
-// Anchor sections v1.0 written by Atsushi Yasumoto on Oct 3rd, 2020.
-document.addEventListener('DOMContentLoaded', function() {
-  // Do nothing if AnchorJS is used
-  if (typeof window.anchors === 'object' && anchors.hasOwnProperty('hasAnchorJSLink')) {
-    return;
-  }
-
-  const h = document.querySelectorAll('h1, h2, h3, h4, h5, h6');
-
-  // Do nothing if sections are already anchored
-  if (Array.from(h).some(x => x.classList.contains('hasAnchor'))) {
-    return null;
-  }
-
-  // Use section id when pandoc runs with --section-divs
-  const section_id = function(x) {
-    return ((x.classList.contains('section') || (x.tagName === 'SECTION'))
-            ? x.id : '');
-  };
-
-  // Add anchors
-  h.forEach(function(x) {
-    const id = x.id || section_id(x.parentElement);
-    if (id === '') {
-      return null;
-    }
-    let anchor = document.createElement('a');
-    anchor.href = '#' + id;
-    anchor.classList = ['anchor-section'];
-    x.classList.add('hasAnchor');
-    x.appendChild(anchor);
-  });
-});
diff --git a/articles/torchscript.html b/articles/torchscript.html
index 0ad8ce98e..74fea9473 100644
--- a/articles/torchscript.html
+++ b/articles/torchscript.html
@@ -4,14 +4,14 @@
 <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
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+<meta name="description" content="torch">
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 <meta property="og:title" content="TorchScript">
 <meta property="og:description" content="torch">
 <!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
@@ -25,128 +25,89 @@
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 </script>
 </head>
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+
 
-      
 
-      </header><script src="torchscript_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header toc-ignore">
-      <h1 data-toc-skip>TorchScript</h1>
+<script src="torchscript_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row">
+  <main id="main"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>TorchScript</h1>
             
       
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/master/vignettes/torchscript.Rmd"><code>vignettes/torchscript.Rmd</code></a></small>
-      <div class="hidden name"><code>torchscript.Rmd</code></div>
-
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/vignettes/torchscript.Rmd" class="external-link"><code>vignettes/torchscript.Rmd</code></a></small>
+      <div class="d-none name"><code>torchscript.Rmd</code></div>
     </div>
 
     
     
 <div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://torch.mlverse.org/docs">torch</a></span><span class="op">)</span></code></pre></div>
-<p><a href="https://pytorch.org/docs/stable/jit_language_reference.html#language-reference">TorchScript</a> is a statically typed subset of Python that can be interpreted by LibTorch without any Python dependency. The torch R package provides interfaces to create, serialize, load and execute TorchScript programs.</p>
+<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://torch.mlverse.org/docs">torch</a></span><span class="op">)</span></code></pre></div>
+<p><a href="https://pytorch.org/docs/stable/jit_language_reference.html#language-reference" class="external-link">TorchScript</a> is a statically typed subset of Python that can be interpreted by LibTorch without any Python dependency. The torch R package provides interfaces to create, serialize, load and execute TorchScript programs.</p>
 <p>Advantages of using TorchScript are:</p>
 <ul>
 <li><p>TorchScript code can be invoked in its own interpreter, which is basically a restricted Python interpreter. This interpreter does not acquire the Global Interpreter Lock, and so many requests can be processed on the same instance simultaneously.</p></li>
@@ -154,12 +115,12 @@
 <li><p>TorchScript gives us a representation in which we can do compiler optimizations on the code to make execution more efficient.</p></li>
 <li><p>TorchScript allows us to interface with many backend/device runtimes that require a broader view of the program than individual operators.</p></li>
 </ul>
-<div id="creating-torchscript-programs" class="section level2">
-<h2 class="hasAnchor">
-<a href="#creating-torchscript-programs" class="anchor"></a>Creating TorchScript programs</h2>
-<div id="tracing" class="section level3">
-<h3 class="hasAnchor">
-<a href="#tracing" class="anchor"></a>Tracing</h3>
+<div class="section level2">
+<h2 id="creating-torchscript-programs">Creating TorchScript programs<a class="anchor" aria-label="anchor" href="#creating-torchscript-programs"></a>
+</h2>
+<div class="section level3">
+<h3 id="tracing">Tracing<a class="anchor" aria-label="anchor" href="#tracing"></a>
+</h3>
 <p>TorchScript programs can be created from R using tracing. When using tracing, code is automatically converted into this subset of Python by recording only the actual operators on tensors and simply executing and discarding the other surrounding R code.</p>
 <p>Currently tracing is the only supported way to create TorchScript programs from R code.</p>
 <p>For example, let’s use the <code>jit_trace</code> function to create a TorchScript program. We pass a regular R function and example inputs.</p>
@@ -168,7 +129,7 @@
   <span class="fu"><a href="../reference/torch_relu.html">torch_relu</a></span><span class="op">(</span><span class="va">x</span><span class="op">)</span>
 <span class="op">}</span>
 
-<span class="va">traced_fn</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/jit_trace.html">jit_trace</a></span><span class="op">(</span><span class="va">fn</span>, <span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="op">-</span><span class="fl">1</span>, <span class="fl">0</span>, <span class="fl">1</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></code></pre></div>
+<span class="va">traced_fn</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/jit_trace.html">jit_trace</a></span><span class="op">(</span><span class="va">fn</span>, <span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="op">-</span><span class="fl">1</span>, <span class="fl">0</span>, <span class="fl">1</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></code></pre></div>
 <p>The <code>jit_trace</code> function has executed the R function with the example input and recorded all torch operations that occurred during execution to create a <em>graph</em>. <em>graph</em> is how we call the intermediate representation of TorchScript programs, and it can be inspected with:</p>
 <div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span class="va">traced_fn</span><span class="op">$</span><span class="va">graph</span>
@@ -179,9 +140,9 @@
 <div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span class="fu">traced_fn</span><span class="op">(</span><span class="fu"><a href="../reference/torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">3</span><span class="op">)</span><span class="op">)</span>
 <span class="co">#&gt; torch_tensor</span>
-<span class="co">#&gt;  1.4243</span>
+<span class="co">#&gt;  0.3837</span>
 <span class="co">#&gt;  0.0000</span>
-<span class="co">#&gt;  1.3047</span>
+<span class="co">#&gt;  2.4086</span>
 <span class="co">#&gt; [ CPUFloatType{3} ]</span></code></pre></div>
 <p>It’s also possible to trace <code>nn_modules()</code> defined in R, for example:</p>
 <div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
@@ -191,9 +152,9 @@
     <span class="va">self</span><span class="op">$</span><span class="va">linear2</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/nn_linear.html">nn_linear</a></span><span class="op">(</span><span class="fl">10</span>, <span class="fl">1</span><span class="op">)</span>
   <span class="op">}</span>,
   forward <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="op">{</span>
-    <span class="va">x</span> <span class="op"><a href="../reference/%&gt;%.html">%&gt;%</a></span> 
-      <span class="va">self</span><span class="op">$</span><span class="fu">linear1</span><span class="op">(</span><span class="op">)</span> <span class="op"><a href="../reference/%&gt;%.html">%&gt;%</a></span> 
-      <span class="fu"><a href="../reference/nnf_relu.html">nnf_relu</a></span><span class="op">(</span><span class="op">)</span> <span class="op"><a href="../reference/%&gt;%.html">%&gt;%</a></span> 
+    <span class="va">x</span> <span class="op"><a href="../reference/pipe.html">%&gt;%</a></span> 
+      <span class="va">self</span><span class="op">$</span><span class="fu">linear1</span><span class="op">(</span><span class="op">)</span> <span class="op"><a href="../reference/pipe.html">%&gt;%</a></span> 
+      <span class="fu"><a href="../reference/nnf_relu.html">nnf_relu</a></span><span class="op">(</span><span class="op">)</span> <span class="op"><a href="../reference/pipe.html">%&gt;%</a></span> 
       <span class="va">self</span><span class="op">$</span><span class="fu">linear2</span><span class="op">(</span><span class="op">)</span>
   <span class="op">}</span>
 <span class="op">)</span>
@@ -202,22 +163,22 @@
 <div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span class="fu">traced_module</span><span class="op">(</span><span class="fu"><a href="../reference/torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">10</span><span class="op">)</span><span class="op">)</span>
 <span class="co">#&gt; torch_tensor</span>
-<span class="co">#&gt; -0.1522</span>
-<span class="co">#&gt; -0.2576</span>
-<span class="co">#&gt; -0.4480</span>
+<span class="co">#&gt;  0.1978</span>
+<span class="co">#&gt;  0.1542</span>
+<span class="co">#&gt;  0.1632</span>
 <span class="co">#&gt; [ CPUFloatType{3,1} ][ grad_fn = &lt;AddBackward0&gt; ]</span></code></pre></div>
-<div id="limitations-of-tracing" class="section level4">
-<h4 class="hasAnchor">
-<a href="#limitations-of-tracing" class="anchor"></a>Limitations of tracing</h4>
+<div class="section level4">
+<h4 id="limitations-of-tracing">Limitations of tracing<a class="anchor" aria-label="anchor" href="#limitations-of-tracing"></a>
+</h4>
 <ol style="list-style-type: decimal">
 <li>Tracing will not record any control flow like if-statements or loops. When this control flow is constant across your module, this is fine and it often inlines the control flow decisions. But sometimes the control flow is actually part of the model itself. For instance, a recurrent network is a loop over the (possibly dynamic) length of an input sequence. For example:</li>
 </ol>
 <div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span class="co"># fn does does an operation for each dimension of a tensor</span>
 <span class="va">fn</span> <span class="op">&lt;-</span> <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="op">{</span>
-  <span class="va">x</span> <span class="op"><a href="../reference/%&gt;%.html">%&gt;%</a></span> 
-    <span class="fu"><a href="../reference/torch_unbind.html">torch_unbind</a></span><span class="op">(</span>dim <span class="op">=</span> <span class="fl">1</span><span class="op">)</span> <span class="op"><a href="../reference/%&gt;%.html">%&gt;%</a></span> 
-    <span class="fu"><a href="https://rdrr.io/r/base/lapply.html">lapply</a></span><span class="op">(</span><span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="va">x</span><span class="op">$</span><span class="fu">sum</span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op"><a href="../reference/%&gt;%.html">%&gt;%</a></span> 
+  <span class="va">x</span> <span class="op"><a href="../reference/pipe.html">%&gt;%</a></span> 
+    <span class="fu"><a href="../reference/torch_unbind.html">torch_unbind</a></span><span class="op">(</span>dim <span class="op">=</span> <span class="fl">1</span><span class="op">)</span> <span class="op"><a href="../reference/pipe.html">%&gt;%</a></span> 
+    <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="va">x</span><span class="op">$</span><span class="fu">sum</span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op"><a href="../reference/pipe.html">%&gt;%</a></span> 
     <span class="fu"><a href="../reference/torch_stack.html">torch_stack</a></span><span class="op">(</span>dim <span class="op">=</span> <span class="fl">1</span><span class="op">)</span>
 <span class="op">}</span>
 <span class="co"># we trace using as an example a tensor with size (10, 5, 5)</span>
@@ -234,17 +195,17 @@
 <code class="sourceCode R"><span class="va">traced_dropout</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/jit_trace.html">jit_trace</a></span><span class="op">(</span><span class="fu"><a href="../reference/nn_dropout.html">nn_dropout</a></span><span class="op">(</span><span class="op">)</span>, <span class="fu"><a href="../reference/torch_ones.html">torch_ones</a></span><span class="op">(</span><span class="fl">5</span>,<span class="fl">5</span><span class="op">)</span><span class="op">)</span>
 <span class="fu">traced_dropout</span><span class="op">(</span><span class="fu"><a href="../reference/torch_ones.html">torch_ones</a></span><span class="op">(</span><span class="fl">3</span>,<span class="fl">3</span><span class="op">)</span><span class="op">)</span>
 <span class="co">#&gt; torch_tensor</span>
+<span class="co">#&gt;  2  2  0</span>
 <span class="co">#&gt;  0  2  0</span>
-<span class="co">#&gt;  2  0  2</span>
-<span class="co">#&gt;  0  2  0</span>
+<span class="co">#&gt;  2  0  0</span>
 <span class="co">#&gt; [ CPUFloatType{3,3} ]</span>
 <span class="va">traced_dropout</span><span class="op">$</span><span class="fu">eval</span><span class="op">(</span><span class="op">)</span>
 <span class="co"># even after setting to eval mode, dropout is applied</span>
 <span class="fu">traced_dropout</span><span class="op">(</span><span class="fu"><a href="../reference/torch_ones.html">torch_ones</a></span><span class="op">(</span><span class="fl">3</span>,<span class="fl">3</span><span class="op">)</span><span class="op">)</span>
 <span class="co">#&gt; torch_tensor</span>
-<span class="co">#&gt;  2  2  0</span>
-<span class="co">#&gt;  0  2  2</span>
-<span class="co">#&gt;  0  0  0</span>
+<span class="co">#&gt;  2  2  2</span>
+<span class="co">#&gt;  0  0  2</span>
+<span class="co">#&gt;  2  0  0</span>
 <span class="co">#&gt; [ CPUFloatType{3,3} ]</span></code></pre></div>
 <ol start="3" style="list-style-type: decimal">
 <li>Tracing proegrams can only take tensors and lists of tensors as input and return tensors and lists of tensors. For example:</li>
@@ -256,76 +217,76 @@
 <span class="fu"><a href="../reference/jit_trace.html">jit_trace</a></span><span class="op">(</span><span class="va">fn</span>, <span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fl">1</span><span class="op">)</span>, <span class="fl">1</span><span class="op">)</span>
 <span class="co">#&gt; Error in cpp_trace_function(tr_fn, list(...), .compilation_unit, strict, : Only tensors or (possibly nested) dict or tuples of tensors can be inputs to traced functions. Got float</span>
 <span class="co">#&gt; Exception raised from addInput at ../torch/csrc/jit/frontend/tracer.cpp:408 (most recent call first):</span>
-<span class="co">#&gt; frame #0: c10::Error::Error(c10::SourceLocation, std::__1::basic_string&lt;char, std::__1::char_traits&lt;char&gt;, std::__1::allocator&lt;char&gt; &gt;) + 98 (0x10e2cb5f2 in libc10.dylib)</span>
-<span class="co">#&gt; frame #1: c10::detail::torchCheckFail(char const*, char const*, unsigned int, std::__1::basic_string&lt;char, std::__1::char_traits&lt;char&gt;, std::__1::allocator&lt;char&gt; &gt; const&amp;) + 106 (0x10e2c9d6a in libc10.dylib)</span>
-<span class="co">#&gt; frame #2: torch::jit::tracer::addInput(std::__1::shared_ptr&lt;torch::jit::tracer::TracingState&gt; const&amp;, c10::IValue const&amp;, std::__1::shared_ptr&lt;c10::Type&gt; const&amp;, torch::jit::Value*) + 6951 (0x113fa8fe7 in libtorch_cpu.dylib)</span>
-<span class="co">#&gt; frame #3: torch::jit::tracer::addInput(std::__1::shared_ptr&lt;torch::jit::tracer::TracingState&gt; const&amp;, c10::IValue const&amp;, std::__1::shared_ptr&lt;c10::Type&gt; const&amp;, torch::jit::Value*) + 4216 (0x113fa8538 in libtorch_cpu.dylib)</span>
-<span class="co">#&gt; frame #4: torch::jit::tracer::trace(std::__1::vector&lt;c10::IValue, std::__1::allocator&lt;c10::IValue&gt; &gt;, std::__1::function&lt;std::__1::vector&lt;c10::IValue, std::__1::allocator&lt;c10::IValue&gt; &gt; (std::__1::vector&lt;c10::IValue, std::__1::allocator&lt;c10::IValue&gt; &gt;)&gt; const&amp;, std::__1::function&lt;std::__1::basic_string&lt;char, std::__1::char_traits&lt;char&gt;, std::__1::allocator&lt;char&gt; &gt; (at::Tensor const&amp;)&gt;, bool, bool, torch::jit::Module*, std::__1::vector&lt;std::__1::basic_string&lt;char, std::__1::char_traits&lt;char&gt;, std::__1::allocator&lt;char&gt; &gt;, std::__1::allocator&lt;std::__1::basic_string&lt;char, std::__1::char_traits&lt;char&gt;, std::__1::allocator&lt;char&gt; &gt; &gt; &gt; const&amp;) + 1034 (0x113fa5b4a in libtorch_cpu.dylib)</span>
-<span class="co">#&gt; frame #5: _lantern_trace_fn + 533 (0x10e0dc7d5 in liblantern.dylib)</span>
-<span class="co">#&gt; frame #6: cpp_trace_function(Rcpp::Function_Impl&lt;Rcpp::PreserveStorage&gt;, XPtrTorchStack, XPtrTorchCompilationUnit, XPtrTorchstring, bool, XPtrTorchScriptModule, bool, bool) + 547 (0x10d6f9703 in torchpkg.so)</span>
-<span class="co">#&gt; frame #7: _torch_cpp_trace_function + 720 (0x10d577190 in torchpkg.so)</span>
-<span class="co">#&gt; frame #8: R_doDotCall + 2679 (0x107782737 in libR.dylib)</span>
-<span class="co">#&gt; frame #9: do_dotcall + 334 (0x107783bfe in libR.dylib)</span>
-<span class="co">#&gt; frame #10: bcEval + 28581 (0x1077bac05 in libR.dylib)</span>
-<span class="co">#&gt; frame #11: Rf_eval + 385 (0x1077b35b1 in libR.dylib)</span>
-<span class="co">#&gt; frame #12: R_execClosure + 2169 (0x1077d3429 in libR.dylib)</span>
-<span class="co">#&gt; frame #13: Rf_applyClosure + 471 (0x1077d2217 in libR.dylib)</span>
-<span class="co">#&gt; frame #14: bcEval + 26782 (0x1077ba4fe in libR.dylib)</span>
-<span class="co">#&gt; frame #15: Rf_eval + 385 (0x1077b35b1 in libR.dylib)</span>
-<span class="co">#&gt; frame #16: R_execClosure + 2169 (0x1077d3429 in libR.dylib)</span>
-<span class="co">#&gt; frame #17: Rf_applyClosure + 471 (0x1077d2217 in libR.dylib)</span>
-<span class="co">#&gt; frame #18: Rf_eval + 1595 (0x1077b3a6b in libR.dylib)</span>
-<span class="co">#&gt; frame #19: do_eval + 625 (0x1077d7461 in libR.dylib)</span>
-<span class="co">#&gt; frame #20: bcEval + 28581 (0x1077bac05 in libR.dylib)</span>
-<span class="co">#&gt; frame #21: Rf_eval + 385 (0x1077b35b1 in libR.dylib)</span>
-<span class="co">#&gt; frame #22: R_execClosure + 2169 (0x1077d3429 in libR.dylib)</span>
-<span class="co">#&gt; frame #23: Rf_applyClosure + 471 (0x1077d2217 in libR.dylib)</span>
-<span class="co">#&gt; frame #24: bcEval + 26782 (0x1077ba4fe in libR.dylib)</span>
-<span class="co">#&gt; frame #25: Rf_eval + 385 (0x1077b35b1 in libR.dylib)</span>
-<span class="co">#&gt; frame #26: forcePromise + 172 (0x1077d192c in libR.dylib)</span>
-<span class="co">#&gt; frame #27: Rf_eval + 1124 (0x1077b3894 in libR.dylib)</span>
-<span class="co">#&gt; frame #28: do_withVisible + 57 (0x1077d7ad9 in libR.dylib)</span>
-<span class="co">#&gt; frame #29: do_internal + 362 (0x10781b2fa in libR.dylib)</span>
-<span class="co">#&gt; frame #30: bcEval + 29053 (0x1077baddd in libR.dylib)</span>
-<span class="co">#&gt; frame #31: Rf_eval + 385 (0x1077b35b1 in libR.dylib)</span>
-<span class="co">#&gt; frame #32: R_execClosure + 2169 (0x1077d3429 in libR.dylib)</span>
-<span class="co">#&gt; frame #33: Rf_applyClosure + 471 (0x1077d2217 in libR.dylib)</span>
-<span class="co">#&gt; frame #34: bcEval + 26782 (0x1077ba4fe in libR.dylib)</span>
-<span class="co">#&gt; frame #35: Rf_eval + 385 (0x1077b35b1 in libR.dylib)</span>
-<span class="co">#&gt; frame #36: forcePromise + 172 (0x1077d192c in libR.dylib)</span>
-<span class="co">#&gt; frame #37: getvar + 778 (0x1077dcf7a in libR.dylib)</span>
-<span class="co">#&gt; frame #38: bcEval + 15063 (0x1077b7737 in libR.dylib)</span>
-<span class="co">#&gt; frame #39: Rf_eval + 385 (0x1077b35b1 in libR.dylib)</span>
-<span class="co">#&gt; frame #40: R_execClosure + 2169 (0x1077d3429 in libR.dylib)</span>
-<span class="co">#&gt; frame #41: Rf_applyClosure + 471 (0x1077d2217 in libR.dylib)</span>
-<span class="co">#&gt; frame #42: bcEval + 26782 (0x1077ba4fe in libR.dylib)</span>
-<span class="co">#&gt; frame #43: Rf_eval + 385 (0x1077b35b1 in libR.dylib)</span>
-<span class="co">#&gt; frame #44: forcePromise + 172 (0x1077d192c in libR.dylib)</span>
-<span class="co">#&gt; frame #45: getvar + 778 (0x1077dcf7a in libR.dylib)</span>
-<span class="co">#&gt; frame #46: bcEval + 15063 (0x1077b7737 in libR.dylib)</span>
-<span class="co">#&gt; frame #47: Rf_eval + 385 (0x1077b35b1 in libR.dylib)</span>
-<span class="co">#&gt; frame #48: forcePromise + 172 (0x1077d192c in libR.dylib)</span>
-<span class="co">#&gt; frame #49: getvar + 778 (0x1077dcf7a in libR.dylib)</span>
-<span class="co">#&gt; frame #50: bcEval + 15063 (0x1077b7737 in libR.dylib)</span>
-<span class="co">#&gt; frame #51: Rf_eval + 385 (0x1077b35b1 in libR.dylib)</span>
-<span class="co">#&gt; frame #52: forcePromise + 172 (0x1077d192c in libR.dylib)</span>
-<span class="co">#&gt; frame #53: getvar + 778 (0x1077dcf7a in libR.dylib)</span>
-<span class="co">#&gt; frame #54: bcEval + 15063 (0x1077b7737 in libR.dylib)</span>
-<span class="co">#&gt; frame #55: Rf_eval + 385 (0x1077b35b1 in libR.dylib)</span>
-<span class="co">#&gt; frame #56: forcePromise + 172 (0x1077d192c in libR.dylib)</span>
-<span class="co">#&gt; frame #57: getvar + 778 (0x1077dcf7a in libR.dylib)</span>
-<span class="co">#&gt; frame #58: bcEval + 15063 (0x1077b7737 in libR.dylib)</span>
-<span class="co">#&gt; frame #59: Rf_eval + 385 (0x1077b35b1 in libR.dylib)</span>
-<span class="co">#&gt; frame #60: forcePromise + 172 (0x1077d192c in libR.dylib)</span>
-<span class="co">#&gt; frame #61: getvar + 778 (0x1077dcf7a in libR.dylib)</span>
-<span class="co">#&gt; frame #62: bcEval + 15063 (0x1077b7737 in libR.dylib)</span>
-<span class="co">#&gt; frame #63: Rf_eval + 385 (0x1077b35b1 in libR.dylib)</span>
+<span class="co">#&gt; frame #0: c10::Error::Error(c10::SourceLocation, std::__1::basic_string&lt;char, std::__1::char_traits&lt;char&gt;, std::__1::allocator&lt;char&gt; &gt;) + 98 (0x1135cb522 in libc10.dylib)</span>
+<span class="co">#&gt; frame #1: c10::detail::torchCheckFail(char const*, char const*, unsigned int, std::__1::basic_string&lt;char, std::__1::char_traits&lt;char&gt;, std::__1::allocator&lt;char&gt; &gt; const&amp;) + 106 (0x1135c9c3a in libc10.dylib)</span>
+<span class="co">#&gt; frame #2: torch::jit::tracer::addInput(std::__1::shared_ptr&lt;torch::jit::tracer::TracingState&gt; const&amp;, c10::IValue const&amp;, std::__1::shared_ptr&lt;c10::Type&gt; const&amp;, torch::jit::Value*) + 6951 (0x1191e58e7 in libtorch_cpu.dylib)</span>
+<span class="co">#&gt; frame #3: torch::jit::tracer::addInput(std::__1::shared_ptr&lt;torch::jit::tracer::TracingState&gt; const&amp;, c10::IValue const&amp;, std::__1::shared_ptr&lt;c10::Type&gt; const&amp;, torch::jit::Value*) + 4216 (0x1191e4e38 in libtorch_cpu.dylib)</span>
+<span class="co">#&gt; frame #4: torch::jit::tracer::trace(std::__1::vector&lt;c10::IValue, std::__1::allocator&lt;c10::IValue&gt; &gt;, std::__1::function&lt;std::__1::vector&lt;c10::IValue, std::__1::allocator&lt;c10::IValue&gt; &gt; (std::__1::vector&lt;c10::IValue, std::__1::allocator&lt;c10::IValue&gt; &gt;)&gt; const&amp;, std::__1::function&lt;std::__1::basic_string&lt;char, std::__1::char_traits&lt;char&gt;, std::__1::allocator&lt;char&gt; &gt; (at::Tensor const&amp;)&gt;, bool, bool, torch::jit::Module*, std::__1::vector&lt;std::__1::basic_string&lt;char, std::__1::char_traits&lt;char&gt;, std::__1::allocator&lt;char&gt; &gt;, std::__1::allocator&lt;std::__1::basic_string&lt;char, std::__1::char_traits&lt;char&gt;, std::__1::allocator&lt;char&gt; &gt; &gt; &gt; const&amp;) + 1034 (0x1191e244a in libtorch_cpu.dylib)</span>
+<span class="co">#&gt; frame #5: _lantern_trace_fn + 541 (0x1160f146d in liblantern.dylib)</span>
+<span class="co">#&gt; frame #6: cpp_trace_function(Rcpp::Function_Impl&lt;Rcpp::PreserveStorage&gt;, XPtrTorchStack, XPtrTorchCompilationUnit, XPtrTorchstring, bool, XPtrTorchScriptModule, bool, bool) + 547 (0x1156609a3 in torchpkg.so)</span>
+<span class="co">#&gt; frame #7: _torch_cpp_trace_function + 727 (0x1154e4357 in torchpkg.so)</span>
+<span class="co">#&gt; frame #8: R_doDotCall + 2679 (0x10d98d737 in libR.dylib)</span>
+<span class="co">#&gt; frame #9: do_dotcall + 334 (0x10d98ebfe in libR.dylib)</span>
+<span class="co">#&gt; frame #10: bcEval + 28581 (0x10d9c5c05 in libR.dylib)</span>
+<span class="co">#&gt; frame #11: Rf_eval + 385 (0x10d9be5b1 in libR.dylib)</span>
+<span class="co">#&gt; frame #12: R_execClosure + 2169 (0x10d9de429 in libR.dylib)</span>
+<span class="co">#&gt; frame #13: Rf_applyClosure + 471 (0x10d9dd217 in libR.dylib)</span>
+<span class="co">#&gt; frame #14: bcEval + 26782 (0x10d9c54fe in libR.dylib)</span>
+<span class="co">#&gt; frame #15: Rf_eval + 385 (0x10d9be5b1 in libR.dylib)</span>
+<span class="co">#&gt; frame #16: R_execClosure + 2169 (0x10d9de429 in libR.dylib)</span>
+<span class="co">#&gt; frame #17: Rf_applyClosure + 471 (0x10d9dd217 in libR.dylib)</span>
+<span class="co">#&gt; frame #18: Rf_eval + 1595 (0x10d9bea6b in libR.dylib)</span>
+<span class="co">#&gt; frame #19: do_eval + 625 (0x10d9e2461 in libR.dylib)</span>
+<span class="co">#&gt; frame #20: bcEval + 28581 (0x10d9c5c05 in libR.dylib)</span>
+<span class="co">#&gt; frame #21: Rf_eval + 385 (0x10d9be5b1 in libR.dylib)</span>
+<span class="co">#&gt; frame #22: R_execClosure + 2169 (0x10d9de429 in libR.dylib)</span>
+<span class="co">#&gt; frame #23: Rf_applyClosure + 471 (0x10d9dd217 in libR.dylib)</span>
+<span class="co">#&gt; frame #24: bcEval + 26782 (0x10d9c54fe in libR.dylib)</span>
+<span class="co">#&gt; frame #25: Rf_eval + 385 (0x10d9be5b1 in libR.dylib)</span>
+<span class="co">#&gt; frame #26: forcePromise + 172 (0x10d9dc92c in libR.dylib)</span>
+<span class="co">#&gt; frame #27: Rf_eval + 1124 (0x10d9be894 in libR.dylib)</span>
+<span class="co">#&gt; frame #28: do_withVisible + 57 (0x10d9e2ad9 in libR.dylib)</span>
+<span class="co">#&gt; frame #29: do_internal + 362 (0x10da262fa in libR.dylib)</span>
+<span class="co">#&gt; frame #30: bcEval + 29053 (0x10d9c5ddd in libR.dylib)</span>
+<span class="co">#&gt; frame #31: Rf_eval + 385 (0x10d9be5b1 in libR.dylib)</span>
+<span class="co">#&gt; frame #32: R_execClosure + 2169 (0x10d9de429 in libR.dylib)</span>
+<span class="co">#&gt; frame #33: Rf_applyClosure + 471 (0x10d9dd217 in libR.dylib)</span>
+<span class="co">#&gt; frame #34: bcEval + 26782 (0x10d9c54fe in libR.dylib)</span>
+<span class="co">#&gt; frame #35: Rf_eval + 385 (0x10d9be5b1 in libR.dylib)</span>
+<span class="co">#&gt; frame #36: forcePromise + 172 (0x10d9dc92c in libR.dylib)</span>
+<span class="co">#&gt; frame #37: getvar + 778 (0x10d9e7f7a in libR.dylib)</span>
+<span class="co">#&gt; frame #38: bcEval + 15063 (0x10d9c2737 in libR.dylib)</span>
+<span class="co">#&gt; frame #39: Rf_eval + 385 (0x10d9be5b1 in libR.dylib)</span>
+<span class="co">#&gt; frame #40: R_execClosure + 2169 (0x10d9de429 in libR.dylib)</span>
+<span class="co">#&gt; frame #41: Rf_applyClosure + 471 (0x10d9dd217 in libR.dylib)</span>
+<span class="co">#&gt; frame #42: bcEval + 26782 (0x10d9c54fe in libR.dylib)</span>
+<span class="co">#&gt; frame #43: Rf_eval + 385 (0x10d9be5b1 in libR.dylib)</span>
+<span class="co">#&gt; frame #44: forcePromise + 172 (0x10d9dc92c in libR.dylib)</span>
+<span class="co">#&gt; frame #45: getvar + 778 (0x10d9e7f7a in libR.dylib)</span>
+<span class="co">#&gt; frame #46: bcEval + 15063 (0x10d9c2737 in libR.dylib)</span>
+<span class="co">#&gt; frame #47: Rf_eval + 385 (0x10d9be5b1 in libR.dylib)</span>
+<span class="co">#&gt; frame #48: forcePromise + 172 (0x10d9dc92c in libR.dylib)</span>
+<span class="co">#&gt; frame #49: getvar + 778 (0x10d9e7f7a in libR.dylib)</span>
+<span class="co">#&gt; frame #50: bcEval + 15063 (0x10d9c2737 in libR.dylib)</span>
+<span class="co">#&gt; frame #51: Rf_eval + 385 (0x10d9be5b1 in libR.dylib)</span>
+<span class="co">#&gt; frame #52: forcePromise + 172 (0x10d9dc92c in libR.dylib)</span>
+<span class="co">#&gt; frame #53: getvar + 778 (0x10d9e7f7a in libR.dylib)</span>
+<span class="co">#&gt; frame #54: bcEval + 15063 (0x10d9c2737 in libR.dylib)</span>
+<span class="co">#&gt; frame #55: Rf_eval + 385 (0x10d9be5b1 in libR.dylib)</span>
+<span class="co">#&gt; frame #56: forcePromise + 172 (0x10d9dc92c in libR.dylib)</span>
+<span class="co">#&gt; frame #57: getvar + 778 (0x10d9e7f7a in libR.dylib)</span>
+<span class="co">#&gt; frame #58: bcEval + 15063 (0x10d9c2737 in libR.dylib)</span>
+<span class="co">#&gt; frame #59: Rf_eval + 385 (0x10d9be5b1 in libR.dylib)</span>
+<span class="co">#&gt; frame #60: forcePromise + 172 (0x10d9dc92c in libR.dylib)</span>
+<span class="co">#&gt; frame #61: getvar + 778 (0x10d9e7f7a in libR.dylib)</span>
+<span class="co">#&gt; frame #62: bcEval + 15063 (0x10d9c2737 in libR.dylib)</span>
+<span class="co">#&gt; frame #63: Rf_eval + 385 (0x10d9be5b1 in libR.dylib)</span>
 <span class="co">#&gt; :</span></code></pre></div>
 </div>
 </div>
-<div id="compiling-torchscript" class="section level3">
-<h3 class="hasAnchor">
-<a href="#compiling-torchscript" class="anchor"></a>Compiling TorchScript</h3>
+<div class="section level3">
+<h3 id="compiling-torchscript">Compiling TorchScript<a class="anchor" aria-label="anchor" href="#compiling-torchscript"></a>
+</h3>
 <p>It’s also possible to create TorchScript programs by compiling TorchScript code. TorchScript code looks a lot like standard python code. For example:</p>
 <div class="sourceCode" id="cb10"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span class="va">tr</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/jit_compile.html">jit_compile</a></span><span class="op">(</span><span class="st">"
@@ -333,7 +294,7 @@ def fn (x: Tensor):
   return torch.relu(x)
 
 "</span><span class="op">)</span>
-<span class="va">tr</span><span class="op">$</span><span class="fu">fn</span><span class="op">(</span><span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="op">-</span><span class="fl">1</span>, <span class="fl">0</span>, <span class="fl">1</span><span class="op">)</span><span class="op">)</span><span class="op">)</span>
+<span class="va">tr</span><span class="op">$</span><span class="fu">fn</span><span class="op">(</span><span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="op">-</span><span class="fl">1</span>, <span class="fl">0</span>, <span class="fl">1</span><span class="op">)</span><span class="op">)</span><span class="op">)</span>
 <span class="co">#&gt; torch_tensor</span>
 <span class="co">#&gt;  0</span>
 <span class="co">#&gt;  0</span>
@@ -341,9 +302,9 @@ def fn (x: Tensor):
 <span class="co">#&gt; [ CPUFloatType{3} ]</span></code></pre></div>
 </div>
 </div>
-<div id="serializing-and-loading" class="section level2">
-<h2 class="hasAnchor">
-<a href="#serializing-and-loading" class="anchor"></a>Serializing and loading</h2>
+<div class="section level2">
+<h2 id="serializing-and-loading">Serializing and loading<a class="anchor" aria-label="anchor" href="#serializing-and-loading"></a>
+</h2>
 <p>TorchScript programs can be serialized using the <code>jit_save</code> function and loaded back from disk with <code>jit_load</code>.</p>
 <p>For example:</p>
 <div class="sourceCode" id="cb11"><pre class="downlit sourceCode r">
@@ -355,7 +316,7 @@ def fn (x: Tensor):
 <span class="va">loaded</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/jit_load.html">jit_load</a></span><span class="op">(</span><span class="st">"path.pt"</span><span class="op">)</span></code></pre></div>
 <p>Loaded programs can be executed as usual:</p>
 <div class="sourceCode" id="cb12"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="fu">loaded</span><span class="op">(</span><span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="op">-</span><span class="fl">1</span>, <span class="fl">0</span>, <span class="fl">1</span><span class="op">)</span><span class="op">)</span><span class="op">)</span>
+<code class="sourceCode R"><span class="fu">loaded</span><span class="op">(</span><span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="op">-</span><span class="fl">1</span>, <span class="fl">0</span>, <span class="fl">1</span><span class="op">)</span><span class="op">)</span><span class="op">)</span>
 <span class="co">#&gt; torch_tensor</span>
 <span class="co">#&gt;  0</span>
 <span class="co">#&gt;  0</span>
@@ -364,9 +325,9 @@ def fn (x: Tensor):
 <p><strong>Note</strong> You can load TorchScript programs that were created in libraries different than <code>torch</code> for R. Eg, a TorchScript program can be created in PyTorch with <code>torch.jit.trace</code> or <code>torch.jit.script</code>, and run from R.</p>
 <p>R objects are automatically converted to their TorchScript counterpart following the Types table in this document. However, sometimes it’s necessary to make type annotations with <code><a href="../reference/jit_tuple.html">jit_tuple()</a></code> and <code><a href="../reference/jit_scalar.html">jit_scalar()</a></code> to disambiguate the conversion.</p>
 </div>
-<div id="types" class="section level2">
-<h2 class="hasAnchor">
-<a href="#types" class="anchor"></a>Types</h2>
+<div class="section level2">
+<h2 id="types">Types<a class="anchor" aria-label="anchor" href="#types"></a>
+</h2>
 <p>The following table lists all TorchScript types and how to convert the to and back to R.</p>
 <table class="table">
 <colgroup>
@@ -384,7 +345,7 @@ def fn (x: Tensor):
 </tr>
 <tr class="even">
 <td><code>Tuple[T0, T1, ..., TN]</code></td>
-<td>A <code><a href="https://rdrr.io/r/base/list.html">list()</a></code> containing subtypes <code>T0</code>, <code>T1</code>, etc. wrapped with <code><a href="../reference/jit_tuple.html">jit_tuple()</a></code> .</td>
+<td>A <code><a href="https://rdrr.io/r/base/list.html" class="external-link">list()</a></code> containing subtypes <code>T0</code>, <code>T1</code>, etc. wrapped with <code><a href="../reference/jit_tuple.html">jit_tuple()</a></code> .</td>
 </tr>
 <tr class="odd">
 <td><code>bool</code></td>
@@ -429,31 +390,28 @@ def fn (x: Tensor):
 </tbody>
 </table>
 </div>
-  </div>
-
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-
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+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside>
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+  <main id="main"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Using autograd</h1>
             
       
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/master/vignettes/using-autograd.Rmd"><code>vignettes/using-autograd.Rmd</code></a></small>
-      <div class="hidden name"><code>using-autograd.Rmd</code></div>
-
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/vignettes/using-autograd.Rmd" class="external-link"><code>vignettes/using-autograd.Rmd</code></a></small>
+      <div class="d-none name"><code>using-autograd.Rmd</code></div>
     </div>
 
     
     
 <div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://torch.mlverse.org/docs">torch</a></span><span class="op">)</span></code></pre></div>
+<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://torch.mlverse.org/docs">torch</a></span><span class="op">)</span></code></pre></div>
 <p>So far, all we’ve been using from torch is <em>tensors</em>, but we’ve been performing all calculations ourselves – the computing the predictions, the loss, the gradients (and thus, the necessary updates to the weights), and the new weight values. In this chapter, we’ll make a significant change: Namely, we spare ourselves the cumbersome calculation of gradients, and have torch do it for us.</p>
 <p>Before we see that in action, let’s get some more background.</p>
-<div id="automatic-differentiation-with-autograd" class="section level2">
-<h2 class="hasAnchor">
-<a href="#automatic-differentiation-with-autograd" class="anchor"></a>Automatic differentiation with autograd</h2>
+<div class="section level2">
+<h2 id="automatic-differentiation-with-autograd">Automatic differentiation with autograd<a class="anchor" aria-label="anchor" href="#automatic-differentiation-with-autograd"></a>
+</h2>
 <p>Torch uses a module called <em>autograd</em> to record operations performed on tensors, and store what has to be done to obtain the respective gradients. These actions are stored as functions, and those functions are applied in order when the gradient of the output (normally, the loss) with respect to those tensors is calculated: starting from the output node and <em>propagating</em> gradients <em>back</em> through the network. This is a form of <em>reverse mode automatic differentiation</em>.</p>
 <p>As users, we can see a bit of this implementation. As a prerequisite for this “recording” to happen, tensors have to be created with <code>requires_grad = TRUE</code>. E.g.</p>
 <div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/torch_ones.html">torch_ones</a></span><span class="op">(</span><span class="fl">2</span>,<span class="fl">2</span>, requires_grad <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></code></pre></div>
-<p>To be clear, this is a tensor <em>with respect to which</em> gradients have to be calculated – normally, a tensor representing a weight or a bias, not the input data <a href="#fn1" class="footnote-ref" id="fnref1"><sup>1</sup></a>. If we now perform some operation on that tensor, assigning the result to <code>y</code></p>
+<p>To be clear, this is a tensor <em>with respect to which</em> gradients have to be calculated – normally, a tensor representing a weight or a bias, not the input data <a class="footnote-ref" tabindex="0" data-bs-toggle="popover" data-bs-content="&lt;p&gt;Unless we &lt;em&gt;want&lt;/em&gt; to change the data, as in adversarial example generation&lt;/p&gt;"><sup>1</sup></a>. If we now perform some operation on that tensor, assigning the result to <code>y</code></p>
 <div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span class="va">y</span> <span class="op">&lt;-</span> <span class="va">x</span><span class="op">$</span><span class="fu">mean</span><span class="op">(</span><span class="op">)</span></code></pre></div>
 <p>we find that <code>y</code> now has a non-empty <code>grad_fn</code> that tells torch how to compute the gradient of <code>y</code> with respect to <code>x</code>:</p>
@@ -234,9 +195,9 @@
 <span class="co">#&gt; [ CPUFloatType{2,2} ]</span></code></pre></div>
 <p>Thus acquainted with autograd, we’re ready to modify our example.</p>
 </div>
-<div id="the-simple-network-now-using-autograd" class="section level2">
-<h2 class="hasAnchor">
-<a href="#the-simple-network-now-using-autograd" class="anchor"></a>The simple network, now using autograd</h2>
+<div class="section level2">
+<h2 id="the-simple-network-now-using-autograd">The simple network, now using autograd<a class="anchor" aria-label="anchor" href="#the-simple-network-now-using-autograd"></a>
+</h2>
 <p>For a single new line calling <code>loss$backward()</code>, now a number of lines (that did manual backprop) are gone:</p>
 <div class="sourceCode" id="cb10"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span class="co">### generate training data -----------------------------------------------------</span>
@@ -270,7 +231,7 @@
     <span class="va">y_pred</span> <span class="op">&lt;-</span> <span class="va">x</span><span class="op">$</span><span class="fu">mm</span><span class="op">(</span><span class="va">w1</span><span class="op">)</span><span class="op">$</span><span class="fu">add</span><span class="op">(</span><span class="va">b1</span><span class="op">)</span><span class="op">$</span><span class="fu">clamp</span><span class="op">(</span>min <span class="op">=</span> <span class="fl">0</span><span class="op">)</span><span class="op">$</span><span class="fu">mm</span><span class="op">(</span><span class="va">w2</span><span class="op">)</span><span class="op">$</span><span class="fu">add</span><span class="op">(</span><span class="va">b2</span><span class="op">)</span>
     <span class="co">### -------- compute loss -------- </span>
     <span class="va">loss</span> <span class="op">&lt;-</span> <span class="op">(</span><span class="va">y_pred</span> <span class="op">-</span> <span class="va">y</span><span class="op">)</span><span class="op">$</span><span class="fu">pow</span><span class="op">(</span><span class="fl">2</span><span class="op">)</span><span class="op">$</span><span class="fu">mean</span><span class="op">(</span><span class="op">)</span>
-    <span class="kw">if</span> <span class="op">(</span><span class="va">t</span> <span class="op"><a href="https://rdrr.io/r/base/Arithmetic.html">%%</a></span> <span class="fl">10</span> <span class="op">==</span> <span class="fl">0</span><span class="op">)</span> <span class="fu"><a href="https://rdrr.io/r/base/cat.html">cat</a></span><span class="op">(</span><span class="va">t</span>, <span class="fu"><a href="../reference/as_array.html">as_array</a></span><span class="op">(</span><span class="va">loss</span><span class="op">)</span>, <span class="st">"\n"</span><span class="op">)</span>
+    <span class="kw">if</span> <span class="op">(</span><span class="va">t</span> <span class="op"><a href="https://rdrr.io/r/base/Arithmetic.html" class="external-link">%%</a></span> <span class="fl">10</span> <span class="op">==</span> <span class="fl">0</span><span class="op">)</span> <span class="fu"><a href="https://rdrr.io/r/base/cat.html" class="external-link">cat</a></span><span class="op">(</span><span class="va">t</span>, <span class="fu"><a href="../reference/as_array.html">as_array</a></span><span class="op">(</span><span class="va">loss</span><span class="op">)</span>, <span class="st">"\n"</span><span class="op">)</span>
     <span class="co">### -------- Backpropagation -------- </span>
     <span class="co"># compute the gradient of loss with respect to all tensors with requires_grad = True.</span>
     <span class="va">loss</span><span class="op">$</span><span class="fu">backward</span><span class="op">(</span><span class="op">)</span>
@@ -294,59 +255,51 @@
     <span class="op">}</span><span class="op">)</span>
     
 <span class="op">}</span>
-<span class="co">#&gt; 10 24.63874 </span>
-<span class="co">#&gt; 20 22.96676 </span>
-<span class="co">#&gt; 30 21.46304 </span>
-<span class="co">#&gt; 40 20.11166 </span>
-<span class="co">#&gt; 50 18.89442 </span>
-<span class="co">#&gt; 60 17.79779 </span>
-<span class="co">#&gt; 70 16.80661 </span>
-<span class="co">#&gt; 80 15.91018 </span>
-<span class="co">#&gt; 90 15.0992 </span>
-<span class="co">#&gt; 100 14.3636 </span>
-<span class="co">#&gt; 110 13.69568 </span>
-<span class="co">#&gt; 120 13.08923 </span>
-<span class="co">#&gt; 130 12.5373 </span>
-<span class="co">#&gt; 140 12.03484 </span>
-<span class="co">#&gt; 150 11.57683 </span>
-<span class="co">#&gt; 160 11.15849 </span>
-<span class="co">#&gt; 170 10.77546 </span>
-<span class="co">#&gt; 180 10.42468 </span>
-<span class="co">#&gt; 190 10.10336 </span>
-<span class="co">#&gt; 200 9.808649</span></code></pre></div>
+<span class="co">#&gt; 10 263.5121 </span>
+<span class="co">#&gt; 20 223.0612 </span>
+<span class="co">#&gt; 30 190.0824 </span>
+<span class="co">#&gt; 40 162.9376 </span>
+<span class="co">#&gt; 50 140.4214 </span>
+<span class="co">#&gt; 60 121.7117 </span>
+<span class="co">#&gt; 70 106.0199 </span>
+<span class="co">#&gt; 80 92.77345 </span>
+<span class="co">#&gt; 90 81.55437 </span>
+<span class="co">#&gt; 100 71.98052 </span>
+<span class="co">#&gt; 110 63.83845 </span>
+<span class="co">#&gt; 120 56.86206 </span>
+<span class="co">#&gt; 130 50.81776 </span>
+<span class="co">#&gt; 140 45.57465 </span>
+<span class="co">#&gt; 150 41.01472 </span>
+<span class="co">#&gt; 160 37.03083 </span>
+<span class="co">#&gt; 170 33.56329 </span>
+<span class="co">#&gt; 180 30.53346 </span>
+<span class="co">#&gt; 190 27.87165 </span>
+<span class="co">#&gt; 200 25.53565</span></code></pre></div>
 <p>We still manually compute the forward pass, and we still manually update the weights. In the last two chapters of this section, we’ll see how these parts of the logic can be made more modular and reusable, as well.</p>
 </div>
-<div class="footnotes">
-<hr>
-<ol>
-<li id="fn1"><p>Unless we <em>want</em> to change the data, as in adversarial example generation<a href="#fnref1" class="footnote-back">↩︎</a></p></li>
-</ol>
-</div>
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deleted file mode 100644
index 07aee5fcb..000000000
--- a/articles/using-autograd_files/anchor-sections-1.0/anchor-sections.css
+++ /dev/null
@@ -1,4 +0,0 @@
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-.hasAnchor:hover a.anchor-section {visibility: visible;}
diff --git a/articles/using-autograd_files/anchor-sections-1.0/anchor-sections.js b/articles/using-autograd_files/anchor-sections-1.0/anchor-sections.js
deleted file mode 100644
index 570f99a0a..000000000
--- a/articles/using-autograd_files/anchor-sections-1.0/anchor-sections.js
+++ /dev/null
@@ -1,33 +0,0 @@
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-  // Do nothing if AnchorJS is used
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-
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diff --git a/authors.html b/authors.html
index e28280d41..0e2d6ca03 100644
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+</script></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+    
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+    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary"><div class="container">
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+    <a class="navbar-brand me-2" href="index.html">torch</a>
+
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.7.0</small>
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+        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
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+    
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+</nav><div class="container template-citation-authors">
 <div class="row">
-  <div class="contents col-md-9">
-    <div class="page-header">
-      <h1>Authors</h1>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Authors and Citation</h1>
     </div>
 
-    <ul class="list-unstyled">
-      <li>
-        <p><strong>Daniel Falbel</strong>. Author, maintainer, copyright holder. 
-        </p>
-      </li>
-      <li>
-        <p><strong>Javier Luraschi</strong>. Author. 
-        </p>
-      </li>
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-        <p><strong>Dmitriy Selivanov</strong>. Contributor. 
-        </p>
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-        <p><strong>Athos Damiani</strong>. Contributor. 
-        </p>
-      </li>
-      <li>
-        <p><strong>Christophe Regouby</strong>. Contributor. 
-        </p>
-      </li>
-      <li>
-        <p><strong>Krzysztof Joachimiak</strong>. Contributor. 
-        </p>
-      </li>
-      <li>
-        <p><strong>Hamada S. Badr</strong>. Contributor. 
-        </p>
-      </li>
-      <li>
-        <p><strong> RStudio</strong>. Copyright holder. 
-        </p>
-      </li>
-    </ul>
-
-  </div>
-
-</div>
-
+    <div class="section level2 citation">
+      <h2>Authors</h2>
+      
+      <ul class="list-unstyled"><li>
+          <p><strong>Daniel Falbel</strong>. Author, maintainer, copyright holder. 
+          </p>
+        </li>
+        <li>
+          <p><strong>Javier Luraschi</strong>. Author. 
+          </p>
+        </li>
+        <li>
+          <p><strong>Dmitriy Selivanov</strong>. Contributor. 
+          </p>
+        </li>
+        <li>
+          <p><strong>Athos Damiani</strong>. Contributor. 
+          </p>
+        </li>
+        <li>
+          <p><strong>Christophe Regouby</strong>. Contributor. 
+          </p>
+        </li>
+        <li>
+          <p><strong>Krzysztof Joachimiak</strong>. Contributor. 
+          </p>
+        </li>
+        <li>
+          <p><strong>Hamada S. Badr</strong>. Contributor. 
+          </p>
+        </li>
+        <li>
+          <p><strong> RStudio</strong>. Copyright holder. 
+          </p>
+        </li>
+      </ul></div>
+
+    <div class="section level2 citation">
+      <h2 id="citation">Citation</h2>
+      <p><small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/DESCRIPTION" class="external-link"><code>DESCRIPTION</code></a></small></p>
+
+      <p>Falbel D, Luraschi J (2022).
+<em>torch: Tensors and Neural Networks with 'GPU' Acceleration</em>.
+https://torch.mlverse.org/docs, https://github.com/mlverse/torch. 
+</p>
+      <pre>@Manual{,
+  title = {torch: Tensors and Neural Networks with 'GPU' Acceleration},
+  author = {Daniel Falbel and Javier Luraschi},
+  year = {2022},
+  note = {https://torch.mlverse.org/docs, https://github.com/mlverse/torch},
+}</pre>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
 </div>
 
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-  <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p>
+<div class="pkgdown-footer-right">
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+    </footer></div>
 
   
 
+  
 
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-</html>
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diff --git a/bootstrap-toc.css b/bootstrap-toc.css
deleted file mode 100644
index 5a859415c..000000000
--- a/bootstrap-toc.css
+++ /dev/null
@@ -1,60 +0,0 @@
-/*!
- * Bootstrap Table of Contents v0.4.1 (http://afeld.github.io/bootstrap-toc/)
- * Copyright 2015 Aidan Feldman
- * Licensed under MIT (https://github.com/afeld/bootstrap-toc/blob/gh-pages/LICENSE.md) */
-
-/* modified from https://github.com/twbs/bootstrap/blob/94b4076dd2efba9af71f0b18d4ee4b163aa9e0dd/docs/assets/css/src/docs.css#L548-L601 */
-
-/* All levels of nav */
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-nav[data-toggle='toc'] .nav > li > a:focus {
-  padding-left: 19px;
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-nav[data-toggle='toc'] .nav > .active:hover > a,
-nav[data-toggle='toc'] .nav > .active:focus > a {
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diff --git a/bootstrap-toc.js b/bootstrap-toc.js
deleted file mode 100644
index 1cdd573b2..000000000
--- a/bootstrap-toc.js
+++ /dev/null
@@ -1,159 +0,0 @@
-/*!
- * Bootstrap Table of Contents v0.4.1 (http://afeld.github.io/bootstrap-toc/)
- * Copyright 2015 Aidan Feldman
- * Licensed under MIT (https://github.com/afeld/bootstrap-toc/blob/gh-pages/LICENSE.md) */
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diff --git a/deps/bootstrap-5.1.0/bootstrap.bundle.min.js b/deps/bootstrap-5.1.0/bootstrap.bundle.min.js
new file mode 100644
index 000000000..b65b161a5
--- /dev/null
+++ b/deps/bootstrap-5.1.0/bootstrap.bundle.min.js
@@ -0,0 +1,7 @@
+/*!
+  * Bootstrap v5.1.0 (https://getbootstrap.com/)
+  * Copyright 2011-2021 The Bootstrap Authors (https://github.com/twbs/bootstrap/graphs/contributors)
+  * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)
+  */
+!function(t,e){"object"==typeof exports&&"undefined"!=typeof module?module.exports=e():"function"==typeof define&&define.amd?define(e):(t="undefined"!=typeof globalThis?globalThis:t||self).bootstrap=e()}(this,(function(){"use strict";const t=t=>{let e=t.getAttribute("data-bs-target");if(!e||"#"===e){let i=t.getAttribute("href");if(!i||!i.includes("#")&&!i.startsWith("."))return null;i.includes("#")&&!i.startsWith("#")&&(i="#"+i.split("#")[1]),e=i&&"#"!==i?i.trim():null}return e},e=e=>{const i=t(e);return i&&document.querySelector(i)?i:null},i=e=>{const i=t(e);return i?document.querySelector(i):null},n=t=>{t.dispatchEvent(new Event("transitionend"))},s=t=>!(!t||"object"!=typeof t)&&(void 0!==t.jquery&&(t=t[0]),void 0!==t.nodeType),o=t=>s(t)?t.jquery?t[0]:t:"string"==typeof t&&t.length>0?document.querySelector(t):null,r=(t,e,i)=>{Object.keys(i).forEach(n=>{const o=i[n],r=e[n],a=r&&s(r)?"element":null==(l=r)?""+l:{}.toString.call(l).match(/\s([a-z]+)/i)[1].toLowerCase();var l;if(!new 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e=t.getBoundingClientRect(),i=e.width/t.offsetWidth||1,n=e.height/t.offsetHeight||1;return 1!==i||1!==n}(e),a=At(e),l=bt(t,r),c={scrollLeft:0,scrollTop:0},h={x:0,y:0};return(o||!o&&!i)&&(("body"!==ht(e)||Vt(a))&&(c=(n=e)!==dt(n)&&ft(n)?{scrollLeft:(s=n).scrollLeft,scrollTop:s.scrollTop}:Ut(n)),ft(e)?((h=bt(e,!0)).x+=e.clientLeft,h.y+=e.clientTop):a&&(h.x=$t(a))),{x:l.left+c.scrollLeft-h.x,y:l.top+c.scrollTop-h.y,width:l.width,height:l.height}}var le={placement:"bottom",modifiers:[],strategy:"absolute"};function ce(){for(var t=arguments.length,e=new Array(t),i=0;i<t;i++)e[i]=arguments[i];return!e.some((function(t){return!(t&&"function"==typeof t.getBoundingClientRect)}))}function he(t){void 0===t&&(t={});var e=t,i=e.defaultModifiers,n=void 0===i?[]:i,s=e.defaultOptions,o=void 0===s?le:s;return function(t,e,i){void 0===i&&(i=o);var s,r,a={placement:"bottom",orderedModifiers:[],options:Object.assign({},le,o),modifiersData:{},elements:{reference:t,popper:e},attributes:{},styles:{}},l=[],c=!1,h={state:a,setOptions:function(i){d(),a.options=Object.assign({},o,a.options,i),a.scrollParents={reference:ut(t)?Kt(t):t.contextElement?Kt(t.contextElement):[],popper:Kt(e)};var s,r,c=function(t){var e=function(t){var e=new Map,i=new Set,n=[];return t.forEach((function(t){e.set(t.name,t)})),t.forEach((function(t){i.has(t.name)||function t(s){i.add(s.name),[].concat(s.requires||[],s.requiresIfExists||[]).forEach((function(n){if(!i.has(n)){var s=e.get(n);s&&t(s)}})),n.push(s)}(t)})),n}(t);return ct.reduce((function(t,i){return t.concat(e.filter((function(t){return t.phase===i})))}),[])}((s=[].concat(n,a.options.modifiers),r=s.reduce((function(t,e){var i=t[e.name];return t[e.name]=i?Object.assign({},i,e,{options:Object.assign({},i.options,e.options),data:Object.assign({},i.data,e.data)}):e,t}),{}),Object.keys(r).map((function(t){return r[t]}))));return a.orderedModifiers=c.filter((function(t){return t.enabled})),a.orderedModifiers.forEach((function(t){var e=t.name,i=t.options,n=void 0===i?{}:i,s=t.effect;if("function"==typeof s){var o=s({state:a,name:e,instance:h,options:n});l.push(o||function(){})}})),h.update()},forceUpdate:function(){if(!c){var t=a.elements,e=t.reference,i=t.popper;if(ce(e,i)){a.rects={reference:ae(e,Ct(i),"fixed"===a.options.strategy),popper:vt(i)},a.reset=!1,a.placement=a.options.placement,a.orderedModifiers.forEach((function(t){return a.modifiersData[t.name]=Object.assign({},t.data)}));for(var n=0;n<a.orderedModifiers.length;n++)if(!0!==a.reset){var s=a.orderedModifiers[n],o=s.fn,r=s.options,l=void 0===r?{}:r,d=s.name;"function"==typeof o&&(a=o({state:a,options:l,name:d,instance:h})||a)}else a.reset=!1,n=-1}}},update:(s=function(){return new Promise((function(t){h.forceUpdate(),t(a)}))},function(){return r||(r=new Promise((function(t){Promise.resolve().then((function(){r=void 0,t(s())}))}))),r}),destroy:function(){d(),c=!0}};if(!ce(t,e))return h;function d(){l.forEach((function(t){return t()})),l=[]}return h.setOptions(i).then((function(t){!c&&i.onFirstUpdate&&i.onFirstUpdate(t)})),h}}var de=he(),ue=he({defaultModifiers:[Rt,oe,Ht,mt]}),fe=he({defaultModifiers:[Rt,oe,Ht,mt,se,te,re,Pt,ne]}),pe=Object.freeze({__proto__:null,popperGenerator:he,detectOverflow:Zt,createPopperBase:de,createPopper:fe,createPopperLite:ue,top:it,bottom:nt,right:st,left:ot,auto:"auto",basePlacements:rt,start:"start",end:"end",clippingParents:"clippingParents",viewport:"viewport",popper:"popper",reference:"reference",variationPlacements:at,placements:lt,beforeRead:"beforeRead",read:"read",afterRead:"afterRead",beforeMain:"beforeMain",main:"main",afterMain:"afterMain",beforeWrite:"beforeWrite",write:"write",afterWrite:"afterWrite",modifierPhases:ct,applyStyles:mt,arrow:Pt,computeStyles:Ht,eventListeners:Rt,flip:te,hide:ne,offset:se,popperOffsets:oe,preventOverflow:re});const me=new RegExp("ArrowUp|ArrowDown|Escape"),ge=p()?"top-end":"top-start",_e=p()?"top-start":"top-end",be=p()?"bottom-end":"bottom-start",ve=p()?"bottom-start":"bottom-end",ye=p()?"left-start":"right-start",we=p()?"right-start":"left-start",Ee={offset:[0,2],boundary:"clippingParents",reference:"toggle",display:"dynamic",popperConfig:null,autoClose:!0},Ae={offset:"(array|string|function)",boundary:"(string|element)",reference:"(string|element|object)",display:"string",popperConfig:"(null|object|function)",autoClose:"(boolean|string)"};class Te extends H{constructor(t,e){super(t),this._popper=null,this._config=this._getConfig(e),this._menu=this._getMenuElement(),this._inNavbar=this._detectNavbar()}static get Default(){return Ee}static get DefaultType(){return Ae}static get NAME(){return"dropdown"}toggle(){return this._isShown()?this.hide():this.show()}show(){if(l(this._element)||this._isShown(this._menu))return;const t={relatedTarget:this._element};if(P.trigger(this._element,"show.bs.dropdown",t).defaultPrevented)return;const e=Te.getParentFromElement(this._element);this._inNavbar?F.setDataAttribute(this._menu,"popper","none"):this._createPopper(e),"ontouchstart"in document.documentElement&&!e.closest(".navbar-nav")&&[].concat(...document.body.children).forEach(t=>P.on(t,"mouseover",h)),this._element.focus(),this._element.setAttribute("aria-expanded",!0),this._menu.classList.add("show"),this._element.classList.add("show"),P.trigger(this._element,"shown.bs.dropdown",t)}hide(){if(l(this._element)||!this._isShown(this._menu))return;const t={relatedTarget:this._element};this._completeHide(t)}dispose(){this._popper&&this._popper.destroy(),super.dispose()}update(){this._inNavbar=this._detectNavbar(),this._popper&&this._popper.update()}_completeHide(t){P.trigger(this._element,"hide.bs.dropdown",t).defaultPrevented||("ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach(t=>P.off(t,"mouseover",h)),this._popper&&this._popper.destroy(),this._menu.classList.remove("show"),this._element.classList.remove("show"),this._element.setAttribute("aria-expanded","false"),F.removeDataAttribute(this._menu,"popper"),P.trigger(this._element,"hidden.bs.dropdown",t))}_getConfig(t){if(t={...this.constructor.Default,...F.getDataAttributes(this._element),...t},r("dropdown",t,this.constructor.DefaultType),"object"==typeof t.reference&&!s(t.reference)&&"function"!=typeof t.reference.getBoundingClientRect)throw new TypeError("dropdown".toUpperCase()+': Option "reference" provided type "object" without a required "getBoundingClientRect" method.');return t}_createPopper(t){if(void 0===pe)throw new TypeError("Bootstrap's dropdowns require Popper (https://popper.js.org)");let e=this._element;"parent"===this._config.reference?e=t:s(this._config.reference)?e=o(this._config.reference):"object"==typeof this._config.reference&&(e=this._config.reference);const i=this._getPopperConfig(),n=i.modifiers.find(t=>"applyStyles"===t.name&&!1===t.enabled);this._popper=fe(e,this._menu,i),n&&F.setDataAttribute(this._menu,"popper","static")}_isShown(t=this._element){return t.classList.contains("show")}_getMenuElement(){return U.next(this._element,".dropdown-menu")[0]}_getPlacement(){const t=this._element.parentNode;if(t.classList.contains("dropend"))return ye;if(t.classList.contains("dropstart"))return we;const e="end"===getComputedStyle(this._menu).getPropertyValue("--bs-position").trim();return t.classList.contains("dropup")?e?_e:ge:e?ve:be}_detectNavbar(){return null!==this._element.closest(".navbar")}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map(t=>Number.parseInt(t,10)):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return"static"===this._config.display&&(t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,..."function"==typeof this._config.popperConfig?this._config.popperConfig(t):this._config.popperConfig}}_selectMenuItem({key:t,target:e}){const i=U.find(".dropdown-menu .dropdown-item:not(.disabled):not(:disabled)",this._menu).filter(a);i.length&&b(i,e,"ArrowDown"===t,!i.includes(e)).focus()}static jQueryInterface(t){return this.each((function(){const e=Te.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}static clearMenus(t){if(t&&(2===t.button||"keyup"===t.type&&"Tab"!==t.key))return;const e=U.find('[data-bs-toggle="dropdown"]');for(let i=0,n=e.length;i<n;i++){const n=Te.getInstance(e[i]);if(!n||!1===n._config.autoClose)continue;if(!n._isShown())continue;const s={relatedTarget:n._element};if(t){const e=t.composedPath(),i=e.includes(n._menu);if(e.includes(n._element)||"inside"===n._config.autoClose&&!i||"outside"===n._config.autoClose&&i)continue;if(n._menu.contains(t.target)&&("keyup"===t.type&&"Tab"===t.key||/input|select|option|textarea|form/i.test(t.target.tagName)))continue;"click"===t.type&&(s.clickEvent=t)}n._completeHide(s)}}static getParentFromElement(t){return i(t)||t.parentNode}static dataApiKeydownHandler(t){if(/input|textarea/i.test(t.target.tagName)?"Space"===t.key||"Escape"!==t.key&&("ArrowDown"!==t.key&&"ArrowUp"!==t.key||t.target.closest(".dropdown-menu")):!me.test(t.key))return;const e=this.classList.contains("show");if(!e&&"Escape"===t.key)return;if(t.preventDefault(),t.stopPropagation(),l(this))return;const i=this.matches('[data-bs-toggle="dropdown"]')?this:U.prev(this,'[data-bs-toggle="dropdown"]')[0],n=Te.getOrCreateInstance(i);if("Escape"!==t.key)return"ArrowUp"===t.key||"ArrowDown"===t.key?(e||n.show(),void n._selectMenuItem(t)):void(e&&"Space"!==t.key||Te.clearMenus());n.hide()}}P.on(document,"keydown.bs.dropdown.data-api",'[data-bs-toggle="dropdown"]',Te.dataApiKeydownHandler),P.on(document,"keydown.bs.dropdown.data-api",".dropdown-menu",Te.dataApiKeydownHandler),P.on(document,"click.bs.dropdown.data-api",Te.clearMenus),P.on(document,"keyup.bs.dropdown.data-api",Te.clearMenus),P.on(document,"click.bs.dropdown.data-api",'[data-bs-toggle="dropdown"]',(function(t){t.preventDefault(),Te.getOrCreateInstance(this).toggle()})),m(Te);class Oe{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,"paddingRight",e=>e+t),this._setElementAttributes(".fixed-top, .fixed-bottom, .is-fixed, .sticky-top","paddingRight",e=>e+t),this._setElementAttributes(".sticky-top","marginRight",e=>e-t)}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t)[e];t.style[e]=i(Number.parseFloat(s))+"px"})}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,"paddingRight"),this._resetElementAttributes(".fixed-top, .fixed-bottom, .is-fixed, .sticky-top","paddingRight"),this._resetElementAttributes(".sticky-top","marginRight")}_saveInitialAttribute(t,e){const i=t.style[e];i&&F.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,t=>{const i=F.getDataAttribute(t,e);void 0===i?t.style.removeProperty(e):(F.removeDataAttribute(t,e),t.style[e]=i)})}_applyManipulationCallback(t,e){s(t)?e(t):U.find(t,this._element).forEach(e)}isOverflowing(){return this.getWidth()>0}}const Ce={className:"modal-backdrop",isVisible:!0,isAnimated:!1,rootElement:"body",clickCallback:null},ke={className:"string",isVisible:"boolean",isAnimated:"boolean",rootElement:"(element|string)",clickCallback:"(function|null)"};class Le{constructor(t){this._config=this._getConfig(t),this._isAppended=!1,this._element=null}show(t){this._config.isVisible?(this._append(),this._config.isAnimated&&d(this._getElement()),this._getElement().classList.add("show"),this._emulateAnimation(()=>{g(t)})):g(t)}hide(t){this._config.isVisible?(this._getElement().classList.remove("show"),this._emulateAnimation(()=>{this.dispose(),g(t)})):g(t)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_getConfig(t){return(t={...Ce,..."object"==typeof t?t:{}}).rootElement=o(t.rootElement),r("backdrop",t,ke),t}_append(){this._isAppended||(this._config.rootElement.append(this._getElement()),P.on(this._getElement(),"mousedown.bs.backdrop",()=>{g(this._config.clickCallback)}),this._isAppended=!0)}dispose(){this._isAppended&&(P.off(this._element,"mousedown.bs.backdrop"),this._element.remove(),this._isAppended=!1)}_emulateAnimation(t){_(t,this._getElement(),this._config.isAnimated)}}const xe={trapElement:null,autofocus:!0},De={trapElement:"element",autofocus:"boolean"};class Se{constructor(t){this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}activate(){const{trapElement:t,autofocus:e}=this._config;this._isActive||(e&&t.focus(),P.off(document,".bs.focustrap"),P.on(document,"focusin.bs.focustrap",t=>this._handleFocusin(t)),P.on(document,"keydown.tab.bs.focustrap",t=>this._handleKeydown(t)),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,P.off(document,".bs.focustrap"))}_handleFocusin(t){const{target:e}=t,{trapElement:i}=this._config;if(e===document||e===i||i.contains(e))return;const n=U.focusableChildren(i);0===n.length?i.focus():"backward"===this._lastTabNavDirection?n[n.length-1].focus():n[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?"backward":"forward")}_getConfig(t){return t={...xe,..."object"==typeof t?t:{}},r("focustrap",t,De),t}}const Ne={backdrop:!0,keyboard:!0,focus:!0},Ie={backdrop:"(boolean|string)",keyboard:"boolean",focus:"boolean"};class Pe extends H{constructor(t,e){super(t),this._config=this._getConfig(e),this._dialog=U.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._ignoreBackdropClick=!1,this._isTransitioning=!1,this._scrollBar=new Oe}static get Default(){return Ne}static get NAME(){return"modal"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||P.trigger(this._element,"show.bs.modal",{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isAnimated()&&(this._isTransitioning=!0),this._scrollBar.hide(),document.body.classList.add("modal-open"),this._adjustDialog(),this._setEscapeEvent(),this._setResizeEvent(),P.on(this._dialog,"mousedown.dismiss.bs.modal",()=>{P.one(this._element,"mouseup.dismiss.bs.modal",t=>{t.target===this._element&&(this._ignoreBackdropClick=!0)})}),this._showBackdrop(()=>this._showElement(t)))}hide(){if(!this._isShown||this._isTransitioning)return;if(P.trigger(this._element,"hide.bs.modal").defaultPrevented)return;this._isShown=!1;const t=this._isAnimated();t&&(this._isTransitioning=!0),this._setEscapeEvent(),this._setResizeEvent(),this._focustrap.deactivate(),this._element.classList.remove("show"),P.off(this._element,"click.dismiss.bs.modal"),P.off(this._dialog,"mousedown.dismiss.bs.modal"),this._queueCallback(()=>this._hideModal(),this._element,t)}dispose(){[window,this._dialog].forEach(t=>P.off(t,".bs.modal")),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new Le({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new Se({trapElement:this._element})}_getConfig(t){return t={...Ne,...F.getDataAttributes(this._element),..."object"==typeof t?t:{}},r("modal",t,Ie),t}_showElement(t){const e=this._isAnimated(),i=U.findOne(".modal-body",this._dialog);this._element.parentNode&&this._element.parentNode.nodeType===Node.ELEMENT_NODE||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0,i&&(i.scrollTop=0),e&&d(this._element),this._element.classList.add("show"),this._queueCallback(()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,P.trigger(this._element,"shown.bs.modal",{relatedTarget:t})},this._dialog,e)}_setEscapeEvent(){this._isShown?P.on(this._element,"keydown.dismiss.bs.modal",t=>{this._config.keyboard&&"Escape"===t.key?(t.preventDefault(),this.hide()):this._config.keyboard||"Escape"!==t.key||this._triggerBackdropTransition()}):P.off(this._element,"keydown.dismiss.bs.modal")}_setResizeEvent(){this._isShown?P.on(window,"resize.bs.modal",()=>this._adjustDialog()):P.off(window,"resize.bs.modal")}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide(()=>{document.body.classList.remove("modal-open"),this._resetAdjustments(),this._scrollBar.reset(),P.trigger(this._element,"hidden.bs.modal")})}_showBackdrop(t){P.on(this._element,"click.dismiss.bs.modal",t=>{this._ignoreBackdropClick?this._ignoreBackdropClick=!1:t.target===t.currentTarget&&(!0===this._config.backdrop?this.hide():"static"===this._config.backdrop&&this._triggerBackdropTransition())}),this._backdrop.show(t)}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(P.trigger(this._element,"hidePrevented.bs.modal").defaultPrevented)return;const{classList:t,scrollHeight:e,style:i}=this._element,n=e>document.documentElement.clientHeight;!n&&"hidden"===i.overflowY||t.contains("modal-static")||(n||(i.overflowY="hidden"),t.add("modal-static"),this._queueCallback(()=>{t.remove("modal-static"),n||this._queueCallback(()=>{i.overflowY=""},this._dialog)},this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;(!i&&t&&!p()||i&&!t&&p())&&(this._element.style.paddingLeft=e+"px"),(i&&!t&&!p()||!i&&t&&p())&&(this._element.style.paddingRight=e+"px")}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=Pe.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}P.on(document,"click.bs.modal.data-api",'[data-bs-toggle="modal"]',(function(t){const e=i(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),P.one(e,"show.bs.modal",t=>{t.defaultPrevented||P.one(e,"hidden.bs.modal",()=>{a(this)&&this.focus()})}),Pe.getOrCreateInstance(e).toggle(this)})),B(Pe),m(Pe);const je={backdrop:!0,keyboard:!0,scroll:!1},Me={backdrop:"boolean",keyboard:"boolean",scroll:"boolean"};class He extends H{constructor(t,e){super(t),this._config=this._getConfig(e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get NAME(){return"offcanvas"}static get Default(){return je}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||P.trigger(this._element,"show.bs.offcanvas",{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._element.style.visibility="visible",this._backdrop.show(),this._config.scroll||(new Oe).hide(),this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add("show"),this._queueCallback(()=>{this._config.scroll||this._focustrap.activate(),P.trigger(this._element,"shown.bs.offcanvas",{relatedTarget:t})},this._element,!0))}hide(){this._isShown&&(P.trigger(this._element,"hide.bs.offcanvas").defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.remove("show"),this._backdrop.hide(),this._queueCallback(()=>{this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._element.style.visibility="hidden",this._config.scroll||(new Oe).reset(),P.trigger(this._element,"hidden.bs.offcanvas")},this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_getConfig(t){return t={...je,...F.getDataAttributes(this._element),..."object"==typeof t?t:{}},r("offcanvas",t,Me),t}_initializeBackDrop(){return new Le({className:"offcanvas-backdrop",isVisible:this._config.backdrop,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:()=>this.hide()})}_initializeFocusTrap(){return new Se({trapElement:this._element})}_addEventListeners(){P.on(this._element,"keydown.dismiss.bs.offcanvas",t=>{this._config.keyboard&&"Escape"===t.key&&this.hide()})}static jQueryInterface(t){return this.each((function(){const e=He.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}P.on(document,"click.bs.offcanvas.data-api",'[data-bs-toggle="offcanvas"]',(function(t){const e=i(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),l(this))return;P.one(e,"hidden.bs.offcanvas",()=>{a(this)&&this.focus()});const n=U.findOne(".offcanvas.show");n&&n!==e&&He.getInstance(n).hide(),He.getOrCreateInstance(e).toggle(this)})),P.on(window,"load.bs.offcanvas.data-api",()=>U.find(".offcanvas.show").forEach(t=>He.getOrCreateInstance(t).show())),B(He),m(He);const Be=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Re=/^(?:(?:https?|mailto|ftp|tel|file):|[^#&/:?]*(?:[#/?]|$))/i,We=/^data:(?:image\/(?:bmp|gif|jpeg|jpg|png|tiff|webp)|video\/(?:mpeg|mp4|ogg|webm)|audio\/(?:mp3|oga|ogg|opus));base64,[\d+/a-z]+=*$/i,ze=(t,e)=>{const i=t.nodeName.toLowerCase();if(e.includes(i))return!Be.has(i)||Boolean(Re.test(t.nodeValue)||We.test(t.nodeValue));const n=e.filter(t=>t instanceof RegExp);for(let t=0,e=n.length;t<e;t++)if(n[t].test(i))return!0;return!1};function qe(t,e,i){if(!t.length)return t;if(i&&"function"==typeof i)return i(t);const n=(new window.DOMParser).parseFromString(t,"text/html"),s=Object.keys(e),o=[].concat(...n.body.querySelectorAll("*"));for(let 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Error("Please use show on visible elements");if(!this.isWithContent()||!this._isEnabled)return;const t=P.trigger(this._element,this.constructor.Event.SHOW),e=c(this._element),i=null===e?this._element.ownerDocument.documentElement.contains(this._element):e.contains(this._element);if(t.defaultPrevented||!i)return;const n=this.getTipElement(),s=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME);n.setAttribute("id",s),this._element.setAttribute("aria-describedby",s),this._config.animation&&n.classList.add("fade");const o="function"==typeof 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t=this.getTipElement();if(P.trigger(this._element,this.constructor.Event.HIDE).defaultPrevented)return;t.classList.remove("show"),"ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach(t=>P.off(t,"mouseover",h)),this._activeTrigger.click=!1,this._activeTrigger.focus=!1,this._activeTrigger.hover=!1;const e=this.tip.classList.contains("fade");this._queueCallback(()=>{this._isWithActiveTrigger()||("show"!==this._hoverState&&t.remove(),this._cleanTipClass(),this._element.removeAttribute("aria-describedby"),P.trigger(this._element,this.constructor.Event.HIDDEN),this._popper&&(this._popper.destroy(),this._popper=null))},this.tip,e),this._hoverState=""}update(){null!==this._popper&&this._popper.update()}isWithContent(){return Boolean(this.getTitle())}getTipElement(){if(this.tip)return this.tip;const t=document.createElement("div");t.innerHTML=this._config.template;const e=t.children[0];return this.setContent(e),e.classList.remove("fade","show"),this.tip=e,this.tip}setContent(t){this._sanitizeAndSetContent(t,this.getTitle(),".tooltip-inner")}_sanitizeAndSetContent(t,e,i){const n=U.findOne(i,t);e||!n?this.setElementContent(n,e):n.remove()}setElementContent(t,e){if(null!==t)return s(e)?(e=o(e),void(this._config.html?e.parentNode!==t&&(t.innerHTML="",t.append(e)):t.textContent=e.textContent)):void(this._config.html?(this._config.sanitize&&(e=qe(e,this._config.allowList,this._config.sanitizeFn)),t.innerHTML=e):t.textContent=e)}getTitle(){const t=this._element.getAttribute("data-bs-original-title")||this._config.title;return this._resolvePossibleFunction(t)}updateAttachment(t){return"right"===t?"end":"left"===t?"start":t}_initializeOnDelegatedTarget(t,e){return e||this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map(t=>Number.parseInt(t,10)):"function"==typeof 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t in this._activeTrigger)if(this._activeTrigger[t])return!0;return!1}_getConfig(t){const e=F.getDataAttributes(this._element);return Object.keys(e).forEach(t=>{Fe.has(t)&&delete e[t]}),(t={...this.constructor.Default,...e,..."object"==typeof t&&t?t:{}}).container=!1===t.container?document.body:o(t.container),"number"==typeof t.delay&&(t.delay={show:t.delay,hide:t.delay}),"number"==typeof t.title&&(t.title=t.title.toString()),"number"==typeof t.content&&(t.content=t.content.toString()),r("tooltip",t,this.constructor.DefaultType),t.sanitize&&(t.template=qe(t.template,t.allowList,t.sanitizeFn)),t}_getDelegateConfig(){const t={};for(const e in this._config)this.constructor.Default[e]!==this._config[e]&&(t[e]=this._config[e]);return t}_cleanTipClass(){const t=this.getTipElement(),e=new 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t=o.getBoundingClientRect();if(t.width||t.height)return[F[i](o).top+n,s]}return null}).filter(t=>t).sort((t,e)=>t[0]-e[0]).forEach(t=>{this._offsets.push(t[0]),this._targets.push(t[1])})}dispose(){P.off(this._scrollElement,".bs.scrollspy"),super.dispose()}_getConfig(t){return(t={...Je,...F.getDataAttributes(this._element),..."object"==typeof t&&t?t:{}}).target=o(t.target)||document.documentElement,r("scrollspy",t,ti),t}_getScrollTop(){return this._scrollElement===window?this._scrollElement.pageYOffset:this._scrollElement.scrollTop}_getScrollHeight(){return this._scrollElement.scrollHeight||Math.max(document.body.scrollHeight,document.documentElement.scrollHeight)}_getOffsetHeight(){return this._scrollElement===window?window.innerHeight:this._scrollElement.getBoundingClientRect().height}_process(){const t=this._getScrollTop()+this._config.offset,e=this._getScrollHeight(),i=this._config.offset+e-this._getOffsetHeight();if(this._scrollHeight!==e&&this.refresh(),t>=i){const 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si={animation:"boolean",autohide:"boolean",delay:"number"},oi={animation:!0,autohide:!0,delay:5e3};class ri extends H{constructor(t,e){super(t),this._config=this._getConfig(e),this._timeout=null,this._hasMouseInteraction=!1,this._hasKeyboardInteraction=!1,this._setListeners()}static get DefaultType(){return si}static get Default(){return oi}static get NAME(){return"toast"}show(){P.trigger(this._element,"show.bs.toast").defaultPrevented||(this._clearTimeout(),this._config.animation&&this._element.classList.add("fade"),this._element.classList.remove("hide"),d(this._element),this._element.classList.add("show"),this._element.classList.add("showing"),this._queueCallback(()=>{this._element.classList.remove("showing"),P.trigger(this._element,"shown.bs.toast"),this._maybeScheduleHide()},this._element,this._config.animation))}hide(){this._element.classList.contains("show")&&(P.trigger(this._element,"hide.bs.toast").defaultPrevented||(this._element.classList.add("showing"),this._queueCallback(()=>{this._element.classList.add("hide"),this._element.classList.remove("showing"),this._element.classList.remove("show"),P.trigger(this._element,"hidden.bs.toast")},this._element,this._config.animation)))}dispose(){this._clearTimeout(),this._element.classList.contains("show")&&this._element.classList.remove("show"),super.dispose()}_getConfig(t){return t={...oi,...F.getDataAttributes(this._element),..."object"==typeof t&&t?t:{}},r("toast",t,this.constructor.DefaultType),t}_maybeScheduleHide(){this._config.autohide&&(this._hasMouseInteraction||this._hasKeyboardInteraction||(this._timeout=setTimeout(()=>{this.hide()},this._config.delay)))}_onInteraction(t,e){switch(t.type){case"mouseover":case"mouseout":this._hasMouseInteraction=e;break;case"focusin":case"focusout":this._hasKeyboardInteraction=e}if(e)return void this._clearTimeout();const i=t.relatedTarget;this._element===i||this._element.contains(i)||this._maybeScheduleHide()}_setListeners(){P.on(this._element,"mouseover.bs.toast",t=>this._onInteraction(t,!0)),P.on(this._element,"mouseout.bs.toast",t=>this._onInteraction(t,!1)),P.on(this._element,"focusin.bs.toast",t=>this._onInteraction(t,!0)),P.on(this._element,"focusout.bs.toast",t=>this._onInteraction(t,!1))}_clearTimeout(){clearTimeout(this._timeout),this._timeout=null}static jQueryInterface(t){return this.each((function(){const e=ri.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}return B(ri),m(ri),{Alert:R,Button:W,Carousel:Z,Collapse:et,Dropdown:Te,Modal:Pe,Offcanvas:He,Popover:Ze,ScrollSpy:ii,Tab:ni,Toast:ri,Tooltip:Xe}}));
+//# sourceMappingURL=bootstrap.bundle.min.js.map
\ No newline at end of file
diff --git a/deps/bootstrap-5.1.0/bootstrap.bundle.min.js.map b/deps/bootstrap-5.1.0/bootstrap.bundle.min.js.map
new file mode 100644
index 000000000..a59a60b89
--- /dev/null
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= element.getAttribute('data-bs-target')\n\n  if (!selector || selector === '#') {\n    let hrefAttr = element.getAttribute('href')\n\n    // The only valid content that could double as a selector are IDs or classes,\n    // so everything starting with `#` or `.`. If a \"real\" URL is used as the selector,\n    // `document.querySelector` will rightfully complain it is invalid.\n    // See https://github.com/twbs/bootstrap/issues/32273\n    if (!hrefAttr || (!hrefAttr.includes('#') && !hrefAttr.startsWith('.'))) {\n      return null\n    }\n\n    // Just in case some CMS puts out a full URL with the anchor appended\n    if (hrefAttr.includes('#') && !hrefAttr.startsWith('#')) {\n      hrefAttr = `#${hrefAttr.split('#')[1]}`\n    }\n\n    selector = hrefAttr && hrefAttr !== '#' ? hrefAttr.trim() : null\n  }\n\n  return selector\n}\n\nconst getSelectorFromElement = element => {\n  const selector = getSelector(element)\n\n  if (selector) {\n    return document.querySelector(selector) ? selector : null\n  }\n\n  return null\n}\n\nconst getElementFromSelector = element => {\n  const selector = getSelector(element)\n\n  return selector ? document.querySelector(selector) : null\n}\n\nconst getTransitionDurationFromElement = element => {\n  if (!element) {\n    return 0\n  }\n\n  // Get transition-duration of the element\n  let { transitionDuration, transitionDelay } = window.getComputedStyle(element)\n\n  const floatTransitionDuration = Number.parseFloat(transitionDuration)\n  const floatTransitionDelay = Number.parseFloat(transitionDelay)\n\n  // Return 0 if element or transition duration is not found\n  if (!floatTransitionDuration && !floatTransitionDelay) {\n    return 0\n  }\n\n  // If multiple durations are defined, take the first\n  transitionDuration = transitionDuration.split(',')[0]\n  transitionDelay = transitionDelay.split(',')[0]\n\n  return (Number.parseFloat(transitionDuration) + Number.parseFloat(transitionDelay)) * MILLISECONDS_MULTIPLIER\n}\n\nconst triggerTransitionEnd = element => {\n  element.dispatchEvent(new Event(TRANSITION_END))\n}\n\nconst isElement = obj => {\n  if (!obj || typeof obj !== 'object') {\n    return false\n  }\n\n  if (typeof obj.jquery !== 'undefined') {\n    obj = obj[0]\n  }\n\n  return typeof obj.nodeType !== 'undefined'\n}\n\nconst getElement = obj => {\n  if (isElement(obj)) { // it's a jQuery object or a node element\n    return obj.jquery ? obj[0] : obj\n  }\n\n  if (typeof obj === 'string' && obj.length > 0) {\n    return document.querySelector(obj)\n  }\n\n  return null\n}\n\nconst typeCheckConfig = (componentName, config, configTypes) => {\n  Object.keys(configTypes).forEach(property => {\n    const expectedTypes = configTypes[property]\n    const value = config[property]\n    const valueType = value && isElement(value) ? 'element' : toType(value)\n\n    if (!new RegExp(expectedTypes).test(valueType)) {\n      throw new TypeError(\n        `${componentName.toUpperCase()}: Option \"${property}\" provided type \"${valueType}\" but expected type \"${expectedTypes}\".`\n      )\n    }\n  })\n}\n\nconst isVisible = element => {\n  if (!isElement(element) || element.getClientRects().length === 0) {\n    return false\n  }\n\n  return getComputedStyle(element).getPropertyValue('visibility') === 'visible'\n}\n\nconst isDisabled = element => {\n  if (!element || element.nodeType !== Node.ELEMENT_NODE) {\n    return true\n  }\n\n  if (element.classList.contains('disabled')) {\n    return true\n  }\n\n  if (typeof element.disabled !== 'undefined') {\n    return element.disabled\n  }\n\n  return element.hasAttribute('disabled') && element.getAttribute('disabled') !== 'false'\n}\n\nconst findShadowRoot = element => {\n  if (!document.documentElement.attachShadow) {\n    return null\n  }\n\n  // Can find the shadow root otherwise it'll return the document\n  if (typeof element.getRootNode === 'function') {\n    const root = element.getRootNode()\n    return root instanceof ShadowRoot ? root : null\n  }\n\n  if (element instanceof ShadowRoot) {\n    return element\n  }\n\n  // when we don't find a shadow root\n  if (!element.parentNode) {\n    return null\n  }\n\n  return findShadowRoot(element.parentNode)\n}\n\nconst noop = () => {}\n\n/**\n * Trick to restart an element's animation\n *\n * @param {HTMLElement} element\n * @return void\n *\n * @see https://www.charistheo.io/blog/2021/02/restart-a-css-animation-with-javascript/#restarting-a-css-animation\n */\nconst reflow = element => {\n  // eslint-disable-next-line no-unused-expressions\n  element.offsetHeight\n}\n\nconst getjQuery = () => {\n  const { jQuery } = window\n\n  if (jQuery && !document.body.hasAttribute('data-bs-no-jquery')) {\n    return jQuery\n  }\n\n  return null\n}\n\nconst DOMContentLoadedCallbacks = []\n\nconst onDOMContentLoaded = callback => {\n  if (document.readyState === 'loading') {\n    // add listener on the first call when the document is in loading state\n    if (!DOMContentLoadedCallbacks.length) {\n      document.addEventListener('DOMContentLoaded', () => {\n        DOMContentLoadedCallbacks.forEach(callback => callback())\n      })\n    }\n\n    DOMContentLoadedCallbacks.push(callback)\n  } else {\n    callback()\n  }\n}\n\nconst isRTL = () => document.documentElement.dir === 'rtl'\n\nconst defineJQueryPlugin = plugin => {\n  onDOMContentLoaded(() => {\n    const $ = getjQuery()\n    /* istanbul ignore if */\n    if ($) {\n      const name = plugin.NAME\n      const JQUERY_NO_CONFLICT = $.fn[name]\n      $.fn[name] = plugin.jQueryInterface\n      $.fn[name].Constructor = plugin\n      $.fn[name].noConflict = () => {\n        $.fn[name] = JQUERY_NO_CONFLICT\n        return plugin.jQueryInterface\n      }\n    }\n  })\n}\n\nconst execute = callback => {\n  if (typeof callback === 'function') {\n    callback()\n  }\n}\n\nconst executeAfterTransition = (callback, transitionElement, waitForTransition = true) => {\n  if (!waitForTransition) {\n    execute(callback)\n    return\n  }\n\n  const durationPadding = 5\n  const emulatedDuration = getTransitionDurationFromElement(transitionElement) + durationPadding\n\n  let called = false\n\n  const handler = ({ target }) => {\n    if (target !== transitionElement) {\n      return\n    }\n\n    called = true\n    transitionElement.removeEventListener(TRANSITION_END, handler)\n    execute(callback)\n  }\n\n  transitionElement.addEventListener(TRANSITION_END, handler)\n  setTimeout(() => {\n    if (!called) {\n      triggerTransitionEnd(transitionElement)\n    }\n  }, emulatedDuration)\n}\n\n/**\n * Return the previous/next element of a list.\n *\n * @param {array} list    The list of elements\n * @param activeElement   The active element\n * @param shouldGetNext   Choose to get next or previous element\n * @param isCycleAllowed\n * @return {Element|elem} The proper element\n */\nconst getNextActiveElement = (list, activeElement, shouldGetNext, isCycleAllowed) => {\n  let index = list.indexOf(activeElement)\n\n  // if the element does not exist in the list return an element depending on the direction and if cycle is allowed\n  if (index === -1) {\n    return list[!shouldGetNext && isCycleAllowed ? list.length - 1 : 0]\n  }\n\n  const listLength = list.length\n\n  index += shouldGetNext ? 1 : -1\n\n  if (isCycleAllowed) {\n    index = (index + listLength) % listLength\n  }\n\n  return list[Math.max(0, Math.min(index, listLength - 1))]\n}\n\nexport {\n  getElement,\n  getUID,\n  getSelectorFromElement,\n  getElementFromSelector,\n  getTransitionDurationFromElement,\n  triggerTransitionEnd,\n  isElement,\n  typeCheckConfig,\n  isVisible,\n  isDisabled,\n  findShadowRoot,\n  noop,\n  getNextActiveElement,\n  reflow,\n  getjQuery,\n  onDOMContentLoaded,\n  isRTL,\n  defineJQueryPlugin,\n  execute,\n  executeAfterTransition\n}\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.1.0): dom/event-handler.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport { getjQuery } from '../util/index'\n\n/**\n * ------------------------------------------------------------------------\n * Constants\n * ------------------------------------------------------------------------\n */\n\nconst namespaceRegex = /[^.]*(?=\\..*)\\.|.*/\nconst stripNameRegex = /\\..*/\nconst stripUidRegex = /::\\d+$/\nconst eventRegistry = {} // Events storage\nlet uidEvent = 1\nconst customEvents = {\n  mouseenter: 'mouseover',\n  mouseleave: 'mouseout'\n}\nconst customEventsRegex = /^(mouseenter|mouseleave)/i\nconst nativeEvents = new Set([\n  'click',\n  'dblclick',\n  'mouseup',\n  'mousedown',\n  'contextmenu',\n  'mousewheel',\n  'DOMMouseScroll',\n  'mouseover',\n  'mouseout',\n  'mousemove',\n  'selectstart',\n  'selectend',\n  'keydown',\n  'keypress',\n  'keyup',\n  'orientationchange',\n  'touchstart',\n  'touchmove',\n  'touchend',\n  'touchcancel',\n  'pointerdown',\n  'pointermove',\n  'pointerup',\n  'pointerleave',\n  'pointercancel',\n  'gesturestart',\n  'gesturechange',\n  'gestureend',\n  'focus',\n  'blur',\n  'change',\n  'reset',\n  'select',\n  'submit',\n  'focusin',\n  'focusout',\n  'load',\n  'unload',\n  'beforeunload',\n  'resize',\n  'move',\n  'DOMContentLoaded',\n  'readystatechange',\n  'error',\n  'abort',\n  'scroll'\n])\n\n/**\n * ------------------------------------------------------------------------\n * Private methods\n * ------------------------------------------------------------------------\n */\n\nfunction getUidEvent(element, uid) {\n  return (uid && `${uid}::${uidEvent++}`) || element.uidEvent || uidEvent++\n}\n\nfunction getEvent(element) {\n  const uid = getUidEvent(element)\n\n  element.uidEvent = uid\n  eventRegistry[uid] = eventRegistry[uid] || {}\n\n  return eventRegistry[uid]\n}\n\nfunction bootstrapHandler(element, fn) {\n  return function handler(event) {\n    event.delegateTarget = element\n\n    if (handler.oneOff) {\n      EventHandler.off(element, event.type, fn)\n    }\n\n    return fn.apply(element, [event])\n  }\n}\n\nfunction bootstrapDelegationHandler(element, selector, fn) {\n  return function handler(event) {\n    const domElements = element.querySelectorAll(selector)\n\n    for (let { target } = event; target && target !== this; target = target.parentNode) {\n      for (let i = domElements.length; i--;) {\n        if (domElements[i] === target) {\n          event.delegateTarget = target\n\n          if (handler.oneOff) {\n            // eslint-disable-next-line unicorn/consistent-destructuring\n            EventHandler.off(element, event.type, selector, fn)\n          }\n\n          return fn.apply(target, [event])\n        }\n      }\n    }\n\n    // To please ESLint\n    return null\n  }\n}\n\nfunction findHandler(events, handler, delegationSelector = null) {\n  const uidEventList = Object.keys(events)\n\n  for (let i = 0, len = uidEventList.length; i < len; i++) {\n    const event = events[uidEventList[i]]\n\n    if (event.originalHandler === handler && event.delegationSelector === delegationSelector) {\n      return event\n    }\n  }\n\n  return null\n}\n\nfunction normalizeParams(originalTypeEvent, handler, delegationFn) {\n  const delegation = typeof handler === 'string'\n  const originalHandler = delegation ? delegationFn : handler\n\n  let typeEvent = getTypeEvent(originalTypeEvent)\n  const isNative = nativeEvents.has(typeEvent)\n\n  if (!isNative) {\n    typeEvent = originalTypeEvent\n  }\n\n  return [delegation, originalHandler, typeEvent]\n}\n\nfunction addHandler(element, originalTypeEvent, handler, delegationFn, oneOff) {\n  if (typeof originalTypeEvent !== 'string' || !element) {\n    return\n  }\n\n  if (!handler) {\n    handler = delegationFn\n    delegationFn = null\n  }\n\n  // in case of mouseenter or mouseleave wrap the handler within a function that checks for its DOM position\n  // this prevents the handler from being dispatched the same way as mouseover or mouseout does\n  if (customEventsRegex.test(originalTypeEvent)) {\n    const wrapFn = fn => {\n      return function (event) {\n        if (!event.relatedTarget || (event.relatedTarget !== event.delegateTarget && !event.delegateTarget.contains(event.relatedTarget))) {\n          return fn.call(this, event)\n        }\n      }\n    }\n\n    if (delegationFn) {\n      delegationFn = wrapFn(delegationFn)\n    } else {\n      handler = wrapFn(handler)\n    }\n  }\n\n  const [delegation, originalHandler, typeEvent] = normalizeParams(originalTypeEvent, handler, delegationFn)\n  const events = getEvent(element)\n  const handlers = events[typeEvent] || (events[typeEvent] = {})\n  const previousFn = findHandler(handlers, originalHandler, delegation ? handler : null)\n\n  if (previousFn) {\n    previousFn.oneOff = previousFn.oneOff && oneOff\n\n    return\n  }\n\n  const uid = getUidEvent(originalHandler, originalTypeEvent.replace(namespaceRegex, ''))\n  const fn = delegation ?\n    bootstrapDelegationHandler(element, handler, delegationFn) :\n    bootstrapHandler(element, handler)\n\n  fn.delegationSelector = delegation ? handler : null\n  fn.originalHandler = originalHandler\n  fn.oneOff = oneOff\n  fn.uidEvent = uid\n  handlers[uid] = fn\n\n  element.addEventListener(typeEvent, fn, delegation)\n}\n\nfunction removeHandler(element, events, typeEvent, handler, delegationSelector) {\n  const fn = findHandler(events[typeEvent], handler, delegationSelector)\n\n  if (!fn) {\n    return\n  }\n\n  element.removeEventListener(typeEvent, fn, Boolean(delegationSelector))\n  delete events[typeEvent][fn.uidEvent]\n}\n\nfunction removeNamespacedHandlers(element, events, typeEvent, namespace) {\n  const storeElementEvent = events[typeEvent] || {}\n\n  Object.keys(storeElementEvent).forEach(handlerKey => {\n    if (handlerKey.includes(namespace)) {\n      const event = storeElementEvent[handlerKey]\n\n      removeHandler(element, events, typeEvent, event.originalHandler, event.delegationSelector)\n    }\n  })\n}\n\nfunction getTypeEvent(event) {\n  // allow to get the native events from namespaced events ('click.bs.button' --> 'click')\n  event = event.replace(stripNameRegex, '')\n  return customEvents[event] || event\n}\n\nconst EventHandler = {\n  on(element, event, handler, delegationFn) {\n    addHandler(element, event, handler, delegationFn, false)\n  },\n\n  one(element, event, handler, delegationFn) {\n    addHandler(element, event, handler, delegationFn, true)\n  },\n\n  off(element, originalTypeEvent, handler, delegationFn) {\n    if (typeof originalTypeEvent !== 'string' || !element) {\n      return\n    }\n\n    const [delegation, originalHandler, typeEvent] = normalizeParams(originalTypeEvent, handler, delegationFn)\n    const inNamespace = typeEvent !== originalTypeEvent\n    const events = getEvent(element)\n    const isNamespace = originalTypeEvent.startsWith('.')\n\n    if (typeof originalHandler !== 'undefined') {\n      // Simplest case: handler is passed, remove that listener ONLY.\n      if (!events || !events[typeEvent]) {\n        return\n      }\n\n      removeHandler(element, events, typeEvent, originalHandler, delegation ? handler : null)\n      return\n    }\n\n    if (isNamespace) {\n      Object.keys(events).forEach(elementEvent => {\n        removeNamespacedHandlers(element, events, elementEvent, originalTypeEvent.slice(1))\n      })\n    }\n\n    const storeElementEvent = events[typeEvent] || {}\n    Object.keys(storeElementEvent).forEach(keyHandlers => {\n      const handlerKey = keyHandlers.replace(stripUidRegex, '')\n\n      if (!inNamespace || originalTypeEvent.includes(handlerKey)) {\n        const event = storeElementEvent[keyHandlers]\n\n        removeHandler(element, events, typeEvent, event.originalHandler, event.delegationSelector)\n      }\n    })\n  },\n\n  trigger(element, event, args) {\n    if (typeof event !== 'string' || !element) {\n      return null\n    }\n\n    const $ = getjQuery()\n    const typeEvent = getTypeEvent(event)\n    const inNamespace = event !== typeEvent\n    const isNative = nativeEvents.has(typeEvent)\n\n    let jQueryEvent\n    let bubbles = true\n    let nativeDispatch = true\n    let defaultPrevented = false\n    let evt = null\n\n    if (inNamespace && $) {\n      jQueryEvent = $.Event(event, args)\n\n      $(element).trigger(jQueryEvent)\n      bubbles = !jQueryEvent.isPropagationStopped()\n      nativeDispatch = !jQueryEvent.isImmediatePropagationStopped()\n      defaultPrevented = jQueryEvent.isDefaultPrevented()\n    }\n\n    if (isNative) {\n      evt = document.createEvent('HTMLEvents')\n      evt.initEvent(typeEvent, bubbles, true)\n    } else {\n      evt = new CustomEvent(event, {\n        bubbles,\n        cancelable: true\n      })\n    }\n\n    // merge custom information in our event\n    if (typeof args !== 'undefined') {\n      Object.keys(args).forEach(key => {\n        Object.defineProperty(evt, key, {\n          get() {\n            return args[key]\n          }\n        })\n      })\n    }\n\n    if (defaultPrevented) {\n      evt.preventDefault()\n    }\n\n    if (nativeDispatch) {\n      element.dispatchEvent(evt)\n    }\n\n    if (evt.defaultPrevented && typeof jQueryEvent !== 'undefined') {\n      jQueryEvent.preventDefault()\n    }\n\n    return evt\n  }\n}\n\nexport default EventHandler\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.1.0): dom/data.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n/**\n * ------------------------------------------------------------------------\n * Constants\n * ------------------------------------------------------------------------\n */\n\nconst elementMap = new Map()\n\nexport default {\n  set(element, key, instance) {\n    if (!elementMap.has(element)) {\n      elementMap.set(element, new Map())\n    }\n\n    const instanceMap = elementMap.get(element)\n\n    // make it clear we only want one instance per element\n    // can be removed later when multiple key/instances are fine to be used\n    if (!instanceMap.has(key) && instanceMap.size !== 0) {\n      // eslint-disable-next-line no-console\n      console.error(`Bootstrap doesn't allow more than one instance per element. Bound instance: ${Array.from(instanceMap.keys())[0]}.`)\n      return\n    }\n\n    instanceMap.set(key, instance)\n  },\n\n  get(element, key) {\n    if (elementMap.has(element)) {\n      return elementMap.get(element).get(key) || null\n    }\n\n    return null\n  },\n\n  remove(element, key) {\n    if (!elementMap.has(element)) {\n      return\n    }\n\n    const instanceMap = elementMap.get(element)\n\n    instanceMap.delete(key)\n\n    // free up element references if there are no instances left for an element\n    if (instanceMap.size === 0) {\n      elementMap.delete(element)\n    }\n  }\n}\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.1.0): base-component.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport Data from './dom/data'\nimport {\n  executeAfterTransition,\n  getElement\n} from './util/index'\nimport EventHandler from './dom/event-handler'\n\n/**\n * ------------------------------------------------------------------------\n * Constants\n * ------------------------------------------------------------------------\n */\n\nconst VERSION = '5.1.0'\n\nclass BaseComponent {\n  constructor(element) {\n    element = getElement(element)\n\n    if (!element) {\n      return\n    }\n\n    this._element = element\n    Data.set(this._element, this.constructor.DATA_KEY, this)\n  }\n\n  dispose() {\n    Data.remove(this._element, this.constructor.DATA_KEY)\n    EventHandler.off(this._element, this.constructor.EVENT_KEY)\n\n    Object.getOwnPropertyNames(this).forEach(propertyName => {\n      this[propertyName] = null\n    })\n  }\n\n  _queueCallback(callback, element, isAnimated = true) {\n    executeAfterTransition(callback, element, isAnimated)\n  }\n\n  /** Static */\n\n  static getInstance(element) {\n    return Data.get(getElement(element), this.DATA_KEY)\n  }\n\n  static getOrCreateInstance(element, config = {}) {\n    return this.getInstance(element) || new this(element, typeof config === 'object' ? config : null)\n  }\n\n  static get VERSION() {\n    return VERSION\n  }\n\n  static get NAME() {\n    throw new Error('You have to implement the static method \"NAME\", for each component!')\n  }\n\n  static get DATA_KEY() {\n    return `bs.${this.NAME}`\n  }\n\n  static get EVENT_KEY() {\n    return `.${this.DATA_KEY}`\n  }\n}\n\nexport default BaseComponent\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.1.0): util/component-functions.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport EventHandler from '../dom/event-handler'\nimport { getElementFromSelector, isDisabled } from './index'\n\nconst enableDismissTrigger = (component, method = 'hide') => {\n  const clickEvent = `click.dismiss${component.EVENT_KEY}`\n  const name = component.NAME\n\n  EventHandler.on(document, clickEvent, `[data-bs-dismiss=\"${name}\"]`, function (event) {\n    if (['A', 'AREA'].includes(this.tagName)) {\n      event.preventDefault()\n    }\n\n    if (isDisabled(this)) {\n      return\n    }\n\n    const target = getElementFromSelector(this) || this.closest(`.${name}`)\n    const instance = component.getOrCreateInstance(target)\n\n    // Method argument is left, for Alert and only, as it doesn't implement the 'hide' method\n    instance[method]()\n  })\n}\n\nexport {\n  enableDismissTrigger\n}\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.1.0): alert.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport { defineJQueryPlugin } from './util/index'\nimport EventHandler from './dom/event-handler'\nimport BaseComponent from './base-component'\nimport { enableDismissTrigger } from './util/component-functions'\n\n/**\n * ------------------------------------------------------------------------\n * Constants\n * ------------------------------------------------------------------------\n */\n\nconst NAME = 'alert'\nconst DATA_KEY = 'bs.alert'\nconst EVENT_KEY = `.${DATA_KEY}`\n\nconst EVENT_CLOSE = `close${EVENT_KEY}`\nconst EVENT_CLOSED = `closed${EVENT_KEY}`\nconst CLASS_NAME_FADE = 'fade'\nconst CLASS_NAME_SHOW = 'show'\n\n/**\n * ------------------------------------------------------------------------\n * Class Definition\n * ------------------------------------------------------------------------\n */\n\nclass Alert extends BaseComponent {\n  // Getters\n\n  static get NAME() {\n    return NAME\n  }\n\n  // Public\n\n  close() {\n    const closeEvent = EventHandler.trigger(this._element, EVENT_CLOSE)\n\n    if (closeEvent.defaultPrevented) {\n      return\n    }\n\n    this._element.classList.remove(CLASS_NAME_SHOW)\n\n    const isAnimated = this._element.classList.contains(CLASS_NAME_FADE)\n    this._queueCallback(() => this._destroyElement(), this._element, isAnimated)\n  }\n\n  // Private\n  _destroyElement() {\n    this._element.remove()\n    EventHandler.trigger(this._element, EVENT_CLOSED)\n    this.dispose()\n  }\n\n  // Static\n\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Alert.getOrCreateInstance(this)\n\n      if (typeof config !== 'string') {\n        return\n      }\n\n      if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n        throw new TypeError(`No method named \"${config}\"`)\n      }\n\n      data[config](this)\n    })\n  }\n}\n\n/**\n * ------------------------------------------------------------------------\n * Data Api implementation\n * ------------------------------------------------------------------------\n */\n\nenableDismissTrigger(Alert, 'close')\n/**\n * ------------------------------------------------------------------------\n * jQuery\n * ------------------------------------------------------------------------\n * add .Alert to jQuery only if jQuery is present\n */\n\ndefineJQueryPlugin(Alert)\n\nexport default Alert\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.1.0): button.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport { defineJQueryPlugin } from './util/index'\nimport EventHandler from './dom/event-handler'\nimport BaseComponent from './base-component'\n\n/**\n * ------------------------------------------------------------------------\n * Constants\n * ------------------------------------------------------------------------\n */\n\nconst NAME = 'button'\nconst DATA_KEY = 'bs.button'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\n\nconst CLASS_NAME_ACTIVE = 'active'\n\nconst SELECTOR_DATA_TOGGLE = '[data-bs-toggle=\"button\"]'\n\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY}${DATA_API_KEY}`\n\n/**\n * ------------------------------------------------------------------------\n * Class Definition\n * ------------------------------------------------------------------------\n */\n\nclass Button extends BaseComponent {\n  // Getters\n\n  static get NAME() {\n    return NAME\n  }\n\n  // Public\n\n  toggle() {\n    // Toggle class and sync the `aria-pressed` attribute with the return value of the `.toggle()` method\n    this._element.setAttribute('aria-pressed', this._element.classList.toggle(CLASS_NAME_ACTIVE))\n  }\n\n  // Static\n\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Button.getOrCreateInstance(this)\n\n      if (config === 'toggle') {\n        data[config]()\n      }\n    })\n  }\n}\n\n/**\n * ------------------------------------------------------------------------\n * Data Api implementation\n * ------------------------------------------------------------------------\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_TOGGLE, event => {\n  event.preventDefault()\n\n  const button = event.target.closest(SELECTOR_DATA_TOGGLE)\n  const data = Button.getOrCreateInstance(button)\n\n  data.toggle()\n})\n\n/**\n * ------------------------------------------------------------------------\n * jQuery\n * ------------------------------------------------------------------------\n * add .Button to jQuery only if jQuery is present\n */\n\ndefineJQueryPlugin(Button)\n\nexport default Button\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.1.0): dom/manipulator.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nfunction normalizeData(val) {\n  if (val === 'true') {\n    return true\n  }\n\n  if (val === 'false') {\n    return false\n  }\n\n  if (val === Number(val).toString()) {\n    return Number(val)\n  }\n\n  if (val === '' || val === 'null') {\n    return null\n  }\n\n  return val\n}\n\nfunction normalizeDataKey(key) {\n  return key.replace(/[A-Z]/g, chr => `-${chr.toLowerCase()}`)\n}\n\nconst Manipulator = {\n  setDataAttribute(element, key, value) {\n    element.setAttribute(`data-bs-${normalizeDataKey(key)}`, value)\n  },\n\n  removeDataAttribute(element, key) {\n    element.removeAttribute(`data-bs-${normalizeDataKey(key)}`)\n  },\n\n  getDataAttributes(element) {\n    if (!element) {\n      return {}\n    }\n\n    const attributes = {}\n\n    Object.keys(element.dataset)\n      .filter(key => key.startsWith('bs'))\n      .forEach(key => {\n        let pureKey = key.replace(/^bs/, '')\n        pureKey = pureKey.charAt(0).toLowerCase() + pureKey.slice(1, pureKey.length)\n        attributes[pureKey] = normalizeData(element.dataset[key])\n      })\n\n    return attributes\n  },\n\n  getDataAttribute(element, key) {\n    return normalizeData(element.getAttribute(`data-bs-${normalizeDataKey(key)}`))\n  },\n\n  offset(element) {\n    const rect = element.getBoundingClientRect()\n\n    return {\n      top: rect.top + window.pageYOffset,\n      left: rect.left + window.pageXOffset\n    }\n  },\n\n  position(element) {\n    return {\n      top: element.offsetTop,\n      left: element.offsetLeft\n    }\n  }\n}\n\nexport default Manipulator\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.1.0): dom/selector-engine.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n/**\n * ------------------------------------------------------------------------\n * Constants\n * ------------------------------------------------------------------------\n */\n\nimport { isDisabled, isVisible } from '../util/index'\n\nconst NODE_TEXT = 3\n\nconst SelectorEngine = {\n  find(selector, element = document.documentElement) {\n    return [].concat(...Element.prototype.querySelectorAll.call(element, selector))\n  },\n\n  findOne(selector, element = document.documentElement) {\n    return Element.prototype.querySelector.call(element, selector)\n  },\n\n  children(element, selector) {\n    return [].concat(...element.children)\n      .filter(child => child.matches(selector))\n  },\n\n  parents(element, selector) {\n    const parents = []\n\n    let ancestor = element.parentNode\n\n    while (ancestor && ancestor.nodeType === Node.ELEMENT_NODE && ancestor.nodeType !== NODE_TEXT) {\n      if (ancestor.matches(selector)) {\n        parents.push(ancestor)\n      }\n\n      ancestor = ancestor.parentNode\n    }\n\n    return parents\n  },\n\n  prev(element, selector) {\n    let previous = element.previousElementSibling\n\n    while (previous) {\n      if (previous.matches(selector)) {\n        return [previous]\n      }\n\n      previous = previous.previousElementSibling\n    }\n\n    return []\n  },\n\n  next(element, selector) {\n    let next = element.nextElementSibling\n\n    while (next) {\n      if (next.matches(selector)) {\n        return [next]\n      }\n\n      next = next.nextElementSibling\n    }\n\n    return []\n  },\n\n  focusableChildren(element) {\n    const focusables = [\n      'a',\n      'button',\n      'input',\n      'textarea',\n      'select',\n      'details',\n      '[tabindex]',\n      '[contenteditable=\"true\"]'\n    ].map(selector => `${selector}:not([tabindex^=\"-\"])`).join(', ')\n\n    return this.find(focusables, element).filter(el => !isDisabled(el) && isVisible(el))\n  }\n}\n\nexport default SelectorEngine\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.1.0): carousel.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport {\n  defineJQueryPlugin,\n  getElementFromSelector,\n  isRTL,\n  isVisible,\n  getNextActiveElement,\n  reflow,\n  triggerTransitionEnd,\n  typeCheckConfig\n} from './util/index'\nimport EventHandler from './dom/event-handler'\nimport Manipulator from './dom/manipulator'\nimport SelectorEngine from './dom/selector-engine'\nimport BaseComponent from './base-component'\n\n/**\n * ------------------------------------------------------------------------\n * Constants\n * ------------------------------------------------------------------------\n */\n\nconst NAME = 'carousel'\nconst DATA_KEY = 'bs.carousel'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\n\nconst ARROW_LEFT_KEY = 'ArrowLeft'\nconst ARROW_RIGHT_KEY = 'ArrowRight'\nconst TOUCHEVENT_COMPAT_WAIT = 500 // Time for mouse compat events to fire after touch\nconst SWIPE_THRESHOLD = 40\n\nconst Default = {\n  interval: 5000,\n  keyboard: true,\n  slide: false,\n  pause: 'hover',\n  wrap: true,\n  touch: true\n}\n\nconst DefaultType = {\n  interval: '(number|boolean)',\n  keyboard: 'boolean',\n  slide: '(boolean|string)',\n  pause: '(string|boolean)',\n  wrap: 'boolean',\n  touch: 'boolean'\n}\n\nconst ORDER_NEXT = 'next'\nconst ORDER_PREV = 'prev'\nconst DIRECTION_LEFT = 'left'\nconst DIRECTION_RIGHT = 'right'\n\nconst KEY_TO_DIRECTION = {\n  [ARROW_LEFT_KEY]: DIRECTION_RIGHT,\n  [ARROW_RIGHT_KEY]: DIRECTION_LEFT\n}\n\nconst EVENT_SLIDE = `slide${EVENT_KEY}`\nconst EVENT_SLID = `slid${EVENT_KEY}`\nconst EVENT_KEYDOWN = `keydown${EVENT_KEY}`\nconst EVENT_MOUSEENTER = `mouseenter${EVENT_KEY}`\nconst EVENT_MOUSELEAVE = `mouseleave${EVENT_KEY}`\nconst EVENT_TOUCHSTART = `touchstart${EVENT_KEY}`\nconst EVENT_TOUCHMOVE = `touchmove${EVENT_KEY}`\nconst EVENT_TOUCHEND = `touchend${EVENT_KEY}`\nconst EVENT_POINTERDOWN = `pointerdown${EVENT_KEY}`\nconst EVENT_POINTERUP = `pointerup${EVENT_KEY}`\nconst EVENT_DRAG_START = `dragstart${EVENT_KEY}`\nconst EVENT_LOAD_DATA_API = `load${EVENT_KEY}${DATA_API_KEY}`\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY}${DATA_API_KEY}`\n\nconst CLASS_NAME_CAROUSEL = 'carousel'\nconst CLASS_NAME_ACTIVE = 'active'\nconst CLASS_NAME_SLIDE = 'slide'\nconst CLASS_NAME_END = 'carousel-item-end'\nconst CLASS_NAME_START = 'carousel-item-start'\nconst CLASS_NAME_NEXT = 'carousel-item-next'\nconst CLASS_NAME_PREV = 'carousel-item-prev'\nconst CLASS_NAME_POINTER_EVENT = 'pointer-event'\n\nconst SELECTOR_ACTIVE = '.active'\nconst SELECTOR_ACTIVE_ITEM = '.active.carousel-item'\nconst SELECTOR_ITEM = '.carousel-item'\nconst SELECTOR_ITEM_IMG = '.carousel-item img'\nconst SELECTOR_NEXT_PREV = '.carousel-item-next, .carousel-item-prev'\nconst SELECTOR_INDICATORS = '.carousel-indicators'\nconst SELECTOR_INDICATOR = '[data-bs-target]'\nconst SELECTOR_DATA_SLIDE = '[data-bs-slide], [data-bs-slide-to]'\nconst SELECTOR_DATA_RIDE = '[data-bs-ride=\"carousel\"]'\n\nconst POINTER_TYPE_TOUCH = 'touch'\nconst POINTER_TYPE_PEN = 'pen'\n\n/**\n * ------------------------------------------------------------------------\n * Class Definition\n * ------------------------------------------------------------------------\n */\nclass Carousel extends BaseComponent {\n  constructor(element, config) {\n    super(element)\n\n    this._items = null\n    this._interval = null\n    this._activeElement = null\n    this._isPaused = false\n    this._isSliding = false\n    this.touchTimeout = null\n    this.touchStartX = 0\n    this.touchDeltaX = 0\n\n    this._config = this._getConfig(config)\n    this._indicatorsElement = SelectorEngine.findOne(SELECTOR_INDICATORS, this._element)\n    this._touchSupported = 'ontouchstart' in document.documentElement || navigator.maxTouchPoints > 0\n    this._pointerEvent = Boolean(window.PointerEvent)\n\n    this._addEventListeners()\n  }\n\n  // Getters\n\n  static get Default() {\n    return Default\n  }\n\n  static get NAME() {\n    return NAME\n  }\n\n  // Public\n\n  next() {\n    this._slide(ORDER_NEXT)\n  }\n\n  nextWhenVisible() {\n    // Don't call next when the page isn't visible\n    // or the carousel or its parent isn't visible\n    if (!document.hidden && isVisible(this._element)) {\n      this.next()\n    }\n  }\n\n  prev() {\n    this._slide(ORDER_PREV)\n  }\n\n  pause(event) {\n    if (!event) {\n      this._isPaused = true\n    }\n\n    if (SelectorEngine.findOne(SELECTOR_NEXT_PREV, this._element)) {\n      triggerTransitionEnd(this._element)\n      this.cycle(true)\n    }\n\n    clearInterval(this._interval)\n    this._interval = null\n  }\n\n  cycle(event) {\n    if (!event) {\n      this._isPaused = false\n    }\n\n    if (this._interval) {\n      clearInterval(this._interval)\n      this._interval = null\n    }\n\n    if (this._config && this._config.interval && !this._isPaused) {\n      this._updateInterval()\n\n      this._interval = setInterval(\n        (document.visibilityState ? this.nextWhenVisible : this.next).bind(this),\n        this._config.interval\n      )\n    }\n  }\n\n  to(index) {\n    this._activeElement = SelectorEngine.findOne(SELECTOR_ACTIVE_ITEM, this._element)\n    const activeIndex = this._getItemIndex(this._activeElement)\n\n    if (index > this._items.length - 1 || index < 0) {\n      return\n    }\n\n    if (this._isSliding) {\n      EventHandler.one(this._element, EVENT_SLID, () => this.to(index))\n      return\n    }\n\n    if (activeIndex === index) {\n      this.pause()\n      this.cycle()\n      return\n    }\n\n    const order = index > activeIndex ?\n      ORDER_NEXT :\n      ORDER_PREV\n\n    this._slide(order, this._items[index])\n  }\n\n  // Private\n\n  _getConfig(config) {\n    config = {\n      ...Default,\n      ...Manipulator.getDataAttributes(this._element),\n      ...(typeof config === 'object' ? config : {})\n    }\n    typeCheckConfig(NAME, config, DefaultType)\n    return config\n  }\n\n  _handleSwipe() {\n    const absDeltax = Math.abs(this.touchDeltaX)\n\n    if (absDeltax <= SWIPE_THRESHOLD) {\n      return\n    }\n\n    const direction = absDeltax / this.touchDeltaX\n\n    this.touchDeltaX = 0\n\n    if (!direction) {\n      return\n    }\n\n    this._slide(direction > 0 ? DIRECTION_RIGHT : DIRECTION_LEFT)\n  }\n\n  _addEventListeners() {\n    if (this._config.keyboard) {\n      EventHandler.on(this._element, EVENT_KEYDOWN, event => this._keydown(event))\n    }\n\n    if (this._config.pause === 'hover') {\n      EventHandler.on(this._element, EVENT_MOUSEENTER, event => this.pause(event))\n      EventHandler.on(this._element, EVENT_MOUSELEAVE, event => this.cycle(event))\n    }\n\n    if (this._config.touch && this._touchSupported) {\n      this._addTouchEventListeners()\n    }\n  }\n\n  _addTouchEventListeners() {\n    const start = event => {\n      if (this._pointerEvent && (event.pointerType === POINTER_TYPE_PEN || event.pointerType === POINTER_TYPE_TOUCH)) {\n        this.touchStartX = event.clientX\n      } else if (!this._pointerEvent) {\n        this.touchStartX = event.touches[0].clientX\n      }\n    }\n\n    const move = event => {\n      // ensure swiping with one touch and not pinching\n      this.touchDeltaX = event.touches && event.touches.length > 1 ?\n        0 :\n        event.touches[0].clientX - this.touchStartX\n    }\n\n    const end = event => {\n      if (this._pointerEvent && (event.pointerType === POINTER_TYPE_PEN || event.pointerType === POINTER_TYPE_TOUCH)) {\n        this.touchDeltaX = event.clientX - this.touchStartX\n      }\n\n      this._handleSwipe()\n      if (this._config.pause === 'hover') {\n        // If it's a touch-enabled device, mouseenter/leave are fired as\n        // part of the mouse compatibility events on first tap - the carousel\n        // would stop cycling until user tapped out of it;\n        // here, we listen for touchend, explicitly pause the carousel\n        // (as if it's the second time we tap on it, mouseenter compat event\n        // is NOT fired) and after a timeout (to allow for mouse compatibility\n        // events to fire) we explicitly restart cycling\n\n        this.pause()\n        if (this.touchTimeout) {\n          clearTimeout(this.touchTimeout)\n        }\n\n        this.touchTimeout = setTimeout(event => this.cycle(event), TOUCHEVENT_COMPAT_WAIT + this._config.interval)\n      }\n    }\n\n    SelectorEngine.find(SELECTOR_ITEM_IMG, this._element).forEach(itemImg => {\n      EventHandler.on(itemImg, EVENT_DRAG_START, e => e.preventDefault())\n    })\n\n    if (this._pointerEvent) {\n      EventHandler.on(this._element, EVENT_POINTERDOWN, event => start(event))\n      EventHandler.on(this._element, EVENT_POINTERUP, event => end(event))\n\n      this._element.classList.add(CLASS_NAME_POINTER_EVENT)\n    } else {\n      EventHandler.on(this._element, EVENT_TOUCHSTART, event => start(event))\n      EventHandler.on(this._element, EVENT_TOUCHMOVE, event => move(event))\n      EventHandler.on(this._element, EVENT_TOUCHEND, event => end(event))\n    }\n  }\n\n  _keydown(event) {\n    if (/input|textarea/i.test(event.target.tagName)) {\n      return\n    }\n\n    const direction = KEY_TO_DIRECTION[event.key]\n    if (direction) {\n      event.preventDefault()\n      this._slide(direction)\n    }\n  }\n\n  _getItemIndex(element) {\n    this._items = element && element.parentNode ?\n      SelectorEngine.find(SELECTOR_ITEM, element.parentNode) :\n      []\n\n    return this._items.indexOf(element)\n  }\n\n  _getItemByOrder(order, activeElement) {\n    const isNext = order === ORDER_NEXT\n    return getNextActiveElement(this._items, activeElement, isNext, this._config.wrap)\n  }\n\n  _triggerSlideEvent(relatedTarget, eventDirectionName) {\n    const targetIndex = this._getItemIndex(relatedTarget)\n    const fromIndex = this._getItemIndex(SelectorEngine.findOne(SELECTOR_ACTIVE_ITEM, this._element))\n\n    return EventHandler.trigger(this._element, EVENT_SLIDE, {\n      relatedTarget,\n      direction: eventDirectionName,\n      from: fromIndex,\n      to: targetIndex\n    })\n  }\n\n  _setActiveIndicatorElement(element) {\n    if (this._indicatorsElement) {\n      const activeIndicator = SelectorEngine.findOne(SELECTOR_ACTIVE, this._indicatorsElement)\n\n      activeIndicator.classList.remove(CLASS_NAME_ACTIVE)\n      activeIndicator.removeAttribute('aria-current')\n\n      const indicators = SelectorEngine.find(SELECTOR_INDICATOR, this._indicatorsElement)\n\n      for (let i = 0; i < indicators.length; i++) {\n        if (Number.parseInt(indicators[i].getAttribute('data-bs-slide-to'), 10) === this._getItemIndex(element)) {\n          indicators[i].classList.add(CLASS_NAME_ACTIVE)\n          indicators[i].setAttribute('aria-current', 'true')\n          break\n        }\n      }\n    }\n  }\n\n  _updateInterval() {\n    const element = this._activeElement || SelectorEngine.findOne(SELECTOR_ACTIVE_ITEM, this._element)\n\n    if (!element) {\n      return\n    }\n\n    const elementInterval = Number.parseInt(element.getAttribute('data-bs-interval'), 10)\n\n    if (elementInterval) {\n      this._config.defaultInterval = this._config.defaultInterval || this._config.interval\n      this._config.interval = elementInterval\n    } else {\n      this._config.interval = this._config.defaultInterval || this._config.interval\n    }\n  }\n\n  _slide(directionOrOrder, element) {\n    const order = this._directionToOrder(directionOrOrder)\n    const activeElement = SelectorEngine.findOne(SELECTOR_ACTIVE_ITEM, this._element)\n    const activeElementIndex = this._getItemIndex(activeElement)\n    const nextElement = element || this._getItemByOrder(order, activeElement)\n\n    const nextElementIndex = this._getItemIndex(nextElement)\n    const isCycling = Boolean(this._interval)\n\n    const isNext = order === ORDER_NEXT\n    const directionalClassName = isNext ? CLASS_NAME_START : CLASS_NAME_END\n    const orderClassName = isNext ? CLASS_NAME_NEXT : CLASS_NAME_PREV\n    const eventDirectionName = this._orderToDirection(order)\n\n    if (nextElement && nextElement.classList.contains(CLASS_NAME_ACTIVE)) {\n      this._isSliding = false\n      return\n    }\n\n    if (this._isSliding) {\n      return\n    }\n\n    const slideEvent = this._triggerSlideEvent(nextElement, eventDirectionName)\n    if (slideEvent.defaultPrevented) {\n      return\n    }\n\n    if (!activeElement || !nextElement) {\n      // Some weirdness is happening, so we bail\n      return\n    }\n\n    this._isSliding = true\n\n    if (isCycling) {\n      this.pause()\n    }\n\n    this._setActiveIndicatorElement(nextElement)\n    this._activeElement = nextElement\n\n    const triggerSlidEvent = () => {\n      EventHandler.trigger(this._element, EVENT_SLID, {\n        relatedTarget: nextElement,\n        direction: eventDirectionName,\n        from: activeElementIndex,\n        to: nextElementIndex\n      })\n    }\n\n    if (this._element.classList.contains(CLASS_NAME_SLIDE)) {\n      nextElement.classList.add(orderClassName)\n\n      reflow(nextElement)\n\n      activeElement.classList.add(directionalClassName)\n      nextElement.classList.add(directionalClassName)\n\n      const completeCallBack = () => {\n        nextElement.classList.remove(directionalClassName, orderClassName)\n        nextElement.classList.add(CLASS_NAME_ACTIVE)\n\n        activeElement.classList.remove(CLASS_NAME_ACTIVE, orderClassName, directionalClassName)\n\n        this._isSliding = false\n\n        setTimeout(triggerSlidEvent, 0)\n      }\n\n      this._queueCallback(completeCallBack, activeElement, true)\n    } else {\n      activeElement.classList.remove(CLASS_NAME_ACTIVE)\n      nextElement.classList.add(CLASS_NAME_ACTIVE)\n\n      this._isSliding = false\n      triggerSlidEvent()\n    }\n\n    if (isCycling) {\n      this.cycle()\n    }\n  }\n\n  _directionToOrder(direction) {\n    if (![DIRECTION_RIGHT, DIRECTION_LEFT].includes(direction)) {\n      return direction\n    }\n\n    if (isRTL()) {\n      return direction === DIRECTION_LEFT ? ORDER_PREV : ORDER_NEXT\n    }\n\n    return direction === DIRECTION_LEFT ? ORDER_NEXT : ORDER_PREV\n  }\n\n  _orderToDirection(order) {\n    if (![ORDER_NEXT, ORDER_PREV].includes(order)) {\n      return order\n    }\n\n    if (isRTL()) {\n      return order === ORDER_PREV ? DIRECTION_LEFT : DIRECTION_RIGHT\n    }\n\n    return order === ORDER_PREV ? DIRECTION_RIGHT : DIRECTION_LEFT\n  }\n\n  // Static\n\n  static carouselInterface(element, config) {\n    const data = Carousel.getOrCreateInstance(element, config)\n\n    let { _config } = data\n    if (typeof config === 'object') {\n      _config = {\n        ..._config,\n        ...config\n      }\n    }\n\n    const action = typeof config === 'string' ? config : _config.slide\n\n    if (typeof config === 'number') {\n      data.to(config)\n    } else if (typeof action === 'string') {\n      if (typeof data[action] === 'undefined') {\n        throw new TypeError(`No method named \"${action}\"`)\n      }\n\n      data[action]()\n    } else if (_config.interval && _config.ride) {\n      data.pause()\n      data.cycle()\n    }\n  }\n\n  static jQueryInterface(config) {\n    return this.each(function () {\n      Carousel.carouselInterface(this, config)\n    })\n  }\n\n  static dataApiClickHandler(event) {\n    const target = getElementFromSelector(this)\n\n    if (!target || !target.classList.contains(CLASS_NAME_CAROUSEL)) {\n      return\n    }\n\n    const config = {\n      ...Manipulator.getDataAttributes(target),\n      ...Manipulator.getDataAttributes(this)\n    }\n    const slideIndex = this.getAttribute('data-bs-slide-to')\n\n    if (slideIndex) {\n      config.interval = false\n    }\n\n    Carousel.carouselInterface(target, config)\n\n    if (slideIndex) {\n      Carousel.getInstance(target).to(slideIndex)\n    }\n\n    event.preventDefault()\n  }\n}\n\n/**\n * ------------------------------------------------------------------------\n * Data Api implementation\n * ------------------------------------------------------------------------\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_SLIDE, Carousel.dataApiClickHandler)\n\nEventHandler.on(window, EVENT_LOAD_DATA_API, () => {\n  const carousels = SelectorEngine.find(SELECTOR_DATA_RIDE)\n\n  for (let i = 0, len = carousels.length; i < len; i++) {\n    Carousel.carouselInterface(carousels[i], Carousel.getInstance(carousels[i]))\n  }\n})\n\n/**\n * ------------------------------------------------------------------------\n * jQuery\n * ------------------------------------------------------------------------\n * add .Carousel to jQuery only if jQuery is present\n */\n\ndefineJQueryPlugin(Carousel)\n\nexport default Carousel\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.1.0): collapse.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport {\n  defineJQueryPlugin,\n  getElement,\n  getSelectorFromElement,\n  getElementFromSelector,\n  reflow,\n  typeCheckConfig\n} from './util/index'\nimport Data from './dom/data'\nimport EventHandler from './dom/event-handler'\nimport Manipulator from './dom/manipulator'\nimport SelectorEngine from './dom/selector-engine'\nimport BaseComponent from './base-component'\n\n/**\n * ------------------------------------------------------------------------\n * Constants\n * ------------------------------------------------------------------------\n */\n\nconst NAME = 'collapse'\nconst DATA_KEY = 'bs.collapse'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\n\nconst Default = {\n  toggle: true,\n  parent: null\n}\n\nconst DefaultType = {\n  toggle: 'boolean',\n  parent: '(null|element)'\n}\n\nconst EVENT_SHOW = `show${EVENT_KEY}`\nconst EVENT_SHOWN = `shown${EVENT_KEY}`\nconst EVENT_HIDE = `hide${EVENT_KEY}`\nconst EVENT_HIDDEN = `hidden${EVENT_KEY}`\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY}${DATA_API_KEY}`\n\nconst CLASS_NAME_SHOW = 'show'\nconst CLASS_NAME_COLLAPSE = 'collapse'\nconst CLASS_NAME_COLLAPSING = 'collapsing'\nconst CLASS_NAME_COLLAPSED = 'collapsed'\nconst CLASS_NAME_HORIZONTAL = 'collapse-horizontal'\n\nconst WIDTH = 'width'\nconst HEIGHT = 'height'\n\nconst SELECTOR_ACTIVES = '.show, .collapsing'\nconst SELECTOR_DATA_TOGGLE = '[data-bs-toggle=\"collapse\"]'\n\n/**\n * ------------------------------------------------------------------------\n * Class Definition\n * ------------------------------------------------------------------------\n */\n\nclass Collapse extends BaseComponent {\n  constructor(element, config) {\n    super(element)\n\n    this._isTransitioning = false\n    this._config = this._getConfig(config)\n    this._triggerArray = []\n\n    const toggleList = SelectorEngine.find(SELECTOR_DATA_TOGGLE)\n\n    for (let i = 0, len = toggleList.length; i < len; i++) {\n      const elem = toggleList[i]\n      const selector = getSelectorFromElement(elem)\n      const filterElement = SelectorEngine.find(selector)\n        .filter(foundElem => foundElem === this._element)\n\n      if (selector !== null && filterElement.length) {\n        this._selector = selector\n        this._triggerArray.push(elem)\n      }\n    }\n\n    this._initializeChildren()\n\n    if (!this._config.parent) {\n      this._addAriaAndCollapsedClass(this._triggerArray, this._isShown())\n    }\n\n    if (this._config.toggle) {\n      this.toggle()\n    }\n  }\n\n  // Getters\n\n  static get Default() {\n    return Default\n  }\n\n  static get NAME() {\n    return NAME\n  }\n\n  // Public\n\n  toggle() {\n    if (this._isShown()) {\n      this.hide()\n    } else {\n      this.show()\n    }\n  }\n\n  show() {\n    if (this._isTransitioning || this._isShown()) {\n      return\n    }\n\n    let actives = []\n    let activesData\n\n    if (this._config.parent) {\n      const children = SelectorEngine.find(`.${CLASS_NAME_COLLAPSE} .${CLASS_NAME_COLLAPSE}`, this._config.parent)\n      actives = SelectorEngine.find(SELECTOR_ACTIVES, this._config.parent).filter(elem => !children.includes(elem)) // remove children if greater depth\n    }\n\n    const container = SelectorEngine.findOne(this._selector)\n    if (actives.length) {\n      const tempActiveData = actives.find(elem => container !== elem)\n      activesData = tempActiveData ? Collapse.getInstance(tempActiveData) : null\n\n      if (activesData && activesData._isTransitioning) {\n        return\n      }\n    }\n\n    const startEvent = EventHandler.trigger(this._element, EVENT_SHOW)\n    if (startEvent.defaultPrevented) {\n      return\n    }\n\n    actives.forEach(elemActive => {\n      if (container !== elemActive) {\n        Collapse.getOrCreateInstance(elemActive, { toggle: false }).hide()\n      }\n\n      if (!activesData) {\n        Data.set(elemActive, DATA_KEY, null)\n      }\n    })\n\n    const dimension = this._getDimension()\n\n    this._element.classList.remove(CLASS_NAME_COLLAPSE)\n    this._element.classList.add(CLASS_NAME_COLLAPSING)\n\n    this._element.style[dimension] = 0\n\n    this._addAriaAndCollapsedClass(this._triggerArray, true)\n    this._isTransitioning = true\n\n    const complete = () => {\n      this._isTransitioning = false\n\n      this._element.classList.remove(CLASS_NAME_COLLAPSING)\n      this._element.classList.add(CLASS_NAME_COLLAPSE, CLASS_NAME_SHOW)\n\n      this._element.style[dimension] = ''\n\n      EventHandler.trigger(this._element, EVENT_SHOWN)\n    }\n\n    const capitalizedDimension = dimension[0].toUpperCase() + dimension.slice(1)\n    const scrollSize = `scroll${capitalizedDimension}`\n\n    this._queueCallback(complete, this._element, true)\n    this._element.style[dimension] = `${this._element[scrollSize]}px`\n  }\n\n  hide() {\n    if (this._isTransitioning || !this._isShown()) {\n      return\n    }\n\n    const startEvent = EventHandler.trigger(this._element, EVENT_HIDE)\n    if (startEvent.defaultPrevented) {\n      return\n    }\n\n    const dimension = this._getDimension()\n\n    this._element.style[dimension] = `${this._element.getBoundingClientRect()[dimension]}px`\n\n    reflow(this._element)\n\n    this._element.classList.add(CLASS_NAME_COLLAPSING)\n    this._element.classList.remove(CLASS_NAME_COLLAPSE, CLASS_NAME_SHOW)\n\n    const triggerArrayLength = this._triggerArray.length\n    for (let i = 0; i < triggerArrayLength; i++) {\n      const trigger = this._triggerArray[i]\n      const elem = getElementFromSelector(trigger)\n\n      if (elem && !this._isShown(elem)) {\n        this._addAriaAndCollapsedClass([trigger], false)\n      }\n    }\n\n    this._isTransitioning = true\n\n    const complete = () => {\n      this._isTransitioning = false\n      this._element.classList.remove(CLASS_NAME_COLLAPSING)\n      this._element.classList.add(CLASS_NAME_COLLAPSE)\n      EventHandler.trigger(this._element, EVENT_HIDDEN)\n    }\n\n    this._element.style[dimension] = ''\n\n    this._queueCallback(complete, this._element, true)\n  }\n\n  _isShown(element = this._element) {\n    return element.classList.contains(CLASS_NAME_SHOW)\n  }\n\n  // Private\n\n  _getConfig(config) {\n    config = {\n      ...Default,\n      ...Manipulator.getDataAttributes(this._element),\n      ...config\n    }\n    config.toggle = Boolean(config.toggle) // Coerce string values\n    config.parent = getElement(config.parent)\n    typeCheckConfig(NAME, config, DefaultType)\n    return config\n  }\n\n  _getDimension() {\n    return this._element.classList.contains(CLASS_NAME_HORIZONTAL) ? WIDTH : HEIGHT\n  }\n\n  _initializeChildren() {\n    if (!this._config.parent) {\n      return\n    }\n\n    const children = SelectorEngine.find(`.${CLASS_NAME_COLLAPSE} .${CLASS_NAME_COLLAPSE}`, this._config.parent)\n    SelectorEngine.find(SELECTOR_DATA_TOGGLE, this._config.parent).filter(elem => !children.includes(elem))\n      .forEach(element => {\n        const selected = getElementFromSelector(element)\n\n        if (selected) {\n          this._addAriaAndCollapsedClass([element], this._isShown(selected))\n        }\n      })\n  }\n\n  _addAriaAndCollapsedClass(triggerArray, isOpen) {\n    if (!triggerArray.length) {\n      return\n    }\n\n    triggerArray.forEach(elem => {\n      if (isOpen) {\n        elem.classList.remove(CLASS_NAME_COLLAPSED)\n      } else {\n        elem.classList.add(CLASS_NAME_COLLAPSED)\n      }\n\n      elem.setAttribute('aria-expanded', isOpen)\n    })\n  }\n\n  // Static\n\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const _config = {}\n      if (typeof config === 'string' && /show|hide/.test(config)) {\n        _config.toggle = false\n      }\n\n      const data = Collapse.getOrCreateInstance(this, _config)\n\n      if (typeof config === 'string') {\n        if (typeof data[config] === 'undefined') {\n          throw new TypeError(`No method named \"${config}\"`)\n        }\n\n        data[config]()\n      }\n    })\n  }\n}\n\n/**\n * ------------------------------------------------------------------------\n * Data Api implementation\n * ------------------------------------------------------------------------\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_TOGGLE, function (event) {\n  // preventDefault only for <a> elements (which change the URL) not inside the collapsible element\n  if (event.target.tagName === 'A' || (event.delegateTarget && event.delegateTarget.tagName === 'A')) {\n    event.preventDefault()\n  }\n\n  const selector = getSelectorFromElement(this)\n  const selectorElements = SelectorEngine.find(selector)\n\n  selectorElements.forEach(element => {\n    Collapse.getOrCreateInstance(element, { toggle: false }).toggle()\n  })\n})\n\n/**\n * ------------------------------------------------------------------------\n * jQuery\n * ------------------------------------------------------------------------\n * add .Collapse to jQuery only if jQuery is present\n */\n\ndefineJQueryPlugin(Collapse)\n\nexport default Collapse\n","export var top = 'top';\nexport var bottom = 'bottom';\nexport var right = 'right';\nexport var left = 'left';\nexport var auto = 'auto';\nexport var basePlacements = [top, bottom, right, left];\nexport var start = 'start';\nexport var end = 'end';\nexport var clippingParents = 'clippingParents';\nexport var viewport = 'viewport';\nexport var popper = 'popper';\nexport var reference = 'reference';\nexport var variationPlacements = /*#__PURE__*/basePlacements.reduce(function (acc, placement) {\n  return acc.concat([placement + \"-\" + start, placement + \"-\" + end]);\n}, []);\nexport var placements = /*#__PURE__*/[].concat(basePlacements, [auto]).reduce(function (acc, placement) {\n  return acc.concat([placement, placement + \"-\" + start, placement + \"-\" + end]);\n}, []); // modifiers that need to read the DOM\n\nexport var beforeRead = 'beforeRead';\nexport var read = 'read';\nexport var afterRead = 'afterRead'; // pure-logic modifiers\n\nexport var beforeMain = 'beforeMain';\nexport var main = 'main';\nexport var afterMain = 'afterMain'; // modifier with the purpose to write to the DOM (or write into a framework state)\n\nexport var beforeWrite = 'beforeWrite';\nexport var write = 'write';\nexport var afterWrite = 'afterWrite';\nexport var modifierPhases = [beforeRead, read, afterRead, beforeMain, main, afterMain, beforeWrite, write, afterWrite];","export default function getNodeName(element) {\n  return element ? (element.nodeName || '').toLowerCase() : null;\n}","export default function getWindow(node) {\n  if (node == null) {\n    return window;\n  }\n\n  if (node.toString() !== '[object Window]') {\n    var ownerDocument = node.ownerDocument;\n    return ownerDocument ? ownerDocument.defaultView || window : window;\n  }\n\n  return node;\n}","import getWindow from \"./getWindow.js\";\n\nfunction isElement(node) {\n  var OwnElement = getWindow(node).Element;\n  return node instanceof OwnElement || node instanceof Element;\n}\n\nfunction isHTMLElement(node) {\n  var OwnElement = getWindow(node).HTMLElement;\n  return node instanceof OwnElement || node instanceof HTMLElement;\n}\n\nfunction isShadowRoot(node) {\n  // IE 11 has no ShadowRoot\n  if (typeof ShadowRoot === 'undefined') {\n    return false;\n  }\n\n  var OwnElement = getWindow(node).ShadowRoot;\n  return node instanceof OwnElement || node instanceof ShadowRoot;\n}\n\nexport { isElement, isHTMLElement, isShadowRoot };","import getNodeName from \"../dom-utils/getNodeName.js\";\nimport { isHTMLElement } from \"../dom-utils/instanceOf.js\"; // This modifier takes the styles prepared by the `computeStyles` modifier\n// and applies them to the HTMLElements such as popper and arrow\n\nfunction applyStyles(_ref) {\n  var state = _ref.state;\n  Object.keys(state.elements).forEach(function (name) {\n    var style = state.styles[name] || {};\n    var attributes = state.attributes[name] || {};\n    var element = state.elements[name]; // arrow is optional + virtual elements\n\n    if (!isHTMLElement(element) || !getNodeName(element)) {\n      return;\n    } // Flow doesn't support to extend this property, but it's the most\n    // effective way to apply styles to an HTMLElement\n    // $FlowFixMe[cannot-write]\n\n\n    Object.assign(element.style, style);\n    Object.keys(attributes).forEach(function (name) {\n      var value = attributes[name];\n\n      if (value === false) {\n        element.removeAttribute(name);\n      } else {\n        element.setAttribute(name, value === true ? '' : value);\n      }\n    });\n  });\n}\n\nfunction effect(_ref2) {\n  var state = _ref2.state;\n  var initialStyles = {\n    popper: {\n      position: state.options.strategy,\n      left: '0',\n      top: '0',\n      margin: '0'\n    },\n    arrow: {\n      position: 'absolute'\n    },\n    reference: {}\n  };\n  Object.assign(state.elements.popper.style, initialStyles.popper);\n  state.styles = initialStyles;\n\n  if (state.elements.arrow) {\n    Object.assign(state.elements.arrow.style, initialStyles.arrow);\n  }\n\n  return function () {\n    Object.keys(state.elements).forEach(function (name) {\n      var element = state.elements[name];\n      var attributes = state.attributes[name] || {};\n      var styleProperties = Object.keys(state.styles.hasOwnProperty(name) ? state.styles[name] : initialStyles[name]); // Set all values to an empty string to unset them\n\n      var style = styleProperties.reduce(function (style, property) {\n        style[property] = '';\n        return style;\n      }, {}); // arrow is optional + virtual elements\n\n      if (!isHTMLElement(element) || !getNodeName(element)) {\n        return;\n      }\n\n      Object.assign(element.style, style);\n      Object.keys(attributes).forEach(function (attribute) {\n        element.removeAttribute(attribute);\n      });\n    });\n  };\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'applyStyles',\n  enabled: true,\n  phase: 'write',\n  fn: applyStyles,\n  effect: effect,\n  requires: ['computeStyles']\n};","import { auto } from \"../enums.js\";\nexport default function getBasePlacement(placement) {\n  return placement.split('-')[0];\n}","import { isHTMLElement } from \"./instanceOf.js\";\nvar round = Math.round;\nexport default function getBoundingClientRect(element, includeScale) {\n  if (includeScale === void 0) {\n    includeScale = false;\n  }\n\n  var rect = element.getBoundingClientRect();\n  var scaleX = 1;\n  var scaleY = 1;\n\n  if (isHTMLElement(element) && includeScale) {\n    // Fallback to 1 in case both values are `0`\n    scaleX = rect.width / element.offsetWidth || 1;\n    scaleY = rect.height / element.offsetHeight || 1;\n  }\n\n  return {\n    width: round(rect.width / scaleX),\n    height: round(rect.height / scaleY),\n    top: round(rect.top / scaleY),\n    right: round(rect.right / scaleX),\n    bottom: round(rect.bottom / scaleY),\n    left: round(rect.left / scaleX),\n    x: round(rect.left / scaleX),\n    y: round(rect.top / scaleY)\n  };\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\"; // Returns the layout rect of an element relative to its offsetParent. Layout\n// means it doesn't take into account transforms.\n\nexport default function getLayoutRect(element) {\n  var clientRect = getBoundingClientRect(element); // Use the clientRect sizes if it's not been transformed.\n  // Fixes https://github.com/popperjs/popper-core/issues/1223\n\n  var width = element.offsetWidth;\n  var height = element.offsetHeight;\n\n  if (Math.abs(clientRect.width - width) <= 1) {\n    width = clientRect.width;\n  }\n\n  if (Math.abs(clientRect.height - height) <= 1) {\n    height = clientRect.height;\n  }\n\n  return {\n    x: element.offsetLeft,\n    y: element.offsetTop,\n    width: width,\n    height: height\n  };\n}","import { isShadowRoot } from \"./instanceOf.js\";\nexport default function contains(parent, child) {\n  var rootNode = child.getRootNode && child.getRootNode(); // First, attempt with faster native method\n\n  if (parent.contains(child)) {\n    return true;\n  } // then fallback to custom implementation with Shadow DOM support\n  else if (rootNode && isShadowRoot(rootNode)) {\n      var next = child;\n\n      do {\n        if (next && parent.isSameNode(next)) {\n          return true;\n        } // $FlowFixMe[prop-missing]: need a better way to handle this...\n\n\n        next = next.parentNode || next.host;\n      } while (next);\n    } // Give up, the result is false\n\n\n  return false;\n}","import getWindow from \"./getWindow.js\";\nexport default function getComputedStyle(element) {\n  return getWindow(element).getComputedStyle(element);\n}","import getNodeName from \"./getNodeName.js\";\nexport default function isTableElement(element) {\n  return ['table', 'td', 'th'].indexOf(getNodeName(element)) >= 0;\n}","import { isElement } from \"./instanceOf.js\";\nexport default function getDocumentElement(element) {\n  // $FlowFixMe[incompatible-return]: assume body is always available\n  return ((isElement(element) ? element.ownerDocument : // $FlowFixMe[prop-missing]\n  element.document) || window.document).documentElement;\n}","import getNodeName from \"./getNodeName.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport { isShadowRoot } from \"./instanceOf.js\";\nexport default function getParentNode(element) {\n  if (getNodeName(element) === 'html') {\n    return element;\n  }\n\n  return (// this is a quicker (but less type safe) way to save quite some bytes from the bundle\n    // $FlowFixMe[incompatible-return]\n    // $FlowFixMe[prop-missing]\n    element.assignedSlot || // step into the shadow DOM of the parent of a slotted node\n    element.parentNode || ( // DOM Element detected\n    isShadowRoot(element) ? element.host : null) || // ShadowRoot detected\n    // $FlowFixMe[incompatible-call]: HTMLElement is a Node\n    getDocumentElement(element) // fallback\n\n  );\n}","import getWindow from \"./getWindow.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nimport isTableElement from \"./isTableElement.js\";\nimport getParentNode from \"./getParentNode.js\";\n\nfunction getTrueOffsetParent(element) {\n  if (!isHTMLElement(element) || // https://github.com/popperjs/popper-core/issues/837\n  getComputedStyle(element).position === 'fixed') {\n    return null;\n  }\n\n  return element.offsetParent;\n} // `.offsetParent` reports `null` for fixed elements, while absolute elements\n// return the containing block\n\n\nfunction getContainingBlock(element) {\n  var isFirefox = navigator.userAgent.toLowerCase().indexOf('firefox') !== -1;\n  var isIE = navigator.userAgent.indexOf('Trident') !== -1;\n\n  if (isIE && isHTMLElement(element)) {\n    // In IE 9, 10 and 11 fixed elements containing block is always established by the viewport\n    var elementCss = getComputedStyle(element);\n\n    if (elementCss.position === 'fixed') {\n      return null;\n    }\n  }\n\n  var currentNode = getParentNode(element);\n\n  while (isHTMLElement(currentNode) && ['html', 'body'].indexOf(getNodeName(currentNode)) < 0) {\n    var css = getComputedStyle(currentNode); // This is non-exhaustive but covers the most common CSS properties that\n    // create a containing block.\n    // https://developer.mozilla.org/en-US/docs/Web/CSS/Containing_block#identifying_the_containing_block\n\n    if (css.transform !== 'none' || css.perspective !== 'none' || css.contain === 'paint' || ['transform', 'perspective'].indexOf(css.willChange) !== -1 || isFirefox && css.willChange === 'filter' || isFirefox && css.filter && css.filter !== 'none') {\n      return currentNode;\n    } else {\n      currentNode = currentNode.parentNode;\n    }\n  }\n\n  return null;\n} // Gets the closest ancestor positioned element. Handles some edge cases,\n// such as table ancestors and cross browser bugs.\n\n\nexport default function getOffsetParent(element) {\n  var window = getWindow(element);\n  var offsetParent = getTrueOffsetParent(element);\n\n  while (offsetParent && isTableElement(offsetParent) && getComputedStyle(offsetParent).position === 'static') {\n    offsetParent = getTrueOffsetParent(offsetParent);\n  }\n\n  if (offsetParent && (getNodeName(offsetParent) === 'html' || getNodeName(offsetParent) === 'body' && getComputedStyle(offsetParent).position === 'static')) {\n    return window;\n  }\n\n  return offsetParent || getContainingBlock(element) || window;\n}","export default function getMainAxisFromPlacement(placement) {\n  return ['top', 'bottom'].indexOf(placement) >= 0 ? 'x' : 'y';\n}","export var max = Math.max;\nexport var min = Math.min;\nexport var round = Math.round;","import { max as mathMax, min as mathMin } from \"./math.js\";\nexport default function within(min, value, max) {\n  return mathMax(min, mathMin(value, max));\n}","import getFreshSideObject from \"./getFreshSideObject.js\";\nexport default function mergePaddingObject(paddingObject) {\n  return Object.assign({}, getFreshSideObject(), paddingObject);\n}","export default function getFreshSideObject() {\n  return {\n    top: 0,\n    right: 0,\n    bottom: 0,\n    left: 0\n  };\n}","export default function expandToHashMap(value, keys) {\n  return keys.reduce(function (hashMap, key) {\n    hashMap[key] = value;\n    return hashMap;\n  }, {});\n}","import getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getLayoutRect from \"../dom-utils/getLayoutRect.js\";\nimport contains from \"../dom-utils/contains.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport getMainAxisFromPlacement from \"../utils/getMainAxisFromPlacement.js\";\nimport within from \"../utils/within.js\";\nimport mergePaddingObject from \"../utils/mergePaddingObject.js\";\nimport expandToHashMap from \"../utils/expandToHashMap.js\";\nimport { left, right, basePlacements, top, bottom } from \"../enums.js\";\nimport { isHTMLElement } from \"../dom-utils/instanceOf.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar toPaddingObject = function toPaddingObject(padding, state) {\n  padding = typeof padding === 'function' ? padding(Object.assign({}, state.rects, {\n    placement: state.placement\n  })) : padding;\n  return mergePaddingObject(typeof padding !== 'number' ? padding : expandToHashMap(padding, basePlacements));\n};\n\nfunction arrow(_ref) {\n  var _state$modifiersData$;\n\n  var state = _ref.state,\n      name = _ref.name,\n      options = _ref.options;\n  var arrowElement = state.elements.arrow;\n  var popperOffsets = state.modifiersData.popperOffsets;\n  var basePlacement = getBasePlacement(state.placement);\n  var axis = getMainAxisFromPlacement(basePlacement);\n  var isVertical = [left, right].indexOf(basePlacement) >= 0;\n  var len = isVertical ? 'height' : 'width';\n\n  if (!arrowElement || !popperOffsets) {\n    return;\n  }\n\n  var paddingObject = toPaddingObject(options.padding, state);\n  var arrowRect = getLayoutRect(arrowElement);\n  var minProp = axis === 'y' ? top : left;\n  var maxProp = axis === 'y' ? bottom : right;\n  var endDiff = state.rects.reference[len] + state.rects.reference[axis] - popperOffsets[axis] - state.rects.popper[len];\n  var startDiff = popperOffsets[axis] - state.rects.reference[axis];\n  var arrowOffsetParent = getOffsetParent(arrowElement);\n  var clientSize = arrowOffsetParent ? axis === 'y' ? arrowOffsetParent.clientHeight || 0 : arrowOffsetParent.clientWidth || 0 : 0;\n  var centerToReference = endDiff / 2 - startDiff / 2; // Make sure the arrow doesn't overflow the popper if the center point is\n  // outside of the popper bounds\n\n  var min = paddingObject[minProp];\n  var max = clientSize - arrowRect[len] - paddingObject[maxProp];\n  var center = clientSize / 2 - arrowRect[len] / 2 + centerToReference;\n  var offset = within(min, center, max); // Prevents breaking syntax highlighting...\n\n  var axisProp = axis;\n  state.modifiersData[name] = (_state$modifiersData$ = {}, _state$modifiersData$[axisProp] = offset, _state$modifiersData$.centerOffset = offset - center, _state$modifiersData$);\n}\n\nfunction effect(_ref2) {\n  var state = _ref2.state,\n      options = _ref2.options;\n  var _options$element = options.element,\n      arrowElement = _options$element === void 0 ? '[data-popper-arrow]' : _options$element;\n\n  if (arrowElement == null) {\n    return;\n  } // CSS selector\n\n\n  if (typeof arrowElement === 'string') {\n    arrowElement = state.elements.popper.querySelector(arrowElement);\n\n    if (!arrowElement) {\n      return;\n    }\n  }\n\n  if (process.env.NODE_ENV !== \"production\") {\n    if (!isHTMLElement(arrowElement)) {\n      console.error(['Popper: \"arrow\" element must be an HTMLElement (not an SVGElement).', 'To use an SVG arrow, wrap it in an HTMLElement that will be used as', 'the arrow.'].join(' '));\n    }\n  }\n\n  if (!contains(state.elements.popper, arrowElement)) {\n    if (process.env.NODE_ENV !== \"production\") {\n      console.error(['Popper: \"arrow\" modifier\\'s `element` must be a child of the popper', 'element.'].join(' '));\n    }\n\n    return;\n  }\n\n  state.elements.arrow = arrowElement;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'arrow',\n  enabled: true,\n  phase: 'main',\n  fn: arrow,\n  effect: effect,\n  requires: ['popperOffsets'],\n  requiresIfExists: ['preventOverflow']\n};","import { top, left, right, bottom } from \"../enums.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport getWindow from \"../dom-utils/getWindow.js\";\nimport getDocumentElement from \"../dom-utils/getDocumentElement.js\";\nimport getComputedStyle from \"../dom-utils/getComputedStyle.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport { round } from \"../utils/math.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar unsetSides = {\n  top: 'auto',\n  right: 'auto',\n  bottom: 'auto',\n  left: 'auto'\n}; // Round the offsets to the nearest suitable subpixel based on the DPR.\n// Zooming can change the DPR, but it seems to report a value that will\n// cleanly divide the values into the appropriate subpixels.\n\nfunction roundOffsetsByDPR(_ref) {\n  var x = _ref.x,\n      y = _ref.y;\n  var win = window;\n  var dpr = win.devicePixelRatio || 1;\n  return {\n    x: round(round(x * dpr) / dpr) || 0,\n    y: round(round(y * dpr) / dpr) || 0\n  };\n}\n\nexport function mapToStyles(_ref2) {\n  var _Object$assign2;\n\n  var popper = _ref2.popper,\n      popperRect = _ref2.popperRect,\n      placement = _ref2.placement,\n      offsets = _ref2.offsets,\n      position = _ref2.position,\n      gpuAcceleration = _ref2.gpuAcceleration,\n      adaptive = _ref2.adaptive,\n      roundOffsets = _ref2.roundOffsets;\n\n  var _ref3 = roundOffsets === true ? roundOffsetsByDPR(offsets) : typeof roundOffsets === 'function' ? roundOffsets(offsets) : offsets,\n      _ref3$x = _ref3.x,\n      x = _ref3$x === void 0 ? 0 : _ref3$x,\n      _ref3$y = _ref3.y,\n      y = _ref3$y === void 0 ? 0 : _ref3$y;\n\n  var hasX = offsets.hasOwnProperty('x');\n  var hasY = offsets.hasOwnProperty('y');\n  var sideX = left;\n  var sideY = top;\n  var win = window;\n\n  if (adaptive) {\n    var offsetParent = getOffsetParent(popper);\n    var heightProp = 'clientHeight';\n    var widthProp = 'clientWidth';\n\n    if (offsetParent === getWindow(popper)) {\n      offsetParent = getDocumentElement(popper);\n\n      if (getComputedStyle(offsetParent).position !== 'static') {\n        heightProp = 'scrollHeight';\n        widthProp = 'scrollWidth';\n      }\n    } // $FlowFixMe[incompatible-cast]: force type refinement, we compare offsetParent with window above, but Flow doesn't detect it\n\n\n    offsetParent = offsetParent;\n\n    if (placement === top) {\n      sideY = bottom; // $FlowFixMe[prop-missing]\n\n      y -= offsetParent[heightProp] - popperRect.height;\n      y *= gpuAcceleration ? 1 : -1;\n    }\n\n    if (placement === left) {\n      sideX = right; // $FlowFixMe[prop-missing]\n\n      x -= offsetParent[widthProp] - popperRect.width;\n      x *= gpuAcceleration ? 1 : -1;\n    }\n  }\n\n  var commonStyles = Object.assign({\n    position: position\n  }, adaptive && unsetSides);\n\n  if (gpuAcceleration) {\n    var _Object$assign;\n\n    return Object.assign({}, commonStyles, (_Object$assign = {}, _Object$assign[sideY] = hasY ? '0' : '', _Object$assign[sideX] = hasX ? '0' : '', _Object$assign.transform = (win.devicePixelRatio || 1) < 2 ? \"translate(\" + x + \"px, \" + y + \"px)\" : \"translate3d(\" + x + \"px, \" + y + \"px, 0)\", _Object$assign));\n  }\n\n  return Object.assign({}, commonStyles, (_Object$assign2 = {}, _Object$assign2[sideY] = hasY ? y + \"px\" : '', _Object$assign2[sideX] = hasX ? x + \"px\" : '', _Object$assign2.transform = '', _Object$assign2));\n}\n\nfunction computeStyles(_ref4) {\n  var state = _ref4.state,\n      options = _ref4.options;\n  var _options$gpuAccelerat = options.gpuAcceleration,\n      gpuAcceleration = _options$gpuAccelerat === void 0 ? true : _options$gpuAccelerat,\n      _options$adaptive = options.adaptive,\n      adaptive = _options$adaptive === void 0 ? true : _options$adaptive,\n      _options$roundOffsets = options.roundOffsets,\n      roundOffsets = _options$roundOffsets === void 0 ? true : _options$roundOffsets;\n\n  if (process.env.NODE_ENV !== \"production\") {\n    var transitionProperty = getComputedStyle(state.elements.popper).transitionProperty || '';\n\n    if (adaptive && ['transform', 'top', 'right', 'bottom', 'left'].some(function (property) {\n      return transitionProperty.indexOf(property) >= 0;\n    })) {\n      console.warn(['Popper: Detected CSS transitions on at least one of the following', 'CSS properties: \"transform\", \"top\", \"right\", \"bottom\", \"left\".', '\\n\\n', 'Disable the \"computeStyles\" modifier\\'s `adaptive` option to allow', 'for smooth transitions, or remove these properties from the CSS', 'transition declaration on the popper element if only transitioning', 'opacity or background-color for example.', '\\n\\n', 'We recommend using the popper element as a wrapper around an inner', 'element that can have any CSS property transitioned for animations.'].join(' '));\n    }\n  }\n\n  var commonStyles = {\n    placement: getBasePlacement(state.placement),\n    popper: state.elements.popper,\n    popperRect: state.rects.popper,\n    gpuAcceleration: gpuAcceleration\n  };\n\n  if (state.modifiersData.popperOffsets != null) {\n    state.styles.popper = Object.assign({}, state.styles.popper, mapToStyles(Object.assign({}, commonStyles, {\n      offsets: state.modifiersData.popperOffsets,\n      position: state.options.strategy,\n      adaptive: adaptive,\n      roundOffsets: roundOffsets\n    })));\n  }\n\n  if (state.modifiersData.arrow != null) {\n    state.styles.arrow = Object.assign({}, state.styles.arrow, mapToStyles(Object.assign({}, commonStyles, {\n      offsets: state.modifiersData.arrow,\n      position: 'absolute',\n      adaptive: false,\n      roundOffsets: roundOffsets\n    })));\n  }\n\n  state.attributes.popper = Object.assign({}, state.attributes.popper, {\n    'data-popper-placement': state.placement\n  });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'computeStyles',\n  enabled: true,\n  phase: 'beforeWrite',\n  fn: computeStyles,\n  data: {}\n};","import getWindow from \"../dom-utils/getWindow.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar passive = {\n  passive: true\n};\n\nfunction effect(_ref) {\n  var state = _ref.state,\n      instance = _ref.instance,\n      options = _ref.options;\n  var _options$scroll = options.scroll,\n      scroll = _options$scroll === void 0 ? true : _options$scroll,\n      _options$resize = options.resize,\n      resize = _options$resize === void 0 ? true : _options$resize;\n  var window = getWindow(state.elements.popper);\n  var scrollParents = [].concat(state.scrollParents.reference, state.scrollParents.popper);\n\n  if (scroll) {\n    scrollParents.forEach(function (scrollParent) {\n      scrollParent.addEventListener('scroll', instance.update, passive);\n    });\n  }\n\n  if (resize) {\n    window.addEventListener('resize', instance.update, passive);\n  }\n\n  return function () {\n    if (scroll) {\n      scrollParents.forEach(function (scrollParent) {\n        scrollParent.removeEventListener('scroll', instance.update, passive);\n      });\n    }\n\n    if (resize) {\n      window.removeEventListener('resize', instance.update, passive);\n    }\n  };\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'eventListeners',\n  enabled: true,\n  phase: 'write',\n  fn: function fn() {},\n  effect: effect,\n  data: {}\n};","var hash = {\n  left: 'right',\n  right: 'left',\n  bottom: 'top',\n  top: 'bottom'\n};\nexport default function getOppositePlacement(placement) {\n  return placement.replace(/left|right|bottom|top/g, function (matched) {\n    return hash[matched];\n  });\n}","var hash = {\n  start: 'end',\n  end: 'start'\n};\nexport default function getOppositeVariationPlacement(placement) {\n  return placement.replace(/start|end/g, function (matched) {\n    return hash[matched];\n  });\n}","import getWindow from \"./getWindow.js\";\nexport default function getWindowScroll(node) {\n  var win = getWindow(node);\n  var scrollLeft = win.pageXOffset;\n  var scrollTop = win.pageYOffset;\n  return {\n    scrollLeft: scrollLeft,\n    scrollTop: scrollTop\n  };\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getWindowScroll from \"./getWindowScroll.js\";\nexport default function getWindowScrollBarX(element) {\n  // If <html> has a CSS width greater than the viewport, then this will be\n  // incorrect for RTL.\n  // Popper 1 is broken in this case and never had a bug report so let's assume\n  // it's not an issue. I don't think anyone ever specifies width on <html>\n  // anyway.\n  // Browsers where the left scrollbar doesn't cause an issue report `0` for\n  // this (e.g. Edge 2019, IE11, Safari)\n  return getBoundingClientRect(getDocumentElement(element)).left + getWindowScroll(element).scrollLeft;\n}","import getComputedStyle from \"./getComputedStyle.js\";\nexport default function isScrollParent(element) {\n  // Firefox wants us to check `-x` and `-y` variations as well\n  var _getComputedStyle = getComputedStyle(element),\n      overflow = _getComputedStyle.overflow,\n      overflowX = _getComputedStyle.overflowX,\n      overflowY = _getComputedStyle.overflowY;\n\n  return /auto|scroll|overlay|hidden/.test(overflow + overflowY + overflowX);\n}","import getScrollParent from \"./getScrollParent.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport getWindow from \"./getWindow.js\";\nimport isScrollParent from \"./isScrollParent.js\";\n/*\ngiven a DOM element, return the list of all scroll parents, up the list of ancesors\nuntil we get to the top window object. This list is what we attach scroll listeners\nto, because if any of these parent elements scroll, we'll need to re-calculate the\nreference element's position.\n*/\n\nexport default function listScrollParents(element, list) {\n  var _element$ownerDocumen;\n\n  if (list === void 0) {\n    list = [];\n  }\n\n  var scrollParent = getScrollParent(element);\n  var isBody = scrollParent === ((_element$ownerDocumen = element.ownerDocument) == null ? void 0 : _element$ownerDocumen.body);\n  var win = getWindow(scrollParent);\n  var target = isBody ? [win].concat(win.visualViewport || [], isScrollParent(scrollParent) ? scrollParent : []) : scrollParent;\n  var updatedList = list.concat(target);\n  return isBody ? updatedList : // $FlowFixMe[incompatible-call]: isBody tells us target will be an HTMLElement here\n  updatedList.concat(listScrollParents(getParentNode(target)));\n}","import getParentNode from \"./getParentNode.js\";\nimport isScrollParent from \"./isScrollParent.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nexport default function getScrollParent(node) {\n  if (['html', 'body', '#document'].indexOf(getNodeName(node)) >= 0) {\n    // $FlowFixMe[incompatible-return]: assume body is always available\n    return node.ownerDocument.body;\n  }\n\n  if (isHTMLElement(node) && isScrollParent(node)) {\n    return node;\n  }\n\n  return getScrollParent(getParentNode(node));\n}","export default function rectToClientRect(rect) {\n  return Object.assign({}, rect, {\n    left: rect.x,\n    top: rect.y,\n    right: rect.x + rect.width,\n    bottom: rect.y + rect.height\n  });\n}","import { viewport } from \"../enums.js\";\nimport getViewportRect from \"./getViewportRect.js\";\nimport getDocumentRect from \"./getDocumentRect.js\";\nimport listScrollParents from \"./listScrollParents.js\";\nimport getOffsetParent from \"./getOffsetParent.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport { isElement, isHTMLElement } from \"./instanceOf.js\";\nimport getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport contains from \"./contains.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport rectToClientRect from \"../utils/rectToClientRect.js\";\nimport { max, min } from \"../utils/math.js\";\n\nfunction getInnerBoundingClientRect(element) {\n  var rect = getBoundingClientRect(element);\n  rect.top = rect.top + element.clientTop;\n  rect.left = rect.left + element.clientLeft;\n  rect.bottom = rect.top + element.clientHeight;\n  rect.right = rect.left + element.clientWidth;\n  rect.width = element.clientWidth;\n  rect.height = element.clientHeight;\n  rect.x = rect.left;\n  rect.y = rect.top;\n  return rect;\n}\n\nfunction getClientRectFromMixedType(element, clippingParent) {\n  return clippingParent === viewport ? rectToClientRect(getViewportRect(element)) : isHTMLElement(clippingParent) ? getInnerBoundingClientRect(clippingParent) : rectToClientRect(getDocumentRect(getDocumentElement(element)));\n} // A \"clipping parent\" is an overflowable container with the characteristic of\n// clipping (or hiding) overflowing elements with a position different from\n// `initial`\n\n\nfunction getClippingParents(element) {\n  var clippingParents = listScrollParents(getParentNode(element));\n  var canEscapeClipping = ['absolute', 'fixed'].indexOf(getComputedStyle(element).position) >= 0;\n  var clipperElement = canEscapeClipping && isHTMLElement(element) ? getOffsetParent(element) : element;\n\n  if (!isElement(clipperElement)) {\n    return [];\n  } // $FlowFixMe[incompatible-return]: https://github.com/facebook/flow/issues/1414\n\n\n  return clippingParents.filter(function (clippingParent) {\n    return isElement(clippingParent) && contains(clippingParent, clipperElement) && getNodeName(clippingParent) !== 'body';\n  });\n} // Gets the maximum area that the element is visible in due to any number of\n// clipping parents\n\n\nexport default function getClippingRect(element, boundary, rootBoundary) {\n  var mainClippingParents = boundary === 'clippingParents' ? getClippingParents(element) : [].concat(boundary);\n  var clippingParents = [].concat(mainClippingParents, [rootBoundary]);\n  var firstClippingParent = clippingParents[0];\n  var clippingRect = clippingParents.reduce(function (accRect, clippingParent) {\n    var rect = getClientRectFromMixedType(element, clippingParent);\n    accRect.top = max(rect.top, accRect.top);\n    accRect.right = min(rect.right, accRect.right);\n    accRect.bottom = min(rect.bottom, accRect.bottom);\n    accRect.left = max(rect.left, accRect.left);\n    return accRect;\n  }, getClientRectFromMixedType(element, firstClippingParent));\n  clippingRect.width = clippingRect.right - clippingRect.left;\n  clippingRect.height = clippingRect.bottom - clippingRect.top;\n  clippingRect.x = clippingRect.left;\n  clippingRect.y = clippingRect.top;\n  return clippingRect;\n}","import getWindow from \"./getWindow.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nexport default function getViewportRect(element) {\n  var win = getWindow(element);\n  var html = getDocumentElement(element);\n  var visualViewport = win.visualViewport;\n  var width = html.clientWidth;\n  var height = html.clientHeight;\n  var x = 0;\n  var y = 0; // NB: This isn't supported on iOS <= 12. If the keyboard is open, the popper\n  // can be obscured underneath it.\n  // Also, `html.clientHeight` adds the bottom bar height in Safari iOS, even\n  // if it isn't open, so if this isn't available, the popper will be detected\n  // to overflow the bottom of the screen too early.\n\n  if (visualViewport) {\n    width = visualViewport.width;\n    height = visualViewport.height; // Uses Layout Viewport (like Chrome; Safari does not currently)\n    // In Chrome, it returns a value very close to 0 (+/-) but contains rounding\n    // errors due to floating point numbers, so we need to check precision.\n    // Safari returns a number <= 0, usually < -1 when pinch-zoomed\n    // Feature detection fails in mobile emulation mode in Chrome.\n    // Math.abs(win.innerWidth / visualViewport.scale - visualViewport.width) <\n    // 0.001\n    // Fallback here: \"Not Safari\" userAgent\n\n    if (!/^((?!chrome|android).)*safari/i.test(navigator.userAgent)) {\n      x = visualViewport.offsetLeft;\n      y = visualViewport.offsetTop;\n    }\n  }\n\n  return {\n    width: width,\n    height: height,\n    x: x + getWindowScrollBarX(element),\n    y: y\n  };\n}","import getDocumentElement from \"./getDocumentElement.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport getWindowScroll from \"./getWindowScroll.js\";\nimport { max } from \"../utils/math.js\"; // Gets the entire size of the scrollable document area, even extending outside\n// of the `<html>` and `<body>` rect bounds if horizontally scrollable\n\nexport default function getDocumentRect(element) {\n  var _element$ownerDocumen;\n\n  var html = getDocumentElement(element);\n  var winScroll = getWindowScroll(element);\n  var body = (_element$ownerDocumen = element.ownerDocument) == null ? void 0 : _element$ownerDocumen.body;\n  var width = max(html.scrollWidth, html.clientWidth, body ? body.scrollWidth : 0, body ? body.clientWidth : 0);\n  var height = max(html.scrollHeight, html.clientHeight, body ? body.scrollHeight : 0, body ? body.clientHeight : 0);\n  var x = -winScroll.scrollLeft + getWindowScrollBarX(element);\n  var y = -winScroll.scrollTop;\n\n  if (getComputedStyle(body || html).direction === 'rtl') {\n    x += max(html.clientWidth, body ? body.clientWidth : 0) - width;\n  }\n\n  return {\n    width: width,\n    height: height,\n    x: x,\n    y: y\n  };\n}","export default function getVariation(placement) {\n  return placement.split('-')[1];\n}","import getBasePlacement from \"./getBasePlacement.js\";\nimport getVariation from \"./getVariation.js\";\nimport getMainAxisFromPlacement from \"./getMainAxisFromPlacement.js\";\nimport { top, right, bottom, left, start, end } from \"../enums.js\";\nexport default function computeOffsets(_ref) {\n  var reference = _ref.reference,\n      element = _ref.element,\n      placement = _ref.placement;\n  var basePlacement = placement ? getBasePlacement(placement) : null;\n  var variation = placement ? getVariation(placement) : null;\n  var commonX = reference.x + reference.width / 2 - element.width / 2;\n  var commonY = reference.y + reference.height / 2 - element.height / 2;\n  var offsets;\n\n  switch (basePlacement) {\n    case top:\n      offsets = {\n        x: commonX,\n        y: reference.y - element.height\n      };\n      break;\n\n    case bottom:\n      offsets = {\n        x: commonX,\n        y: reference.y + reference.height\n      };\n      break;\n\n    case right:\n      offsets = {\n        x: reference.x + reference.width,\n        y: commonY\n      };\n      break;\n\n    case left:\n      offsets = {\n        x: reference.x - element.width,\n        y: commonY\n      };\n      break;\n\n    default:\n      offsets = {\n        x: reference.x,\n        y: reference.y\n      };\n  }\n\n  var mainAxis = basePlacement ? getMainAxisFromPlacement(basePlacement) : null;\n\n  if (mainAxis != null) {\n    var len = mainAxis === 'y' ? 'height' : 'width';\n\n    switch (variation) {\n      case start:\n        offsets[mainAxis] = offsets[mainAxis] - (reference[len] / 2 - element[len] / 2);\n        break;\n\n      case end:\n        offsets[mainAxis] = offsets[mainAxis] + (reference[len] / 2 - element[len] / 2);\n        break;\n\n      default:\n    }\n  }\n\n  return offsets;\n}","import getBoundingClientRect from \"../dom-utils/getBoundingClientRect.js\";\nimport getClippingRect from \"../dom-utils/getClippingRect.js\";\nimport getDocumentElement from \"../dom-utils/getDocumentElement.js\";\nimport computeOffsets from \"./computeOffsets.js\";\nimport rectToClientRect from \"./rectToClientRect.js\";\nimport { clippingParents, reference, popper, bottom, top, right, basePlacements, viewport } from \"../enums.js\";\nimport { isElement } from \"../dom-utils/instanceOf.js\";\nimport mergePaddingObject from \"./mergePaddingObject.js\";\nimport expandToHashMap from \"./expandToHashMap.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport default function detectOverflow(state, options) {\n  if (options === void 0) {\n    options = {};\n  }\n\n  var _options = options,\n      _options$placement = _options.placement,\n      placement = _options$placement === void 0 ? state.placement : _options$placement,\n      _options$boundary = _options.boundary,\n      boundary = _options$boundary === void 0 ? clippingParents : _options$boundary,\n      _options$rootBoundary = _options.rootBoundary,\n      rootBoundary = _options$rootBoundary === void 0 ? viewport : _options$rootBoundary,\n      _options$elementConte = _options.elementContext,\n      elementContext = _options$elementConte === void 0 ? popper : _options$elementConte,\n      _options$altBoundary = _options.altBoundary,\n      altBoundary = _options$altBoundary === void 0 ? false : _options$altBoundary,\n      _options$padding = _options.padding,\n      padding = _options$padding === void 0 ? 0 : _options$padding;\n  var paddingObject = mergePaddingObject(typeof padding !== 'number' ? padding : expandToHashMap(padding, basePlacements));\n  var altContext = elementContext === popper ? reference : popper;\n  var referenceElement = state.elements.reference;\n  var popperRect = state.rects.popper;\n  var element = state.elements[altBoundary ? altContext : elementContext];\n  var clippingClientRect = getClippingRect(isElement(element) ? element : element.contextElement || getDocumentElement(state.elements.popper), boundary, rootBoundary);\n  var referenceClientRect = getBoundingClientRect(referenceElement);\n  var popperOffsets = computeOffsets({\n    reference: referenceClientRect,\n    element: popperRect,\n    strategy: 'absolute',\n    placement: placement\n  });\n  var popperClientRect = rectToClientRect(Object.assign({}, popperRect, popperOffsets));\n  var elementClientRect = elementContext === popper ? popperClientRect : referenceClientRect; // positive = overflowing the clipping rect\n  // 0 or negative = within the clipping rect\n\n  var overflowOffsets = {\n    top: clippingClientRect.top - elementClientRect.top + paddingObject.top,\n    bottom: elementClientRect.bottom - clippingClientRect.bottom + paddingObject.bottom,\n    left: clippingClientRect.left - elementClientRect.left + paddingObject.left,\n    right: elementClientRect.right - clippingClientRect.right + paddingObject.right\n  };\n  var offsetData = state.modifiersData.offset; // Offsets can be applied only to the popper element\n\n  if (elementContext === popper && offsetData) {\n    var offset = offsetData[placement];\n    Object.keys(overflowOffsets).forEach(function (key) {\n      var multiply = [right, bottom].indexOf(key) >= 0 ? 1 : -1;\n      var axis = [top, bottom].indexOf(key) >= 0 ? 'y' : 'x';\n      overflowOffsets[key] += offset[axis] * multiply;\n    });\n  }\n\n  return overflowOffsets;\n}","import getVariation from \"./getVariation.js\";\nimport { variationPlacements, basePlacements, placements as allPlacements } from \"../enums.js\";\nimport detectOverflow from \"./detectOverflow.js\";\nimport getBasePlacement from \"./getBasePlacement.js\";\nexport default function computeAutoPlacement(state, options) {\n  if (options === void 0) {\n    options = {};\n  }\n\n  var _options = options,\n      placement = _options.placement,\n      boundary = _options.boundary,\n      rootBoundary = _options.rootBoundary,\n      padding = _options.padding,\n      flipVariations = _options.flipVariations,\n      _options$allowedAutoP = _options.allowedAutoPlacements,\n      allowedAutoPlacements = _options$allowedAutoP === void 0 ? allPlacements : _options$allowedAutoP;\n  var variation = getVariation(placement);\n  var placements = variation ? flipVariations ? variationPlacements : variationPlacements.filter(function (placement) {\n    return getVariation(placement) === variation;\n  }) : basePlacements;\n  var allowedPlacements = placements.filter(function (placement) {\n    return allowedAutoPlacements.indexOf(placement) >= 0;\n  });\n\n  if (allowedPlacements.length === 0) {\n    allowedPlacements = placements;\n\n    if (process.env.NODE_ENV !== \"production\") {\n      console.error(['Popper: The `allowedAutoPlacements` option did not allow any', 'placements. Ensure the `placement` option matches the variation', 'of the allowed placements.', 'For example, \"auto\" cannot be used to allow \"bottom-start\".', 'Use \"auto-start\" instead.'].join(' '));\n    }\n  } // $FlowFixMe[incompatible-type]: Flow seems to have problems with two array unions...\n\n\n  var overflows = allowedPlacements.reduce(function (acc, placement) {\n    acc[placement] = detectOverflow(state, {\n      placement: placement,\n      boundary: boundary,\n      rootBoundary: rootBoundary,\n      padding: padding\n    })[getBasePlacement(placement)];\n    return acc;\n  }, {});\n  return Object.keys(overflows).sort(function (a, b) {\n    return overflows[a] - overflows[b];\n  });\n}","import getOppositePlacement from \"../utils/getOppositePlacement.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getOppositeVariationPlacement from \"../utils/getOppositeVariationPlacement.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\nimport computeAutoPlacement from \"../utils/computeAutoPlacement.js\";\nimport { bottom, top, start, right, left, auto } from \"../enums.js\";\nimport getVariation from \"../utils/getVariation.js\"; // eslint-disable-next-line import/no-unused-modules\n\nfunction getExpandedFallbackPlacements(placement) {\n  if (getBasePlacement(placement) === auto) {\n    return [];\n  }\n\n  var oppositePlacement = getOppositePlacement(placement);\n  return [getOppositeVariationPlacement(placement), oppositePlacement, getOppositeVariationPlacement(oppositePlacement)];\n}\n\nfunction flip(_ref) {\n  var state = _ref.state,\n      options = _ref.options,\n      name = _ref.name;\n\n  if (state.modifiersData[name]._skip) {\n    return;\n  }\n\n  var _options$mainAxis = options.mainAxis,\n      checkMainAxis = _options$mainAxis === void 0 ? true : _options$mainAxis,\n      _options$altAxis = options.altAxis,\n      checkAltAxis = _options$altAxis === void 0 ? true : _options$altAxis,\n      specifiedFallbackPlacements = options.fallbackPlacements,\n      padding = options.padding,\n      boundary = options.boundary,\n      rootBoundary = options.rootBoundary,\n      altBoundary = options.altBoundary,\n      _options$flipVariatio = options.flipVariations,\n      flipVariations = _options$flipVariatio === void 0 ? true : _options$flipVariatio,\n      allowedAutoPlacements = options.allowedAutoPlacements;\n  var preferredPlacement = state.options.placement;\n  var basePlacement = getBasePlacement(preferredPlacement);\n  var isBasePlacement = basePlacement === preferredPlacement;\n  var fallbackPlacements = specifiedFallbackPlacements || (isBasePlacement || !flipVariations ? [getOppositePlacement(preferredPlacement)] : getExpandedFallbackPlacements(preferredPlacement));\n  var placements = [preferredPlacement].concat(fallbackPlacements).reduce(function (acc, placement) {\n    return acc.concat(getBasePlacement(placement) === auto ? computeAutoPlacement(state, {\n      placement: placement,\n      boundary: boundary,\n      rootBoundary: rootBoundary,\n      padding: padding,\n      flipVariations: flipVariations,\n      allowedAutoPlacements: allowedAutoPlacements\n    }) : placement);\n  }, []);\n  var referenceRect = state.rects.reference;\n  var popperRect = state.rects.popper;\n  var checksMap = new Map();\n  var makeFallbackChecks = true;\n  var firstFittingPlacement = placements[0];\n\n  for (var i = 0; i < placements.length; i++) {\n    var placement = placements[i];\n\n    var _basePlacement = getBasePlacement(placement);\n\n    var isStartVariation = getVariation(placement) === start;\n    var isVertical = [top, bottom].indexOf(_basePlacement) >= 0;\n    var len = isVertical ? 'width' : 'height';\n    var overflow = detectOverflow(state, {\n      placement: placement,\n      boundary: boundary,\n      rootBoundary: rootBoundary,\n      altBoundary: altBoundary,\n      padding: padding\n    });\n    var mainVariationSide = isVertical ? isStartVariation ? right : left : isStartVariation ? bottom : top;\n\n    if (referenceRect[len] > popperRect[len]) {\n      mainVariationSide = getOppositePlacement(mainVariationSide);\n    }\n\n    var altVariationSide = getOppositePlacement(mainVariationSide);\n    var checks = [];\n\n    if (checkMainAxis) {\n      checks.push(overflow[_basePlacement] <= 0);\n    }\n\n    if (checkAltAxis) {\n      checks.push(overflow[mainVariationSide] <= 0, overflow[altVariationSide] <= 0);\n    }\n\n    if (checks.every(function (check) {\n      return check;\n    })) {\n      firstFittingPlacement = placement;\n      makeFallbackChecks = false;\n      break;\n    }\n\n    checksMap.set(placement, checks);\n  }\n\n  if (makeFallbackChecks) {\n    // `2` may be desired in some cases – research later\n    var numberOfChecks = flipVariations ? 3 : 1;\n\n    var _loop = function _loop(_i) {\n      var fittingPlacement = placements.find(function (placement) {\n        var checks = checksMap.get(placement);\n\n        if (checks) {\n          return checks.slice(0, _i).every(function (check) {\n            return check;\n          });\n        }\n      });\n\n      if (fittingPlacement) {\n        firstFittingPlacement = fittingPlacement;\n        return \"break\";\n      }\n    };\n\n    for (var _i = numberOfChecks; _i > 0; _i--) {\n      var _ret = _loop(_i);\n\n      if (_ret === \"break\") break;\n    }\n  }\n\n  if (state.placement !== firstFittingPlacement) {\n    state.modifiersData[name]._skip = true;\n    state.placement = firstFittingPlacement;\n    state.reset = true;\n  }\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'flip',\n  enabled: true,\n  phase: 'main',\n  fn: flip,\n  requiresIfExists: ['offset'],\n  data: {\n    _skip: false\n  }\n};","import { top, bottom, left, right } from \"../enums.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\n\nfunction getSideOffsets(overflow, rect, preventedOffsets) {\n  if (preventedOffsets === void 0) {\n    preventedOffsets = {\n      x: 0,\n      y: 0\n    };\n  }\n\n  return {\n    top: overflow.top - rect.height - preventedOffsets.y,\n    right: overflow.right - rect.width + preventedOffsets.x,\n    bottom: overflow.bottom - rect.height + preventedOffsets.y,\n    left: overflow.left - rect.width - preventedOffsets.x\n  };\n}\n\nfunction isAnySideFullyClipped(overflow) {\n  return [top, right, bottom, left].some(function (side) {\n    return overflow[side] >= 0;\n  });\n}\n\nfunction hide(_ref) {\n  var state = _ref.state,\n      name = _ref.name;\n  var referenceRect = state.rects.reference;\n  var popperRect = state.rects.popper;\n  var preventedOffsets = state.modifiersData.preventOverflow;\n  var referenceOverflow = detectOverflow(state, {\n    elementContext: 'reference'\n  });\n  var popperAltOverflow = detectOverflow(state, {\n    altBoundary: true\n  });\n  var referenceClippingOffsets = getSideOffsets(referenceOverflow, referenceRect);\n  var popperEscapeOffsets = getSideOffsets(popperAltOverflow, popperRect, preventedOffsets);\n  var isReferenceHidden = isAnySideFullyClipped(referenceClippingOffsets);\n  var hasPopperEscaped = isAnySideFullyClipped(popperEscapeOffsets);\n  state.modifiersData[name] = {\n    referenceClippingOffsets: referenceClippingOffsets,\n    popperEscapeOffsets: popperEscapeOffsets,\n    isReferenceHidden: isReferenceHidden,\n    hasPopperEscaped: hasPopperEscaped\n  };\n  state.attributes.popper = Object.assign({}, state.attributes.popper, {\n    'data-popper-reference-hidden': isReferenceHidden,\n    'data-popper-escaped': hasPopperEscaped\n  });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'hide',\n  enabled: true,\n  phase: 'main',\n  requiresIfExists: ['preventOverflow'],\n  fn: hide\n};","import getBasePlacement from \"../utils/getBasePlacement.js\";\nimport { top, left, right, placements } from \"../enums.js\";\nexport function distanceAndSkiddingToXY(placement, rects, offset) {\n  var basePlacement = getBasePlacement(placement);\n  var invertDistance = [left, top].indexOf(basePlacement) >= 0 ? -1 : 1;\n\n  var _ref = typeof offset === 'function' ? offset(Object.assign({}, rects, {\n    placement: placement\n  })) : offset,\n      skidding = _ref[0],\n      distance = _ref[1];\n\n  skidding = skidding || 0;\n  distance = (distance || 0) * invertDistance;\n  return [left, right].indexOf(basePlacement) >= 0 ? {\n    x: distance,\n    y: skidding\n  } : {\n    x: skidding,\n    y: distance\n  };\n}\n\nfunction offset(_ref2) {\n  var state = _ref2.state,\n      options = _ref2.options,\n      name = _ref2.name;\n  var _options$offset = options.offset,\n      offset = _options$offset === void 0 ? [0, 0] : _options$offset;\n  var data = placements.reduce(function (acc, placement) {\n    acc[placement] = distanceAndSkiddingToXY(placement, state.rects, offset);\n    return acc;\n  }, {});\n  var _data$state$placement = data[state.placement],\n      x = _data$state$placement.x,\n      y = _data$state$placement.y;\n\n  if (state.modifiersData.popperOffsets != null) {\n    state.modifiersData.popperOffsets.x += x;\n    state.modifiersData.popperOffsets.y += y;\n  }\n\n  state.modifiersData[name] = data;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'offset',\n  enabled: true,\n  phase: 'main',\n  requires: ['popperOffsets'],\n  fn: offset\n};","import computeOffsets from \"../utils/computeOffsets.js\";\n\nfunction popperOffsets(_ref) {\n  var state = _ref.state,\n      name = _ref.name;\n  // Offsets are the actual position the popper needs to have to be\n  // properly positioned near its reference element\n  // This is the most basic placement, and will be adjusted by\n  // the modifiers in the next step\n  state.modifiersData[name] = computeOffsets({\n    reference: state.rects.reference,\n    element: state.rects.popper,\n    strategy: 'absolute',\n    placement: state.placement\n  });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'popperOffsets',\n  enabled: true,\n  phase: 'read',\n  fn: popperOffsets,\n  data: {}\n};","import { top, left, right, bottom, start } from \"../enums.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getMainAxisFromPlacement from \"../utils/getMainAxisFromPlacement.js\";\nimport getAltAxis from \"../utils/getAltAxis.js\";\nimport within from \"../utils/within.js\";\nimport getLayoutRect from \"../dom-utils/getLayoutRect.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\nimport getVariation from \"../utils/getVariation.js\";\nimport getFreshSideObject from \"../utils/getFreshSideObject.js\";\nimport { max as mathMax, min as mathMin } from \"../utils/math.js\";\n\nfunction preventOverflow(_ref) {\n  var state = _ref.state,\n      options = _ref.options,\n      name = _ref.name;\n  var _options$mainAxis = options.mainAxis,\n      checkMainAxis = _options$mainAxis === void 0 ? true : _options$mainAxis,\n      _options$altAxis = options.altAxis,\n      checkAltAxis = _options$altAxis === void 0 ? false : _options$altAxis,\n      boundary = options.boundary,\n      rootBoundary = options.rootBoundary,\n      altBoundary = options.altBoundary,\n      padding = options.padding,\n      _options$tether = options.tether,\n      tether = _options$tether === void 0 ? true : _options$tether,\n      _options$tetherOffset = options.tetherOffset,\n      tetherOffset = _options$tetherOffset === void 0 ? 0 : _options$tetherOffset;\n  var overflow = detectOverflow(state, {\n    boundary: boundary,\n    rootBoundary: rootBoundary,\n    padding: padding,\n    altBoundary: altBoundary\n  });\n  var basePlacement = getBasePlacement(state.placement);\n  var variation = getVariation(state.placement);\n  var isBasePlacement = !variation;\n  var mainAxis = getMainAxisFromPlacement(basePlacement);\n  var altAxis = getAltAxis(mainAxis);\n  var popperOffsets = state.modifiersData.popperOffsets;\n  var referenceRect = state.rects.reference;\n  var popperRect = state.rects.popper;\n  var tetherOffsetValue = typeof tetherOffset === 'function' ? tetherOffset(Object.assign({}, state.rects, {\n    placement: state.placement\n  })) : tetherOffset;\n  var data = {\n    x: 0,\n    y: 0\n  };\n\n  if (!popperOffsets) {\n    return;\n  }\n\n  if (checkMainAxis || checkAltAxis) {\n    var mainSide = mainAxis === 'y' ? top : left;\n    var altSide = mainAxis === 'y' ? bottom : right;\n    var len = mainAxis === 'y' ? 'height' : 'width';\n    var offset = popperOffsets[mainAxis];\n    var min = popperOffsets[mainAxis] + overflow[mainSide];\n    var max = popperOffsets[mainAxis] - overflow[altSide];\n    var additive = tether ? -popperRect[len] / 2 : 0;\n    var minLen = variation === start ? referenceRect[len] : popperRect[len];\n    var maxLen = variation === start ? -popperRect[len] : -referenceRect[len]; // We need to include the arrow in the calculation so the arrow doesn't go\n    // outside the reference bounds\n\n    var arrowElement = state.elements.arrow;\n    var arrowRect = tether && arrowElement ? getLayoutRect(arrowElement) : {\n      width: 0,\n      height: 0\n    };\n    var arrowPaddingObject = state.modifiersData['arrow#persistent'] ? state.modifiersData['arrow#persistent'].padding : getFreshSideObject();\n    var arrowPaddingMin = arrowPaddingObject[mainSide];\n    var arrowPaddingMax = arrowPaddingObject[altSide]; // If the reference length is smaller than the arrow length, we don't want\n    // to include its full size in the calculation. If the reference is small\n    // and near the edge of a boundary, the popper can overflow even if the\n    // reference is not overflowing as well (e.g. virtual elements with no\n    // width or height)\n\n    var arrowLen = within(0, referenceRect[len], arrowRect[len]);\n    var minOffset = isBasePlacement ? referenceRect[len] / 2 - additive - arrowLen - arrowPaddingMin - tetherOffsetValue : minLen - arrowLen - arrowPaddingMin - tetherOffsetValue;\n    var maxOffset = isBasePlacement ? -referenceRect[len] / 2 + additive + arrowLen + arrowPaddingMax + tetherOffsetValue : maxLen + arrowLen + arrowPaddingMax + tetherOffsetValue;\n    var arrowOffsetParent = state.elements.arrow && getOffsetParent(state.elements.arrow);\n    var clientOffset = arrowOffsetParent ? mainAxis === 'y' ? arrowOffsetParent.clientTop || 0 : arrowOffsetParent.clientLeft || 0 : 0;\n    var offsetModifierValue = state.modifiersData.offset ? state.modifiersData.offset[state.placement][mainAxis] : 0;\n    var tetherMin = popperOffsets[mainAxis] + minOffset - offsetModifierValue - clientOffset;\n    var tetherMax = popperOffsets[mainAxis] + maxOffset - offsetModifierValue;\n\n    if (checkMainAxis) {\n      var preventedOffset = within(tether ? mathMin(min, tetherMin) : min, offset, tether ? mathMax(max, tetherMax) : max);\n      popperOffsets[mainAxis] = preventedOffset;\n      data[mainAxis] = preventedOffset - offset;\n    }\n\n    if (checkAltAxis) {\n      var _mainSide = mainAxis === 'x' ? top : left;\n\n      var _altSide = mainAxis === 'x' ? bottom : right;\n\n      var _offset = popperOffsets[altAxis];\n\n      var _min = _offset + overflow[_mainSide];\n\n      var _max = _offset - overflow[_altSide];\n\n      var _preventedOffset = within(tether ? mathMin(_min, tetherMin) : _min, _offset, tether ? mathMax(_max, tetherMax) : _max);\n\n      popperOffsets[altAxis] = _preventedOffset;\n      data[altAxis] = _preventedOffset - _offset;\n    }\n  }\n\n  state.modifiersData[name] = data;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'preventOverflow',\n  enabled: true,\n  phase: 'main',\n  fn: preventOverflow,\n  requiresIfExists: ['offset']\n};","export default function getAltAxis(axis) {\n  return axis === 'x' ? 'y' : 'x';\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getNodeScroll from \"./getNodeScroll.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport isScrollParent from \"./isScrollParent.js\";\n\nfunction isElementScaled(element) {\n  var rect = element.getBoundingClientRect();\n  var scaleX = rect.width / element.offsetWidth || 1;\n  var scaleY = rect.height / element.offsetHeight || 1;\n  return scaleX !== 1 || scaleY !== 1;\n} // Returns the composite rect of an element relative to its offsetParent.\n// Composite means it takes into account transforms as well as layout.\n\n\nexport default function getCompositeRect(elementOrVirtualElement, offsetParent, isFixed) {\n  if (isFixed === void 0) {\n    isFixed = false;\n  }\n\n  var isOffsetParentAnElement = isHTMLElement(offsetParent);\n  var offsetParentIsScaled = isHTMLElement(offsetParent) && isElementScaled(offsetParent);\n  var documentElement = getDocumentElement(offsetParent);\n  var rect = getBoundingClientRect(elementOrVirtualElement, offsetParentIsScaled);\n  var scroll = {\n    scrollLeft: 0,\n    scrollTop: 0\n  };\n  var offsets = {\n    x: 0,\n    y: 0\n  };\n\n  if (isOffsetParentAnElement || !isOffsetParentAnElement && !isFixed) {\n    if (getNodeName(offsetParent) !== 'body' || // https://github.com/popperjs/popper-core/issues/1078\n    isScrollParent(documentElement)) {\n      scroll = getNodeScroll(offsetParent);\n    }\n\n    if (isHTMLElement(offsetParent)) {\n      offsets = getBoundingClientRect(offsetParent, true);\n      offsets.x += offsetParent.clientLeft;\n      offsets.y += offsetParent.clientTop;\n    } else if (documentElement) {\n      offsets.x = getWindowScrollBarX(documentElement);\n    }\n  }\n\n  return {\n    x: rect.left + scroll.scrollLeft - offsets.x,\n    y: rect.top + scroll.scrollTop - offsets.y,\n    width: rect.width,\n    height: rect.height\n  };\n}","import getWindowScroll from \"./getWindowScroll.js\";\nimport getWindow from \"./getWindow.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nimport getHTMLElementScroll from \"./getHTMLElementScroll.js\";\nexport default function getNodeScroll(node) {\n  if (node === getWindow(node) || !isHTMLElement(node)) {\n    return getWindowScroll(node);\n  } else {\n    return getHTMLElementScroll(node);\n  }\n}","export default function getHTMLElementScroll(element) {\n  return {\n    scrollLeft: element.scrollLeft,\n    scrollTop: element.scrollTop\n  };\n}","import getCompositeRect from \"./dom-utils/getCompositeRect.js\";\nimport getLayoutRect from \"./dom-utils/getLayoutRect.js\";\nimport listScrollParents from \"./dom-utils/listScrollParents.js\";\nimport getOffsetParent from \"./dom-utils/getOffsetParent.js\";\nimport getComputedStyle from \"./dom-utils/getComputedStyle.js\";\nimport orderModifiers from \"./utils/orderModifiers.js\";\nimport debounce from \"./utils/debounce.js\";\nimport validateModifiers from \"./utils/validateModifiers.js\";\nimport uniqueBy from \"./utils/uniqueBy.js\";\nimport getBasePlacement from \"./utils/getBasePlacement.js\";\nimport mergeByName from \"./utils/mergeByName.js\";\nimport detectOverflow from \"./utils/detectOverflow.js\";\nimport { isElement } from \"./dom-utils/instanceOf.js\";\nimport { auto } from \"./enums.js\";\nvar INVALID_ELEMENT_ERROR = 'Popper: Invalid reference or popper argument provided. They must be either a DOM element or virtual element.';\nvar INFINITE_LOOP_ERROR = 'Popper: An infinite loop in the modifiers cycle has been detected! The cycle has been interrupted to prevent a browser crash.';\nvar DEFAULT_OPTIONS = {\n  placement: 'bottom',\n  modifiers: [],\n  strategy: 'absolute'\n};\n\nfunction areValidElements() {\n  for (var _len = arguments.length, args = new Array(_len), _key = 0; _key < _len; _key++) {\n    args[_key] = arguments[_key];\n  }\n\n  return !args.some(function (element) {\n    return !(element && typeof element.getBoundingClientRect === 'function');\n  });\n}\n\nexport function popperGenerator(generatorOptions) {\n  if (generatorOptions === void 0) {\n    generatorOptions = {};\n  }\n\n  var _generatorOptions = generatorOptions,\n      _generatorOptions$def = _generatorOptions.defaultModifiers,\n      defaultModifiers = _generatorOptions$def === void 0 ? [] : _generatorOptions$def,\n      _generatorOptions$def2 = _generatorOptions.defaultOptions,\n      defaultOptions = _generatorOptions$def2 === void 0 ? DEFAULT_OPTIONS : _generatorOptions$def2;\n  return function createPopper(reference, popper, options) {\n    if (options === void 0) {\n      options = defaultOptions;\n    }\n\n    var state = {\n      placement: 'bottom',\n      orderedModifiers: [],\n      options: Object.assign({}, DEFAULT_OPTIONS, defaultOptions),\n      modifiersData: {},\n      elements: {\n        reference: reference,\n        popper: popper\n      },\n      attributes: {},\n      styles: {}\n    };\n    var effectCleanupFns = [];\n    var isDestroyed = false;\n    var instance = {\n      state: state,\n      setOptions: function setOptions(options) {\n        cleanupModifierEffects();\n        state.options = Object.assign({}, defaultOptions, state.options, options);\n        state.scrollParents = {\n          reference: isElement(reference) ? listScrollParents(reference) : reference.contextElement ? listScrollParents(reference.contextElement) : [],\n          popper: listScrollParents(popper)\n        }; // Orders the modifiers based on their dependencies and `phase`\n        // properties\n\n        var orderedModifiers = orderModifiers(mergeByName([].concat(defaultModifiers, state.options.modifiers))); // Strip out disabled modifiers\n\n        state.orderedModifiers = orderedModifiers.filter(function (m) {\n          return m.enabled;\n        }); // Validate the provided modifiers so that the consumer will get warned\n        // if one of the modifiers is invalid for any reason\n\n        if (process.env.NODE_ENV !== \"production\") {\n          var modifiers = uniqueBy([].concat(orderedModifiers, state.options.modifiers), function (_ref) {\n            var name = _ref.name;\n            return name;\n          });\n          validateModifiers(modifiers);\n\n          if (getBasePlacement(state.options.placement) === auto) {\n            var flipModifier = state.orderedModifiers.find(function (_ref2) {\n              var name = _ref2.name;\n              return name === 'flip';\n            });\n\n            if (!flipModifier) {\n              console.error(['Popper: \"auto\" placements require the \"flip\" modifier be', 'present and enabled to work.'].join(' '));\n            }\n          }\n\n          var _getComputedStyle = getComputedStyle(popper),\n              marginTop = _getComputedStyle.marginTop,\n              marginRight = _getComputedStyle.marginRight,\n              marginBottom = _getComputedStyle.marginBottom,\n              marginLeft = _getComputedStyle.marginLeft; // We no longer take into account `margins` on the popper, and it can\n          // cause bugs with positioning, so we'll warn the consumer\n\n\n          if ([marginTop, marginRight, marginBottom, marginLeft].some(function (margin) {\n            return parseFloat(margin);\n          })) {\n            console.warn(['Popper: CSS \"margin\" styles cannot be used to apply padding', 'between the popper and its reference element or boundary.', 'To replicate margin, use the `offset` modifier, as well as', 'the `padding` option in the `preventOverflow` and `flip`', 'modifiers.'].join(' '));\n          }\n        }\n\n        runModifierEffects();\n        return instance.update();\n      },\n      // Sync update – it will always be executed, even if not necessary. This\n      // is useful for low frequency updates where sync behavior simplifies the\n      // logic.\n      // For high frequency updates (e.g. `resize` and `scroll` events), always\n      // prefer the async Popper#update method\n      forceUpdate: function forceUpdate() {\n        if (isDestroyed) {\n          return;\n        }\n\n        var _state$elements = state.elements,\n            reference = _state$elements.reference,\n            popper = _state$elements.popper; // Don't proceed if `reference` or `popper` are not valid elements\n        // anymore\n\n        if (!areValidElements(reference, popper)) {\n          if (process.env.NODE_ENV !== \"production\") {\n            console.error(INVALID_ELEMENT_ERROR);\n          }\n\n          return;\n        } // Store the reference and popper rects to be read by modifiers\n\n\n        state.rects = {\n          reference: getCompositeRect(reference, getOffsetParent(popper), state.options.strategy === 'fixed'),\n          popper: getLayoutRect(popper)\n        }; // Modifiers have the ability to reset the current update cycle. The\n        // most common use case for this is the `flip` modifier changing the\n        // placement, which then needs to re-run all the modifiers, because the\n        // logic was previously ran for the previous placement and is therefore\n        // stale/incorrect\n\n        state.reset = false;\n        state.placement = state.options.placement; // On each update cycle, the `modifiersData` property for each modifier\n        // is filled with the initial data specified by the modifier. This means\n        // it doesn't persist and is fresh on each update.\n        // To ensure persistent data, use `${name}#persistent`\n\n        state.orderedModifiers.forEach(function (modifier) {\n          return state.modifiersData[modifier.name] = Object.assign({}, modifier.data);\n        });\n        var __debug_loops__ = 0;\n\n        for (var index = 0; index < state.orderedModifiers.length; index++) {\n          if (process.env.NODE_ENV !== \"production\") {\n            __debug_loops__ += 1;\n\n            if (__debug_loops__ > 100) {\n              console.error(INFINITE_LOOP_ERROR);\n              break;\n            }\n          }\n\n          if (state.reset === true) {\n            state.reset = false;\n            index = -1;\n            continue;\n          }\n\n          var _state$orderedModifie = state.orderedModifiers[index],\n              fn = _state$orderedModifie.fn,\n              _state$orderedModifie2 = _state$orderedModifie.options,\n              _options = _state$orderedModifie2 === void 0 ? {} : _state$orderedModifie2,\n              name = _state$orderedModifie.name;\n\n          if (typeof fn === 'function') {\n            state = fn({\n              state: state,\n              options: _options,\n              name: name,\n              instance: instance\n            }) || state;\n          }\n        }\n      },\n      // Async and optimistically optimized update – it will not be executed if\n      // not necessary (debounced to run at most once-per-tick)\n      update: debounce(function () {\n        return new Promise(function (resolve) {\n          instance.forceUpdate();\n          resolve(state);\n        });\n      }),\n      destroy: function destroy() {\n        cleanupModifierEffects();\n        isDestroyed = true;\n      }\n    };\n\n    if (!areValidElements(reference, popper)) {\n      if (process.env.NODE_ENV !== \"production\") {\n        console.error(INVALID_ELEMENT_ERROR);\n      }\n\n      return instance;\n    }\n\n    instance.setOptions(options).then(function (state) {\n      if (!isDestroyed && options.onFirstUpdate) {\n        options.onFirstUpdate(state);\n      }\n    }); // Modifiers have the ability to execute arbitrary code before the first\n    // update cycle runs. They will be executed in the same order as the update\n    // cycle. This is useful when a modifier adds some persistent data that\n    // other modifiers need to use, but the modifier is run after the dependent\n    // one.\n\n    function runModifierEffects() {\n      state.orderedModifiers.forEach(function (_ref3) {\n        var name = _ref3.name,\n            _ref3$options = _ref3.options,\n            options = _ref3$options === void 0 ? {} : _ref3$options,\n            effect = _ref3.effect;\n\n        if (typeof effect === 'function') {\n          var cleanupFn = effect({\n            state: state,\n            name: name,\n            instance: instance,\n            options: options\n          });\n\n          var noopFn = function noopFn() {};\n\n          effectCleanupFns.push(cleanupFn || noopFn);\n        }\n      });\n    }\n\n    function cleanupModifierEffects() {\n      effectCleanupFns.forEach(function (fn) {\n        return fn();\n      });\n      effectCleanupFns = [];\n    }\n\n    return instance;\n  };\n}\nexport var createPopper = /*#__PURE__*/popperGenerator(); // eslint-disable-next-line import/no-unused-modules\n\nexport { detectOverflow };","export default function debounce(fn) {\n  var pending;\n  return function () {\n    if (!pending) {\n      pending = new Promise(function (resolve) {\n        Promise.resolve().then(function () {\n          pending = undefined;\n          resolve(fn());\n        });\n      });\n    }\n\n    return pending;\n  };\n}","export default function mergeByName(modifiers) {\n  var merged = modifiers.reduce(function (merged, current) {\n    var existing = merged[current.name];\n    merged[current.name] = existing ? Object.assign({}, existing, current, {\n      options: Object.assign({}, existing.options, current.options),\n      data: Object.assign({}, existing.data, current.data)\n    }) : current;\n    return merged;\n  }, {}); // IE11 does not support Object.values\n\n  return Object.keys(merged).map(function (key) {\n    return merged[key];\n  });\n}","import { modifierPhases } from \"../enums.js\"; // source: https://stackoverflow.com/questions/49875255\n\nfunction order(modifiers) {\n  var map = new Map();\n  var visited = new Set();\n  var result = [];\n  modifiers.forEach(function (modifier) {\n    map.set(modifier.name, modifier);\n  }); // On visiting object, check for its dependencies and visit them recursively\n\n  function sort(modifier) {\n    visited.add(modifier.name);\n    var requires = [].concat(modifier.requires || [], modifier.requiresIfExists || []);\n    requires.forEach(function (dep) {\n      if (!visited.has(dep)) {\n        var depModifier = map.get(dep);\n\n        if (depModifier) {\n          sort(depModifier);\n        }\n      }\n    });\n    result.push(modifier);\n  }\n\n  modifiers.forEach(function (modifier) {\n    if (!visited.has(modifier.name)) {\n      // check for visited object\n      sort(modifier);\n    }\n  });\n  return result;\n}\n\nexport default function orderModifiers(modifiers) {\n  // order based on dependencies\n  var orderedModifiers = order(modifiers); // order based on phase\n\n  return modifierPhases.reduce(function (acc, phase) {\n    return acc.concat(orderedModifiers.filter(function (modifier) {\n      return modifier.phase === phase;\n    }));\n  }, []);\n}","import { popperGenerator, detectOverflow } from \"./createPopper.js\";\nimport eventListeners from \"./modifiers/eventListeners.js\";\nimport popperOffsets from \"./modifiers/popperOffsets.js\";\nimport computeStyles from \"./modifiers/computeStyles.js\";\nimport applyStyles from \"./modifiers/applyStyles.js\";\nvar defaultModifiers = [eventListeners, popperOffsets, computeStyles, applyStyles];\nvar createPopper = /*#__PURE__*/popperGenerator({\n  defaultModifiers: defaultModifiers\n}); // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper, popperGenerator, defaultModifiers, detectOverflow };","import { popperGenerator, detectOverflow } from \"./createPopper.js\";\nimport eventListeners from \"./modifiers/eventListeners.js\";\nimport popperOffsets from \"./modifiers/popperOffsets.js\";\nimport computeStyles from \"./modifiers/computeStyles.js\";\nimport applyStyles from \"./modifiers/applyStyles.js\";\nimport offset from \"./modifiers/offset.js\";\nimport flip from \"./modifiers/flip.js\";\nimport preventOverflow from \"./modifiers/preventOverflow.js\";\nimport arrow from \"./modifiers/arrow.js\";\nimport hide from \"./modifiers/hide.js\";\nvar defaultModifiers = [eventListeners, popperOffsets, computeStyles, applyStyles, offset, flip, preventOverflow, arrow, hide];\nvar createPopper = /*#__PURE__*/popperGenerator({\n  defaultModifiers: defaultModifiers\n}); // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper, popperGenerator, defaultModifiers, detectOverflow }; // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper as createPopperLite } from \"./popper-lite.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport * from \"./modifiers/index.js\";","/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.1.0): dropdown.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport * as Popper from '@popperjs/core'\n\nimport {\n  defineJQueryPlugin,\n  getElement,\n  getElementFromSelector,\n  getNextActiveElement,\n  isDisabled,\n  isElement,\n  isRTL,\n  isVisible,\n  noop,\n  typeCheckConfig\n} from './util/index'\nimport EventHandler from './dom/event-handler'\nimport Manipulator from './dom/manipulator'\nimport SelectorEngine from './dom/selector-engine'\nimport BaseComponent from './base-component'\n\n/**\n * ------------------------------------------------------------------------\n * Constants\n * ------------------------------------------------------------------------\n */\n\nconst NAME = 'dropdown'\nconst DATA_KEY = 'bs.dropdown'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\n\nconst ESCAPE_KEY = 'Escape'\nconst SPACE_KEY = 'Space'\nconst TAB_KEY = 'Tab'\nconst ARROW_UP_KEY = 'ArrowUp'\nconst ARROW_DOWN_KEY = 'ArrowDown'\nconst RIGHT_MOUSE_BUTTON = 2 // MouseEvent.button value for the secondary button, usually the right button\n\nconst REGEXP_KEYDOWN = new RegExp(`${ARROW_UP_KEY}|${ARROW_DOWN_KEY}|${ESCAPE_KEY}`)\n\nconst EVENT_HIDE = `hide${EVENT_KEY}`\nconst EVENT_HIDDEN = `hidden${EVENT_KEY}`\nconst EVENT_SHOW = `show${EVENT_KEY}`\nconst EVENT_SHOWN = `shown${EVENT_KEY}`\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY}${DATA_API_KEY}`\nconst EVENT_KEYDOWN_DATA_API = `keydown${EVENT_KEY}${DATA_API_KEY}`\nconst EVENT_KEYUP_DATA_API = `keyup${EVENT_KEY}${DATA_API_KEY}`\n\nconst CLASS_NAME_SHOW = 'show'\nconst CLASS_NAME_DROPUP = 'dropup'\nconst CLASS_NAME_DROPEND = 'dropend'\nconst CLASS_NAME_DROPSTART = 'dropstart'\nconst CLASS_NAME_NAVBAR = 'navbar'\n\nconst SELECTOR_DATA_TOGGLE = '[data-bs-toggle=\"dropdown\"]'\nconst SELECTOR_MENU = '.dropdown-menu'\nconst SELECTOR_NAVBAR_NAV = '.navbar-nav'\nconst SELECTOR_VISIBLE_ITEMS = '.dropdown-menu .dropdown-item:not(.disabled):not(:disabled)'\n\nconst PLACEMENT_TOP = isRTL() ? 'top-end' : 'top-start'\nconst PLACEMENT_TOPEND = isRTL() ? 'top-start' : 'top-end'\nconst PLACEMENT_BOTTOM = isRTL() ? 'bottom-end' : 'bottom-start'\nconst PLACEMENT_BOTTOMEND = isRTL() ? 'bottom-start' : 'bottom-end'\nconst PLACEMENT_RIGHT = isRTL() ? 'left-start' : 'right-start'\nconst PLACEMENT_LEFT = isRTL() ? 'right-start' : 'left-start'\n\nconst Default = {\n  offset: [0, 2],\n  boundary: 'clippingParents',\n  reference: 'toggle',\n  display: 'dynamic',\n  popperConfig: null,\n  autoClose: true\n}\n\nconst DefaultType = {\n  offset: '(array|string|function)',\n  boundary: '(string|element)',\n  reference: '(string|element|object)',\n  display: 'string',\n  popperConfig: '(null|object|function)',\n  autoClose: '(boolean|string)'\n}\n\n/**\n * ------------------------------------------------------------------------\n * Class Definition\n * ------------------------------------------------------------------------\n */\n\nclass Dropdown extends BaseComponent {\n  constructor(element, config) {\n    super(element)\n\n    this._popper = null\n    this._config = this._getConfig(config)\n    this._menu = this._getMenuElement()\n    this._inNavbar = this._detectNavbar()\n  }\n\n  // Getters\n\n  static get Default() {\n    return Default\n  }\n\n  static get DefaultType() {\n    return DefaultType\n  }\n\n  static get NAME() {\n    return NAME\n  }\n\n  // Public\n\n  toggle() {\n    return this._isShown() ? this.hide() : this.show()\n  }\n\n  show() {\n    if (isDisabled(this._element) || this._isShown(this._menu)) {\n      return\n    }\n\n    const relatedTarget = {\n      relatedTarget: this._element\n    }\n\n    const showEvent = EventHandler.trigger(this._element, EVENT_SHOW, relatedTarget)\n\n    if (showEvent.defaultPrevented) {\n      return\n    }\n\n    const parent = Dropdown.getParentFromElement(this._element)\n    // Totally disable Popper for Dropdowns in Navbar\n    if (this._inNavbar) {\n      Manipulator.setDataAttribute(this._menu, 'popper', 'none')\n    } else {\n      this._createPopper(parent)\n    }\n\n    // If this is a touch-enabled device we add extra\n    // empty mouseover listeners to the body's immediate children;\n    // only needed because of broken event delegation on iOS\n    // https://www.quirksmode.org/blog/archives/2014/02/mouse_event_bub.html\n    if ('ontouchstart' in document.documentElement &&\n      !parent.closest(SELECTOR_NAVBAR_NAV)) {\n      [].concat(...document.body.children)\n        .forEach(elem => EventHandler.on(elem, 'mouseover', noop))\n    }\n\n    this._element.focus()\n    this._element.setAttribute('aria-expanded', true)\n\n    this._menu.classList.add(CLASS_NAME_SHOW)\n    this._element.classList.add(CLASS_NAME_SHOW)\n    EventHandler.trigger(this._element, EVENT_SHOWN, relatedTarget)\n  }\n\n  hide() {\n    if (isDisabled(this._element) || !this._isShown(this._menu)) {\n      return\n    }\n\n    const relatedTarget = {\n      relatedTarget: this._element\n    }\n\n    this._completeHide(relatedTarget)\n  }\n\n  dispose() {\n    if (this._popper) {\n      this._popper.destroy()\n    }\n\n    super.dispose()\n  }\n\n  update() {\n    this._inNavbar = this._detectNavbar()\n    if (this._popper) {\n      this._popper.update()\n    }\n  }\n\n  // Private\n\n  _completeHide(relatedTarget) {\n    const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE, relatedTarget)\n    if (hideEvent.defaultPrevented) {\n      return\n    }\n\n    // If this is a touch-enabled device we remove the extra\n    // empty mouseover listeners we added for iOS support\n    if ('ontouchstart' in document.documentElement) {\n      [].concat(...document.body.children)\n        .forEach(elem => EventHandler.off(elem, 'mouseover', noop))\n    }\n\n    if (this._popper) {\n      this._popper.destroy()\n    }\n\n    this._menu.classList.remove(CLASS_NAME_SHOW)\n    this._element.classList.remove(CLASS_NAME_SHOW)\n    this._element.setAttribute('aria-expanded', 'false')\n    Manipulator.removeDataAttribute(this._menu, 'popper')\n    EventHandler.trigger(this._element, EVENT_HIDDEN, relatedTarget)\n  }\n\n  _getConfig(config) {\n    config = {\n      ...this.constructor.Default,\n      ...Manipulator.getDataAttributes(this._element),\n      ...config\n    }\n\n    typeCheckConfig(NAME, config, this.constructor.DefaultType)\n\n    if (typeof config.reference === 'object' && !isElement(config.reference) &&\n      typeof config.reference.getBoundingClientRect !== 'function'\n    ) {\n      // Popper virtual elements require a getBoundingClientRect method\n      throw new TypeError(`${NAME.toUpperCase()}: Option \"reference\" provided type \"object\" without a required \"getBoundingClientRect\" method.`)\n    }\n\n    return config\n  }\n\n  _createPopper(parent) {\n    if (typeof Popper === 'undefined') {\n      throw new TypeError('Bootstrap\\'s dropdowns require Popper (https://popper.js.org)')\n    }\n\n    let referenceElement = this._element\n\n    if (this._config.reference === 'parent') {\n      referenceElement = parent\n    } else if (isElement(this._config.reference)) {\n      referenceElement = getElement(this._config.reference)\n    } else if (typeof this._config.reference === 'object') {\n      referenceElement = this._config.reference\n    }\n\n    const popperConfig = this._getPopperConfig()\n    const isDisplayStatic = popperConfig.modifiers.find(modifier => modifier.name === 'applyStyles' && modifier.enabled === false)\n\n    this._popper = Popper.createPopper(referenceElement, this._menu, popperConfig)\n\n    if (isDisplayStatic) {\n      Manipulator.setDataAttribute(this._menu, 'popper', 'static')\n    }\n  }\n\n  _isShown(element = this._element) {\n    return element.classList.contains(CLASS_NAME_SHOW)\n  }\n\n  _getMenuElement() {\n    return SelectorEngine.next(this._element, SELECTOR_MENU)[0]\n  }\n\n  _getPlacement() {\n    const parentDropdown = this._element.parentNode\n\n    if (parentDropdown.classList.contains(CLASS_NAME_DROPEND)) {\n      return PLACEMENT_RIGHT\n    }\n\n    if (parentDropdown.classList.contains(CLASS_NAME_DROPSTART)) {\n      return PLACEMENT_LEFT\n    }\n\n    // We need to trim the value because custom properties can also include spaces\n    const isEnd = getComputedStyle(this._menu).getPropertyValue('--bs-position').trim() === 'end'\n\n    if (parentDropdown.classList.contains(CLASS_NAME_DROPUP)) {\n      return isEnd ? PLACEMENT_TOPEND : PLACEMENT_TOP\n    }\n\n    return isEnd ? PLACEMENT_BOTTOMEND : PLACEMENT_BOTTOM\n  }\n\n  _detectNavbar() {\n    return this._element.closest(`.${CLASS_NAME_NAVBAR}`) !== null\n  }\n\n  _getOffset() {\n    const { offset } = this._config\n\n    if (typeof offset === 'string') {\n      return offset.split(',').map(val => Number.parseInt(val, 10))\n    }\n\n    if (typeof offset === 'function') {\n      return popperData => offset(popperData, this._element)\n    }\n\n    return offset\n  }\n\n  _getPopperConfig() {\n    const defaultBsPopperConfig = {\n      placement: this._getPlacement(),\n      modifiers: [{\n        name: 'preventOverflow',\n        options: {\n          boundary: this._config.boundary\n        }\n      },\n      {\n        name: 'offset',\n        options: {\n          offset: this._getOffset()\n        }\n      }]\n    }\n\n    // Disable Popper if we have a static display\n    if (this._config.display === 'static') {\n      defaultBsPopperConfig.modifiers = [{\n        name: 'applyStyles',\n        enabled: false\n      }]\n    }\n\n    return {\n      ...defaultBsPopperConfig,\n      ...(typeof this._config.popperConfig === 'function' ? this._config.popperConfig(defaultBsPopperConfig) : this._config.popperConfig)\n    }\n  }\n\n  _selectMenuItem({ key, target }) {\n    const items = SelectorEngine.find(SELECTOR_VISIBLE_ITEMS, this._menu).filter(isVisible)\n\n    if (!items.length) {\n      return\n    }\n\n    // if target isn't included in items (e.g. when expanding the dropdown)\n    // allow cycling to get the last item in case key equals ARROW_UP_KEY\n    getNextActiveElement(items, target, key === ARROW_DOWN_KEY, !items.includes(target)).focus()\n  }\n\n  // Static\n\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Dropdown.getOrCreateInstance(this, config)\n\n      if (typeof config !== 'string') {\n        return\n      }\n\n      if (typeof data[config] === 'undefined') {\n        throw new TypeError(`No method named \"${config}\"`)\n      }\n\n      data[config]()\n    })\n  }\n\n  static clearMenus(event) {\n    if (event && (event.button === RIGHT_MOUSE_BUTTON || (event.type === 'keyup' && event.key !== TAB_KEY))) {\n      return\n    }\n\n    const toggles = SelectorEngine.find(SELECTOR_DATA_TOGGLE)\n\n    for (let i = 0, len = toggles.length; i < len; i++) {\n      const context = Dropdown.getInstance(toggles[i])\n      if (!context || context._config.autoClose === false) {\n        continue\n      }\n\n      if (!context._isShown()) {\n        continue\n      }\n\n      const relatedTarget = {\n        relatedTarget: context._element\n      }\n\n      if (event) {\n        const composedPath = event.composedPath()\n        const isMenuTarget = composedPath.includes(context._menu)\n        if (\n          composedPath.includes(context._element) ||\n          (context._config.autoClose === 'inside' && !isMenuTarget) ||\n          (context._config.autoClose === 'outside' && isMenuTarget)\n        ) {\n          continue\n        }\n\n        // Tab navigation through the dropdown menu or events from contained inputs shouldn't close the menu\n        if (context._menu.contains(event.target) && ((event.type === 'keyup' && event.key === TAB_KEY) || /input|select|option|textarea|form/i.test(event.target.tagName))) {\n          continue\n        }\n\n        if (event.type === 'click') {\n          relatedTarget.clickEvent = event\n        }\n      }\n\n      context._completeHide(relatedTarget)\n    }\n  }\n\n  static getParentFromElement(element) {\n    return getElementFromSelector(element) || element.parentNode\n  }\n\n  static dataApiKeydownHandler(event) {\n    // If not input/textarea:\n    //  - And not a key in REGEXP_KEYDOWN => not a dropdown command\n    // If input/textarea:\n    //  - If space key => not a dropdown command\n    //  - If key is other than escape\n    //    - If key is not up or down => not a dropdown command\n    //    - If trigger inside the menu => not a dropdown command\n    if (/input|textarea/i.test(event.target.tagName) ?\n      event.key === SPACE_KEY || (event.key !== ESCAPE_KEY &&\n      ((event.key !== ARROW_DOWN_KEY && event.key !== ARROW_UP_KEY) ||\n        event.target.closest(SELECTOR_MENU))) :\n      !REGEXP_KEYDOWN.test(event.key)) {\n      return\n    }\n\n    const isActive = this.classList.contains(CLASS_NAME_SHOW)\n\n    if (!isActive && event.key === ESCAPE_KEY) {\n      return\n    }\n\n    event.preventDefault()\n    event.stopPropagation()\n\n    if (isDisabled(this)) {\n      return\n    }\n\n    const getToggleButton = this.matches(SELECTOR_DATA_TOGGLE) ? this : SelectorEngine.prev(this, SELECTOR_DATA_TOGGLE)[0]\n    const instance = Dropdown.getOrCreateInstance(getToggleButton)\n\n    if (event.key === ESCAPE_KEY) {\n      instance.hide()\n      return\n    }\n\n    if (event.key === ARROW_UP_KEY || event.key === ARROW_DOWN_KEY) {\n      if (!isActive) {\n        instance.show()\n      }\n\n      instance._selectMenuItem(event)\n      return\n    }\n\n    if (!isActive || event.key === SPACE_KEY) {\n      Dropdown.clearMenus()\n    }\n  }\n}\n\n/**\n * ------------------------------------------------------------------------\n * Data Api implementation\n * ------------------------------------------------------------------------\n */\n\nEventHandler.on(document, EVENT_KEYDOWN_DATA_API, SELECTOR_DATA_TOGGLE, Dropdown.dataApiKeydownHandler)\nEventHandler.on(document, EVENT_KEYDOWN_DATA_API, SELECTOR_MENU, Dropdown.dataApiKeydownHandler)\nEventHandler.on(document, EVENT_CLICK_DATA_API, Dropdown.clearMenus)\nEventHandler.on(document, EVENT_KEYUP_DATA_API, Dropdown.clearMenus)\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_TOGGLE, function (event) {\n  event.preventDefault()\n  Dropdown.getOrCreateInstance(this).toggle()\n})\n\n/**\n * ------------------------------------------------------------------------\n * jQuery\n * ------------------------------------------------------------------------\n * add .Dropdown to jQuery only if jQuery is present\n */\n\ndefineJQueryPlugin(Dropdown)\n\nexport default Dropdown\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.1.0): util/scrollBar.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport SelectorEngine from '../dom/selector-engine'\nimport Manipulator from '../dom/manipulator'\nimport { isElement } from './index'\n\nconst SELECTOR_FIXED_CONTENT = '.fixed-top, .fixed-bottom, .is-fixed, .sticky-top'\nconst SELECTOR_STICKY_CONTENT = '.sticky-top'\n\nclass ScrollBarHelper {\n  constructor() {\n    this._element = document.body\n  }\n\n  getWidth() {\n    // https://developer.mozilla.org/en-US/docs/Web/API/Window/innerWidth#usage_notes\n    const documentWidth = document.documentElement.clientWidth\n    return Math.abs(window.innerWidth - documentWidth)\n  }\n\n  hide() {\n    const width = this.getWidth()\n    this._disableOverFlow()\n    // give padding to element to balance the hidden scrollbar width\n    this._setElementAttributes(this._element, 'paddingRight', calculatedValue => calculatedValue + width)\n    // trick: We adjust positive paddingRight and negative marginRight to sticky-top elements to keep showing fullwidth\n    this._setElementAttributes(SELECTOR_FIXED_CONTENT, 'paddingRight', calculatedValue => calculatedValue + width)\n    this._setElementAttributes(SELECTOR_STICKY_CONTENT, 'marginRight', calculatedValue => calculatedValue - width)\n  }\n\n  _disableOverFlow() {\n    this._saveInitialAttribute(this._element, 'overflow')\n    this._element.style.overflow = 'hidden'\n  }\n\n  _setElementAttributes(selector, styleProp, callback) {\n    const scrollbarWidth = this.getWidth()\n    const manipulationCallBack = element => {\n      if (element !== this._element && window.innerWidth > element.clientWidth + scrollbarWidth) {\n        return\n      }\n\n      this._saveInitialAttribute(element, styleProp)\n      const calculatedValue = window.getComputedStyle(element)[styleProp]\n      element.style[styleProp] = `${callback(Number.parseFloat(calculatedValue))}px`\n    }\n\n    this._applyManipulationCallback(selector, manipulationCallBack)\n  }\n\n  reset() {\n    this._resetElementAttributes(this._element, 'overflow')\n    this._resetElementAttributes(this._element, 'paddingRight')\n    this._resetElementAttributes(SELECTOR_FIXED_CONTENT, 'paddingRight')\n    this._resetElementAttributes(SELECTOR_STICKY_CONTENT, 'marginRight')\n  }\n\n  _saveInitialAttribute(element, styleProp) {\n    const actualValue = element.style[styleProp]\n    if (actualValue) {\n      Manipulator.setDataAttribute(element, styleProp, actualValue)\n    }\n  }\n\n  _resetElementAttributes(selector, styleProp) {\n    const manipulationCallBack = element => {\n      const value = Manipulator.getDataAttribute(element, styleProp)\n      if (typeof value === 'undefined') {\n        element.style.removeProperty(styleProp)\n      } else {\n        Manipulator.removeDataAttribute(element, styleProp)\n        element.style[styleProp] = value\n      }\n    }\n\n    this._applyManipulationCallback(selector, manipulationCallBack)\n  }\n\n  _applyManipulationCallback(selector, callBack) {\n    if (isElement(selector)) {\n      callBack(selector)\n    } else {\n      SelectorEngine.find(selector, this._element).forEach(callBack)\n    }\n  }\n\n  isOverflowing() {\n    return this.getWidth() > 0\n  }\n}\n\nexport default ScrollBarHelper\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.1.0): util/backdrop.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport EventHandler from '../dom/event-handler'\nimport { execute, executeAfterTransition, getElement, reflow, typeCheckConfig } from './index'\n\nconst Default = {\n  className: 'modal-backdrop',\n  isVisible: true, // if false, we use the backdrop helper without adding any element to the dom\n  isAnimated: false,\n  rootElement: 'body', // give the choice to place backdrop under different elements\n  clickCallback: null\n}\n\nconst DefaultType = {\n  className: 'string',\n  isVisible: 'boolean',\n  isAnimated: 'boolean',\n  rootElement: '(element|string)',\n  clickCallback: '(function|null)'\n}\nconst NAME = 'backdrop'\nconst CLASS_NAME_FADE = 'fade'\nconst CLASS_NAME_SHOW = 'show'\n\nconst EVENT_MOUSEDOWN = `mousedown.bs.${NAME}`\n\nclass Backdrop {\n  constructor(config) {\n    this._config = this._getConfig(config)\n    this._isAppended = false\n    this._element = null\n  }\n\n  show(callback) {\n    if (!this._config.isVisible) {\n      execute(callback)\n      return\n    }\n\n    this._append()\n\n    if (this._config.isAnimated) {\n      reflow(this._getElement())\n    }\n\n    this._getElement().classList.add(CLASS_NAME_SHOW)\n\n    this._emulateAnimation(() => {\n      execute(callback)\n    })\n  }\n\n  hide(callback) {\n    if (!this._config.isVisible) {\n      execute(callback)\n      return\n    }\n\n    this._getElement().classList.remove(CLASS_NAME_SHOW)\n\n    this._emulateAnimation(() => {\n      this.dispose()\n      execute(callback)\n    })\n  }\n\n  // Private\n\n  _getElement() {\n    if (!this._element) {\n      const backdrop = document.createElement('div')\n      backdrop.className = this._config.className\n      if (this._config.isAnimated) {\n        backdrop.classList.add(CLASS_NAME_FADE)\n      }\n\n      this._element = backdrop\n    }\n\n    return this._element\n  }\n\n  _getConfig(config) {\n    config = {\n      ...Default,\n      ...(typeof config === 'object' ? config : {})\n    }\n\n    // use getElement() with the default \"body\" to get a fresh Element on each instantiation\n    config.rootElement = getElement(config.rootElement)\n    typeCheckConfig(NAME, config, DefaultType)\n    return config\n  }\n\n  _append() {\n    if (this._isAppended) {\n      return\n    }\n\n    this._config.rootElement.append(this._getElement())\n\n    EventHandler.on(this._getElement(), EVENT_MOUSEDOWN, () => {\n      execute(this._config.clickCallback)\n    })\n\n    this._isAppended = true\n  }\n\n  dispose() {\n    if (!this._isAppended) {\n      return\n    }\n\n    EventHandler.off(this._element, EVENT_MOUSEDOWN)\n\n    this._element.remove()\n    this._isAppended = false\n  }\n\n  _emulateAnimation(callback) {\n    executeAfterTransition(callback, this._getElement(), this._config.isAnimated)\n  }\n}\n\nexport default Backdrop\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.1.0): util/focustrap.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport EventHandler from '../dom/event-handler'\nimport SelectorEngine from '../dom/selector-engine'\nimport { typeCheckConfig } from './index'\n\nconst Default = {\n  trapElement: null, // The element to trap focus inside of\n  autofocus: true\n}\n\nconst DefaultType = {\n  trapElement: 'element',\n  autofocus: 'boolean'\n}\n\nconst NAME = 'focustrap'\nconst DATA_KEY = 'bs.focustrap'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst EVENT_FOCUSIN = `focusin${EVENT_KEY}`\nconst EVENT_KEYDOWN_TAB = `keydown.tab${EVENT_KEY}`\n\nconst TAB_KEY = 'Tab'\nconst TAB_NAV_FORWARD = 'forward'\nconst TAB_NAV_BACKWARD = 'backward'\n\nclass FocusTrap {\n  constructor(config) {\n    this._config = this._getConfig(config)\n    this._isActive = false\n    this._lastTabNavDirection = null\n  }\n\n  activate() {\n    const { trapElement, autofocus } = this._config\n\n    if (this._isActive) {\n      return\n    }\n\n    if (autofocus) {\n      trapElement.focus()\n    }\n\n    EventHandler.off(document, EVENT_KEY) // guard against infinite focus loop\n    EventHandler.on(document, EVENT_FOCUSIN, event => this._handleFocusin(event))\n    EventHandler.on(document, EVENT_KEYDOWN_TAB, event => this._handleKeydown(event))\n\n    this._isActive = true\n  }\n\n  deactivate() {\n    if (!this._isActive) {\n      return\n    }\n\n    this._isActive = false\n    EventHandler.off(document, EVENT_KEY)\n  }\n\n  // Private\n\n  _handleFocusin(event) {\n    const { target } = event\n    const { trapElement } = this._config\n\n    if (\n      target === document ||\n      target === trapElement ||\n      trapElement.contains(target)\n    ) {\n      return\n    }\n\n    const elements = SelectorEngine.focusableChildren(trapElement)\n\n    if (elements.length === 0) {\n      trapElement.focus()\n    } else if (this._lastTabNavDirection === TAB_NAV_BACKWARD) {\n      elements[elements.length - 1].focus()\n    } else {\n      elements[0].focus()\n    }\n  }\n\n  _handleKeydown(event) {\n    if (event.key !== TAB_KEY) {\n      return\n    }\n\n    this._lastTabNavDirection = event.shiftKey ? TAB_NAV_BACKWARD : TAB_NAV_FORWARD\n  }\n\n  _getConfig(config) {\n    config = {\n      ...Default,\n      ...(typeof config === 'object' ? config : {})\n    }\n    typeCheckConfig(NAME, config, DefaultType)\n    return config\n  }\n}\n\nexport default FocusTrap\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.1.0): modal.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport {\n  defineJQueryPlugin,\n  getElementFromSelector,\n  isRTL,\n  isVisible,\n  reflow,\n  typeCheckConfig\n} from './util/index'\nimport EventHandler from './dom/event-handler'\nimport Manipulator from './dom/manipulator'\nimport SelectorEngine from './dom/selector-engine'\nimport ScrollBarHelper from './util/scrollbar'\nimport BaseComponent from './base-component'\nimport Backdrop from './util/backdrop'\nimport FocusTrap from './util/focustrap'\nimport { enableDismissTrigger } from './util/component-functions'\n\n/**\n * ------------------------------------------------------------------------\n * Constants\n * ------------------------------------------------------------------------\n */\n\nconst NAME = 'modal'\nconst DATA_KEY = 'bs.modal'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\nconst ESCAPE_KEY = 'Escape'\n\nconst Default = {\n  backdrop: true,\n  keyboard: true,\n  focus: true\n}\n\nconst DefaultType = {\n  backdrop: '(boolean|string)',\n  keyboard: 'boolean',\n  focus: 'boolean'\n}\n\nconst EVENT_HIDE = `hide${EVENT_KEY}`\nconst EVENT_HIDE_PREVENTED = `hidePrevented${EVENT_KEY}`\nconst EVENT_HIDDEN = `hidden${EVENT_KEY}`\nconst EVENT_SHOW = `show${EVENT_KEY}`\nconst EVENT_SHOWN = `shown${EVENT_KEY}`\nconst EVENT_RESIZE = `resize${EVENT_KEY}`\nconst EVENT_CLICK_DISMISS = `click.dismiss${EVENT_KEY}`\nconst EVENT_KEYDOWN_DISMISS = `keydown.dismiss${EVENT_KEY}`\nconst EVENT_MOUSEUP_DISMISS = `mouseup.dismiss${EVENT_KEY}`\nconst EVENT_MOUSEDOWN_DISMISS = `mousedown.dismiss${EVENT_KEY}`\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY}${DATA_API_KEY}`\n\nconst CLASS_NAME_OPEN = 'modal-open'\nconst CLASS_NAME_FADE = 'fade'\nconst CLASS_NAME_SHOW = 'show'\nconst CLASS_NAME_STATIC = 'modal-static'\n\nconst SELECTOR_DIALOG = '.modal-dialog'\nconst SELECTOR_MODAL_BODY = '.modal-body'\nconst SELECTOR_DATA_TOGGLE = '[data-bs-toggle=\"modal\"]'\n\n/**\n * ------------------------------------------------------------------------\n * Class Definition\n * ------------------------------------------------------------------------\n */\n\nclass Modal extends BaseComponent {\n  constructor(element, config) {\n    super(element)\n\n    this._config = this._getConfig(config)\n    this._dialog = SelectorEngine.findOne(SELECTOR_DIALOG, this._element)\n    this._backdrop = this._initializeBackDrop()\n    this._focustrap = this._initializeFocusTrap()\n    this._isShown = false\n    this._ignoreBackdropClick = false\n    this._isTransitioning = false\n    this._scrollBar = new ScrollBarHelper()\n  }\n\n  // Getters\n\n  static get Default() {\n    return Default\n  }\n\n  static get NAME() {\n    return NAME\n  }\n\n  // Public\n\n  toggle(relatedTarget) {\n    return this._isShown ? this.hide() : this.show(relatedTarget)\n  }\n\n  show(relatedTarget) {\n    if (this._isShown || this._isTransitioning) {\n      return\n    }\n\n    const showEvent = EventHandler.trigger(this._element, EVENT_SHOW, {\n      relatedTarget\n    })\n\n    if (showEvent.defaultPrevented) {\n      return\n    }\n\n    this._isShown = true\n\n    if (this._isAnimated()) {\n      this._isTransitioning = true\n    }\n\n    this._scrollBar.hide()\n\n    document.body.classList.add(CLASS_NAME_OPEN)\n\n    this._adjustDialog()\n\n    this._setEscapeEvent()\n    this._setResizeEvent()\n\n    EventHandler.on(this._dialog, EVENT_MOUSEDOWN_DISMISS, () => {\n      EventHandler.one(this._element, EVENT_MOUSEUP_DISMISS, event => {\n        if (event.target === this._element) {\n          this._ignoreBackdropClick = true\n        }\n      })\n    })\n\n    this._showBackdrop(() => this._showElement(relatedTarget))\n  }\n\n  hide() {\n    if (!this._isShown || this._isTransitioning) {\n      return\n    }\n\n    const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE)\n\n    if (hideEvent.defaultPrevented) {\n      return\n    }\n\n    this._isShown = false\n    const isAnimated = this._isAnimated()\n\n    if (isAnimated) {\n      this._isTransitioning = true\n    }\n\n    this._setEscapeEvent()\n    this._setResizeEvent()\n\n    this._focustrap.deactivate()\n\n    this._element.classList.remove(CLASS_NAME_SHOW)\n\n    EventHandler.off(this._element, EVENT_CLICK_DISMISS)\n    EventHandler.off(this._dialog, EVENT_MOUSEDOWN_DISMISS)\n\n    this._queueCallback(() => this._hideModal(), this._element, isAnimated)\n  }\n\n  dispose() {\n    [window, this._dialog]\n      .forEach(htmlElement => EventHandler.off(htmlElement, EVENT_KEY))\n\n    this._backdrop.dispose()\n    this._focustrap.deactivate()\n    super.dispose()\n  }\n\n  handleUpdate() {\n    this._adjustDialog()\n  }\n\n  // Private\n\n  _initializeBackDrop() {\n    return new Backdrop({\n      isVisible: Boolean(this._config.backdrop), // 'static' option will be translated to true, and booleans will keep their value\n      isAnimated: this._isAnimated()\n    })\n  }\n\n  _initializeFocusTrap() {\n    return new FocusTrap({\n      trapElement: this._element\n    })\n  }\n\n  _getConfig(config) {\n    config = {\n      ...Default,\n      ...Manipulator.getDataAttributes(this._element),\n      ...(typeof config === 'object' ? config : {})\n    }\n    typeCheckConfig(NAME, config, DefaultType)\n    return config\n  }\n\n  _showElement(relatedTarget) {\n    const isAnimated = this._isAnimated()\n    const modalBody = SelectorEngine.findOne(SELECTOR_MODAL_BODY, this._dialog)\n\n    if (!this._element.parentNode || this._element.parentNode.nodeType !== Node.ELEMENT_NODE) {\n      // Don't move modal's DOM position\n      document.body.append(this._element)\n    }\n\n    this._element.style.display = 'block'\n    this._element.removeAttribute('aria-hidden')\n    this._element.setAttribute('aria-modal', true)\n    this._element.setAttribute('role', 'dialog')\n    this._element.scrollTop = 0\n\n    if (modalBody) {\n      modalBody.scrollTop = 0\n    }\n\n    if (isAnimated) {\n      reflow(this._element)\n    }\n\n    this._element.classList.add(CLASS_NAME_SHOW)\n\n    const transitionComplete = () => {\n      if (this._config.focus) {\n        this._focustrap.activate()\n      }\n\n      this._isTransitioning = false\n      EventHandler.trigger(this._element, EVENT_SHOWN, {\n        relatedTarget\n      })\n    }\n\n    this._queueCallback(transitionComplete, this._dialog, isAnimated)\n  }\n\n  _setEscapeEvent() {\n    if (this._isShown) {\n      EventHandler.on(this._element, EVENT_KEYDOWN_DISMISS, event => {\n        if (this._config.keyboard && event.key === ESCAPE_KEY) {\n          event.preventDefault()\n          this.hide()\n        } else if (!this._config.keyboard && event.key === ESCAPE_KEY) {\n          this._triggerBackdropTransition()\n        }\n      })\n    } else {\n      EventHandler.off(this._element, EVENT_KEYDOWN_DISMISS)\n    }\n  }\n\n  _setResizeEvent() {\n    if (this._isShown) {\n      EventHandler.on(window, EVENT_RESIZE, () => this._adjustDialog())\n    } else {\n      EventHandler.off(window, EVENT_RESIZE)\n    }\n  }\n\n  _hideModal() {\n    this._element.style.display = 'none'\n    this._element.setAttribute('aria-hidden', true)\n    this._element.removeAttribute('aria-modal')\n    this._element.removeAttribute('role')\n    this._isTransitioning = false\n    this._backdrop.hide(() => {\n      document.body.classList.remove(CLASS_NAME_OPEN)\n      this._resetAdjustments()\n      this._scrollBar.reset()\n      EventHandler.trigger(this._element, EVENT_HIDDEN)\n    })\n  }\n\n  _showBackdrop(callback) {\n    EventHandler.on(this._element, EVENT_CLICK_DISMISS, event => {\n      if (this._ignoreBackdropClick) {\n        this._ignoreBackdropClick = false\n        return\n      }\n\n      if (event.target !== event.currentTarget) {\n        return\n      }\n\n      if (this._config.backdrop === true) {\n        this.hide()\n      } else if (this._config.backdrop === 'static') {\n        this._triggerBackdropTransition()\n      }\n    })\n\n    this._backdrop.show(callback)\n  }\n\n  _isAnimated() {\n    return this._element.classList.contains(CLASS_NAME_FADE)\n  }\n\n  _triggerBackdropTransition() {\n    const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED)\n    if (hideEvent.defaultPrevented) {\n      return\n    }\n\n    const { classList, scrollHeight, style } = this._element\n    const isModalOverflowing = scrollHeight > document.documentElement.clientHeight\n\n    // return if the following background transition hasn't yet completed\n    if ((!isModalOverflowing && style.overflowY === 'hidden') || classList.contains(CLASS_NAME_STATIC)) {\n      return\n    }\n\n    if (!isModalOverflowing) {\n      style.overflowY = 'hidden'\n    }\n\n    classList.add(CLASS_NAME_STATIC)\n    this._queueCallback(() => {\n      classList.remove(CLASS_NAME_STATIC)\n      if (!isModalOverflowing) {\n        this._queueCallback(() => {\n          style.overflowY = ''\n        }, this._dialog)\n      }\n    }, this._dialog)\n\n    this._element.focus()\n  }\n\n  // ----------------------------------------------------------------------\n  // the following methods are used to handle overflowing modals\n  // ----------------------------------------------------------------------\n\n  _adjustDialog() {\n    const isModalOverflowing = this._element.scrollHeight > document.documentElement.clientHeight\n    const scrollbarWidth = this._scrollBar.getWidth()\n    const isBodyOverflowing = scrollbarWidth > 0\n\n    if ((!isBodyOverflowing && isModalOverflowing && !isRTL()) || (isBodyOverflowing && !isModalOverflowing && isRTL())) {\n      this._element.style.paddingLeft = `${scrollbarWidth}px`\n    }\n\n    if ((isBodyOverflowing && !isModalOverflowing && !isRTL()) || (!isBodyOverflowing && isModalOverflowing && isRTL())) {\n      this._element.style.paddingRight = `${scrollbarWidth}px`\n    }\n  }\n\n  _resetAdjustments() {\n    this._element.style.paddingLeft = ''\n    this._element.style.paddingRight = ''\n  }\n\n  // Static\n\n  static jQueryInterface(config, relatedTarget) {\n    return this.each(function () {\n      const data = Modal.getOrCreateInstance(this, config)\n\n      if (typeof config !== 'string') {\n        return\n      }\n\n      if (typeof data[config] === 'undefined') {\n        throw new TypeError(`No method named \"${config}\"`)\n      }\n\n      data[config](relatedTarget)\n    })\n  }\n}\n\n/**\n * ------------------------------------------------------------------------\n * Data Api implementation\n * ------------------------------------------------------------------------\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_TOGGLE, function (event) {\n  const target = getElementFromSelector(this)\n\n  if (['A', 'AREA'].includes(this.tagName)) {\n    event.preventDefault()\n  }\n\n  EventHandler.one(target, EVENT_SHOW, showEvent => {\n    if (showEvent.defaultPrevented) {\n      // only register focus restorer if modal will actually get shown\n      return\n    }\n\n    EventHandler.one(target, EVENT_HIDDEN, () => {\n      if (isVisible(this)) {\n        this.focus()\n      }\n    })\n  })\n\n  const data = Modal.getOrCreateInstance(target)\n\n  data.toggle(this)\n})\n\nenableDismissTrigger(Modal)\n\n/**\n * ------------------------------------------------------------------------\n * jQuery\n * ------------------------------------------------------------------------\n * add .Modal to jQuery only if jQuery is present\n */\n\ndefineJQueryPlugin(Modal)\n\nexport default Modal\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.1.0): offcanvas.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport {\n  defineJQueryPlugin,\n  getElementFromSelector,\n  isDisabled,\n  isVisible,\n  typeCheckConfig\n} from './util/index'\nimport ScrollBarHelper from './util/scrollbar'\nimport EventHandler from './dom/event-handler'\nimport BaseComponent from './base-component'\nimport SelectorEngine from './dom/selector-engine'\nimport Manipulator from './dom/manipulator'\nimport Backdrop from './util/backdrop'\nimport FocusTrap from './util/focustrap'\nimport { enableDismissTrigger } from './util/component-functions'\n\n/**\n * ------------------------------------------------------------------------\n * Constants\n * ------------------------------------------------------------------------\n */\n\nconst NAME = 'offcanvas'\nconst DATA_KEY = 'bs.offcanvas'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\nconst EVENT_LOAD_DATA_API = `load${EVENT_KEY}${DATA_API_KEY}`\nconst ESCAPE_KEY = 'Escape'\n\nconst Default = {\n  backdrop: true,\n  keyboard: true,\n  scroll: false\n}\n\nconst DefaultType = {\n  backdrop: 'boolean',\n  keyboard: 'boolean',\n  scroll: 'boolean'\n}\n\nconst CLASS_NAME_SHOW = 'show'\nconst CLASS_NAME_BACKDROP = 'offcanvas-backdrop'\nconst OPEN_SELECTOR = '.offcanvas.show'\n\nconst EVENT_SHOW = `show${EVENT_KEY}`\nconst EVENT_SHOWN = `shown${EVENT_KEY}`\nconst EVENT_HIDE = `hide${EVENT_KEY}`\nconst EVENT_HIDDEN = `hidden${EVENT_KEY}`\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY}${DATA_API_KEY}`\nconst EVENT_KEYDOWN_DISMISS = `keydown.dismiss${EVENT_KEY}`\n\nconst SELECTOR_DATA_TOGGLE = '[data-bs-toggle=\"offcanvas\"]'\n\n/**\n * ------------------------------------------------------------------------\n * Class Definition\n * ------------------------------------------------------------------------\n */\n\nclass Offcanvas extends BaseComponent {\n  constructor(element, config) {\n    super(element)\n\n    this._config = this._getConfig(config)\n    this._isShown = false\n    this._backdrop = this._initializeBackDrop()\n    this._focustrap = this._initializeFocusTrap()\n    this._addEventListeners()\n  }\n\n  // Getters\n\n  static get NAME() {\n    return NAME\n  }\n\n  static get Default() {\n    return Default\n  }\n\n  // Public\n\n  toggle(relatedTarget) {\n    return this._isShown ? this.hide() : this.show(relatedTarget)\n  }\n\n  show(relatedTarget) {\n    if (this._isShown) {\n      return\n    }\n\n    const showEvent = EventHandler.trigger(this._element, EVENT_SHOW, { relatedTarget })\n\n    if (showEvent.defaultPrevented) {\n      return\n    }\n\n    this._isShown = true\n    this._element.style.visibility = 'visible'\n\n    this._backdrop.show()\n\n    if (!this._config.scroll) {\n      new ScrollBarHelper().hide()\n    }\n\n    this._element.removeAttribute('aria-hidden')\n    this._element.setAttribute('aria-modal', true)\n    this._element.setAttribute('role', 'dialog')\n    this._element.classList.add(CLASS_NAME_SHOW)\n\n    const completeCallBack = () => {\n      if (!this._config.scroll) {\n        this._focustrap.activate()\n      }\n\n      EventHandler.trigger(this._element, EVENT_SHOWN, { relatedTarget })\n    }\n\n    this._queueCallback(completeCallBack, this._element, true)\n  }\n\n  hide() {\n    if (!this._isShown) {\n      return\n    }\n\n    const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE)\n\n    if (hideEvent.defaultPrevented) {\n      return\n    }\n\n    this._focustrap.deactivate()\n    this._element.blur()\n    this._isShown = false\n    this._element.classList.remove(CLASS_NAME_SHOW)\n    this._backdrop.hide()\n\n    const completeCallback = () => {\n      this._element.setAttribute('aria-hidden', true)\n      this._element.removeAttribute('aria-modal')\n      this._element.removeAttribute('role')\n      this._element.style.visibility = 'hidden'\n\n      if (!this._config.scroll) {\n        new ScrollBarHelper().reset()\n      }\n\n      EventHandler.trigger(this._element, EVENT_HIDDEN)\n    }\n\n    this._queueCallback(completeCallback, this._element, true)\n  }\n\n  dispose() {\n    this._backdrop.dispose()\n    this._focustrap.deactivate()\n    super.dispose()\n  }\n\n  // Private\n\n  _getConfig(config) {\n    config = {\n      ...Default,\n      ...Manipulator.getDataAttributes(this._element),\n      ...(typeof config === 'object' ? config : {})\n    }\n    typeCheckConfig(NAME, config, DefaultType)\n    return config\n  }\n\n  _initializeBackDrop() {\n    return new Backdrop({\n      className: CLASS_NAME_BACKDROP,\n      isVisible: this._config.backdrop,\n      isAnimated: true,\n      rootElement: this._element.parentNode,\n      clickCallback: () => this.hide()\n    })\n  }\n\n  _initializeFocusTrap() {\n    return new FocusTrap({\n      trapElement: this._element\n    })\n  }\n\n  _addEventListeners() {\n    EventHandler.on(this._element, EVENT_KEYDOWN_DISMISS, event => {\n      if (this._config.keyboard && event.key === ESCAPE_KEY) {\n        this.hide()\n      }\n    })\n  }\n\n  // Static\n\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Offcanvas.getOrCreateInstance(this, config)\n\n      if (typeof config !== 'string') {\n        return\n      }\n\n      if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n        throw new TypeError(`No method named \"${config}\"`)\n      }\n\n      data[config](this)\n    })\n  }\n}\n\n/**\n * ------------------------------------------------------------------------\n * Data Api implementation\n * ------------------------------------------------------------------------\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_TOGGLE, function (event) {\n  const target = getElementFromSelector(this)\n\n  if (['A', 'AREA'].includes(this.tagName)) {\n    event.preventDefault()\n  }\n\n  if (isDisabled(this)) {\n    return\n  }\n\n  EventHandler.one(target, EVENT_HIDDEN, () => {\n    // focus on trigger when it is closed\n    if (isVisible(this)) {\n      this.focus()\n    }\n  })\n\n  // avoid conflict when clicking a toggler of an offcanvas, while another is open\n  const allReadyOpen = SelectorEngine.findOne(OPEN_SELECTOR)\n  if (allReadyOpen && allReadyOpen !== target) {\n    Offcanvas.getInstance(allReadyOpen).hide()\n  }\n\n  const data = Offcanvas.getOrCreateInstance(target)\n  data.toggle(this)\n})\n\nEventHandler.on(window, EVENT_LOAD_DATA_API, () =>\n  SelectorEngine.find(OPEN_SELECTOR).forEach(el => Offcanvas.getOrCreateInstance(el).show())\n)\n\nenableDismissTrigger(Offcanvas)\n/**\n * ------------------------------------------------------------------------\n * jQuery\n * ------------------------------------------------------------------------\n */\n\ndefineJQueryPlugin(Offcanvas)\n\nexport default Offcanvas\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.1.0): util/sanitizer.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nconst uriAttrs = new Set([\n  'background',\n  'cite',\n  'href',\n  'itemtype',\n  'longdesc',\n  'poster',\n  'src',\n  'xlink:href'\n])\n\nconst ARIA_ATTRIBUTE_PATTERN = /^aria-[\\w-]*$/i\n\n/**\n * A pattern that recognizes a commonly useful subset of URLs that are safe.\n *\n * Shoutout to Angular 7 https://github.com/angular/angular/blob/7.2.4/packages/core/src/sanitization/url_sanitizer.ts\n */\nconst SAFE_URL_PATTERN = /^(?:(?:https?|mailto|ftp|tel|file):|[^#&/:?]*(?:[#/?]|$))/i\n\n/**\n * A pattern that matches safe data URLs. Only matches image, video and audio types.\n *\n * Shoutout to Angular 7 https://github.com/angular/angular/blob/7.2.4/packages/core/src/sanitization/url_sanitizer.ts\n */\nconst DATA_URL_PATTERN = /^data:(?:image\\/(?:bmp|gif|jpeg|jpg|png|tiff|webp)|video\\/(?:mpeg|mp4|ogg|webm)|audio\\/(?:mp3|oga|ogg|opus));base64,[\\d+/a-z]+=*$/i\n\nconst allowedAttribute = (attr, allowedAttributeList) => {\n  const attrName = attr.nodeName.toLowerCase()\n\n  if (allowedAttributeList.includes(attrName)) {\n    if (uriAttrs.has(attrName)) {\n      return Boolean(SAFE_URL_PATTERN.test(attr.nodeValue) || DATA_URL_PATTERN.test(attr.nodeValue))\n    }\n\n    return true\n  }\n\n  const regExp = allowedAttributeList.filter(attrRegex => attrRegex instanceof RegExp)\n\n  // Check if a regular expression validates the attribute.\n  for (let i = 0, len = regExp.length; i < len; i++) {\n    if (regExp[i].test(attrName)) {\n      return true\n    }\n  }\n\n  return false\n}\n\nexport const DefaultAllowlist = {\n  // Global attributes allowed on any supplied element below.\n  '*': ['class', 'dir', 'id', 'lang', 'role', ARIA_ATTRIBUTE_PATTERN],\n  a: ['target', 'href', 'title', 'rel'],\n  area: [],\n  b: [],\n  br: [],\n  col: [],\n  code: [],\n  div: [],\n  em: [],\n  hr: [],\n  h1: [],\n  h2: [],\n  h3: [],\n  h4: [],\n  h5: [],\n  h6: [],\n  i: [],\n  img: ['src', 'srcset', 'alt', 'title', 'width', 'height'],\n  li: [],\n  ol: [],\n  p: [],\n  pre: [],\n  s: [],\n  small: [],\n  span: [],\n  sub: [],\n  sup: [],\n  strong: [],\n  u: [],\n  ul: []\n}\n\nexport function sanitizeHtml(unsafeHtml, allowList, sanitizeFn) {\n  if (!unsafeHtml.length) {\n    return unsafeHtml\n  }\n\n  if (sanitizeFn && typeof sanitizeFn === 'function') {\n    return sanitizeFn(unsafeHtml)\n  }\n\n  const domParser = new window.DOMParser()\n  const createdDocument = domParser.parseFromString(unsafeHtml, 'text/html')\n  const allowlistKeys = Object.keys(allowList)\n  const elements = [].concat(...createdDocument.body.querySelectorAll('*'))\n\n  for (let i = 0, len = elements.length; i < len; i++) {\n    const el = elements[i]\n    const elName = el.nodeName.toLowerCase()\n\n    if (!allowlistKeys.includes(elName)) {\n      el.remove()\n\n      continue\n    }\n\n    const attributeList = [].concat(...el.attributes)\n    const allowedAttributes = [].concat(allowList['*'] || [], allowList[elName] || [])\n\n    attributeList.forEach(attr => {\n      if (!allowedAttribute(attr, allowedAttributes)) {\n        el.removeAttribute(attr.nodeName)\n      }\n    })\n  }\n\n  return createdDocument.body.innerHTML\n}\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.1.0): tooltip.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport * as Popper from '@popperjs/core'\n\nimport {\n  defineJQueryPlugin,\n  findShadowRoot,\n  getElement,\n  getUID,\n  isElement,\n  isRTL,\n  noop,\n  typeCheckConfig\n} from './util/index'\nimport { DefaultAllowlist, sanitizeHtml } from './util/sanitizer'\nimport Data from './dom/data'\nimport EventHandler from './dom/event-handler'\nimport Manipulator from './dom/manipulator'\nimport SelectorEngine from './dom/selector-engine'\nimport BaseComponent from './base-component'\n\n/**\n * ------------------------------------------------------------------------\n * Constants\n * ------------------------------------------------------------------------\n */\n\nconst NAME = 'tooltip'\nconst DATA_KEY = 'bs.tooltip'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst CLASS_PREFIX = 'bs-tooltip'\nconst DISALLOWED_ATTRIBUTES = new Set(['sanitize', 'allowList', 'sanitizeFn'])\n\nconst DefaultType = {\n  animation: 'boolean',\n  template: 'string',\n  title: '(string|element|function)',\n  trigger: 'string',\n  delay: '(number|object)',\n  html: 'boolean',\n  selector: '(string|boolean)',\n  placement: '(string|function)',\n  offset: '(array|string|function)',\n  container: '(string|element|boolean)',\n  fallbackPlacements: 'array',\n  boundary: '(string|element)',\n  customClass: '(string|function)',\n  sanitize: 'boolean',\n  sanitizeFn: '(null|function)',\n  allowList: 'object',\n  popperConfig: '(null|object|function)'\n}\n\nconst AttachmentMap = {\n  AUTO: 'auto',\n  TOP: 'top',\n  RIGHT: isRTL() ? 'left' : 'right',\n  BOTTOM: 'bottom',\n  LEFT: isRTL() ? 'right' : 'left'\n}\n\nconst Default = {\n  animation: true,\n  template: '<div class=\"tooltip\" role=\"tooltip\">' +\n              '<div class=\"tooltip-arrow\"></div>' +\n              '<div class=\"tooltip-inner\"></div>' +\n            '</div>',\n  trigger: 'hover focus',\n  title: '',\n  delay: 0,\n  html: false,\n  selector: false,\n  placement: 'top',\n  offset: [0, 0],\n  container: false,\n  fallbackPlacements: ['top', 'right', 'bottom', 'left'],\n  boundary: 'clippingParents',\n  customClass: '',\n  sanitize: true,\n  sanitizeFn: null,\n  allowList: DefaultAllowlist,\n  popperConfig: null\n}\n\nconst Event = {\n  HIDE: `hide${EVENT_KEY}`,\n  HIDDEN: `hidden${EVENT_KEY}`,\n  SHOW: `show${EVENT_KEY}`,\n  SHOWN: `shown${EVENT_KEY}`,\n  INSERTED: `inserted${EVENT_KEY}`,\n  CLICK: `click${EVENT_KEY}`,\n  FOCUSIN: `focusin${EVENT_KEY}`,\n  FOCUSOUT: `focusout${EVENT_KEY}`,\n  MOUSEENTER: `mouseenter${EVENT_KEY}`,\n  MOUSELEAVE: `mouseleave${EVENT_KEY}`\n}\n\nconst CLASS_NAME_FADE = 'fade'\nconst CLASS_NAME_MODAL = 'modal'\nconst CLASS_NAME_SHOW = 'show'\n\nconst HOVER_STATE_SHOW = 'show'\nconst HOVER_STATE_OUT = 'out'\n\nconst SELECTOR_TOOLTIP_INNER = '.tooltip-inner'\nconst SELECTOR_MODAL = `.${CLASS_NAME_MODAL}`\n\nconst EVENT_MODAL_HIDE = 'hide.bs.modal'\n\nconst TRIGGER_HOVER = 'hover'\nconst TRIGGER_FOCUS = 'focus'\nconst TRIGGER_CLICK = 'click'\nconst TRIGGER_MANUAL = 'manual'\n\n/**\n * ------------------------------------------------------------------------\n * Class Definition\n * ------------------------------------------------------------------------\n */\n\nclass Tooltip extends BaseComponent {\n  constructor(element, config) {\n    if (typeof Popper === 'undefined') {\n      throw new TypeError('Bootstrap\\'s tooltips require Popper (https://popper.js.org)')\n    }\n\n    super(element)\n\n    // private\n    this._isEnabled = true\n    this._timeout = 0\n    this._hoverState = ''\n    this._activeTrigger = {}\n    this._popper = null\n\n    // Protected\n    this._config = this._getConfig(config)\n    this.tip = null\n\n    this._setListeners()\n  }\n\n  // Getters\n\n  static get Default() {\n    return Default\n  }\n\n  static get NAME() {\n    return NAME\n  }\n\n  static get Event() {\n    return Event\n  }\n\n  static get DefaultType() {\n    return DefaultType\n  }\n\n  // Public\n\n  enable() {\n    this._isEnabled = true\n  }\n\n  disable() {\n    this._isEnabled = false\n  }\n\n  toggleEnabled() {\n    this._isEnabled = !this._isEnabled\n  }\n\n  toggle(event) {\n    if (!this._isEnabled) {\n      return\n    }\n\n    if (event) {\n      const context = this._initializeOnDelegatedTarget(event)\n\n      context._activeTrigger.click = !context._activeTrigger.click\n\n      if (context._isWithActiveTrigger()) {\n        context._enter(null, context)\n      } else {\n        context._leave(null, context)\n      }\n    } else {\n      if (this.getTipElement().classList.contains(CLASS_NAME_SHOW)) {\n        this._leave(null, this)\n        return\n      }\n\n      this._enter(null, this)\n    }\n  }\n\n  dispose() {\n    clearTimeout(this._timeout)\n\n    EventHandler.off(this._element.closest(SELECTOR_MODAL), EVENT_MODAL_HIDE, this._hideModalHandler)\n\n    if (this.tip) {\n      this.tip.remove()\n    }\n\n    if (this._popper) {\n      this._popper.destroy()\n    }\n\n    super.dispose()\n  }\n\n  show() {\n    if (this._element.style.display === 'none') {\n      throw new Error('Please use show on visible elements')\n    }\n\n    if (!(this.isWithContent() && this._isEnabled)) {\n      return\n    }\n\n    const showEvent = EventHandler.trigger(this._element, this.constructor.Event.SHOW)\n    const shadowRoot = findShadowRoot(this._element)\n    const isInTheDom = shadowRoot === null ?\n      this._element.ownerDocument.documentElement.contains(this._element) :\n      shadowRoot.contains(this._element)\n\n    if (showEvent.defaultPrevented || !isInTheDom) {\n      return\n    }\n\n    const tip = this.getTipElement()\n    const tipId = getUID(this.constructor.NAME)\n\n    tip.setAttribute('id', tipId)\n    this._element.setAttribute('aria-describedby', tipId)\n\n    if (this._config.animation) {\n      tip.classList.add(CLASS_NAME_FADE)\n    }\n\n    const placement = typeof this._config.placement === 'function' ?\n      this._config.placement.call(this, tip, this._element) :\n      this._config.placement\n\n    const attachment = this._getAttachment(placement)\n    this._addAttachmentClass(attachment)\n\n    const { container } = this._config\n    Data.set(tip, this.constructor.DATA_KEY, this)\n\n    if (!this._element.ownerDocument.documentElement.contains(this.tip)) {\n      container.append(tip)\n      EventHandler.trigger(this._element, this.constructor.Event.INSERTED)\n    }\n\n    if (this._popper) {\n      this._popper.update()\n    } else {\n      this._popper = Popper.createPopper(this._element, tip, this._getPopperConfig(attachment))\n    }\n\n    tip.classList.add(CLASS_NAME_SHOW)\n\n    const customClass = this._resolvePossibleFunction(this._config.customClass)\n    if (customClass) {\n      tip.classList.add(...customClass.split(' '))\n    }\n\n    // If this is a touch-enabled device we add extra\n    // empty mouseover listeners to the body's immediate children;\n    // only needed because of broken event delegation on iOS\n    // https://www.quirksmode.org/blog/archives/2014/02/mouse_event_bub.html\n    if ('ontouchstart' in document.documentElement) {\n      [].concat(...document.body.children).forEach(element => {\n        EventHandler.on(element, 'mouseover', noop)\n      })\n    }\n\n    const complete = () => {\n      const prevHoverState = this._hoverState\n\n      this._hoverState = null\n      EventHandler.trigger(this._element, this.constructor.Event.SHOWN)\n\n      if (prevHoverState === HOVER_STATE_OUT) {\n        this._leave(null, this)\n      }\n    }\n\n    const isAnimated = this.tip.classList.contains(CLASS_NAME_FADE)\n    this._queueCallback(complete, this.tip, isAnimated)\n  }\n\n  hide() {\n    if (!this._popper) {\n      return\n    }\n\n    const tip = this.getTipElement()\n    const complete = () => {\n      if (this._isWithActiveTrigger()) {\n        return\n      }\n\n      if (this._hoverState !== HOVER_STATE_SHOW) {\n        tip.remove()\n      }\n\n      this._cleanTipClass()\n      this._element.removeAttribute('aria-describedby')\n      EventHandler.trigger(this._element, this.constructor.Event.HIDDEN)\n\n      if (this._popper) {\n        this._popper.destroy()\n        this._popper = null\n      }\n    }\n\n    const hideEvent = EventHandler.trigger(this._element, this.constructor.Event.HIDE)\n    if (hideEvent.defaultPrevented) {\n      return\n    }\n\n    tip.classList.remove(CLASS_NAME_SHOW)\n\n    // If this is a touch-enabled device we remove the extra\n    // empty mouseover listeners we added for iOS support\n    if ('ontouchstart' in document.documentElement) {\n      [].concat(...document.body.children)\n        .forEach(element => EventHandler.off(element, 'mouseover', noop))\n    }\n\n    this._activeTrigger[TRIGGER_CLICK] = false\n    this._activeTrigger[TRIGGER_FOCUS] = false\n    this._activeTrigger[TRIGGER_HOVER] = false\n\n    const isAnimated = this.tip.classList.contains(CLASS_NAME_FADE)\n    this._queueCallback(complete, this.tip, isAnimated)\n    this._hoverState = ''\n  }\n\n  update() {\n    if (this._popper !== null) {\n      this._popper.update()\n    }\n  }\n\n  // Protected\n\n  isWithContent() {\n    return Boolean(this.getTitle())\n  }\n\n  getTipElement() {\n    if (this.tip) {\n      return this.tip\n    }\n\n    const element = document.createElement('div')\n    element.innerHTML = this._config.template\n\n    const tip = element.children[0]\n    this.setContent(tip)\n    tip.classList.remove(CLASS_NAME_FADE, CLASS_NAME_SHOW)\n\n    this.tip = tip\n    return this.tip\n  }\n\n  setContent(tip) {\n    this._sanitizeAndSetContent(tip, this.getTitle(), SELECTOR_TOOLTIP_INNER)\n  }\n\n  _sanitizeAndSetContent(template, content, selector) {\n    const templateElement = SelectorEngine.findOne(selector, template)\n\n    if (!content && templateElement) {\n      templateElement.remove()\n      return\n    }\n\n    // we use append for html objects to maintain js events\n    this.setElementContent(templateElement, content)\n  }\n\n  setElementContent(element, content) {\n    if (element === null) {\n      return\n    }\n\n    if (isElement(content)) {\n      content = getElement(content)\n\n      // content is a DOM node or a jQuery\n      if (this._config.html) {\n        if (content.parentNode !== element) {\n          element.innerHTML = ''\n          element.append(content)\n        }\n      } else {\n        element.textContent = content.textContent\n      }\n\n      return\n    }\n\n    if (this._config.html) {\n      if (this._config.sanitize) {\n        content = sanitizeHtml(content, this._config.allowList, this._config.sanitizeFn)\n      }\n\n      element.innerHTML = content\n    } else {\n      element.textContent = content\n    }\n  }\n\n  getTitle() {\n    const title = this._element.getAttribute('data-bs-original-title') || this._config.title\n\n    return this._resolvePossibleFunction(title)\n  }\n\n  updateAttachment(attachment) {\n    if (attachment === 'right') {\n      return 'end'\n    }\n\n    if (attachment === 'left') {\n      return 'start'\n    }\n\n    return attachment\n  }\n\n  // Private\n\n  _initializeOnDelegatedTarget(event, context) {\n    return context || this.constructor.getOrCreateInstance(event.delegateTarget, this._getDelegateConfig())\n  }\n\n  _getOffset() {\n    const { offset } = this._config\n\n    if (typeof offset === 'string') {\n      return offset.split(',').map(val => Number.parseInt(val, 10))\n    }\n\n    if (typeof offset === 'function') {\n      return popperData => offset(popperData, this._element)\n    }\n\n    return offset\n  }\n\n  _resolvePossibleFunction(content) {\n    return typeof content === 'function' ? content.call(this._element) : content\n  }\n\n  _getPopperConfig(attachment) {\n    const defaultBsPopperConfig = {\n      placement: attachment,\n      modifiers: [\n        {\n          name: 'flip',\n          options: {\n            fallbackPlacements: this._config.fallbackPlacements\n          }\n        },\n        {\n          name: 'offset',\n          options: {\n            offset: this._getOffset()\n          }\n        },\n        {\n          name: 'preventOverflow',\n          options: {\n            boundary: this._config.boundary\n          }\n        },\n        {\n          name: 'arrow',\n          options: {\n            element: `.${this.constructor.NAME}-arrow`\n          }\n        },\n        {\n          name: 'onChange',\n          enabled: true,\n          phase: 'afterWrite',\n          fn: data => this._handlePopperPlacementChange(data)\n        }\n      ],\n      onFirstUpdate: data => {\n        if (data.options.placement !== data.placement) {\n          this._handlePopperPlacementChange(data)\n        }\n      }\n    }\n\n    return {\n      ...defaultBsPopperConfig,\n      ...(typeof this._config.popperConfig === 'function' ? this._config.popperConfig(defaultBsPopperConfig) : this._config.popperConfig)\n    }\n  }\n\n  _addAttachmentClass(attachment) {\n    this.getTipElement().classList.add(`${this._getBasicClassPrefix()}-${this.updateAttachment(attachment)}`)\n  }\n\n  _getAttachment(placement) {\n    return AttachmentMap[placement.toUpperCase()]\n  }\n\n  _setListeners() {\n    const triggers = this._config.trigger.split(' ')\n\n    triggers.forEach(trigger => {\n      if (trigger === 'click') {\n        EventHandler.on(this._element, this.constructor.Event.CLICK, this._config.selector, event => this.toggle(event))\n      } else if (trigger !== TRIGGER_MANUAL) {\n        const eventIn = trigger === TRIGGER_HOVER ?\n          this.constructor.Event.MOUSEENTER :\n          this.constructor.Event.FOCUSIN\n        const eventOut = trigger === TRIGGER_HOVER ?\n          this.constructor.Event.MOUSELEAVE :\n          this.constructor.Event.FOCUSOUT\n\n        EventHandler.on(this._element, eventIn, this._config.selector, event => this._enter(event))\n        EventHandler.on(this._element, eventOut, this._config.selector, event => this._leave(event))\n      }\n    })\n\n    this._hideModalHandler = () => {\n      if (this._element) {\n        this.hide()\n      }\n    }\n\n    EventHandler.on(this._element.closest(SELECTOR_MODAL), EVENT_MODAL_HIDE, this._hideModalHandler)\n\n    if (this._config.selector) {\n      this._config = {\n        ...this._config,\n        trigger: 'manual',\n        selector: ''\n      }\n    } else {\n      this._fixTitle()\n    }\n  }\n\n  _fixTitle() {\n    const title = this._element.getAttribute('title')\n    const originalTitleType = typeof this._element.getAttribute('data-bs-original-title')\n\n    if (title || originalTitleType !== 'string') {\n      this._element.setAttribute('data-bs-original-title', title || '')\n      if (title && !this._element.getAttribute('aria-label') && !this._element.textContent) {\n        this._element.setAttribute('aria-label', title)\n      }\n\n      this._element.setAttribute('title', '')\n    }\n  }\n\n  _enter(event, context) {\n    context = this._initializeOnDelegatedTarget(event, context)\n\n    if (event) {\n      context._activeTrigger[\n        event.type === 'focusin' ? TRIGGER_FOCUS : TRIGGER_HOVER\n      ] = true\n    }\n\n    if (context.getTipElement().classList.contains(CLASS_NAME_SHOW) || context._hoverState === HOVER_STATE_SHOW) {\n      context._hoverState = HOVER_STATE_SHOW\n      return\n    }\n\n    clearTimeout(context._timeout)\n\n    context._hoverState = HOVER_STATE_SHOW\n\n    if (!context._config.delay || !context._config.delay.show) {\n      context.show()\n      return\n    }\n\n    context._timeout = setTimeout(() => {\n      if (context._hoverState === HOVER_STATE_SHOW) {\n        context.show()\n      }\n    }, context._config.delay.show)\n  }\n\n  _leave(event, context) {\n    context = this._initializeOnDelegatedTarget(event, context)\n\n    if (event) {\n      context._activeTrigger[\n        event.type === 'focusout' ? TRIGGER_FOCUS : TRIGGER_HOVER\n      ] = context._element.contains(event.relatedTarget)\n    }\n\n    if (context._isWithActiveTrigger()) {\n      return\n    }\n\n    clearTimeout(context._timeout)\n\n    context._hoverState = HOVER_STATE_OUT\n\n    if (!context._config.delay || !context._config.delay.hide) {\n      context.hide()\n      return\n    }\n\n    context._timeout = setTimeout(() => {\n      if (context._hoverState === HOVER_STATE_OUT) {\n        context.hide()\n      }\n    }, context._config.delay.hide)\n  }\n\n  _isWithActiveTrigger() {\n    for (const trigger in this._activeTrigger) {\n      if (this._activeTrigger[trigger]) {\n        return true\n      }\n    }\n\n    return false\n  }\n\n  _getConfig(config) {\n    const dataAttributes = Manipulator.getDataAttributes(this._element)\n\n    Object.keys(dataAttributes).forEach(dataAttr => {\n      if (DISALLOWED_ATTRIBUTES.has(dataAttr)) {\n        delete dataAttributes[dataAttr]\n      }\n    })\n\n    config = {\n      ...this.constructor.Default,\n      ...dataAttributes,\n      ...(typeof config === 'object' && config ? config : {})\n    }\n\n    config.container = config.container === false ? document.body : getElement(config.container)\n\n    if (typeof config.delay === 'number') {\n      config.delay = {\n        show: config.delay,\n        hide: config.delay\n      }\n    }\n\n    if (typeof config.title === 'number') {\n      config.title = config.title.toString()\n    }\n\n    if (typeof config.content === 'number') {\n      config.content = config.content.toString()\n    }\n\n    typeCheckConfig(NAME, config, this.constructor.DefaultType)\n\n    if (config.sanitize) {\n      config.template = sanitizeHtml(config.template, config.allowList, config.sanitizeFn)\n    }\n\n    return config\n  }\n\n  _getDelegateConfig() {\n    const config = {}\n\n    for (const key in this._config) {\n      if (this.constructor.Default[key] !== this._config[key]) {\n        config[key] = this._config[key]\n      }\n    }\n\n    // In the future can be replaced with:\n    // const keysWithDifferentValues = Object.entries(this._config).filter(entry => this.constructor.Default[entry[0]] !== this._config[entry[0]])\n    // `Object.fromEntries(keysWithDifferentValues)`\n    return config\n  }\n\n  _cleanTipClass() {\n    const tip = this.getTipElement()\n    const basicClassPrefixRegex = new RegExp(`(^|\\\\s)${this._getBasicClassPrefix()}\\\\S+`, 'g')\n    const tabClass = tip.getAttribute('class').match(basicClassPrefixRegex)\n    if (tabClass !== null && tabClass.length > 0) {\n      tabClass.map(token => token.trim())\n        .forEach(tClass => tip.classList.remove(tClass))\n    }\n  }\n\n  _getBasicClassPrefix() {\n    return CLASS_PREFIX\n  }\n\n  _handlePopperPlacementChange(popperData) {\n    const { state } = popperData\n\n    if (!state) {\n      return\n    }\n\n    this.tip = state.elements.popper\n    this._cleanTipClass()\n    this._addAttachmentClass(this._getAttachment(state.placement))\n  }\n\n  // Static\n\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Tooltip.getOrCreateInstance(this, config)\n\n      if (typeof config === 'string') {\n        if (typeof data[config] === 'undefined') {\n          throw new TypeError(`No method named \"${config}\"`)\n        }\n\n        data[config]()\n      }\n    })\n  }\n}\n\n/**\n * ------------------------------------------------------------------------\n * jQuery\n * ------------------------------------------------------------------------\n * add .Tooltip to jQuery only if jQuery is present\n */\n\ndefineJQueryPlugin(Tooltip)\n\nexport default Tooltip\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.1.0): popover.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport { defineJQueryPlugin } from './util/index'\nimport Tooltip from './tooltip'\n\n/**\n * ------------------------------------------------------------------------\n * Constants\n * ------------------------------------------------------------------------\n */\n\nconst NAME = 'popover'\nconst DATA_KEY = 'bs.popover'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst CLASS_PREFIX = 'bs-popover'\n\nconst Default = {\n  ...Tooltip.Default,\n  placement: 'right',\n  offset: [0, 8],\n  trigger: 'click',\n  content: '',\n  template: '<div class=\"popover\" role=\"tooltip\">' +\n              '<div class=\"popover-arrow\"></div>' +\n              '<h3 class=\"popover-header\"></h3>' +\n              '<div class=\"popover-body\"></div>' +\n            '</div>'\n}\n\nconst DefaultType = {\n  ...Tooltip.DefaultType,\n  content: '(string|element|function)'\n}\n\nconst Event = {\n  HIDE: `hide${EVENT_KEY}`,\n  HIDDEN: `hidden${EVENT_KEY}`,\n  SHOW: `show${EVENT_KEY}`,\n  SHOWN: `shown${EVENT_KEY}`,\n  INSERTED: `inserted${EVENT_KEY}`,\n  CLICK: `click${EVENT_KEY}`,\n  FOCUSIN: `focusin${EVENT_KEY}`,\n  FOCUSOUT: `focusout${EVENT_KEY}`,\n  MOUSEENTER: `mouseenter${EVENT_KEY}`,\n  MOUSELEAVE: `mouseleave${EVENT_KEY}`\n}\n\nconst SELECTOR_TITLE = '.popover-header'\nconst SELECTOR_CONTENT = '.popover-body'\n\n/**\n * ------------------------------------------------------------------------\n * Class Definition\n * ------------------------------------------------------------------------\n */\n\nclass Popover extends Tooltip {\n  // Getters\n\n  static get Default() {\n    return Default\n  }\n\n  static get NAME() {\n    return NAME\n  }\n\n  static get Event() {\n    return Event\n  }\n\n  static get DefaultType() {\n    return DefaultType\n  }\n\n  // Overrides\n\n  isWithContent() {\n    return this.getTitle() || this._getContent()\n  }\n\n  setContent(tip) {\n    this._sanitizeAndSetContent(tip, this.getTitle(), SELECTOR_TITLE)\n    this._sanitizeAndSetContent(tip, this._getContent(), SELECTOR_CONTENT)\n  }\n\n  // Private\n\n  _getContent() {\n    return this._resolvePossibleFunction(this._config.content)\n  }\n\n  _getBasicClassPrefix() {\n    return CLASS_PREFIX\n  }\n\n  // Static\n\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Popover.getOrCreateInstance(this, config)\n\n      if (typeof config === 'string') {\n        if (typeof data[config] === 'undefined') {\n          throw new TypeError(`No method named \"${config}\"`)\n        }\n\n        data[config]()\n      }\n    })\n  }\n}\n\n/**\n * ------------------------------------------------------------------------\n * jQuery\n * ------------------------------------------------------------------------\n * add .Popover to jQuery only if jQuery is present\n */\n\ndefineJQueryPlugin(Popover)\n\nexport default Popover\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.1.0): scrollspy.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport {\n  defineJQueryPlugin,\n  getElement,\n  getSelectorFromElement,\n  typeCheckConfig\n} from './util/index'\nimport EventHandler from './dom/event-handler'\nimport Manipulator from './dom/manipulator'\nimport SelectorEngine from './dom/selector-engine'\nimport BaseComponent from './base-component'\n\n/**\n * ------------------------------------------------------------------------\n * Constants\n * ------------------------------------------------------------------------\n */\n\nconst NAME = 'scrollspy'\nconst DATA_KEY = 'bs.scrollspy'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\n\nconst Default = {\n  offset: 10,\n  method: 'auto',\n  target: ''\n}\n\nconst DefaultType = {\n  offset: 'number',\n  method: 'string',\n  target: '(string|element)'\n}\n\nconst EVENT_ACTIVATE = `activate${EVENT_KEY}`\nconst EVENT_SCROLL = `scroll${EVENT_KEY}`\nconst EVENT_LOAD_DATA_API = `load${EVENT_KEY}${DATA_API_KEY}`\n\nconst CLASS_NAME_DROPDOWN_ITEM = 'dropdown-item'\nconst CLASS_NAME_ACTIVE = 'active'\n\nconst SELECTOR_DATA_SPY = '[data-bs-spy=\"scroll\"]'\nconst SELECTOR_NAV_LIST_GROUP = '.nav, .list-group'\nconst SELECTOR_NAV_LINKS = '.nav-link'\nconst SELECTOR_NAV_ITEMS = '.nav-item'\nconst SELECTOR_LIST_ITEMS = '.list-group-item'\nconst SELECTOR_LINK_ITEMS = `${SELECTOR_NAV_LINKS}, ${SELECTOR_LIST_ITEMS}, .${CLASS_NAME_DROPDOWN_ITEM}`\nconst SELECTOR_DROPDOWN = '.dropdown'\nconst SELECTOR_DROPDOWN_TOGGLE = '.dropdown-toggle'\n\nconst METHOD_OFFSET = 'offset'\nconst METHOD_POSITION = 'position'\n\n/**\n * ------------------------------------------------------------------------\n * Class Definition\n * ------------------------------------------------------------------------\n */\n\nclass ScrollSpy extends BaseComponent {\n  constructor(element, config) {\n    super(element)\n    this._scrollElement = this._element.tagName === 'BODY' ? window : this._element\n    this._config = this._getConfig(config)\n    this._offsets = []\n    this._targets = []\n    this._activeTarget = null\n    this._scrollHeight = 0\n\n    EventHandler.on(this._scrollElement, EVENT_SCROLL, () => this._process())\n\n    this.refresh()\n    this._process()\n  }\n\n  // Getters\n\n  static get Default() {\n    return Default\n  }\n\n  static get NAME() {\n    return NAME\n  }\n\n  // Public\n\n  refresh() {\n    const autoMethod = this._scrollElement === this._scrollElement.window ?\n      METHOD_OFFSET :\n      METHOD_POSITION\n\n    const offsetMethod = this._config.method === 'auto' ?\n      autoMethod :\n      this._config.method\n\n    const offsetBase = offsetMethod === METHOD_POSITION ?\n      this._getScrollTop() :\n      0\n\n    this._offsets = []\n    this._targets = []\n    this._scrollHeight = this._getScrollHeight()\n\n    const targets = SelectorEngine.find(SELECTOR_LINK_ITEMS, this._config.target)\n\n    targets.map(element => {\n      const targetSelector = getSelectorFromElement(element)\n      const target = targetSelector ? SelectorEngine.findOne(targetSelector) : null\n\n      if (target) {\n        const targetBCR = target.getBoundingClientRect()\n        if (targetBCR.width || targetBCR.height) {\n          return [\n            Manipulator[offsetMethod](target).top + offsetBase,\n            targetSelector\n          ]\n        }\n      }\n\n      return null\n    })\n      .filter(item => item)\n      .sort((a, b) => a[0] - b[0])\n      .forEach(item => {\n        this._offsets.push(item[0])\n        this._targets.push(item[1])\n      })\n  }\n\n  dispose() {\n    EventHandler.off(this._scrollElement, EVENT_KEY)\n    super.dispose()\n  }\n\n  // Private\n\n  _getConfig(config) {\n    config = {\n      ...Default,\n      ...Manipulator.getDataAttributes(this._element),\n      ...(typeof config === 'object' && config ? config : {})\n    }\n\n    config.target = getElement(config.target) || document.documentElement\n\n    typeCheckConfig(NAME, config, DefaultType)\n\n    return config\n  }\n\n  _getScrollTop() {\n    return this._scrollElement === window ?\n      this._scrollElement.pageYOffset :\n      this._scrollElement.scrollTop\n  }\n\n  _getScrollHeight() {\n    return this._scrollElement.scrollHeight || Math.max(\n      document.body.scrollHeight,\n      document.documentElement.scrollHeight\n    )\n  }\n\n  _getOffsetHeight() {\n    return this._scrollElement === window ?\n      window.innerHeight :\n      this._scrollElement.getBoundingClientRect().height\n  }\n\n  _process() {\n    const scrollTop = this._getScrollTop() + this._config.offset\n    const scrollHeight = this._getScrollHeight()\n    const maxScroll = this._config.offset + scrollHeight - this._getOffsetHeight()\n\n    if (this._scrollHeight !== scrollHeight) {\n      this.refresh()\n    }\n\n    if (scrollTop >= maxScroll) {\n      const target = this._targets[this._targets.length - 1]\n\n      if (this._activeTarget !== target) {\n        this._activate(target)\n      }\n\n      return\n    }\n\n    if (this._activeTarget && scrollTop < this._offsets[0] && this._offsets[0] > 0) {\n      this._activeTarget = null\n      this._clear()\n      return\n    }\n\n    for (let i = this._offsets.length; i--;) {\n      const isActiveTarget = this._activeTarget !== this._targets[i] &&\n          scrollTop >= this._offsets[i] &&\n          (typeof this._offsets[i + 1] === 'undefined' || scrollTop < this._offsets[i + 1])\n\n      if (isActiveTarget) {\n        this._activate(this._targets[i])\n      }\n    }\n  }\n\n  _activate(target) {\n    this._activeTarget = target\n\n    this._clear()\n\n    const queries = SELECTOR_LINK_ITEMS.split(',')\n      .map(selector => `${selector}[data-bs-target=\"${target}\"],${selector}[href=\"${target}\"]`)\n\n    const link = SelectorEngine.findOne(queries.join(','), this._config.target)\n\n    link.classList.add(CLASS_NAME_ACTIVE)\n    if (link.classList.contains(CLASS_NAME_DROPDOWN_ITEM)) {\n      SelectorEngine.findOne(SELECTOR_DROPDOWN_TOGGLE, link.closest(SELECTOR_DROPDOWN))\n        .classList.add(CLASS_NAME_ACTIVE)\n    } else {\n      SelectorEngine.parents(link, SELECTOR_NAV_LIST_GROUP)\n        .forEach(listGroup => {\n          // Set triggered links parents as active\n          // With both <ul> and <nav> markup a parent is the previous sibling of any nav ancestor\n          SelectorEngine.prev(listGroup, `${SELECTOR_NAV_LINKS}, ${SELECTOR_LIST_ITEMS}`)\n            .forEach(item => item.classList.add(CLASS_NAME_ACTIVE))\n\n          // Handle special case when .nav-link is inside .nav-item\n          SelectorEngine.prev(listGroup, SELECTOR_NAV_ITEMS)\n            .forEach(navItem => {\n              SelectorEngine.children(navItem, SELECTOR_NAV_LINKS)\n                .forEach(item => item.classList.add(CLASS_NAME_ACTIVE))\n            })\n        })\n    }\n\n    EventHandler.trigger(this._scrollElement, EVENT_ACTIVATE, {\n      relatedTarget: target\n    })\n  }\n\n  _clear() {\n    SelectorEngine.find(SELECTOR_LINK_ITEMS, this._config.target)\n      .filter(node => node.classList.contains(CLASS_NAME_ACTIVE))\n      .forEach(node => node.classList.remove(CLASS_NAME_ACTIVE))\n  }\n\n  // Static\n\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = ScrollSpy.getOrCreateInstance(this, config)\n\n      if (typeof config !== 'string') {\n        return\n      }\n\n      if (typeof data[config] === 'undefined') {\n        throw new TypeError(`No method named \"${config}\"`)\n      }\n\n      data[config]()\n    })\n  }\n}\n\n/**\n * ------------------------------------------------------------------------\n * Data Api implementation\n * ------------------------------------------------------------------------\n */\n\nEventHandler.on(window, EVENT_LOAD_DATA_API, () => {\n  SelectorEngine.find(SELECTOR_DATA_SPY)\n    .forEach(spy => new ScrollSpy(spy))\n})\n\n/**\n * ------------------------------------------------------------------------\n * jQuery\n * ------------------------------------------------------------------------\n * add .ScrollSpy to jQuery only if jQuery is present\n */\n\ndefineJQueryPlugin(ScrollSpy)\n\nexport default ScrollSpy\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.1.0): tab.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport {\n  defineJQueryPlugin,\n  getElementFromSelector,\n  isDisabled,\n  reflow\n} from './util/index'\nimport EventHandler from './dom/event-handler'\nimport SelectorEngine from './dom/selector-engine'\nimport BaseComponent from './base-component'\n\n/**\n * ------------------------------------------------------------------------\n * Constants\n * ------------------------------------------------------------------------\n */\n\nconst NAME = 'tab'\nconst DATA_KEY = 'bs.tab'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\n\nconst EVENT_HIDE = `hide${EVENT_KEY}`\nconst EVENT_HIDDEN = `hidden${EVENT_KEY}`\nconst EVENT_SHOW = `show${EVENT_KEY}`\nconst EVENT_SHOWN = `shown${EVENT_KEY}`\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY}${DATA_API_KEY}`\n\nconst CLASS_NAME_DROPDOWN_MENU = 'dropdown-menu'\nconst CLASS_NAME_ACTIVE = 'active'\nconst CLASS_NAME_FADE = 'fade'\nconst CLASS_NAME_SHOW = 'show'\n\nconst SELECTOR_DROPDOWN = '.dropdown'\nconst SELECTOR_NAV_LIST_GROUP = '.nav, .list-group'\nconst SELECTOR_ACTIVE = '.active'\nconst SELECTOR_ACTIVE_UL = ':scope > li > .active'\nconst SELECTOR_DATA_TOGGLE = '[data-bs-toggle=\"tab\"], [data-bs-toggle=\"pill\"], [data-bs-toggle=\"list\"]'\nconst SELECTOR_DROPDOWN_TOGGLE = '.dropdown-toggle'\nconst SELECTOR_DROPDOWN_ACTIVE_CHILD = ':scope > .dropdown-menu .active'\n\n/**\n * ------------------------------------------------------------------------\n * Class Definition\n * ------------------------------------------------------------------------\n */\n\nclass Tab extends BaseComponent {\n  // Getters\n\n  static get NAME() {\n    return NAME\n  }\n\n  // Public\n\n  show() {\n    if ((this._element.parentNode &&\n      this._element.parentNode.nodeType === Node.ELEMENT_NODE &&\n      this._element.classList.contains(CLASS_NAME_ACTIVE))) {\n      return\n    }\n\n    let previous\n    const target = getElementFromSelector(this._element)\n    const listElement = this._element.closest(SELECTOR_NAV_LIST_GROUP)\n\n    if (listElement) {\n      const itemSelector = listElement.nodeName === 'UL' || listElement.nodeName === 'OL' ? SELECTOR_ACTIVE_UL : SELECTOR_ACTIVE\n      previous = SelectorEngine.find(itemSelector, listElement)\n      previous = previous[previous.length - 1]\n    }\n\n    const hideEvent = previous ?\n      EventHandler.trigger(previous, EVENT_HIDE, {\n        relatedTarget: this._element\n      }) :\n      null\n\n    const showEvent = EventHandler.trigger(this._element, EVENT_SHOW, {\n      relatedTarget: previous\n    })\n\n    if (showEvent.defaultPrevented || (hideEvent !== null && hideEvent.defaultPrevented)) {\n      return\n    }\n\n    this._activate(this._element, listElement)\n\n    const complete = () => {\n      EventHandler.trigger(previous, EVENT_HIDDEN, {\n        relatedTarget: this._element\n      })\n      EventHandler.trigger(this._element, EVENT_SHOWN, {\n        relatedTarget: previous\n      })\n    }\n\n    if (target) {\n      this._activate(target, target.parentNode, complete)\n    } else {\n      complete()\n    }\n  }\n\n  // Private\n\n  _activate(element, container, callback) {\n    const activeElements = container && (container.nodeName === 'UL' || container.nodeName === 'OL') ?\n      SelectorEngine.find(SELECTOR_ACTIVE_UL, container) :\n      SelectorEngine.children(container, SELECTOR_ACTIVE)\n\n    const active = activeElements[0]\n    const isTransitioning = callback && (active && active.classList.contains(CLASS_NAME_FADE))\n\n    const complete = () => this._transitionComplete(element, active, callback)\n\n    if (active && isTransitioning) {\n      active.classList.remove(CLASS_NAME_SHOW)\n      this._queueCallback(complete, element, true)\n    } else {\n      complete()\n    }\n  }\n\n  _transitionComplete(element, active, callback) {\n    if (active) {\n      active.classList.remove(CLASS_NAME_ACTIVE)\n\n      const dropdownChild = SelectorEngine.findOne(SELECTOR_DROPDOWN_ACTIVE_CHILD, active.parentNode)\n\n      if (dropdownChild) {\n        dropdownChild.classList.remove(CLASS_NAME_ACTIVE)\n      }\n\n      if (active.getAttribute('role') === 'tab') {\n        active.setAttribute('aria-selected', false)\n      }\n    }\n\n    element.classList.add(CLASS_NAME_ACTIVE)\n    if (element.getAttribute('role') === 'tab') {\n      element.setAttribute('aria-selected', true)\n    }\n\n    reflow(element)\n\n    if (element.classList.contains(CLASS_NAME_FADE)) {\n      element.classList.add(CLASS_NAME_SHOW)\n    }\n\n    let parent = element.parentNode\n    if (parent && parent.nodeName === 'LI') {\n      parent = parent.parentNode\n    }\n\n    if (parent && parent.classList.contains(CLASS_NAME_DROPDOWN_MENU)) {\n      const dropdownElement = element.closest(SELECTOR_DROPDOWN)\n\n      if (dropdownElement) {\n        SelectorEngine.find(SELECTOR_DROPDOWN_TOGGLE, dropdownElement)\n          .forEach(dropdown => dropdown.classList.add(CLASS_NAME_ACTIVE))\n      }\n\n      element.setAttribute('aria-expanded', true)\n    }\n\n    if (callback) {\n      callback()\n    }\n  }\n\n  // Static\n\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Tab.getOrCreateInstance(this)\n\n      if (typeof config === 'string') {\n        if (typeof data[config] === 'undefined') {\n          throw new TypeError(`No method named \"${config}\"`)\n        }\n\n        data[config]()\n      }\n    })\n  }\n}\n\n/**\n * ------------------------------------------------------------------------\n * Data Api implementation\n * ------------------------------------------------------------------------\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_TOGGLE, function (event) {\n  if (['A', 'AREA'].includes(this.tagName)) {\n    event.preventDefault()\n  }\n\n  if (isDisabled(this)) {\n    return\n  }\n\n  const data = Tab.getOrCreateInstance(this)\n  data.show()\n})\n\n/**\n * ------------------------------------------------------------------------\n * jQuery\n * ------------------------------------------------------------------------\n * add .Tab to jQuery only if jQuery is present\n */\n\ndefineJQueryPlugin(Tab)\n\nexport default Tab\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.1.0): toast.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport {\n  defineJQueryPlugin,\n  reflow,\n  typeCheckConfig\n} from './util/index'\nimport EventHandler from './dom/event-handler'\nimport Manipulator from './dom/manipulator'\nimport BaseComponent from './base-component'\nimport { enableDismissTrigger } from './util/component-functions'\n\n/**\n * ------------------------------------------------------------------------\n * Constants\n * ------------------------------------------------------------------------\n */\n\nconst NAME = 'toast'\nconst DATA_KEY = 'bs.toast'\nconst EVENT_KEY = `.${DATA_KEY}`\n\nconst EVENT_MOUSEOVER = `mouseover${EVENT_KEY}`\nconst EVENT_MOUSEOUT = `mouseout${EVENT_KEY}`\nconst EVENT_FOCUSIN = `focusin${EVENT_KEY}`\nconst EVENT_FOCUSOUT = `focusout${EVENT_KEY}`\nconst EVENT_HIDE = `hide${EVENT_KEY}`\nconst EVENT_HIDDEN = `hidden${EVENT_KEY}`\nconst EVENT_SHOW = `show${EVENT_KEY}`\nconst EVENT_SHOWN = `shown${EVENT_KEY}`\n\nconst CLASS_NAME_FADE = 'fade'\nconst CLASS_NAME_HIDE = 'hide' // @deprecated - kept here only for backwards compatibility\nconst CLASS_NAME_SHOW = 'show'\nconst CLASS_NAME_SHOWING = 'showing'\n\nconst DefaultType = {\n  animation: 'boolean',\n  autohide: 'boolean',\n  delay: 'number'\n}\n\nconst Default = {\n  animation: true,\n  autohide: true,\n  delay: 5000\n}\n\n/**\n * ------------------------------------------------------------------------\n * Class Definition\n * ------------------------------------------------------------------------\n */\n\nclass Toast extends BaseComponent {\n  constructor(element, config) {\n    super(element)\n\n    this._config = this._getConfig(config)\n    this._timeout = null\n    this._hasMouseInteraction = false\n    this._hasKeyboardInteraction = false\n    this._setListeners()\n  }\n\n  // Getters\n\n  static get DefaultType() {\n    return DefaultType\n  }\n\n  static get Default() {\n    return Default\n  }\n\n  static get NAME() {\n    return NAME\n  }\n\n  // Public\n\n  show() {\n    const showEvent = EventHandler.trigger(this._element, EVENT_SHOW)\n\n    if (showEvent.defaultPrevented) {\n      return\n    }\n\n    this._clearTimeout()\n\n    if (this._config.animation) {\n      this._element.classList.add(CLASS_NAME_FADE)\n    }\n\n    const complete = () => {\n      this._element.classList.remove(CLASS_NAME_SHOWING)\n      EventHandler.trigger(this._element, EVENT_SHOWN)\n\n      this._maybeScheduleHide()\n    }\n\n    this._element.classList.remove(CLASS_NAME_HIDE) // @deprecated\n    reflow(this._element)\n    this._element.classList.add(CLASS_NAME_SHOW)\n    this._element.classList.add(CLASS_NAME_SHOWING)\n\n    this._queueCallback(complete, this._element, this._config.animation)\n  }\n\n  hide() {\n    if (!this._element.classList.contains(CLASS_NAME_SHOW)) {\n      return\n    }\n\n    const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE)\n\n    if (hideEvent.defaultPrevented) {\n      return\n    }\n\n    const complete = () => {\n      this._element.classList.add(CLASS_NAME_HIDE) // @deprecated\n      this._element.classList.remove(CLASS_NAME_SHOWING)\n      this._element.classList.remove(CLASS_NAME_SHOW)\n      EventHandler.trigger(this._element, EVENT_HIDDEN)\n    }\n\n    this._element.classList.add(CLASS_NAME_SHOWING)\n    this._queueCallback(complete, this._element, this._config.animation)\n  }\n\n  dispose() {\n    this._clearTimeout()\n\n    if (this._element.classList.contains(CLASS_NAME_SHOW)) {\n      this._element.classList.remove(CLASS_NAME_SHOW)\n    }\n\n    super.dispose()\n  }\n\n  // Private\n\n  _getConfig(config) {\n    config = {\n      ...Default,\n      ...Manipulator.getDataAttributes(this._element),\n      ...(typeof config === 'object' && config ? config : {})\n    }\n\n    typeCheckConfig(NAME, config, this.constructor.DefaultType)\n\n    return config\n  }\n\n  _maybeScheduleHide() {\n    if (!this._config.autohide) {\n      return\n    }\n\n    if (this._hasMouseInteraction || this._hasKeyboardInteraction) {\n      return\n    }\n\n    this._timeout = setTimeout(() => {\n      this.hide()\n    }, this._config.delay)\n  }\n\n  _onInteraction(event, isInteracting) {\n    switch (event.type) {\n      case 'mouseover':\n      case 'mouseout':\n        this._hasMouseInteraction = isInteracting\n        break\n      case 'focusin':\n      case 'focusout':\n        this._hasKeyboardInteraction = isInteracting\n        break\n      default:\n        break\n    }\n\n    if (isInteracting) {\n      this._clearTimeout()\n      return\n    }\n\n    const nextElement = event.relatedTarget\n    if (this._element === nextElement || this._element.contains(nextElement)) {\n      return\n    }\n\n    this._maybeScheduleHide()\n  }\n\n  _setListeners() {\n    EventHandler.on(this._element, EVENT_MOUSEOVER, event => this._onInteraction(event, true))\n    EventHandler.on(this._element, EVENT_MOUSEOUT, event => this._onInteraction(event, false))\n    EventHandler.on(this._element, EVENT_FOCUSIN, event => this._onInteraction(event, true))\n    EventHandler.on(this._element, EVENT_FOCUSOUT, event => this._onInteraction(event, false))\n  }\n\n  _clearTimeout() {\n    clearTimeout(this._timeout)\n    this._timeout = null\n  }\n\n  // Static\n\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Toast.getOrCreateInstance(this, config)\n\n      if (typeof config === 'string') {\n        if (typeof data[config] === 'undefined') {\n          throw new TypeError(`No method named \"${config}\"`)\n        }\n\n        data[config](this)\n      }\n    })\n  }\n}\n\nenableDismissTrigger(Toast)\n\n/**\n * ------------------------------------------------------------------------\n * jQuery\n * ------------------------------------------------------------------------\n * add .Toast to jQuery only if jQuery is present\n */\n\ndefineJQueryPlugin(Toast)\n\nexport default Toast\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.1.0): index.umd.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport Alert from './src/alert'\nimport Button from './src/button'\nimport Carousel from './src/carousel'\nimport Collapse from './src/collapse'\nimport Dropdown from './src/dropdown'\nimport Modal from './src/modal'\nimport Offcanvas from './src/offcanvas'\nimport Popover from './src/popover'\nimport ScrollSpy from './src/scrollspy'\nimport Tab from './src/tab'\nimport Toast from './src/toast'\nimport Tooltip from './src/tooltip'\n\nexport default {\n  Alert,\n  Button,\n  Carousel,\n  Collapse,\n  Dropdown,\n  Modal,\n  Offcanvas,\n  Popover,\n  ScrollSpy,\n  Tab,\n  Toast,\n  Tooltip\n}\n"]}
\ No newline at end of file
diff --git a/deps/bootstrap-5.1.0/bootstrap.min.css b/deps/bootstrap-5.1.0/bootstrap.min.css
new file mode 100644
index 000000000..171450eb2
--- /dev/null
+++ b/deps/bootstrap-5.1.0/bootstrap.min.css
@@ -0,0 +1 @@
+:root{--bs-blue: #0d6efd;--bs-indigo: #6610f2;--bs-purple: #6f42c1;--bs-pink: #d63384;--bs-red: #dc3545;--bs-orange: #fd7e14;--bs-yellow: #ffc107;--bs-green: #198754;--bs-teal: #20c997;--bs-cyan: #0dcaf0;--bs-white: #fff;--bs-gray: #6c757d;--bs-gray-dark: #343a40;--bs-gray-100: #f8f9fa;--bs-gray-200: #e9ecef;--bs-gray-300: #dee2e6;--bs-gray-400: #ced4da;--bs-gray-500: #adb5bd;--bs-gray-600: #6c757d;--bs-gray-700: #495057;--bs-gray-800: #343a40;--bs-gray-900: #212529;--bs-default: #dee2e6;--bs-primary: #7E1F77;--bs-secondary: #6c757d;--bs-success: #198754;--bs-info: #0dcaf0;--bs-warning: #ffc107;--bs-danger: #dc3545;--bs-light: #f8f9fa;--bs-dark: #212529;--bs-default-rgb: 222,226,230;--bs-primary-rgb: 126,31,119;--bs-secondary-rgb: 108,117,125;--bs-success-rgb: 25,135,84;--bs-info-rgb: 13,202,240;--bs-warning-rgb: 255,193,7;--bs-danger-rgb: 220,53,69;--bs-light-rgb: 248,249,250;--bs-dark-rgb: 33,37,41;--bs-white-rgb: 255,255,255;--bs-black-rgb: 0,0,0;--bs-body-rgb: 33,37,41;--bs-font-sans-serif: system-ui, -apple-system, "Segoe UI", Roboto, "Helvetica Neue", Arial, "Noto Sans", "Liberation Sans", sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol", "Noto Color Emoji";--bs-font-monospace: SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace;--bs-gradient: linear-gradient(180deg, rgba(255,255,255,0.15), rgba(255,255,255,0));--bs-body-font-family: var(--bs-font-sans-serif);--bs-body-font-size: 1rem;--bs-body-font-weight: 400;--bs-body-line-height: 1.5;--bs-body-color: #212529;--bs-body-bg: #fff}*,*::before,*::after{box-sizing:border-box}@media (prefers-reduced-motion: no-preference){:root{scroll-behavior:smooth}}body{margin:0;font-family:var(--bs-body-font-family);font-size:var(--bs-body-font-size);font-weight:var(--bs-body-font-weight);line-height:var(--bs-body-line-height);color:var(--bs-body-color);text-align:var(--bs-body-text-align);background-color:var(--bs-body-bg);-webkit-text-size-adjust:100%;-webkit-tap-highlight-color:rgba(0,0,0,0)}hr{margin:1rem 0;color:inherit;background-color:currentColor;border:0;opacity:.25}hr:not([size]){height:1px}h6,.h6,h5,.h5,h4,.h4,h3,.h3,h2,.h2,h1,.h1{margin-top:0;margin-bottom:.5rem;font-weight:500;line-height:1.2}h1,.h1{font-size:calc(1.375rem + 1.5vw)}@media (min-width: 1200px){h1,.h1{font-size:2.5rem}}h2,.h2{font-size:calc(1.325rem + .9vw)}@media (min-width: 1200px){h2,.h2{font-size:2rem}}h3,.h3{font-size:calc(1.3rem + .6vw)}@media (min-width: 1200px){h3,.h3{font-size:1.75rem}}h4,.h4{font-size:calc(1.275rem + .3vw)}@media (min-width: 1200px){h4,.h4{font-size:1.5rem}}h5,.h5{font-size:1.25rem}h6,.h6{font-size:1rem}p{margin-top:0;margin-bottom:1rem}abbr[title],abbr[data-bs-original-title]{text-decoration:underline dotted;-webkit-text-decoration:underline dotted;-moz-text-decoration:underline dotted;-ms-text-decoration:underline dotted;-o-text-decoration:underline dotted;cursor:help;text-decoration-skip-ink:none}address{margin-bottom:1rem;font-style:normal;line-height:inherit}ol,ul{padding-left:2rem}ol,ul,dl{margin-top:0;margin-bottom:1rem}ol ol,ul ul,ol ul,ul ol{margin-bottom:0}dt{font-weight:700}dd{margin-bottom:.5rem;margin-left:0}blockquote{margin:0 0 1rem;padding:.625rem 1.25rem;border-left:.25rem solid #e9ecef}blockquote p:last-child,blockquote ul:last-child,blockquote ol:last-child{margin-bottom:0}b,strong{font-weight:bolder}small,.small{font-size:.875em}mark,.mark{padding:.2em;background-color:#fcf8e3}sub,sup{position:relative;font-size:.75em;line-height:0;vertical-align:baseline}sub{bottom:-.25em}sup{top:-.5em}a{color:#7E1F77;text-decoration:underline;-webkit-text-decoration:underline;-moz-text-decoration:underline;-ms-text-decoration:underline;-o-text-decoration:underline}a:hover{color:#65195f}a:not([href]):not([class]),a:not([href]):not([class]):hover{color:inherit;text-decoration:none}pre,code,kbd,samp{font-family:var(--bs-font-monospace);font-size:1em;direction:ltr /* rtl:ignore */;unicode-bidi:bidi-override}pre{display:block;margin-top:0;margin-bottom:1rem;overflow:auto;font-size:.875em;color:#000;background-color:#f6f6f6;padding:.5rem;border:1px solid #dee2e6;border-radius:.25rem}pre 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div.csl-entry{margin-left:2em;text-indent:-2em}div.csl-left-margin{min-width:2em;float:left}div.csl-right-inline{margin-left:2em;padding-left:1em}div.csl-indent{margin-left:2em}pre,pre code{word-wrap:normal}code{overflow-wrap:break-word}.hasCopyButton{position:relative}.btn-copy-ex{position:absolute;right:5px;top:5px;visibility:hidden}.hasCopyButton:hover button.btn-copy-ex{visibility:visible}pre{padding:1rem 0.5rem}@media (max-width: 575.98px){div>div>pre{margin-left:calc(var(--bs-gutter-x) * -.5);margin-right:calc(var(--bs-gutter-x) * -.5);border-radius:0;padding-left:1rem;padding-right:1rem}.btn-copy-ex{right:calc(var(--bs-gutter-x) * -.5 + 5px)}}pre code{padding:0;background:transparent}pre code a:any-link{color:inherit;text-decoration-color:#6c757d}pre code .error,pre code .warning{font-weight:bolder}pre .img img,pre .r-plt img{margin:5px 0;background-color:#fff}@media print{code a:link:after,code a:visited:after{content:""}}a.sourceLine:hover{text-decoration:none}mark,.mark{background:linear-gradient(-100deg, rgba(13,202,240,0.2), rgba(13,202,240,0.7) 95%, rgba(13,202,240,0.1))}.algolia-autocomplete .aa-hint{color:#212529}.algolia-autocomplete .aa-dropdown-menu{width:Max(100%, 20rem);background-color:#fff;border:1px solid #dee2e6;margin-top:2px;max-height:50vh;overflow-y:auto}.algolia-autocomplete .aa-dropdown-menu .aa-suggestion{cursor:pointer;padding:5px 4px;border-bottom:1px #e9ecef solid;font-size:0.9rem;color:#212529}.search-details{font-size:0.9rem;color:#7E1F77;display:inline;font-weight:bolder}.algolia-autocomplete .aa-dropdown-menu .aa-suggestion.aa-cursor{background-color:#f2e9f1}pre{background-color:#f1f3f5}pre code{color:#007BA5}pre code span.al{color:#AD0000}pre code span.an{color:#5E5E5E}pre code span.bn{color:#AD0000}pre code span.cf{color:#007BA5}pre code span.ch{color:#20794D}pre code span.cn{color:#8f5902}pre code span.co{color:#5E5E5E}pre code 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diff --git a/deps/data-deps.txt b/deps/data-deps.txt
new file mode 100644
index 000000000..aef7effd6
--- /dev/null
+++ b/deps/data-deps.txt
@@ -0,0 +1,4 @@
+<script src="deps/jquery-3.6.0/jquery-3.6.0.min.js"></script>
+<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no" />
+<link href="deps/bootstrap-5.1.0/bootstrap.min.css" rel="stylesheet" />
+<script src="deps/bootstrap-5.1.0/bootstrap.bundle.min.js"></script>
diff --git a/deps/jquery-3.6.0/jquery-3.6.0.js b/deps/jquery-3.6.0/jquery-3.6.0.js
new file mode 100644
index 000000000..fc6c299b7
--- /dev/null
+++ b/deps/jquery-3.6.0/jquery-3.6.0.js
@@ -0,0 +1,10881 @@
+/*!
+ * jQuery JavaScript Library v3.6.0
+ * https://jquery.com/
+ *
+ * Includes Sizzle.js
+ * https://sizzlejs.com/
+ *
+ * Copyright OpenJS Foundation and other contributors
+ * Released under the MIT license
+ * https://jquery.org/license
+ *
+ * Date: 2021-03-02T17:08Z
+ */
+( function( global, factory ) {
+
+	"use strict";
+
+	if ( typeof module === "object" && typeof module.exports === "object" ) {
+
+		// For CommonJS and CommonJS-like environments where a proper `window`
+		// is present, execute the factory and get jQuery.
+		// For environments that do not have a `window` with a `document`
+		// (such as Node.js), expose a factory as module.exports.
+		// This accentuates the need for the creation of a real `window`.
+		// e.g. var jQuery = require("jquery")(window);
+		// See ticket #14549 for more info.
+		module.exports = global.document ?
+			factory( global, true ) :
+			function( w ) {
+				if ( !w.document ) {
+					throw new Error( "jQuery requires a window with a document" );
+				}
+				return factory( w );
+			};
+	} else {
+		factory( global );
+	}
+
+// Pass this if window is not defined yet
+} )( typeof window !== "undefined" ? window : this, function( window, noGlobal ) {
+
+// Edge <= 12 - 13+, Firefox <=18 - 45+, IE 10 - 11, Safari 5.1 - 9+, iOS 6 - 9.1
+// throw exceptions when non-strict code (e.g., ASP.NET 4.5) accesses strict mode
+// arguments.callee.caller (trac-13335). But as of jQuery 3.0 (2016), strict mode should be common
+// enough that all such attempts are guarded in a try block.
+"use strict";
+
+var arr = [];
+
+var getProto = Object.getPrototypeOf;
+
+var slice = arr.slice;
+
+var flat = arr.flat ? function( array ) {
+	return arr.flat.call( array );
+} : function( array ) {
+	return arr.concat.apply( [], array );
+};
+
+
+var push = arr.push;
+
+var indexOf = arr.indexOf;
+
+var class2type = {};
+
+var toString = class2type.toString;
+
+var hasOwn = class2type.hasOwnProperty;
+
+var fnToString = hasOwn.toString;
+
+var ObjectFunctionString = fnToString.call( Object );
+
+var support = {};
+
+var isFunction = function isFunction( obj ) {
+
+		// Support: Chrome <=57, Firefox <=52
+		// In some browsers, typeof returns "function" for HTML <object> elements
+		// (i.e., `typeof document.createElement( "object" ) === "function"`).
+		// We don't want to classify *any* DOM node as a function.
+		// Support: QtWeb <=3.8.5, WebKit <=534.34, wkhtmltopdf tool <=0.12.5
+		// Plus for old WebKit, typeof returns "function" for HTML collections
+		// (e.g., `typeof document.getElementsByTagName("div") === "function"`). (gh-4756)
+		return typeof obj === "function" && typeof obj.nodeType !== "number" &&
+			typeof obj.item !== "function";
+	};
+
+
+var isWindow = function isWindow( obj ) {
+		return obj != null && obj === obj.window;
+	};
+
+
+var document = window.document;
+
+
+
+	var preservedScriptAttributes = {
+		type: true,
+		src: true,
+		nonce: true,
+		noModule: true
+	};
+
+	function DOMEval( code, node, doc ) {
+		doc = doc || document;
+
+		var i, val,
+			script = doc.createElement( "script" );
+
+		script.text = code;
+		if ( node ) {
+			for ( i in preservedScriptAttributes ) {
+
+				// Support: Firefox 64+, Edge 18+
+				// Some browsers don't support the "nonce" property on scripts.
+				// On the other hand, just using `getAttribute` is not enough as
+				// the `nonce` attribute is reset to an empty string whenever it
+				// becomes browsing-context connected.
+				// See https://github.com/whatwg/html/issues/2369
+				// See https://html.spec.whatwg.org/#nonce-attributes
+				// The `node.getAttribute` check was added for the sake of
+				// `jQuery.globalEval` so that it can fake a nonce-containing node
+				// via an object.
+				val = node[ i ] || node.getAttribute && node.getAttribute( i );
+				if ( val ) {
+					script.setAttribute( i, val );
+				}
+			}
+		}
+		doc.head.appendChild( script ).parentNode.removeChild( script );
+	}
+
+
+function toType( obj ) {
+	if ( obj == null ) {
+		return obj + "";
+	}
+
+	// Support: Android <=2.3 only (functionish RegExp)
+	return typeof obj === "object" || typeof obj === "function" ?
+		class2type[ toString.call( obj ) ] || "object" :
+		typeof obj;
+}
+/* global Symbol */
+// Defining this global in .eslintrc.json would create a danger of using the global
+// unguarded in another place, it seems safer to define global only for this module
+
+
+
+var
+	version = "3.6.0",
+
+	// Define a local copy of jQuery
+	jQuery = function( selector, context ) {
+
+		// The jQuery object is actually just the init constructor 'enhanced'
+		// Need init if jQuery is called (just allow error to be thrown if not included)
+		return new jQuery.fn.init( selector, context );
+	};
+
+jQuery.fn = jQuery.prototype = {
+
+	// The current version of jQuery being used
+	jquery: version,
+
+	constructor: jQuery,
+
+	// The default length of a jQuery object is 0
+	length: 0,
+
+	toArray: function() {
+		return slice.call( this );
+	},
+
+	// Get the Nth element in the matched element set OR
+	// Get the whole matched element set as a clean array
+	get: function( num ) {
+
+		// Return all the elements in a clean array
+		if ( num == null ) {
+			return slice.call( this );
+		}
+
+		// Return just the one element from the set
+		return num < 0 ? this[ num + this.length ] : this[ num ];
+	},
+
+	// Take an array of elements and push it onto the stack
+	// (returning the new matched element set)
+	pushStack: function( elems ) {
+
+		// Build a new jQuery matched element set
+		var ret = jQuery.merge( this.constructor(), elems );
+
+		// Add the old object onto the stack (as a reference)
+		ret.prevObject = this;
+
+		// Return the newly-formed element set
+		return ret;
+	},
+
+	// Execute a callback for every element in the matched set.
+	each: function( callback ) {
+		return jQuery.each( this, callback );
+	},
+
+	map: function( callback ) {
+		return this.pushStack( jQuery.map( this, function( elem, i ) {
+			return callback.call( elem, i, elem );
+		} ) );
+	},
+
+	slice: function() {
+		return this.pushStack( slice.apply( this, arguments ) );
+	},
+
+	first: function() {
+		return this.eq( 0 );
+	},
+
+	last: function() {
+		return this.eq( -1 );
+	},
+
+	even: function() {
+		return this.pushStack( jQuery.grep( this, function( _elem, i ) {
+			return ( i + 1 ) % 2;
+		} ) );
+	},
+
+	odd: function() {
+		return this.pushStack( jQuery.grep( this, function( _elem, i ) {
+			return i % 2;
+		} ) );
+	},
+
+	eq: function( i ) {
+		var len = this.length,
+			j = +i + ( i < 0 ? len : 0 );
+		return this.pushStack( j >= 0 && j < len ? [ this[ j ] ] : [] );
+	},
+
+	end: function() {
+		return this.prevObject || this.constructor();
+	},
+
+	// For internal use only.
+	// Behaves like an Array's method, not like a jQuery method.
+	push: push,
+	sort: arr.sort,
+	splice: arr.splice
+};
+
+jQuery.extend = jQuery.fn.extend = function() {
+	var options, name, src, copy, copyIsArray, clone,
+		target = arguments[ 0 ] || {},
+		i = 1,
+		length = arguments.length,
+		deep = false;
+
+	// Handle a deep copy situation
+	if ( typeof target === "boolean" ) {
+		deep = target;
+
+		// Skip the boolean and the target
+		target = arguments[ i ] || {};
+		i++;
+	}
+
+	// Handle case when target is a string or something (possible in deep copy)
+	if ( typeof target !== "object" && !isFunction( target ) ) {
+		target = {};
+	}
+
+	// Extend jQuery itself if only one argument is passed
+	if ( i === length ) {
+		target = this;
+		i--;
+	}
+
+	for ( ; i < length; i++ ) {
+
+		// Only deal with non-null/undefined values
+		if ( ( options = arguments[ i ] ) != null ) {
+
+			// Extend the base object
+			for ( name in options ) {
+				copy = options[ name ];
+
+				// Prevent Object.prototype pollution
+				// Prevent never-ending loop
+				if ( name === "__proto__" || target === copy ) {
+					continue;
+				}
+
+				// Recurse if we're merging plain objects or arrays
+				if ( deep && copy && ( jQuery.isPlainObject( copy ) ||
+					( copyIsArray = Array.isArray( copy ) ) ) ) {
+					src = target[ name ];
+
+					// Ensure proper type for the source value
+					if ( copyIsArray && !Array.isArray( src ) ) {
+						clone = [];
+					} else if ( !copyIsArray && !jQuery.isPlainObject( src ) ) {
+						clone = {};
+					} else {
+						clone = src;
+					}
+					copyIsArray = false;
+
+					// Never move original objects, clone them
+					target[ name ] = jQuery.extend( deep, clone, copy );
+
+				// Don't bring in undefined values
+				} else if ( copy !== undefined ) {
+					target[ name ] = copy;
+				}
+			}
+		}
+	}
+
+	// Return the modified object
+	return target;
+};
+
+jQuery.extend( {
+
+	// Unique for each copy of jQuery on the page
+	expando: "jQuery" + ( version + Math.random() ).replace( /\D/g, "" ),
+
+	// Assume jQuery is ready without the ready module
+	isReady: true,
+
+	error: function( msg ) {
+		throw new Error( msg );
+	},
+
+	noop: function() {},
+
+	isPlainObject: function( obj ) {
+		var proto, Ctor;
+
+		// Detect obvious negatives
+		// Use toString instead of jQuery.type to catch host objects
+		if ( !obj || toString.call( obj ) !== "[object Object]" ) {
+			return false;
+		}
+
+		proto = getProto( obj );
+
+		// Objects with no prototype (e.g., `Object.create( null )`) are plain
+		if ( !proto ) {
+			return true;
+		}
+
+		// Objects with prototype are plain iff they were constructed by a global Object function
+		Ctor = hasOwn.call( proto, "constructor" ) && proto.constructor;
+		return typeof Ctor === "function" && fnToString.call( Ctor ) === ObjectFunctionString;
+	},
+
+	isEmptyObject: function( obj ) {
+		var name;
+
+		for ( name in obj ) {
+			return false;
+		}
+		return true;
+	},
+
+	// Evaluates a script in a provided context; falls back to the global one
+	// if not specified.
+	globalEval: function( code, options, doc ) {
+		DOMEval( code, { nonce: options && options.nonce }, doc );
+	},
+
+	each: function( obj, callback ) {
+		var length, i = 0;
+
+		if ( isArrayLike( obj ) ) {
+			length = obj.length;
+			for ( ; i < length; i++ ) {
+				if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) {
+					break;
+				}
+			}
+		} else {
+			for ( i in obj ) {
+				if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) {
+					break;
+				}
+			}
+		}
+
+		return obj;
+	},
+
+	// results is for internal usage only
+	makeArray: function( arr, results ) {
+		var ret = results || [];
+
+		if ( arr != null ) {
+			if ( isArrayLike( Object( arr ) ) ) {
+				jQuery.merge( ret,
+					typeof arr === "string" ?
+						[ arr ] : arr
+				);
+			} else {
+				push.call( ret, arr );
+			}
+		}
+
+		return ret;
+	},
+
+	inArray: function( elem, arr, i ) {
+		return arr == null ? -1 : indexOf.call( arr, elem, i );
+	},
+
+	// Support: Android <=4.0 only, PhantomJS 1 only
+	// push.apply(_, arraylike) throws on ancient WebKit
+	merge: function( first, second ) {
+		var len = +second.length,
+			j = 0,
+			i = first.length;
+
+		for ( ; j < len; j++ ) {
+			first[ i++ ] = second[ j ];
+		}
+
+		first.length = i;
+
+		return first;
+	},
+
+	grep: function( elems, callback, invert ) {
+		var callbackInverse,
+			matches = [],
+			i = 0,
+			length = elems.length,
+			callbackExpect = !invert;
+
+		// Go through the array, only saving the items
+		// that pass the validator function
+		for ( ; i < length; i++ ) {
+			callbackInverse = !callback( elems[ i ], i );
+			if ( callbackInverse !== callbackExpect ) {
+				matches.push( elems[ i ] );
+			}
+		}
+
+		return matches;
+	},
+
+	// arg is for internal usage only
+	map: function( elems, callback, arg ) {
+		var length, value,
+			i = 0,
+			ret = [];
+
+		// Go through the array, translating each of the items to their new values
+		if ( isArrayLike( elems ) ) {
+			length = elems.length;
+			for ( ; i < length; i++ ) {
+				value = callback( elems[ i ], i, arg );
+
+				if ( value != null ) {
+					ret.push( value );
+				}
+			}
+
+		// Go through every key on the object,
+		} else {
+			for ( i in elems ) {
+				value = callback( elems[ i ], i, arg );
+
+				if ( value != null ) {
+					ret.push( value );
+				}
+			}
+		}
+
+		// Flatten any nested arrays
+		return flat( ret );
+	},
+
+	// A global GUID counter for objects
+	guid: 1,
+
+	// jQuery.support is not used in Core but other projects attach their
+	// properties to it so it needs to exist.
+	support: support
+} );
+
+if ( typeof Symbol === "function" ) {
+	jQuery.fn[ Symbol.iterator ] = arr[ Symbol.iterator ];
+}
+
+// Populate the class2type map
+jQuery.each( "Boolean Number String Function Array Date RegExp Object Error Symbol".split( " " ),
+	function( _i, name ) {
+		class2type[ "[object " + name + "]" ] = name.toLowerCase();
+	} );
+
+function isArrayLike( obj ) {
+
+	// Support: real iOS 8.2 only (not reproducible in simulator)
+	// `in` check used to prevent JIT error (gh-2145)
+	// hasOwn isn't used here due to false negatives
+	// regarding Nodelist length in IE
+	var length = !!obj && "length" in obj && obj.length,
+		type = toType( obj );
+
+	if ( isFunction( obj ) || isWindow( obj ) ) {
+		return false;
+	}
+
+	return type === "array" || length === 0 ||
+		typeof length === "number" && length > 0 && ( length - 1 ) in obj;
+}
+var Sizzle =
+/*!
+ * Sizzle CSS Selector Engine v2.3.6
+ * https://sizzlejs.com/
+ *
+ * Copyright JS Foundation and other contributors
+ * Released under the MIT license
+ * https://js.foundation/
+ *
+ * Date: 2021-02-16
+ */
+( function( window ) {
+var i,
+	support,
+	Expr,
+	getText,
+	isXML,
+	tokenize,
+	compile,
+	select,
+	outermostContext,
+	sortInput,
+	hasDuplicate,
+
+	// Local document vars
+	setDocument,
+	document,
+	docElem,
+	documentIsHTML,
+	rbuggyQSA,
+	rbuggyMatches,
+	matches,
+	contains,
+
+	// Instance-specific data
+	expando = "sizzle" + 1 * new Date(),
+	preferredDoc = window.document,
+	dirruns = 0,
+	done = 0,
+	classCache = createCache(),
+	tokenCache = createCache(),
+	compilerCache = createCache(),
+	nonnativeSelectorCache = createCache(),
+	sortOrder = function( a, b ) {
+		if ( a === b ) {
+			hasDuplicate = true;
+		}
+		return 0;
+	},
+
+	// Instance methods
+	hasOwn = ( {} ).hasOwnProperty,
+	arr = [],
+	pop = arr.pop,
+	pushNative = arr.push,
+	push = arr.push,
+	slice = arr.slice,
+
+	// Use a stripped-down indexOf as it's faster than native
+	// https://jsperf.com/thor-indexof-vs-for/5
+	indexOf = function( list, elem ) {
+		var i = 0,
+			len = list.length;
+		for ( ; i < len; i++ ) {
+			if ( list[ i ] === elem ) {
+				return i;
+			}
+		}
+		return -1;
+	},
+
+	booleans = "checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|" +
+		"ismap|loop|multiple|open|readonly|required|scoped",
+
+	// Regular expressions
+
+	// http://www.w3.org/TR/css3-selectors/#whitespace
+	whitespace = "[\\x20\\t\\r\\n\\f]",
+
+	// https://www.w3.org/TR/css-syntax-3/#ident-token-diagram
+	identifier = "(?:\\\\[\\da-fA-F]{1,6}" + whitespace +
+		"?|\\\\[^\\r\\n\\f]|[\\w-]|[^\0-\\x7f])+",
+
+	// Attribute selectors: http://www.w3.org/TR/selectors/#attribute-selectors
+	attributes = "\\[" + whitespace + "*(" + identifier + ")(?:" + whitespace +
+
+		// Operator (capture 2)
+		"*([*^$|!~]?=)" + whitespace +
+
+		// "Attribute values must be CSS identifiers [capture 5]
+		// or strings [capture 3 or capture 4]"
+		"*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|(" + identifier + "))|)" +
+		whitespace + "*\\]",
+
+	pseudos = ":(" + identifier + ")(?:\\((" +
+
+		// To reduce the number of selectors needing tokenize in the preFilter, prefer arguments:
+		// 1. quoted (capture 3; capture 4 or capture 5)
+		"('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|" +
+
+		// 2. simple (capture 6)
+		"((?:\\\\.|[^\\\\()[\\]]|" + attributes + ")*)|" +
+
+		// 3. anything else (capture 2)
+		".*" +
+		")\\)|)",
+
+	// Leading and non-escaped trailing whitespace, capturing some non-whitespace characters preceding the latter
+	rwhitespace = new RegExp( whitespace + "+", "g" ),
+	rtrim = new RegExp( "^" + whitespace + "+|((?:^|[^\\\\])(?:\\\\.)*)" +
+		whitespace + "+$", "g" ),
+
+	rcomma = new RegExp( "^" + whitespace + "*," + whitespace + "*" ),
+	rcombinators = new RegExp( "^" + whitespace + "*([>+~]|" + whitespace + ")" + whitespace +
+		"*" ),
+	rdescend = new RegExp( whitespace + "|>" ),
+
+	rpseudo = new RegExp( pseudos ),
+	ridentifier = new RegExp( "^" + identifier + "$" ),
+
+	matchExpr = {
+		"ID": new RegExp( "^#(" + identifier + ")" ),
+		"CLASS": new RegExp( "^\\.(" + identifier + ")" ),
+		"TAG": new RegExp( "^(" + identifier + "|[*])" ),
+		"ATTR": new RegExp( "^" + attributes ),
+		"PSEUDO": new RegExp( "^" + pseudos ),
+		"CHILD": new RegExp( "^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\(" +
+			whitespace + "*(even|odd|(([+-]|)(\\d*)n|)" + whitespace + "*(?:([+-]|)" +
+			whitespace + "*(\\d+)|))" + whitespace + "*\\)|)", "i" ),
+		"bool": new RegExp( "^(?:" + booleans + ")$", "i" ),
+
+		// For use in libraries implementing .is()
+		// We use this for POS matching in `select`
+		"needsContext": new RegExp( "^" + whitespace +
+			"*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\(" + whitespace +
+			"*((?:-\\d)?\\d*)" + whitespace + "*\\)|)(?=[^-]|$)", "i" )
+	},
+
+	rhtml = /HTML$/i,
+	rinputs = /^(?:input|select|textarea|button)$/i,
+	rheader = /^h\d$/i,
+
+	rnative = /^[^{]+\{\s*\[native \w/,
+
+	// Easily-parseable/retrievable ID or TAG or CLASS selectors
+	rquickExpr = /^(?:#([\w-]+)|(\w+)|\.([\w-]+))$/,
+
+	rsibling = /[+~]/,
+
+	// CSS escapes
+	// http://www.w3.org/TR/CSS21/syndata.html#escaped-characters
+	runescape = new RegExp( "\\\\[\\da-fA-F]{1,6}" + whitespace + "?|\\\\([^\\r\\n\\f])", "g" ),
+	funescape = function( escape, nonHex ) {
+		var high = "0x" + escape.slice( 1 ) - 0x10000;
+
+		return nonHex ?
+
+			// Strip the backslash prefix from a non-hex escape sequence
+			nonHex :
+
+			// Replace a hexadecimal escape sequence with the encoded Unicode code point
+			// Support: IE <=11+
+			// For values outside the Basic Multilingual Plane (BMP), manually construct a
+			// surrogate pair
+			high < 0 ?
+				String.fromCharCode( high + 0x10000 ) :
+				String.fromCharCode( high >> 10 | 0xD800, high & 0x3FF | 0xDC00 );
+	},
+
+	// CSS string/identifier serialization
+	// https://drafts.csswg.org/cssom/#common-serializing-idioms
+	rcssescape = /([\0-\x1f\x7f]|^-?\d)|^-$|[^\0-\x1f\x7f-\uFFFF\w-]/g,
+	fcssescape = function( ch, asCodePoint ) {
+		if ( asCodePoint ) {
+
+			// U+0000 NULL becomes U+FFFD REPLACEMENT CHARACTER
+			if ( ch === "\0" ) {
+				return "\uFFFD";
+			}
+
+			// Control characters and (dependent upon position) numbers get escaped as code points
+			return ch.slice( 0, -1 ) + "\\" +
+				ch.charCodeAt( ch.length - 1 ).toString( 16 ) + " ";
+		}
+
+		// Other potentially-special ASCII characters get backslash-escaped
+		return "\\" + ch;
+	},
+
+	// Used for iframes
+	// See setDocument()
+	// Removing the function wrapper causes a "Permission Denied"
+	// error in IE
+	unloadHandler = function() {
+		setDocument();
+	},
+
+	inDisabledFieldset = addCombinator(
+		function( elem ) {
+			return elem.disabled === true && elem.nodeName.toLowerCase() === "fieldset";
+		},
+		{ dir: "parentNode", next: "legend" }
+	);
+
+// Optimize for push.apply( _, NodeList )
+try {
+	push.apply(
+		( arr = slice.call( preferredDoc.childNodes ) ),
+		preferredDoc.childNodes
+	);
+
+	// Support: Android<4.0
+	// Detect silently failing push.apply
+	// eslint-disable-next-line no-unused-expressions
+	arr[ preferredDoc.childNodes.length ].nodeType;
+} catch ( e ) {
+	push = { apply: arr.length ?
+
+		// Leverage slice if possible
+		function( target, els ) {
+			pushNative.apply( target, slice.call( els ) );
+		} :
+
+		// Support: IE<9
+		// Otherwise append directly
+		function( target, els ) {
+			var j = target.length,
+				i = 0;
+
+			// Can't trust NodeList.length
+			while ( ( target[ j++ ] = els[ i++ ] ) ) {}
+			target.length = j - 1;
+		}
+	};
+}
+
+function Sizzle( selector, context, results, seed ) {
+	var m, i, elem, nid, match, groups, newSelector,
+		newContext = context && context.ownerDocument,
+
+		// nodeType defaults to 9, since context defaults to document
+		nodeType = context ? context.nodeType : 9;
+
+	results = results || [];
+
+	// Return early from calls with invalid selector or context
+	if ( typeof selector !== "string" || !selector ||
+		nodeType !== 1 && nodeType !== 9 && nodeType !== 11 ) {
+
+		return results;
+	}
+
+	// Try to shortcut find operations (as opposed to filters) in HTML documents
+	if ( !seed ) {
+		setDocument( context );
+		context = context || document;
+
+		if ( documentIsHTML ) {
+
+			// If the selector is sufficiently simple, try using a "get*By*" DOM method
+			// (excepting DocumentFragment context, where the methods don't exist)
+			if ( nodeType !== 11 && ( match = rquickExpr.exec( selector ) ) ) {
+
+				// ID selector
+				if ( ( m = match[ 1 ] ) ) {
+
+					// Document context
+					if ( nodeType === 9 ) {
+						if ( ( elem = context.getElementById( m ) ) ) {
+
+							// Support: IE, Opera, Webkit
+							// TODO: identify versions
+							// getElementById can match elements by name instead of ID
+							if ( elem.id === m ) {
+								results.push( elem );
+								return results;
+							}
+						} else {
+							return results;
+						}
+
+					// Element context
+					} else {
+
+						// Support: IE, Opera, Webkit
+						// TODO: identify versions
+						// getElementById can match elements by name instead of ID
+						if ( newContext && ( elem = newContext.getElementById( m ) ) &&
+							contains( context, elem ) &&
+							elem.id === m ) {
+
+							results.push( elem );
+							return results;
+						}
+					}
+
+				// Type selector
+				} else if ( match[ 2 ] ) {
+					push.apply( results, context.getElementsByTagName( selector ) );
+					return results;
+
+				// Class selector
+				} else if ( ( m = match[ 3 ] ) && support.getElementsByClassName &&
+					context.getElementsByClassName ) {
+
+					push.apply( results, context.getElementsByClassName( m ) );
+					return results;
+				}
+			}
+
+			// Take advantage of querySelectorAll
+			if ( support.qsa &&
+				!nonnativeSelectorCache[ selector + " " ] &&
+				( !rbuggyQSA || !rbuggyQSA.test( selector ) ) &&
+
+				// Support: IE 8 only
+				// Exclude object elements
+				( nodeType !== 1 || context.nodeName.toLowerCase() !== "object" ) ) {
+
+				newSelector = selector;
+				newContext = context;
+
+				// qSA considers elements outside a scoping root when evaluating child or
+				// descendant combinators, which is not what we want.
+				// In such cases, we work around the behavior by prefixing every selector in the
+				// list with an ID selector referencing the scope context.
+				// The technique has to be used as well when a leading combinator is used
+				// as such selectors are not recognized by querySelectorAll.
+				// Thanks to Andrew Dupont for this technique.
+				if ( nodeType === 1 &&
+					( rdescend.test( selector ) || rcombinators.test( selector ) ) ) {
+
+					// Expand context for sibling selectors
+					newContext = rsibling.test( selector ) && testContext( context.parentNode ) ||
+						context;
+
+					// We can use :scope instead of the ID hack if the browser
+					// supports it & if we're not changing the context.
+					if ( newContext !== context || !support.scope ) {
+
+						// Capture the context ID, setting it first if necessary
+						if ( ( nid = context.getAttribute( "id" ) ) ) {
+							nid = nid.replace( rcssescape, fcssescape );
+						} else {
+							context.setAttribute( "id", ( nid = expando ) );
+						}
+					}
+
+					// Prefix every selector in the list
+					groups = tokenize( selector );
+					i = groups.length;
+					while ( i-- ) {
+						groups[ i ] = ( nid ? "#" + nid : ":scope" ) + " " +
+							toSelector( groups[ i ] );
+					}
+					newSelector = groups.join( "," );
+				}
+
+				try {
+					push.apply( results,
+						newContext.querySelectorAll( newSelector )
+					);
+					return results;
+				} catch ( qsaError ) {
+					nonnativeSelectorCache( selector, true );
+				} finally {
+					if ( nid === expando ) {
+						context.removeAttribute( "id" );
+					}
+				}
+			}
+		}
+	}
+
+	// All others
+	return select( selector.replace( rtrim, "$1" ), context, results, seed );
+}
+
+/**
+ * Create key-value caches of limited size
+ * @returns {function(string, object)} Returns the Object data after storing it on itself with
+ *	property name the (space-suffixed) string and (if the cache is larger than Expr.cacheLength)
+ *	deleting the oldest entry
+ */
+function createCache() {
+	var keys = [];
+
+	function cache( key, value ) {
+
+		// Use (key + " ") to avoid collision with native prototype properties (see Issue #157)
+		if ( keys.push( key + " " ) > Expr.cacheLength ) {
+
+			// Only keep the most recent entries
+			delete cache[ keys.shift() ];
+		}
+		return ( cache[ key + " " ] = value );
+	}
+	return cache;
+}
+
+/**
+ * Mark a function for special use by Sizzle
+ * @param {Function} fn The function to mark
+ */
+function markFunction( fn ) {
+	fn[ expando ] = true;
+	return fn;
+}
+
+/**
+ * Support testing using an element
+ * @param {Function} fn Passed the created element and returns a boolean result
+ */
+function assert( fn ) {
+	var el = document.createElement( "fieldset" );
+
+	try {
+		return !!fn( el );
+	} catch ( e ) {
+		return false;
+	} finally {
+
+		// Remove from its parent by default
+		if ( el.parentNode ) {
+			el.parentNode.removeChild( el );
+		}
+
+		// release memory in IE
+		el = null;
+	}
+}
+
+/**
+ * Adds the same handler for all of the specified attrs
+ * @param {String} attrs Pipe-separated list of attributes
+ * @param {Function} handler The method that will be applied
+ */
+function addHandle( attrs, handler ) {
+	var arr = attrs.split( "|" ),
+		i = arr.length;
+
+	while ( i-- ) {
+		Expr.attrHandle[ arr[ i ] ] = handler;
+	}
+}
+
+/**
+ * Checks document order of two siblings
+ * @param {Element} a
+ * @param {Element} b
+ * @returns {Number} Returns less than 0 if a precedes b, greater than 0 if a follows b
+ */
+function siblingCheck( a, b ) {
+	var cur = b && a,
+		diff = cur && a.nodeType === 1 && b.nodeType === 1 &&
+			a.sourceIndex - b.sourceIndex;
+
+	// Use IE sourceIndex if available on both nodes
+	if ( diff ) {
+		return diff;
+	}
+
+	// Check if b follows a
+	if ( cur ) {
+		while ( ( cur = cur.nextSibling ) ) {
+			if ( cur === b ) {
+				return -1;
+			}
+		}
+	}
+
+	return a ? 1 : -1;
+}
+
+/**
+ * Returns a function to use in pseudos for input types
+ * @param {String} type
+ */
+function createInputPseudo( type ) {
+	return function( elem ) {
+		var name = elem.nodeName.toLowerCase();
+		return name === "input" && elem.type === type;
+	};
+}
+
+/**
+ * Returns a function to use in pseudos for buttons
+ * @param {String} type
+ */
+function createButtonPseudo( type ) {
+	return function( elem ) {
+		var name = elem.nodeName.toLowerCase();
+		return ( name === "input" || name === "button" ) && elem.type === type;
+	};
+}
+
+/**
+ * Returns a function to use in pseudos for :enabled/:disabled
+ * @param {Boolean} disabled true for :disabled; false for :enabled
+ */
+function createDisabledPseudo( disabled ) {
+
+	// Known :disabled false positives: fieldset[disabled] > legend:nth-of-type(n+2) :can-disable
+	return function( elem ) {
+
+		// Only certain elements can match :enabled or :disabled
+		// https://html.spec.whatwg.org/multipage/scripting.html#selector-enabled
+		// https://html.spec.whatwg.org/multipage/scripting.html#selector-disabled
+		if ( "form" in elem ) {
+
+			// Check for inherited disabledness on relevant non-disabled elements:
+			// * listed form-associated elements in a disabled fieldset
+			//   https://html.spec.whatwg.org/multipage/forms.html#category-listed
+			//   https://html.spec.whatwg.org/multipage/forms.html#concept-fe-disabled
+			// * option elements in a disabled optgroup
+			//   https://html.spec.whatwg.org/multipage/forms.html#concept-option-disabled
+			// All such elements have a "form" property.
+			if ( elem.parentNode && elem.disabled === false ) {
+
+				// Option elements defer to a parent optgroup if present
+				if ( "label" in elem ) {
+					if ( "label" in elem.parentNode ) {
+						return elem.parentNode.disabled === disabled;
+					} else {
+						return elem.disabled === disabled;
+					}
+				}
+
+				// Support: IE 6 - 11
+				// Use the isDisabled shortcut property to check for disabled fieldset ancestors
+				return elem.isDisabled === disabled ||
+
+					// Where there is no isDisabled, check manually
+					/* jshint -W018 */
+					elem.isDisabled !== !disabled &&
+					inDisabledFieldset( elem ) === disabled;
+			}
+
+			return elem.disabled === disabled;
+
+		// Try to winnow out elements that can't be disabled before trusting the disabled property.
+		// Some victims get caught in our net (label, legend, menu, track), but it shouldn't
+		// even exist on them, let alone have a boolean value.
+		} else if ( "label" in elem ) {
+			return elem.disabled === disabled;
+		}
+
+		// Remaining elements are neither :enabled nor :disabled
+		return false;
+	};
+}
+
+/**
+ * Returns a function to use in pseudos for positionals
+ * @param {Function} fn
+ */
+function createPositionalPseudo( fn ) {
+	return markFunction( function( argument ) {
+		argument = +argument;
+		return markFunction( function( seed, matches ) {
+			var j,
+				matchIndexes = fn( [], seed.length, argument ),
+				i = matchIndexes.length;
+
+			// Match elements found at the specified indexes
+			while ( i-- ) {
+				if ( seed[ ( j = matchIndexes[ i ] ) ] ) {
+					seed[ j ] = !( matches[ j ] = seed[ j ] );
+				}
+			}
+		} );
+	} );
+}
+
+/**
+ * Checks a node for validity as a Sizzle context
+ * @param {Element|Object=} context
+ * @returns {Element|Object|Boolean} The input node if acceptable, otherwise a falsy value
+ */
+function testContext( context ) {
+	return context && typeof context.getElementsByTagName !== "undefined" && context;
+}
+
+// Expose support vars for convenience
+support = Sizzle.support = {};
+
+/**
+ * Detects XML nodes
+ * @param {Element|Object} elem An element or a document
+ * @returns {Boolean} True iff elem is a non-HTML XML node
+ */
+isXML = Sizzle.isXML = function( elem ) {
+	var namespace = elem && elem.namespaceURI,
+		docElem = elem && ( elem.ownerDocument || elem ).documentElement;
+
+	// Support: IE <=8
+	// Assume HTML when documentElement doesn't yet exist, such as inside loading iframes
+	// https://bugs.jquery.com/ticket/4833
+	return !rhtml.test( namespace || docElem && docElem.nodeName || "HTML" );
+};
+
+/**
+ * Sets document-related variables once based on the current document
+ * @param {Element|Object} [doc] An element or document object to use to set the document
+ * @returns {Object} Returns the current document
+ */
+setDocument = Sizzle.setDocument = function( node ) {
+	var hasCompare, subWindow,
+		doc = node ? node.ownerDocument || node : preferredDoc;
+
+	// Return early if doc is invalid or already selected
+	// Support: IE 11+, Edge 17 - 18+
+	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+	// two documents; shallow comparisons work.
+	// eslint-disable-next-line eqeqeq
+	if ( doc == document || doc.nodeType !== 9 || !doc.documentElement ) {
+		return document;
+	}
+
+	// Update global variables
+	document = doc;
+	docElem = document.documentElement;
+	documentIsHTML = !isXML( document );
+
+	// Support: IE 9 - 11+, Edge 12 - 18+
+	// Accessing iframe documents after unload throws "permission denied" errors (jQuery #13936)
+	// Support: IE 11+, Edge 17 - 18+
+	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+	// two documents; shallow comparisons work.
+	// eslint-disable-next-line eqeqeq
+	if ( preferredDoc != document &&
+		( subWindow = document.defaultView ) && subWindow.top !== subWindow ) {
+
+		// Support: IE 11, Edge
+		if ( subWindow.addEventListener ) {
+			subWindow.addEventListener( "unload", unloadHandler, false );
+
+		// Support: IE 9 - 10 only
+		} else if ( subWindow.attachEvent ) {
+			subWindow.attachEvent( "onunload", unloadHandler );
+		}
+	}
+
+	// Support: IE 8 - 11+, Edge 12 - 18+, Chrome <=16 - 25 only, Firefox <=3.6 - 31 only,
+	// Safari 4 - 5 only, Opera <=11.6 - 12.x only
+	// IE/Edge & older browsers don't support the :scope pseudo-class.
+	// Support: Safari 6.0 only
+	// Safari 6.0 supports :scope but it's an alias of :root there.
+	support.scope = assert( function( el ) {
+		docElem.appendChild( el ).appendChild( document.createElement( "div" ) );
+		return typeof el.querySelectorAll !== "undefined" &&
+			!el.querySelectorAll( ":scope fieldset div" ).length;
+	} );
+
+	/* Attributes
+	---------------------------------------------------------------------- */
+
+	// Support: IE<8
+	// Verify that getAttribute really returns attributes and not properties
+	// (excepting IE8 booleans)
+	support.attributes = assert( function( el ) {
+		el.className = "i";
+		return !el.getAttribute( "className" );
+	} );
+
+	/* getElement(s)By*
+	---------------------------------------------------------------------- */
+
+	// Check if getElementsByTagName("*") returns only elements
+	support.getElementsByTagName = assert( function( el ) {
+		el.appendChild( document.createComment( "" ) );
+		return !el.getElementsByTagName( "*" ).length;
+	} );
+
+	// Support: IE<9
+	support.getElementsByClassName = rnative.test( document.getElementsByClassName );
+
+	// Support: IE<10
+	// Check if getElementById returns elements by name
+	// The broken getElementById methods don't pick up programmatically-set names,
+	// so use a roundabout getElementsByName test
+	support.getById = assert( function( el ) {
+		docElem.appendChild( el ).id = expando;
+		return !document.getElementsByName || !document.getElementsByName( expando ).length;
+	} );
+
+	// ID filter and find
+	if ( support.getById ) {
+		Expr.filter[ "ID" ] = function( id ) {
+			var attrId = id.replace( runescape, funescape );
+			return function( elem ) {
+				return elem.getAttribute( "id" ) === attrId;
+			};
+		};
+		Expr.find[ "ID" ] = function( id, context ) {
+			if ( typeof context.getElementById !== "undefined" && documentIsHTML ) {
+				var elem = context.getElementById( id );
+				return elem ? [ elem ] : [];
+			}
+		};
+	} else {
+		Expr.filter[ "ID" ] =  function( id ) {
+			var attrId = id.replace( runescape, funescape );
+			return function( elem ) {
+				var node = typeof elem.getAttributeNode !== "undefined" &&
+					elem.getAttributeNode( "id" );
+				return node && node.value === attrId;
+			};
+		};
+
+		// Support: IE 6 - 7 only
+		// getElementById is not reliable as a find shortcut
+		Expr.find[ "ID" ] = function( id, context ) {
+			if ( typeof context.getElementById !== "undefined" && documentIsHTML ) {
+				var node, i, elems,
+					elem = context.getElementById( id );
+
+				if ( elem ) {
+
+					// Verify the id attribute
+					node = elem.getAttributeNode( "id" );
+					if ( node && node.value === id ) {
+						return [ elem ];
+					}
+
+					// Fall back on getElementsByName
+					elems = context.getElementsByName( id );
+					i = 0;
+					while ( ( elem = elems[ i++ ] ) ) {
+						node = elem.getAttributeNode( "id" );
+						if ( node && node.value === id ) {
+							return [ elem ];
+						}
+					}
+				}
+
+				return [];
+			}
+		};
+	}
+
+	// Tag
+	Expr.find[ "TAG" ] = support.getElementsByTagName ?
+		function( tag, context ) {
+			if ( typeof context.getElementsByTagName !== "undefined" ) {
+				return context.getElementsByTagName( tag );
+
+			// DocumentFragment nodes don't have gEBTN
+			} else if ( support.qsa ) {
+				return context.querySelectorAll( tag );
+			}
+		} :
+
+		function( tag, context ) {
+			var elem,
+				tmp = [],
+				i = 0,
+
+				// By happy coincidence, a (broken) gEBTN appears on DocumentFragment nodes too
+				results = context.getElementsByTagName( tag );
+
+			// Filter out possible comments
+			if ( tag === "*" ) {
+				while ( ( elem = results[ i++ ] ) ) {
+					if ( elem.nodeType === 1 ) {
+						tmp.push( elem );
+					}
+				}
+
+				return tmp;
+			}
+			return results;
+		};
+
+	// Class
+	Expr.find[ "CLASS" ] = support.getElementsByClassName && function( className, context ) {
+		if ( typeof context.getElementsByClassName !== "undefined" && documentIsHTML ) {
+			return context.getElementsByClassName( className );
+		}
+	};
+
+	/* QSA/matchesSelector
+	---------------------------------------------------------------------- */
+
+	// QSA and matchesSelector support
+
+	// matchesSelector(:active) reports false when true (IE9/Opera 11.5)
+	rbuggyMatches = [];
+
+	// qSa(:focus) reports false when true (Chrome 21)
+	// We allow this because of a bug in IE8/9 that throws an error
+	// whenever `document.activeElement` is accessed on an iframe
+	// So, we allow :focus to pass through QSA all the time to avoid the IE error
+	// See https://bugs.jquery.com/ticket/13378
+	rbuggyQSA = [];
+
+	if ( ( support.qsa = rnative.test( document.querySelectorAll ) ) ) {
+
+		// Build QSA regex
+		// Regex strategy adopted from Diego Perini
+		assert( function( el ) {
+
+			var input;
+
+			// Select is set to empty string on purpose
+			// This is to test IE's treatment of not explicitly
+			// setting a boolean content attribute,
+			// since its presence should be enough
+			// https://bugs.jquery.com/ticket/12359
+			docElem.appendChild( el ).innerHTML = "<a id='" + expando + "'></a>" +
+				"<select id='" + expando + "-\r\\' msallowcapture=''>" +
+				"<option selected=''></option></select>";
+
+			// Support: IE8, Opera 11-12.16
+			// Nothing should be selected when empty strings follow ^= or $= or *=
+			// The test attribute must be unknown in Opera but "safe" for WinRT
+			// https://msdn.microsoft.com/en-us/library/ie/hh465388.aspx#attribute_section
+			if ( el.querySelectorAll( "[msallowcapture^='']" ).length ) {
+				rbuggyQSA.push( "[*^$]=" + whitespace + "*(?:''|\"\")" );
+			}
+
+			// Support: IE8
+			// Boolean attributes and "value" are not treated correctly
+			if ( !el.querySelectorAll( "[selected]" ).length ) {
+				rbuggyQSA.push( "\\[" + whitespace + "*(?:value|" + booleans + ")" );
+			}
+
+			// Support: Chrome<29, Android<4.4, Safari<7.0+, iOS<7.0+, PhantomJS<1.9.8+
+			if ( !el.querySelectorAll( "[id~=" + expando + "-]" ).length ) {
+				rbuggyQSA.push( "~=" );
+			}
+
+			// Support: IE 11+, Edge 15 - 18+
+			// IE 11/Edge don't find elements on a `[name='']` query in some cases.
+			// Adding a temporary attribute to the document before the selection works
+			// around the issue.
+			// Interestingly, IE 10 & older don't seem to have the issue.
+			input = document.createElement( "input" );
+			input.setAttribute( "name", "" );
+			el.appendChild( input );
+			if ( !el.querySelectorAll( "[name='']" ).length ) {
+				rbuggyQSA.push( "\\[" + whitespace + "*name" + whitespace + "*=" +
+					whitespace + "*(?:''|\"\")" );
+			}
+
+			// Webkit/Opera - :checked should return selected option elements
+			// http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked
+			// IE8 throws error here and will not see later tests
+			if ( !el.querySelectorAll( ":checked" ).length ) {
+				rbuggyQSA.push( ":checked" );
+			}
+
+			// Support: Safari 8+, iOS 8+
+			// https://bugs.webkit.org/show_bug.cgi?id=136851
+			// In-page `selector#id sibling-combinator selector` fails
+			if ( !el.querySelectorAll( "a#" + expando + "+*" ).length ) {
+				rbuggyQSA.push( ".#.+[+~]" );
+			}
+
+			// Support: Firefox <=3.6 - 5 only
+			// Old Firefox doesn't throw on a badly-escaped identifier.
+			el.querySelectorAll( "\\\f" );
+			rbuggyQSA.push( "[\\r\\n\\f]" );
+		} );
+
+		assert( function( el ) {
+			el.innerHTML = "<a href='' disabled='disabled'></a>" +
+				"<select disabled='disabled'><option/></select>";
+
+			// Support: Windows 8 Native Apps
+			// The type and name attributes are restricted during .innerHTML assignment
+			var input = document.createElement( "input" );
+			input.setAttribute( "type", "hidden" );
+			el.appendChild( input ).setAttribute( "name", "D" );
+
+			// Support: IE8
+			// Enforce case-sensitivity of name attribute
+			if ( el.querySelectorAll( "[name=d]" ).length ) {
+				rbuggyQSA.push( "name" + whitespace + "*[*^$|!~]?=" );
+			}
+
+			// FF 3.5 - :enabled/:disabled and hidden elements (hidden elements are still enabled)
+			// IE8 throws error here and will not see later tests
+			if ( el.querySelectorAll( ":enabled" ).length !== 2 ) {
+				rbuggyQSA.push( ":enabled", ":disabled" );
+			}
+
+			// Support: IE9-11+
+			// IE's :disabled selector does not pick up the children of disabled fieldsets
+			docElem.appendChild( el ).disabled = true;
+			if ( el.querySelectorAll( ":disabled" ).length !== 2 ) {
+				rbuggyQSA.push( ":enabled", ":disabled" );
+			}
+
+			// Support: Opera 10 - 11 only
+			// Opera 10-11 does not throw on post-comma invalid pseudos
+			el.querySelectorAll( "*,:x" );
+			rbuggyQSA.push( ",.*:" );
+		} );
+	}
+
+	if ( ( support.matchesSelector = rnative.test( ( matches = docElem.matches ||
+		docElem.webkitMatchesSelector ||
+		docElem.mozMatchesSelector ||
+		docElem.oMatchesSelector ||
+		docElem.msMatchesSelector ) ) ) ) {
+
+		assert( function( el ) {
+
+			// Check to see if it's possible to do matchesSelector
+			// on a disconnected node (IE 9)
+			support.disconnectedMatch = matches.call( el, "*" );
+
+			// This should fail with an exception
+			// Gecko does not error, returns false instead
+			matches.call( el, "[s!='']:x" );
+			rbuggyMatches.push( "!=", pseudos );
+		} );
+	}
+
+	rbuggyQSA = rbuggyQSA.length && new RegExp( rbuggyQSA.join( "|" ) );
+	rbuggyMatches = rbuggyMatches.length && new RegExp( rbuggyMatches.join( "|" ) );
+
+	/* Contains
+	---------------------------------------------------------------------- */
+	hasCompare = rnative.test( docElem.compareDocumentPosition );
+
+	// Element contains another
+	// Purposefully self-exclusive
+	// As in, an element does not contain itself
+	contains = hasCompare || rnative.test( docElem.contains ) ?
+		function( a, b ) {
+			var adown = a.nodeType === 9 ? a.documentElement : a,
+				bup = b && b.parentNode;
+			return a === bup || !!( bup && bup.nodeType === 1 && (
+				adown.contains ?
+					adown.contains( bup ) :
+					a.compareDocumentPosition && a.compareDocumentPosition( bup ) & 16
+			) );
+		} :
+		function( a, b ) {
+			if ( b ) {
+				while ( ( b = b.parentNode ) ) {
+					if ( b === a ) {
+						return true;
+					}
+				}
+			}
+			return false;
+		};
+
+	/* Sorting
+	---------------------------------------------------------------------- */
+
+	// Document order sorting
+	sortOrder = hasCompare ?
+	function( a, b ) {
+
+		// Flag for duplicate removal
+		if ( a === b ) {
+			hasDuplicate = true;
+			return 0;
+		}
+
+		// Sort on method existence if only one input has compareDocumentPosition
+		var compare = !a.compareDocumentPosition - !b.compareDocumentPosition;
+		if ( compare ) {
+			return compare;
+		}
+
+		// Calculate position if both inputs belong to the same document
+		// Support: IE 11+, Edge 17 - 18+
+		// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+		// two documents; shallow comparisons work.
+		// eslint-disable-next-line eqeqeq
+		compare = ( a.ownerDocument || a ) == ( b.ownerDocument || b ) ?
+			a.compareDocumentPosition( b ) :
+
+			// Otherwise we know they are disconnected
+			1;
+
+		// Disconnected nodes
+		if ( compare & 1 ||
+			( !support.sortDetached && b.compareDocumentPosition( a ) === compare ) ) {
+
+			// Choose the first element that is related to our preferred document
+			// Support: IE 11+, Edge 17 - 18+
+			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+			// two documents; shallow comparisons work.
+			// eslint-disable-next-line eqeqeq
+			if ( a == document || a.ownerDocument == preferredDoc &&
+				contains( preferredDoc, a ) ) {
+				return -1;
+			}
+
+			// Support: IE 11+, Edge 17 - 18+
+			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+			// two documents; shallow comparisons work.
+			// eslint-disable-next-line eqeqeq
+			if ( b == document || b.ownerDocument == preferredDoc &&
+				contains( preferredDoc, b ) ) {
+				return 1;
+			}
+
+			// Maintain original order
+			return sortInput ?
+				( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) :
+				0;
+		}
+
+		return compare & 4 ? -1 : 1;
+	} :
+	function( a, b ) {
+
+		// Exit early if the nodes are identical
+		if ( a === b ) {
+			hasDuplicate = true;
+			return 0;
+		}
+
+		var cur,
+			i = 0,
+			aup = a.parentNode,
+			bup = b.parentNode,
+			ap = [ a ],
+			bp = [ b ];
+
+		// Parentless nodes are either documents or disconnected
+		if ( !aup || !bup ) {
+
+			// Support: IE 11+, Edge 17 - 18+
+			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+			// two documents; shallow comparisons work.
+			/* eslint-disable eqeqeq */
+			return a == document ? -1 :
+				b == document ? 1 :
+				/* eslint-enable eqeqeq */
+				aup ? -1 :
+				bup ? 1 :
+				sortInput ?
+				( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) :
+				0;
+
+		// If the nodes are siblings, we can do a quick check
+		} else if ( aup === bup ) {
+			return siblingCheck( a, b );
+		}
+
+		// Otherwise we need full lists of their ancestors for comparison
+		cur = a;
+		while ( ( cur = cur.parentNode ) ) {
+			ap.unshift( cur );
+		}
+		cur = b;
+		while ( ( cur = cur.parentNode ) ) {
+			bp.unshift( cur );
+		}
+
+		// Walk down the tree looking for a discrepancy
+		while ( ap[ i ] === bp[ i ] ) {
+			i++;
+		}
+
+		return i ?
+
+			// Do a sibling check if the nodes have a common ancestor
+			siblingCheck( ap[ i ], bp[ i ] ) :
+
+			// Otherwise nodes in our document sort first
+			// Support: IE 11+, Edge 17 - 18+
+			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+			// two documents; shallow comparisons work.
+			/* eslint-disable eqeqeq */
+			ap[ i ] == preferredDoc ? -1 :
+			bp[ i ] == preferredDoc ? 1 :
+			/* eslint-enable eqeqeq */
+			0;
+	};
+
+	return document;
+};
+
+Sizzle.matches = function( expr, elements ) {
+	return Sizzle( expr, null, null, elements );
+};
+
+Sizzle.matchesSelector = function( elem, expr ) {
+	setDocument( elem );
+
+	if ( support.matchesSelector && documentIsHTML &&
+		!nonnativeSelectorCache[ expr + " " ] &&
+		( !rbuggyMatches || !rbuggyMatches.test( expr ) ) &&
+		( !rbuggyQSA     || !rbuggyQSA.test( expr ) ) ) {
+
+		try {
+			var ret = matches.call( elem, expr );
+
+			// IE 9's matchesSelector returns false on disconnected nodes
+			if ( ret || support.disconnectedMatch ||
+
+				// As well, disconnected nodes are said to be in a document
+				// fragment in IE 9
+				elem.document && elem.document.nodeType !== 11 ) {
+				return ret;
+			}
+		} catch ( e ) {
+			nonnativeSelectorCache( expr, true );
+		}
+	}
+
+	return Sizzle( expr, document, null, [ elem ] ).length > 0;
+};
+
+Sizzle.contains = function( context, elem ) {
+
+	// Set document vars if needed
+	// Support: IE 11+, Edge 17 - 18+
+	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+	// two documents; shallow comparisons work.
+	// eslint-disable-next-line eqeqeq
+	if ( ( context.ownerDocument || context ) != document ) {
+		setDocument( context );
+	}
+	return contains( context, elem );
+};
+
+Sizzle.attr = function( elem, name ) {
+
+	// Set document vars if needed
+	// Support: IE 11+, Edge 17 - 18+
+	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+	// two documents; shallow comparisons work.
+	// eslint-disable-next-line eqeqeq
+	if ( ( elem.ownerDocument || elem ) != document ) {
+		setDocument( elem );
+	}
+
+	var fn = Expr.attrHandle[ name.toLowerCase() ],
+
+		// Don't get fooled by Object.prototype properties (jQuery #13807)
+		val = fn && hasOwn.call( Expr.attrHandle, name.toLowerCase() ) ?
+			fn( elem, name, !documentIsHTML ) :
+			undefined;
+
+	return val !== undefined ?
+		val :
+		support.attributes || !documentIsHTML ?
+			elem.getAttribute( name ) :
+			( val = elem.getAttributeNode( name ) ) && val.specified ?
+				val.value :
+				null;
+};
+
+Sizzle.escape = function( sel ) {
+	return ( sel + "" ).replace( rcssescape, fcssescape );
+};
+
+Sizzle.error = function( msg ) {
+	throw new Error( "Syntax error, unrecognized expression: " + msg );
+};
+
+/**
+ * Document sorting and removing duplicates
+ * @param {ArrayLike} results
+ */
+Sizzle.uniqueSort = function( results ) {
+	var elem,
+		duplicates = [],
+		j = 0,
+		i = 0;
+
+	// Unless we *know* we can detect duplicates, assume their presence
+	hasDuplicate = !support.detectDuplicates;
+	sortInput = !support.sortStable && results.slice( 0 );
+	results.sort( sortOrder );
+
+	if ( hasDuplicate ) {
+		while ( ( elem = results[ i++ ] ) ) {
+			if ( elem === results[ i ] ) {
+				j = duplicates.push( i );
+			}
+		}
+		while ( j-- ) {
+			results.splice( duplicates[ j ], 1 );
+		}
+	}
+
+	// Clear input after sorting to release objects
+	// See https://github.com/jquery/sizzle/pull/225
+	sortInput = null;
+
+	return results;
+};
+
+/**
+ * Utility function for retrieving the text value of an array of DOM nodes
+ * @param {Array|Element} elem
+ */
+getText = Sizzle.getText = function( elem ) {
+	var node,
+		ret = "",
+		i = 0,
+		nodeType = elem.nodeType;
+
+	if ( !nodeType ) {
+
+		// If no nodeType, this is expected to be an array
+		while ( ( node = elem[ i++ ] ) ) {
+
+			// Do not traverse comment nodes
+			ret += getText( node );
+		}
+	} else if ( nodeType === 1 || nodeType === 9 || nodeType === 11 ) {
+
+		// Use textContent for elements
+		// innerText usage removed for consistency of new lines (jQuery #11153)
+		if ( typeof elem.textContent === "string" ) {
+			return elem.textContent;
+		} else {
+
+			// Traverse its children
+			for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) {
+				ret += getText( elem );
+			}
+		}
+	} else if ( nodeType === 3 || nodeType === 4 ) {
+		return elem.nodeValue;
+	}
+
+	// Do not include comment or processing instruction nodes
+
+	return ret;
+};
+
+Expr = Sizzle.selectors = {
+
+	// Can be adjusted by the user
+	cacheLength: 50,
+
+	createPseudo: markFunction,
+
+	match: matchExpr,
+
+	attrHandle: {},
+
+	find: {},
+
+	relative: {
+		">": { dir: "parentNode", first: true },
+		" ": { dir: "parentNode" },
+		"+": { dir: "previousSibling", first: true },
+		"~": { dir: "previousSibling" }
+	},
+
+	preFilter: {
+		"ATTR": function( match ) {
+			match[ 1 ] = match[ 1 ].replace( runescape, funescape );
+
+			// Move the given value to match[3] whether quoted or unquoted
+			match[ 3 ] = ( match[ 3 ] || match[ 4 ] ||
+				match[ 5 ] || "" ).replace( runescape, funescape );
+
+			if ( match[ 2 ] === "~=" ) {
+				match[ 3 ] = " " + match[ 3 ] + " ";
+			}
+
+			return match.slice( 0, 4 );
+		},
+
+		"CHILD": function( match ) {
+
+			/* matches from matchExpr["CHILD"]
+				1 type (only|nth|...)
+				2 what (child|of-type)
+				3 argument (even|odd|\d*|\d*n([+-]\d+)?|...)
+				4 xn-component of xn+y argument ([+-]?\d*n|)
+				5 sign of xn-component
+				6 x of xn-component
+				7 sign of y-component
+				8 y of y-component
+			*/
+			match[ 1 ] = match[ 1 ].toLowerCase();
+
+			if ( match[ 1 ].slice( 0, 3 ) === "nth" ) {
+
+				// nth-* requires argument
+				if ( !match[ 3 ] ) {
+					Sizzle.error( match[ 0 ] );
+				}
+
+				// numeric x and y parameters for Expr.filter.CHILD
+				// remember that false/true cast respectively to 0/1
+				match[ 4 ] = +( match[ 4 ] ?
+					match[ 5 ] + ( match[ 6 ] || 1 ) :
+					2 * ( match[ 3 ] === "even" || match[ 3 ] === "odd" ) );
+				match[ 5 ] = +( ( match[ 7 ] + match[ 8 ] ) || match[ 3 ] === "odd" );
+
+				// other types prohibit arguments
+			} else if ( match[ 3 ] ) {
+				Sizzle.error( match[ 0 ] );
+			}
+
+			return match;
+		},
+
+		"PSEUDO": function( match ) {
+			var excess,
+				unquoted = !match[ 6 ] && match[ 2 ];
+
+			if ( matchExpr[ "CHILD" ].test( match[ 0 ] ) ) {
+				return null;
+			}
+
+			// Accept quoted arguments as-is
+			if ( match[ 3 ] ) {
+				match[ 2 ] = match[ 4 ] || match[ 5 ] || "";
+
+			// Strip excess characters from unquoted arguments
+			} else if ( unquoted && rpseudo.test( unquoted ) &&
+
+				// Get excess from tokenize (recursively)
+				( excess = tokenize( unquoted, true ) ) &&
+
+				// advance to the next closing parenthesis
+				( excess = unquoted.indexOf( ")", unquoted.length - excess ) - unquoted.length ) ) {
+
+				// excess is a negative index
+				match[ 0 ] = match[ 0 ].slice( 0, excess );
+				match[ 2 ] = unquoted.slice( 0, excess );
+			}
+
+			// Return only captures needed by the pseudo filter method (type and argument)
+			return match.slice( 0, 3 );
+		}
+	},
+
+	filter: {
+
+		"TAG": function( nodeNameSelector ) {
+			var nodeName = nodeNameSelector.replace( runescape, funescape ).toLowerCase();
+			return nodeNameSelector === "*" ?
+				function() {
+					return true;
+				} :
+				function( elem ) {
+					return elem.nodeName && elem.nodeName.toLowerCase() === nodeName;
+				};
+		},
+
+		"CLASS": function( className ) {
+			var pattern = classCache[ className + " " ];
+
+			return pattern ||
+				( pattern = new RegExp( "(^|" + whitespace +
+					")" + className + "(" + whitespace + "|$)" ) ) && classCache(
+						className, function( elem ) {
+							return pattern.test(
+								typeof elem.className === "string" && elem.className ||
+								typeof elem.getAttribute !== "undefined" &&
+									elem.getAttribute( "class" ) ||
+								""
+							);
+				} );
+		},
+
+		"ATTR": function( name, operator, check ) {
+			return function( elem ) {
+				var result = Sizzle.attr( elem, name );
+
+				if ( result == null ) {
+					return operator === "!=";
+				}
+				if ( !operator ) {
+					return true;
+				}
+
+				result += "";
+
+				/* eslint-disable max-len */
+
+				return operator === "=" ? result === check :
+					operator === "!=" ? result !== check :
+					operator === "^=" ? check && result.indexOf( check ) === 0 :
+					operator === "*=" ? check && result.indexOf( check ) > -1 :
+					operator === "$=" ? check && result.slice( -check.length ) === check :
+					operator === "~=" ? ( " " + result.replace( rwhitespace, " " ) + " " ).indexOf( check ) > -1 :
+					operator === "|=" ? result === check || result.slice( 0, check.length + 1 ) === check + "-" :
+					false;
+				/* eslint-enable max-len */
+
+			};
+		},
+
+		"CHILD": function( type, what, _argument, first, last ) {
+			var simple = type.slice( 0, 3 ) !== "nth",
+				forward = type.slice( -4 ) !== "last",
+				ofType = what === "of-type";
+
+			return first === 1 && last === 0 ?
+
+				// Shortcut for :nth-*(n)
+				function( elem ) {
+					return !!elem.parentNode;
+				} :
+
+				function( elem, _context, xml ) {
+					var cache, uniqueCache, outerCache, node, nodeIndex, start,
+						dir = simple !== forward ? "nextSibling" : "previousSibling",
+						parent = elem.parentNode,
+						name = ofType && elem.nodeName.toLowerCase(),
+						useCache = !xml && !ofType,
+						diff = false;
+
+					if ( parent ) {
+
+						// :(first|last|only)-(child|of-type)
+						if ( simple ) {
+							while ( dir ) {
+								node = elem;
+								while ( ( node = node[ dir ] ) ) {
+									if ( ofType ?
+										node.nodeName.toLowerCase() === name :
+										node.nodeType === 1 ) {
+
+										return false;
+									}
+								}
+
+								// Reverse direction for :only-* (if we haven't yet done so)
+								start = dir = type === "only" && !start && "nextSibling";
+							}
+							return true;
+						}
+
+						start = [ forward ? parent.firstChild : parent.lastChild ];
+
+						// non-xml :nth-child(...) stores cache data on `parent`
+						if ( forward && useCache ) {
+
+							// Seek `elem` from a previously-cached index
+
+							// ...in a gzip-friendly way
+							node = parent;
+							outerCache = node[ expando ] || ( node[ expando ] = {} );
+
+							// Support: IE <9 only
+							// Defend against cloned attroperties (jQuery gh-1709)
+							uniqueCache = outerCache[ node.uniqueID ] ||
+								( outerCache[ node.uniqueID ] = {} );
+
+							cache = uniqueCache[ type ] || [];
+							nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ];
+							diff = nodeIndex && cache[ 2 ];
+							node = nodeIndex && parent.childNodes[ nodeIndex ];
+
+							while ( ( node = ++nodeIndex && node && node[ dir ] ||
+
+								// Fallback to seeking `elem` from the start
+								( diff = nodeIndex = 0 ) || start.pop() ) ) {
+
+								// When found, cache indexes on `parent` and break
+								if ( node.nodeType === 1 && ++diff && node === elem ) {
+									uniqueCache[ type ] = [ dirruns, nodeIndex, diff ];
+									break;
+								}
+							}
+
+						} else {
+
+							// Use previously-cached element index if available
+							if ( useCache ) {
+
+								// ...in a gzip-friendly way
+								node = elem;
+								outerCache = node[ expando ] || ( node[ expando ] = {} );
+
+								// Support: IE <9 only
+								// Defend against cloned attroperties (jQuery gh-1709)
+								uniqueCache = outerCache[ node.uniqueID ] ||
+									( outerCache[ node.uniqueID ] = {} );
+
+								cache = uniqueCache[ type ] || [];
+								nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ];
+								diff = nodeIndex;
+							}
+
+							// xml :nth-child(...)
+							// or :nth-last-child(...) or :nth(-last)?-of-type(...)
+							if ( diff === false ) {
+
+								// Use the same loop as above to seek `elem` from the start
+								while ( ( node = ++nodeIndex && node && node[ dir ] ||
+									( diff = nodeIndex = 0 ) || start.pop() ) ) {
+
+									if ( ( ofType ?
+										node.nodeName.toLowerCase() === name :
+										node.nodeType === 1 ) &&
+										++diff ) {
+
+										// Cache the index of each encountered element
+										if ( useCache ) {
+											outerCache = node[ expando ] ||
+												( node[ expando ] = {} );
+
+											// Support: IE <9 only
+											// Defend against cloned attroperties (jQuery gh-1709)
+											uniqueCache = outerCache[ node.uniqueID ] ||
+												( outerCache[ node.uniqueID ] = {} );
+
+											uniqueCache[ type ] = [ dirruns, diff ];
+										}
+
+										if ( node === elem ) {
+											break;
+										}
+									}
+								}
+							}
+						}
+
+						// Incorporate the offset, then check against cycle size
+						diff -= last;
+						return diff === first || ( diff % first === 0 && diff / first >= 0 );
+					}
+				};
+		},
+
+		"PSEUDO": function( pseudo, argument ) {
+
+			// pseudo-class names are case-insensitive
+			// http://www.w3.org/TR/selectors/#pseudo-classes
+			// Prioritize by case sensitivity in case custom pseudos are added with uppercase letters
+			// Remember that setFilters inherits from pseudos
+			var args,
+				fn = Expr.pseudos[ pseudo ] || Expr.setFilters[ pseudo.toLowerCase() ] ||
+					Sizzle.error( "unsupported pseudo: " + pseudo );
+
+			// The user may use createPseudo to indicate that
+			// arguments are needed to create the filter function
+			// just as Sizzle does
+			if ( fn[ expando ] ) {
+				return fn( argument );
+			}
+
+			// But maintain support for old signatures
+			if ( fn.length > 1 ) {
+				args = [ pseudo, pseudo, "", argument ];
+				return Expr.setFilters.hasOwnProperty( pseudo.toLowerCase() ) ?
+					markFunction( function( seed, matches ) {
+						var idx,
+							matched = fn( seed, argument ),
+							i = matched.length;
+						while ( i-- ) {
+							idx = indexOf( seed, matched[ i ] );
+							seed[ idx ] = !( matches[ idx ] = matched[ i ] );
+						}
+					} ) :
+					function( elem ) {
+						return fn( elem, 0, args );
+					};
+			}
+
+			return fn;
+		}
+	},
+
+	pseudos: {
+
+		// Potentially complex pseudos
+		"not": markFunction( function( selector ) {
+
+			// Trim the selector passed to compile
+			// to avoid treating leading and trailing
+			// spaces as combinators
+			var input = [],
+				results = [],
+				matcher = compile( selector.replace( rtrim, "$1" ) );
+
+			return matcher[ expando ] ?
+				markFunction( function( seed, matches, _context, xml ) {
+					var elem,
+						unmatched = matcher( seed, null, xml, [] ),
+						i = seed.length;
+
+					// Match elements unmatched by `matcher`
+					while ( i-- ) {
+						if ( ( elem = unmatched[ i ] ) ) {
+							seed[ i ] = !( matches[ i ] = elem );
+						}
+					}
+				} ) :
+				function( elem, _context, xml ) {
+					input[ 0 ] = elem;
+					matcher( input, null, xml, results );
+
+					// Don't keep the element (issue #299)
+					input[ 0 ] = null;
+					return !results.pop();
+				};
+		} ),
+
+		"has": markFunction( function( selector ) {
+			return function( elem ) {
+				return Sizzle( selector, elem ).length > 0;
+			};
+		} ),
+
+		"contains": markFunction( function( text ) {
+			text = text.replace( runescape, funescape );
+			return function( elem ) {
+				return ( elem.textContent || getText( elem ) ).indexOf( text ) > -1;
+			};
+		} ),
+
+		// "Whether an element is represented by a :lang() selector
+		// is based solely on the element's language value
+		// being equal to the identifier C,
+		// or beginning with the identifier C immediately followed by "-".
+		// The matching of C against the element's language value is performed case-insensitively.
+		// The identifier C does not have to be a valid language name."
+		// http://www.w3.org/TR/selectors/#lang-pseudo
+		"lang": markFunction( function( lang ) {
+
+			// lang value must be a valid identifier
+			if ( !ridentifier.test( lang || "" ) ) {
+				Sizzle.error( "unsupported lang: " + lang );
+			}
+			lang = lang.replace( runescape, funescape ).toLowerCase();
+			return function( elem ) {
+				var elemLang;
+				do {
+					if ( ( elemLang = documentIsHTML ?
+						elem.lang :
+						elem.getAttribute( "xml:lang" ) || elem.getAttribute( "lang" ) ) ) {
+
+						elemLang = elemLang.toLowerCase();
+						return elemLang === lang || elemLang.indexOf( lang + "-" ) === 0;
+					}
+				} while ( ( elem = elem.parentNode ) && elem.nodeType === 1 );
+				return false;
+			};
+		} ),
+
+		// Miscellaneous
+		"target": function( elem ) {
+			var hash = window.location && window.location.hash;
+			return hash && hash.slice( 1 ) === elem.id;
+		},
+
+		"root": function( elem ) {
+			return elem === docElem;
+		},
+
+		"focus": function( elem ) {
+			return elem === document.activeElement &&
+				( !document.hasFocus || document.hasFocus() ) &&
+				!!( elem.type || elem.href || ~elem.tabIndex );
+		},
+
+		// Boolean properties
+		"enabled": createDisabledPseudo( false ),
+		"disabled": createDisabledPseudo( true ),
+
+		"checked": function( elem ) {
+
+			// In CSS3, :checked should return both checked and selected elements
+			// http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked
+			var nodeName = elem.nodeName.toLowerCase();
+			return ( nodeName === "input" && !!elem.checked ) ||
+				( nodeName === "option" && !!elem.selected );
+		},
+
+		"selected": function( elem ) {
+
+			// Accessing this property makes selected-by-default
+			// options in Safari work properly
+			if ( elem.parentNode ) {
+				// eslint-disable-next-line no-unused-expressions
+				elem.parentNode.selectedIndex;
+			}
+
+			return elem.selected === true;
+		},
+
+		// Contents
+		"empty": function( elem ) {
+
+			// http://www.w3.org/TR/selectors/#empty-pseudo
+			// :empty is negated by element (1) or content nodes (text: 3; cdata: 4; entity ref: 5),
+			//   but not by others (comment: 8; processing instruction: 7; etc.)
+			// nodeType < 6 works because attributes (2) do not appear as children
+			for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) {
+				if ( elem.nodeType < 6 ) {
+					return false;
+				}
+			}
+			return true;
+		},
+
+		"parent": function( elem ) {
+			return !Expr.pseudos[ "empty" ]( elem );
+		},
+
+		// Element/input types
+		"header": function( elem ) {
+			return rheader.test( elem.nodeName );
+		},
+
+		"input": function( elem ) {
+			return rinputs.test( elem.nodeName );
+		},
+
+		"button": function( elem ) {
+			var name = elem.nodeName.toLowerCase();
+			return name === "input" && elem.type === "button" || name === "button";
+		},
+
+		"text": function( elem ) {
+			var attr;
+			return elem.nodeName.toLowerCase() === "input" &&
+				elem.type === "text" &&
+
+				// Support: IE<8
+				// New HTML5 attribute values (e.g., "search") appear with elem.type === "text"
+				( ( attr = elem.getAttribute( "type" ) ) == null ||
+					attr.toLowerCase() === "text" );
+		},
+
+		// Position-in-collection
+		"first": createPositionalPseudo( function() {
+			return [ 0 ];
+		} ),
+
+		"last": createPositionalPseudo( function( _matchIndexes, length ) {
+			return [ length - 1 ];
+		} ),
+
+		"eq": createPositionalPseudo( function( _matchIndexes, length, argument ) {
+			return [ argument < 0 ? argument + length : argument ];
+		} ),
+
+		"even": createPositionalPseudo( function( matchIndexes, length ) {
+			var i = 0;
+			for ( ; i < length; i += 2 ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		} ),
+
+		"odd": createPositionalPseudo( function( matchIndexes, length ) {
+			var i = 1;
+			for ( ; i < length; i += 2 ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		} ),
+
+		"lt": createPositionalPseudo( function( matchIndexes, length, argument ) {
+			var i = argument < 0 ?
+				argument + length :
+				argument > length ?
+					length :
+					argument;
+			for ( ; --i >= 0; ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		} ),
+
+		"gt": createPositionalPseudo( function( matchIndexes, length, argument ) {
+			var i = argument < 0 ? argument + length : argument;
+			for ( ; ++i < length; ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		} )
+	}
+};
+
+Expr.pseudos[ "nth" ] = Expr.pseudos[ "eq" ];
+
+// Add button/input type pseudos
+for ( i in { radio: true, checkbox: true, file: true, password: true, image: true } ) {
+	Expr.pseudos[ i ] = createInputPseudo( i );
+}
+for ( i in { submit: true, reset: true } ) {
+	Expr.pseudos[ i ] = createButtonPseudo( i );
+}
+
+// Easy API for creating new setFilters
+function setFilters() {}
+setFilters.prototype = Expr.filters = Expr.pseudos;
+Expr.setFilters = new setFilters();
+
+tokenize = Sizzle.tokenize = function( selector, parseOnly ) {
+	var matched, match, tokens, type,
+		soFar, groups, preFilters,
+		cached = tokenCache[ selector + " " ];
+
+	if ( cached ) {
+		return parseOnly ? 0 : cached.slice( 0 );
+	}
+
+	soFar = selector;
+	groups = [];
+	preFilters = Expr.preFilter;
+
+	while ( soFar ) {
+
+		// Comma and first run
+		if ( !matched || ( match = rcomma.exec( soFar ) ) ) {
+			if ( match ) {
+
+				// Don't consume trailing commas as valid
+				soFar = soFar.slice( match[ 0 ].length ) || soFar;
+			}
+			groups.push( ( tokens = [] ) );
+		}
+
+		matched = false;
+
+		// Combinators
+		if ( ( match = rcombinators.exec( soFar ) ) ) {
+			matched = match.shift();
+			tokens.push( {
+				value: matched,
+
+				// Cast descendant combinators to space
+				type: match[ 0 ].replace( rtrim, " " )
+			} );
+			soFar = soFar.slice( matched.length );
+		}
+
+		// Filters
+		for ( type in Expr.filter ) {
+			if ( ( match = matchExpr[ type ].exec( soFar ) ) && ( !preFilters[ type ] ||
+				( match = preFilters[ type ]( match ) ) ) ) {
+				matched = match.shift();
+				tokens.push( {
+					value: matched,
+					type: type,
+					matches: match
+				} );
+				soFar = soFar.slice( matched.length );
+			}
+		}
+
+		if ( !matched ) {
+			break;
+		}
+	}
+
+	// Return the length of the invalid excess
+	// if we're just parsing
+	// Otherwise, throw an error or return tokens
+	return parseOnly ?
+		soFar.length :
+		soFar ?
+			Sizzle.error( selector ) :
+
+			// Cache the tokens
+			tokenCache( selector, groups ).slice( 0 );
+};
+
+function toSelector( tokens ) {
+	var i = 0,
+		len = tokens.length,
+		selector = "";
+	for ( ; i < len; i++ ) {
+		selector += tokens[ i ].value;
+	}
+	return selector;
+}
+
+function addCombinator( matcher, combinator, base ) {
+	var dir = combinator.dir,
+		skip = combinator.next,
+		key = skip || dir,
+		checkNonElements = base && key === "parentNode",
+		doneName = done++;
+
+	return combinator.first ?
+
+		// Check against closest ancestor/preceding element
+		function( elem, context, xml ) {
+			while ( ( elem = elem[ dir ] ) ) {
+				if ( elem.nodeType === 1 || checkNonElements ) {
+					return matcher( elem, context, xml );
+				}
+			}
+			return false;
+		} :
+
+		// Check against all ancestor/preceding elements
+		function( elem, context, xml ) {
+			var oldCache, uniqueCache, outerCache,
+				newCache = [ dirruns, doneName ];
+
+			// We can't set arbitrary data on XML nodes, so they don't benefit from combinator caching
+			if ( xml ) {
+				while ( ( elem = elem[ dir ] ) ) {
+					if ( elem.nodeType === 1 || checkNonElements ) {
+						if ( matcher( elem, context, xml ) ) {
+							return true;
+						}
+					}
+				}
+			} else {
+				while ( ( elem = elem[ dir ] ) ) {
+					if ( elem.nodeType === 1 || checkNonElements ) {
+						outerCache = elem[ expando ] || ( elem[ expando ] = {} );
+
+						// Support: IE <9 only
+						// Defend against cloned attroperties (jQuery gh-1709)
+						uniqueCache = outerCache[ elem.uniqueID ] ||
+							( outerCache[ elem.uniqueID ] = {} );
+
+						if ( skip && skip === elem.nodeName.toLowerCase() ) {
+							elem = elem[ dir ] || elem;
+						} else if ( ( oldCache = uniqueCache[ key ] ) &&
+							oldCache[ 0 ] === dirruns && oldCache[ 1 ] === doneName ) {
+
+							// Assign to newCache so results back-propagate to previous elements
+							return ( newCache[ 2 ] = oldCache[ 2 ] );
+						} else {
+
+							// Reuse newcache so results back-propagate to previous elements
+							uniqueCache[ key ] = newCache;
+
+							// A match means we're done; a fail means we have to keep checking
+							if ( ( newCache[ 2 ] = matcher( elem, context, xml ) ) ) {
+								return true;
+							}
+						}
+					}
+				}
+			}
+			return false;
+		};
+}
+
+function elementMatcher( matchers ) {
+	return matchers.length > 1 ?
+		function( elem, context, xml ) {
+			var i = matchers.length;
+			while ( i-- ) {
+				if ( !matchers[ i ]( elem, context, xml ) ) {
+					return false;
+				}
+			}
+			return true;
+		} :
+		matchers[ 0 ];
+}
+
+function multipleContexts( selector, contexts, results ) {
+	var i = 0,
+		len = contexts.length;
+	for ( ; i < len; i++ ) {
+		Sizzle( selector, contexts[ i ], results );
+	}
+	return results;
+}
+
+function condense( unmatched, map, filter, context, xml ) {
+	var elem,
+		newUnmatched = [],
+		i = 0,
+		len = unmatched.length,
+		mapped = map != null;
+
+	for ( ; i < len; i++ ) {
+		if ( ( elem = unmatched[ i ] ) ) {
+			if ( !filter || filter( elem, context, xml ) ) {
+				newUnmatched.push( elem );
+				if ( mapped ) {
+					map.push( i );
+				}
+			}
+		}
+	}
+
+	return newUnmatched;
+}
+
+function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postSelector ) {
+	if ( postFilter && !postFilter[ expando ] ) {
+		postFilter = setMatcher( postFilter );
+	}
+	if ( postFinder && !postFinder[ expando ] ) {
+		postFinder = setMatcher( postFinder, postSelector );
+	}
+	return markFunction( function( seed, results, context, xml ) {
+		var temp, i, elem,
+			preMap = [],
+			postMap = [],
+			preexisting = results.length,
+
+			// Get initial elements from seed or context
+			elems = seed || multipleContexts(
+				selector || "*",
+				context.nodeType ? [ context ] : context,
+				[]
+			),
+
+			// Prefilter to get matcher input, preserving a map for seed-results synchronization
+			matcherIn = preFilter && ( seed || !selector ) ?
+				condense( elems, preMap, preFilter, context, xml ) :
+				elems,
+
+			matcherOut = matcher ?
+
+				// If we have a postFinder, or filtered seed, or non-seed postFilter or preexisting results,
+				postFinder || ( seed ? preFilter : preexisting || postFilter ) ?
+
+					// ...intermediate processing is necessary
+					[] :
+
+					// ...otherwise use results directly
+					results :
+				matcherIn;
+
+		// Find primary matches
+		if ( matcher ) {
+			matcher( matcherIn, matcherOut, context, xml );
+		}
+
+		// Apply postFilter
+		if ( postFilter ) {
+			temp = condense( matcherOut, postMap );
+			postFilter( temp, [], context, xml );
+
+			// Un-match failing elements by moving them back to matcherIn
+			i = temp.length;
+			while ( i-- ) {
+				if ( ( elem = temp[ i ] ) ) {
+					matcherOut[ postMap[ i ] ] = !( matcherIn[ postMap[ i ] ] = elem );
+				}
+			}
+		}
+
+		if ( seed ) {
+			if ( postFinder || preFilter ) {
+				if ( postFinder ) {
+
+					// Get the final matcherOut by condensing this intermediate into postFinder contexts
+					temp = [];
+					i = matcherOut.length;
+					while ( i-- ) {
+						if ( ( elem = matcherOut[ i ] ) ) {
+
+							// Restore matcherIn since elem is not yet a final match
+							temp.push( ( matcherIn[ i ] = elem ) );
+						}
+					}
+					postFinder( null, ( matcherOut = [] ), temp, xml );
+				}
+
+				// Move matched elements from seed to results to keep them synchronized
+				i = matcherOut.length;
+				while ( i-- ) {
+					if ( ( elem = matcherOut[ i ] ) &&
+						( temp = postFinder ? indexOf( seed, elem ) : preMap[ i ] ) > -1 ) {
+
+						seed[ temp ] = !( results[ temp ] = elem );
+					}
+				}
+			}
+
+		// Add elements to results, through postFinder if defined
+		} else {
+			matcherOut = condense(
+				matcherOut === results ?
+					matcherOut.splice( preexisting, matcherOut.length ) :
+					matcherOut
+			);
+			if ( postFinder ) {
+				postFinder( null, results, matcherOut, xml );
+			} else {
+				push.apply( results, matcherOut );
+			}
+		}
+	} );
+}
+
+function matcherFromTokens( tokens ) {
+	var checkContext, matcher, j,
+		len = tokens.length,
+		leadingRelative = Expr.relative[ tokens[ 0 ].type ],
+		implicitRelative = leadingRelative || Expr.relative[ " " ],
+		i = leadingRelative ? 1 : 0,
+
+		// The foundational matcher ensures that elements are reachable from top-level context(s)
+		matchContext = addCombinator( function( elem ) {
+			return elem === checkContext;
+		}, implicitRelative, true ),
+		matchAnyContext = addCombinator( function( elem ) {
+			return indexOf( checkContext, elem ) > -1;
+		}, implicitRelative, true ),
+		matchers = [ function( elem, context, xml ) {
+			var ret = ( !leadingRelative && ( xml || context !== outermostContext ) ) || (
+				( checkContext = context ).nodeType ?
+					matchContext( elem, context, xml ) :
+					matchAnyContext( elem, context, xml ) );
+
+			// Avoid hanging onto element (issue #299)
+			checkContext = null;
+			return ret;
+		} ];
+
+	for ( ; i < len; i++ ) {
+		if ( ( matcher = Expr.relative[ tokens[ i ].type ] ) ) {
+			matchers = [ addCombinator( elementMatcher( matchers ), matcher ) ];
+		} else {
+			matcher = Expr.filter[ tokens[ i ].type ].apply( null, tokens[ i ].matches );
+
+			// Return special upon seeing a positional matcher
+			if ( matcher[ expando ] ) {
+
+				// Find the next relative operator (if any) for proper handling
+				j = ++i;
+				for ( ; j < len; j++ ) {
+					if ( Expr.relative[ tokens[ j ].type ] ) {
+						break;
+					}
+				}
+				return setMatcher(
+					i > 1 && elementMatcher( matchers ),
+					i > 1 && toSelector(
+
+					// If the preceding token was a descendant combinator, insert an implicit any-element `*`
+					tokens
+						.slice( 0, i - 1 )
+						.concat( { value: tokens[ i - 2 ].type === " " ? "*" : "" } )
+					).replace( rtrim, "$1" ),
+					matcher,
+					i < j && matcherFromTokens( tokens.slice( i, j ) ),
+					j < len && matcherFromTokens( ( tokens = tokens.slice( j ) ) ),
+					j < len && toSelector( tokens )
+				);
+			}
+			matchers.push( matcher );
+		}
+	}
+
+	return elementMatcher( matchers );
+}
+
+function matcherFromGroupMatchers( elementMatchers, setMatchers ) {
+	var bySet = setMatchers.length > 0,
+		byElement = elementMatchers.length > 0,
+		superMatcher = function( seed, context, xml, results, outermost ) {
+			var elem, j, matcher,
+				matchedCount = 0,
+				i = "0",
+				unmatched = seed && [],
+				setMatched = [],
+				contextBackup = outermostContext,
+
+				// We must always have either seed elements or outermost context
+				elems = seed || byElement && Expr.find[ "TAG" ]( "*", outermost ),
+
+				// Use integer dirruns iff this is the outermost matcher
+				dirrunsUnique = ( dirruns += contextBackup == null ? 1 : Math.random() || 0.1 ),
+				len = elems.length;
+
+			if ( outermost ) {
+
+				// Support: IE 11+, Edge 17 - 18+
+				// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+				// two documents; shallow comparisons work.
+				// eslint-disable-next-line eqeqeq
+				outermostContext = context == document || context || outermost;
+			}
+
+			// Add elements passing elementMatchers directly to results
+			// Support: IE<9, Safari
+			// Tolerate NodeList properties (IE: "length"; Safari: <number>) matching elements by id
+			for ( ; i !== len && ( elem = elems[ i ] ) != null; i++ ) {
+				if ( byElement && elem ) {
+					j = 0;
+
+					// Support: IE 11+, Edge 17 - 18+
+					// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+					// two documents; shallow comparisons work.
+					// eslint-disable-next-line eqeqeq
+					if ( !context && elem.ownerDocument != document ) {
+						setDocument( elem );
+						xml = !documentIsHTML;
+					}
+					while ( ( matcher = elementMatchers[ j++ ] ) ) {
+						if ( matcher( elem, context || document, xml ) ) {
+							results.push( elem );
+							break;
+						}
+					}
+					if ( outermost ) {
+						dirruns = dirrunsUnique;
+					}
+				}
+
+				// Track unmatched elements for set filters
+				if ( bySet ) {
+
+					// They will have gone through all possible matchers
+					if ( ( elem = !matcher && elem ) ) {
+						matchedCount--;
+					}
+
+					// Lengthen the array for every element, matched or not
+					if ( seed ) {
+						unmatched.push( elem );
+					}
+				}
+			}
+
+			// `i` is now the count of elements visited above, and adding it to `matchedCount`
+			// makes the latter nonnegative.
+			matchedCount += i;
+
+			// Apply set filters to unmatched elements
+			// NOTE: This can be skipped if there are no unmatched elements (i.e., `matchedCount`
+			// equals `i`), unless we didn't visit _any_ elements in the above loop because we have
+			// no element matchers and no seed.
+			// Incrementing an initially-string "0" `i` allows `i` to remain a string only in that
+			// case, which will result in a "00" `matchedCount` that differs from `i` but is also
+			// numerically zero.
+			if ( bySet && i !== matchedCount ) {
+				j = 0;
+				while ( ( matcher = setMatchers[ j++ ] ) ) {
+					matcher( unmatched, setMatched, context, xml );
+				}
+
+				if ( seed ) {
+
+					// Reintegrate element matches to eliminate the need for sorting
+					if ( matchedCount > 0 ) {
+						while ( i-- ) {
+							if ( !( unmatched[ i ] || setMatched[ i ] ) ) {
+								setMatched[ i ] = pop.call( results );
+							}
+						}
+					}
+
+					// Discard index placeholder values to get only actual matches
+					setMatched = condense( setMatched );
+				}
+
+				// Add matches to results
+				push.apply( results, setMatched );
+
+				// Seedless set matches succeeding multiple successful matchers stipulate sorting
+				if ( outermost && !seed && setMatched.length > 0 &&
+					( matchedCount + setMatchers.length ) > 1 ) {
+
+					Sizzle.uniqueSort( results );
+				}
+			}
+
+			// Override manipulation of globals by nested matchers
+			if ( outermost ) {
+				dirruns = dirrunsUnique;
+				outermostContext = contextBackup;
+			}
+
+			return unmatched;
+		};
+
+	return bySet ?
+		markFunction( superMatcher ) :
+		superMatcher;
+}
+
+compile = Sizzle.compile = function( selector, match /* Internal Use Only */ ) {
+	var i,
+		setMatchers = [],
+		elementMatchers = [],
+		cached = compilerCache[ selector + " " ];
+
+	if ( !cached ) {
+
+		// Generate a function of recursive functions that can be used to check each element
+		if ( !match ) {
+			match = tokenize( selector );
+		}
+		i = match.length;
+		while ( i-- ) {
+			cached = matcherFromTokens( match[ i ] );
+			if ( cached[ expando ] ) {
+				setMatchers.push( cached );
+			} else {
+				elementMatchers.push( cached );
+			}
+		}
+
+		// Cache the compiled function
+		cached = compilerCache(
+			selector,
+			matcherFromGroupMatchers( elementMatchers, setMatchers )
+		);
+
+		// Save selector and tokenization
+		cached.selector = selector;
+	}
+	return cached;
+};
+
+/**
+ * A low-level selection function that works with Sizzle's compiled
+ *  selector functions
+ * @param {String|Function} selector A selector or a pre-compiled
+ *  selector function built with Sizzle.compile
+ * @param {Element} context
+ * @param {Array} [results]
+ * @param {Array} [seed] A set of elements to match against
+ */
+select = Sizzle.select = function( selector, context, results, seed ) {
+	var i, tokens, token, type, find,
+		compiled = typeof selector === "function" && selector,
+		match = !seed && tokenize( ( selector = compiled.selector || selector ) );
+
+	results = results || [];
+
+	// Try to minimize operations if there is only one selector in the list and no seed
+	// (the latter of which guarantees us context)
+	if ( match.length === 1 ) {
+
+		// Reduce context if the leading compound selector is an ID
+		tokens = match[ 0 ] = match[ 0 ].slice( 0 );
+		if ( tokens.length > 2 && ( token = tokens[ 0 ] ).type === "ID" &&
+			context.nodeType === 9 && documentIsHTML && Expr.relative[ tokens[ 1 ].type ] ) {
+
+			context = ( Expr.find[ "ID" ]( token.matches[ 0 ]
+				.replace( runescape, funescape ), context ) || [] )[ 0 ];
+			if ( !context ) {
+				return results;
+
+			// Precompiled matchers will still verify ancestry, so step up a level
+			} else if ( compiled ) {
+				context = context.parentNode;
+			}
+
+			selector = selector.slice( tokens.shift().value.length );
+		}
+
+		// Fetch a seed set for right-to-left matching
+		i = matchExpr[ "needsContext" ].test( selector ) ? 0 : tokens.length;
+		while ( i-- ) {
+			token = tokens[ i ];
+
+			// Abort if we hit a combinator
+			if ( Expr.relative[ ( type = token.type ) ] ) {
+				break;
+			}
+			if ( ( find = Expr.find[ type ] ) ) {
+
+				// Search, expanding context for leading sibling combinators
+				if ( ( seed = find(
+					token.matches[ 0 ].replace( runescape, funescape ),
+					rsibling.test( tokens[ 0 ].type ) && testContext( context.parentNode ) ||
+						context
+				) ) ) {
+
+					// If seed is empty or no tokens remain, we can return early
+					tokens.splice( i, 1 );
+					selector = seed.length && toSelector( tokens );
+					if ( !selector ) {
+						push.apply( results, seed );
+						return results;
+					}
+
+					break;
+				}
+			}
+		}
+	}
+
+	// Compile and execute a filtering function if one is not provided
+	// Provide `match` to avoid retokenization if we modified the selector above
+	( compiled || compile( selector, match ) )(
+		seed,
+		context,
+		!documentIsHTML,
+		results,
+		!context || rsibling.test( selector ) && testContext( context.parentNode ) || context
+	);
+	return results;
+};
+
+// One-time assignments
+
+// Sort stability
+support.sortStable = expando.split( "" ).sort( sortOrder ).join( "" ) === expando;
+
+// Support: Chrome 14-35+
+// Always assume duplicates if they aren't passed to the comparison function
+support.detectDuplicates = !!hasDuplicate;
+
+// Initialize against the default document
+setDocument();
+
+// Support: Webkit<537.32 - Safari 6.0.3/Chrome 25 (fixed in Chrome 27)
+// Detached nodes confoundingly follow *each other*
+support.sortDetached = assert( function( el ) {
+
+	// Should return 1, but returns 4 (following)
+	return el.compareDocumentPosition( document.createElement( "fieldset" ) ) & 1;
+} );
+
+// Support: IE<8
+// Prevent attribute/property "interpolation"
+// https://msdn.microsoft.com/en-us/library/ms536429%28VS.85%29.aspx
+if ( !assert( function( el ) {
+	el.innerHTML = "<a href='#'></a>";
+	return el.firstChild.getAttribute( "href" ) === "#";
+} ) ) {
+	addHandle( "type|href|height|width", function( elem, name, isXML ) {
+		if ( !isXML ) {
+			return elem.getAttribute( name, name.toLowerCase() === "type" ? 1 : 2 );
+		}
+	} );
+}
+
+// Support: IE<9
+// Use defaultValue in place of getAttribute("value")
+if ( !support.attributes || !assert( function( el ) {
+	el.innerHTML = "<input/>";
+	el.firstChild.setAttribute( "value", "" );
+	return el.firstChild.getAttribute( "value" ) === "";
+} ) ) {
+	addHandle( "value", function( elem, _name, isXML ) {
+		if ( !isXML && elem.nodeName.toLowerCase() === "input" ) {
+			return elem.defaultValue;
+		}
+	} );
+}
+
+// Support: IE<9
+// Use getAttributeNode to fetch booleans when getAttribute lies
+if ( !assert( function( el ) {
+	return el.getAttribute( "disabled" ) == null;
+} ) ) {
+	addHandle( booleans, function( elem, name, isXML ) {
+		var val;
+		if ( !isXML ) {
+			return elem[ name ] === true ? name.toLowerCase() :
+				( val = elem.getAttributeNode( name ) ) && val.specified ?
+					val.value :
+					null;
+		}
+	} );
+}
+
+return Sizzle;
+
+} )( window );
+
+
+
+jQuery.find = Sizzle;
+jQuery.expr = Sizzle.selectors;
+
+// Deprecated
+jQuery.expr[ ":" ] = jQuery.expr.pseudos;
+jQuery.uniqueSort = jQuery.unique = Sizzle.uniqueSort;
+jQuery.text = Sizzle.getText;
+jQuery.isXMLDoc = Sizzle.isXML;
+jQuery.contains = Sizzle.contains;
+jQuery.escapeSelector = Sizzle.escape;
+
+
+
+
+var dir = function( elem, dir, until ) {
+	var matched = [],
+		truncate = until !== undefined;
+
+	while ( ( elem = elem[ dir ] ) && elem.nodeType !== 9 ) {
+		if ( elem.nodeType === 1 ) {
+			if ( truncate && jQuery( elem ).is( until ) ) {
+				break;
+			}
+			matched.push( elem );
+		}
+	}
+	return matched;
+};
+
+
+var siblings = function( n, elem ) {
+	var matched = [];
+
+	for ( ; n; n = n.nextSibling ) {
+		if ( n.nodeType === 1 && n !== elem ) {
+			matched.push( n );
+		}
+	}
+
+	return matched;
+};
+
+
+var rneedsContext = jQuery.expr.match.needsContext;
+
+
+
+function nodeName( elem, name ) {
+
+	return elem.nodeName && elem.nodeName.toLowerCase() === name.toLowerCase();
+
+}
+var rsingleTag = ( /^<([a-z][^\/\0>:\x20\t\r\n\f]*)[\x20\t\r\n\f]*\/?>(?:<\/\1>|)$/i );
+
+
+
+// Implement the identical functionality for filter and not
+function winnow( elements, qualifier, not ) {
+	if ( isFunction( qualifier ) ) {
+		return jQuery.grep( elements, function( elem, i ) {
+			return !!qualifier.call( elem, i, elem ) !== not;
+		} );
+	}
+
+	// Single element
+	if ( qualifier.nodeType ) {
+		return jQuery.grep( elements, function( elem ) {
+			return ( elem === qualifier ) !== not;
+		} );
+	}
+
+	// Arraylike of elements (jQuery, arguments, Array)
+	if ( typeof qualifier !== "string" ) {
+		return jQuery.grep( elements, function( elem ) {
+			return ( indexOf.call( qualifier, elem ) > -1 ) !== not;
+		} );
+	}
+
+	// Filtered directly for both simple and complex selectors
+	return jQuery.filter( qualifier, elements, not );
+}
+
+jQuery.filter = function( expr, elems, not ) {
+	var elem = elems[ 0 ];
+
+	if ( not ) {
+		expr = ":not(" + expr + ")";
+	}
+
+	if ( elems.length === 1 && elem.nodeType === 1 ) {
+		return jQuery.find.matchesSelector( elem, expr ) ? [ elem ] : [];
+	}
+
+	return jQuery.find.matches( expr, jQuery.grep( elems, function( elem ) {
+		return elem.nodeType === 1;
+	} ) );
+};
+
+jQuery.fn.extend( {
+	find: function( selector ) {
+		var i, ret,
+			len = this.length,
+			self = this;
+
+		if ( typeof selector !== "string" ) {
+			return this.pushStack( jQuery( selector ).filter( function() {
+				for ( i = 0; i < len; i++ ) {
+					if ( jQuery.contains( self[ i ], this ) ) {
+						return true;
+					}
+				}
+			} ) );
+		}
+
+		ret = this.pushStack( [] );
+
+		for ( i = 0; i < len; i++ ) {
+			jQuery.find( selector, self[ i ], ret );
+		}
+
+		return len > 1 ? jQuery.uniqueSort( ret ) : ret;
+	},
+	filter: function( selector ) {
+		return this.pushStack( winnow( this, selector || [], false ) );
+	},
+	not: function( selector ) {
+		return this.pushStack( winnow( this, selector || [], true ) );
+	},
+	is: function( selector ) {
+		return !!winnow(
+			this,
+
+			// If this is a positional/relative selector, check membership in the returned set
+			// so $("p:first").is("p:last") won't return true for a doc with two "p".
+			typeof selector === "string" && rneedsContext.test( selector ) ?
+				jQuery( selector ) :
+				selector || [],
+			false
+		).length;
+	}
+} );
+
+
+// Initialize a jQuery object
+
+
+// A central reference to the root jQuery(document)
+var rootjQuery,
+
+	// A simple way to check for HTML strings
+	// Prioritize #id over <tag> to avoid XSS via location.hash (#9521)
+	// Strict HTML recognition (#11290: must start with <)
+	// Shortcut simple #id case for speed
+	rquickExpr = /^(?:\s*(<[\w\W]+>)[^>]*|#([\w-]+))$/,
+
+	init = jQuery.fn.init = function( selector, context, root ) {
+		var match, elem;
+
+		// HANDLE: $(""), $(null), $(undefined), $(false)
+		if ( !selector ) {
+			return this;
+		}
+
+		// Method init() accepts an alternate rootjQuery
+		// so migrate can support jQuery.sub (gh-2101)
+		root = root || rootjQuery;
+
+		// Handle HTML strings
+		if ( typeof selector === "string" ) {
+			if ( selector[ 0 ] === "<" &&
+				selector[ selector.length - 1 ] === ">" &&
+				selector.length >= 3 ) {
+
+				// Assume that strings that start and end with <> are HTML and skip the regex check
+				match = [ null, selector, null ];
+
+			} else {
+				match = rquickExpr.exec( selector );
+			}
+
+			// Match html or make sure no context is specified for #id
+			if ( match && ( match[ 1 ] || !context ) ) {
+
+				// HANDLE: $(html) -> $(array)
+				if ( match[ 1 ] ) {
+					context = context instanceof jQuery ? context[ 0 ] : context;
+
+					// Option to run scripts is true for back-compat
+					// Intentionally let the error be thrown if parseHTML is not present
+					jQuery.merge( this, jQuery.parseHTML(
+						match[ 1 ],
+						context && context.nodeType ? context.ownerDocument || context : document,
+						true
+					) );
+
+					// HANDLE: $(html, props)
+					if ( rsingleTag.test( match[ 1 ] ) && jQuery.isPlainObject( context ) ) {
+						for ( match in context ) {
+
+							// Properties of context are called as methods if possible
+							if ( isFunction( this[ match ] ) ) {
+								this[ match ]( context[ match ] );
+
+							// ...and otherwise set as attributes
+							} else {
+								this.attr( match, context[ match ] );
+							}
+						}
+					}
+
+					return this;
+
+				// HANDLE: $(#id)
+				} else {
+					elem = document.getElementById( match[ 2 ] );
+
+					if ( elem ) {
+
+						// Inject the element directly into the jQuery object
+						this[ 0 ] = elem;
+						this.length = 1;
+					}
+					return this;
+				}
+
+			// HANDLE: $(expr, $(...))
+			} else if ( !context || context.jquery ) {
+				return ( context || root ).find( selector );
+
+			// HANDLE: $(expr, context)
+			// (which is just equivalent to: $(context).find(expr)
+			} else {
+				return this.constructor( context ).find( selector );
+			}
+
+		// HANDLE: $(DOMElement)
+		} else if ( selector.nodeType ) {
+			this[ 0 ] = selector;
+			this.length = 1;
+			return this;
+
+		// HANDLE: $(function)
+		// Shortcut for document ready
+		} else if ( isFunction( selector ) ) {
+			return root.ready !== undefined ?
+				root.ready( selector ) :
+
+				// Execute immediately if ready is not present
+				selector( jQuery );
+		}
+
+		return jQuery.makeArray( selector, this );
+	};
+
+// Give the init function the jQuery prototype for later instantiation
+init.prototype = jQuery.fn;
+
+// Initialize central reference
+rootjQuery = jQuery( document );
+
+
+var rparentsprev = /^(?:parents|prev(?:Until|All))/,
+
+	// Methods guaranteed to produce a unique set when starting from a unique set
+	guaranteedUnique = {
+		children: true,
+		contents: true,
+		next: true,
+		prev: true
+	};
+
+jQuery.fn.extend( {
+	has: function( target ) {
+		var targets = jQuery( target, this ),
+			l = targets.length;
+
+		return this.filter( function() {
+			var i = 0;
+			for ( ; i < l; i++ ) {
+				if ( jQuery.contains( this, targets[ i ] ) ) {
+					return true;
+				}
+			}
+		} );
+	},
+
+	closest: function( selectors, context ) {
+		var cur,
+			i = 0,
+			l = this.length,
+			matched = [],
+			targets = typeof selectors !== "string" && jQuery( selectors );
+
+		// Positional selectors never match, since there's no _selection_ context
+		if ( !rneedsContext.test( selectors ) ) {
+			for ( ; i < l; i++ ) {
+				for ( cur = this[ i ]; cur && cur !== context; cur = cur.parentNode ) {
+
+					// Always skip document fragments
+					if ( cur.nodeType < 11 && ( targets ?
+						targets.index( cur ) > -1 :
+
+						// Don't pass non-elements to Sizzle
+						cur.nodeType === 1 &&
+							jQuery.find.matchesSelector( cur, selectors ) ) ) {
+
+						matched.push( cur );
+						break;
+					}
+				}
+			}
+		}
+
+		return this.pushStack( matched.length > 1 ? jQuery.uniqueSort( matched ) : matched );
+	},
+
+	// Determine the position of an element within the set
+	index: function( elem ) {
+
+		// No argument, return index in parent
+		if ( !elem ) {
+			return ( this[ 0 ] && this[ 0 ].parentNode ) ? this.first().prevAll().length : -1;
+		}
+
+		// Index in selector
+		if ( typeof elem === "string" ) {
+			return indexOf.call( jQuery( elem ), this[ 0 ] );
+		}
+
+		// Locate the position of the desired element
+		return indexOf.call( this,
+
+			// If it receives a jQuery object, the first element is used
+			elem.jquery ? elem[ 0 ] : elem
+		);
+	},
+
+	add: function( selector, context ) {
+		return this.pushStack(
+			jQuery.uniqueSort(
+				jQuery.merge( this.get(), jQuery( selector, context ) )
+			)
+		);
+	},
+
+	addBack: function( selector ) {
+		return this.add( selector == null ?
+			this.prevObject : this.prevObject.filter( selector )
+		);
+	}
+} );
+
+function sibling( cur, dir ) {
+	while ( ( cur = cur[ dir ] ) && cur.nodeType !== 1 ) {}
+	return cur;
+}
+
+jQuery.each( {
+	parent: function( elem ) {
+		var parent = elem.parentNode;
+		return parent && parent.nodeType !== 11 ? parent : null;
+	},
+	parents: function( elem ) {
+		return dir( elem, "parentNode" );
+	},
+	parentsUntil: function( elem, _i, until ) {
+		return dir( elem, "parentNode", until );
+	},
+	next: function( elem ) {
+		return sibling( elem, "nextSibling" );
+	},
+	prev: function( elem ) {
+		return sibling( elem, "previousSibling" );
+	},
+	nextAll: function( elem ) {
+		return dir( elem, "nextSibling" );
+	},
+	prevAll: function( elem ) {
+		return dir( elem, "previousSibling" );
+	},
+	nextUntil: function( elem, _i, until ) {
+		return dir( elem, "nextSibling", until );
+	},
+	prevUntil: function( elem, _i, until ) {
+		return dir( elem, "previousSibling", until );
+	},
+	siblings: function( elem ) {
+		return siblings( ( elem.parentNode || {} ).firstChild, elem );
+	},
+	children: function( elem ) {
+		return siblings( elem.firstChild );
+	},
+	contents: function( elem ) {
+		if ( elem.contentDocument != null &&
+
+			// Support: IE 11+
+			// <object> elements with no `data` attribute has an object
+			// `contentDocument` with a `null` prototype.
+			getProto( elem.contentDocument ) ) {
+
+			return elem.contentDocument;
+		}
+
+		// Support: IE 9 - 11 only, iOS 7 only, Android Browser <=4.3 only
+		// Treat the template element as a regular one in browsers that
+		// don't support it.
+		if ( nodeName( elem, "template" ) ) {
+			elem = elem.content || elem;
+		}
+
+		return jQuery.merge( [], elem.childNodes );
+	}
+}, function( name, fn ) {
+	jQuery.fn[ name ] = function( until, selector ) {
+		var matched = jQuery.map( this, fn, until );
+
+		if ( name.slice( -5 ) !== "Until" ) {
+			selector = until;
+		}
+
+		if ( selector && typeof selector === "string" ) {
+			matched = jQuery.filter( selector, matched );
+		}
+
+		if ( this.length > 1 ) {
+
+			// Remove duplicates
+			if ( !guaranteedUnique[ name ] ) {
+				jQuery.uniqueSort( matched );
+			}
+
+			// Reverse order for parents* and prev-derivatives
+			if ( rparentsprev.test( name ) ) {
+				matched.reverse();
+			}
+		}
+
+		return this.pushStack( matched );
+	};
+} );
+var rnothtmlwhite = ( /[^\x20\t\r\n\f]+/g );
+
+
+
+// Convert String-formatted options into Object-formatted ones
+function createOptions( options ) {
+	var object = {};
+	jQuery.each( options.match( rnothtmlwhite ) || [], function( _, flag ) {
+		object[ flag ] = true;
+	} );
+	return object;
+}
+
+/*
+ * Create a callback list using the following parameters:
+ *
+ *	options: an optional list of space-separated options that will change how
+ *			the callback list behaves or a more traditional option object
+ *
+ * By default a callback list will act like an event callback list and can be
+ * "fired" multiple times.
+ *
+ * Possible options:
+ *
+ *	once:			will ensure the callback list can only be fired once (like a Deferred)
+ *
+ *	memory:			will keep track of previous values and will call any callback added
+ *					after the list has been fired right away with the latest "memorized"
+ *					values (like a Deferred)
+ *
+ *	unique:			will ensure a callback can only be added once (no duplicate in the list)
+ *
+ *	stopOnFalse:	interrupt callings when a callback returns false
+ *
+ */
+jQuery.Callbacks = function( options ) {
+
+	// Convert options from String-formatted to Object-formatted if needed
+	// (we check in cache first)
+	options = typeof options === "string" ?
+		createOptions( options ) :
+		jQuery.extend( {}, options );
+
+	var // Flag to know if list is currently firing
+		firing,
+
+		// Last fire value for non-forgettable lists
+		memory,
+
+		// Flag to know if list was already fired
+		fired,
+
+		// Flag to prevent firing
+		locked,
+
+		// Actual callback list
+		list = [],
+
+		// Queue of execution data for repeatable lists
+		queue = [],
+
+		// Index of currently firing callback (modified by add/remove as needed)
+		firingIndex = -1,
+
+		// Fire callbacks
+		fire = function() {
+
+			// Enforce single-firing
+			locked = locked || options.once;
+
+			// Execute callbacks for all pending executions,
+			// respecting firingIndex overrides and runtime changes
+			fired = firing = true;
+			for ( ; queue.length; firingIndex = -1 ) {
+				memory = queue.shift();
+				while ( ++firingIndex < list.length ) {
+
+					// Run callback and check for early termination
+					if ( list[ firingIndex ].apply( memory[ 0 ], memory[ 1 ] ) === false &&
+						options.stopOnFalse ) {
+
+						// Jump to end and forget the data so .add doesn't re-fire
+						firingIndex = list.length;
+						memory = false;
+					}
+				}
+			}
+
+			// Forget the data if we're done with it
+			if ( !options.memory ) {
+				memory = false;
+			}
+
+			firing = false;
+
+			// Clean up if we're done firing for good
+			if ( locked ) {
+
+				// Keep an empty list if we have data for future add calls
+				if ( memory ) {
+					list = [];
+
+				// Otherwise, this object is spent
+				} else {
+					list = "";
+				}
+			}
+		},
+
+		// Actual Callbacks object
+		self = {
+
+			// Add a callback or a collection of callbacks to the list
+			add: function() {
+				if ( list ) {
+
+					// If we have memory from a past run, we should fire after adding
+					if ( memory && !firing ) {
+						firingIndex = list.length - 1;
+						queue.push( memory );
+					}
+
+					( function add( args ) {
+						jQuery.each( args, function( _, arg ) {
+							if ( isFunction( arg ) ) {
+								if ( !options.unique || !self.has( arg ) ) {
+									list.push( arg );
+								}
+							} else if ( arg && arg.length && toType( arg ) !== "string" ) {
+
+								// Inspect recursively
+								add( arg );
+							}
+						} );
+					} )( arguments );
+
+					if ( memory && !firing ) {
+						fire();
+					}
+				}
+				return this;
+			},
+
+			// Remove a callback from the list
+			remove: function() {
+				jQuery.each( arguments, function( _, arg ) {
+					var index;
+					while ( ( index = jQuery.inArray( arg, list, index ) ) > -1 ) {
+						list.splice( index, 1 );
+
+						// Handle firing indexes
+						if ( index <= firingIndex ) {
+							firingIndex--;
+						}
+					}
+				} );
+				return this;
+			},
+
+			// Check if a given callback is in the list.
+			// If no argument is given, return whether or not list has callbacks attached.
+			has: function( fn ) {
+				return fn ?
+					jQuery.inArray( fn, list ) > -1 :
+					list.length > 0;
+			},
+
+			// Remove all callbacks from the list
+			empty: function() {
+				if ( list ) {
+					list = [];
+				}
+				return this;
+			},
+
+			// Disable .fire and .add
+			// Abort any current/pending executions
+			// Clear all callbacks and values
+			disable: function() {
+				locked = queue = [];
+				list = memory = "";
+				return this;
+			},
+			disabled: function() {
+				return !list;
+			},
+
+			// Disable .fire
+			// Also disable .add unless we have memory (since it would have no effect)
+			// Abort any pending executions
+			lock: function() {
+				locked = queue = [];
+				if ( !memory && !firing ) {
+					list = memory = "";
+				}
+				return this;
+			},
+			locked: function() {
+				return !!locked;
+			},
+
+			// Call all callbacks with the given context and arguments
+			fireWith: function( context, args ) {
+				if ( !locked ) {
+					args = args || [];
+					args = [ context, args.slice ? args.slice() : args ];
+					queue.push( args );
+					if ( !firing ) {
+						fire();
+					}
+				}
+				return this;
+			},
+
+			// Call all the callbacks with the given arguments
+			fire: function() {
+				self.fireWith( this, arguments );
+				return this;
+			},
+
+			// To know if the callbacks have already been called at least once
+			fired: function() {
+				return !!fired;
+			}
+		};
+
+	return self;
+};
+
+
+function Identity( v ) {
+	return v;
+}
+function Thrower( ex ) {
+	throw ex;
+}
+
+function adoptValue( value, resolve, reject, noValue ) {
+	var method;
+
+	try {
+
+		// Check for promise aspect first to privilege synchronous behavior
+		if ( value && isFunction( ( method = value.promise ) ) ) {
+			method.call( value ).done( resolve ).fail( reject );
+
+		// Other thenables
+		} else if ( value && isFunction( ( method = value.then ) ) ) {
+			method.call( value, resolve, reject );
+
+		// Other non-thenables
+		} else {
+
+			// Control `resolve` arguments by letting Array#slice cast boolean `noValue` to integer:
+			// * false: [ value ].slice( 0 ) => resolve( value )
+			// * true: [ value ].slice( 1 ) => resolve()
+			resolve.apply( undefined, [ value ].slice( noValue ) );
+		}
+
+	// For Promises/A+, convert exceptions into rejections
+	// Since jQuery.when doesn't unwrap thenables, we can skip the extra checks appearing in
+	// Deferred#then to conditionally suppress rejection.
+	} catch ( value ) {
+
+		// Support: Android 4.0 only
+		// Strict mode functions invoked without .call/.apply get global-object context
+		reject.apply( undefined, [ value ] );
+	}
+}
+
+jQuery.extend( {
+
+	Deferred: function( func ) {
+		var tuples = [
+
+				// action, add listener, callbacks,
+				// ... .then handlers, argument index, [final state]
+				[ "notify", "progress", jQuery.Callbacks( "memory" ),
+					jQuery.Callbacks( "memory" ), 2 ],
+				[ "resolve", "done", jQuery.Callbacks( "once memory" ),
+					jQuery.Callbacks( "once memory" ), 0, "resolved" ],
+				[ "reject", "fail", jQuery.Callbacks( "once memory" ),
+					jQuery.Callbacks( "once memory" ), 1, "rejected" ]
+			],
+			state = "pending",
+			promise = {
+				state: function() {
+					return state;
+				},
+				always: function() {
+					deferred.done( arguments ).fail( arguments );
+					return this;
+				},
+				"catch": function( fn ) {
+					return promise.then( null, fn );
+				},
+
+				// Keep pipe for back-compat
+				pipe: function( /* fnDone, fnFail, fnProgress */ ) {
+					var fns = arguments;
+
+					return jQuery.Deferred( function( newDefer ) {
+						jQuery.each( tuples, function( _i, tuple ) {
+
+							// Map tuples (progress, done, fail) to arguments (done, fail, progress)
+							var fn = isFunction( fns[ tuple[ 4 ] ] ) && fns[ tuple[ 4 ] ];
+
+							// deferred.progress(function() { bind to newDefer or newDefer.notify })
+							// deferred.done(function() { bind to newDefer or newDefer.resolve })
+							// deferred.fail(function() { bind to newDefer or newDefer.reject })
+							deferred[ tuple[ 1 ] ]( function() {
+								var returned = fn && fn.apply( this, arguments );
+								if ( returned && isFunction( returned.promise ) ) {
+									returned.promise()
+										.progress( newDefer.notify )
+										.done( newDefer.resolve )
+										.fail( newDefer.reject );
+								} else {
+									newDefer[ tuple[ 0 ] + "With" ](
+										this,
+										fn ? [ returned ] : arguments
+									);
+								}
+							} );
+						} );
+						fns = null;
+					} ).promise();
+				},
+				then: function( onFulfilled, onRejected, onProgress ) {
+					var maxDepth = 0;
+					function resolve( depth, deferred, handler, special ) {
+						return function() {
+							var that = this,
+								args = arguments,
+								mightThrow = function() {
+									var returned, then;
+
+									// Support: Promises/A+ section 2.3.3.3.3
+									// https://promisesaplus.com/#point-59
+									// Ignore double-resolution attempts
+									if ( depth < maxDepth ) {
+										return;
+									}
+
+									returned = handler.apply( that, args );
+
+									// Support: Promises/A+ section 2.3.1
+									// https://promisesaplus.com/#point-48
+									if ( returned === deferred.promise() ) {
+										throw new TypeError( "Thenable self-resolution" );
+									}
+
+									// Support: Promises/A+ sections 2.3.3.1, 3.5
+									// https://promisesaplus.com/#point-54
+									// https://promisesaplus.com/#point-75
+									// Retrieve `then` only once
+									then = returned &&
+
+										// Support: Promises/A+ section 2.3.4
+										// https://promisesaplus.com/#point-64
+										// Only check objects and functions for thenability
+										( typeof returned === "object" ||
+											typeof returned === "function" ) &&
+										returned.then;
+
+									// Handle a returned thenable
+									if ( isFunction( then ) ) {
+
+										// Special processors (notify) just wait for resolution
+										if ( special ) {
+											then.call(
+												returned,
+												resolve( maxDepth, deferred, Identity, special ),
+												resolve( maxDepth, deferred, Thrower, special )
+											);
+
+										// Normal processors (resolve) also hook into progress
+										} else {
+
+											// ...and disregard older resolution values
+											maxDepth++;
+
+											then.call(
+												returned,
+												resolve( maxDepth, deferred, Identity, special ),
+												resolve( maxDepth, deferred, Thrower, special ),
+												resolve( maxDepth, deferred, Identity,
+													deferred.notifyWith )
+											);
+										}
+
+									// Handle all other returned values
+									} else {
+
+										// Only substitute handlers pass on context
+										// and multiple values (non-spec behavior)
+										if ( handler !== Identity ) {
+											that = undefined;
+											args = [ returned ];
+										}
+
+										// Process the value(s)
+										// Default process is resolve
+										( special || deferred.resolveWith )( that, args );
+									}
+								},
+
+								// Only normal processors (resolve) catch and reject exceptions
+								process = special ?
+									mightThrow :
+									function() {
+										try {
+											mightThrow();
+										} catch ( e ) {
+
+											if ( jQuery.Deferred.exceptionHook ) {
+												jQuery.Deferred.exceptionHook( e,
+													process.stackTrace );
+											}
+
+											// Support: Promises/A+ section 2.3.3.3.4.1
+											// https://promisesaplus.com/#point-61
+											// Ignore post-resolution exceptions
+											if ( depth + 1 >= maxDepth ) {
+
+												// Only substitute handlers pass on context
+												// and multiple values (non-spec behavior)
+												if ( handler !== Thrower ) {
+													that = undefined;
+													args = [ e ];
+												}
+
+												deferred.rejectWith( that, args );
+											}
+										}
+									};
+
+							// Support: Promises/A+ section 2.3.3.3.1
+							// https://promisesaplus.com/#point-57
+							// Re-resolve promises immediately to dodge false rejection from
+							// subsequent errors
+							if ( depth ) {
+								process();
+							} else {
+
+								// Call an optional hook to record the stack, in case of exception
+								// since it's otherwise lost when execution goes async
+								if ( jQuery.Deferred.getStackHook ) {
+									process.stackTrace = jQuery.Deferred.getStackHook();
+								}
+								window.setTimeout( process );
+							}
+						};
+					}
+
+					return jQuery.Deferred( function( newDefer ) {
+
+						// progress_handlers.add( ... )
+						tuples[ 0 ][ 3 ].add(
+							resolve(
+								0,
+								newDefer,
+								isFunction( onProgress ) ?
+									onProgress :
+									Identity,
+								newDefer.notifyWith
+							)
+						);
+
+						// fulfilled_handlers.add( ... )
+						tuples[ 1 ][ 3 ].add(
+							resolve(
+								0,
+								newDefer,
+								isFunction( onFulfilled ) ?
+									onFulfilled :
+									Identity
+							)
+						);
+
+						// rejected_handlers.add( ... )
+						tuples[ 2 ][ 3 ].add(
+							resolve(
+								0,
+								newDefer,
+								isFunction( onRejected ) ?
+									onRejected :
+									Thrower
+							)
+						);
+					} ).promise();
+				},
+
+				// Get a promise for this deferred
+				// If obj is provided, the promise aspect is added to the object
+				promise: function( obj ) {
+					return obj != null ? jQuery.extend( obj, promise ) : promise;
+				}
+			},
+			deferred = {};
+
+		// Add list-specific methods
+		jQuery.each( tuples, function( i, tuple ) {
+			var list = tuple[ 2 ],
+				stateString = tuple[ 5 ];
+
+			// promise.progress = list.add
+			// promise.done = list.add
+			// promise.fail = list.add
+			promise[ tuple[ 1 ] ] = list.add;
+
+			// Handle state
+			if ( stateString ) {
+				list.add(
+					function() {
+
+						// state = "resolved" (i.e., fulfilled)
+						// state = "rejected"
+						state = stateString;
+					},
+
+					// rejected_callbacks.disable
+					// fulfilled_callbacks.disable
+					tuples[ 3 - i ][ 2 ].disable,
+
+					// rejected_handlers.disable
+					// fulfilled_handlers.disable
+					tuples[ 3 - i ][ 3 ].disable,
+
+					// progress_callbacks.lock
+					tuples[ 0 ][ 2 ].lock,
+
+					// progress_handlers.lock
+					tuples[ 0 ][ 3 ].lock
+				);
+			}
+
+			// progress_handlers.fire
+			// fulfilled_handlers.fire
+			// rejected_handlers.fire
+			list.add( tuple[ 3 ].fire );
+
+			// deferred.notify = function() { deferred.notifyWith(...) }
+			// deferred.resolve = function() { deferred.resolveWith(...) }
+			// deferred.reject = function() { deferred.rejectWith(...) }
+			deferred[ tuple[ 0 ] ] = function() {
+				deferred[ tuple[ 0 ] + "With" ]( this === deferred ? undefined : this, arguments );
+				return this;
+			};
+
+			// deferred.notifyWith = list.fireWith
+			// deferred.resolveWith = list.fireWith
+			// deferred.rejectWith = list.fireWith
+			deferred[ tuple[ 0 ] + "With" ] = list.fireWith;
+		} );
+
+		// Make the deferred a promise
+		promise.promise( deferred );
+
+		// Call given func if any
+		if ( func ) {
+			func.call( deferred, deferred );
+		}
+
+		// All done!
+		return deferred;
+	},
+
+	// Deferred helper
+	when: function( singleValue ) {
+		var
+
+			// count of uncompleted subordinates
+			remaining = arguments.length,
+
+			// count of unprocessed arguments
+			i = remaining,
+
+			// subordinate fulfillment data
+			resolveContexts = Array( i ),
+			resolveValues = slice.call( arguments ),
+
+			// the primary Deferred
+			primary = jQuery.Deferred(),
+
+			// subordinate callback factory
+			updateFunc = function( i ) {
+				return function( value ) {
+					resolveContexts[ i ] = this;
+					resolveValues[ i ] = arguments.length > 1 ? slice.call( arguments ) : value;
+					if ( !( --remaining ) ) {
+						primary.resolveWith( resolveContexts, resolveValues );
+					}
+				};
+			};
+
+		// Single- and empty arguments are adopted like Promise.resolve
+		if ( remaining <= 1 ) {
+			adoptValue( singleValue, primary.done( updateFunc( i ) ).resolve, primary.reject,
+				!remaining );
+
+			// Use .then() to unwrap secondary thenables (cf. gh-3000)
+			if ( primary.state() === "pending" ||
+				isFunction( resolveValues[ i ] && resolveValues[ i ].then ) ) {
+
+				return primary.then();
+			}
+		}
+
+		// Multiple arguments are aggregated like Promise.all array elements
+		while ( i-- ) {
+			adoptValue( resolveValues[ i ], updateFunc( i ), primary.reject );
+		}
+
+		return primary.promise();
+	}
+} );
+
+
+// These usually indicate a programmer mistake during development,
+// warn about them ASAP rather than swallowing them by default.
+var rerrorNames = /^(Eval|Internal|Range|Reference|Syntax|Type|URI)Error$/;
+
+jQuery.Deferred.exceptionHook = function( error, stack ) {
+
+	// Support: IE 8 - 9 only
+	// Console exists when dev tools are open, which can happen at any time
+	if ( window.console && window.console.warn && error && rerrorNames.test( error.name ) ) {
+		window.console.warn( "jQuery.Deferred exception: " + error.message, error.stack, stack );
+	}
+};
+
+
+
+
+jQuery.readyException = function( error ) {
+	window.setTimeout( function() {
+		throw error;
+	} );
+};
+
+
+
+
+// The deferred used on DOM ready
+var readyList = jQuery.Deferred();
+
+jQuery.fn.ready = function( fn ) {
+
+	readyList
+		.then( fn )
+
+		// Wrap jQuery.readyException in a function so that the lookup
+		// happens at the time of error handling instead of callback
+		// registration.
+		.catch( function( error ) {
+			jQuery.readyException( error );
+		} );
+
+	return this;
+};
+
+jQuery.extend( {
+
+	// Is the DOM ready to be used? Set to true once it occurs.
+	isReady: false,
+
+	// A counter to track how many items to wait for before
+	// the ready event fires. See #6781
+	readyWait: 1,
+
+	// Handle when the DOM is ready
+	ready: function( wait ) {
+
+		// Abort if there are pending holds or we're already ready
+		if ( wait === true ? --jQuery.readyWait : jQuery.isReady ) {
+			return;
+		}
+
+		// Remember that the DOM is ready
+		jQuery.isReady = true;
+
+		// If a normal DOM Ready event fired, decrement, and wait if need be
+		if ( wait !== true && --jQuery.readyWait > 0 ) {
+			return;
+		}
+
+		// If there are functions bound, to execute
+		readyList.resolveWith( document, [ jQuery ] );
+	}
+} );
+
+jQuery.ready.then = readyList.then;
+
+// The ready event handler and self cleanup method
+function completed() {
+	document.removeEventListener( "DOMContentLoaded", completed );
+	window.removeEventListener( "load", completed );
+	jQuery.ready();
+}
+
+// Catch cases where $(document).ready() is called
+// after the browser event has already occurred.
+// Support: IE <=9 - 10 only
+// Older IE sometimes signals "interactive" too soon
+if ( document.readyState === "complete" ||
+	( document.readyState !== "loading" && !document.documentElement.doScroll ) ) {
+
+	// Handle it asynchronously to allow scripts the opportunity to delay ready
+	window.setTimeout( jQuery.ready );
+
+} else {
+
+	// Use the handy event callback
+	document.addEventListener( "DOMContentLoaded", completed );
+
+	// A fallback to window.onload, that will always work
+	window.addEventListener( "load", completed );
+}
+
+
+
+
+// Multifunctional method to get and set values of a collection
+// The value/s can optionally be executed if it's a function
+var access = function( elems, fn, key, value, chainable, emptyGet, raw ) {
+	var i = 0,
+		len = elems.length,
+		bulk = key == null;
+
+	// Sets many values
+	if ( toType( key ) === "object" ) {
+		chainable = true;
+		for ( i in key ) {
+			access( elems, fn, i, key[ i ], true, emptyGet, raw );
+		}
+
+	// Sets one value
+	} else if ( value !== undefined ) {
+		chainable = true;
+
+		if ( !isFunction( value ) ) {
+			raw = true;
+		}
+
+		if ( bulk ) {
+
+			// Bulk operations run against the entire set
+			if ( raw ) {
+				fn.call( elems, value );
+				fn = null;
+
+			// ...except when executing function values
+			} else {
+				bulk = fn;
+				fn = function( elem, _key, value ) {
+					return bulk.call( jQuery( elem ), value );
+				};
+			}
+		}
+
+		if ( fn ) {
+			for ( ; i < len; i++ ) {
+				fn(
+					elems[ i ], key, raw ?
+						value :
+						value.call( elems[ i ], i, fn( elems[ i ], key ) )
+				);
+			}
+		}
+	}
+
+	if ( chainable ) {
+		return elems;
+	}
+
+	// Gets
+	if ( bulk ) {
+		return fn.call( elems );
+	}
+
+	return len ? fn( elems[ 0 ], key ) : emptyGet;
+};
+
+
+// Matches dashed string for camelizing
+var rmsPrefix = /^-ms-/,
+	rdashAlpha = /-([a-z])/g;
+
+// Used by camelCase as callback to replace()
+function fcamelCase( _all, letter ) {
+	return letter.toUpperCase();
+}
+
+// Convert dashed to camelCase; used by the css and data modules
+// Support: IE <=9 - 11, Edge 12 - 15
+// Microsoft forgot to hump their vendor prefix (#9572)
+function camelCase( string ) {
+	return string.replace( rmsPrefix, "ms-" ).replace( rdashAlpha, fcamelCase );
+}
+var acceptData = function( owner ) {
+
+	// Accepts only:
+	//  - Node
+	//    - Node.ELEMENT_NODE
+	//    - Node.DOCUMENT_NODE
+	//  - Object
+	//    - Any
+	return owner.nodeType === 1 || owner.nodeType === 9 || !( +owner.nodeType );
+};
+
+
+
+
+function Data() {
+	this.expando = jQuery.expando + Data.uid++;
+}
+
+Data.uid = 1;
+
+Data.prototype = {
+
+	cache: function( owner ) {
+
+		// Check if the owner object already has a cache
+		var value = owner[ this.expando ];
+
+		// If not, create one
+		if ( !value ) {
+			value = {};
+
+			// We can accept data for non-element nodes in modern browsers,
+			// but we should not, see #8335.
+			// Always return an empty object.
+			if ( acceptData( owner ) ) {
+
+				// If it is a node unlikely to be stringify-ed or looped over
+				// use plain assignment
+				if ( owner.nodeType ) {
+					owner[ this.expando ] = value;
+
+				// Otherwise secure it in a non-enumerable property
+				// configurable must be true to allow the property to be
+				// deleted when data is removed
+				} else {
+					Object.defineProperty( owner, this.expando, {
+						value: value,
+						configurable: true
+					} );
+				}
+			}
+		}
+
+		return value;
+	},
+	set: function( owner, data, value ) {
+		var prop,
+			cache = this.cache( owner );
+
+		// Handle: [ owner, key, value ] args
+		// Always use camelCase key (gh-2257)
+		if ( typeof data === "string" ) {
+			cache[ camelCase( data ) ] = value;
+
+		// Handle: [ owner, { properties } ] args
+		} else {
+
+			// Copy the properties one-by-one to the cache object
+			for ( prop in data ) {
+				cache[ camelCase( prop ) ] = data[ prop ];
+			}
+		}
+		return cache;
+	},
+	get: function( owner, key ) {
+		return key === undefined ?
+			this.cache( owner ) :
+
+			// Always use camelCase key (gh-2257)
+			owner[ this.expando ] && owner[ this.expando ][ camelCase( key ) ];
+	},
+	access: function( owner, key, value ) {
+
+		// In cases where either:
+		//
+		//   1. No key was specified
+		//   2. A string key was specified, but no value provided
+		//
+		// Take the "read" path and allow the get method to determine
+		// which value to return, respectively either:
+		//
+		//   1. The entire cache object
+		//   2. The data stored at the key
+		//
+		if ( key === undefined ||
+				( ( key && typeof key === "string" ) && value === undefined ) ) {
+
+			return this.get( owner, key );
+		}
+
+		// When the key is not a string, or both a key and value
+		// are specified, set or extend (existing objects) with either:
+		//
+		//   1. An object of properties
+		//   2. A key and value
+		//
+		this.set( owner, key, value );
+
+		// Since the "set" path can have two possible entry points
+		// return the expected data based on which path was taken[*]
+		return value !== undefined ? value : key;
+	},
+	remove: function( owner, key ) {
+		var i,
+			cache = owner[ this.expando ];
+
+		if ( cache === undefined ) {
+			return;
+		}
+
+		if ( key !== undefined ) {
+
+			// Support array or space separated string of keys
+			if ( Array.isArray( key ) ) {
+
+				// If key is an array of keys...
+				// We always set camelCase keys, so remove that.
+				key = key.map( camelCase );
+			} else {
+				key = camelCase( key );
+
+				// If a key with the spaces exists, use it.
+				// Otherwise, create an array by matching non-whitespace
+				key = key in cache ?
+					[ key ] :
+					( key.match( rnothtmlwhite ) || [] );
+			}
+
+			i = key.length;
+
+			while ( i-- ) {
+				delete cache[ key[ i ] ];
+			}
+		}
+
+		// Remove the expando if there's no more data
+		if ( key === undefined || jQuery.isEmptyObject( cache ) ) {
+
+			// Support: Chrome <=35 - 45
+			// Webkit & Blink performance suffers when deleting properties
+			// from DOM nodes, so set to undefined instead
+			// https://bugs.chromium.org/p/chromium/issues/detail?id=378607 (bug restricted)
+			if ( owner.nodeType ) {
+				owner[ this.expando ] = undefined;
+			} else {
+				delete owner[ this.expando ];
+			}
+		}
+	},
+	hasData: function( owner ) {
+		var cache = owner[ this.expando ];
+		return cache !== undefined && !jQuery.isEmptyObject( cache );
+	}
+};
+var dataPriv = new Data();
+
+var dataUser = new Data();
+
+
+
+//	Implementation Summary
+//
+//	1. Enforce API surface and semantic compatibility with 1.9.x branch
+//	2. Improve the module's maintainability by reducing the storage
+//		paths to a single mechanism.
+//	3. Use the same single mechanism to support "private" and "user" data.
+//	4. _Never_ expose "private" data to user code (TODO: Drop _data, _removeData)
+//	5. Avoid exposing implementation details on user objects (eg. expando properties)
+//	6. Provide a clear path for implementation upgrade to WeakMap in 2014
+
+var rbrace = /^(?:\{[\w\W]*\}|\[[\w\W]*\])$/,
+	rmultiDash = /[A-Z]/g;
+
+function getData( data ) {
+	if ( data === "true" ) {
+		return true;
+	}
+
+	if ( data === "false" ) {
+		return false;
+	}
+
+	if ( data === "null" ) {
+		return null;
+	}
+
+	// Only convert to a number if it doesn't change the string
+	if ( data === +data + "" ) {
+		return +data;
+	}
+
+	if ( rbrace.test( data ) ) {
+		return JSON.parse( data );
+	}
+
+	return data;
+}
+
+function dataAttr( elem, key, data ) {
+	var name;
+
+	// If nothing was found internally, try to fetch any
+	// data from the HTML5 data-* attribute
+	if ( data === undefined && elem.nodeType === 1 ) {
+		name = "data-" + key.replace( rmultiDash, "-$&" ).toLowerCase();
+		data = elem.getAttribute( name );
+
+		if ( typeof data === "string" ) {
+			try {
+				data = getData( data );
+			} catch ( e ) {}
+
+			// Make sure we set the data so it isn't changed later
+			dataUser.set( elem, key, data );
+		} else {
+			data = undefined;
+		}
+	}
+	return data;
+}
+
+jQuery.extend( {
+	hasData: function( elem ) {
+		return dataUser.hasData( elem ) || dataPriv.hasData( elem );
+	},
+
+	data: function( elem, name, data ) {
+		return dataUser.access( elem, name, data );
+	},
+
+	removeData: function( elem, name ) {
+		dataUser.remove( elem, name );
+	},
+
+	// TODO: Now that all calls to _data and _removeData have been replaced
+	// with direct calls to dataPriv methods, these can be deprecated.
+	_data: function( elem, name, data ) {
+		return dataPriv.access( elem, name, data );
+	},
+
+	_removeData: function( elem, name ) {
+		dataPriv.remove( elem, name );
+	}
+} );
+
+jQuery.fn.extend( {
+	data: function( key, value ) {
+		var i, name, data,
+			elem = this[ 0 ],
+			attrs = elem && elem.attributes;
+
+		// Gets all values
+		if ( key === undefined ) {
+			if ( this.length ) {
+				data = dataUser.get( elem );
+
+				if ( elem.nodeType === 1 && !dataPriv.get( elem, "hasDataAttrs" ) ) {
+					i = attrs.length;
+					while ( i-- ) {
+
+						// Support: IE 11 only
+						// The attrs elements can be null (#14894)
+						if ( attrs[ i ] ) {
+							name = attrs[ i ].name;
+							if ( name.indexOf( "data-" ) === 0 ) {
+								name = camelCase( name.slice( 5 ) );
+								dataAttr( elem, name, data[ name ] );
+							}
+						}
+					}
+					dataPriv.set( elem, "hasDataAttrs", true );
+				}
+			}
+
+			return data;
+		}
+
+		// Sets multiple values
+		if ( typeof key === "object" ) {
+			return this.each( function() {
+				dataUser.set( this, key );
+			} );
+		}
+
+		return access( this, function( value ) {
+			var data;
+
+			// The calling jQuery object (element matches) is not empty
+			// (and therefore has an element appears at this[ 0 ]) and the
+			// `value` parameter was not undefined. An empty jQuery object
+			// will result in `undefined` for elem = this[ 0 ] which will
+			// throw an exception if an attempt to read a data cache is made.
+			if ( elem && value === undefined ) {
+
+				// Attempt to get data from the cache
+				// The key will always be camelCased in Data
+				data = dataUser.get( elem, key );
+				if ( data !== undefined ) {
+					return data;
+				}
+
+				// Attempt to "discover" the data in
+				// HTML5 custom data-* attrs
+				data = dataAttr( elem, key );
+				if ( data !== undefined ) {
+					return data;
+				}
+
+				// We tried really hard, but the data doesn't exist.
+				return;
+			}
+
+			// Set the data...
+			this.each( function() {
+
+				// We always store the camelCased key
+				dataUser.set( this, key, value );
+			} );
+		}, null, value, arguments.length > 1, null, true );
+	},
+
+	removeData: function( key ) {
+		return this.each( function() {
+			dataUser.remove( this, key );
+		} );
+	}
+} );
+
+
+jQuery.extend( {
+	queue: function( elem, type, data ) {
+		var queue;
+
+		if ( elem ) {
+			type = ( type || "fx" ) + "queue";
+			queue = dataPriv.get( elem, type );
+
+			// Speed up dequeue by getting out quickly if this is just a lookup
+			if ( data ) {
+				if ( !queue || Array.isArray( data ) ) {
+					queue = dataPriv.access( elem, type, jQuery.makeArray( data ) );
+				} else {
+					queue.push( data );
+				}
+			}
+			return queue || [];
+		}
+	},
+
+	dequeue: function( elem, type ) {
+		type = type || "fx";
+
+		var queue = jQuery.queue( elem, type ),
+			startLength = queue.length,
+			fn = queue.shift(),
+			hooks = jQuery._queueHooks( elem, type ),
+			next = function() {
+				jQuery.dequeue( elem, type );
+			};
+
+		// If the fx queue is dequeued, always remove the progress sentinel
+		if ( fn === "inprogress" ) {
+			fn = queue.shift();
+			startLength--;
+		}
+
+		if ( fn ) {
+
+			// Add a progress sentinel to prevent the fx queue from being
+			// automatically dequeued
+			if ( type === "fx" ) {
+				queue.unshift( "inprogress" );
+			}
+
+			// Clear up the last queue stop function
+			delete hooks.stop;
+			fn.call( elem, next, hooks );
+		}
+
+		if ( !startLength && hooks ) {
+			hooks.empty.fire();
+		}
+	},
+
+	// Not public - generate a queueHooks object, or return the current one
+	_queueHooks: function( elem, type ) {
+		var key = type + "queueHooks";
+		return dataPriv.get( elem, key ) || dataPriv.access( elem, key, {
+			empty: jQuery.Callbacks( "once memory" ).add( function() {
+				dataPriv.remove( elem, [ type + "queue", key ] );
+			} )
+		} );
+	}
+} );
+
+jQuery.fn.extend( {
+	queue: function( type, data ) {
+		var setter = 2;
+
+		if ( typeof type !== "string" ) {
+			data = type;
+			type = "fx";
+			setter--;
+		}
+
+		if ( arguments.length < setter ) {
+			return jQuery.queue( this[ 0 ], type );
+		}
+
+		return data === undefined ?
+			this :
+			this.each( function() {
+				var queue = jQuery.queue( this, type, data );
+
+				// Ensure a hooks for this queue
+				jQuery._queueHooks( this, type );
+
+				if ( type === "fx" && queue[ 0 ] !== "inprogress" ) {
+					jQuery.dequeue( this, type );
+				}
+			} );
+	},
+	dequeue: function( type ) {
+		return this.each( function() {
+			jQuery.dequeue( this, type );
+		} );
+	},
+	clearQueue: function( type ) {
+		return this.queue( type || "fx", [] );
+	},
+
+	// Get a promise resolved when queues of a certain type
+	// are emptied (fx is the type by default)
+	promise: function( type, obj ) {
+		var tmp,
+			count = 1,
+			defer = jQuery.Deferred(),
+			elements = this,
+			i = this.length,
+			resolve = function() {
+				if ( !( --count ) ) {
+					defer.resolveWith( elements, [ elements ] );
+				}
+			};
+
+		if ( typeof type !== "string" ) {
+			obj = type;
+			type = undefined;
+		}
+		type = type || "fx";
+
+		while ( i-- ) {
+			tmp = dataPriv.get( elements[ i ], type + "queueHooks" );
+			if ( tmp && tmp.empty ) {
+				count++;
+				tmp.empty.add( resolve );
+			}
+		}
+		resolve();
+		return defer.promise( obj );
+	}
+} );
+var pnum = ( /[+-]?(?:\d*\.|)\d+(?:[eE][+-]?\d+|)/ ).source;
+
+var rcssNum = new RegExp( "^(?:([+-])=|)(" + pnum + ")([a-z%]*)$", "i" );
+
+
+var cssExpand = [ "Top", "Right", "Bottom", "Left" ];
+
+var documentElement = document.documentElement;
+
+
+
+	var isAttached = function( elem ) {
+			return jQuery.contains( elem.ownerDocument, elem );
+		},
+		composed = { composed: true };
+
+	// Support: IE 9 - 11+, Edge 12 - 18+, iOS 10.0 - 10.2 only
+	// Check attachment across shadow DOM boundaries when possible (gh-3504)
+	// Support: iOS 10.0-10.2 only
+	// Early iOS 10 versions support `attachShadow` but not `getRootNode`,
+	// leading to errors. We need to check for `getRootNode`.
+	if ( documentElement.getRootNode ) {
+		isAttached = function( elem ) {
+			return jQuery.contains( elem.ownerDocument, elem ) ||
+				elem.getRootNode( composed ) === elem.ownerDocument;
+		};
+	}
+var isHiddenWithinTree = function( elem, el ) {
+
+		// isHiddenWithinTree might be called from jQuery#filter function;
+		// in that case, element will be second argument
+		elem = el || elem;
+
+		// Inline style trumps all
+		return elem.style.display === "none" ||
+			elem.style.display === "" &&
+
+			// Otherwise, check computed style
+			// Support: Firefox <=43 - 45
+			// Disconnected elements can have computed display: none, so first confirm that elem is
+			// in the document.
+			isAttached( elem ) &&
+
+			jQuery.css( elem, "display" ) === "none";
+	};
+
+
+
+function adjustCSS( elem, prop, valueParts, tween ) {
+	var adjusted, scale,
+		maxIterations = 20,
+		currentValue = tween ?
+			function() {
+				return tween.cur();
+			} :
+			function() {
+				return jQuery.css( elem, prop, "" );
+			},
+		initial = currentValue(),
+		unit = valueParts && valueParts[ 3 ] || ( jQuery.cssNumber[ prop ] ? "" : "px" ),
+
+		// Starting value computation is required for potential unit mismatches
+		initialInUnit = elem.nodeType &&
+			( jQuery.cssNumber[ prop ] || unit !== "px" && +initial ) &&
+			rcssNum.exec( jQuery.css( elem, prop ) );
+
+	if ( initialInUnit && initialInUnit[ 3 ] !== unit ) {
+
+		// Support: Firefox <=54
+		// Halve the iteration target value to prevent interference from CSS upper bounds (gh-2144)
+		initial = initial / 2;
+
+		// Trust units reported by jQuery.css
+		unit = unit || initialInUnit[ 3 ];
+
+		// Iteratively approximate from a nonzero starting point
+		initialInUnit = +initial || 1;
+
+		while ( maxIterations-- ) {
+
+			// Evaluate and update our best guess (doubling guesses that zero out).
+			// Finish if the scale equals or crosses 1 (making the old*new product non-positive).
+			jQuery.style( elem, prop, initialInUnit + unit );
+			if ( ( 1 - scale ) * ( 1 - ( scale = currentValue() / initial || 0.5 ) ) <= 0 ) {
+				maxIterations = 0;
+			}
+			initialInUnit = initialInUnit / scale;
+
+		}
+
+		initialInUnit = initialInUnit * 2;
+		jQuery.style( elem, prop, initialInUnit + unit );
+
+		// Make sure we update the tween properties later on
+		valueParts = valueParts || [];
+	}
+
+	if ( valueParts ) {
+		initialInUnit = +initialInUnit || +initial || 0;
+
+		// Apply relative offset (+=/-=) if specified
+		adjusted = valueParts[ 1 ] ?
+			initialInUnit + ( valueParts[ 1 ] + 1 ) * valueParts[ 2 ] :
+			+valueParts[ 2 ];
+		if ( tween ) {
+			tween.unit = unit;
+			tween.start = initialInUnit;
+			tween.end = adjusted;
+		}
+	}
+	return adjusted;
+}
+
+
+var defaultDisplayMap = {};
+
+function getDefaultDisplay( elem ) {
+	var temp,
+		doc = elem.ownerDocument,
+		nodeName = elem.nodeName,
+		display = defaultDisplayMap[ nodeName ];
+
+	if ( display ) {
+		return display;
+	}
+
+	temp = doc.body.appendChild( doc.createElement( nodeName ) );
+	display = jQuery.css( temp, "display" );
+
+	temp.parentNode.removeChild( temp );
+
+	if ( display === "none" ) {
+		display = "block";
+	}
+	defaultDisplayMap[ nodeName ] = display;
+
+	return display;
+}
+
+function showHide( elements, show ) {
+	var display, elem,
+		values = [],
+		index = 0,
+		length = elements.length;
+
+	// Determine new display value for elements that need to change
+	for ( ; index < length; index++ ) {
+		elem = elements[ index ];
+		if ( !elem.style ) {
+			continue;
+		}
+
+		display = elem.style.display;
+		if ( show ) {
+
+			// Since we force visibility upon cascade-hidden elements, an immediate (and slow)
+			// check is required in this first loop unless we have a nonempty display value (either
+			// inline or about-to-be-restored)
+			if ( display === "none" ) {
+				values[ index ] = dataPriv.get( elem, "display" ) || null;
+				if ( !values[ index ] ) {
+					elem.style.display = "";
+				}
+			}
+			if ( elem.style.display === "" && isHiddenWithinTree( elem ) ) {
+				values[ index ] = getDefaultDisplay( elem );
+			}
+		} else {
+			if ( display !== "none" ) {
+				values[ index ] = "none";
+
+				// Remember what we're overwriting
+				dataPriv.set( elem, "display", display );
+			}
+		}
+	}
+
+	// Set the display of the elements in a second loop to avoid constant reflow
+	for ( index = 0; index < length; index++ ) {
+		if ( values[ index ] != null ) {
+			elements[ index ].style.display = values[ index ];
+		}
+	}
+
+	return elements;
+}
+
+jQuery.fn.extend( {
+	show: function() {
+		return showHide( this, true );
+	},
+	hide: function() {
+		return showHide( this );
+	},
+	toggle: function( state ) {
+		if ( typeof state === "boolean" ) {
+			return state ? this.show() : this.hide();
+		}
+
+		return this.each( function() {
+			if ( isHiddenWithinTree( this ) ) {
+				jQuery( this ).show();
+			} else {
+				jQuery( this ).hide();
+			}
+		} );
+	}
+} );
+var rcheckableType = ( /^(?:checkbox|radio)$/i );
+
+var rtagName = ( /<([a-z][^\/\0>\x20\t\r\n\f]*)/i );
+
+var rscriptType = ( /^$|^module$|\/(?:java|ecma)script/i );
+
+
+
+( function() {
+	var fragment = document.createDocumentFragment(),
+		div = fragment.appendChild( document.createElement( "div" ) ),
+		input = document.createElement( "input" );
+
+	// Support: Android 4.0 - 4.3 only
+	// Check state lost if the name is set (#11217)
+	// Support: Windows Web Apps (WWA)
+	// `name` and `type` must use .setAttribute for WWA (#14901)
+	input.setAttribute( "type", "radio" );
+	input.setAttribute( "checked", "checked" );
+	input.setAttribute( "name", "t" );
+
+	div.appendChild( input );
+
+	// Support: Android <=4.1 only
+	// Older WebKit doesn't clone checked state correctly in fragments
+	support.checkClone = div.cloneNode( true ).cloneNode( true ).lastChild.checked;
+
+	// Support: IE <=11 only
+	// Make sure textarea (and checkbox) defaultValue is properly cloned
+	div.innerHTML = "<textarea>x</textarea>";
+	support.noCloneChecked = !!div.cloneNode( true ).lastChild.defaultValue;
+
+	// Support: IE <=9 only
+	// IE <=9 replaces <option> tags with their contents when inserted outside of
+	// the select element.
+	div.innerHTML = "<option></option>";
+	support.option = !!div.lastChild;
+} )();
+
+
+// We have to close these tags to support XHTML (#13200)
+var wrapMap = {
+
+	// XHTML parsers do not magically insert elements in the
+	// same way that tag soup parsers do. So we cannot shorten
+	// this by omitting <tbody> or other required elements.
+	thead: [ 1, "<table>", "</table>" ],
+	col: [ 2, "<table><colgroup>", "</colgroup></table>" ],
+	tr: [ 2, "<table><tbody>", "</tbody></table>" ],
+	td: [ 3, "<table><tbody><tr>", "</tr></tbody></table>" ],
+
+	_default: [ 0, "", "" ]
+};
+
+wrapMap.tbody = wrapMap.tfoot = wrapMap.colgroup = wrapMap.caption = wrapMap.thead;
+wrapMap.th = wrapMap.td;
+
+// Support: IE <=9 only
+if ( !support.option ) {
+	wrapMap.optgroup = wrapMap.option = [ 1, "<select multiple='multiple'>", "</select>" ];
+}
+
+
+function getAll( context, tag ) {
+
+	// Support: IE <=9 - 11 only
+	// Use typeof to avoid zero-argument method invocation on host objects (#15151)
+	var ret;
+
+	if ( typeof context.getElementsByTagName !== "undefined" ) {
+		ret = context.getElementsByTagName( tag || "*" );
+
+	} else if ( typeof context.querySelectorAll !== "undefined" ) {
+		ret = context.querySelectorAll( tag || "*" );
+
+	} else {
+		ret = [];
+	}
+
+	if ( tag === undefined || tag && nodeName( context, tag ) ) {
+		return jQuery.merge( [ context ], ret );
+	}
+
+	return ret;
+}
+
+
+// Mark scripts as having already been evaluated
+function setGlobalEval( elems, refElements ) {
+	var i = 0,
+		l = elems.length;
+
+	for ( ; i < l; i++ ) {
+		dataPriv.set(
+			elems[ i ],
+			"globalEval",
+			!refElements || dataPriv.get( refElements[ i ], "globalEval" )
+		);
+	}
+}
+
+
+var rhtml = /<|&#?\w+;/;
+
+function buildFragment( elems, context, scripts, selection, ignored ) {
+	var elem, tmp, tag, wrap, attached, j,
+		fragment = context.createDocumentFragment(),
+		nodes = [],
+		i = 0,
+		l = elems.length;
+
+	for ( ; i < l; i++ ) {
+		elem = elems[ i ];
+
+		if ( elem || elem === 0 ) {
+
+			// Add nodes directly
+			if ( toType( elem ) === "object" ) {
+
+				// Support: Android <=4.0 only, PhantomJS 1 only
+				// push.apply(_, arraylike) throws on ancient WebKit
+				jQuery.merge( nodes, elem.nodeType ? [ elem ] : elem );
+
+			// Convert non-html into a text node
+			} else if ( !rhtml.test( elem ) ) {
+				nodes.push( context.createTextNode( elem ) );
+
+			// Convert html into DOM nodes
+			} else {
+				tmp = tmp || fragment.appendChild( context.createElement( "div" ) );
+
+				// Deserialize a standard representation
+				tag = ( rtagName.exec( elem ) || [ "", "" ] )[ 1 ].toLowerCase();
+				wrap = wrapMap[ tag ] || wrapMap._default;
+				tmp.innerHTML = wrap[ 1 ] + jQuery.htmlPrefilter( elem ) + wrap[ 2 ];
+
+				// Descend through wrappers to the right content
+				j = wrap[ 0 ];
+				while ( j-- ) {
+					tmp = tmp.lastChild;
+				}
+
+				// Support: Android <=4.0 only, PhantomJS 1 only
+				// push.apply(_, arraylike) throws on ancient WebKit
+				jQuery.merge( nodes, tmp.childNodes );
+
+				// Remember the top-level container
+				tmp = fragment.firstChild;
+
+				// Ensure the created nodes are orphaned (#12392)
+				tmp.textContent = "";
+			}
+		}
+	}
+
+	// Remove wrapper from fragment
+	fragment.textContent = "";
+
+	i = 0;
+	while ( ( elem = nodes[ i++ ] ) ) {
+
+		// Skip elements already in the context collection (trac-4087)
+		if ( selection && jQuery.inArray( elem, selection ) > -1 ) {
+			if ( ignored ) {
+				ignored.push( elem );
+			}
+			continue;
+		}
+
+		attached = isAttached( elem );
+
+		// Append to fragment
+		tmp = getAll( fragment.appendChild( elem ), "script" );
+
+		// Preserve script evaluation history
+		if ( attached ) {
+			setGlobalEval( tmp );
+		}
+
+		// Capture executables
+		if ( scripts ) {
+			j = 0;
+			while ( ( elem = tmp[ j++ ] ) ) {
+				if ( rscriptType.test( elem.type || "" ) ) {
+					scripts.push( elem );
+				}
+			}
+		}
+	}
+
+	return fragment;
+}
+
+
+var rtypenamespace = /^([^.]*)(?:\.(.+)|)/;
+
+function returnTrue() {
+	return true;
+}
+
+function returnFalse() {
+	return false;
+}
+
+// Support: IE <=9 - 11+
+// focus() and blur() are asynchronous, except when they are no-op.
+// So expect focus to be synchronous when the element is already active,
+// and blur to be synchronous when the element is not already active.
+// (focus and blur are always synchronous in other supported browsers,
+// this just defines when we can count on it).
+function expectSync( elem, type ) {
+	return ( elem === safeActiveElement() ) === ( type === "focus" );
+}
+
+// Support: IE <=9 only
+// Accessing document.activeElement can throw unexpectedly
+// https://bugs.jquery.com/ticket/13393
+function safeActiveElement() {
+	try {
+		return document.activeElement;
+	} catch ( err ) { }
+}
+
+function on( elem, types, selector, data, fn, one ) {
+	var origFn, type;
+
+	// Types can be a map of types/handlers
+	if ( typeof types === "object" ) {
+
+		// ( types-Object, selector, data )
+		if ( typeof selector !== "string" ) {
+
+			// ( types-Object, data )
+			data = data || selector;
+			selector = undefined;
+		}
+		for ( type in types ) {
+			on( elem, type, selector, data, types[ type ], one );
+		}
+		return elem;
+	}
+
+	if ( data == null && fn == null ) {
+
+		// ( types, fn )
+		fn = selector;
+		data = selector = undefined;
+	} else if ( fn == null ) {
+		if ( typeof selector === "string" ) {
+
+			// ( types, selector, fn )
+			fn = data;
+			data = undefined;
+		} else {
+
+			// ( types, data, fn )
+			fn = data;
+			data = selector;
+			selector = undefined;
+		}
+	}
+	if ( fn === false ) {
+		fn = returnFalse;
+	} else if ( !fn ) {
+		return elem;
+	}
+
+	if ( one === 1 ) {
+		origFn = fn;
+		fn = function( event ) {
+
+			// Can use an empty set, since event contains the info
+			jQuery().off( event );
+			return origFn.apply( this, arguments );
+		};
+
+		// Use same guid so caller can remove using origFn
+		fn.guid = origFn.guid || ( origFn.guid = jQuery.guid++ );
+	}
+	return elem.each( function() {
+		jQuery.event.add( this, types, fn, data, selector );
+	} );
+}
+
+/*
+ * Helper functions for managing events -- not part of the public interface.
+ * Props to Dean Edwards' addEvent library for many of the ideas.
+ */
+jQuery.event = {
+
+	global: {},
+
+	add: function( elem, types, handler, data, selector ) {
+
+		var handleObjIn, eventHandle, tmp,
+			events, t, handleObj,
+			special, handlers, type, namespaces, origType,
+			elemData = dataPriv.get( elem );
+
+		// Only attach events to objects that accept data
+		if ( !acceptData( elem ) ) {
+			return;
+		}
+
+		// Caller can pass in an object of custom data in lieu of the handler
+		if ( handler.handler ) {
+			handleObjIn = handler;
+			handler = handleObjIn.handler;
+			selector = handleObjIn.selector;
+		}
+
+		// Ensure that invalid selectors throw exceptions at attach time
+		// Evaluate against documentElement in case elem is a non-element node (e.g., document)
+		if ( selector ) {
+			jQuery.find.matchesSelector( documentElement, selector );
+		}
+
+		// Make sure that the handler has a unique ID, used to find/remove it later
+		if ( !handler.guid ) {
+			handler.guid = jQuery.guid++;
+		}
+
+		// Init the element's event structure and main handler, if this is the first
+		if ( !( events = elemData.events ) ) {
+			events = elemData.events = Object.create( null );
+		}
+		if ( !( eventHandle = elemData.handle ) ) {
+			eventHandle = elemData.handle = function( e ) {
+
+				// Discard the second event of a jQuery.event.trigger() and
+				// when an event is called after a page has unloaded
+				return typeof jQuery !== "undefined" && jQuery.event.triggered !== e.type ?
+					jQuery.event.dispatch.apply( elem, arguments ) : undefined;
+			};
+		}
+
+		// Handle multiple events separated by a space
+		types = ( types || "" ).match( rnothtmlwhite ) || [ "" ];
+		t = types.length;
+		while ( t-- ) {
+			tmp = rtypenamespace.exec( types[ t ] ) || [];
+			type = origType = tmp[ 1 ];
+			namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort();
+
+			// There *must* be a type, no attaching namespace-only handlers
+			if ( !type ) {
+				continue;
+			}
+
+			// If event changes its type, use the special event handlers for the changed type
+			special = jQuery.event.special[ type ] || {};
+
+			// If selector defined, determine special event api type, otherwise given type
+			type = ( selector ? special.delegateType : special.bindType ) || type;
+
+			// Update special based on newly reset type
+			special = jQuery.event.special[ type ] || {};
+
+			// handleObj is passed to all event handlers
+			handleObj = jQuery.extend( {
+				type: type,
+				origType: origType,
+				data: data,
+				handler: handler,
+				guid: handler.guid,
+				selector: selector,
+				needsContext: selector && jQuery.expr.match.needsContext.test( selector ),
+				namespace: namespaces.join( "." )
+			}, handleObjIn );
+
+			// Init the event handler queue if we're the first
+			if ( !( handlers = events[ type ] ) ) {
+				handlers = events[ type ] = [];
+				handlers.delegateCount = 0;
+
+				// Only use addEventListener if the special events handler returns false
+				if ( !special.setup ||
+					special.setup.call( elem, data, namespaces, eventHandle ) === false ) {
+
+					if ( elem.addEventListener ) {
+						elem.addEventListener( type, eventHandle );
+					}
+				}
+			}
+
+			if ( special.add ) {
+				special.add.call( elem, handleObj );
+
+				if ( !handleObj.handler.guid ) {
+					handleObj.handler.guid = handler.guid;
+				}
+			}
+
+			// Add to the element's handler list, delegates in front
+			if ( selector ) {
+				handlers.splice( handlers.delegateCount++, 0, handleObj );
+			} else {
+				handlers.push( handleObj );
+			}
+
+			// Keep track of which events have ever been used, for event optimization
+			jQuery.event.global[ type ] = true;
+		}
+
+	},
+
+	// Detach an event or set of events from an element
+	remove: function( elem, types, handler, selector, mappedTypes ) {
+
+		var j, origCount, tmp,
+			events, t, handleObj,
+			special, handlers, type, namespaces, origType,
+			elemData = dataPriv.hasData( elem ) && dataPriv.get( elem );
+
+		if ( !elemData || !( events = elemData.events ) ) {
+			return;
+		}
+
+		// Once for each type.namespace in types; type may be omitted
+		types = ( types || "" ).match( rnothtmlwhite ) || [ "" ];
+		t = types.length;
+		while ( t-- ) {
+			tmp = rtypenamespace.exec( types[ t ] ) || [];
+			type = origType = tmp[ 1 ];
+			namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort();
+
+			// Unbind all events (on this namespace, if provided) for the element
+			if ( !type ) {
+				for ( type in events ) {
+					jQuery.event.remove( elem, type + types[ t ], handler, selector, true );
+				}
+				continue;
+			}
+
+			special = jQuery.event.special[ type ] || {};
+			type = ( selector ? special.delegateType : special.bindType ) || type;
+			handlers = events[ type ] || [];
+			tmp = tmp[ 2 ] &&
+				new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" );
+
+			// Remove matching events
+			origCount = j = handlers.length;
+			while ( j-- ) {
+				handleObj = handlers[ j ];
+
+				if ( ( mappedTypes || origType === handleObj.origType ) &&
+					( !handler || handler.guid === handleObj.guid ) &&
+					( !tmp || tmp.test( handleObj.namespace ) ) &&
+					( !selector || selector === handleObj.selector ||
+						selector === "**" && handleObj.selector ) ) {
+					handlers.splice( j, 1 );
+
+					if ( handleObj.selector ) {
+						handlers.delegateCount--;
+					}
+					if ( special.remove ) {
+						special.remove.call( elem, handleObj );
+					}
+				}
+			}
+
+			// Remove generic event handler if we removed something and no more handlers exist
+			// (avoids potential for endless recursion during removal of special event handlers)
+			if ( origCount && !handlers.length ) {
+				if ( !special.teardown ||
+					special.teardown.call( elem, namespaces, elemData.handle ) === false ) {
+
+					jQuery.removeEvent( elem, type, elemData.handle );
+				}
+
+				delete events[ type ];
+			}
+		}
+
+		// Remove data and the expando if it's no longer used
+		if ( jQuery.isEmptyObject( events ) ) {
+			dataPriv.remove( elem, "handle events" );
+		}
+	},
+
+	dispatch: function( nativeEvent ) {
+
+		var i, j, ret, matched, handleObj, handlerQueue,
+			args = new Array( arguments.length ),
+
+			// Make a writable jQuery.Event from the native event object
+			event = jQuery.event.fix( nativeEvent ),
+
+			handlers = (
+				dataPriv.get( this, "events" ) || Object.create( null )
+			)[ event.type ] || [],
+			special = jQuery.event.special[ event.type ] || {};
+
+		// Use the fix-ed jQuery.Event rather than the (read-only) native event
+		args[ 0 ] = event;
+
+		for ( i = 1; i < arguments.length; i++ ) {
+			args[ i ] = arguments[ i ];
+		}
+
+		event.delegateTarget = this;
+
+		// Call the preDispatch hook for the mapped type, and let it bail if desired
+		if ( special.preDispatch && special.preDispatch.call( this, event ) === false ) {
+			return;
+		}
+
+		// Determine handlers
+		handlerQueue = jQuery.event.handlers.call( this, event, handlers );
+
+		// Run delegates first; they may want to stop propagation beneath us
+		i = 0;
+		while ( ( matched = handlerQueue[ i++ ] ) && !event.isPropagationStopped() ) {
+			event.currentTarget = matched.elem;
+
+			j = 0;
+			while ( ( handleObj = matched.handlers[ j++ ] ) &&
+				!event.isImmediatePropagationStopped() ) {
+
+				// If the event is namespaced, then each handler is only invoked if it is
+				// specially universal or its namespaces are a superset of the event's.
+				if ( !event.rnamespace || handleObj.namespace === false ||
+					event.rnamespace.test( handleObj.namespace ) ) {
+
+					event.handleObj = handleObj;
+					event.data = handleObj.data;
+
+					ret = ( ( jQuery.event.special[ handleObj.origType ] || {} ).handle ||
+						handleObj.handler ).apply( matched.elem, args );
+
+					if ( ret !== undefined ) {
+						if ( ( event.result = ret ) === false ) {
+							event.preventDefault();
+							event.stopPropagation();
+						}
+					}
+				}
+			}
+		}
+
+		// Call the postDispatch hook for the mapped type
+		if ( special.postDispatch ) {
+			special.postDispatch.call( this, event );
+		}
+
+		return event.result;
+	},
+
+	handlers: function( event, handlers ) {
+		var i, handleObj, sel, matchedHandlers, matchedSelectors,
+			handlerQueue = [],
+			delegateCount = handlers.delegateCount,
+			cur = event.target;
+
+		// Find delegate handlers
+		if ( delegateCount &&
+
+			// Support: IE <=9
+			// Black-hole SVG <use> instance trees (trac-13180)
+			cur.nodeType &&
+
+			// Support: Firefox <=42
+			// Suppress spec-violating clicks indicating a non-primary pointer button (trac-3861)
+			// https://www.w3.org/TR/DOM-Level-3-Events/#event-type-click
+			// Support: IE 11 only
+			// ...but not arrow key "clicks" of radio inputs, which can have `button` -1 (gh-2343)
+			!( event.type === "click" && event.button >= 1 ) ) {
+
+			for ( ; cur !== this; cur = cur.parentNode || this ) {
+
+				// Don't check non-elements (#13208)
+				// Don't process clicks on disabled elements (#6911, #8165, #11382, #11764)
+				if ( cur.nodeType === 1 && !( event.type === "click" && cur.disabled === true ) ) {
+					matchedHandlers = [];
+					matchedSelectors = {};
+					for ( i = 0; i < delegateCount; i++ ) {
+						handleObj = handlers[ i ];
+
+						// Don't conflict with Object.prototype properties (#13203)
+						sel = handleObj.selector + " ";
+
+						if ( matchedSelectors[ sel ] === undefined ) {
+							matchedSelectors[ sel ] = handleObj.needsContext ?
+								jQuery( sel, this ).index( cur ) > -1 :
+								jQuery.find( sel, this, null, [ cur ] ).length;
+						}
+						if ( matchedSelectors[ sel ] ) {
+							matchedHandlers.push( handleObj );
+						}
+					}
+					if ( matchedHandlers.length ) {
+						handlerQueue.push( { elem: cur, handlers: matchedHandlers } );
+					}
+				}
+			}
+		}
+
+		// Add the remaining (directly-bound) handlers
+		cur = this;
+		if ( delegateCount < handlers.length ) {
+			handlerQueue.push( { elem: cur, handlers: handlers.slice( delegateCount ) } );
+		}
+
+		return handlerQueue;
+	},
+
+	addProp: function( name, hook ) {
+		Object.defineProperty( jQuery.Event.prototype, name, {
+			enumerable: true,
+			configurable: true,
+
+			get: isFunction( hook ) ?
+				function() {
+					if ( this.originalEvent ) {
+						return hook( this.originalEvent );
+					}
+				} :
+				function() {
+					if ( this.originalEvent ) {
+						return this.originalEvent[ name ];
+					}
+				},
+
+			set: function( value ) {
+				Object.defineProperty( this, name, {
+					enumerable: true,
+					configurable: true,
+					writable: true,
+					value: value
+				} );
+			}
+		} );
+	},
+
+	fix: function( originalEvent ) {
+		return originalEvent[ jQuery.expando ] ?
+			originalEvent :
+			new jQuery.Event( originalEvent );
+	},
+
+	special: {
+		load: {
+
+			// Prevent triggered image.load events from bubbling to window.load
+			noBubble: true
+		},
+		click: {
+
+			// Utilize native event to ensure correct state for checkable inputs
+			setup: function( data ) {
+
+				// For mutual compressibility with _default, replace `this` access with a local var.
+				// `|| data` is dead code meant only to preserve the variable through minification.
+				var el = this || data;
+
+				// Claim the first handler
+				if ( rcheckableType.test( el.type ) &&
+					el.click && nodeName( el, "input" ) ) {
+
+					// dataPriv.set( el, "click", ... )
+					leverageNative( el, "click", returnTrue );
+				}
+
+				// Return false to allow normal processing in the caller
+				return false;
+			},
+			trigger: function( data ) {
+
+				// For mutual compressibility with _default, replace `this` access with a local var.
+				// `|| data` is dead code meant only to preserve the variable through minification.
+				var el = this || data;
+
+				// Force setup before triggering a click
+				if ( rcheckableType.test( el.type ) &&
+					el.click && nodeName( el, "input" ) ) {
+
+					leverageNative( el, "click" );
+				}
+
+				// Return non-false to allow normal event-path propagation
+				return true;
+			},
+
+			// For cross-browser consistency, suppress native .click() on links
+			// Also prevent it if we're currently inside a leveraged native-event stack
+			_default: function( event ) {
+				var target = event.target;
+				return rcheckableType.test( target.type ) &&
+					target.click && nodeName( target, "input" ) &&
+					dataPriv.get( target, "click" ) ||
+					nodeName( target, "a" );
+			}
+		},
+
+		beforeunload: {
+			postDispatch: function( event ) {
+
+				// Support: Firefox 20+
+				// Firefox doesn't alert if the returnValue field is not set.
+				if ( event.result !== undefined && event.originalEvent ) {
+					event.originalEvent.returnValue = event.result;
+				}
+			}
+		}
+	}
+};
+
+// Ensure the presence of an event listener that handles manually-triggered
+// synthetic events by interrupting progress until reinvoked in response to
+// *native* events that it fires directly, ensuring that state changes have
+// already occurred before other listeners are invoked.
+function leverageNative( el, type, expectSync ) {
+
+	// Missing expectSync indicates a trigger call, which must force setup through jQuery.event.add
+	if ( !expectSync ) {
+		if ( dataPriv.get( el, type ) === undefined ) {
+			jQuery.event.add( el, type, returnTrue );
+		}
+		return;
+	}
+
+	// Register the controller as a special universal handler for all event namespaces
+	dataPriv.set( el, type, false );
+	jQuery.event.add( el, type, {
+		namespace: false,
+		handler: function( event ) {
+			var notAsync, result,
+				saved = dataPriv.get( this, type );
+
+			if ( ( event.isTrigger & 1 ) && this[ type ] ) {
+
+				// Interrupt processing of the outer synthetic .trigger()ed event
+				// Saved data should be false in such cases, but might be a leftover capture object
+				// from an async native handler (gh-4350)
+				if ( !saved.length ) {
+
+					// Store arguments for use when handling the inner native event
+					// There will always be at least one argument (an event object), so this array
+					// will not be confused with a leftover capture object.
+					saved = slice.call( arguments );
+					dataPriv.set( this, type, saved );
+
+					// Trigger the native event and capture its result
+					// Support: IE <=9 - 11+
+					// focus() and blur() are asynchronous
+					notAsync = expectSync( this, type );
+					this[ type ]();
+					result = dataPriv.get( this, type );
+					if ( saved !== result || notAsync ) {
+						dataPriv.set( this, type, false );
+					} else {
+						result = {};
+					}
+					if ( saved !== result ) {
+
+						// Cancel the outer synthetic event
+						event.stopImmediatePropagation();
+						event.preventDefault();
+
+						// Support: Chrome 86+
+						// In Chrome, if an element having a focusout handler is blurred by
+						// clicking outside of it, it invokes the handler synchronously. If
+						// that handler calls `.remove()` on the element, the data is cleared,
+						// leaving `result` undefined. We need to guard against this.
+						return result && result.value;
+					}
+
+				// If this is an inner synthetic event for an event with a bubbling surrogate
+				// (focus or blur), assume that the surrogate already propagated from triggering the
+				// native event and prevent that from happening again here.
+				// This technically gets the ordering wrong w.r.t. to `.trigger()` (in which the
+				// bubbling surrogate propagates *after* the non-bubbling base), but that seems
+				// less bad than duplication.
+				} else if ( ( jQuery.event.special[ type ] || {} ).delegateType ) {
+					event.stopPropagation();
+				}
+
+			// If this is a native event triggered above, everything is now in order
+			// Fire an inner synthetic event with the original arguments
+			} else if ( saved.length ) {
+
+				// ...and capture the result
+				dataPriv.set( this, type, {
+					value: jQuery.event.trigger(
+
+						// Support: IE <=9 - 11+
+						// Extend with the prototype to reset the above stopImmediatePropagation()
+						jQuery.extend( saved[ 0 ], jQuery.Event.prototype ),
+						saved.slice( 1 ),
+						this
+					)
+				} );
+
+				// Abort handling of the native event
+				event.stopImmediatePropagation();
+			}
+		}
+	} );
+}
+
+jQuery.removeEvent = function( elem, type, handle ) {
+
+	// This "if" is needed for plain objects
+	if ( elem.removeEventListener ) {
+		elem.removeEventListener( type, handle );
+	}
+};
+
+jQuery.Event = function( src, props ) {
+
+	// Allow instantiation without the 'new' keyword
+	if ( !( this instanceof jQuery.Event ) ) {
+		return new jQuery.Event( src, props );
+	}
+
+	// Event object
+	if ( src && src.type ) {
+		this.originalEvent = src;
+		this.type = src.type;
+
+		// Events bubbling up the document may have been marked as prevented
+		// by a handler lower down the tree; reflect the correct value.
+		this.isDefaultPrevented = src.defaultPrevented ||
+				src.defaultPrevented === undefined &&
+
+				// Support: Android <=2.3 only
+				src.returnValue === false ?
+			returnTrue :
+			returnFalse;
+
+		// Create target properties
+		// Support: Safari <=6 - 7 only
+		// Target should not be a text node (#504, #13143)
+		this.target = ( src.target && src.target.nodeType === 3 ) ?
+			src.target.parentNode :
+			src.target;
+
+		this.currentTarget = src.currentTarget;
+		this.relatedTarget = src.relatedTarget;
+
+	// Event type
+	} else {
+		this.type = src;
+	}
+
+	// Put explicitly provided properties onto the event object
+	if ( props ) {
+		jQuery.extend( this, props );
+	}
+
+	// Create a timestamp if incoming event doesn't have one
+	this.timeStamp = src && src.timeStamp || Date.now();
+
+	// Mark it as fixed
+	this[ jQuery.expando ] = true;
+};
+
+// jQuery.Event is based on DOM3 Events as specified by the ECMAScript Language Binding
+// https://www.w3.org/TR/2003/WD-DOM-Level-3-Events-20030331/ecma-script-binding.html
+jQuery.Event.prototype = {
+	constructor: jQuery.Event,
+	isDefaultPrevented: returnFalse,
+	isPropagationStopped: returnFalse,
+	isImmediatePropagationStopped: returnFalse,
+	isSimulated: false,
+
+	preventDefault: function() {
+		var e = this.originalEvent;
+
+		this.isDefaultPrevented = returnTrue;
+
+		if ( e && !this.isSimulated ) {
+			e.preventDefault();
+		}
+	},
+	stopPropagation: function() {
+		var e = this.originalEvent;
+
+		this.isPropagationStopped = returnTrue;
+
+		if ( e && !this.isSimulated ) {
+			e.stopPropagation();
+		}
+	},
+	stopImmediatePropagation: function() {
+		var e = this.originalEvent;
+
+		this.isImmediatePropagationStopped = returnTrue;
+
+		if ( e && !this.isSimulated ) {
+			e.stopImmediatePropagation();
+		}
+
+		this.stopPropagation();
+	}
+};
+
+// Includes all common event props including KeyEvent and MouseEvent specific props
+jQuery.each( {
+	altKey: true,
+	bubbles: true,
+	cancelable: true,
+	changedTouches: true,
+	ctrlKey: true,
+	detail: true,
+	eventPhase: true,
+	metaKey: true,
+	pageX: true,
+	pageY: true,
+	shiftKey: true,
+	view: true,
+	"char": true,
+	code: true,
+	charCode: true,
+	key: true,
+	keyCode: true,
+	button: true,
+	buttons: true,
+	clientX: true,
+	clientY: true,
+	offsetX: true,
+	offsetY: true,
+	pointerId: true,
+	pointerType: true,
+	screenX: true,
+	screenY: true,
+	targetTouches: true,
+	toElement: true,
+	touches: true,
+	which: true
+}, jQuery.event.addProp );
+
+jQuery.each( { focus: "focusin", blur: "focusout" }, function( type, delegateType ) {
+	jQuery.event.special[ type ] = {
+
+		// Utilize native event if possible so blur/focus sequence is correct
+		setup: function() {
+
+			// Claim the first handler
+			// dataPriv.set( this, "focus", ... )
+			// dataPriv.set( this, "blur", ... )
+			leverageNative( this, type, expectSync );
+
+			// Return false to allow normal processing in the caller
+			return false;
+		},
+		trigger: function() {
+
+			// Force setup before trigger
+			leverageNative( this, type );
+
+			// Return non-false to allow normal event-path propagation
+			return true;
+		},
+
+		// Suppress native focus or blur as it's already being fired
+		// in leverageNative.
+		_default: function() {
+			return true;
+		},
+
+		delegateType: delegateType
+	};
+} );
+
+// Create mouseenter/leave events using mouseover/out and event-time checks
+// so that event delegation works in jQuery.
+// Do the same for pointerenter/pointerleave and pointerover/pointerout
+//
+// Support: Safari 7 only
+// Safari sends mouseenter too often; see:
+// https://bugs.chromium.org/p/chromium/issues/detail?id=470258
+// for the description of the bug (it existed in older Chrome versions as well).
+jQuery.each( {
+	mouseenter: "mouseover",
+	mouseleave: "mouseout",
+	pointerenter: "pointerover",
+	pointerleave: "pointerout"
+}, function( orig, fix ) {
+	jQuery.event.special[ orig ] = {
+		delegateType: fix,
+		bindType: fix,
+
+		handle: function( event ) {
+			var ret,
+				target = this,
+				related = event.relatedTarget,
+				handleObj = event.handleObj;
+
+			// For mouseenter/leave call the handler if related is outside the target.
+			// NB: No relatedTarget if the mouse left/entered the browser window
+			if ( !related || ( related !== target && !jQuery.contains( target, related ) ) ) {
+				event.type = handleObj.origType;
+				ret = handleObj.handler.apply( this, arguments );
+				event.type = fix;
+			}
+			return ret;
+		}
+	};
+} );
+
+jQuery.fn.extend( {
+
+	on: function( types, selector, data, fn ) {
+		return on( this, types, selector, data, fn );
+	},
+	one: function( types, selector, data, fn ) {
+		return on( this, types, selector, data, fn, 1 );
+	},
+	off: function( types, selector, fn ) {
+		var handleObj, type;
+		if ( types && types.preventDefault && types.handleObj ) {
+
+			// ( event )  dispatched jQuery.Event
+			handleObj = types.handleObj;
+			jQuery( types.delegateTarget ).off(
+				handleObj.namespace ?
+					handleObj.origType + "." + handleObj.namespace :
+					handleObj.origType,
+				handleObj.selector,
+				handleObj.handler
+			);
+			return this;
+		}
+		if ( typeof types === "object" ) {
+
+			// ( types-object [, selector] )
+			for ( type in types ) {
+				this.off( type, selector, types[ type ] );
+			}
+			return this;
+		}
+		if ( selector === false || typeof selector === "function" ) {
+
+			// ( types [, fn] )
+			fn = selector;
+			selector = undefined;
+		}
+		if ( fn === false ) {
+			fn = returnFalse;
+		}
+		return this.each( function() {
+			jQuery.event.remove( this, types, fn, selector );
+		} );
+	}
+} );
+
+
+var
+
+	// Support: IE <=10 - 11, Edge 12 - 13 only
+	// In IE/Edge using regex groups here causes severe slowdowns.
+	// See https://connect.microsoft.com/IE/feedback/details/1736512/
+	rnoInnerhtml = /<script|<style|<link/i,
+
+	// checked="checked" or checked
+	rchecked = /checked\s*(?:[^=]|=\s*.checked.)/i,
+	rcleanScript = /^\s*<!(?:\[CDATA\[|--)|(?:\]\]|--)>\s*$/g;
+
+// Prefer a tbody over its parent table for containing new rows
+function manipulationTarget( elem, content ) {
+	if ( nodeName( elem, "table" ) &&
+		nodeName( content.nodeType !== 11 ? content : content.firstChild, "tr" ) ) {
+
+		return jQuery( elem ).children( "tbody" )[ 0 ] || elem;
+	}
+
+	return elem;
+}
+
+// Replace/restore the type attribute of script elements for safe DOM manipulation
+function disableScript( elem ) {
+	elem.type = ( elem.getAttribute( "type" ) !== null ) + "/" + elem.type;
+	return elem;
+}
+function restoreScript( elem ) {
+	if ( ( elem.type || "" ).slice( 0, 5 ) === "true/" ) {
+		elem.type = elem.type.slice( 5 );
+	} else {
+		elem.removeAttribute( "type" );
+	}
+
+	return elem;
+}
+
+function cloneCopyEvent( src, dest ) {
+	var i, l, type, pdataOld, udataOld, udataCur, events;
+
+	if ( dest.nodeType !== 1 ) {
+		return;
+	}
+
+	// 1. Copy private data: events, handlers, etc.
+	if ( dataPriv.hasData( src ) ) {
+		pdataOld = dataPriv.get( src );
+		events = pdataOld.events;
+
+		if ( events ) {
+			dataPriv.remove( dest, "handle events" );
+
+			for ( type in events ) {
+				for ( i = 0, l = events[ type ].length; i < l; i++ ) {
+					jQuery.event.add( dest, type, events[ type ][ i ] );
+				}
+			}
+		}
+	}
+
+	// 2. Copy user data
+	if ( dataUser.hasData( src ) ) {
+		udataOld = dataUser.access( src );
+		udataCur = jQuery.extend( {}, udataOld );
+
+		dataUser.set( dest, udataCur );
+	}
+}
+
+// Fix IE bugs, see support tests
+function fixInput( src, dest ) {
+	var nodeName = dest.nodeName.toLowerCase();
+
+	// Fails to persist the checked state of a cloned checkbox or radio button.
+	if ( nodeName === "input" && rcheckableType.test( src.type ) ) {
+		dest.checked = src.checked;
+
+	// Fails to return the selected option to the default selected state when cloning options
+	} else if ( nodeName === "input" || nodeName === "textarea" ) {
+		dest.defaultValue = src.defaultValue;
+	}
+}
+
+function domManip( collection, args, callback, ignored ) {
+
+	// Flatten any nested arrays
+	args = flat( args );
+
+	var fragment, first, scripts, hasScripts, node, doc,
+		i = 0,
+		l = collection.length,
+		iNoClone = l - 1,
+		value = args[ 0 ],
+		valueIsFunction = isFunction( value );
+
+	// We can't cloneNode fragments that contain checked, in WebKit
+	if ( valueIsFunction ||
+			( l > 1 && typeof value === "string" &&
+				!support.checkClone && rchecked.test( value ) ) ) {
+		return collection.each( function( index ) {
+			var self = collection.eq( index );
+			if ( valueIsFunction ) {
+				args[ 0 ] = value.call( this, index, self.html() );
+			}
+			domManip( self, args, callback, ignored );
+		} );
+	}
+
+	if ( l ) {
+		fragment = buildFragment( args, collection[ 0 ].ownerDocument, false, collection, ignored );
+		first = fragment.firstChild;
+
+		if ( fragment.childNodes.length === 1 ) {
+			fragment = first;
+		}
+
+		// Require either new content or an interest in ignored elements to invoke the callback
+		if ( first || ignored ) {
+			scripts = jQuery.map( getAll( fragment, "script" ), disableScript );
+			hasScripts = scripts.length;
+
+			// Use the original fragment for the last item
+			// instead of the first because it can end up
+			// being emptied incorrectly in certain situations (#8070).
+			for ( ; i < l; i++ ) {
+				node = fragment;
+
+				if ( i !== iNoClone ) {
+					node = jQuery.clone( node, true, true );
+
+					// Keep references to cloned scripts for later restoration
+					if ( hasScripts ) {
+
+						// Support: Android <=4.0 only, PhantomJS 1 only
+						// push.apply(_, arraylike) throws on ancient WebKit
+						jQuery.merge( scripts, getAll( node, "script" ) );
+					}
+				}
+
+				callback.call( collection[ i ], node, i );
+			}
+
+			if ( hasScripts ) {
+				doc = scripts[ scripts.length - 1 ].ownerDocument;
+
+				// Reenable scripts
+				jQuery.map( scripts, restoreScript );
+
+				// Evaluate executable scripts on first document insertion
+				for ( i = 0; i < hasScripts; i++ ) {
+					node = scripts[ i ];
+					if ( rscriptType.test( node.type || "" ) &&
+						!dataPriv.access( node, "globalEval" ) &&
+						jQuery.contains( doc, node ) ) {
+
+						if ( node.src && ( node.type || "" ).toLowerCase()  !== "module" ) {
+
+							// Optional AJAX dependency, but won't run scripts if not present
+							if ( jQuery._evalUrl && !node.noModule ) {
+								jQuery._evalUrl( node.src, {
+									nonce: node.nonce || node.getAttribute( "nonce" )
+								}, doc );
+							}
+						} else {
+							DOMEval( node.textContent.replace( rcleanScript, "" ), node, doc );
+						}
+					}
+				}
+			}
+		}
+	}
+
+	return collection;
+}
+
+function remove( elem, selector, keepData ) {
+	var node,
+		nodes = selector ? jQuery.filter( selector, elem ) : elem,
+		i = 0;
+
+	for ( ; ( node = nodes[ i ] ) != null; i++ ) {
+		if ( !keepData && node.nodeType === 1 ) {
+			jQuery.cleanData( getAll( node ) );
+		}
+
+		if ( node.parentNode ) {
+			if ( keepData && isAttached( node ) ) {
+				setGlobalEval( getAll( node, "script" ) );
+			}
+			node.parentNode.removeChild( node );
+		}
+	}
+
+	return elem;
+}
+
+jQuery.extend( {
+	htmlPrefilter: function( html ) {
+		return html;
+	},
+
+	clone: function( elem, dataAndEvents, deepDataAndEvents ) {
+		var i, l, srcElements, destElements,
+			clone = elem.cloneNode( true ),
+			inPage = isAttached( elem );
+
+		// Fix IE cloning issues
+		if ( !support.noCloneChecked && ( elem.nodeType === 1 || elem.nodeType === 11 ) &&
+				!jQuery.isXMLDoc( elem ) ) {
+
+			// We eschew Sizzle here for performance reasons: https://jsperf.com/getall-vs-sizzle/2
+			destElements = getAll( clone );
+			srcElements = getAll( elem );
+
+			for ( i = 0, l = srcElements.length; i < l; i++ ) {
+				fixInput( srcElements[ i ], destElements[ i ] );
+			}
+		}
+
+		// Copy the events from the original to the clone
+		if ( dataAndEvents ) {
+			if ( deepDataAndEvents ) {
+				srcElements = srcElements || getAll( elem );
+				destElements = destElements || getAll( clone );
+
+				for ( i = 0, l = srcElements.length; i < l; i++ ) {
+					cloneCopyEvent( srcElements[ i ], destElements[ i ] );
+				}
+			} else {
+				cloneCopyEvent( elem, clone );
+			}
+		}
+
+		// Preserve script evaluation history
+		destElements = getAll( clone, "script" );
+		if ( destElements.length > 0 ) {
+			setGlobalEval( destElements, !inPage && getAll( elem, "script" ) );
+		}
+
+		// Return the cloned set
+		return clone;
+	},
+
+	cleanData: function( elems ) {
+		var data, elem, type,
+			special = jQuery.event.special,
+			i = 0;
+
+		for ( ; ( elem = elems[ i ] ) !== undefined; i++ ) {
+			if ( acceptData( elem ) ) {
+				if ( ( data = elem[ dataPriv.expando ] ) ) {
+					if ( data.events ) {
+						for ( type in data.events ) {
+							if ( special[ type ] ) {
+								jQuery.event.remove( elem, type );
+
+							// This is a shortcut to avoid jQuery.event.remove's overhead
+							} else {
+								jQuery.removeEvent( elem, type, data.handle );
+							}
+						}
+					}
+
+					// Support: Chrome <=35 - 45+
+					// Assign undefined instead of using delete, see Data#remove
+					elem[ dataPriv.expando ] = undefined;
+				}
+				if ( elem[ dataUser.expando ] ) {
+
+					// Support: Chrome <=35 - 45+
+					// Assign undefined instead of using delete, see Data#remove
+					elem[ dataUser.expando ] = undefined;
+				}
+			}
+		}
+	}
+} );
+
+jQuery.fn.extend( {
+	detach: function( selector ) {
+		return remove( this, selector, true );
+	},
+
+	remove: function( selector ) {
+		return remove( this, selector );
+	},
+
+	text: function( value ) {
+		return access( this, function( value ) {
+			return value === undefined ?
+				jQuery.text( this ) :
+				this.empty().each( function() {
+					if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
+						this.textContent = value;
+					}
+				} );
+		}, null, value, arguments.length );
+	},
+
+	append: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
+				var target = manipulationTarget( this, elem );
+				target.appendChild( elem );
+			}
+		} );
+	},
+
+	prepend: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
+				var target = manipulationTarget( this, elem );
+				target.insertBefore( elem, target.firstChild );
+			}
+		} );
+	},
+
+	before: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.parentNode ) {
+				this.parentNode.insertBefore( elem, this );
+			}
+		} );
+	},
+
+	after: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.parentNode ) {
+				this.parentNode.insertBefore( elem, this.nextSibling );
+			}
+		} );
+	},
+
+	empty: function() {
+		var elem,
+			i = 0;
+
+		for ( ; ( elem = this[ i ] ) != null; i++ ) {
+			if ( elem.nodeType === 1 ) {
+
+				// Prevent memory leaks
+				jQuery.cleanData( getAll( elem, false ) );
+
+				// Remove any remaining nodes
+				elem.textContent = "";
+			}
+		}
+
+		return this;
+	},
+
+	clone: function( dataAndEvents, deepDataAndEvents ) {
+		dataAndEvents = dataAndEvents == null ? false : dataAndEvents;
+		deepDataAndEvents = deepDataAndEvents == null ? dataAndEvents : deepDataAndEvents;
+
+		return this.map( function() {
+			return jQuery.clone( this, dataAndEvents, deepDataAndEvents );
+		} );
+	},
+
+	html: function( value ) {
+		return access( this, function( value ) {
+			var elem = this[ 0 ] || {},
+				i = 0,
+				l = this.length;
+
+			if ( value === undefined && elem.nodeType === 1 ) {
+				return elem.innerHTML;
+			}
+
+			// See if we can take a shortcut and just use innerHTML
+			if ( typeof value === "string" && !rnoInnerhtml.test( value ) &&
+				!wrapMap[ ( rtagName.exec( value ) || [ "", "" ] )[ 1 ].toLowerCase() ] ) {
+
+				value = jQuery.htmlPrefilter( value );
+
+				try {
+					for ( ; i < l; i++ ) {
+						elem = this[ i ] || {};
+
+						// Remove element nodes and prevent memory leaks
+						if ( elem.nodeType === 1 ) {
+							jQuery.cleanData( getAll( elem, false ) );
+							elem.innerHTML = value;
+						}
+					}
+
+					elem = 0;
+
+				// If using innerHTML throws an exception, use the fallback method
+				} catch ( e ) {}
+			}
+
+			if ( elem ) {
+				this.empty().append( value );
+			}
+		}, null, value, arguments.length );
+	},
+
+	replaceWith: function() {
+		var ignored = [];
+
+		// Make the changes, replacing each non-ignored context element with the new content
+		return domManip( this, arguments, function( elem ) {
+			var parent = this.parentNode;
+
+			if ( jQuery.inArray( this, ignored ) < 0 ) {
+				jQuery.cleanData( getAll( this ) );
+				if ( parent ) {
+					parent.replaceChild( elem, this );
+				}
+			}
+
+		// Force callback invocation
+		}, ignored );
+	}
+} );
+
+jQuery.each( {
+	appendTo: "append",
+	prependTo: "prepend",
+	insertBefore: "before",
+	insertAfter: "after",
+	replaceAll: "replaceWith"
+}, function( name, original ) {
+	jQuery.fn[ name ] = function( selector ) {
+		var elems,
+			ret = [],
+			insert = jQuery( selector ),
+			last = insert.length - 1,
+			i = 0;
+
+		for ( ; i <= last; i++ ) {
+			elems = i === last ? this : this.clone( true );
+			jQuery( insert[ i ] )[ original ]( elems );
+
+			// Support: Android <=4.0 only, PhantomJS 1 only
+			// .get() because push.apply(_, arraylike) throws on ancient WebKit
+			push.apply( ret, elems.get() );
+		}
+
+		return this.pushStack( ret );
+	};
+} );
+var rnumnonpx = new RegExp( "^(" + pnum + ")(?!px)[a-z%]+$", "i" );
+
+var getStyles = function( elem ) {
+
+		// Support: IE <=11 only, Firefox <=30 (#15098, #14150)
+		// IE throws on elements created in popups
+		// FF meanwhile throws on frame elements through "defaultView.getComputedStyle"
+		var view = elem.ownerDocument.defaultView;
+
+		if ( !view || !view.opener ) {
+			view = window;
+		}
+
+		return view.getComputedStyle( elem );
+	};
+
+var swap = function( elem, options, callback ) {
+	var ret, name,
+		old = {};
+
+	// Remember the old values, and insert the new ones
+	for ( name in options ) {
+		old[ name ] = elem.style[ name ];
+		elem.style[ name ] = options[ name ];
+	}
+
+	ret = callback.call( elem );
+
+	// Revert the old values
+	for ( name in options ) {
+		elem.style[ name ] = old[ name ];
+	}
+
+	return ret;
+};
+
+
+var rboxStyle = new RegExp( cssExpand.join( "|" ), "i" );
+
+
+
+( function() {
+
+	// Executing both pixelPosition & boxSizingReliable tests require only one layout
+	// so they're executed at the same time to save the second computation.
+	function computeStyleTests() {
+
+		// This is a singleton, we need to execute it only once
+		if ( !div ) {
+			return;
+		}
+
+		container.style.cssText = "position:absolute;left:-11111px;width:60px;" +
+			"margin-top:1px;padding:0;border:0";
+		div.style.cssText =
+			"position:relative;display:block;box-sizing:border-box;overflow:scroll;" +
+			"margin:auto;border:1px;padding:1px;" +
+			"width:60%;top:1%";
+		documentElement.appendChild( container ).appendChild( div );
+
+		var divStyle = window.getComputedStyle( div );
+		pixelPositionVal = divStyle.top !== "1%";
+
+		// Support: Android 4.0 - 4.3 only, Firefox <=3 - 44
+		reliableMarginLeftVal = roundPixelMeasures( divStyle.marginLeft ) === 12;
+
+		// Support: Android 4.0 - 4.3 only, Safari <=9.1 - 10.1, iOS <=7.0 - 9.3
+		// Some styles come back with percentage values, even though they shouldn't
+		div.style.right = "60%";
+		pixelBoxStylesVal = roundPixelMeasures( divStyle.right ) === 36;
+
+		// Support: IE 9 - 11 only
+		// Detect misreporting of content dimensions for box-sizing:border-box elements
+		boxSizingReliableVal = roundPixelMeasures( divStyle.width ) === 36;
+
+		// Support: IE 9 only
+		// Detect overflow:scroll screwiness (gh-3699)
+		// Support: Chrome <=64
+		// Don't get tricked when zoom affects offsetWidth (gh-4029)
+		div.style.position = "absolute";
+		scrollboxSizeVal = roundPixelMeasures( div.offsetWidth / 3 ) === 12;
+
+		documentElement.removeChild( container );
+
+		// Nullify the div so it wouldn't be stored in the memory and
+		// it will also be a sign that checks already performed
+		div = null;
+	}
+
+	function roundPixelMeasures( measure ) {
+		return Math.round( parseFloat( measure ) );
+	}
+
+	var pixelPositionVal, boxSizingReliableVal, scrollboxSizeVal, pixelBoxStylesVal,
+		reliableTrDimensionsVal, reliableMarginLeftVal,
+		container = document.createElement( "div" ),
+		div = document.createElement( "div" );
+
+	// Finish early in limited (non-browser) environments
+	if ( !div.style ) {
+		return;
+	}
+
+	// Support: IE <=9 - 11 only
+	// Style of cloned element affects source element cloned (#8908)
+	div.style.backgroundClip = "content-box";
+	div.cloneNode( true ).style.backgroundClip = "";
+	support.clearCloneStyle = div.style.backgroundClip === "content-box";
+
+	jQuery.extend( support, {
+		boxSizingReliable: function() {
+			computeStyleTests();
+			return boxSizingReliableVal;
+		},
+		pixelBoxStyles: function() {
+			computeStyleTests();
+			return pixelBoxStylesVal;
+		},
+		pixelPosition: function() {
+			computeStyleTests();
+			return pixelPositionVal;
+		},
+		reliableMarginLeft: function() {
+			computeStyleTests();
+			return reliableMarginLeftVal;
+		},
+		scrollboxSize: function() {
+			computeStyleTests();
+			return scrollboxSizeVal;
+		},
+
+		// Support: IE 9 - 11+, Edge 15 - 18+
+		// IE/Edge misreport `getComputedStyle` of table rows with width/height
+		// set in CSS while `offset*` properties report correct values.
+		// Behavior in IE 9 is more subtle than in newer versions & it passes
+		// some versions of this test; make sure not to make it pass there!
+		//
+		// Support: Firefox 70+
+		// Only Firefox includes border widths
+		// in computed dimensions. (gh-4529)
+		reliableTrDimensions: function() {
+			var table, tr, trChild, trStyle;
+			if ( reliableTrDimensionsVal == null ) {
+				table = document.createElement( "table" );
+				tr = document.createElement( "tr" );
+				trChild = document.createElement( "div" );
+
+				table.style.cssText = "position:absolute;left:-11111px;border-collapse:separate";
+				tr.style.cssText = "border:1px solid";
+
+				// Support: Chrome 86+
+				// Height set through cssText does not get applied.
+				// Computed height then comes back as 0.
+				tr.style.height = "1px";
+				trChild.style.height = "9px";
+
+				// Support: Android 8 Chrome 86+
+				// In our bodyBackground.html iframe,
+				// display for all div elements is set to "inline",
+				// which causes a problem only in Android 8 Chrome 86.
+				// Ensuring the div is display: block
+				// gets around this issue.
+				trChild.style.display = "block";
+
+				documentElement
+					.appendChild( table )
+					.appendChild( tr )
+					.appendChild( trChild );
+
+				trStyle = window.getComputedStyle( tr );
+				reliableTrDimensionsVal = ( parseInt( trStyle.height, 10 ) +
+					parseInt( trStyle.borderTopWidth, 10 ) +
+					parseInt( trStyle.borderBottomWidth, 10 ) ) === tr.offsetHeight;
+
+				documentElement.removeChild( table );
+			}
+			return reliableTrDimensionsVal;
+		}
+	} );
+} )();
+
+
+function curCSS( elem, name, computed ) {
+	var width, minWidth, maxWidth, ret,
+
+		// Support: Firefox 51+
+		// Retrieving style before computed somehow
+		// fixes an issue with getting wrong values
+		// on detached elements
+		style = elem.style;
+
+	computed = computed || getStyles( elem );
+
+	// getPropertyValue is needed for:
+	//   .css('filter') (IE 9 only, #12537)
+	//   .css('--customProperty) (#3144)
+	if ( computed ) {
+		ret = computed.getPropertyValue( name ) || computed[ name ];
+
+		if ( ret === "" && !isAttached( elem ) ) {
+			ret = jQuery.style( elem, name );
+		}
+
+		// A tribute to the "awesome hack by Dean Edwards"
+		// Android Browser returns percentage for some values,
+		// but width seems to be reliably pixels.
+		// This is against the CSSOM draft spec:
+		// https://drafts.csswg.org/cssom/#resolved-values
+		if ( !support.pixelBoxStyles() && rnumnonpx.test( ret ) && rboxStyle.test( name ) ) {
+
+			// Remember the original values
+			width = style.width;
+			minWidth = style.minWidth;
+			maxWidth = style.maxWidth;
+
+			// Put in the new values to get a computed value out
+			style.minWidth = style.maxWidth = style.width = ret;
+			ret = computed.width;
+
+			// Revert the changed values
+			style.width = width;
+			style.minWidth = minWidth;
+			style.maxWidth = maxWidth;
+		}
+	}
+
+	return ret !== undefined ?
+
+		// Support: IE <=9 - 11 only
+		// IE returns zIndex value as an integer.
+		ret + "" :
+		ret;
+}
+
+
+function addGetHookIf( conditionFn, hookFn ) {
+
+	// Define the hook, we'll check on the first run if it's really needed.
+	return {
+		get: function() {
+			if ( conditionFn() ) {
+
+				// Hook not needed (or it's not possible to use it due
+				// to missing dependency), remove it.
+				delete this.get;
+				return;
+			}
+
+			// Hook needed; redefine it so that the support test is not executed again.
+			return ( this.get = hookFn ).apply( this, arguments );
+		}
+	};
+}
+
+
+var cssPrefixes = [ "Webkit", "Moz", "ms" ],
+	emptyStyle = document.createElement( "div" ).style,
+	vendorProps = {};
+
+// Return a vendor-prefixed property or undefined
+function vendorPropName( name ) {
+
+	// Check for vendor prefixed names
+	var capName = name[ 0 ].toUpperCase() + name.slice( 1 ),
+		i = cssPrefixes.length;
+
+	while ( i-- ) {
+		name = cssPrefixes[ i ] + capName;
+		if ( name in emptyStyle ) {
+			return name;
+		}
+	}
+}
+
+// Return a potentially-mapped jQuery.cssProps or vendor prefixed property
+function finalPropName( name ) {
+	var final = jQuery.cssProps[ name ] || vendorProps[ name ];
+
+	if ( final ) {
+		return final;
+	}
+	if ( name in emptyStyle ) {
+		return name;
+	}
+	return vendorProps[ name ] = vendorPropName( name ) || name;
+}
+
+
+var
+
+	// Swappable if display is none or starts with table
+	// except "table", "table-cell", or "table-caption"
+	// See here for display values: https://developer.mozilla.org/en-US/docs/CSS/display
+	rdisplayswap = /^(none|table(?!-c[ea]).+)/,
+	rcustomProp = /^--/,
+	cssShow = { position: "absolute", visibility: "hidden", display: "block" },
+	cssNormalTransform = {
+		letterSpacing: "0",
+		fontWeight: "400"
+	};
+
+function setPositiveNumber( _elem, value, subtract ) {
+
+	// Any relative (+/-) values have already been
+	// normalized at this point
+	var matches = rcssNum.exec( value );
+	return matches ?
+
+		// Guard against undefined "subtract", e.g., when used as in cssHooks
+		Math.max( 0, matches[ 2 ] - ( subtract || 0 ) ) + ( matches[ 3 ] || "px" ) :
+		value;
+}
+
+function boxModelAdjustment( elem, dimension, box, isBorderBox, styles, computedVal ) {
+	var i = dimension === "width" ? 1 : 0,
+		extra = 0,
+		delta = 0;
+
+	// Adjustment may not be necessary
+	if ( box === ( isBorderBox ? "border" : "content" ) ) {
+		return 0;
+	}
+
+	for ( ; i < 4; i += 2 ) {
+
+		// Both box models exclude margin
+		if ( box === "margin" ) {
+			delta += jQuery.css( elem, box + cssExpand[ i ], true, styles );
+		}
+
+		// If we get here with a content-box, we're seeking "padding" or "border" or "margin"
+		if ( !isBorderBox ) {
+
+			// Add padding
+			delta += jQuery.css( elem, "padding" + cssExpand[ i ], true, styles );
+
+			// For "border" or "margin", add border
+			if ( box !== "padding" ) {
+				delta += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
+
+			// But still keep track of it otherwise
+			} else {
+				extra += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
+			}
+
+		// If we get here with a border-box (content + padding + border), we're seeking "content" or
+		// "padding" or "margin"
+		} else {
+
+			// For "content", subtract padding
+			if ( box === "content" ) {
+				delta -= jQuery.css( elem, "padding" + cssExpand[ i ], true, styles );
+			}
+
+			// For "content" or "padding", subtract border
+			if ( box !== "margin" ) {
+				delta -= jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
+			}
+		}
+	}
+
+	// Account for positive content-box scroll gutter when requested by providing computedVal
+	if ( !isBorderBox && computedVal >= 0 ) {
+
+		// offsetWidth/offsetHeight is a rounded sum of content, padding, scroll gutter, and border
+		// Assuming integer scroll gutter, subtract the rest and round down
+		delta += Math.max( 0, Math.ceil(
+			elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] -
+			computedVal -
+			delta -
+			extra -
+			0.5
+
+		// If offsetWidth/offsetHeight is unknown, then we can't determine content-box scroll gutter
+		// Use an explicit zero to avoid NaN (gh-3964)
+		) ) || 0;
+	}
+
+	return delta;
+}
+
+function getWidthOrHeight( elem, dimension, extra ) {
+
+	// Start with computed style
+	var styles = getStyles( elem ),
+
+		// To avoid forcing a reflow, only fetch boxSizing if we need it (gh-4322).
+		// Fake content-box until we know it's needed to know the true value.
+		boxSizingNeeded = !support.boxSizingReliable() || extra,
+		isBorderBox = boxSizingNeeded &&
+			jQuery.css( elem, "boxSizing", false, styles ) === "border-box",
+		valueIsBorderBox = isBorderBox,
+
+		val = curCSS( elem, dimension, styles ),
+		offsetProp = "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 );
+
+	// Support: Firefox <=54
+	// Return a confounding non-pixel value or feign ignorance, as appropriate.
+	if ( rnumnonpx.test( val ) ) {
+		if ( !extra ) {
+			return val;
+		}
+		val = "auto";
+	}
+
+
+	// Support: IE 9 - 11 only
+	// Use offsetWidth/offsetHeight for when box sizing is unreliable.
+	// In those cases, the computed value can be trusted to be border-box.
+	if ( ( !support.boxSizingReliable() && isBorderBox ||
+
+		// Support: IE 10 - 11+, Edge 15 - 18+
+		// IE/Edge misreport `getComputedStyle` of table rows with width/height
+		// set in CSS while `offset*` properties report correct values.
+		// Interestingly, in some cases IE 9 doesn't suffer from this issue.
+		!support.reliableTrDimensions() && nodeName( elem, "tr" ) ||
+
+		// Fall back to offsetWidth/offsetHeight when value is "auto"
+		// This happens for inline elements with no explicit setting (gh-3571)
+		val === "auto" ||
+
+		// Support: Android <=4.1 - 4.3 only
+		// Also use offsetWidth/offsetHeight for misreported inline dimensions (gh-3602)
+		!parseFloat( val ) && jQuery.css( elem, "display", false, styles ) === "inline" ) &&
+
+		// Make sure the element is visible & connected
+		elem.getClientRects().length ) {
+
+		isBorderBox = jQuery.css( elem, "boxSizing", false, styles ) === "border-box";
+
+		// Where available, offsetWidth/offsetHeight approximate border box dimensions.
+		// Where not available (e.g., SVG), assume unreliable box-sizing and interpret the
+		// retrieved value as a content box dimension.
+		valueIsBorderBox = offsetProp in elem;
+		if ( valueIsBorderBox ) {
+			val = elem[ offsetProp ];
+		}
+	}
+
+	// Normalize "" and auto
+	val = parseFloat( val ) || 0;
+
+	// Adjust for the element's box model
+	return ( val +
+		boxModelAdjustment(
+			elem,
+			dimension,
+			extra || ( isBorderBox ? "border" : "content" ),
+			valueIsBorderBox,
+			styles,
+
+			// Provide the current computed size to request scroll gutter calculation (gh-3589)
+			val
+		)
+	) + "px";
+}
+
+jQuery.extend( {
+
+	// Add in style property hooks for overriding the default
+	// behavior of getting and setting a style property
+	cssHooks: {
+		opacity: {
+			get: function( elem, computed ) {
+				if ( computed ) {
+
+					// We should always get a number back from opacity
+					var ret = curCSS( elem, "opacity" );
+					return ret === "" ? "1" : ret;
+				}
+			}
+		}
+	},
+
+	// Don't automatically add "px" to these possibly-unitless properties
+	cssNumber: {
+		"animationIterationCount": true,
+		"columnCount": true,
+		"fillOpacity": true,
+		"flexGrow": true,
+		"flexShrink": true,
+		"fontWeight": true,
+		"gridArea": true,
+		"gridColumn": true,
+		"gridColumnEnd": true,
+		"gridColumnStart": true,
+		"gridRow": true,
+		"gridRowEnd": true,
+		"gridRowStart": true,
+		"lineHeight": true,
+		"opacity": true,
+		"order": true,
+		"orphans": true,
+		"widows": true,
+		"zIndex": true,
+		"zoom": true
+	},
+
+	// Add in properties whose names you wish to fix before
+	// setting or getting the value
+	cssProps: {},
+
+	// Get and set the style property on a DOM Node
+	style: function( elem, name, value, extra ) {
+
+		// Don't set styles on text and comment nodes
+		if ( !elem || elem.nodeType === 3 || elem.nodeType === 8 || !elem.style ) {
+			return;
+		}
+
+		// Make sure that we're working with the right name
+		var ret, type, hooks,
+			origName = camelCase( name ),
+			isCustomProp = rcustomProp.test( name ),
+			style = elem.style;
+
+		// Make sure that we're working with the right name. We don't
+		// want to query the value if it is a CSS custom property
+		// since they are user-defined.
+		if ( !isCustomProp ) {
+			name = finalPropName( origName );
+		}
+
+		// Gets hook for the prefixed version, then unprefixed version
+		hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ];
+
+		// Check if we're setting a value
+		if ( value !== undefined ) {
+			type = typeof value;
+
+			// Convert "+=" or "-=" to relative numbers (#7345)
+			if ( type === "string" && ( ret = rcssNum.exec( value ) ) && ret[ 1 ] ) {
+				value = adjustCSS( elem, name, ret );
+
+				// Fixes bug #9237
+				type = "number";
+			}
+
+			// Make sure that null and NaN values aren't set (#7116)
+			if ( value == null || value !== value ) {
+				return;
+			}
+
+			// If a number was passed in, add the unit (except for certain CSS properties)
+			// The isCustomProp check can be removed in jQuery 4.0 when we only auto-append
+			// "px" to a few hardcoded values.
+			if ( type === "number" && !isCustomProp ) {
+				value += ret && ret[ 3 ] || ( jQuery.cssNumber[ origName ] ? "" : "px" );
+			}
+
+			// background-* props affect original clone's values
+			if ( !support.clearCloneStyle && value === "" && name.indexOf( "background" ) === 0 ) {
+				style[ name ] = "inherit";
+			}
+
+			// If a hook was provided, use that value, otherwise just set the specified value
+			if ( !hooks || !( "set" in hooks ) ||
+				( value = hooks.set( elem, value, extra ) ) !== undefined ) {
+
+				if ( isCustomProp ) {
+					style.setProperty( name, value );
+				} else {
+					style[ name ] = value;
+				}
+			}
+
+		} else {
+
+			// If a hook was provided get the non-computed value from there
+			if ( hooks && "get" in hooks &&
+				( ret = hooks.get( elem, false, extra ) ) !== undefined ) {
+
+				return ret;
+			}
+
+			// Otherwise just get the value from the style object
+			return style[ name ];
+		}
+	},
+
+	css: function( elem, name, extra, styles ) {
+		var val, num, hooks,
+			origName = camelCase( name ),
+			isCustomProp = rcustomProp.test( name );
+
+		// Make sure that we're working with the right name. We don't
+		// want to modify the value if it is a CSS custom property
+		// since they are user-defined.
+		if ( !isCustomProp ) {
+			name = finalPropName( origName );
+		}
+
+		// Try prefixed name followed by the unprefixed name
+		hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ];
+
+		// If a hook was provided get the computed value from there
+		if ( hooks && "get" in hooks ) {
+			val = hooks.get( elem, true, extra );
+		}
+
+		// Otherwise, if a way to get the computed value exists, use that
+		if ( val === undefined ) {
+			val = curCSS( elem, name, styles );
+		}
+
+		// Convert "normal" to computed value
+		if ( val === "normal" && name in cssNormalTransform ) {
+			val = cssNormalTransform[ name ];
+		}
+
+		// Make numeric if forced or a qualifier was provided and val looks numeric
+		if ( extra === "" || extra ) {
+			num = parseFloat( val );
+			return extra === true || isFinite( num ) ? num || 0 : val;
+		}
+
+		return val;
+	}
+} );
+
+jQuery.each( [ "height", "width" ], function( _i, dimension ) {
+	jQuery.cssHooks[ dimension ] = {
+		get: function( elem, computed, extra ) {
+			if ( computed ) {
+
+				// Certain elements can have dimension info if we invisibly show them
+				// but it must have a current display style that would benefit
+				return rdisplayswap.test( jQuery.css( elem, "display" ) ) &&
+
+					// Support: Safari 8+
+					// Table columns in Safari have non-zero offsetWidth & zero
+					// getBoundingClientRect().width unless display is changed.
+					// Support: IE <=11 only
+					// Running getBoundingClientRect on a disconnected node
+					// in IE throws an error.
+					( !elem.getClientRects().length || !elem.getBoundingClientRect().width ) ?
+					swap( elem, cssShow, function() {
+						return getWidthOrHeight( elem, dimension, extra );
+					} ) :
+					getWidthOrHeight( elem, dimension, extra );
+			}
+		},
+
+		set: function( elem, value, extra ) {
+			var matches,
+				styles = getStyles( elem ),
+
+				// Only read styles.position if the test has a chance to fail
+				// to avoid forcing a reflow.
+				scrollboxSizeBuggy = !support.scrollboxSize() &&
+					styles.position === "absolute",
+
+				// To avoid forcing a reflow, only fetch boxSizing if we need it (gh-3991)
+				boxSizingNeeded = scrollboxSizeBuggy || extra,
+				isBorderBox = boxSizingNeeded &&
+					jQuery.css( elem, "boxSizing", false, styles ) === "border-box",
+				subtract = extra ?
+					boxModelAdjustment(
+						elem,
+						dimension,
+						extra,
+						isBorderBox,
+						styles
+					) :
+					0;
+
+			// Account for unreliable border-box dimensions by comparing offset* to computed and
+			// faking a content-box to get border and padding (gh-3699)
+			if ( isBorderBox && scrollboxSizeBuggy ) {
+				subtract -= Math.ceil(
+					elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] -
+					parseFloat( styles[ dimension ] ) -
+					boxModelAdjustment( elem, dimension, "border", false, styles ) -
+					0.5
+				);
+			}
+
+			// Convert to pixels if value adjustment is needed
+			if ( subtract && ( matches = rcssNum.exec( value ) ) &&
+				( matches[ 3 ] || "px" ) !== "px" ) {
+
+				elem.style[ dimension ] = value;
+				value = jQuery.css( elem, dimension );
+			}
+
+			return setPositiveNumber( elem, value, subtract );
+		}
+	};
+} );
+
+jQuery.cssHooks.marginLeft = addGetHookIf( support.reliableMarginLeft,
+	function( elem, computed ) {
+		if ( computed ) {
+			return ( parseFloat( curCSS( elem, "marginLeft" ) ) ||
+				elem.getBoundingClientRect().left -
+					swap( elem, { marginLeft: 0 }, function() {
+						return elem.getBoundingClientRect().left;
+					} )
+			) + "px";
+		}
+	}
+);
+
+// These hooks are used by animate to expand properties
+jQuery.each( {
+	margin: "",
+	padding: "",
+	border: "Width"
+}, function( prefix, suffix ) {
+	jQuery.cssHooks[ prefix + suffix ] = {
+		expand: function( value ) {
+			var i = 0,
+				expanded = {},
+
+				// Assumes a single number if not a string
+				parts = typeof value === "string" ? value.split( " " ) : [ value ];
+
+			for ( ; i < 4; i++ ) {
+				expanded[ prefix + cssExpand[ i ] + suffix ] =
+					parts[ i ] || parts[ i - 2 ] || parts[ 0 ];
+			}
+
+			return expanded;
+		}
+	};
+
+	if ( prefix !== "margin" ) {
+		jQuery.cssHooks[ prefix + suffix ].set = setPositiveNumber;
+	}
+} );
+
+jQuery.fn.extend( {
+	css: function( name, value ) {
+		return access( this, function( elem, name, value ) {
+			var styles, len,
+				map = {},
+				i = 0;
+
+			if ( Array.isArray( name ) ) {
+				styles = getStyles( elem );
+				len = name.length;
+
+				for ( ; i < len; i++ ) {
+					map[ name[ i ] ] = jQuery.css( elem, name[ i ], false, styles );
+				}
+
+				return map;
+			}
+
+			return value !== undefined ?
+				jQuery.style( elem, name, value ) :
+				jQuery.css( elem, name );
+		}, name, value, arguments.length > 1 );
+	}
+} );
+
+
+function Tween( elem, options, prop, end, easing ) {
+	return new Tween.prototype.init( elem, options, prop, end, easing );
+}
+jQuery.Tween = Tween;
+
+Tween.prototype = {
+	constructor: Tween,
+	init: function( elem, options, prop, end, easing, unit ) {
+		this.elem = elem;
+		this.prop = prop;
+		this.easing = easing || jQuery.easing._default;
+		this.options = options;
+		this.start = this.now = this.cur();
+		this.end = end;
+		this.unit = unit || ( jQuery.cssNumber[ prop ] ? "" : "px" );
+	},
+	cur: function() {
+		var hooks = Tween.propHooks[ this.prop ];
+
+		return hooks && hooks.get ?
+			hooks.get( this ) :
+			Tween.propHooks._default.get( this );
+	},
+	run: function( percent ) {
+		var eased,
+			hooks = Tween.propHooks[ this.prop ];
+
+		if ( this.options.duration ) {
+			this.pos = eased = jQuery.easing[ this.easing ](
+				percent, this.options.duration * percent, 0, 1, this.options.duration
+			);
+		} else {
+			this.pos = eased = percent;
+		}
+		this.now = ( this.end - this.start ) * eased + this.start;
+
+		if ( this.options.step ) {
+			this.options.step.call( this.elem, this.now, this );
+		}
+
+		if ( hooks && hooks.set ) {
+			hooks.set( this );
+		} else {
+			Tween.propHooks._default.set( this );
+		}
+		return this;
+	}
+};
+
+Tween.prototype.init.prototype = Tween.prototype;
+
+Tween.propHooks = {
+	_default: {
+		get: function( tween ) {
+			var result;
+
+			// Use a property on the element directly when it is not a DOM element,
+			// or when there is no matching style property that exists.
+			if ( tween.elem.nodeType !== 1 ||
+				tween.elem[ tween.prop ] != null && tween.elem.style[ tween.prop ] == null ) {
+				return tween.elem[ tween.prop ];
+			}
+
+			// Passing an empty string as a 3rd parameter to .css will automatically
+			// attempt a parseFloat and fallback to a string if the parse fails.
+			// Simple values such as "10px" are parsed to Float;
+			// complex values such as "rotate(1rad)" are returned as-is.
+			result = jQuery.css( tween.elem, tween.prop, "" );
+
+			// Empty strings, null, undefined and "auto" are converted to 0.
+			return !result || result === "auto" ? 0 : result;
+		},
+		set: function( tween ) {
+
+			// Use step hook for back compat.
+			// Use cssHook if its there.
+			// Use .style if available and use plain properties where available.
+			if ( jQuery.fx.step[ tween.prop ] ) {
+				jQuery.fx.step[ tween.prop ]( tween );
+			} else if ( tween.elem.nodeType === 1 && (
+				jQuery.cssHooks[ tween.prop ] ||
+					tween.elem.style[ finalPropName( tween.prop ) ] != null ) ) {
+				jQuery.style( tween.elem, tween.prop, tween.now + tween.unit );
+			} else {
+				tween.elem[ tween.prop ] = tween.now;
+			}
+		}
+	}
+};
+
+// Support: IE <=9 only
+// Panic based approach to setting things on disconnected nodes
+Tween.propHooks.scrollTop = Tween.propHooks.scrollLeft = {
+	set: function( tween ) {
+		if ( tween.elem.nodeType && tween.elem.parentNode ) {
+			tween.elem[ tween.prop ] = tween.now;
+		}
+	}
+};
+
+jQuery.easing = {
+	linear: function( p ) {
+		return p;
+	},
+	swing: function( p ) {
+		return 0.5 - Math.cos( p * Math.PI ) / 2;
+	},
+	_default: "swing"
+};
+
+jQuery.fx = Tween.prototype.init;
+
+// Back compat <1.8 extension point
+jQuery.fx.step = {};
+
+
+
+
+var
+	fxNow, inProgress,
+	rfxtypes = /^(?:toggle|show|hide)$/,
+	rrun = /queueHooks$/;
+
+function schedule() {
+	if ( inProgress ) {
+		if ( document.hidden === false && window.requestAnimationFrame ) {
+			window.requestAnimationFrame( schedule );
+		} else {
+			window.setTimeout( schedule, jQuery.fx.interval );
+		}
+
+		jQuery.fx.tick();
+	}
+}
+
+// Animations created synchronously will run synchronously
+function createFxNow() {
+	window.setTimeout( function() {
+		fxNow = undefined;
+	} );
+	return ( fxNow = Date.now() );
+}
+
+// Generate parameters to create a standard animation
+function genFx( type, includeWidth ) {
+	var which,
+		i = 0,
+		attrs = { height: type };
+
+	// If we include width, step value is 1 to do all cssExpand values,
+	// otherwise step value is 2 to skip over Left and Right
+	includeWidth = includeWidth ? 1 : 0;
+	for ( ; i < 4; i += 2 - includeWidth ) {
+		which = cssExpand[ i ];
+		attrs[ "margin" + which ] = attrs[ "padding" + which ] = type;
+	}
+
+	if ( includeWidth ) {
+		attrs.opacity = attrs.width = type;
+	}
+
+	return attrs;
+}
+
+function createTween( value, prop, animation ) {
+	var tween,
+		collection = ( Animation.tweeners[ prop ] || [] ).concat( Animation.tweeners[ "*" ] ),
+		index = 0,
+		length = collection.length;
+	for ( ; index < length; index++ ) {
+		if ( ( tween = collection[ index ].call( animation, prop, value ) ) ) {
+
+			// We're done with this property
+			return tween;
+		}
+	}
+}
+
+function defaultPrefilter( elem, props, opts ) {
+	var prop, value, toggle, hooks, oldfire, propTween, restoreDisplay, display,
+		isBox = "width" in props || "height" in props,
+		anim = this,
+		orig = {},
+		style = elem.style,
+		hidden = elem.nodeType && isHiddenWithinTree( elem ),
+		dataShow = dataPriv.get( elem, "fxshow" );
+
+	// Queue-skipping animations hijack the fx hooks
+	if ( !opts.queue ) {
+		hooks = jQuery._queueHooks( elem, "fx" );
+		if ( hooks.unqueued == null ) {
+			hooks.unqueued = 0;
+			oldfire = hooks.empty.fire;
+			hooks.empty.fire = function() {
+				if ( !hooks.unqueued ) {
+					oldfire();
+				}
+			};
+		}
+		hooks.unqueued++;
+
+		anim.always( function() {
+
+			// Ensure the complete handler is called before this completes
+			anim.always( function() {
+				hooks.unqueued--;
+				if ( !jQuery.queue( elem, "fx" ).length ) {
+					hooks.empty.fire();
+				}
+			} );
+		} );
+	}
+
+	// Detect show/hide animations
+	for ( prop in props ) {
+		value = props[ prop ];
+		if ( rfxtypes.test( value ) ) {
+			delete props[ prop ];
+			toggle = toggle || value === "toggle";
+			if ( value === ( hidden ? "hide" : "show" ) ) {
+
+				// Pretend to be hidden if this is a "show" and
+				// there is still data from a stopped show/hide
+				if ( value === "show" && dataShow && dataShow[ prop ] !== undefined ) {
+					hidden = true;
+
+				// Ignore all other no-op show/hide data
+				} else {
+					continue;
+				}
+			}
+			orig[ prop ] = dataShow && dataShow[ prop ] || jQuery.style( elem, prop );
+		}
+	}
+
+	// Bail out if this is a no-op like .hide().hide()
+	propTween = !jQuery.isEmptyObject( props );
+	if ( !propTween && jQuery.isEmptyObject( orig ) ) {
+		return;
+	}
+
+	// Restrict "overflow" and "display" styles during box animations
+	if ( isBox && elem.nodeType === 1 ) {
+
+		// Support: IE <=9 - 11, Edge 12 - 15
+		// Record all 3 overflow attributes because IE does not infer the shorthand
+		// from identically-valued overflowX and overflowY and Edge just mirrors
+		// the overflowX value there.
+		opts.overflow = [ style.overflow, style.overflowX, style.overflowY ];
+
+		// Identify a display type, preferring old show/hide data over the CSS cascade
+		restoreDisplay = dataShow && dataShow.display;
+		if ( restoreDisplay == null ) {
+			restoreDisplay = dataPriv.get( elem, "display" );
+		}
+		display = jQuery.css( elem, "display" );
+		if ( display === "none" ) {
+			if ( restoreDisplay ) {
+				display = restoreDisplay;
+			} else {
+
+				// Get nonempty value(s) by temporarily forcing visibility
+				showHide( [ elem ], true );
+				restoreDisplay = elem.style.display || restoreDisplay;
+				display = jQuery.css( elem, "display" );
+				showHide( [ elem ] );
+			}
+		}
+
+		// Animate inline elements as inline-block
+		if ( display === "inline" || display === "inline-block" && restoreDisplay != null ) {
+			if ( jQuery.css( elem, "float" ) === "none" ) {
+
+				// Restore the original display value at the end of pure show/hide animations
+				if ( !propTween ) {
+					anim.done( function() {
+						style.display = restoreDisplay;
+					} );
+					if ( restoreDisplay == null ) {
+						display = style.display;
+						restoreDisplay = display === "none" ? "" : display;
+					}
+				}
+				style.display = "inline-block";
+			}
+		}
+	}
+
+	if ( opts.overflow ) {
+		style.overflow = "hidden";
+		anim.always( function() {
+			style.overflow = opts.overflow[ 0 ];
+			style.overflowX = opts.overflow[ 1 ];
+			style.overflowY = opts.overflow[ 2 ];
+		} );
+	}
+
+	// Implement show/hide animations
+	propTween = false;
+	for ( prop in orig ) {
+
+		// General show/hide setup for this element animation
+		if ( !propTween ) {
+			if ( dataShow ) {
+				if ( "hidden" in dataShow ) {
+					hidden = dataShow.hidden;
+				}
+			} else {
+				dataShow = dataPriv.access( elem, "fxshow", { display: restoreDisplay } );
+			}
+
+			// Store hidden/visible for toggle so `.stop().toggle()` "reverses"
+			if ( toggle ) {
+				dataShow.hidden = !hidden;
+			}
+
+			// Show elements before animating them
+			if ( hidden ) {
+				showHide( [ elem ], true );
+			}
+
+			/* eslint-disable no-loop-func */
+
+			anim.done( function() {
+
+				/* eslint-enable no-loop-func */
+
+				// The final step of a "hide" animation is actually hiding the element
+				if ( !hidden ) {
+					showHide( [ elem ] );
+				}
+				dataPriv.remove( elem, "fxshow" );
+				for ( prop in orig ) {
+					jQuery.style( elem, prop, orig[ prop ] );
+				}
+			} );
+		}
+
+		// Per-property setup
+		propTween = createTween( hidden ? dataShow[ prop ] : 0, prop, anim );
+		if ( !( prop in dataShow ) ) {
+			dataShow[ prop ] = propTween.start;
+			if ( hidden ) {
+				propTween.end = propTween.start;
+				propTween.start = 0;
+			}
+		}
+	}
+}
+
+function propFilter( props, specialEasing ) {
+	var index, name, easing, value, hooks;
+
+	// camelCase, specialEasing and expand cssHook pass
+	for ( index in props ) {
+		name = camelCase( index );
+		easing = specialEasing[ name ];
+		value = props[ index ];
+		if ( Array.isArray( value ) ) {
+			easing = value[ 1 ];
+			value = props[ index ] = value[ 0 ];
+		}
+
+		if ( index !== name ) {
+			props[ name ] = value;
+			delete props[ index ];
+		}
+
+		hooks = jQuery.cssHooks[ name ];
+		if ( hooks && "expand" in hooks ) {
+			value = hooks.expand( value );
+			delete props[ name ];
+
+			// Not quite $.extend, this won't overwrite existing keys.
+			// Reusing 'index' because we have the correct "name"
+			for ( index in value ) {
+				if ( !( index in props ) ) {
+					props[ index ] = value[ index ];
+					specialEasing[ index ] = easing;
+				}
+			}
+		} else {
+			specialEasing[ name ] = easing;
+		}
+	}
+}
+
+function Animation( elem, properties, options ) {
+	var result,
+		stopped,
+		index = 0,
+		length = Animation.prefilters.length,
+		deferred = jQuery.Deferred().always( function() {
+
+			// Don't match elem in the :animated selector
+			delete tick.elem;
+		} ),
+		tick = function() {
+			if ( stopped ) {
+				return false;
+			}
+			var currentTime = fxNow || createFxNow(),
+				remaining = Math.max( 0, animation.startTime + animation.duration - currentTime ),
+
+				// Support: Android 2.3 only
+				// Archaic crash bug won't allow us to use `1 - ( 0.5 || 0 )` (#12497)
+				temp = remaining / animation.duration || 0,
+				percent = 1 - temp,
+				index = 0,
+				length = animation.tweens.length;
+
+			for ( ; index < length; index++ ) {
+				animation.tweens[ index ].run( percent );
+			}
+
+			deferred.notifyWith( elem, [ animation, percent, remaining ] );
+
+			// If there's more to do, yield
+			if ( percent < 1 && length ) {
+				return remaining;
+			}
+
+			// If this was an empty animation, synthesize a final progress notification
+			if ( !length ) {
+				deferred.notifyWith( elem, [ animation, 1, 0 ] );
+			}
+
+			// Resolve the animation and report its conclusion
+			deferred.resolveWith( elem, [ animation ] );
+			return false;
+		},
+		animation = deferred.promise( {
+			elem: elem,
+			props: jQuery.extend( {}, properties ),
+			opts: jQuery.extend( true, {
+				specialEasing: {},
+				easing: jQuery.easing._default
+			}, options ),
+			originalProperties: properties,
+			originalOptions: options,
+			startTime: fxNow || createFxNow(),
+			duration: options.duration,
+			tweens: [],
+			createTween: function( prop, end ) {
+				var tween = jQuery.Tween( elem, animation.opts, prop, end,
+					animation.opts.specialEasing[ prop ] || animation.opts.easing );
+				animation.tweens.push( tween );
+				return tween;
+			},
+			stop: function( gotoEnd ) {
+				var index = 0,
+
+					// If we are going to the end, we want to run all the tweens
+					// otherwise we skip this part
+					length = gotoEnd ? animation.tweens.length : 0;
+				if ( stopped ) {
+					return this;
+				}
+				stopped = true;
+				for ( ; index < length; index++ ) {
+					animation.tweens[ index ].run( 1 );
+				}
+
+				// Resolve when we played the last frame; otherwise, reject
+				if ( gotoEnd ) {
+					deferred.notifyWith( elem, [ animation, 1, 0 ] );
+					deferred.resolveWith( elem, [ animation, gotoEnd ] );
+				} else {
+					deferred.rejectWith( elem, [ animation, gotoEnd ] );
+				}
+				return this;
+			}
+		} ),
+		props = animation.props;
+
+	propFilter( props, animation.opts.specialEasing );
+
+	for ( ; index < length; index++ ) {
+		result = Animation.prefilters[ index ].call( animation, elem, props, animation.opts );
+		if ( result ) {
+			if ( isFunction( result.stop ) ) {
+				jQuery._queueHooks( animation.elem, animation.opts.queue ).stop =
+					result.stop.bind( result );
+			}
+			return result;
+		}
+	}
+
+	jQuery.map( props, createTween, animation );
+
+	if ( isFunction( animation.opts.start ) ) {
+		animation.opts.start.call( elem, animation );
+	}
+
+	// Attach callbacks from options
+	animation
+		.progress( animation.opts.progress )
+		.done( animation.opts.done, animation.opts.complete )
+		.fail( animation.opts.fail )
+		.always( animation.opts.always );
+
+	jQuery.fx.timer(
+		jQuery.extend( tick, {
+			elem: elem,
+			anim: animation,
+			queue: animation.opts.queue
+		} )
+	);
+
+	return animation;
+}
+
+jQuery.Animation = jQuery.extend( Animation, {
+
+	tweeners: {
+		"*": [ function( prop, value ) {
+			var tween = this.createTween( prop, value );
+			adjustCSS( tween.elem, prop, rcssNum.exec( value ), tween );
+			return tween;
+		} ]
+	},
+
+	tweener: function( props, callback ) {
+		if ( isFunction( props ) ) {
+			callback = props;
+			props = [ "*" ];
+		} else {
+			props = props.match( rnothtmlwhite );
+		}
+
+		var prop,
+			index = 0,
+			length = props.length;
+
+		for ( ; index < length; index++ ) {
+			prop = props[ index ];
+			Animation.tweeners[ prop ] = Animation.tweeners[ prop ] || [];
+			Animation.tweeners[ prop ].unshift( callback );
+		}
+	},
+
+	prefilters: [ defaultPrefilter ],
+
+	prefilter: function( callback, prepend ) {
+		if ( prepend ) {
+			Animation.prefilters.unshift( callback );
+		} else {
+			Animation.prefilters.push( callback );
+		}
+	}
+} );
+
+jQuery.speed = function( speed, easing, fn ) {
+	var opt = speed && typeof speed === "object" ? jQuery.extend( {}, speed ) : {
+		complete: fn || !fn && easing ||
+			isFunction( speed ) && speed,
+		duration: speed,
+		easing: fn && easing || easing && !isFunction( easing ) && easing
+	};
+
+	// Go to the end state if fx are off
+	if ( jQuery.fx.off ) {
+		opt.duration = 0;
+
+	} else {
+		if ( typeof opt.duration !== "number" ) {
+			if ( opt.duration in jQuery.fx.speeds ) {
+				opt.duration = jQuery.fx.speeds[ opt.duration ];
+
+			} else {
+				opt.duration = jQuery.fx.speeds._default;
+			}
+		}
+	}
+
+	// Normalize opt.queue - true/undefined/null -> "fx"
+	if ( opt.queue == null || opt.queue === true ) {
+		opt.queue = "fx";
+	}
+
+	// Queueing
+	opt.old = opt.complete;
+
+	opt.complete = function() {
+		if ( isFunction( opt.old ) ) {
+			opt.old.call( this );
+		}
+
+		if ( opt.queue ) {
+			jQuery.dequeue( this, opt.queue );
+		}
+	};
+
+	return opt;
+};
+
+jQuery.fn.extend( {
+	fadeTo: function( speed, to, easing, callback ) {
+
+		// Show any hidden elements after setting opacity to 0
+		return this.filter( isHiddenWithinTree ).css( "opacity", 0 ).show()
+
+			// Animate to the value specified
+			.end().animate( { opacity: to }, speed, easing, callback );
+	},
+	animate: function( prop, speed, easing, callback ) {
+		var empty = jQuery.isEmptyObject( prop ),
+			optall = jQuery.speed( speed, easing, callback ),
+			doAnimation = function() {
+
+				// Operate on a copy of prop so per-property easing won't be lost
+				var anim = Animation( this, jQuery.extend( {}, prop ), optall );
+
+				// Empty animations, or finishing resolves immediately
+				if ( empty || dataPriv.get( this, "finish" ) ) {
+					anim.stop( true );
+				}
+			};
+
+		doAnimation.finish = doAnimation;
+
+		return empty || optall.queue === false ?
+			this.each( doAnimation ) :
+			this.queue( optall.queue, doAnimation );
+	},
+	stop: function( type, clearQueue, gotoEnd ) {
+		var stopQueue = function( hooks ) {
+			var stop = hooks.stop;
+			delete hooks.stop;
+			stop( gotoEnd );
+		};
+
+		if ( typeof type !== "string" ) {
+			gotoEnd = clearQueue;
+			clearQueue = type;
+			type = undefined;
+		}
+		if ( clearQueue ) {
+			this.queue( type || "fx", [] );
+		}
+
+		return this.each( function() {
+			var dequeue = true,
+				index = type != null && type + "queueHooks",
+				timers = jQuery.timers,
+				data = dataPriv.get( this );
+
+			if ( index ) {
+				if ( data[ index ] && data[ index ].stop ) {
+					stopQueue( data[ index ] );
+				}
+			} else {
+				for ( index in data ) {
+					if ( data[ index ] && data[ index ].stop && rrun.test( index ) ) {
+						stopQueue( data[ index ] );
+					}
+				}
+			}
+
+			for ( index = timers.length; index--; ) {
+				if ( timers[ index ].elem === this &&
+					( type == null || timers[ index ].queue === type ) ) {
+
+					timers[ index ].anim.stop( gotoEnd );
+					dequeue = false;
+					timers.splice( index, 1 );
+				}
+			}
+
+			// Start the next in the queue if the last step wasn't forced.
+			// Timers currently will call their complete callbacks, which
+			// will dequeue but only if they were gotoEnd.
+			if ( dequeue || !gotoEnd ) {
+				jQuery.dequeue( this, type );
+			}
+		} );
+	},
+	finish: function( type ) {
+		if ( type !== false ) {
+			type = type || "fx";
+		}
+		return this.each( function() {
+			var index,
+				data = dataPriv.get( this ),
+				queue = data[ type + "queue" ],
+				hooks = data[ type + "queueHooks" ],
+				timers = jQuery.timers,
+				length = queue ? queue.length : 0;
+
+			// Enable finishing flag on private data
+			data.finish = true;
+
+			// Empty the queue first
+			jQuery.queue( this, type, [] );
+
+			if ( hooks && hooks.stop ) {
+				hooks.stop.call( this, true );
+			}
+
+			// Look for any active animations, and finish them
+			for ( index = timers.length; index--; ) {
+				if ( timers[ index ].elem === this && timers[ index ].queue === type ) {
+					timers[ index ].anim.stop( true );
+					timers.splice( index, 1 );
+				}
+			}
+
+			// Look for any animations in the old queue and finish them
+			for ( index = 0; index < length; index++ ) {
+				if ( queue[ index ] && queue[ index ].finish ) {
+					queue[ index ].finish.call( this );
+				}
+			}
+
+			// Turn off finishing flag
+			delete data.finish;
+		} );
+	}
+} );
+
+jQuery.each( [ "toggle", "show", "hide" ], function( _i, name ) {
+	var cssFn = jQuery.fn[ name ];
+	jQuery.fn[ name ] = function( speed, easing, callback ) {
+		return speed == null || typeof speed === "boolean" ?
+			cssFn.apply( this, arguments ) :
+			this.animate( genFx( name, true ), speed, easing, callback );
+	};
+} );
+
+// Generate shortcuts for custom animations
+jQuery.each( {
+	slideDown: genFx( "show" ),
+	slideUp: genFx( "hide" ),
+	slideToggle: genFx( "toggle" ),
+	fadeIn: { opacity: "show" },
+	fadeOut: { opacity: "hide" },
+	fadeToggle: { opacity: "toggle" }
+}, function( name, props ) {
+	jQuery.fn[ name ] = function( speed, easing, callback ) {
+		return this.animate( props, speed, easing, callback );
+	};
+} );
+
+jQuery.timers = [];
+jQuery.fx.tick = function() {
+	var timer,
+		i = 0,
+		timers = jQuery.timers;
+
+	fxNow = Date.now();
+
+	for ( ; i < timers.length; i++ ) {
+		timer = timers[ i ];
+
+		// Run the timer and safely remove it when done (allowing for external removal)
+		if ( !timer() && timers[ i ] === timer ) {
+			timers.splice( i--, 1 );
+		}
+	}
+
+	if ( !timers.length ) {
+		jQuery.fx.stop();
+	}
+	fxNow = undefined;
+};
+
+jQuery.fx.timer = function( timer ) {
+	jQuery.timers.push( timer );
+	jQuery.fx.start();
+};
+
+jQuery.fx.interval = 13;
+jQuery.fx.start = function() {
+	if ( inProgress ) {
+		return;
+	}
+
+	inProgress = true;
+	schedule();
+};
+
+jQuery.fx.stop = function() {
+	inProgress = null;
+};
+
+jQuery.fx.speeds = {
+	slow: 600,
+	fast: 200,
+
+	// Default speed
+	_default: 400
+};
+
+
+// Based off of the plugin by Clint Helfers, with permission.
+// https://web.archive.org/web/20100324014747/http://blindsignals.com/index.php/2009/07/jquery-delay/
+jQuery.fn.delay = function( time, type ) {
+	time = jQuery.fx ? jQuery.fx.speeds[ time ] || time : time;
+	type = type || "fx";
+
+	return this.queue( type, function( next, hooks ) {
+		var timeout = window.setTimeout( next, time );
+		hooks.stop = function() {
+			window.clearTimeout( timeout );
+		};
+	} );
+};
+
+
+( function() {
+	var input = document.createElement( "input" ),
+		select = document.createElement( "select" ),
+		opt = select.appendChild( document.createElement( "option" ) );
+
+	input.type = "checkbox";
+
+	// Support: Android <=4.3 only
+	// Default value for a checkbox should be "on"
+	support.checkOn = input.value !== "";
+
+	// Support: IE <=11 only
+	// Must access selectedIndex to make default options select
+	support.optSelected = opt.selected;
+
+	// Support: IE <=11 only
+	// An input loses its value after becoming a radio
+	input = document.createElement( "input" );
+	input.value = "t";
+	input.type = "radio";
+	support.radioValue = input.value === "t";
+} )();
+
+
+var boolHook,
+	attrHandle = jQuery.expr.attrHandle;
+
+jQuery.fn.extend( {
+	attr: function( name, value ) {
+		return access( this, jQuery.attr, name, value, arguments.length > 1 );
+	},
+
+	removeAttr: function( name ) {
+		return this.each( function() {
+			jQuery.removeAttr( this, name );
+		} );
+	}
+} );
+
+jQuery.extend( {
+	attr: function( elem, name, value ) {
+		var ret, hooks,
+			nType = elem.nodeType;
+
+		// Don't get/set attributes on text, comment and attribute nodes
+		if ( nType === 3 || nType === 8 || nType === 2 ) {
+			return;
+		}
+
+		// Fallback to prop when attributes are not supported
+		if ( typeof elem.getAttribute === "undefined" ) {
+			return jQuery.prop( elem, name, value );
+		}
+
+		// Attribute hooks are determined by the lowercase version
+		// Grab necessary hook if one is defined
+		if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) {
+			hooks = jQuery.attrHooks[ name.toLowerCase() ] ||
+				( jQuery.expr.match.bool.test( name ) ? boolHook : undefined );
+		}
+
+		if ( value !== undefined ) {
+			if ( value === null ) {
+				jQuery.removeAttr( elem, name );
+				return;
+			}
+
+			if ( hooks && "set" in hooks &&
+				( ret = hooks.set( elem, value, name ) ) !== undefined ) {
+				return ret;
+			}
+
+			elem.setAttribute( name, value + "" );
+			return value;
+		}
+
+		if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) {
+			return ret;
+		}
+
+		ret = jQuery.find.attr( elem, name );
+
+		// Non-existent attributes return null, we normalize to undefined
+		return ret == null ? undefined : ret;
+	},
+
+	attrHooks: {
+		type: {
+			set: function( elem, value ) {
+				if ( !support.radioValue && value === "radio" &&
+					nodeName( elem, "input" ) ) {
+					var val = elem.value;
+					elem.setAttribute( "type", value );
+					if ( val ) {
+						elem.value = val;
+					}
+					return value;
+				}
+			}
+		}
+	},
+
+	removeAttr: function( elem, value ) {
+		var name,
+			i = 0,
+
+			// Attribute names can contain non-HTML whitespace characters
+			// https://html.spec.whatwg.org/multipage/syntax.html#attributes-2
+			attrNames = value && value.match( rnothtmlwhite );
+
+		if ( attrNames && elem.nodeType === 1 ) {
+			while ( ( name = attrNames[ i++ ] ) ) {
+				elem.removeAttribute( name );
+			}
+		}
+	}
+} );
+
+// Hooks for boolean attributes
+boolHook = {
+	set: function( elem, value, name ) {
+		if ( value === false ) {
+
+			// Remove boolean attributes when set to false
+			jQuery.removeAttr( elem, name );
+		} else {
+			elem.setAttribute( name, name );
+		}
+		return name;
+	}
+};
+
+jQuery.each( jQuery.expr.match.bool.source.match( /\w+/g ), function( _i, name ) {
+	var getter = attrHandle[ name ] || jQuery.find.attr;
+
+	attrHandle[ name ] = function( elem, name, isXML ) {
+		var ret, handle,
+			lowercaseName = name.toLowerCase();
+
+		if ( !isXML ) {
+
+			// Avoid an infinite loop by temporarily removing this function from the getter
+			handle = attrHandle[ lowercaseName ];
+			attrHandle[ lowercaseName ] = ret;
+			ret = getter( elem, name, isXML ) != null ?
+				lowercaseName :
+				null;
+			attrHandle[ lowercaseName ] = handle;
+		}
+		return ret;
+	};
+} );
+
+
+
+
+var rfocusable = /^(?:input|select|textarea|button)$/i,
+	rclickable = /^(?:a|area)$/i;
+
+jQuery.fn.extend( {
+	prop: function( name, value ) {
+		return access( this, jQuery.prop, name, value, arguments.length > 1 );
+	},
+
+	removeProp: function( name ) {
+		return this.each( function() {
+			delete this[ jQuery.propFix[ name ] || name ];
+		} );
+	}
+} );
+
+jQuery.extend( {
+	prop: function( elem, name, value ) {
+		var ret, hooks,
+			nType = elem.nodeType;
+
+		// Don't get/set properties on text, comment and attribute nodes
+		if ( nType === 3 || nType === 8 || nType === 2 ) {
+			return;
+		}
+
+		if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) {
+
+			// Fix name and attach hooks
+			name = jQuery.propFix[ name ] || name;
+			hooks = jQuery.propHooks[ name ];
+		}
+
+		if ( value !== undefined ) {
+			if ( hooks && "set" in hooks &&
+				( ret = hooks.set( elem, value, name ) ) !== undefined ) {
+				return ret;
+			}
+
+			return ( elem[ name ] = value );
+		}
+
+		if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) {
+			return ret;
+		}
+
+		return elem[ name ];
+	},
+
+	propHooks: {
+		tabIndex: {
+			get: function( elem ) {
+
+				// Support: IE <=9 - 11 only
+				// elem.tabIndex doesn't always return the
+				// correct value when it hasn't been explicitly set
+				// https://web.archive.org/web/20141116233347/http://fluidproject.org/blog/2008/01/09/getting-setting-and-removing-tabindex-values-with-javascript/
+				// Use proper attribute retrieval(#12072)
+				var tabindex = jQuery.find.attr( elem, "tabindex" );
+
+				if ( tabindex ) {
+					return parseInt( tabindex, 10 );
+				}
+
+				if (
+					rfocusable.test( elem.nodeName ) ||
+					rclickable.test( elem.nodeName ) &&
+					elem.href
+				) {
+					return 0;
+				}
+
+				return -1;
+			}
+		}
+	},
+
+	propFix: {
+		"for": "htmlFor",
+		"class": "className"
+	}
+} );
+
+// Support: IE <=11 only
+// Accessing the selectedIndex property
+// forces the browser to respect setting selected
+// on the option
+// The getter ensures a default option is selected
+// when in an optgroup
+// eslint rule "no-unused-expressions" is disabled for this code
+// since it considers such accessions noop
+if ( !support.optSelected ) {
+	jQuery.propHooks.selected = {
+		get: function( elem ) {
+
+			/* eslint no-unused-expressions: "off" */
+
+			var parent = elem.parentNode;
+			if ( parent && parent.parentNode ) {
+				parent.parentNode.selectedIndex;
+			}
+			return null;
+		},
+		set: function( elem ) {
+
+			/* eslint no-unused-expressions: "off" */
+
+			var parent = elem.parentNode;
+			if ( parent ) {
+				parent.selectedIndex;
+
+				if ( parent.parentNode ) {
+					parent.parentNode.selectedIndex;
+				}
+			}
+		}
+	};
+}
+
+jQuery.each( [
+	"tabIndex",
+	"readOnly",
+	"maxLength",
+	"cellSpacing",
+	"cellPadding",
+	"rowSpan",
+	"colSpan",
+	"useMap",
+	"frameBorder",
+	"contentEditable"
+], function() {
+	jQuery.propFix[ this.toLowerCase() ] = this;
+} );
+
+
+
+
+	// Strip and collapse whitespace according to HTML spec
+	// https://infra.spec.whatwg.org/#strip-and-collapse-ascii-whitespace
+	function stripAndCollapse( value ) {
+		var tokens = value.match( rnothtmlwhite ) || [];
+		return tokens.join( " " );
+	}
+
+
+function getClass( elem ) {
+	return elem.getAttribute && elem.getAttribute( "class" ) || "";
+}
+
+function classesToArray( value ) {
+	if ( Array.isArray( value ) ) {
+		return value;
+	}
+	if ( typeof value === "string" ) {
+		return value.match( rnothtmlwhite ) || [];
+	}
+	return [];
+}
+
+jQuery.fn.extend( {
+	addClass: function( value ) {
+		var classes, elem, cur, curValue, clazz, j, finalValue,
+			i = 0;
+
+		if ( isFunction( value ) ) {
+			return this.each( function( j ) {
+				jQuery( this ).addClass( value.call( this, j, getClass( this ) ) );
+			} );
+		}
+
+		classes = classesToArray( value );
+
+		if ( classes.length ) {
+			while ( ( elem = this[ i++ ] ) ) {
+				curValue = getClass( elem );
+				cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " );
+
+				if ( cur ) {
+					j = 0;
+					while ( ( clazz = classes[ j++ ] ) ) {
+						if ( cur.indexOf( " " + clazz + " " ) < 0 ) {
+							cur += clazz + " ";
+						}
+					}
+
+					// Only assign if different to avoid unneeded rendering.
+					finalValue = stripAndCollapse( cur );
+					if ( curValue !== finalValue ) {
+						elem.setAttribute( "class", finalValue );
+					}
+				}
+			}
+		}
+
+		return this;
+	},
+
+	removeClass: function( value ) {
+		var classes, elem, cur, curValue, clazz, j, finalValue,
+			i = 0;
+
+		if ( isFunction( value ) ) {
+			return this.each( function( j ) {
+				jQuery( this ).removeClass( value.call( this, j, getClass( this ) ) );
+			} );
+		}
+
+		if ( !arguments.length ) {
+			return this.attr( "class", "" );
+		}
+
+		classes = classesToArray( value );
+
+		if ( classes.length ) {
+			while ( ( elem = this[ i++ ] ) ) {
+				curValue = getClass( elem );
+
+				// This expression is here for better compressibility (see addClass)
+				cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " );
+
+				if ( cur ) {
+					j = 0;
+					while ( ( clazz = classes[ j++ ] ) ) {
+
+						// Remove *all* instances
+						while ( cur.indexOf( " " + clazz + " " ) > -1 ) {
+							cur = cur.replace( " " + clazz + " ", " " );
+						}
+					}
+
+					// Only assign if different to avoid unneeded rendering.
+					finalValue = stripAndCollapse( cur );
+					if ( curValue !== finalValue ) {
+						elem.setAttribute( "class", finalValue );
+					}
+				}
+			}
+		}
+
+		return this;
+	},
+
+	toggleClass: function( value, stateVal ) {
+		var type = typeof value,
+			isValidValue = type === "string" || Array.isArray( value );
+
+		if ( typeof stateVal === "boolean" && isValidValue ) {
+			return stateVal ? this.addClass( value ) : this.removeClass( value );
+		}
+
+		if ( isFunction( value ) ) {
+			return this.each( function( i ) {
+				jQuery( this ).toggleClass(
+					value.call( this, i, getClass( this ), stateVal ),
+					stateVal
+				);
+			} );
+		}
+
+		return this.each( function() {
+			var className, i, self, classNames;
+
+			if ( isValidValue ) {
+
+				// Toggle individual class names
+				i = 0;
+				self = jQuery( this );
+				classNames = classesToArray( value );
+
+				while ( ( className = classNames[ i++ ] ) ) {
+
+					// Check each className given, space separated list
+					if ( self.hasClass( className ) ) {
+						self.removeClass( className );
+					} else {
+						self.addClass( className );
+					}
+				}
+
+			// Toggle whole class name
+			} else if ( value === undefined || type === "boolean" ) {
+				className = getClass( this );
+				if ( className ) {
+
+					// Store className if set
+					dataPriv.set( this, "__className__", className );
+				}
+
+				// If the element has a class name or if we're passed `false`,
+				// then remove the whole classname (if there was one, the above saved it).
+				// Otherwise bring back whatever was previously saved (if anything),
+				// falling back to the empty string if nothing was stored.
+				if ( this.setAttribute ) {
+					this.setAttribute( "class",
+						className || value === false ?
+							"" :
+							dataPriv.get( this, "__className__" ) || ""
+					);
+				}
+			}
+		} );
+	},
+
+	hasClass: function( selector ) {
+		var className, elem,
+			i = 0;
+
+		className = " " + selector + " ";
+		while ( ( elem = this[ i++ ] ) ) {
+			if ( elem.nodeType === 1 &&
+				( " " + stripAndCollapse( getClass( elem ) ) + " " ).indexOf( className ) > -1 ) {
+				return true;
+			}
+		}
+
+		return false;
+	}
+} );
+
+
+
+
+var rreturn = /\r/g;
+
+jQuery.fn.extend( {
+	val: function( value ) {
+		var hooks, ret, valueIsFunction,
+			elem = this[ 0 ];
+
+		if ( !arguments.length ) {
+			if ( elem ) {
+				hooks = jQuery.valHooks[ elem.type ] ||
+					jQuery.valHooks[ elem.nodeName.toLowerCase() ];
+
+				if ( hooks &&
+					"get" in hooks &&
+					( ret = hooks.get( elem, "value" ) ) !== undefined
+				) {
+					return ret;
+				}
+
+				ret = elem.value;
+
+				// Handle most common string cases
+				if ( typeof ret === "string" ) {
+					return ret.replace( rreturn, "" );
+				}
+
+				// Handle cases where value is null/undef or number
+				return ret == null ? "" : ret;
+			}
+
+			return;
+		}
+
+		valueIsFunction = isFunction( value );
+
+		return this.each( function( i ) {
+			var val;
+
+			if ( this.nodeType !== 1 ) {
+				return;
+			}
+
+			if ( valueIsFunction ) {
+				val = value.call( this, i, jQuery( this ).val() );
+			} else {
+				val = value;
+			}
+
+			// Treat null/undefined as ""; convert numbers to string
+			if ( val == null ) {
+				val = "";
+
+			} else if ( typeof val === "number" ) {
+				val += "";
+
+			} else if ( Array.isArray( val ) ) {
+				val = jQuery.map( val, function( value ) {
+					return value == null ? "" : value + "";
+				} );
+			}
+
+			hooks = jQuery.valHooks[ this.type ] || jQuery.valHooks[ this.nodeName.toLowerCase() ];
+
+			// If set returns undefined, fall back to normal setting
+			if ( !hooks || !( "set" in hooks ) || hooks.set( this, val, "value" ) === undefined ) {
+				this.value = val;
+			}
+		} );
+	}
+} );
+
+jQuery.extend( {
+	valHooks: {
+		option: {
+			get: function( elem ) {
+
+				var val = jQuery.find.attr( elem, "value" );
+				return val != null ?
+					val :
+
+					// Support: IE <=10 - 11 only
+					// option.text throws exceptions (#14686, #14858)
+					// Strip and collapse whitespace
+					// https://html.spec.whatwg.org/#strip-and-collapse-whitespace
+					stripAndCollapse( jQuery.text( elem ) );
+			}
+		},
+		select: {
+			get: function( elem ) {
+				var value, option, i,
+					options = elem.options,
+					index = elem.selectedIndex,
+					one = elem.type === "select-one",
+					values = one ? null : [],
+					max = one ? index + 1 : options.length;
+
+				if ( index < 0 ) {
+					i = max;
+
+				} else {
+					i = one ? index : 0;
+				}
+
+				// Loop through all the selected options
+				for ( ; i < max; i++ ) {
+					option = options[ i ];
+
+					// Support: IE <=9 only
+					// IE8-9 doesn't update selected after form reset (#2551)
+					if ( ( option.selected || i === index ) &&
+
+							// Don't return options that are disabled or in a disabled optgroup
+							!option.disabled &&
+							( !option.parentNode.disabled ||
+								!nodeName( option.parentNode, "optgroup" ) ) ) {
+
+						// Get the specific value for the option
+						value = jQuery( option ).val();
+
+						// We don't need an array for one selects
+						if ( one ) {
+							return value;
+						}
+
+						// Multi-Selects return an array
+						values.push( value );
+					}
+				}
+
+				return values;
+			},
+
+			set: function( elem, value ) {
+				var optionSet, option,
+					options = elem.options,
+					values = jQuery.makeArray( value ),
+					i = options.length;
+
+				while ( i-- ) {
+					option = options[ i ];
+
+					/* eslint-disable no-cond-assign */
+
+					if ( option.selected =
+						jQuery.inArray( jQuery.valHooks.option.get( option ), values ) > -1
+					) {
+						optionSet = true;
+					}
+
+					/* eslint-enable no-cond-assign */
+				}
+
+				// Force browsers to behave consistently when non-matching value is set
+				if ( !optionSet ) {
+					elem.selectedIndex = -1;
+				}
+				return values;
+			}
+		}
+	}
+} );
+
+// Radios and checkboxes getter/setter
+jQuery.each( [ "radio", "checkbox" ], function() {
+	jQuery.valHooks[ this ] = {
+		set: function( elem, value ) {
+			if ( Array.isArray( value ) ) {
+				return ( elem.checked = jQuery.inArray( jQuery( elem ).val(), value ) > -1 );
+			}
+		}
+	};
+	if ( !support.checkOn ) {
+		jQuery.valHooks[ this ].get = function( elem ) {
+			return elem.getAttribute( "value" ) === null ? "on" : elem.value;
+		};
+	}
+} );
+
+
+
+
+// Return jQuery for attributes-only inclusion
+
+
+support.focusin = "onfocusin" in window;
+
+
+var rfocusMorph = /^(?:focusinfocus|focusoutblur)$/,
+	stopPropagationCallback = function( e ) {
+		e.stopPropagation();
+	};
+
+jQuery.extend( jQuery.event, {
+
+	trigger: function( event, data, elem, onlyHandlers ) {
+
+		var i, cur, tmp, bubbleType, ontype, handle, special, lastElement,
+			eventPath = [ elem || document ],
+			type = hasOwn.call( event, "type" ) ? event.type : event,
+			namespaces = hasOwn.call( event, "namespace" ) ? event.namespace.split( "." ) : [];
+
+		cur = lastElement = tmp = elem = elem || document;
+
+		// Don't do events on text and comment nodes
+		if ( elem.nodeType === 3 || elem.nodeType === 8 ) {
+			return;
+		}
+
+		// focus/blur morphs to focusin/out; ensure we're not firing them right now
+		if ( rfocusMorph.test( type + jQuery.event.triggered ) ) {
+			return;
+		}
+
+		if ( type.indexOf( "." ) > -1 ) {
+
+			// Namespaced trigger; create a regexp to match event type in handle()
+			namespaces = type.split( "." );
+			type = namespaces.shift();
+			namespaces.sort();
+		}
+		ontype = type.indexOf( ":" ) < 0 && "on" + type;
+
+		// Caller can pass in a jQuery.Event object, Object, or just an event type string
+		event = event[ jQuery.expando ] ?
+			event :
+			new jQuery.Event( type, typeof event === "object" && event );
+
+		// Trigger bitmask: & 1 for native handlers; & 2 for jQuery (always true)
+		event.isTrigger = onlyHandlers ? 2 : 3;
+		event.namespace = namespaces.join( "." );
+		event.rnamespace = event.namespace ?
+			new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ) :
+			null;
+
+		// Clean up the event in case it is being reused
+		event.result = undefined;
+		if ( !event.target ) {
+			event.target = elem;
+		}
+
+		// Clone any incoming data and prepend the event, creating the handler arg list
+		data = data == null ?
+			[ event ] :
+			jQuery.makeArray( data, [ event ] );
+
+		// Allow special events to draw outside the lines
+		special = jQuery.event.special[ type ] || {};
+		if ( !onlyHandlers && special.trigger && special.trigger.apply( elem, data ) === false ) {
+			return;
+		}
+
+		// Determine event propagation path in advance, per W3C events spec (#9951)
+		// Bubble up to document, then to window; watch for a global ownerDocument var (#9724)
+		if ( !onlyHandlers && !special.noBubble && !isWindow( elem ) ) {
+
+			bubbleType = special.delegateType || type;
+			if ( !rfocusMorph.test( bubbleType + type ) ) {
+				cur = cur.parentNode;
+			}
+			for ( ; cur; cur = cur.parentNode ) {
+				eventPath.push( cur );
+				tmp = cur;
+			}
+
+			// Only add window if we got to document (e.g., not plain obj or detached DOM)
+			if ( tmp === ( elem.ownerDocument || document ) ) {
+				eventPath.push( tmp.defaultView || tmp.parentWindow || window );
+			}
+		}
+
+		// Fire handlers on the event path
+		i = 0;
+		while ( ( cur = eventPath[ i++ ] ) && !event.isPropagationStopped() ) {
+			lastElement = cur;
+			event.type = i > 1 ?
+				bubbleType :
+				special.bindType || type;
+
+			// jQuery handler
+			handle = ( dataPriv.get( cur, "events" ) || Object.create( null ) )[ event.type ] &&
+				dataPriv.get( cur, "handle" );
+			if ( handle ) {
+				handle.apply( cur, data );
+			}
+
+			// Native handler
+			handle = ontype && cur[ ontype ];
+			if ( handle && handle.apply && acceptData( cur ) ) {
+				event.result = handle.apply( cur, data );
+				if ( event.result === false ) {
+					event.preventDefault();
+				}
+			}
+		}
+		event.type = type;
+
+		// If nobody prevented the default action, do it now
+		if ( !onlyHandlers && !event.isDefaultPrevented() ) {
+
+			if ( ( !special._default ||
+				special._default.apply( eventPath.pop(), data ) === false ) &&
+				acceptData( elem ) ) {
+
+				// Call a native DOM method on the target with the same name as the event.
+				// Don't do default actions on window, that's where global variables be (#6170)
+				if ( ontype && isFunction( elem[ type ] ) && !isWindow( elem ) ) {
+
+					// Don't re-trigger an onFOO event when we call its FOO() method
+					tmp = elem[ ontype ];
+
+					if ( tmp ) {
+						elem[ ontype ] = null;
+					}
+
+					// Prevent re-triggering of the same event, since we already bubbled it above
+					jQuery.event.triggered = type;
+
+					if ( event.isPropagationStopped() ) {
+						lastElement.addEventListener( type, stopPropagationCallback );
+					}
+
+					elem[ type ]();
+
+					if ( event.isPropagationStopped() ) {
+						lastElement.removeEventListener( type, stopPropagationCallback );
+					}
+
+					jQuery.event.triggered = undefined;
+
+					if ( tmp ) {
+						elem[ ontype ] = tmp;
+					}
+				}
+			}
+		}
+
+		return event.result;
+	},
+
+	// Piggyback on a donor event to simulate a different one
+	// Used only for `focus(in | out)` events
+	simulate: function( type, elem, event ) {
+		var e = jQuery.extend(
+			new jQuery.Event(),
+			event,
+			{
+				type: type,
+				isSimulated: true
+			}
+		);
+
+		jQuery.event.trigger( e, null, elem );
+	}
+
+} );
+
+jQuery.fn.extend( {
+
+	trigger: function( type, data ) {
+		return this.each( function() {
+			jQuery.event.trigger( type, data, this );
+		} );
+	},
+	triggerHandler: function( type, data ) {
+		var elem = this[ 0 ];
+		if ( elem ) {
+			return jQuery.event.trigger( type, data, elem, true );
+		}
+	}
+} );
+
+
+// Support: Firefox <=44
+// Firefox doesn't have focus(in | out) events
+// Related ticket - https://bugzilla.mozilla.org/show_bug.cgi?id=687787
+//
+// Support: Chrome <=48 - 49, Safari <=9.0 - 9.1
+// focus(in | out) events fire after focus & blur events,
+// which is spec violation - http://www.w3.org/TR/DOM-Level-3-Events/#events-focusevent-event-order
+// Related ticket - https://bugs.chromium.org/p/chromium/issues/detail?id=449857
+if ( !support.focusin ) {
+	jQuery.each( { focus: "focusin", blur: "focusout" }, function( orig, fix ) {
+
+		// Attach a single capturing handler on the document while someone wants focusin/focusout
+		var handler = function( event ) {
+			jQuery.event.simulate( fix, event.target, jQuery.event.fix( event ) );
+		};
+
+		jQuery.event.special[ fix ] = {
+			setup: function() {
+
+				// Handle: regular nodes (via `this.ownerDocument`), window
+				// (via `this.document`) & document (via `this`).
+				var doc = this.ownerDocument || this.document || this,
+					attaches = dataPriv.access( doc, fix );
+
+				if ( !attaches ) {
+					doc.addEventListener( orig, handler, true );
+				}
+				dataPriv.access( doc, fix, ( attaches || 0 ) + 1 );
+			},
+			teardown: function() {
+				var doc = this.ownerDocument || this.document || this,
+					attaches = dataPriv.access( doc, fix ) - 1;
+
+				if ( !attaches ) {
+					doc.removeEventListener( orig, handler, true );
+					dataPriv.remove( doc, fix );
+
+				} else {
+					dataPriv.access( doc, fix, attaches );
+				}
+			}
+		};
+	} );
+}
+var location = window.location;
+
+var nonce = { guid: Date.now() };
+
+var rquery = ( /\?/ );
+
+
+
+// Cross-browser xml parsing
+jQuery.parseXML = function( data ) {
+	var xml, parserErrorElem;
+	if ( !data || typeof data !== "string" ) {
+		return null;
+	}
+
+	// Support: IE 9 - 11 only
+	// IE throws on parseFromString with invalid input.
+	try {
+		xml = ( new window.DOMParser() ).parseFromString( data, "text/xml" );
+	} catch ( e ) {}
+
+	parserErrorElem = xml && xml.getElementsByTagName( "parsererror" )[ 0 ];
+	if ( !xml || parserErrorElem ) {
+		jQuery.error( "Invalid XML: " + (
+			parserErrorElem ?
+				jQuery.map( parserErrorElem.childNodes, function( el ) {
+					return el.textContent;
+				} ).join( "\n" ) :
+				data
+		) );
+	}
+	return xml;
+};
+
+
+var
+	rbracket = /\[\]$/,
+	rCRLF = /\r?\n/g,
+	rsubmitterTypes = /^(?:submit|button|image|reset|file)$/i,
+	rsubmittable = /^(?:input|select|textarea|keygen)/i;
+
+function buildParams( prefix, obj, traditional, add ) {
+	var name;
+
+	if ( Array.isArray( obj ) ) {
+
+		// Serialize array item.
+		jQuery.each( obj, function( i, v ) {
+			if ( traditional || rbracket.test( prefix ) ) {
+
+				// Treat each array item as a scalar.
+				add( prefix, v );
+
+			} else {
+
+				// Item is non-scalar (array or object), encode its numeric index.
+				buildParams(
+					prefix + "[" + ( typeof v === "object" && v != null ? i : "" ) + "]",
+					v,
+					traditional,
+					add
+				);
+			}
+		} );
+
+	} else if ( !traditional && toType( obj ) === "object" ) {
+
+		// Serialize object item.
+		for ( name in obj ) {
+			buildParams( prefix + "[" + name + "]", obj[ name ], traditional, add );
+		}
+
+	} else {
+
+		// Serialize scalar item.
+		add( prefix, obj );
+	}
+}
+
+// Serialize an array of form elements or a set of
+// key/values into a query string
+jQuery.param = function( a, traditional ) {
+	var prefix,
+		s = [],
+		add = function( key, valueOrFunction ) {
+
+			// If value is a function, invoke it and use its return value
+			var value = isFunction( valueOrFunction ) ?
+				valueOrFunction() :
+				valueOrFunction;
+
+			s[ s.length ] = encodeURIComponent( key ) + "=" +
+				encodeURIComponent( value == null ? "" : value );
+		};
+
+	if ( a == null ) {
+		return "";
+	}
+
+	// If an array was passed in, assume that it is an array of form elements.
+	if ( Array.isArray( a ) || ( a.jquery && !jQuery.isPlainObject( a ) ) ) {
+
+		// Serialize the form elements
+		jQuery.each( a, function() {
+			add( this.name, this.value );
+		} );
+
+	} else {
+
+		// If traditional, encode the "old" way (the way 1.3.2 or older
+		// did it), otherwise encode params recursively.
+		for ( prefix in a ) {
+			buildParams( prefix, a[ prefix ], traditional, add );
+		}
+	}
+
+	// Return the resulting serialization
+	return s.join( "&" );
+};
+
+jQuery.fn.extend( {
+	serialize: function() {
+		return jQuery.param( this.serializeArray() );
+	},
+	serializeArray: function() {
+		return this.map( function() {
+
+			// Can add propHook for "elements" to filter or add form elements
+			var elements = jQuery.prop( this, "elements" );
+			return elements ? jQuery.makeArray( elements ) : this;
+		} ).filter( function() {
+			var type = this.type;
+
+			// Use .is( ":disabled" ) so that fieldset[disabled] works
+			return this.name && !jQuery( this ).is( ":disabled" ) &&
+				rsubmittable.test( this.nodeName ) && !rsubmitterTypes.test( type ) &&
+				( this.checked || !rcheckableType.test( type ) );
+		} ).map( function( _i, elem ) {
+			var val = jQuery( this ).val();
+
+			if ( val == null ) {
+				return null;
+			}
+
+			if ( Array.isArray( val ) ) {
+				return jQuery.map( val, function( val ) {
+					return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) };
+				} );
+			}
+
+			return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) };
+		} ).get();
+	}
+} );
+
+
+var
+	r20 = /%20/g,
+	rhash = /#.*$/,
+	rantiCache = /([?&])_=[^&]*/,
+	rheaders = /^(.*?):[ \t]*([^\r\n]*)$/mg,
+
+	// #7653, #8125, #8152: local protocol detection
+	rlocalProtocol = /^(?:about|app|app-storage|.+-extension|file|res|widget):$/,
+	rnoContent = /^(?:GET|HEAD)$/,
+	rprotocol = /^\/\//,
+
+	/* Prefilters
+	 * 1) They are useful to introduce custom dataTypes (see ajax/jsonp.js for an example)
+	 * 2) These are called:
+	 *    - BEFORE asking for a transport
+	 *    - AFTER param serialization (s.data is a string if s.processData is true)
+	 * 3) key is the dataType
+	 * 4) the catchall symbol "*" can be used
+	 * 5) execution will start with transport dataType and THEN continue down to "*" if needed
+	 */
+	prefilters = {},
+
+	/* Transports bindings
+	 * 1) key is the dataType
+	 * 2) the catchall symbol "*" can be used
+	 * 3) selection will start with transport dataType and THEN go to "*" if needed
+	 */
+	transports = {},
+
+	// Avoid comment-prolog char sequence (#10098); must appease lint and evade compression
+	allTypes = "*/".concat( "*" ),
+
+	// Anchor tag for parsing the document origin
+	originAnchor = document.createElement( "a" );
+
+originAnchor.href = location.href;
+
+// Base "constructor" for jQuery.ajaxPrefilter and jQuery.ajaxTransport
+function addToPrefiltersOrTransports( structure ) {
+
+	// dataTypeExpression is optional and defaults to "*"
+	return function( dataTypeExpression, func ) {
+
+		if ( typeof dataTypeExpression !== "string" ) {
+			func = dataTypeExpression;
+			dataTypeExpression = "*";
+		}
+
+		var dataType,
+			i = 0,
+			dataTypes = dataTypeExpression.toLowerCase().match( rnothtmlwhite ) || [];
+
+		if ( isFunction( func ) ) {
+
+			// For each dataType in the dataTypeExpression
+			while ( ( dataType = dataTypes[ i++ ] ) ) {
+
+				// Prepend if requested
+				if ( dataType[ 0 ] === "+" ) {
+					dataType = dataType.slice( 1 ) || "*";
+					( structure[ dataType ] = structure[ dataType ] || [] ).unshift( func );
+
+				// Otherwise append
+				} else {
+					( structure[ dataType ] = structure[ dataType ] || [] ).push( func );
+				}
+			}
+		}
+	};
+}
+
+// Base inspection function for prefilters and transports
+function inspectPrefiltersOrTransports( structure, options, originalOptions, jqXHR ) {
+
+	var inspected = {},
+		seekingTransport = ( structure === transports );
+
+	function inspect( dataType ) {
+		var selected;
+		inspected[ dataType ] = true;
+		jQuery.each( structure[ dataType ] || [], function( _, prefilterOrFactory ) {
+			var dataTypeOrTransport = prefilterOrFactory( options, originalOptions, jqXHR );
+			if ( typeof dataTypeOrTransport === "string" &&
+				!seekingTransport && !inspected[ dataTypeOrTransport ] ) {
+
+				options.dataTypes.unshift( dataTypeOrTransport );
+				inspect( dataTypeOrTransport );
+				return false;
+			} else if ( seekingTransport ) {
+				return !( selected = dataTypeOrTransport );
+			}
+		} );
+		return selected;
+	}
+
+	return inspect( options.dataTypes[ 0 ] ) || !inspected[ "*" ] && inspect( "*" );
+}
+
+// A special extend for ajax options
+// that takes "flat" options (not to be deep extended)
+// Fixes #9887
+function ajaxExtend( target, src ) {
+	var key, deep,
+		flatOptions = jQuery.ajaxSettings.flatOptions || {};
+
+	for ( key in src ) {
+		if ( src[ key ] !== undefined ) {
+			( flatOptions[ key ] ? target : ( deep || ( deep = {} ) ) )[ key ] = src[ key ];
+		}
+	}
+	if ( deep ) {
+		jQuery.extend( true, target, deep );
+	}
+
+	return target;
+}
+
+/* Handles responses to an ajax request:
+ * - finds the right dataType (mediates between content-type and expected dataType)
+ * - returns the corresponding response
+ */
+function ajaxHandleResponses( s, jqXHR, responses ) {
+
+	var ct, type, finalDataType, firstDataType,
+		contents = s.contents,
+		dataTypes = s.dataTypes;
+
+	// Remove auto dataType and get content-type in the process
+	while ( dataTypes[ 0 ] === "*" ) {
+		dataTypes.shift();
+		if ( ct === undefined ) {
+			ct = s.mimeType || jqXHR.getResponseHeader( "Content-Type" );
+		}
+	}
+
+	// Check if we're dealing with a known content-type
+	if ( ct ) {
+		for ( type in contents ) {
+			if ( contents[ type ] && contents[ type ].test( ct ) ) {
+				dataTypes.unshift( type );
+				break;
+			}
+		}
+	}
+
+	// Check to see if we have a response for the expected dataType
+	if ( dataTypes[ 0 ] in responses ) {
+		finalDataType = dataTypes[ 0 ];
+	} else {
+
+		// Try convertible dataTypes
+		for ( type in responses ) {
+			if ( !dataTypes[ 0 ] || s.converters[ type + " " + dataTypes[ 0 ] ] ) {
+				finalDataType = type;
+				break;
+			}
+			if ( !firstDataType ) {
+				firstDataType = type;
+			}
+		}
+
+		// Or just use first one
+		finalDataType = finalDataType || firstDataType;
+	}
+
+	// If we found a dataType
+	// We add the dataType to the list if needed
+	// and return the corresponding response
+	if ( finalDataType ) {
+		if ( finalDataType !== dataTypes[ 0 ] ) {
+			dataTypes.unshift( finalDataType );
+		}
+		return responses[ finalDataType ];
+	}
+}
+
+/* Chain conversions given the request and the original response
+ * Also sets the responseXXX fields on the jqXHR instance
+ */
+function ajaxConvert( s, response, jqXHR, isSuccess ) {
+	var conv2, current, conv, tmp, prev,
+		converters = {},
+
+		// Work with a copy of dataTypes in case we need to modify it for conversion
+		dataTypes = s.dataTypes.slice();
+
+	// Create converters map with lowercased keys
+	if ( dataTypes[ 1 ] ) {
+		for ( conv in s.converters ) {
+			converters[ conv.toLowerCase() ] = s.converters[ conv ];
+		}
+	}
+
+	current = dataTypes.shift();
+
+	// Convert to each sequential dataType
+	while ( current ) {
+
+		if ( s.responseFields[ current ] ) {
+			jqXHR[ s.responseFields[ current ] ] = response;
+		}
+
+		// Apply the dataFilter if provided
+		if ( !prev && isSuccess && s.dataFilter ) {
+			response = s.dataFilter( response, s.dataType );
+		}
+
+		prev = current;
+		current = dataTypes.shift();
+
+		if ( current ) {
+
+			// There's only work to do if current dataType is non-auto
+			if ( current === "*" ) {
+
+				current = prev;
+
+			// Convert response if prev dataType is non-auto and differs from current
+			} else if ( prev !== "*" && prev !== current ) {
+
+				// Seek a direct converter
+				conv = converters[ prev + " " + current ] || converters[ "* " + current ];
+
+				// If none found, seek a pair
+				if ( !conv ) {
+					for ( conv2 in converters ) {
+
+						// If conv2 outputs current
+						tmp = conv2.split( " " );
+						if ( tmp[ 1 ] === current ) {
+
+							// If prev can be converted to accepted input
+							conv = converters[ prev + " " + tmp[ 0 ] ] ||
+								converters[ "* " + tmp[ 0 ] ];
+							if ( conv ) {
+
+								// Condense equivalence converters
+								if ( conv === true ) {
+									conv = converters[ conv2 ];
+
+								// Otherwise, insert the intermediate dataType
+								} else if ( converters[ conv2 ] !== true ) {
+									current = tmp[ 0 ];
+									dataTypes.unshift( tmp[ 1 ] );
+								}
+								break;
+							}
+						}
+					}
+				}
+
+				// Apply converter (if not an equivalence)
+				if ( conv !== true ) {
+
+					// Unless errors are allowed to bubble, catch and return them
+					if ( conv && s.throws ) {
+						response = conv( response );
+					} else {
+						try {
+							response = conv( response );
+						} catch ( e ) {
+							return {
+								state: "parsererror",
+								error: conv ? e : "No conversion from " + prev + " to " + current
+							};
+						}
+					}
+				}
+			}
+		}
+	}
+
+	return { state: "success", data: response };
+}
+
+jQuery.extend( {
+
+	// Counter for holding the number of active queries
+	active: 0,
+
+	// Last-Modified header cache for next request
+	lastModified: {},
+	etag: {},
+
+	ajaxSettings: {
+		url: location.href,
+		type: "GET",
+		isLocal: rlocalProtocol.test( location.protocol ),
+		global: true,
+		processData: true,
+		async: true,
+		contentType: "application/x-www-form-urlencoded; charset=UTF-8",
+
+		/*
+		timeout: 0,
+		data: null,
+		dataType: null,
+		username: null,
+		password: null,
+		cache: null,
+		throws: false,
+		traditional: false,
+		headers: {},
+		*/
+
+		accepts: {
+			"*": allTypes,
+			text: "text/plain",
+			html: "text/html",
+			xml: "application/xml, text/xml",
+			json: "application/json, text/javascript"
+		},
+
+		contents: {
+			xml: /\bxml\b/,
+			html: /\bhtml/,
+			json: /\bjson\b/
+		},
+
+		responseFields: {
+			xml: "responseXML",
+			text: "responseText",
+			json: "responseJSON"
+		},
+
+		// Data converters
+		// Keys separate source (or catchall "*") and destination types with a single space
+		converters: {
+
+			// Convert anything to text
+			"* text": String,
+
+			// Text to html (true = no transformation)
+			"text html": true,
+
+			// Evaluate text as a json expression
+			"text json": JSON.parse,
+
+			// Parse text as xml
+			"text xml": jQuery.parseXML
+		},
+
+		// For options that shouldn't be deep extended:
+		// you can add your own custom options here if
+		// and when you create one that shouldn't be
+		// deep extended (see ajaxExtend)
+		flatOptions: {
+			url: true,
+			context: true
+		}
+	},
+
+	// Creates a full fledged settings object into target
+	// with both ajaxSettings and settings fields.
+	// If target is omitted, writes into ajaxSettings.
+	ajaxSetup: function( target, settings ) {
+		return settings ?
+
+			// Building a settings object
+			ajaxExtend( ajaxExtend( target, jQuery.ajaxSettings ), settings ) :
+
+			// Extending ajaxSettings
+			ajaxExtend( jQuery.ajaxSettings, target );
+	},
+
+	ajaxPrefilter: addToPrefiltersOrTransports( prefilters ),
+	ajaxTransport: addToPrefiltersOrTransports( transports ),
+
+	// Main method
+	ajax: function( url, options ) {
+
+		// If url is an object, simulate pre-1.5 signature
+		if ( typeof url === "object" ) {
+			options = url;
+			url = undefined;
+		}
+
+		// Force options to be an object
+		options = options || {};
+
+		var transport,
+
+			// URL without anti-cache param
+			cacheURL,
+
+			// Response headers
+			responseHeadersString,
+			responseHeaders,
+
+			// timeout handle
+			timeoutTimer,
+
+			// Url cleanup var
+			urlAnchor,
+
+			// Request state (becomes false upon send and true upon completion)
+			completed,
+
+			// To know if global events are to be dispatched
+			fireGlobals,
+
+			// Loop variable
+			i,
+
+			// uncached part of the url
+			uncached,
+
+			// Create the final options object
+			s = jQuery.ajaxSetup( {}, options ),
+
+			// Callbacks context
+			callbackContext = s.context || s,
+
+			// Context for global events is callbackContext if it is a DOM node or jQuery collection
+			globalEventContext = s.context &&
+				( callbackContext.nodeType || callbackContext.jquery ) ?
+				jQuery( callbackContext ) :
+				jQuery.event,
+
+			// Deferreds
+			deferred = jQuery.Deferred(),
+			completeDeferred = jQuery.Callbacks( "once memory" ),
+
+			// Status-dependent callbacks
+			statusCode = s.statusCode || {},
+
+			// Headers (they are sent all at once)
+			requestHeaders = {},
+			requestHeadersNames = {},
+
+			// Default abort message
+			strAbort = "canceled",
+
+			// Fake xhr
+			jqXHR = {
+				readyState: 0,
+
+				// Builds headers hashtable if needed
+				getResponseHeader: function( key ) {
+					var match;
+					if ( completed ) {
+						if ( !responseHeaders ) {
+							responseHeaders = {};
+							while ( ( match = rheaders.exec( responseHeadersString ) ) ) {
+								responseHeaders[ match[ 1 ].toLowerCase() + " " ] =
+									( responseHeaders[ match[ 1 ].toLowerCase() + " " ] || [] )
+										.concat( match[ 2 ] );
+							}
+						}
+						match = responseHeaders[ key.toLowerCase() + " " ];
+					}
+					return match == null ? null : match.join( ", " );
+				},
+
+				// Raw string
+				getAllResponseHeaders: function() {
+					return completed ? responseHeadersString : null;
+				},
+
+				// Caches the header
+				setRequestHeader: function( name, value ) {
+					if ( completed == null ) {
+						name = requestHeadersNames[ name.toLowerCase() ] =
+							requestHeadersNames[ name.toLowerCase() ] || name;
+						requestHeaders[ name ] = value;
+					}
+					return this;
+				},
+
+				// Overrides response content-type header
+				overrideMimeType: function( type ) {
+					if ( completed == null ) {
+						s.mimeType = type;
+					}
+					return this;
+				},
+
+				// Status-dependent callbacks
+				statusCode: function( map ) {
+					var code;
+					if ( map ) {
+						if ( completed ) {
+
+							// Execute the appropriate callbacks
+							jqXHR.always( map[ jqXHR.status ] );
+						} else {
+
+							// Lazy-add the new callbacks in a way that preserves old ones
+							for ( code in map ) {
+								statusCode[ code ] = [ statusCode[ code ], map[ code ] ];
+							}
+						}
+					}
+					return this;
+				},
+
+				// Cancel the request
+				abort: function( statusText ) {
+					var finalText = statusText || strAbort;
+					if ( transport ) {
+						transport.abort( finalText );
+					}
+					done( 0, finalText );
+					return this;
+				}
+			};
+
+		// Attach deferreds
+		deferred.promise( jqXHR );
+
+		// Add protocol if not provided (prefilters might expect it)
+		// Handle falsy url in the settings object (#10093: consistency with old signature)
+		// We also use the url parameter if available
+		s.url = ( ( url || s.url || location.href ) + "" )
+			.replace( rprotocol, location.protocol + "//" );
+
+		// Alias method option to type as per ticket #12004
+		s.type = options.method || options.type || s.method || s.type;
+
+		// Extract dataTypes list
+		s.dataTypes = ( s.dataType || "*" ).toLowerCase().match( rnothtmlwhite ) || [ "" ];
+
+		// A cross-domain request is in order when the origin doesn't match the current origin.
+		if ( s.crossDomain == null ) {
+			urlAnchor = document.createElement( "a" );
+
+			// Support: IE <=8 - 11, Edge 12 - 15
+			// IE throws exception on accessing the href property if url is malformed,
+			// e.g. http://example.com:80x/
+			try {
+				urlAnchor.href = s.url;
+
+				// Support: IE <=8 - 11 only
+				// Anchor's host property isn't correctly set when s.url is relative
+				urlAnchor.href = urlAnchor.href;
+				s.crossDomain = originAnchor.protocol + "//" + originAnchor.host !==
+					urlAnchor.protocol + "//" + urlAnchor.host;
+			} catch ( e ) {
+
+				// If there is an error parsing the URL, assume it is crossDomain,
+				// it can be rejected by the transport if it is invalid
+				s.crossDomain = true;
+			}
+		}
+
+		// Convert data if not already a string
+		if ( s.data && s.processData && typeof s.data !== "string" ) {
+			s.data = jQuery.param( s.data, s.traditional );
+		}
+
+		// Apply prefilters
+		inspectPrefiltersOrTransports( prefilters, s, options, jqXHR );
+
+		// If request was aborted inside a prefilter, stop there
+		if ( completed ) {
+			return jqXHR;
+		}
+
+		// We can fire global events as of now if asked to
+		// Don't fire events if jQuery.event is undefined in an AMD-usage scenario (#15118)
+		fireGlobals = jQuery.event && s.global;
+
+		// Watch for a new set of requests
+		if ( fireGlobals && jQuery.active++ === 0 ) {
+			jQuery.event.trigger( "ajaxStart" );
+		}
+
+		// Uppercase the type
+		s.type = s.type.toUpperCase();
+
+		// Determine if request has content
+		s.hasContent = !rnoContent.test( s.type );
+
+		// Save the URL in case we're toying with the If-Modified-Since
+		// and/or If-None-Match header later on
+		// Remove hash to simplify url manipulation
+		cacheURL = s.url.replace( rhash, "" );
+
+		// More options handling for requests with no content
+		if ( !s.hasContent ) {
+
+			// Remember the hash so we can put it back
+			uncached = s.url.slice( cacheURL.length );
+
+			// If data is available and should be processed, append data to url
+			if ( s.data && ( s.processData || typeof s.data === "string" ) ) {
+				cacheURL += ( rquery.test( cacheURL ) ? "&" : "?" ) + s.data;
+
+				// #9682: remove data so that it's not used in an eventual retry
+				delete s.data;
+			}
+
+			// Add or update anti-cache param if needed
+			if ( s.cache === false ) {
+				cacheURL = cacheURL.replace( rantiCache, "$1" );
+				uncached = ( rquery.test( cacheURL ) ? "&" : "?" ) + "_=" + ( nonce.guid++ ) +
+					uncached;
+			}
+
+			// Put hash and anti-cache on the URL that will be requested (gh-1732)
+			s.url = cacheURL + uncached;
+
+		// Change '%20' to '+' if this is encoded form body content (gh-2658)
+		} else if ( s.data && s.processData &&
+			( s.contentType || "" ).indexOf( "application/x-www-form-urlencoded" ) === 0 ) {
+			s.data = s.data.replace( r20, "+" );
+		}
+
+		// Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode.
+		if ( s.ifModified ) {
+			if ( jQuery.lastModified[ cacheURL ] ) {
+				jqXHR.setRequestHeader( "If-Modified-Since", jQuery.lastModified[ cacheURL ] );
+			}
+			if ( jQuery.etag[ cacheURL ] ) {
+				jqXHR.setRequestHeader( "If-None-Match", jQuery.etag[ cacheURL ] );
+			}
+		}
+
+		// Set the correct header, if data is being sent
+		if ( s.data && s.hasContent && s.contentType !== false || options.contentType ) {
+			jqXHR.setRequestHeader( "Content-Type", s.contentType );
+		}
+
+		// Set the Accepts header for the server, depending on the dataType
+		jqXHR.setRequestHeader(
+			"Accept",
+			s.dataTypes[ 0 ] && s.accepts[ s.dataTypes[ 0 ] ] ?
+				s.accepts[ s.dataTypes[ 0 ] ] +
+					( s.dataTypes[ 0 ] !== "*" ? ", " + allTypes + "; q=0.01" : "" ) :
+				s.accepts[ "*" ]
+		);
+
+		// Check for headers option
+		for ( i in s.headers ) {
+			jqXHR.setRequestHeader( i, s.headers[ i ] );
+		}
+
+		// Allow custom headers/mimetypes and early abort
+		if ( s.beforeSend &&
+			( s.beforeSend.call( callbackContext, jqXHR, s ) === false || completed ) ) {
+
+			// Abort if not done already and return
+			return jqXHR.abort();
+		}
+
+		// Aborting is no longer a cancellation
+		strAbort = "abort";
+
+		// Install callbacks on deferreds
+		completeDeferred.add( s.complete );
+		jqXHR.done( s.success );
+		jqXHR.fail( s.error );
+
+		// Get transport
+		transport = inspectPrefiltersOrTransports( transports, s, options, jqXHR );
+
+		// If no transport, we auto-abort
+		if ( !transport ) {
+			done( -1, "No Transport" );
+		} else {
+			jqXHR.readyState = 1;
+
+			// Send global event
+			if ( fireGlobals ) {
+				globalEventContext.trigger( "ajaxSend", [ jqXHR, s ] );
+			}
+
+			// If request was aborted inside ajaxSend, stop there
+			if ( completed ) {
+				return jqXHR;
+			}
+
+			// Timeout
+			if ( s.async && s.timeout > 0 ) {
+				timeoutTimer = window.setTimeout( function() {
+					jqXHR.abort( "timeout" );
+				}, s.timeout );
+			}
+
+			try {
+				completed = false;
+				transport.send( requestHeaders, done );
+			} catch ( e ) {
+
+				// Rethrow post-completion exceptions
+				if ( completed ) {
+					throw e;
+				}
+
+				// Propagate others as results
+				done( -1, e );
+			}
+		}
+
+		// Callback for when everything is done
+		function done( status, nativeStatusText, responses, headers ) {
+			var isSuccess, success, error, response, modified,
+				statusText = nativeStatusText;
+
+			// Ignore repeat invocations
+			if ( completed ) {
+				return;
+			}
+
+			completed = true;
+
+			// Clear timeout if it exists
+			if ( timeoutTimer ) {
+				window.clearTimeout( timeoutTimer );
+			}
+
+			// Dereference transport for early garbage collection
+			// (no matter how long the jqXHR object will be used)
+			transport = undefined;
+
+			// Cache response headers
+			responseHeadersString = headers || "";
+
+			// Set readyState
+			jqXHR.readyState = status > 0 ? 4 : 0;
+
+			// Determine if successful
+			isSuccess = status >= 200 && status < 300 || status === 304;
+
+			// Get response data
+			if ( responses ) {
+				response = ajaxHandleResponses( s, jqXHR, responses );
+			}
+
+			// Use a noop converter for missing script but not if jsonp
+			if ( !isSuccess &&
+				jQuery.inArray( "script", s.dataTypes ) > -1 &&
+				jQuery.inArray( "json", s.dataTypes ) < 0 ) {
+				s.converters[ "text script" ] = function() {};
+			}
+
+			// Convert no matter what (that way responseXXX fields are always set)
+			response = ajaxConvert( s, response, jqXHR, isSuccess );
+
+			// If successful, handle type chaining
+			if ( isSuccess ) {
+
+				// Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode.
+				if ( s.ifModified ) {
+					modified = jqXHR.getResponseHeader( "Last-Modified" );
+					if ( modified ) {
+						jQuery.lastModified[ cacheURL ] = modified;
+					}
+					modified = jqXHR.getResponseHeader( "etag" );
+					if ( modified ) {
+						jQuery.etag[ cacheURL ] = modified;
+					}
+				}
+
+				// if no content
+				if ( status === 204 || s.type === "HEAD" ) {
+					statusText = "nocontent";
+
+				// if not modified
+				} else if ( status === 304 ) {
+					statusText = "notmodified";
+
+				// If we have data, let's convert it
+				} else {
+					statusText = response.state;
+					success = response.data;
+					error = response.error;
+					isSuccess = !error;
+				}
+			} else {
+
+				// Extract error from statusText and normalize for non-aborts
+				error = statusText;
+				if ( status || !statusText ) {
+					statusText = "error";
+					if ( status < 0 ) {
+						status = 0;
+					}
+				}
+			}
+
+			// Set data for the fake xhr object
+			jqXHR.status = status;
+			jqXHR.statusText = ( nativeStatusText || statusText ) + "";
+
+			// Success/Error
+			if ( isSuccess ) {
+				deferred.resolveWith( callbackContext, [ success, statusText, jqXHR ] );
+			} else {
+				deferred.rejectWith( callbackContext, [ jqXHR, statusText, error ] );
+			}
+
+			// Status-dependent callbacks
+			jqXHR.statusCode( statusCode );
+			statusCode = undefined;
+
+			if ( fireGlobals ) {
+				globalEventContext.trigger( isSuccess ? "ajaxSuccess" : "ajaxError",
+					[ jqXHR, s, isSuccess ? success : error ] );
+			}
+
+			// Complete
+			completeDeferred.fireWith( callbackContext, [ jqXHR, statusText ] );
+
+			if ( fireGlobals ) {
+				globalEventContext.trigger( "ajaxComplete", [ jqXHR, s ] );
+
+				// Handle the global AJAX counter
+				if ( !( --jQuery.active ) ) {
+					jQuery.event.trigger( "ajaxStop" );
+				}
+			}
+		}
+
+		return jqXHR;
+	},
+
+	getJSON: function( url, data, callback ) {
+		return jQuery.get( url, data, callback, "json" );
+	},
+
+	getScript: function( url, callback ) {
+		return jQuery.get( url, undefined, callback, "script" );
+	}
+} );
+
+jQuery.each( [ "get", "post" ], function( _i, method ) {
+	jQuery[ method ] = function( url, data, callback, type ) {
+
+		// Shift arguments if data argument was omitted
+		if ( isFunction( data ) ) {
+			type = type || callback;
+			callback = data;
+			data = undefined;
+		}
+
+		// The url can be an options object (which then must have .url)
+		return jQuery.ajax( jQuery.extend( {
+			url: url,
+			type: method,
+			dataType: type,
+			data: data,
+			success: callback
+		}, jQuery.isPlainObject( url ) && url ) );
+	};
+} );
+
+jQuery.ajaxPrefilter( function( s ) {
+	var i;
+	for ( i in s.headers ) {
+		if ( i.toLowerCase() === "content-type" ) {
+			s.contentType = s.headers[ i ] || "";
+		}
+	}
+} );
+
+
+jQuery._evalUrl = function( url, options, doc ) {
+	return jQuery.ajax( {
+		url: url,
+
+		// Make this explicit, since user can override this through ajaxSetup (#11264)
+		type: "GET",
+		dataType: "script",
+		cache: true,
+		async: false,
+		global: false,
+
+		// Only evaluate the response if it is successful (gh-4126)
+		// dataFilter is not invoked for failure responses, so using it instead
+		// of the default converter is kludgy but it works.
+		converters: {
+			"text script": function() {}
+		},
+		dataFilter: function( response ) {
+			jQuery.globalEval( response, options, doc );
+		}
+	} );
+};
+
+
+jQuery.fn.extend( {
+	wrapAll: function( html ) {
+		var wrap;
+
+		if ( this[ 0 ] ) {
+			if ( isFunction( html ) ) {
+				html = html.call( this[ 0 ] );
+			}
+
+			// The elements to wrap the target around
+			wrap = jQuery( html, this[ 0 ].ownerDocument ).eq( 0 ).clone( true );
+
+			if ( this[ 0 ].parentNode ) {
+				wrap.insertBefore( this[ 0 ] );
+			}
+
+			wrap.map( function() {
+				var elem = this;
+
+				while ( elem.firstElementChild ) {
+					elem = elem.firstElementChild;
+				}
+
+				return elem;
+			} ).append( this );
+		}
+
+		return this;
+	},
+
+	wrapInner: function( html ) {
+		if ( isFunction( html ) ) {
+			return this.each( function( i ) {
+				jQuery( this ).wrapInner( html.call( this, i ) );
+			} );
+		}
+
+		return this.each( function() {
+			var self = jQuery( this ),
+				contents = self.contents();
+
+			if ( contents.length ) {
+				contents.wrapAll( html );
+
+			} else {
+				self.append( html );
+			}
+		} );
+	},
+
+	wrap: function( html ) {
+		var htmlIsFunction = isFunction( html );
+
+		return this.each( function( i ) {
+			jQuery( this ).wrapAll( htmlIsFunction ? html.call( this, i ) : html );
+		} );
+	},
+
+	unwrap: function( selector ) {
+		this.parent( selector ).not( "body" ).each( function() {
+			jQuery( this ).replaceWith( this.childNodes );
+		} );
+		return this;
+	}
+} );
+
+
+jQuery.expr.pseudos.hidden = function( elem ) {
+	return !jQuery.expr.pseudos.visible( elem );
+};
+jQuery.expr.pseudos.visible = function( elem ) {
+	return !!( elem.offsetWidth || elem.offsetHeight || elem.getClientRects().length );
+};
+
+
+
+
+jQuery.ajaxSettings.xhr = function() {
+	try {
+		return new window.XMLHttpRequest();
+	} catch ( e ) {}
+};
+
+var xhrSuccessStatus = {
+
+		// File protocol always yields status code 0, assume 200
+		0: 200,
+
+		// Support: IE <=9 only
+		// #1450: sometimes IE returns 1223 when it should be 204
+		1223: 204
+	},
+	xhrSupported = jQuery.ajaxSettings.xhr();
+
+support.cors = !!xhrSupported && ( "withCredentials" in xhrSupported );
+support.ajax = xhrSupported = !!xhrSupported;
+
+jQuery.ajaxTransport( function( options ) {
+	var callback, errorCallback;
+
+	// Cross domain only allowed if supported through XMLHttpRequest
+	if ( support.cors || xhrSupported && !options.crossDomain ) {
+		return {
+			send: function( headers, complete ) {
+				var i,
+					xhr = options.xhr();
+
+				xhr.open(
+					options.type,
+					options.url,
+					options.async,
+					options.username,
+					options.password
+				);
+
+				// Apply custom fields if provided
+				if ( options.xhrFields ) {
+					for ( i in options.xhrFields ) {
+						xhr[ i ] = options.xhrFields[ i ];
+					}
+				}
+
+				// Override mime type if needed
+				if ( options.mimeType && xhr.overrideMimeType ) {
+					xhr.overrideMimeType( options.mimeType );
+				}
+
+				// X-Requested-With header
+				// For cross-domain requests, seeing as conditions for a preflight are
+				// akin to a jigsaw puzzle, we simply never set it to be sure.
+				// (it can always be set on a per-request basis or even using ajaxSetup)
+				// For same-domain requests, won't change header if already provided.
+				if ( !options.crossDomain && !headers[ "X-Requested-With" ] ) {
+					headers[ "X-Requested-With" ] = "XMLHttpRequest";
+				}
+
+				// Set headers
+				for ( i in headers ) {
+					xhr.setRequestHeader( i, headers[ i ] );
+				}
+
+				// Callback
+				callback = function( type ) {
+					return function() {
+						if ( callback ) {
+							callback = errorCallback = xhr.onload =
+								xhr.onerror = xhr.onabort = xhr.ontimeout =
+									xhr.onreadystatechange = null;
+
+							if ( type === "abort" ) {
+								xhr.abort();
+							} else if ( type === "error" ) {
+
+								// Support: IE <=9 only
+								// On a manual native abort, IE9 throws
+								// errors on any property access that is not readyState
+								if ( typeof xhr.status !== "number" ) {
+									complete( 0, "error" );
+								} else {
+									complete(
+
+										// File: protocol always yields status 0; see #8605, #14207
+										xhr.status,
+										xhr.statusText
+									);
+								}
+							} else {
+								complete(
+									xhrSuccessStatus[ xhr.status ] || xhr.status,
+									xhr.statusText,
+
+									// Support: IE <=9 only
+									// IE9 has no XHR2 but throws on binary (trac-11426)
+									// For XHR2 non-text, let the caller handle it (gh-2498)
+									( xhr.responseType || "text" ) !== "text"  ||
+									typeof xhr.responseText !== "string" ?
+										{ binary: xhr.response } :
+										{ text: xhr.responseText },
+									xhr.getAllResponseHeaders()
+								);
+							}
+						}
+					};
+				};
+
+				// Listen to events
+				xhr.onload = callback();
+				errorCallback = xhr.onerror = xhr.ontimeout = callback( "error" );
+
+				// Support: IE 9 only
+				// Use onreadystatechange to replace onabort
+				// to handle uncaught aborts
+				if ( xhr.onabort !== undefined ) {
+					xhr.onabort = errorCallback;
+				} else {
+					xhr.onreadystatechange = function() {
+
+						// Check readyState before timeout as it changes
+						if ( xhr.readyState === 4 ) {
+
+							// Allow onerror to be called first,
+							// but that will not handle a native abort
+							// Also, save errorCallback to a variable
+							// as xhr.onerror cannot be accessed
+							window.setTimeout( function() {
+								if ( callback ) {
+									errorCallback();
+								}
+							} );
+						}
+					};
+				}
+
+				// Create the abort callback
+				callback = callback( "abort" );
+
+				try {
+
+					// Do send the request (this may raise an exception)
+					xhr.send( options.hasContent && options.data || null );
+				} catch ( e ) {
+
+					// #14683: Only rethrow if this hasn't been notified as an error yet
+					if ( callback ) {
+						throw e;
+					}
+				}
+			},
+
+			abort: function() {
+				if ( callback ) {
+					callback();
+				}
+			}
+		};
+	}
+} );
+
+
+
+
+// Prevent auto-execution of scripts when no explicit dataType was provided (See gh-2432)
+jQuery.ajaxPrefilter( function( s ) {
+	if ( s.crossDomain ) {
+		s.contents.script = false;
+	}
+} );
+
+// Install script dataType
+jQuery.ajaxSetup( {
+	accepts: {
+		script: "text/javascript, application/javascript, " +
+			"application/ecmascript, application/x-ecmascript"
+	},
+	contents: {
+		script: /\b(?:java|ecma)script\b/
+	},
+	converters: {
+		"text script": function( text ) {
+			jQuery.globalEval( text );
+			return text;
+		}
+	}
+} );
+
+// Handle cache's special case and crossDomain
+jQuery.ajaxPrefilter( "script", function( s ) {
+	if ( s.cache === undefined ) {
+		s.cache = false;
+	}
+	if ( s.crossDomain ) {
+		s.type = "GET";
+	}
+} );
+
+// Bind script tag hack transport
+jQuery.ajaxTransport( "script", function( s ) {
+
+	// This transport only deals with cross domain or forced-by-attrs requests
+	if ( s.crossDomain || s.scriptAttrs ) {
+		var script, callback;
+		return {
+			send: function( _, complete ) {
+				script = jQuery( "<script>" )
+					.attr( s.scriptAttrs || {} )
+					.prop( { charset: s.scriptCharset, src: s.url } )
+					.on( "load error", callback = function( evt ) {
+						script.remove();
+						callback = null;
+						if ( evt ) {
+							complete( evt.type === "error" ? 404 : 200, evt.type );
+						}
+					} );
+
+				// Use native DOM manipulation to avoid our domManip AJAX trickery
+				document.head.appendChild( script[ 0 ] );
+			},
+			abort: function() {
+				if ( callback ) {
+					callback();
+				}
+			}
+		};
+	}
+} );
+
+
+
+
+var oldCallbacks = [],
+	rjsonp = /(=)\?(?=&|$)|\?\?/;
+
+// Default jsonp settings
+jQuery.ajaxSetup( {
+	jsonp: "callback",
+	jsonpCallback: function() {
+		var callback = oldCallbacks.pop() || ( jQuery.expando + "_" + ( nonce.guid++ ) );
+		this[ callback ] = true;
+		return callback;
+	}
+} );
+
+// Detect, normalize options and install callbacks for jsonp requests
+jQuery.ajaxPrefilter( "json jsonp", function( s, originalSettings, jqXHR ) {
+
+	var callbackName, overwritten, responseContainer,
+		jsonProp = s.jsonp !== false && ( rjsonp.test( s.url ) ?
+			"url" :
+			typeof s.data === "string" &&
+				( s.contentType || "" )
+					.indexOf( "application/x-www-form-urlencoded" ) === 0 &&
+				rjsonp.test( s.data ) && "data"
+		);
+
+	// Handle iff the expected data type is "jsonp" or we have a parameter to set
+	if ( jsonProp || s.dataTypes[ 0 ] === "jsonp" ) {
+
+		// Get callback name, remembering preexisting value associated with it
+		callbackName = s.jsonpCallback = isFunction( s.jsonpCallback ) ?
+			s.jsonpCallback() :
+			s.jsonpCallback;
+
+		// Insert callback into url or form data
+		if ( jsonProp ) {
+			s[ jsonProp ] = s[ jsonProp ].replace( rjsonp, "$1" + callbackName );
+		} else if ( s.jsonp !== false ) {
+			s.url += ( rquery.test( s.url ) ? "&" : "?" ) + s.jsonp + "=" + callbackName;
+		}
+
+		// Use data converter to retrieve json after script execution
+		s.converters[ "script json" ] = function() {
+			if ( !responseContainer ) {
+				jQuery.error( callbackName + " was not called" );
+			}
+			return responseContainer[ 0 ];
+		};
+
+		// Force json dataType
+		s.dataTypes[ 0 ] = "json";
+
+		// Install callback
+		overwritten = window[ callbackName ];
+		window[ callbackName ] = function() {
+			responseContainer = arguments;
+		};
+
+		// Clean-up function (fires after converters)
+		jqXHR.always( function() {
+
+			// If previous value didn't exist - remove it
+			if ( overwritten === undefined ) {
+				jQuery( window ).removeProp( callbackName );
+
+			// Otherwise restore preexisting value
+			} else {
+				window[ callbackName ] = overwritten;
+			}
+
+			// Save back as free
+			if ( s[ callbackName ] ) {
+
+				// Make sure that re-using the options doesn't screw things around
+				s.jsonpCallback = originalSettings.jsonpCallback;
+
+				// Save the callback name for future use
+				oldCallbacks.push( callbackName );
+			}
+
+			// Call if it was a function and we have a response
+			if ( responseContainer && isFunction( overwritten ) ) {
+				overwritten( responseContainer[ 0 ] );
+			}
+
+			responseContainer = overwritten = undefined;
+		} );
+
+		// Delegate to script
+		return "script";
+	}
+} );
+
+
+
+
+// Support: Safari 8 only
+// In Safari 8 documents created via document.implementation.createHTMLDocument
+// collapse sibling forms: the second one becomes a child of the first one.
+// Because of that, this security measure has to be disabled in Safari 8.
+// https://bugs.webkit.org/show_bug.cgi?id=137337
+support.createHTMLDocument = ( function() {
+	var body = document.implementation.createHTMLDocument( "" ).body;
+	body.innerHTML = "<form></form><form></form>";
+	return body.childNodes.length === 2;
+} )();
+
+
+// Argument "data" should be string of html
+// context (optional): If specified, the fragment will be created in this context,
+// defaults to document
+// keepScripts (optional): If true, will include scripts passed in the html string
+jQuery.parseHTML = function( data, context, keepScripts ) {
+	if ( typeof data !== "string" ) {
+		return [];
+	}
+	if ( typeof context === "boolean" ) {
+		keepScripts = context;
+		context = false;
+	}
+
+	var base, parsed, scripts;
+
+	if ( !context ) {
+
+		// Stop scripts or inline event handlers from being executed immediately
+		// by using document.implementation
+		if ( support.createHTMLDocument ) {
+			context = document.implementation.createHTMLDocument( "" );
+
+			// Set the base href for the created document
+			// so any parsed elements with URLs
+			// are based on the document's URL (gh-2965)
+			base = context.createElement( "base" );
+			base.href = document.location.href;
+			context.head.appendChild( base );
+		} else {
+			context = document;
+		}
+	}
+
+	parsed = rsingleTag.exec( data );
+	scripts = !keepScripts && [];
+
+	// Single tag
+	if ( parsed ) {
+		return [ context.createElement( parsed[ 1 ] ) ];
+	}
+
+	parsed = buildFragment( [ data ], context, scripts );
+
+	if ( scripts && scripts.length ) {
+		jQuery( scripts ).remove();
+	}
+
+	return jQuery.merge( [], parsed.childNodes );
+};
+
+
+/**
+ * Load a url into a page
+ */
+jQuery.fn.load = function( url, params, callback ) {
+	var selector, type, response,
+		self = this,
+		off = url.indexOf( " " );
+
+	if ( off > -1 ) {
+		selector = stripAndCollapse( url.slice( off ) );
+		url = url.slice( 0, off );
+	}
+
+	// If it's a function
+	if ( isFunction( params ) ) {
+
+		// We assume that it's the callback
+		callback = params;
+		params = undefined;
+
+	// Otherwise, build a param string
+	} else if ( params && typeof params === "object" ) {
+		type = "POST";
+	}
+
+	// If we have elements to modify, make the request
+	if ( self.length > 0 ) {
+		jQuery.ajax( {
+			url: url,
+
+			// If "type" variable is undefined, then "GET" method will be used.
+			// Make value of this field explicit since
+			// user can override it through ajaxSetup method
+			type: type || "GET",
+			dataType: "html",
+			data: params
+		} ).done( function( responseText ) {
+
+			// Save response for use in complete callback
+			response = arguments;
+
+			self.html( selector ?
+
+				// If a selector was specified, locate the right elements in a dummy div
+				// Exclude scripts to avoid IE 'Permission Denied' errors
+				jQuery( "<div>" ).append( jQuery.parseHTML( responseText ) ).find( selector ) :
+
+				// Otherwise use the full result
+				responseText );
+
+		// If the request succeeds, this function gets "data", "status", "jqXHR"
+		// but they are ignored because response was set above.
+		// If it fails, this function gets "jqXHR", "status", "error"
+		} ).always( callback && function( jqXHR, status ) {
+			self.each( function() {
+				callback.apply( this, response || [ jqXHR.responseText, status, jqXHR ] );
+			} );
+		} );
+	}
+
+	return this;
+};
+
+
+
+
+jQuery.expr.pseudos.animated = function( elem ) {
+	return jQuery.grep( jQuery.timers, function( fn ) {
+		return elem === fn.elem;
+	} ).length;
+};
+
+
+
+
+jQuery.offset = {
+	setOffset: function( elem, options, i ) {
+		var curPosition, curLeft, curCSSTop, curTop, curOffset, curCSSLeft, calculatePosition,
+			position = jQuery.css( elem, "position" ),
+			curElem = jQuery( elem ),
+			props = {};
+
+		// Set position first, in-case top/left are set even on static elem
+		if ( position === "static" ) {
+			elem.style.position = "relative";
+		}
+
+		curOffset = curElem.offset();
+		curCSSTop = jQuery.css( elem, "top" );
+		curCSSLeft = jQuery.css( elem, "left" );
+		calculatePosition = ( position === "absolute" || position === "fixed" ) &&
+			( curCSSTop + curCSSLeft ).indexOf( "auto" ) > -1;
+
+		// Need to be able to calculate position if either
+		// top or left is auto and position is either absolute or fixed
+		if ( calculatePosition ) {
+			curPosition = curElem.position();
+			curTop = curPosition.top;
+			curLeft = curPosition.left;
+
+		} else {
+			curTop = parseFloat( curCSSTop ) || 0;
+			curLeft = parseFloat( curCSSLeft ) || 0;
+		}
+
+		if ( isFunction( options ) ) {
+
+			// Use jQuery.extend here to allow modification of coordinates argument (gh-1848)
+			options = options.call( elem, i, jQuery.extend( {}, curOffset ) );
+		}
+
+		if ( options.top != null ) {
+			props.top = ( options.top - curOffset.top ) + curTop;
+		}
+		if ( options.left != null ) {
+			props.left = ( options.left - curOffset.left ) + curLeft;
+		}
+
+		if ( "using" in options ) {
+			options.using.call( elem, props );
+
+		} else {
+			curElem.css( props );
+		}
+	}
+};
+
+jQuery.fn.extend( {
+
+	// offset() relates an element's border box to the document origin
+	offset: function( options ) {
+
+		// Preserve chaining for setter
+		if ( arguments.length ) {
+			return options === undefined ?
+				this :
+				this.each( function( i ) {
+					jQuery.offset.setOffset( this, options, i );
+				} );
+		}
+
+		var rect, win,
+			elem = this[ 0 ];
+
+		if ( !elem ) {
+			return;
+		}
+
+		// Return zeros for disconnected and hidden (display: none) elements (gh-2310)
+		// Support: IE <=11 only
+		// Running getBoundingClientRect on a
+		// disconnected node in IE throws an error
+		if ( !elem.getClientRects().length ) {
+			return { top: 0, left: 0 };
+		}
+
+		// Get document-relative position by adding viewport scroll to viewport-relative gBCR
+		rect = elem.getBoundingClientRect();
+		win = elem.ownerDocument.defaultView;
+		return {
+			top: rect.top + win.pageYOffset,
+			left: rect.left + win.pageXOffset
+		};
+	},
+
+	// position() relates an element's margin box to its offset parent's padding box
+	// This corresponds to the behavior of CSS absolute positioning
+	position: function() {
+		if ( !this[ 0 ] ) {
+			return;
+		}
+
+		var offsetParent, offset, doc,
+			elem = this[ 0 ],
+			parentOffset = { top: 0, left: 0 };
+
+		// position:fixed elements are offset from the viewport, which itself always has zero offset
+		if ( jQuery.css( elem, "position" ) === "fixed" ) {
+
+			// Assume position:fixed implies availability of getBoundingClientRect
+			offset = elem.getBoundingClientRect();
+
+		} else {
+			offset = this.offset();
+
+			// Account for the *real* offset parent, which can be the document or its root element
+			// when a statically positioned element is identified
+			doc = elem.ownerDocument;
+			offsetParent = elem.offsetParent || doc.documentElement;
+			while ( offsetParent &&
+				( offsetParent === doc.body || offsetParent === doc.documentElement ) &&
+				jQuery.css( offsetParent, "position" ) === "static" ) {
+
+				offsetParent = offsetParent.parentNode;
+			}
+			if ( offsetParent && offsetParent !== elem && offsetParent.nodeType === 1 ) {
+
+				// Incorporate borders into its offset, since they are outside its content origin
+				parentOffset = jQuery( offsetParent ).offset();
+				parentOffset.top += jQuery.css( offsetParent, "borderTopWidth", true );
+				parentOffset.left += jQuery.css( offsetParent, "borderLeftWidth", true );
+			}
+		}
+
+		// Subtract parent offsets and element margins
+		return {
+			top: offset.top - parentOffset.top - jQuery.css( elem, "marginTop", true ),
+			left: offset.left - parentOffset.left - jQuery.css( elem, "marginLeft", true )
+		};
+	},
+
+	// This method will return documentElement in the following cases:
+	// 1) For the element inside the iframe without offsetParent, this method will return
+	//    documentElement of the parent window
+	// 2) For the hidden or detached element
+	// 3) For body or html element, i.e. in case of the html node - it will return itself
+	//
+	// but those exceptions were never presented as a real life use-cases
+	// and might be considered as more preferable results.
+	//
+	// This logic, however, is not guaranteed and can change at any point in the future
+	offsetParent: function() {
+		return this.map( function() {
+			var offsetParent = this.offsetParent;
+
+			while ( offsetParent && jQuery.css( offsetParent, "position" ) === "static" ) {
+				offsetParent = offsetParent.offsetParent;
+			}
+
+			return offsetParent || documentElement;
+		} );
+	}
+} );
+
+// Create scrollLeft and scrollTop methods
+jQuery.each( { scrollLeft: "pageXOffset", scrollTop: "pageYOffset" }, function( method, prop ) {
+	var top = "pageYOffset" === prop;
+
+	jQuery.fn[ method ] = function( val ) {
+		return access( this, function( elem, method, val ) {
+
+			// Coalesce documents and windows
+			var win;
+			if ( isWindow( elem ) ) {
+				win = elem;
+			} else if ( elem.nodeType === 9 ) {
+				win = elem.defaultView;
+			}
+
+			if ( val === undefined ) {
+				return win ? win[ prop ] : elem[ method ];
+			}
+
+			if ( win ) {
+				win.scrollTo(
+					!top ? val : win.pageXOffset,
+					top ? val : win.pageYOffset
+				);
+
+			} else {
+				elem[ method ] = val;
+			}
+		}, method, val, arguments.length );
+	};
+} );
+
+// Support: Safari <=7 - 9.1, Chrome <=37 - 49
+// Add the top/left cssHooks using jQuery.fn.position
+// Webkit bug: https://bugs.webkit.org/show_bug.cgi?id=29084
+// Blink bug: https://bugs.chromium.org/p/chromium/issues/detail?id=589347
+// getComputedStyle returns percent when specified for top/left/bottom/right;
+// rather than make the css module depend on the offset module, just check for it here
+jQuery.each( [ "top", "left" ], function( _i, prop ) {
+	jQuery.cssHooks[ prop ] = addGetHookIf( support.pixelPosition,
+		function( elem, computed ) {
+			if ( computed ) {
+				computed = curCSS( elem, prop );
+
+				// If curCSS returns percentage, fallback to offset
+				return rnumnonpx.test( computed ) ?
+					jQuery( elem ).position()[ prop ] + "px" :
+					computed;
+			}
+		}
+	);
+} );
+
+
+// Create innerHeight, innerWidth, height, width, outerHeight and outerWidth methods
+jQuery.each( { Height: "height", Width: "width" }, function( name, type ) {
+	jQuery.each( {
+		padding: "inner" + name,
+		content: type,
+		"": "outer" + name
+	}, function( defaultExtra, funcName ) {
+
+		// Margin is only for outerHeight, outerWidth
+		jQuery.fn[ funcName ] = function( margin, value ) {
+			var chainable = arguments.length && ( defaultExtra || typeof margin !== "boolean" ),
+				extra = defaultExtra || ( margin === true || value === true ? "margin" : "border" );
+
+			return access( this, function( elem, type, value ) {
+				var doc;
+
+				if ( isWindow( elem ) ) {
+
+					// $( window ).outerWidth/Height return w/h including scrollbars (gh-1729)
+					return funcName.indexOf( "outer" ) === 0 ?
+						elem[ "inner" + name ] :
+						elem.document.documentElement[ "client" + name ];
+				}
+
+				// Get document width or height
+				if ( elem.nodeType === 9 ) {
+					doc = elem.documentElement;
+
+					// Either scroll[Width/Height] or offset[Width/Height] or client[Width/Height],
+					// whichever is greatest
+					return Math.max(
+						elem.body[ "scroll" + name ], doc[ "scroll" + name ],
+						elem.body[ "offset" + name ], doc[ "offset" + name ],
+						doc[ "client" + name ]
+					);
+				}
+
+				return value === undefined ?
+
+					// Get width or height on the element, requesting but not forcing parseFloat
+					jQuery.css( elem, type, extra ) :
+
+					// Set width or height on the element
+					jQuery.style( elem, type, value, extra );
+			}, type, chainable ? margin : undefined, chainable );
+		};
+	} );
+} );
+
+
+jQuery.each( [
+	"ajaxStart",
+	"ajaxStop",
+	"ajaxComplete",
+	"ajaxError",
+	"ajaxSuccess",
+	"ajaxSend"
+], function( _i, type ) {
+	jQuery.fn[ type ] = function( fn ) {
+		return this.on( type, fn );
+	};
+} );
+
+
+
+
+jQuery.fn.extend( {
+
+	bind: function( types, data, fn ) {
+		return this.on( types, null, data, fn );
+	},
+	unbind: function( types, fn ) {
+		return this.off( types, null, fn );
+	},
+
+	delegate: function( selector, types, data, fn ) {
+		return this.on( types, selector, data, fn );
+	},
+	undelegate: function( selector, types, fn ) {
+
+		// ( namespace ) or ( selector, types [, fn] )
+		return arguments.length === 1 ?
+			this.off( selector, "**" ) :
+			this.off( types, selector || "**", fn );
+	},
+
+	hover: function( fnOver, fnOut ) {
+		return this.mouseenter( fnOver ).mouseleave( fnOut || fnOver );
+	}
+} );
+
+jQuery.each(
+	( "blur focus focusin focusout resize scroll click dblclick " +
+	"mousedown mouseup mousemove mouseover mouseout mouseenter mouseleave " +
+	"change select submit keydown keypress keyup contextmenu" ).split( " " ),
+	function( _i, name ) {
+
+		// Handle event binding
+		jQuery.fn[ name ] = function( data, fn ) {
+			return arguments.length > 0 ?
+				this.on( name, null, data, fn ) :
+				this.trigger( name );
+		};
+	}
+);
+
+
+
+
+// Support: Android <=4.0 only
+// Make sure we trim BOM and NBSP
+var rtrim = /^[\s\uFEFF\xA0]+|[\s\uFEFF\xA0]+$/g;
+
+// Bind a function to a context, optionally partially applying any
+// arguments.
+// jQuery.proxy is deprecated to promote standards (specifically Function#bind)
+// However, it is not slated for removal any time soon
+jQuery.proxy = function( fn, context ) {
+	var tmp, args, proxy;
+
+	if ( typeof context === "string" ) {
+		tmp = fn[ context ];
+		context = fn;
+		fn = tmp;
+	}
+
+	// Quick check to determine if target is callable, in the spec
+	// this throws a TypeError, but we will just return undefined.
+	if ( !isFunction( fn ) ) {
+		return undefined;
+	}
+
+	// Simulated bind
+	args = slice.call( arguments, 2 );
+	proxy = function() {
+		return fn.apply( context || this, args.concat( slice.call( arguments ) ) );
+	};
+
+	// Set the guid of unique handler to the same of original handler, so it can be removed
+	proxy.guid = fn.guid = fn.guid || jQuery.guid++;
+
+	return proxy;
+};
+
+jQuery.holdReady = function( hold ) {
+	if ( hold ) {
+		jQuery.readyWait++;
+	} else {
+		jQuery.ready( true );
+	}
+};
+jQuery.isArray = Array.isArray;
+jQuery.parseJSON = JSON.parse;
+jQuery.nodeName = nodeName;
+jQuery.isFunction = isFunction;
+jQuery.isWindow = isWindow;
+jQuery.camelCase = camelCase;
+jQuery.type = toType;
+
+jQuery.now = Date.now;
+
+jQuery.isNumeric = function( obj ) {
+
+	// As of jQuery 3.0, isNumeric is limited to
+	// strings and numbers (primitives or objects)
+	// that can be coerced to finite numbers (gh-2662)
+	var type = jQuery.type( obj );
+	return ( type === "number" || type === "string" ) &&
+
+		// parseFloat NaNs numeric-cast false positives ("")
+		// ...but misinterprets leading-number strings, particularly hex literals ("0x...")
+		// subtraction forces infinities to NaN
+		!isNaN( obj - parseFloat( obj ) );
+};
+
+jQuery.trim = function( text ) {
+	return text == null ?
+		"" :
+		( text + "" ).replace( rtrim, "" );
+};
+
+
+
+// Register as a named AMD module, since jQuery can be concatenated with other
+// files that may use define, but not via a proper concatenation script that
+// understands anonymous AMD modules. A named AMD is safest and most robust
+// way to register. Lowercase jquery is used because AMD module names are
+// derived from file names, and jQuery is normally delivered in a lowercase
+// file name. Do this after creating the global so that if an AMD module wants
+// to call noConflict to hide this version of jQuery, it will work.
+
+// Note that for maximum portability, libraries that are not jQuery should
+// declare themselves as anonymous modules, and avoid setting a global if an
+// AMD loader is present. jQuery is a special case. For more information, see
+// https://github.com/jrburke/requirejs/wiki/Updating-existing-libraries#wiki-anon
+
+if ( typeof define === "function" && define.amd ) {
+	define( "jquery", [], function() {
+		return jQuery;
+	} );
+}
+
+
+
+
+var
+
+	// Map over jQuery in case of overwrite
+	_jQuery = window.jQuery,
+
+	// Map over the $ in case of overwrite
+	_$ = window.$;
+
+jQuery.noConflict = function( deep ) {
+	if ( window.$ === jQuery ) {
+		window.$ = _$;
+	}
+
+	if ( deep && window.jQuery === jQuery ) {
+		window.jQuery = _jQuery;
+	}
+
+	return jQuery;
+};
+
+// Expose jQuery and $ identifiers, even in AMD
+// (#7102#comment:10, https://github.com/jquery/jquery/pull/557)
+// and CommonJS for browser emulators (#13566)
+if ( typeof noGlobal === "undefined" ) {
+	window.jQuery = window.$ = jQuery;
+}
+
+
+
+
+return jQuery;
+} );
diff --git a/deps/jquery-3.6.0/jquery-3.6.0.min.js b/deps/jquery-3.6.0/jquery-3.6.0.min.js
new file mode 100644
index 000000000..c4c6022f2
--- /dev/null
+++ b/deps/jquery-3.6.0/jquery-3.6.0.min.js
@@ -0,0 +1,2 @@
+/*! jQuery v3.6.0 | (c) OpenJS Foundation and other contributors | jquery.org/license */
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\ No newline at end of file
diff --git a/docsearch.css b/docsearch.css
deleted file mode 100644
index e5f1fe1df..000000000
--- a/docsearch.css
+++ /dev/null
@@ -1,148 +0,0 @@
-/* Docsearch -------------------------------------------------------------- */
-/*
-  Source: https://github.com/algolia/docsearch/
-  License: MIT
-*/
-
-.algolia-autocomplete {
-  display: block;
-  -webkit-box-flex: 1;
-  -ms-flex: 1;
-  flex: 1
-}
-
-.algolia-autocomplete .ds-dropdown-menu {
-  width: 100%;
-  min-width: none;
-  max-width: none;
-  padding: .75rem 0;
-  background-color: #fff;
-  background-clip: padding-box;
-  border: 1px solid rgba(0, 0, 0, .1);
-  box-shadow: 0 .5rem 1rem rgba(0, 0, 0, .175);
-}
-
-@media (min-width:768px) {
-  .algolia-autocomplete .ds-dropdown-menu {
-      width: 175%
-  }
-}
-
-.algolia-autocomplete .ds-dropdown-menu::before {
-  display: none
-}
-
-.algolia-autocomplete .ds-dropdown-menu [class^=ds-dataset-] {
-  padding: 0;
-  background-color: rgb(255,255,255);
-  border: 0;
-  max-height: 80vh;
-}
-
-.algolia-autocomplete .ds-dropdown-menu .ds-suggestions {
-  margin-top: 0
-}
-
-.algolia-autocomplete .algolia-docsearch-suggestion {
-  padding: 0;
-  overflow: visible
-}
-
-.algolia-autocomplete .algolia-docsearch-suggestion--category-header {
-  padding: .125rem 1rem;
-  margin-top: 0;
-  font-size: 1.3em;
-  font-weight: 500;
-  color: #00008B;
-  border-bottom: 0
-}
-
-.algolia-autocomplete .algolia-docsearch-suggestion--wrapper {
-    float: none;
-    padding-top: 0
-}
-
-.algolia-autocomplete .algolia-docsearch-suggestion--subcategory-column {
-  float: none;
-  width: auto;
-  padding: 0;
-  text-align: left
-}
-
-.algolia-autocomplete .algolia-docsearch-suggestion--content {
-  float: none;
-  width: auto;
-  padding: 0
-}
-
-.algolia-autocomplete .algolia-docsearch-suggestion--content::before {
-  display: none
-}
-
-.algolia-autocomplete .ds-suggestion:not(:first-child) .algolia-docsearch-suggestion--category-header {
-  padding-top: .75rem;
-  margin-top: .75rem;
-  border-top: 1px solid rgba(0, 0, 0, .1)
-}
-
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-}
-
-.algolia-autocomplete .algolia-docsearch-suggestion--highlight {
-  color: #FF8C00;
-  background: rgba(232, 189, 54, 0.1)
-}
-
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-.algolia-autocomplete .algolia-docsearch-suggestion--text .algolia-docsearch-suggestion--highlight {
-  box-shadow: inset 0 -2px 0 0 rgba(105, 105, 105, .5)
-}
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-.algolia-autocomplete .ds-suggestion.ds-cursor .algolia-docsearch-suggestion--content {
-  background-color: rgba(192, 192, 192, .15)
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diff --git a/docsearch.js b/docsearch.js
deleted file mode 100644
index b35504cd3..000000000
--- a/docsearch.js
+++ /dev/null
@@ -1,85 +0,0 @@
-$(function() {
-
-  // register a handler to move the focus to the search bar
-  // upon pressing shift + "/" (i.e. "?")
-  $(document).on('keydown', function(e) {
-    if (e.shiftKey && e.keyCode == 191) {
-      e.preventDefault();
-      $("#search-input").focus();
-    }
-  });
-
-  $(document).ready(function() {
-    // do keyword highlighting
-    /* modified from https://jsfiddle.net/julmot/bL6bb5oo/ */
-    var mark = function() {
-
-      var referrer = document.URL ;
-      var paramKey = "q" ;
-
-      if (referrer.indexOf("?") !== -1) {
-        var qs = referrer.substr(referrer.indexOf('?') + 1);
-        var qs_noanchor = qs.split('#')[0];
-        var qsa = qs_noanchor.split('&');
-        var keyword = "";
-
-        for (var i = 0; i < qsa.length; i++) {
-          var currentParam = qsa[i].split('=');
-
-          if (currentParam.length !== 2) {
-            continue;
-          }
-
-          if (currentParam[0] == paramKey) {
-            keyword = decodeURIComponent(currentParam[1].replace(/\+/g, "%20"));
-          }
-        }
-
-        if (keyword !== "") {
-          $(".contents").unmark({
-            done: function() {
-              $(".contents").mark(keyword);
-            }
-          });
-        }
-      }
-    };
-
-    mark();
-  });
-});
-
-/* Search term highlighting ------------------------------*/
-
-function matchedWords(hit) {
-  var words = [];
-
-  var hierarchy = hit._highlightResult.hierarchy;
-  // loop to fetch from lvl0, lvl1, etc.
-  for (var idx in hierarchy) {
-    words = words.concat(hierarchy[idx].matchedWords);
-  }
-
-  var content = hit._highlightResult.content;
-  if (content) {
-    words = words.concat(content.matchedWords);
-  }
-
-  // return unique words
-  var words_uniq = [...new Set(words)];
-  return words_uniq;
-}
-
-function updateHitURL(hit) {
-
-  var words = matchedWords(hit);
-  var url = "";
-
-  if (hit.anchor) {
-    url = hit.url_without_anchor + '?q=' + escape(words.join(" ")) + '#' + hit.anchor;
-  } else {
-    url = hit.url + '?q=' + escape(words.join(" "));
-  }
-
-  return url;
-}
diff --git a/extra.css b/extra.css
index d8c4f4705..13a3ea9ad 100644
--- a/extra.css
+++ b/extra.css
@@ -1,44 +1,26 @@
-.navbar-default {
-  background-color: #7e1f77;
-  border-color: #7e1f77;
+#torch- img {
+    max-width: 20%;
+    height: auto;
 }
 
-.navbar-default .navbar-toggle:hover, .navbar-default .navbar-toggle:focus {
-  background-color: #a953a2;
+.navbar-dark .navbar-nav .active>.nav-link {
+    background: #65195f;
+    color: #f0f0f0;
 }
 
-.navbar-default .navbar-collapse, .navbar-default .navbar-form {
-  border-color: #ffffff;
+.navbar-dark .navbar-nav .nav-link {
+    color: #f0f0f0;
 }
 
-.navbar-default .navbar-nav .open .dropdown-menu>.dropdown-header {
-  border-color: #ffffff !important;
+.navbar-dark input[type="search"] {
+    border-color: #fff;
+    background-color: #fff;
+    color: #7e1f77;
+    caret-color: #7e1f77;
 }
 
-.navbar-default .navbar-nav>li>a:hover, .navbar-default .navbar-nav>li>a:focus {
-  color: #ffffff;
-  background-color: #a953a2;
-}
-
-.navbar-default .navbar-nav>.active>a, .navbar-default .navbar-nav>.active>a:hover, .navbar-default .navbar-nav>.active>a:focus {
-  background-color: #a953a2;
-}
-
-.navbar-default .navbar-nav>.open>a, .navbar-default .navbar-nav>.open>a:hover, .navbar-default .navbar-nav>.open>a:focus {
-  background-color: #a953a2;
-}
-
-.nav-pills>li.active>a, .nav-pills>li.active>a:hover, .nav-pills>li.active>a:focus {
-  background-color: #a953a2;
-}
-
-.dropdown-menu>.active>a, 
-.dropdown-menu>.active>a:hover, 
-.dropdown-menu>.active>a:focus {
-    background-color: #7e1f77;
-}
-
-.dropdown-menu>a:hover, 
-.dropdown-menu>a:focus {
-    background-color: #7e1f77;
+body > nav > div > small {
+    background-color: #ced4da;
+    padding: 3px;
+    border-radius: 5px;
 }
\ No newline at end of file
diff --git a/index.html b/index.html
index a80039574..a4e1c95c5 100644
--- a/index.html
+++ b/index.html
@@ -4,18 +4,21 @@
 <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
 <meta charset="utf-8">
 <meta http-equiv="X-UA-Compatible" content="IE=edge">
-<meta name="viewport" content="width=device-width, initial-scale=1.0">
+<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
+<meta name="description" content="Provides functionality to define and train neural networks similar to
+    PyTorch by Paszke et al (2019) &lt;arXiv:1912.01703&gt; but written entirely in R
+    using the libtorch library. Also supports low-level tensor operations and
+    GPU acceleration.">
 <title>Tensors and Neural Networks with GPU Acceleration • torch</title>
-<!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link href="https://cdnjs.cloudflare.com/ajax/libs/bootswatch/3.4.0/united/bootstrap.min.css" rel="stylesheet" crossorigin="anonymous">
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-<script src="bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous">
+<script src="deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
+<link href="deps/bootstrap-5.1.0/bootstrap.min.css" rel="stylesheet">
+<script src="deps/bootstrap-5.1.0/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous">
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-<!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="pkgdown.css" rel="stylesheet">
-<script src="pkgdown.js"></script><link href="extra.css" rel="stylesheet">
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 <meta property="og:title" content="Tensors and Neural Networks with GPU Acceleration">
-<meta property="og:description" content="Provides functionality to define and train neural networks similar to 
-    PyTorch by Paszke et al (2019) &lt;arXiv:1912.01703&gt; but written entirely in R 
-    using the libtorch library. Also supports low-level tensor operations and 
+<meta property="og:description" content="Provides functionality to define and train neural networks similar to
+    PyTorch by Paszke et al (2019) &lt;arXiv:1912.01703&gt; but written entirely in R
+    using the libtorch library. Also supports low-level tensor operations and
     GPU acceleration.">
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-<div id="torch-" class="section level1">
-<div class="page-header"><h1 class="hasAnchor">
-<a href="#torch-" class="anchor"></a>torch <a href="https://torch.mlverse.org"><img src="reference/figures/torch.png" align="right" height="139"></a>
+    
+  </div>
+</nav><div class="container template-home">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="section level1">
+<div class="page-header"><h1 id="torch-">torch <a href="https://torch.mlverse.org" class="external-link"><img src="reference/figures/torch.png" align="right" height="139"></a>
+<a class="anchor" aria-label="anchor" href="#torch-"></a>
 </h1></div>
 
-<div id="installation" class="section level2">
-<h2 class="hasAnchor">
-<a href="#installation" class="anchor"></a>Installation</h2>
+<div class="section level2">
+<h2 id="installation">Installation<a class="anchor" aria-label="anchor" href="#installation"></a>
+</h2>
 <p>torch can be installed from CRAN with:</p>
 <div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/utils/install.packages.html">install.packages</a></span><span class="op">(</span><span class="st">"torch"</span><span class="op">)</span></code></pre></div>
+<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/utils/install.packages.html" class="external-link">install.packages</a></span><span class="op">(</span><span class="st">"torch"</span><span class="op">)</span></code></pre></div>
 <p>You can also install the development version with:</p>
 <div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="fu">remotes</span><span class="fu">::</span><span class="fu"><a href="https://remotes.r-lib.org/reference/install_github.html">install_github</a></span><span class="op">(</span><span class="st">"mlverse/torch"</span><span class="op">)</span></code></pre></div>
+<code class="sourceCode R"><span class="fu">remotes</span><span class="fu">::</span><span class="fu"><a href="https://remotes.r-lib.org/reference/install_github.html" class="external-link">install_github</a></span><span class="op">(</span><span class="st">"mlverse/torch"</span><span class="op">)</span></code></pre></div>
 <p>At the first package load additional software will be installed.</p>
 </div>
-<div id="installation-with-docker" class="section level2">
-<h2 class="hasAnchor">
-<a href="#installation-with-docker" class="anchor"></a>Installation with Docker</h2>
+<div class="section level2">
+<h2 id="installation-with-docker">Installation with Docker<a class="anchor" aria-label="anchor" href="#installation-with-docker"></a>
+</h2>
 <p>If you would like to install with Docker, please read following document.</p>
 <ul>
-<li><a href="https://github.com/mlverse/torch/blob/master/docker/build_env_guide.md">The way of installation with Docker</a></li>
+<li><a href="https://github.com/mlverse/torch/blob/master/docker/build_env_guide.md" class="external-link">The way of installation with Docker</a></li>
 </ul>
 </div>
-<div id="examples" class="section level2">
-<h2 class="hasAnchor">
-<a href="#examples" class="anchor"></a>Examples</h2>
+<div class="section level2">
+<h2 id="examples">Examples<a class="anchor" aria-label="anchor" href="#examples"></a>
+</h2>
 <p>You can create torch tensors from R objects with the <code>torch_tensor</code> function and convert them back to R objects with <code>as_array</code>.</p>
 <div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://torch.mlverse.org/docs">torch</a></span><span class="op">)</span>
-<span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/array.html">array</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/stats/Uniform.html">runif</a></span><span class="op">(</span><span class="fl">8</span><span class="op">)</span>, dim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">2</span>, <span class="fl">2</span><span class="op">)</span><span class="op">)</span>
+<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://torch.mlverse.org/docs">torch</a></span><span class="op">)</span>
+<span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/array.html" class="external-link">array</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/stats/Uniform.html" class="external-link">runif</a></span><span class="op">(</span><span class="fl">8</span><span class="op">)</span>, dim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">2</span>, <span class="fl">2</span><span class="op">)</span><span class="op">)</span>
 <span class="va">y</span> <span class="op">&lt;-</span> <span class="fu"><a href="reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="va">x</span>, dtype <span class="op">=</span> <span class="fu"><a href="reference/torch_dtype.html">torch_float64</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span>
 <span class="va">y</span>
 <span class="co">#&gt; torch_tensor</span>
 <span class="co">#&gt; (1,.,.) = </span>
-<span class="co">#&gt;   0.5955  0.3436</span>
-<span class="co">#&gt;   0.4946  0.4344</span>
+<span class="co">#&gt;   0.7658  0.6123</span>
+<span class="co">#&gt;   0.3150  0.4639</span>
 <span class="co">#&gt; </span>
 <span class="co">#&gt; (2,.,.) = </span>
-<span class="co">#&gt;   0.9322  0.7824</span>
-<span class="co">#&gt;   0.6503  0.7516</span>
+<span class="co">#&gt;   0.0604  0.0290</span>
+<span class="co">#&gt;   0.9553  0.6541</span>
 <span class="co">#&gt; [ CPUDoubleType{2,2,2} ]</span>
-<span class="fu"><a href="https://rdrr.io/r/base/identical.html">identical</a></span><span class="op">(</span><span class="va">x</span>, <span class="fu"><a href="reference/as_array.html">as_array</a></span><span class="op">(</span><span class="va">y</span><span class="op">)</span><span class="op">)</span>
+<span class="fu"><a href="https://rdrr.io/r/base/identical.html" class="external-link">identical</a></span><span class="op">(</span><span class="va">x</span>, <span class="fu"><a href="reference/as_array.html">as_array</a></span><span class="op">(</span><span class="va">y</span><span class="op">)</span><span class="op">)</span>
 <span class="co">#&gt; [1] TRUE</span></code></pre></div>
-<div id="simple-autograd-example" class="section level3">
-<h3 class="hasAnchor">
-<a href="#simple-autograd-example" class="anchor"></a>Simple Autograd Example</h3>
+<div class="section level3">
+<h3 id="simple-autograd-example">Simple Autograd Example<a class="anchor" aria-label="anchor" href="#simple-autograd-example"></a>
+</h3>
 <p>In the following snippet we let torch, using the autograd feature, calculate the derivatives:</p>
 <div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fl">1</span>, requires_grad <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span>
@@ -204,68 +166,82 @@
 <span class="co">#&gt; [ CPUFloatType{1} ]</span></code></pre></div>
 </div>
 </div>
-<div id="contributing" class="section level2">
-<h2 class="hasAnchor">
-<a href="#contributing" class="anchor"></a>Contributing</h2>
+<div class="section level2">
+<h2 id="contributing">Contributing<a class="anchor" aria-label="anchor" href="#contributing"></a>
+</h2>
 <p>No matter your current skills it’s possible to contribute to <code>torch</code> development. See the <a href="https://torch.mlverse.org/docs/contributing">contributing guide</a> for more information.</p>
 </div>
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-    <div class="links">
-<h2>Links</h2>
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+<h2 data-toc-skip>Links</h2>
 <ul class="list-unstyled">
-<li>Download from CRAN at <br><a href="https://cloud.r-project.org/package=torch">https://​cloud.r-project.org/​package=torch</a>
-</li>
-<li>Browse source code at <br><a href="https://github.com/mlverse/torch/">https://​github.com/​mlverse/​torch/​</a>
-</li>
-<li>Report a bug at <br><a href="https://github.com/mlverse/torch/issues">https://​github.com/​mlverse/​torch/​issues</a>
-</li>
+<li><a href="https://cloud.r-project.org/package=torch" class="external-link">View on CRAN</a></li>
+<li><a href="https://github.com/mlverse/torch/" class="external-link">Browse source code</a></li>
+<li><a href="https://github.com/mlverse/torch/issues" class="external-link">Report a bug</a></li>
 </ul>
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 <div class="license">
-<h2>License</h2>
+<h2 data-toc-skip>License</h2>
 <ul class="list-unstyled">
 <li><a href="LICENSE.html">Full license</a></li>
-<li><small><a href="https://opensource.org/licenses/mit-license.php">MIT</a> + file <a href="LICENSE-text.html">LICENSE</a></small></li>
+<li><small><a href="https://opensource.org/licenses/mit-license.php" class="external-link">MIT</a> + file <a href="LICENSE-text.html">LICENSE</a></small></li>
+</ul>
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+<div class="community">
+<h2 data-toc-skip>Community</h2>
+<ul class="list-unstyled">
+<li><a href="CONTRIBUTING.html">Contributing guide</a></li>
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+</div>
+
+<div class="citation">
+<h2 data-toc-skip>Citation</h2>
+<ul class="list-unstyled">
+<li><a href="authors.html#citation">Citing torch</a></li>
 </ul>
 </div>
+
 <div class="developers">
-<h2>Developers</h2>
+<h2 data-toc-skip>Developers</h2>
 <ul class="list-unstyled">
 <li>Daniel Falbel <br><small class="roles"> Author, maintainer, copyright holder </small>  </li>
 <li>Javier Luraschi <br><small class="roles"> Author </small>  </li>
-<li><a href="authors.html">All authors...</a></li>
+<li><a href="authors.html">More about authors...</a></li>
 </ul>
 </div>
 
-  <div class="dev-status">
-<h2>Dev status</h2>
+<div class="dev-status">
+<h2 data-toc-skip>Dev status</h2>
 <ul class="list-unstyled">
-<li><a href="https://lifecycle.r-lib.org/articles/stages.html"><img src="https://img.shields.io/badge/lifecycle-experimental-orange.svg" alt="Lifecycle: experimental"></a></li>
-<li><a href="https://CRAN.R-project.org/package=torch"><img src="https://www.r-pkg.org/badges/version/torch" alt="CRAN status"></a></li>
-<li><a href="https://cran.r-project.org/package=torch"><img src="https://cranlogs.r-pkg.org/badges/torch"></a></li>
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-    <div id="torch-061" class="section level1">
-<h1 class="page-header" data-toc-text="0.6.1">
-<a href="#torch-061" class="anchor"></a>torch 0.6.1<small> Unreleased </small>
-</h1>
-<ul>
-<li>Fixed valgrind errors on CRAN by requiring a more recent version of knitr.</li>
-<li>Updated LibTorch to version 1.9.1 (<a href='https://github.com/mlverse/torch/issues/725'>#725</a> <a href='https://github.com/hsbadr'>@hsbadr</a>)</li>
-<li>We now check if lantern DLL’s are loaded before calling any lantern function. This avoids segfaults when Lantern is not installed. (<a href='https://github.com/mlverse/torch/issues/723'>#723</a>).</li>
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-</h1>
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-<h2 class="hasAnchor">
-<a href="#breaking-changes" class="anchor"></a>Breaking changes</h2>
-<ul>
-<li>
-<code>nn_sequential</code> is now a bare <code>nn_module</code>, allowing to easily inherit from it. This is a breaking change if you used the <code>name</code> argument. The <code>name</code> behavior can be achieved by subclassing; see the tests in the PR. (<a href='https://github.com/mlverse/torch/issues/699'>#699</a>)</li>
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-<div id="new-features" class="section level2">
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-<a href="#new-features" class="anchor"></a>New features</h2>
-<ul>
-<li>Additional info is showed when printing tensors like if it requires grad and the grad fn. (<a href='https://github.com/mlverse/torch/issues/668'>#668</a>, <a href='https://github.com/mlverse/torch/issues/669'>#669</a>, <a href='https://github.com/mlverse/torch/issues/673'>#673</a>, <a href='https://github.com/mohamed-180'>@mohamed-180</a>)</li>
-<li>We can now subset <code>nn_sequential</code> modules using <code>[</code>. (<a href='https://github.com/mlverse/torch/issues/678'>#678</a>, <a href='https://github.com/mohamed-180'>@mohamed-180</a>)</li>
-<li>We now allow <code>padding='same'</code> and <code>padding='valid'</code> when using convolutions. (<a href='https://github.com/mlverse/torch/issues/679'>#679</a>)</li>
-<li>
-<code>nnf_cross_entropy</code> now uses the ATen <code>cross_entropy</code> operation directly instead of doing logsoftmax + NLLLoss. (<a href='https://github.com/mlverse/torch/issues/680'>#680</a>)</li>
-<li>Inherited classes are now persisted by subclasses. This is specially useful if you subclass <code>nn_sequential</code> and still want that the specific S3 methods still work. (<a href='https://github.com/mlverse/torch/issues/701'>#701</a>)</li>
-</ul>
-</div>
-<div id="bug-fixes" class="section level2">
-<h2 class="hasAnchor">
-<a href="#bug-fixes" class="anchor"></a>Bug fixes</h2>
-<ul>
-<li>Fixed bug when indexing with numeric vectors. (<a href='https://github.com/mlverse/torch/issues/693'>#693</a>, <a href='https://github.com/mohamed-180'>@mohamed-180</a>)</li>
-<li>Fixed bug when indexing tensors with ellipsis and a tensor. (<a href='https://github.com/mlverse/torch/issues/696'>#696</a>)</li>
-</ul>
-</div>
-<div id="documentation" class="section level2">
-<h2 class="hasAnchor">
-<a href="#documentation" class="anchor"></a>Documentation</h2>
-<ul>
-<li>Improved optimizer documentation by adding a ‘Warning’ regarding the creation and usage order. (<a href='https://github.com/mlverse/torch/issues/698'>#698</a>)</li>
-</ul>
-</div>
-</div>
-    <div id="torch-050" class="section level1">
-<h1 class="page-header" data-toc-text="0.5.0">
-<a href="#torch-050" class="anchor"></a>torch 0.5.0<small> 2021-08-17 </small>
-</h1>
-<div id="breaking-changes-1" class="section level2">
-<h2 class="hasAnchor">
-<a href="#breaking-changes-1" class="anchor"></a>Breaking changes</h2>
-<ul>
-<li>Droped support for CUDA 10.1 (<a href='https://github.com/mlverse/torch/issues/610'>#610</a>)</li>
+    <div class="section level2">
+<h2 class="pkg-version" data-toc-text="0.7.0" id="torch-070">torch 0.7.0<a class="anchor" aria-label="anchor" href="#torch-070"></a></h2><p class="text-muted">CRAN release: 2022-02-18</p>
+<div class="section level3">
+<h3 id="breaking-changes-0-7-0">Breaking changes<a class="anchor" aria-label="anchor" href="#breaking-changes-0-7-0"></a></h3>
+<ul><li>
+<code>torch_sort</code> and <code>Tensor$sort</code> now return 1-indexed results. (<a href="https://github.com/mlverse/torch/issues/709" class="external-link">#709</a>, <a href="https://github.com/mohamed-180" class="external-link">@mohamed-180</a>)</li>
+<li>Support for LibTorch 1.10.2. See also <a href="https://github.com/pytorch/pytorch/releases/tag/v1.10.0" class="external-link">release notes</a> for the PyTorch v1.10. (<a href="https://github.com/mlverse/torch/issues/758" class="external-link">#758</a>, <a href="https://github.com/mlverse/torch/issues/763" class="external-link">#763</a>, <a href="https://github.com/mlverse/torch/issues/775" class="external-link">#775</a>, <a href="https://github.com/hsbadr" class="external-link">@hsbadr</a>).</li>
+<li>Changed default <code>dim</code> from <code>1</code> to <code>2</code> in <code>nnf_cosine_similarity</code>. (<a href="https://github.com/mlverse/torch/issues/769" class="external-link">#769</a>)</li>
+<li>The default value for arguments of various functions have changed. A bug in the code generation was truncating the default values specially if they were float values that needed more than 6 digit precision. (<a href="https://github.com/mlverse/torch/issues/770" class="external-link">#770</a>)</li>
+</ul></div>
+<div class="section level3">
+<h3 id="new-features-0-7-0">New features<a class="anchor" aria-label="anchor" href="#new-features-0-7-0"></a></h3>
+<ul><li>
+<code>jit_save_for_mobile</code> allows to save a traced model in bytecode form, to be loaded by a <code>LiteModuleLoader</code>. (<a href="https://github.com/mlverse/torch/issues/713" class="external-link">#713</a>)</li>
+<li>Exported <code>is_torch_tensor</code> to check wether an object is a tensor or not. (<a href="https://github.com/mlverse/torch/issues/730" class="external-link">#730</a>, <a href="https://github.com/rdinnager" class="external-link">@rdinnager</a>)</li>
+<li>Adds <code>cuda_get_device_properties(device)</code> that allows one to query device capability and other properties. (<a href="https://github.com/mlverse/torch/issues/734" class="external-link">#734</a>, <a href="https://github.com/rdinnager" class="external-link">@rdinnager</a>)</li>
+<li>Implemented <code><a href="../reference/call_torch_function.html">call_torch_function()</a></code> to allow calling potentially unexported torch core functions. (<a href="https://github.com/mlverse/torch/issues/743" class="external-link">#743</a>, <a href="https://github.com/rdinnager" class="external-link">@rdinnager</a>)</li>
+<li>Now when installing torch all of LibTorch and Lantern headers will be installed within the <code>inst</code> directory. This will allow for packages extending torch to bind directly to its C++ library. (<a href="https://github.com/mlverse/torch/issues/718" class="external-link">#718</a>)</li>
 <li>
-<code><a href="../reference/torch_manual_seed.html">torch_manual_seed()</a></code> now matches PyTorch’s behavior so we can more easily compare implementations. Since this is a breaking change we added the <code>torch.old_seed_behavior=TRUE</code> option so users can stick to the old behavior. (<a href='https://github.com/mlverse/torch/issues/639'>#639</a>)</li>
-<li>Indexing with vectors has a now the same behavior as R indexing, making it easier to understand. Users can still use the old behavior by using <code>torch_index</code> or <code>torch_index_put</code>. (<a href='https://github.com/mlverse/torch/issues/649'>#649</a>)</li>
-</ul>
-</div>
-<div id="new-features-1" class="section level2">
-<h2 class="hasAnchor">
-<a href="#new-features-1" class="anchor"></a>New features</h2>
-<ul>
-<li>Added support for ScriptModule. Loaded JIT modules now operate as <code>nn_module</code>s. (<a href='https://github.com/mlverse/torch/issues/593'>#593</a>)</li>
-<li>Added a <code>jit_compile</code> function that allows compiling arbitrary TorchScript code into script function that can be serialized and executed. (<a href='https://github.com/mlverse/torch/issues/601'>#601</a>)</li>
-<li>Added <code>jit_trace</code> support for <code>nn_module</code> created from R. (<a href='https://github.com/mlverse/torch/issues/604'>#604</a>)</li>
-<li>Updated LibTorch to version 1.9.0 (<a href='https://github.com/mlverse/torch/issues/610'>#610</a>)</li>
-<li>Added Linear Algebra functions (<a href='https://github.com/mlverse/torch/issues/612'>#612</a>)</li>
-<li>Added <code>contrib_sort_vertices</code> to efficiently sort vertices on CUDA. (<a href='https://github.com/mlverse/torch/issues/619'>#619</a>)</li>
-<li>Allows querying the graph from traced modules. (<a href='https://github.com/mlverse/torch/issues/623'>#623</a>)</li>
-<li>Added <code>with_detect_anomaly</code> to debug autograd errors. (<a href='https://github.com/mlverse/torch/issues/628'>#628</a>)</li>
-<li>Implemented <code>traced_module$graph_for()</code> to allow inspecting the optimized jit graph. (<a href='https://github.com/mlverse/torch/issues/643'>#643</a>)</li>
-<li>Added <code>slc</code> to allow dynamically creating slices when indexing tensors. (<a href='https://github.com/mlverse/torch/issues/648'>#648</a>)</li>
-</ul>
-</div>
-<div id="bug-fixes-1" class="section level2">
-<h2 class="hasAnchor">
-<a href="#bug-fixes-1" class="anchor"></a>Bug fixes</h2>
-<ul>
-<li>Fixed a bug when using a <code>.getbatch</code> method that didn’t return a <code>torch_tensor</code>. (<a href='https://github.com/mlverse/torch/issues/615'>#615</a>)</li>
-<li>Fixed warning when using <code>%/%</code> caused by a call to deprecated <code>torch_floor_divide</code> (<a href='https://github.com/mlverse/torch/issues/616'>#616</a>)</li>
-<li>Improved CUDA version auto-detection (<a href='https://github.com/mlverse/torch/issues/644'>#644</a>)</li>
-</ul>
-</div>
-<div id="internal-changes" class="section level2">
-<h2 class="hasAnchor">
-<a href="#internal-changes" class="anchor"></a>Internal changes</h2>
-<ul>
-<li>Improved R &lt;-&gt; JIT types conversion. (<a href='https://github.com/mlverse/torch/issues/593'>#593</a>)</li>
-<li>Added Dockerfiles for CUDA 11.1 (<a href='https://github.com/mlverse/torch/issues/597'>#597</a>)</li>
-<li>A warning is raised when an incompatible dataset is passed to a parallel dataloader. (<a href='https://github.com/mlverse/torch/issues/626'>#626</a>)</li>
-<li>Additionally to calling <code>gc</code> when CUDA memory is exhausted we now call <code>R_RunPendingFinalizers</code>. This should improve memory usage, because we will now delete tensors earlier. (<a href='https://github.com/mlverse/torch/issues/654'>#654</a>)</li>
-<li>Fix rchk issues (<a href='https://github.com/mlverse/torch/issues/667'>#667</a>)</li>
-</ul>
-</div>
-</div>
-    <div id="torch-040" class="section level1">
-<h1 class="page-header" data-toc-text="0.4.0">
-<a href="#torch-040" class="anchor"></a>torch 0.4.0<small> 2021-06-10 </small>
-</h1>
-<div id="breaking-changes-2" class="section level2">
-<h2 class="hasAnchor">
-<a href="#breaking-changes-2" class="anchor"></a>Breaking changes</h2>
-<ul>
+<code>dataset_subset</code> will use the <code>.getbatch</code> method of the wrapped dataset if one is available. (<a href="https://github.com/mlverse/torch/issues/742" class="external-link">#742</a>, <a href="https://github.com/egillax" class="external-link">@egillax</a>)</li>
+<li>Added <code>nn_flatten</code> and <code>nn_unflatten</code> modules. (<a href="https://github.com/mlverse/torch/issues/773" class="external-link">#773</a>)</li>
+<li>Added <code><a href="../reference/cuda_memory_stats.html">cuda_memory_stats()</a></code> and <code><a href="../reference/cuda_memory_stats.html">cuda_memory_summary()</a></code> to verify the amount of memory torch is using from the GPU. (<a href="https://github.com/mlverse/torch/issues/774" class="external-link">#774</a>)</li>
+<li>Added <code><a href="../reference/backends_cudnn_version.html">backends_cudnn_version()</a></code> to query the CuDNN version found by torch. (<a href="https://github.com/mlverse/torch/issues/774" class="external-link">#774</a>)</li>
+</ul></div>
+<div class="section level3">
+<h3 id="bug-fixes-0-7-0">Bug fixes<a class="anchor" aria-label="anchor" href="#bug-fixes-0-7-0"></a></h3>
+<ul><li>Fixed a bug in <code>.validate_sample</code> for the <code>Distribution</code> class that would incorrectly check for tensors. (<a href="https://github.com/mlverse/torch/issues/739" class="external-link">#739</a>, <a href="https://github.com/hsbadr" class="external-link">@hsbadr</a>)</li>
+<li>Fixed memory leak when applying custom <code>autograd_function</code>s. (<a href="https://github.com/mlverse/torch/issues/750" class="external-link">#750</a>)</li>
+<li>Fixed a bug that caused <code>autograd_grad</code> to deadlock when used with custom autograd functions. (<a href="https://github.com/mlverse/torch/issues/771" class="external-link">#771</a>)</li>
+<li>Fixed a bug in <code>torch_max</code> and <code>torch_min</code> that would fail with <code>length=2</code> Tensors. (<a href="https://github.com/mlverse/torch/issues/772" class="external-link">#772</a>)</li>
+</ul></div>
+<div class="section level3">
+<h3 id="documentation-0-7-0">Documentation<a class="anchor" aria-label="anchor" href="#documentation-0-7-0"></a></h3>
+<ul><li>Improved the ‘Loading data’ vignette and datasets documentation. (<a href="https://github.com/mlverse/torch/issues/780" class="external-link">#780</a>, <a href="https://github.com/jnolis" class="external-link">@jnolis</a>)</li>
+</ul></div>
+<div class="section level3">
+<h3 id="internal-0-7-0">Internal<a class="anchor" aria-label="anchor" href="#internal-0-7-0"></a></h3>
+<ul><li>Refactored the internal Lantern types and Rcpp types and made clearer which are the exported types that can be used in the C++ extensions. (<a href="https://github.com/mlverse/torch/issues/718" class="external-link">#718</a>)</li>
+<li>Simplified concurrency related constructs in autograd. (<a href="https://github.com/mlverse/torch/issues/755" class="external-link">#755</a>, <a href="https://github.com/yitao-li" class="external-link">@yitao-li</a>)</li>
+<li>R and C++ code cleanup, styling, and formatting. (<a href="https://github.com/mlverse/torch/issues/753" class="external-link">#753</a>, <a href="https://github.com/hsbadr" class="external-link">@hsbadr</a>)</li>
+<li>Dataloaders are slightly faster with a new transpose function. (<a href="https://github.com/mlverse/torch/issues/783" class="external-link">#783</a>)</li>
 <li>
-<code>torch_multinomial</code> now returns 1-based indexes to comply with 1-based indexing across torch. (<a href='https://github.com/mlverse/torch/issues/588'>#588</a>)</li>
-</ul>
-</div>
-<div id="new-features-2" class="section level2">
-<h2 class="hasAnchor">
-<a href="#new-features-2" class="anchor"></a>New features</h2>
-<ul>
-<li>Added parameter to multihead attention module to allow output of unaveraged attention weights. (<a href='https://github.com/jonathanbratt'>@jonathanbratt</a> <a href='https://github.com/mlverse/torch/issues/542'>#542</a>)</li>
-<li>We now allow <code>jit_trace</code> functions with more than 1 argument. (<a href='https://github.com/mlverse/torch/issues/544'>#544</a>)</li>
-<li>Added Multivariate normal distribution (<a href='https://github.com/mlverse/torch/issues/552'>#552</a>)</li>
-<li>Export the <code>torch_diff</code> function and added docs for it. (<a href='https://github.com/mlverse/torch/issues/565'>#565</a>)</li>
-<li>Added a <code>device</code> argument to <code><a href="../reference/torch_load.html">torch_load()</a></code> allowing one to select to which device parameters should be loaded. (<a href='https://github.com/mlverse/torch/issues/578'>#578</a>)</li>
-<li>Added <code><a href="../reference/distr_categorical.html">distr_categorical()</a></code> (<a href='https://github.com/mlverse/torch/issues/576'>#576</a>)</li>
-<li>Added <code><a href="../reference/distr_mixture_same_family.html">distr_mixture_same_family()</a></code> (<a href='https://github.com/mlverse/torch/issues/576'>#576</a>)</li>
-<li>Improve handling of optimizers state and implement <code><a href="../reference/load_state_dict.html">load_state_dict()</a></code> and <code>state_dict()</code> for optimizers. (<a href='https://github.com/mlverse/torch/issues/585'>#585</a>)</li>
-<li>Added the ability to save R <code>list</code>s containing <code>torch_tensor</code>s using <code>torch_save</code>. This allows us to save the state of optimizers and modules using <code><a href="../reference/torch_save.html">torch_save()</a></code>. (<a href='https://github.com/mlverse/torch/issues/586'>#586</a>)</li>
-</ul>
-</div>
-<div id="bug-fixes-2" class="section level2">
-<h2 class="hasAnchor">
-<a href="#bug-fixes-2" class="anchor"></a>Bug fixes</h2>
-<ul>
-<li>Fixed bug in <code>nn_multihead_attention</code> when q,k,v inputs not all the same. (<a href='https://github.com/jonathanbratt'>@jonathanbratt</a> <a href='https://github.com/mlverse/torch/issues/540'>#540</a>)</li>
-<li>Fixed <code>$copy_</code> so it correctly respects the src <code>requires_grad()</code> when reloading saved models with <code><a href="../reference/torch_load.html">torch_load()</a></code>. (<a href='https://github.com/mlverse/torch/issues/545'>#545</a>)</li>
-<li>Fixed <code><a href="../reference/nn_init_xavier_normal_.html">nn_init_xavier_normal_()</a></code> and <code><a href="../reference/nn_init_xavier_uniform_.html">nn_init_xavier_uniform_()</a></code> standard deviation calculation. (<a href='https://github.com/mlverse/torch/issues/557'>#557</a>)</li>
-<li>Fixed bug in <code><a href="../reference/torch_tensordot.html">torch_tensordot()</a></code> when called when infering dimensions. (<a href='https://github.com/mlverse/torch/issues/563'>#563</a>)</li>
-<li>Dataset’s <code>.getbatch</code> now takes an integer vector as input instead of a <code><a href="https://rdrr.io/r/base/list.html">list()</a></code>. (<a href='https://github.com/mlverse/torch/issues/572'>#572</a>)</li>
-<li>Fixed bug with <code>tensor$size()</code> when indexing with negative numbers. (<a href='https://github.com/mlverse/torch/issues/570'>#570</a>)</li>
-<li>Fixed bug in the <code>log_prob</code> of <code><a href="../reference/distr_bernoulli.html">distr_bernoulli()</a></code> (<a href='https://github.com/mlverse/torch/issues/581'>#581</a>)</li>
-</ul>
-</div>
-<div id="internal-changes-1" class="section level2">
-<h2 class="hasAnchor">
-<a href="#internal-changes-1" class="anchor"></a>Internal changes</h2>
-<ul>
-<li>Better handling optional Tensor arguments by using an explicit <code>XPtrTorchOptionalTensor</code> class. (<a href='https://github.com/mlverse/torch/issues/565'>#565</a>)</li>
-<li>Tensors in the R side that point to the same C++ Tensor are now guaranteed to be the same object. This allows to easily determine unique model parameters. (<a href='https://github.com/mlverse/torch/issues/582'>#582</a>)</li>
-</ul>
-</div>
-</div>
-    <div id="torch-030" class="section level1">
-<h1 class="page-header" data-toc-text="0.3.0">
-<a href="#torch-030" class="anchor"></a>torch 0.3.0<small> 2021-04-28 </small>
-</h1>
-<div id="breaking-changes-3" class="section level2">
-<h2 class="hasAnchor">
-<a href="#breaking-changes-3" class="anchor"></a>Breaking changes</h2>
-<ul>
+<code>torch_tensor</code> is now a C++ only function slighly increasing performance in a few situations. (<a href="https://github.com/mlverse/torch/issues/784" class="external-link">#784</a>)</li>
+</ul></div>
+</div>
+    <div class="section level2">
+<h2 class="pkg-version" data-toc-text="0.6.1" id="torch-061">torch 0.6.1<a class="anchor" aria-label="anchor" href="#torch-061"></a></h2>
+<div class="section level3">
+<h3 id="new-features-0-6-1">New features<a class="anchor" aria-label="anchor" href="#new-features-0-6-1"></a></h3>
+<ul><li>Fixed valgrind errors on CRAN by requiring a more recent version of knitr.</li>
+<li>Updated LibTorch to version 1.9.1 (<a href="https://github.com/mlverse/torch/issues/725" class="external-link">#725</a> <a href="https://github.com/hsbadr" class="external-link">@hsbadr</a>)</li>
+<li>We now check if lantern DLL’s are loaded before calling any lantern function. This avoids segfaults when Lantern is not installed. (<a href="https://github.com/mlverse/torch/issues/723" class="external-link">#723</a>).</li>
+</ul></div>
+</div>
+    <div class="section level2">
+<h2 class="pkg-version" data-toc-text="0.6.0" id="torch-060">torch 0.6.0<a class="anchor" aria-label="anchor" href="#torch-060"></a></h2><p class="text-muted">CRAN release: 2021-10-07</p>
+<div class="section level3">
+<h3 id="breaking-changes-0-6-0">Breaking changes<a class="anchor" aria-label="anchor" href="#breaking-changes-0-6-0"></a></h3>
+<ul><li>
+<code>nn_sequential</code> is now a bare <code>nn_module</code>, allowing to easily inherit from it. This is a breaking change if you used the <code>name</code> argument. The <code>name</code> behavior can be achieved by subclassing; see the tests in the PR. (<a href="https://github.com/mlverse/torch/issues/699" class="external-link">#699</a>)</li>
+</ul></div>
+<div class="section level3">
+<h3 id="new-features-0-6-0">New features<a class="anchor" aria-label="anchor" href="#new-features-0-6-0"></a></h3>
+<ul><li>Additional info is showed when printing tensors like if it requires grad and the grad fn. (<a href="https://github.com/mlverse/torch/issues/668" class="external-link">#668</a>, <a href="https://github.com/mlverse/torch/issues/669" class="external-link">#669</a>, <a href="https://github.com/mlverse/torch/issues/673" class="external-link">#673</a>, <a href="https://github.com/mohamed-180" class="external-link">@mohamed-180</a>)</li>
+<li>We can now subset <code>nn_sequential</code> modules using <code>[</code>. (<a href="https://github.com/mlverse/torch/issues/678" class="external-link">#678</a>, <a href="https://github.com/mohamed-180" class="external-link">@mohamed-180</a>)</li>
+<li>We now allow <code>padding='same'</code> and <code>padding='valid'</code> when using convolutions. (<a href="https://github.com/mlverse/torch/issues/679" class="external-link">#679</a>)</li>
 <li>
-<code>torch_nonzero</code> and <code>tensor$nonzero()</code> now return 1-based indexes. (<a href='https://github.com/mlverse/torch/issues/432'>#432</a>)</li>
-<li>Breaking change: <code>torch_arange</code> returns in the closed interval <code>[start, end]</code> instead of the half open <code>[start, end)</code>. This makes it behave similar to R’s <code>seq</code>. (<a href='https://github.com/mlverse/torch/issues/506'>#506</a>)</li>
-</ul>
-</div>
-<div id="new-features-3" class="section level2">
-<h2 class="hasAnchor">
-<a href="#new-features-3" class="anchor"></a>New features</h2>
-<ul>
+<code>nnf_cross_entropy</code> now uses the ATen <code>cross_entropy</code> operation directly instead of doing logsoftmax + NLLLoss. (<a href="https://github.com/mlverse/torch/issues/680" class="external-link">#680</a>)</li>
+<li>Inherited classes are now persisted by subclasses. This is specially useful if you subclass <code>nn_sequential</code> and still want that the specific S3 methods still work. (<a href="https://github.com/mlverse/torch/issues/701" class="external-link">#701</a>)</li>
+</ul></div>
+<div class="section level3">
+<h3 id="bug-fixes-0-6-0">Bug fixes<a class="anchor" aria-label="anchor" href="#bug-fixes-0-6-0"></a></h3>
+<ul><li>Fixed bug when indexing with numeric vectors. (<a href="https://github.com/mlverse/torch/issues/693" class="external-link">#693</a>, <a href="https://github.com/mohamed-180" class="external-link">@mohamed-180</a>)</li>
+<li>Fixed bug when indexing tensors with ellipsis and a tensor. (<a href="https://github.com/mlverse/torch/issues/696" class="external-link">#696</a>)</li>
+</ul></div>
+<div class="section level3">
+<h3 id="documentation-0-6-0">Documentation<a class="anchor" aria-label="anchor" href="#documentation-0-6-0"></a></h3>
+<ul><li>Improved optimizer documentation by adding a ‘Warning’ regarding the creation and usage order. (<a href="https://github.com/mlverse/torch/issues/698" class="external-link">#698</a>)</li>
+</ul></div>
+</div>
+    <div class="section level2">
+<h2 class="pkg-version" data-toc-text="0.5.0" id="torch-050">torch 0.5.0<a class="anchor" aria-label="anchor" href="#torch-050"></a></h2><p class="text-muted">CRAN release: 2021-08-17</p>
+<div class="section level3">
+<h3 id="breaking-changes-0-5-0">Breaking changes<a class="anchor" aria-label="anchor" href="#breaking-changes-0-5-0"></a></h3>
+<ul><li>Droped support for CUDA 10.1 (<a href="https://github.com/mlverse/torch/issues/610" class="external-link">#610</a>)</li>
 <li>
-<code>torch_split</code> now accepts a list of sizes as well as a fixed size. (<a href='https://github.com/mlverse/torch/issues/429'>#429</a>)</li>
-<li>Added <code>nn_layer_norm</code>. (<a href='https://github.com/mlverse/torch/issues/435'>#435</a>)</li>
-<li>Allow <code>timeout=360</code> as <code><a href="../reference/install_torch.html">install_torch()</a></code> parameter for large file download (<a href='https://github.com/cregouby'>@cregouby</a> <a href='https://github.com/mlverse/torch/issues/438'>#438</a>)</li>
-<li>Added <code><a href="../reference/install_torch_from_file.html">install_torch_from_file()</a></code> and <code><a href="../reference/get_install_libs_url.html">get_install_libs_url()</a></code>for setup cases where direct download is not possible (<a href='https://github.com/cregouby'>@cregouby</a> <a href='https://github.com/mlverse/torch/issues/439'>#439</a>)</li>
-<li>Added <code>mean.torch_tensor</code> (<a href='https://github.com/mlverse/torch/issues/448'>#448</a>)</li>
-<li>New arguments <code>worker_globals</code> and <code>worker_packages</code> allowing to easily pass objects to workers in parallel dataloaders (<a href='https://github.com/mlverse/torch/issues/449'>#449</a>).</li>
-<li>We now call R garbage collector when there’s no memory available on GPU, this can help in a few cases when the laziness of the garbage collector allows too many tensors to be on memory even though they are no longer referenced in R. (<a href='https://github.com/mlverse/torch/issues/456'>#456</a>)</li>
-<li>Implemented <code>nn_group_norm</code> and fixed a bug in <code>nnf_group_norm</code> (<a href='https://github.com/mlverse/torch/issues/474'>#474</a>)</li>
-<li>Added backend functions allowing us to query which optimizations LibTorch was compiled with (<a href='https://github.com/mlverse/torch/issues/476'>#476</a>)</li>
-<li>Added normal distribution (<a href='https://github.com/mlverse/torch/issues/462'>#462</a>)</li>
-<li>Added bernoulli distribution (<a href='https://github.com/mlverse/torch/issues/484'>#484</a>)</li>
+<code><a href="../reference/torch_manual_seed.html">torch_manual_seed()</a></code> now matches PyTorch’s behavior so we can more easily compare implementations. Since this is a breaking change we added the <code>torch.old_seed_behavior=TRUE</code> option so users can stick to the old behavior. (<a href="https://github.com/mlverse/torch/issues/639" class="external-link">#639</a>)</li>
+<li>Indexing with vectors has a now the same behavior as R indexing, making it easier to understand. Users can still use the old behavior by using <code>torch_index</code> or <code>torch_index_put</code>. (<a href="https://github.com/mlverse/torch/issues/649" class="external-link">#649</a>)</li>
+</ul></div>
+<div class="section level3">
+<h3 id="new-features-0-5-0">New features<a class="anchor" aria-label="anchor" href="#new-features-0-5-0"></a></h3>
+<ul><li>Added support for ScriptModule. Loaded JIT modules now operate as <code>nn_module</code>s. (<a href="https://github.com/mlverse/torch/issues/593" class="external-link">#593</a>)</li>
+<li>Added a <code>jit_compile</code> function that allows compiling arbitrary TorchScript code into script function that can be serialized and executed. (<a href="https://github.com/mlverse/torch/issues/601" class="external-link">#601</a>)</li>
+<li>Added <code>jit_trace</code> support for <code>nn_module</code> created from R. (<a href="https://github.com/mlverse/torch/issues/604" class="external-link">#604</a>)</li>
+<li>Updated LibTorch to version 1.9.0 (<a href="https://github.com/mlverse/torch/issues/610" class="external-link">#610</a>)</li>
+<li>Added Linear Algebra functions (<a href="https://github.com/mlverse/torch/issues/612" class="external-link">#612</a>)</li>
+<li>Added <code>contrib_sort_vertices</code> to efficiently sort vertices on CUDA. (<a href="https://github.com/mlverse/torch/issues/619" class="external-link">#619</a>)</li>
+<li>Allows querying the graph from traced modules. (<a href="https://github.com/mlverse/torch/issues/623" class="external-link">#623</a>)</li>
+<li>Added <code>with_detect_anomaly</code> to debug autograd errors. (<a href="https://github.com/mlverse/torch/issues/628" class="external-link">#628</a>)</li>
+<li>Implemented <code>traced_module$graph_for()</code> to allow inspecting the optimized jit graph. (<a href="https://github.com/mlverse/torch/issues/643" class="external-link">#643</a>)</li>
+<li>Added <code>slc</code> to allow dynamically creating slices when indexing tensors. (<a href="https://github.com/mlverse/torch/issues/648" class="external-link">#648</a>)</li>
+</ul></div>
+<div class="section level3">
+<h3 id="bug-fixes-0-5-0">Bug fixes<a class="anchor" aria-label="anchor" href="#bug-fixes-0-5-0"></a></h3>
+<ul><li>Fixed a bug when using a <code>.getbatch</code> method that didn’t return a <code>torch_tensor</code>. (<a href="https://github.com/mlverse/torch/issues/615" class="external-link">#615</a>)</li>
+<li>Fixed warning when using <code>%/%</code> caused by a call to deprecated <code>torch_floor_divide</code> (<a href="https://github.com/mlverse/torch/issues/616" class="external-link">#616</a>)</li>
+<li>Improved CUDA version auto-detection (<a href="https://github.com/mlverse/torch/issues/644" class="external-link">#644</a>)</li>
+</ul></div>
+<div class="section level3">
+<h3 id="internal-changes-0-5-0">Internal changes<a class="anchor" aria-label="anchor" href="#internal-changes-0-5-0"></a></h3>
+<ul><li>Improved R &lt;-&gt; JIT types conversion. (<a href="https://github.com/mlverse/torch/issues/593" class="external-link">#593</a>)</li>
+<li>Added Dockerfiles for CUDA 11.1 (<a href="https://github.com/mlverse/torch/issues/597" class="external-link">#597</a>)</li>
+<li>A warning is raised when an incompatible dataset is passed to a parallel dataloader. (<a href="https://github.com/mlverse/torch/issues/626" class="external-link">#626</a>)</li>
+<li>Additionally to calling <code>gc</code> when CUDA memory is exhausted we now call <code>R_RunPendingFinalizers</code>. This should improve memory usage, because we will now delete tensors earlier. (<a href="https://github.com/mlverse/torch/issues/654" class="external-link">#654</a>)</li>
+<li>Fix rchk issues (<a href="https://github.com/mlverse/torch/issues/667" class="external-link">#667</a>)</li>
+</ul></div>
+</div>
+    <div class="section level2">
+<h2 class="pkg-version" data-toc-text="0.4.0" id="torch-040">torch 0.4.0<a class="anchor" aria-label="anchor" href="#torch-040"></a></h2><p class="text-muted">CRAN release: 2021-06-10</p>
+<div class="section level3">
+<h3 id="breaking-changes-0-4-0">Breaking changes<a class="anchor" aria-label="anchor" href="#breaking-changes-0-4-0"></a></h3>
+<ul><li>
+<code>torch_multinomial</code> now returns 1-based indexes to comply with 1-based indexing across torch. (<a href="https://github.com/mlverse/torch/issues/588" class="external-link">#588</a>)</li>
+</ul></div>
+<div class="section level3">
+<h3 id="new-features-0-4-0">New features<a class="anchor" aria-label="anchor" href="#new-features-0-4-0"></a></h3>
+<ul><li>Added parameter to multihead attention module to allow output of unaveraged attention weights. (<a href="https://github.com/jonathanbratt" class="external-link">@jonathanbratt</a> <a href="https://github.com/mlverse/torch/issues/542" class="external-link">#542</a>)</li>
+<li>We now allow <code>jit_trace</code> functions with more than 1 argument. (<a href="https://github.com/mlverse/torch/issues/544" class="external-link">#544</a>)</li>
+<li>Added Multivariate normal distribution (<a href="https://github.com/mlverse/torch/issues/552" class="external-link">#552</a>)</li>
+<li>Export the <code>torch_diff</code> function and added docs for it. (<a href="https://github.com/mlverse/torch/issues/565" class="external-link">#565</a>)</li>
+<li>Added a <code>device</code> argument to <code><a href="../reference/torch_load.html">torch_load()</a></code> allowing one to select to which device parameters should be loaded. (<a href="https://github.com/mlverse/torch/issues/578" class="external-link">#578</a>)</li>
+<li>Added <code><a href="../reference/distr_categorical.html">distr_categorical()</a></code> (<a href="https://github.com/mlverse/torch/issues/576" class="external-link">#576</a>)</li>
+<li>Added <code><a href="../reference/distr_mixture_same_family.html">distr_mixture_same_family()</a></code> (<a href="https://github.com/mlverse/torch/issues/576" class="external-link">#576</a>)</li>
+<li>Improve handling of optimizers state and implement <code><a href="../reference/load_state_dict.html">load_state_dict()</a></code> and <code>state_dict()</code> for optimizers. (<a href="https://github.com/mlverse/torch/issues/585" class="external-link">#585</a>)</li>
+<li>Added the ability to save R <code>list</code>s containing <code>torch_tensor</code>s using <code>torch_save</code>. This allows us to save the state of optimizers and modules using <code><a href="../reference/torch_save.html">torch_save()</a></code>. (<a href="https://github.com/mlverse/torch/issues/586" class="external-link">#586</a>)</li>
+</ul></div>
+<div class="section level3">
+<h3 id="bug-fixes-0-4-0">Bug fixes<a class="anchor" aria-label="anchor" href="#bug-fixes-0-4-0"></a></h3>
+<ul><li>Fixed bug in <code>nn_multihead_attention</code> when q,k,v inputs not all the same. (<a href="https://github.com/jonathanbratt" class="external-link">@jonathanbratt</a> <a href="https://github.com/mlverse/torch/issues/540" class="external-link">#540</a>)</li>
+<li>Fixed <code>$copy_</code> so it correctly respects the src <code>requires_grad()</code> when reloading saved models with <code><a href="../reference/torch_load.html">torch_load()</a></code>. (<a href="https://github.com/mlverse/torch/issues/545" class="external-link">#545</a>)</li>
+<li>Fixed <code><a href="../reference/nn_init_xavier_normal_.html">nn_init_xavier_normal_()</a></code> and <code><a href="../reference/nn_init_xavier_uniform_.html">nn_init_xavier_uniform_()</a></code> standard deviation calculation. (<a href="https://github.com/mlverse/torch/issues/557" class="external-link">#557</a>)</li>
+<li>Fixed bug in <code><a href="../reference/torch_tensordot.html">torch_tensordot()</a></code> when called when infering dimensions. (<a href="https://github.com/mlverse/torch/issues/563" class="external-link">#563</a>)</li>
+<li>Dataset’s <code>.getbatch</code> now takes an integer vector as input instead of a <code><a href="https://rdrr.io/r/base/list.html" class="external-link">list()</a></code>. (<a href="https://github.com/mlverse/torch/issues/572" class="external-link">#572</a>)</li>
+<li>Fixed bug with <code>tensor$size()</code> when indexing with negative numbers. (<a href="https://github.com/mlverse/torch/issues/570" class="external-link">#570</a>)</li>
+<li>Fixed bug in the <code>log_prob</code> of <code><a href="../reference/distr_bernoulli.html">distr_bernoulli()</a></code> (<a href="https://github.com/mlverse/torch/issues/581" class="external-link">#581</a>)</li>
+</ul></div>
+<div class="section level3">
+<h3 id="internal-changes-0-4-0">Internal changes<a class="anchor" aria-label="anchor" href="#internal-changes-0-4-0"></a></h3>
+<ul><li>Better handling optional Tensor arguments by using an explicit <code>XPtrTorchOptionalTensor</code> class. (<a href="https://github.com/mlverse/torch/issues/565" class="external-link">#565</a>)</li>
+<li>Tensors in the R side that point to the same C++ Tensor are now guaranteed to be the same object. This allows to easily determine unique model parameters. (<a href="https://github.com/mlverse/torch/issues/582" class="external-link">#582</a>)</li>
+</ul></div>
+</div>
+    <div class="section level2">
+<h2 class="pkg-version" data-toc-text="0.3.0" id="torch-030">torch 0.3.0<a class="anchor" aria-label="anchor" href="#torch-030"></a></h2><p class="text-muted">CRAN release: 2021-04-28</p>
+<div class="section level3">
+<h3 id="breaking-changes-0-3-0">Breaking changes<a class="anchor" aria-label="anchor" href="#breaking-changes-0-3-0"></a></h3>
+<ul><li>
+<code>torch_nonzero</code> and <code>tensor$nonzero()</code> now return 1-based indexes. (<a href="https://github.com/mlverse/torch/issues/432" class="external-link">#432</a>)</li>
+<li>Breaking change: <code>torch_arange</code> returns in the closed interval <code>[start, end]</code> instead of the half open <code>[start, end)</code>. This makes it behave similar to R’s <code>seq</code>. (<a href="https://github.com/mlverse/torch/issues/506" class="external-link">#506</a>)</li>
+</ul></div>
+<div class="section level3">
+<h3 id="new-features-0-3-0">New features<a class="anchor" aria-label="anchor" href="#new-features-0-3-0"></a></h3>
+<ul><li>
+<code>torch_split</code> now accepts a list of sizes as well as a fixed size. (<a href="https://github.com/mlverse/torch/issues/429" class="external-link">#429</a>)</li>
+<li>Added <code>nn_layer_norm</code>. (<a href="https://github.com/mlverse/torch/issues/435" class="external-link">#435</a>)</li>
+<li>Allow <code>timeout=360</code> as <code><a href="../reference/install_torch.html">install_torch()</a></code> parameter for large file download (<a href="https://github.com/cregouby" class="external-link">@cregouby</a> <a href="https://github.com/mlverse/torch/issues/438" class="external-link">#438</a>)</li>
+<li>Added <code><a href="../reference/install_torch_from_file.html">install_torch_from_file()</a></code> and <code><a href="../reference/get_install_libs_url.html">get_install_libs_url()</a></code>for setup cases where direct download is not possible (<a href="https://github.com/cregouby" class="external-link">@cregouby</a> <a href="https://github.com/mlverse/torch/issues/439" class="external-link">#439</a>)</li>
+<li>Added <code>mean.torch_tensor</code> (<a href="https://github.com/mlverse/torch/issues/448" class="external-link">#448</a>)</li>
+<li>New arguments <code>worker_globals</code> and <code>worker_packages</code> allowing to easily pass objects to workers in parallel dataloaders (<a href="https://github.com/mlverse/torch/issues/449" class="external-link">#449</a>).</li>
+<li>We now call R garbage collector when there’s no memory available on GPU, this can help in a few cases when the laziness of the garbage collector allows too many tensors to be on memory even though they are no longer referenced in R. (<a href="https://github.com/mlverse/torch/issues/456" class="external-link">#456</a>)</li>
+<li>Implemented <code>nn_group_norm</code> and fixed a bug in <code>nnf_group_norm</code> (<a href="https://github.com/mlverse/torch/issues/474" class="external-link">#474</a>)</li>
+<li>Added backend functions allowing us to query which optimizations LibTorch was compiled with (<a href="https://github.com/mlverse/torch/issues/476" class="external-link">#476</a>)</li>
+<li>Added normal distribution (<a href="https://github.com/mlverse/torch/issues/462" class="external-link">#462</a>)</li>
+<li>Added bernoulli distribution (<a href="https://github.com/mlverse/torch/issues/484" class="external-link">#484</a>)</li>
 <li>
-<code>as.list</code> for <code>nn_modules</code> (<a href='https://github.com/mlverse/torch/issues/492'>#492</a>)</li>
-<li>Enumerate support in Bernoulli distribution (<a href='https://github.com/mlverse/torch/issues/490'>#490</a>)</li>
-<li>Added Poisson Distriibution (<a href='https://github.com/mlverse/torch/issues/495'>#495</a>)</li>
-<li>Allow optional .getbatch in datasets/dataloaders (<a href='https://github.com/mlverse/torch/issues/498'>#498</a>)</li>
+<code>as.list</code> for <code>nn_modules</code> (<a href="https://github.com/mlverse/torch/issues/492" class="external-link">#492</a>)</li>
+<li>Enumerate support in Bernoulli distribution (<a href="https://github.com/mlverse/torch/issues/490" class="external-link">#490</a>)</li>
+<li>Added Poisson Distriibution (<a href="https://github.com/mlverse/torch/issues/495" class="external-link">#495</a>)</li>
+<li>Allow optional .getbatch in datasets/dataloaders (<a href="https://github.com/mlverse/torch/issues/498" class="external-link">#498</a>)</li>
 <li>
-<code>nn_lstm</code>, <code>nn_gru</code> and <code>nn_gru</code> can now use cudnn accelerations when available (<a href='https://github.com/mlverse/torch/issues/503'>#503</a>).</li>
-<li>Added Gamma distribution (<a href='https://github.com/mlverse/torch/issues/489'>#489</a>)</li>
-<li>We now respect the TORCH_HOME env var to automatically install torch. (<a href='https://github.com/mlverse/torch/issues/522'>#522</a>)</li>
-<li>Implement comparison operator <code>!=</code> for torch dtypes. (<a href='https://github.com/mlverse/torch/issues/524'>#524</a>)</li>
-<li>Added Chi-square distribution. (<a href='https://github.com/mlverse/torch/issues/518'>#518</a>)</li>
-<li>Added <code>optimizer</code> function allowing to easily implement custom optimizers. (<a href='https://github.com/mlverse/torch/issues/527'>#527</a>)</li>
-</ul>
-</div>
-<div id="bug-fixes-3" class="section level2">
-<h2 class="hasAnchor">
-<a href="#bug-fixes-3" class="anchor"></a>Bug fixes</h2>
-<ul>
-<li>Fixed bug in <code>optim_lbfgs</code> that would make model objects exponentially big. (<a href='https://github.com/mlverse/torch/issues/431'>#431</a>)</li>
-<li>Correctly handle <code>NaN</code>s in L-BFGS optimizer (<a href='https://github.com/mlverse/torch/issues/433'>#433</a>)</li>
-<li>The default collate function now respects the data type when converting to a tensor (if the dataset returns an R object) (<a href='https://github.com/mlverse/torch/issues/434'>#434</a>)</li>
-<li>Fixed <code>torch_normal</code>. (<a href='https://github.com/mlverse/torch/issues/450'>#450</a>)</li>
-<li>Fixed backward compatibility issue when loading models saved in older versions of torch. This bug was introduced in <a href='https://github.com/mlverse/torch/issues/452'>#452</a> and is now fixed and we also added a regression test. (<a href='https://github.com/mlverse/torch/issues/458'>#458</a>)</li>
-<li>Fixed bug when using RNN’s on the GPU (<a href='https://github.com/mlverse/torch/issues/460'>#460</a>)</li>
-<li>Found and fixed some memory leaks, specially when creating datatypes from strings and when saving models with <code>torch_save</code>. (<a href='https://github.com/mlverse/torch/issues/454'>#454</a>)</li>
-<li>Fixed bug in <code>nnf_pad</code> when using <code>mode='circular'</code>. (<a href='https://github.com/mlverse/torch/issues/471'>#471</a>)</li>
-<li>Bugfixes in <code>nn_multihead_attention</code> (<a href='https://github.com/mlverse/torch/issues/496'>#496</a>)</li>
-<li>Fixed bug when using packed sequences with <code>nn_lstm</code> (<a href='https://github.com/mlverse/torch/issues/500'>#500</a>)</li>
-<li>Fixed bug in the <code>to</code> method of <code>nn_module</code> that would reset the <code>requires_grad</code> attribute of parameters. (<a href='https://github.com/mlverse/torch/issues/501'>#501</a>)</li>
-<li>Added <code>strong_wolfe</code> option to <code>optim_lbfgs</code>. (<a href='https://github.com/mlverse/torch/issues/517'>#517</a>)</li>
-<li>Fixed default argument of <code>nn_init_trunc_normal_</code> initializer function. (<a href='https://github.com/mlverse/torch/issues/535'>#535</a>)</li>
-</ul>
-</div>
-<div id="documentation-1" class="section level2">
-<h2 class="hasAnchor">
-<a href="#documentation-1" class="anchor"></a>Documentation</h2>
-<ul>
-<li>Added vignette on reading models from Python (<a href='https://github.com/mlverse/torch/issues/469'>#469</a>)</li>
-</ul>
-</div>
-<div id="internal-changes-2" class="section level2">
-<h2 class="hasAnchor">
-<a href="#internal-changes-2" class="anchor"></a>Internal changes</h2>
-<ul>
-<li>Removed the PerformanceReporter from tests to get easier to read stack traces. (<a href='https://github.com/mlverse/torch/issues/449'>#449</a>)</li>
-<li>Internal change in the R7 classes so R7 objects are simple external pointer instead of environments. This might cause breaking change if you relied on saving any kind of state in the Tensor object. (<a href='https://github.com/mlverse/torch/issues/452'>#452</a>)</li>
-<li>Internal refactoring making Rcpp aware of some XPtrTorch* types so making it simpler to return them from Rcpp code. This might cause a breaking change if you are relying on <code><a href="../reference/torch_dtype.html">torch_dtype()</a></code> being an R6 class. (<a href='https://github.com/mlverse/torch/issues/451'>#451</a>)</li>
-<li>Internal changes to auto unwrap arguments from SEXP’s in Rcpp. This will make easier to move the dispatcher system to C++ in the future, but already allows us to gain ~30% speedups in small operations. (<a href='https://github.com/mlverse/torch/issues/454'>#454</a>)</li>
-<li>Added a Windows GPU CI workflow (<a href='https://github.com/mlverse/torch/issues/508'>#508</a>).</li>
-<li>Update to LibTorch v1.8 (<a href='https://github.com/mlverse/torch/issues/513'>#513</a>)</li>
-<li>Moved some parts of the dispatcher to C++ to make it faster. (<a href='https://github.com/mlverse/torch/issues/520'>#520</a>)</li>
-</ul>
-</div>
-</div>
-    <div id="torch-021" class="section level1">
-<h1 class="page-header" data-toc-text="0.2.1">
-<a href="#torch-021" class="anchor"></a>torch 0.2.1<small> 2021-01-05 </small>
-</h1>
-<div id="breaking-changes-4" class="section level2">
-<h2 class="hasAnchor">
-<a href="#breaking-changes-4" class="anchor"></a>Breaking changes</h2>
-<ul>
-<li>Made <code>torch_one_hot</code> and <code>nnf_one_hot</code> use 1-based indexing. (<a href='https://github.com/mlverse/torch/issues/410'>#410</a>)</li>
+<code>nn_lstm</code>, <code>nn_gru</code> and <code>nn_gru</code> can now use cudnn accelerations when available (<a href="https://github.com/mlverse/torch/issues/503" class="external-link">#503</a>).</li>
+<li>Added Gamma distribution (<a href="https://github.com/mlverse/torch/issues/489" class="external-link">#489</a>)</li>
+<li>We now respect the TORCH_HOME env var to automatically install torch. (<a href="https://github.com/mlverse/torch/issues/522" class="external-link">#522</a>)</li>
+<li>Implement comparison operator <code>!=</code> for torch dtypes. (<a href="https://github.com/mlverse/torch/issues/524" class="external-link">#524</a>)</li>
+<li>Added Chi-square distribution. (<a href="https://github.com/mlverse/torch/issues/518" class="external-link">#518</a>)</li>
+<li>Added <code>optimizer</code> function allowing to easily implement custom optimizers. (<a href="https://github.com/mlverse/torch/issues/527" class="external-link">#527</a>)</li>
+</ul></div>
+<div class="section level3">
+<h3 id="bug-fixes-0-3-0">Bug fixes<a class="anchor" aria-label="anchor" href="#bug-fixes-0-3-0"></a></h3>
+<ul><li>Fixed bug in <code>optim_lbfgs</code> that would make model objects exponentially big. (<a href="https://github.com/mlverse/torch/issues/431" class="external-link">#431</a>)</li>
+<li>Correctly handle <code>NaN</code>s in L-BFGS optimizer (<a href="https://github.com/mlverse/torch/issues/433" class="external-link">#433</a>)</li>
+<li>The default collate function now respects the data type when converting to a tensor (if the dataset returns an R object) (<a href="https://github.com/mlverse/torch/issues/434" class="external-link">#434</a>)</li>
+<li>Fixed <code>torch_normal</code>. (<a href="https://github.com/mlverse/torch/issues/450" class="external-link">#450</a>)</li>
+<li>Fixed backward compatibility issue when loading models saved in older versions of torch. This bug was introduced in <a href="https://github.com/mlverse/torch/issues/452" class="external-link">#452</a> and is now fixed and we also added a regression test. (<a href="https://github.com/mlverse/torch/issues/458" class="external-link">#458</a>)</li>
+<li>Fixed bug when using RNN’s on the GPU (<a href="https://github.com/mlverse/torch/issues/460" class="external-link">#460</a>)</li>
+<li>Found and fixed some memory leaks, specially when creating datatypes from strings and when saving models with <code>torch_save</code>. (<a href="https://github.com/mlverse/torch/issues/454" class="external-link">#454</a>)</li>
+<li>Fixed bug in <code>nnf_pad</code> when using <code>mode='circular'</code>. (<a href="https://github.com/mlverse/torch/issues/471" class="external-link">#471</a>)</li>
+<li>Bugfixes in <code>nn_multihead_attention</code> (<a href="https://github.com/mlverse/torch/issues/496" class="external-link">#496</a>)</li>
+<li>Fixed bug when using packed sequences with <code>nn_lstm</code> (<a href="https://github.com/mlverse/torch/issues/500" class="external-link">#500</a>)</li>
+<li>Fixed bug in the <code>to</code> method of <code>nn_module</code> that would reset the <code>requires_grad</code> attribute of parameters. (<a href="https://github.com/mlverse/torch/issues/501" class="external-link">#501</a>)</li>
+<li>Added <code>strong_wolfe</code> option to <code>optim_lbfgs</code>. (<a href="https://github.com/mlverse/torch/issues/517" class="external-link">#517</a>)</li>
+<li>Fixed default argument of <code>nn_init_trunc_normal_</code> initializer function. (<a href="https://github.com/mlverse/torch/issues/535" class="external-link">#535</a>)</li>
+</ul></div>
+<div class="section level3">
+<h3 id="documentation-0-3-0">Documentation<a class="anchor" aria-label="anchor" href="#documentation-0-3-0"></a></h3>
+<ul><li>Added vignette on reading models from Python (<a href="https://github.com/mlverse/torch/issues/469" class="external-link">#469</a>)</li>
+</ul></div>
+<div class="section level3">
+<h3 id="internal-changes-0-3-0">Internal changes<a class="anchor" aria-label="anchor" href="#internal-changes-0-3-0"></a></h3>
+<ul><li>Removed the PerformanceReporter from tests to get easier to read stack traces. (<a href="https://github.com/mlverse/torch/issues/449" class="external-link">#449</a>)</li>
+<li>Internal change in the R7 classes so R7 objects are simple external pointer instead of environments. This might cause breaking change if you relied on saving any kind of state in the Tensor object. (<a href="https://github.com/mlverse/torch/issues/452" class="external-link">#452</a>)</li>
+<li>Internal refactoring making Rcpp aware of some XPtrTorch* types so making it simpler to return them from Rcpp code. This might cause a breaking change if you are relying on <code><a href="../reference/torch_dtype.html">torch_dtype()</a></code> being an R6 class. (<a href="https://github.com/mlverse/torch/issues/451" class="external-link">#451</a>)</li>
+<li>Internal changes to auto unwrap arguments from SEXP’s in Rcpp. This will make easier to move the dispatcher system to C++ in the future, but already allows us to gain ~30% speedups in small operations. (<a href="https://github.com/mlverse/torch/issues/454" class="external-link">#454</a>)</li>
+<li>Added a Windows GPU CI workflow (<a href="https://github.com/mlverse/torch/issues/508" class="external-link">#508</a>).</li>
+<li>Update to LibTorch v1.8 (<a href="https://github.com/mlverse/torch/issues/513" class="external-link">#513</a>)</li>
+<li>Moved some parts of the dispatcher to C++ to make it faster. (<a href="https://github.com/mlverse/torch/issues/520" class="external-link">#520</a>)</li>
+</ul></div>
+</div>
+    <div class="section level2">
+<h2 class="pkg-version" data-toc-text="0.2.1" id="torch-021">torch 0.2.1<a class="anchor" aria-label="anchor" href="#torch-021"></a></h2><p class="text-muted">CRAN release: 2021-01-05</p>
+<div class="section level3">
+<h3 id="breaking-changes-0-2-1">Breaking changes<a class="anchor" aria-label="anchor" href="#breaking-changes-0-2-1"></a></h3>
+<ul><li>Made <code>torch_one_hot</code> and <code>nnf_one_hot</code> use 1-based indexing. (<a href="https://github.com/mlverse/torch/issues/410" class="external-link">#410</a>)</li>
 <li>
-<code>nn_module$eval()</code> and <code>nn_module$train()</code> now return a callable <code>nn_module</code> instead of a <code>nn_Module</code>. (<a href='https://github.com/mlverse/torch/issues/425'>#425</a>)</li>
-</ul>
-</div>
-<div id="new-features-4" class="section level2">
-<h2 class="hasAnchor">
-<a href="#new-features-4" class="anchor"></a>New features</h2>
-<ul>
-<li>Added a custom CPU allocator to call <code>gc</code> when torch might need more memory (<a href='https://github.com/mlverse/torch/issues/402'>#402</a>)</li>
-<li>Updated to LibTorch 1.7.1 (<a href='https://github.com/mlverse/torch/issues/412'>#412</a>)</li>
-<li>Allow listing all nested modules in a <code>nn_module</code> (<a href='https://github.com/mlverse/torch/issues/417'>#417</a>)</li>
-<li>Allow modifying the <code>requires_grad</code> attribute using the <code>$&lt;-</code> operator (<a href='https://github.com/mlverse/torch/issues/419'>#419</a>)</li>
-<li>Added <code>length</code> method for the <code>nn_sequential</code> container. (<a href='https://github.com/mlverse/torch/issues/423'>#423</a>)</li>
-<li>Added support for CUDA 11 on linux (<a href='https://github.com/mlverse/torch/issues/424'>#424</a>)</li>
-</ul>
-</div>
-<div id="bug-fixes-4" class="section level2">
-<h2 class="hasAnchor">
-<a href="#bug-fixes-4" class="anchor"></a>Bug fixes</h2>
-<ul>
-<li>Fix support for cuda 9.2 (<a href='https://github.com/mlverse/torch/issues/398'>#398</a>)</li>
-<li>Fixed GPU CI that was skipping tests. (<a href='https://github.com/mlverse/torch/issues/398'>#398</a>)</li>
-<li>Fixed a memory leak when printing tensors (<a href='https://github.com/mlverse/torch/issues/402'>#402</a>)</li>
-<li>Fixed a memory leak when passing integer vectors to lantern. (<a href='https://github.com/mlverse/torch/issues/402'>#402</a>)</li>
-<li>Fixed a few more memory leaks related to autograd context (<a href='https://github.com/mlverse/torch/issues/405'>#405</a>)</li>
-<li>Fixed <code>nnf_normalize</code> and <code>x$norm()</code> as they were not able to be called (<a href='https://github.com/mlverse/torch/issues/409'>#409</a>)</li>
-</ul>
-</div>
-<div id="documentation-2" class="section level2">
-<h2 class="hasAnchor">
-<a href="#documentation-2" class="anchor"></a>Documentation</h2>
-<ul>
-<li>Small improvement to <code>nn_module</code> documentation (<a href='https://github.com/mlverse/torch/issues/399'>#399</a>).</li>
-<li>The getting started section has been removed from the pkgdown website in favor of the new guide in the landing page (<a href='https://github.com/mlverse/torch/issues/401'>#401</a>)</li>
-<li>Updated the landing page to include a getting started tutorial (<a href='https://github.com/mlverse/torch/issues/400'>#400</a>)</li>
-</ul>
-</div>
-</div>
-    <div id="torch-020" class="section level1">
-<h1 class="page-header" data-toc-text="0.2.0">
-<a href="#torch-020" class="anchor"></a>torch 0.2.0<small> 2020-12-15 </small>
-</h1>
-<div id="breaking-changes-5" class="section level2">
-<h2 class="hasAnchor">
-<a href="#breaking-changes-5" class="anchor"></a>Breaking changes</h2>
-<ul>
-<li>Dataloaders now returns a <code><a href="https://rdrr.io/pkg/coro/man/iterator.html">coro::exhausted</a></code> intead of raising <code>stop_iteration_error</code> when the dataloader exceeds. (<a href='https://github.com/mlverse/torch/issues/366'>#366</a>)</li>
-<li>Fixed bug that would happen with functions that need to transform tensors from 0-based to 1-based in the GPU. (<a href='https://github.com/mlverse/torch/issues/317'>#317</a>)</li>
-<li>Fixed <code>torch_argsort</code> and <code>x$argsort</code> to return 1-based indexes (<a href='https://github.com/mlverse/torch/issues/342'>#342</a>)</li>
-<li>Fixed <code>torch_argmax</code>, <code>torch_argmin</code>, <code>x$argmax()</code> and <code>x$argmin()</code> return 1-based indexes. (<a href='https://github.com/mlverse/torch/issues/389'>#389</a>)</li>
-</ul>
-</div>
-<div id="new-features-5" class="section level2">
-<h2 class="hasAnchor">
-<a href="#new-features-5" class="anchor"></a>New features</h2>
-<ul>
-<li>Added <code>$element_size()</code> method (<a href='https://github.com/dirkschumacher'>@dirkschumacher</a> <a href='https://github.com/mlverse/torch/issues/322'>#322</a>)</li>
-<li>Added <code>$bool()</code> method (<a href='https://github.com/dirkschumacher'>@dirkschumacher</a> <a href='https://github.com/mlverse/torch/issues/323'>#323</a>)</li>
+<code>nn_module$eval()</code> and <code>nn_module$train()</code> now return a callable <code>nn_module</code> instead of a <code>nn_Module</code>. (<a href="https://github.com/mlverse/torch/issues/425" class="external-link">#425</a>)</li>
+</ul></div>
+<div class="section level3">
+<h3 id="new-features-0-2-1">New features<a class="anchor" aria-label="anchor" href="#new-features-0-2-1"></a></h3>
+<ul><li>Added a custom CPU allocator to call <code>gc</code> when torch might need more memory (<a href="https://github.com/mlverse/torch/issues/402" class="external-link">#402</a>)</li>
+<li>Updated to LibTorch 1.7.1 (<a href="https://github.com/mlverse/torch/issues/412" class="external-link">#412</a>)</li>
+<li>Allow listing all nested modules in a <code>nn_module</code> (<a href="https://github.com/mlverse/torch/issues/417" class="external-link">#417</a>)</li>
+<li>Allow modifying the <code>requires_grad</code> attribute using the <code>$&lt;-</code> operator (<a href="https://github.com/mlverse/torch/issues/419" class="external-link">#419</a>)</li>
+<li>Added <code>length</code> method for the <code>nn_sequential</code> container. (<a href="https://github.com/mlverse/torch/issues/423" class="external-link">#423</a>)</li>
+<li>Added support for CUDA 11 on linux (<a href="https://github.com/mlverse/torch/issues/424" class="external-link">#424</a>)</li>
+</ul></div>
+<div class="section level3">
+<h3 id="bug-fixes-0-2-1">Bug fixes<a class="anchor" aria-label="anchor" href="#bug-fixes-0-2-1"></a></h3>
+<ul><li>Fix support for cuda 9.2 (<a href="https://github.com/mlverse/torch/issues/398" class="external-link">#398</a>)</li>
+<li>Fixed GPU CI that was skipping tests. (<a href="https://github.com/mlverse/torch/issues/398" class="external-link">#398</a>)</li>
+<li>Fixed a memory leak when printing tensors (<a href="https://github.com/mlverse/torch/issues/402" class="external-link">#402</a>)</li>
+<li>Fixed a memory leak when passing integer vectors to lantern. (<a href="https://github.com/mlverse/torch/issues/402" class="external-link">#402</a>)</li>
+<li>Fixed a few more memory leaks related to autograd context (<a href="https://github.com/mlverse/torch/issues/405" class="external-link">#405</a>)</li>
+<li>Fixed <code>nnf_normalize</code> and <code>x$norm()</code> as they were not able to be called (<a href="https://github.com/mlverse/torch/issues/409" class="external-link">#409</a>)</li>
+</ul></div>
+<div class="section level3">
+<h3 id="documentation-0-2-1">Documentation<a class="anchor" aria-label="anchor" href="#documentation-0-2-1"></a></h3>
+<ul><li>Small improvement to <code>nn_module</code> documentation (<a href="https://github.com/mlverse/torch/issues/399" class="external-link">#399</a>).</li>
+<li>The getting started section has been removed from the pkgdown website in favor of the new guide in the landing page (<a href="https://github.com/mlverse/torch/issues/401" class="external-link">#401</a>)</li>
+<li>Updated the landing page to include a getting started tutorial (<a href="https://github.com/mlverse/torch/issues/400" class="external-link">#400</a>)</li>
+</ul></div>
+</div>
+    <div class="section level2">
+<h2 class="pkg-version" data-toc-text="0.2.0" id="torch-020">torch 0.2.0<a class="anchor" aria-label="anchor" href="#torch-020"></a></h2><p class="text-muted">CRAN release: 2020-12-15</p>
+<div class="section level3">
+<h3 id="breaking-changes-0-2-0">Breaking changes<a class="anchor" aria-label="anchor" href="#breaking-changes-0-2-0"></a></h3>
+<ul><li>Dataloaders now returns a <code><a href="https://rdrr.io/pkg/coro/man/iterator.html" class="external-link">coro::exhausted</a></code> intead of raising <code>stop_iteration_error</code> when the dataloader exceeds. (<a href="https://github.com/mlverse/torch/issues/366" class="external-link">#366</a>)</li>
+<li>Fixed bug that would happen with functions that need to transform tensors from 0-based to 1-based in the GPU. (<a href="https://github.com/mlverse/torch/issues/317" class="external-link">#317</a>)</li>
+<li>Fixed <code>torch_argsort</code> and <code>x$argsort</code> to return 1-based indexes (<a href="https://github.com/mlverse/torch/issues/342" class="external-link">#342</a>)</li>
+<li>Fixed <code>torch_argmax</code>, <code>torch_argmin</code>, <code>x$argmax()</code> and <code>x$argmin()</code> return 1-based indexes. (<a href="https://github.com/mlverse/torch/issues/389" class="external-link">#389</a>)</li>
+</ul></div>
+<div class="section level3">
+<h3 id="new-features-0-2-0">New features<a class="anchor" aria-label="anchor" href="#new-features-0-2-0"></a></h3>
+<ul><li>Added <code>$element_size()</code> method (<a href="https://github.com/dirkschumacher" class="external-link">@dirkschumacher</a> <a href="https://github.com/mlverse/torch/issues/322" class="external-link">#322</a>)</li>
+<li>Added <code>$bool()</code> method (<a href="https://github.com/dirkschumacher" class="external-link">@dirkschumacher</a> <a href="https://github.com/mlverse/torch/issues/323" class="external-link">#323</a>)</li>
 <li>
 <code>torch__addr</code> and <code>torch__addr_</code> have been removed as they are no longer available in LibTorch 1.7.</li>
-<li>We now check the MD5 hashes of downloaded LibTorch binaries. (<a href='https://github.com/dirkschumacher'>@dirkschumacher</a> <a href='https://github.com/mlverse/torch/issues/325'>#325</a>)</li>
-<li>Added a Distribution abstract class (<a href='https://github.com/krzjoa'>@krzjoa</a> <a href='https://github.com/mlverse/torch/issues/333'>#333</a>)</li>
-<li>Updated to LibTorch 1.7 (<a href='https://github.com/mlverse/torch/issues/337'>#337</a>)</li>
-<li>We now warn when converting <code>long</code> tensors to R and there’s a chance of an integer overflow. (<a href='https://github.com/mlverse/torch/issues/347'>#347</a>)</li>
-<li>Allow <code>private</code> and <code>active</code> methods in <code>nn_module</code>’s and <code>dataset</code>’s. (<a href='https://github.com/mlverse/torch/issues/349'>#349</a>)</li>
-<li>Added <code>nn_batch_norm3d</code> (<a href='https://github.com/mattwarkentin'>@mattwarkentin</a> <a href='https://github.com/mlverse/torch/issues/354'>#354</a>)</li>
-<li>Added <code>nn_lstm</code> and <code>nn_gru</code> modules. (<a href='https://github.com/mlverse/torch/issues/362'>#362</a>)</li>
-<li>Added distribution constraints (<a href='https://github.com/krzjoa'>@krzjoa</a> <a href='https://github.com/mlverse/torch/issues/364'>#364</a>)</li>
-<li>Dataloaders now use the num_workers argument to load data in parallel (<a href='https://github.com/mlverse/torch/issues/366'>#366</a>)</li>
-<li>Added Exponential Family classs to distributions (<a href='https://github.com/mlverse/torch/issues/373'>#373</a>)</li>
-<li>Added Dockerfile and docker compose file with GPU support, with a how-to guide. (<a href='https://github.com/mlverse/torch/issues/380'>#380</a> <a href='https://github.com/mlverse/torch/issues/386'>#386</a>)</li>
-<li>Added R 3.6 to the CI system and fixed compilation from source with it on Windows (<a href='https://github.com/mlverse/torch/issues/387'>#387</a>)</li>
-<li>Initial support for JIT tracing (<a href='https://github.com/mlverse/torch/issues/377'>#377</a>)</li>
-<li>Added LBFGS optimizer (<a href='https://github.com/mlverse/torch/issues/392'>#392</a>)</li>
-<li>Improved the <code>nn_module</code> UI by improving autocomplete support and adding a print method (<a href='https://github.com/mlverse/torch/issues/391'>#391</a>)</li>
-</ul>
-</div>
-<div id="bug-fixes-5" class="section level2">
-<h2 class="hasAnchor">
-<a href="#bug-fixes-5" class="anchor"></a>Bug fixes</h2>
-<ul>
-<li>Fixed bug when trying to print the <code>grad_fn</code> of a Tensor that doesn’t have one. See (<a href='https://github.com/mlverse/torch/issues/321'>#321</a>)</li>
-<li>Refactored the optimizers code to avoid duplication of parameter checks, etc. (<a href='https://github.com/dirkschumacher'>@dirkschumacher</a> <a href='https://github.com/mlverse/torch/issues/328'>#328</a>)</li>
-<li>Fixed <code>torch_norm</code> so it can be called with a <code>dim</code> argument. (<a href='https://github.com/mlverse/torch/issues/345'>#345</a>)</li>
-<li>Fixed crash when calling <code>torch_hann_window</code> with an invalid <code>NULL</code> <code>window_length</code>. (<a href='https://github.com/mlverse/torch/issues/351'>#351</a>)</li>
-<li>Fixed <code>torch_stft</code> calls for LibTorch 1.7 (added the <code>return_complex</code> argument) (<a href='https://github.com/mlverse/torch/issues/355'>#355</a>)</li>
-<li>Fixed bug when strides were NULL in some pooling operations. (<a href='https://github.com/mlverse/torch/issues/361'>#361</a>)</li>
-<li>Use <code>nvcc --version</code> instead of <code>nvidia-smi</code> to find the CUDA version as <code>nvidia-smi</code> reports the latest supported version and not the installed one. (<a href='https://github.com/mlverse/torch/issues/363'>#363</a>)</li>
-<li>Corrected URL to download LibTorch under Linux with CUDA 10.2 (<a href='https://github.com/mlverse/torch/issues/367'>#367</a>)</li>
-<li>Fixed handling of integer tensors when indexing tensors (<a href='https://github.com/mlverse/torch/issues/385'>#385</a>)</li>
-<li>Fixed bug when passing length zero vectors to lantern/libtorch. (<a href='https://github.com/mlverse/torch/issues/388'>#388</a>)</li>
-</ul>
-</div>
-</div>
-    <div id="torch-011" class="section level1">
-<h1 class="page-header" data-toc-text="0.1.1">
-<a href="#torch-011" class="anchor"></a>torch 0.1.1<small> 2020-10-20 </small>
-</h1>
-<div id="bug-fixes-6" class="section level2">
-<h2 class="hasAnchor">
-<a href="#bug-fixes-6" class="anchor"></a>Bug fixes</h2>
-<ul>
-<li>Fixed bug that made <code>RandomSampler(replacement = TRUE)</code> to never take the last element in the dataset. (84861fa)</li>
-<li>Fixed <code>torch_topk</code> and <code>x$topk</code> so the returned indexes are 1-based (<a href='https://github.com/mlverse/torch/issues/280'>#280</a>)</li>
-<li>Fixed a bug (<a href='https://github.com/mlverse/torch/issues/275'>#275</a>) that would cause <code>1 - torch_tensor(1, device = "cuda")</code> to fail because <code>1</code> was created in the CPU. (<a href='https://github.com/mlverse/torch/issues/279'>#279</a>)</li>
-<li>We now preserve names in the <code>dataloader</code> output (<a href='https://github.com/mlverse/torch/issues/286'>#286</a>)</li>
+<li>We now check the MD5 hashes of downloaded LibTorch binaries. (<a href="https://github.com/dirkschumacher" class="external-link">@dirkschumacher</a> <a href="https://github.com/mlverse/torch/issues/325" class="external-link">#325</a>)</li>
+<li>Added a Distribution abstract class (<a href="https://github.com/krzjoa" class="external-link">@krzjoa</a> <a href="https://github.com/mlverse/torch/issues/333" class="external-link">#333</a>)</li>
+<li>Updated to LibTorch 1.7 (<a href="https://github.com/mlverse/torch/issues/337" class="external-link">#337</a>)</li>
+<li>We now warn when converting <code>long</code> tensors to R and there’s a chance of an integer overflow. (<a href="https://github.com/mlverse/torch/issues/347" class="external-link">#347</a>)</li>
+<li>Allow <code>private</code> and <code>active</code> methods in <code>nn_module</code>’s and <code>dataset</code>’s. (<a href="https://github.com/mlverse/torch/issues/349" class="external-link">#349</a>)</li>
+<li>Added <code>nn_batch_norm3d</code> (<a href="https://github.com/mattwarkentin" class="external-link">@mattwarkentin</a> <a href="https://github.com/mlverse/torch/issues/354" class="external-link">#354</a>)</li>
+<li>Added <code>nn_lstm</code> and <code>nn_gru</code> modules. (<a href="https://github.com/mlverse/torch/issues/362" class="external-link">#362</a>)</li>
+<li>Added distribution constraints (<a href="https://github.com/krzjoa" class="external-link">@krzjoa</a> <a href="https://github.com/mlverse/torch/issues/364" class="external-link">#364</a>)</li>
+<li>Dataloaders now use the num_workers argument to load data in parallel (<a href="https://github.com/mlverse/torch/issues/366" class="external-link">#366</a>)</li>
+<li>Added Exponential Family classs to distributions (<a href="https://github.com/mlverse/torch/issues/373" class="external-link">#373</a>)</li>
+<li>Added Dockerfile and docker compose file with GPU support, with a how-to guide. (<a href="https://github.com/mlverse/torch/issues/380" class="external-link">#380</a> <a href="https://github.com/mlverse/torch/issues/386" class="external-link">#386</a>)</li>
+<li>Added R 3.6 to the CI system and fixed compilation from source with it on Windows (<a href="https://github.com/mlverse/torch/issues/387" class="external-link">#387</a>)</li>
+<li>Initial support for JIT tracing (<a href="https://github.com/mlverse/torch/issues/377" class="external-link">#377</a>)</li>
+<li>Added LBFGS optimizer (<a href="https://github.com/mlverse/torch/issues/392" class="external-link">#392</a>)</li>
+<li>Improved the <code>nn_module</code> UI by improving autocomplete support and adding a print method (<a href="https://github.com/mlverse/torch/issues/391" class="external-link">#391</a>)</li>
+</ul></div>
+<div class="section level3">
+<h3 id="bug-fixes-0-2-0">Bug fixes<a class="anchor" aria-label="anchor" href="#bug-fixes-0-2-0"></a></h3>
+<ul><li>Fixed bug when trying to print the <code>grad_fn</code> of a Tensor that doesn’t have one. See (<a href="https://github.com/mlverse/torch/issues/321" class="external-link">#321</a>)</li>
+<li>Refactored the optimizers code to avoid duplication of parameter checks, etc. (<a href="https://github.com/dirkschumacher" class="external-link">@dirkschumacher</a> <a href="https://github.com/mlverse/torch/issues/328" class="external-link">#328</a>)</li>
+<li>Fixed <code>torch_norm</code> so it can be called with a <code>dim</code> argument. (<a href="https://github.com/mlverse/torch/issues/345" class="external-link">#345</a>)</li>
+<li>Fixed crash when calling <code>torch_hann_window</code> with an invalid <code>NULL</code> <code>window_length</code>. (<a href="https://github.com/mlverse/torch/issues/351" class="external-link">#351</a>)</li>
+<li>Fixed <code>torch_stft</code> calls for LibTorch 1.7 (added the <code>return_complex</code> argument) (<a href="https://github.com/mlverse/torch/issues/355" class="external-link">#355</a>)</li>
+<li>Fixed bug when strides were NULL in some pooling operations. (<a href="https://github.com/mlverse/torch/issues/361" class="external-link">#361</a>)</li>
+<li>Use <code>nvcc --version</code> instead of <code>nvidia-smi</code> to find the CUDA version as <code>nvidia-smi</code> reports the latest supported version and not the installed one. (<a href="https://github.com/mlverse/torch/issues/363" class="external-link">#363</a>)</li>
+<li>Corrected URL to download LibTorch under Linux with CUDA 10.2 (<a href="https://github.com/mlverse/torch/issues/367" class="external-link">#367</a>)</li>
+<li>Fixed handling of integer tensors when indexing tensors (<a href="https://github.com/mlverse/torch/issues/385" class="external-link">#385</a>)</li>
+<li>Fixed bug when passing length zero vectors to lantern/libtorch. (<a href="https://github.com/mlverse/torch/issues/388" class="external-link">#388</a>)</li>
+</ul></div>
+</div>
+    <div class="section level2">
+<h2 class="pkg-version" data-toc-text="0.1.1" id="torch-011">torch 0.1.1<a class="anchor" aria-label="anchor" href="#torch-011"></a></h2><p class="text-muted">CRAN release: 2020-10-20</p>
+<div class="section level3">
+<h3 id="bug-fixes-0-1-1">Bug fixes<a class="anchor" aria-label="anchor" href="#bug-fixes-0-1-1"></a></h3>
+<ul><li>Fixed bug that made <code>RandomSampler(replacement = TRUE)</code> to never take the last element in the dataset. (84861fa)</li>
+<li>Fixed <code>torch_topk</code> and <code>x$topk</code> so the returned indexes are 1-based (<a href="https://github.com/mlverse/torch/issues/280" class="external-link">#280</a>)</li>
+<li>Fixed a bug (<a href="https://github.com/mlverse/torch/issues/275" class="external-link">#275</a>) that would cause <code>1 - torch_tensor(1, device = "cuda")</code> to fail because <code>1</code> was created in the CPU. (<a href="https://github.com/mlverse/torch/issues/279" class="external-link">#279</a>)</li>
+<li>We now preserve names in the <code>dataloader</code> output (<a href="https://github.com/mlverse/torch/issues/286" class="external-link">#286</a>)</li>
 <li>
-<code>torch_narrow</code>, <code>Tensor$narrow()</code> and <code>Tensor$narrow_copy</code> are now indexed starting at 1. (<a href='https://github.com/mlverse/torch/issues/294'>#294</a>)</li>
+<code>torch_narrow</code>, <code>Tensor$narrow()</code> and <code>Tensor$narrow_copy</code> are now indexed starting at 1. (<a href="https://github.com/mlverse/torch/issues/294" class="external-link">#294</a>)</li>
 <li>
-<code>Tensor$is_leaf</code> is now an active method. (<a href='https://github.com/mlverse/torch/issues/295'>#295</a>)</li>
-<li>Fixed bug when passing equations to <code>torch_einsum</code>. (<a href='https://github.com/mlverse/torch/issues/296'>#296</a>)<br>
-</li>
-<li>Fixed <code><a href="../reference/nn_module_list.html">nn_module_list()</a></code> to correctly name added modules, otherwise they are not returned when doing <code>state_dict()</code> on it. (<a href='https://github.com/mlverse/torch/issues/300'>#300</a>)</li>
-<li>Fixed bug related to random number seeds when using in-place methods. (<a href='https://github.com/mlverse/torch/issues/303'>#303</a>)</li>
-<li>Fixed <code>nn_batchnorm*</code> so it returns the same results as PyTorch (<a href='https://github.com/mlverse/torch/issues/302'>#302</a>)</li>
-<li>Fixed a bug that made <code>nn_module$parameter</code> when there were shared parameters between layers. (<a href='https://github.com/mlverse/torch/issues/306'>#306</a>)</li>
-<li>Fixed <code>$max</code> and <code>$min</code> to return 1-based indexes. (<a href='https://github.com/mlverse/torch/issues/315'>#315</a>)</li>
-</ul>
-</div>
-<div id="new-features-6" class="section level2">
-<h2 class="hasAnchor">
-<a href="#new-features-6" class="anchor"></a>New features</h2>
-<ul>
-<li>Expanded the <code>utils_data_default_collate</code> to support converting R objects to torch tensors when needed. (<a href='https://github.com/mlverse/torch/issues/269'>#269</a>)</li>
-<li>Added an <code>as.matrix</code> method for torch Tensors. (<a href='https://github.com/mlverse/torch/issues/282'>#282</a>)</li>
-<li>By default we now truncate the output of <code>print(totrch_tensor(1:40))</code> if it spans for more than 30 lines. This is useful for not spamming the console or taking very long to print when you print a very large tensor. (<a href='https://github.com/mlverse/torch/issues/283'>#283</a>)</li>
-<li>Added the Adadelta optimizer (<a href='https://github.com/krzjoa'>@krzjoa</a> <a href='https://github.com/mlverse/torch/issues/284'>#284</a>)</li>
-<li>Added support for GPU’s on Windows (<a href='https://github.com/mlverse/torch/issues/281'>#281</a>)</li>
-<li>Added the Adagrad optimizer (<a href='https://github.com/krzjoa'>@krzjoa</a> <a href='https://github.com/mlverse/torch/issues/289'>#289</a>)</li>
-<li>Added RMSprop optimizer (<a href='https://github.com/krzjoa'>@krzjoa</a> <a href='https://github.com/mlverse/torch/issues/290'>#290</a>)</li>
-<li>Added the Rprop optimizer (<a href='https://github.com/krzjoa'>@krzjoa</a> <a href='https://github.com/mlverse/torch/issues/297'>#297</a>)</li>
-<li>Added gradient clipping utilities (<a href='https://github.com/mlverse/torch/issues/299'>#299</a>)</li>
-<li>Added <code>nnf_contrib_sparsemax</code> and <code>nn_contrib_sparsemax</code>. (<a href='https://github.com/mlverse/torch/issues/309'>#309</a>)</li>
-<li>Added ASGD optimizer (<a href='https://github.com/krzjoa'>@krzjoa</a> <a href='https://github.com/mlverse/torch/issues/307'>#307</a>)</li>
-<li>Getters and setters for the number of threads used by torch (<a href='https://github.com/mlverse/torch/issues/311'>#311</a>)</li>
-</ul>
-</div>
-</div>
-    <div id="torch-010" class="section level1">
-<h1 class="page-header" data-toc-text="0.1.0">
-<a href="#torch-010" class="anchor"></a>torch 0.1.0<small> 2020-09-28 </small>
-</h1>
-<ul>
-<li>Added many missing losses (<a href='https://github.com/mlverse/torch/issues/252'>#252</a>)</li>
-<li>Implemented the <code>$&lt;-</code> and <code>[[&lt;-</code> operators for the <code>nn_module</code> class. (<a href='https://github.com/mlverse/torch/issues/253'>#253</a>)</li>
+<code>Tensor$is_leaf</code> is now an active method. (<a href="https://github.com/mlverse/torch/issues/295" class="external-link">#295</a>)</li>
+<li>Fixed bug when passing equations to <code>torch_einsum</code>. (<a href="https://github.com/mlverse/torch/issues/296" class="external-link">#296</a>)<br></li>
+<li>Fixed <code><a href="../reference/nn_module_list.html">nn_module_list()</a></code> to correctly name added modules, otherwise they are not returned when doing <code>state_dict()</code> on it. (<a href="https://github.com/mlverse/torch/issues/300" class="external-link">#300</a>)</li>
+<li>Fixed bug related to random number seeds when using in-place methods. (<a href="https://github.com/mlverse/torch/issues/303" class="external-link">#303</a>)</li>
+<li>Fixed <code>nn_batchnorm*</code> so it returns the same results as PyTorch (<a href="https://github.com/mlverse/torch/issues/302" class="external-link">#302</a>)</li>
+<li>Fixed a bug that made <code>nn_module$parameter</code> when there were shared parameters between layers. (<a href="https://github.com/mlverse/torch/issues/306" class="external-link">#306</a>)</li>
+<li>Fixed <code>$max</code> and <code>$min</code> to return 1-based indexes. (<a href="https://github.com/mlverse/torch/issues/315" class="external-link">#315</a>)</li>
+</ul></div>
+<div class="section level3">
+<h3 id="new-features-0-1-1">New features<a class="anchor" aria-label="anchor" href="#new-features-0-1-1"></a></h3>
+<ul><li>Expanded the <code>utils_data_default_collate</code> to support converting R objects to torch tensors when needed. (<a href="https://github.com/mlverse/torch/issues/269" class="external-link">#269</a>)</li>
+<li>Added an <code>as.matrix</code> method for torch Tensors. (<a href="https://github.com/mlverse/torch/issues/282" class="external-link">#282</a>)</li>
+<li>By default we now truncate the output of <code>print(totrch_tensor(1:40))</code> if it spans for more than 30 lines. This is useful for not spamming the console or taking very long to print when you print a very large tensor. (<a href="https://github.com/mlverse/torch/issues/283" class="external-link">#283</a>)</li>
+<li>Added the Adadelta optimizer (<a href="https://github.com/krzjoa" class="external-link">@krzjoa</a> <a href="https://github.com/mlverse/torch/issues/284" class="external-link">#284</a>)</li>
+<li>Added support for GPU’s on Windows (<a href="https://github.com/mlverse/torch/issues/281" class="external-link">#281</a>)</li>
+<li>Added the Adagrad optimizer (<a href="https://github.com/krzjoa" class="external-link">@krzjoa</a> <a href="https://github.com/mlverse/torch/issues/289" class="external-link">#289</a>)</li>
+<li>Added RMSprop optimizer (<a href="https://github.com/krzjoa" class="external-link">@krzjoa</a> <a href="https://github.com/mlverse/torch/issues/290" class="external-link">#290</a>)</li>
+<li>Added the Rprop optimizer (<a href="https://github.com/krzjoa" class="external-link">@krzjoa</a> <a href="https://github.com/mlverse/torch/issues/297" class="external-link">#297</a>)</li>
+<li>Added gradient clipping utilities (<a href="https://github.com/mlverse/torch/issues/299" class="external-link">#299</a>)</li>
+<li>Added <code>nnf_contrib_sparsemax</code> and <code>nn_contrib_sparsemax</code>. (<a href="https://github.com/mlverse/torch/issues/309" class="external-link">#309</a>)</li>
+<li>Added ASGD optimizer (<a href="https://github.com/krzjoa" class="external-link">@krzjoa</a> <a href="https://github.com/mlverse/torch/issues/307" class="external-link">#307</a>)</li>
+<li>Getters and setters for the number of threads used by torch (<a href="https://github.com/mlverse/torch/issues/311" class="external-link">#311</a>)</li>
+</ul></div>
+</div>
+    <div class="section level2">
+<h2 class="pkg-version" data-toc-text="0.1.0" id="torch-010">torch 0.1.0<a class="anchor" aria-label="anchor" href="#torch-010"></a></h2><p class="text-muted">CRAN release: 2020-09-28</p>
+<ul><li>Added many missing losses (<a href="https://github.com/mlverse/torch/issues/252" class="external-link">#252</a>)</li>
+<li>Implemented the <code>$&lt;-</code> and <code>[[&lt;-</code> operators for the <code>nn_module</code> class. (<a href="https://github.com/mlverse/torch/issues/253" class="external-link">#253</a>)</li>
 <li>Export <code>nn_parameter</code>, <code>nn_buffer</code>, and <code>is_*</code> auxiliary functions.</li>
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-<li>Added a few learning rate schedulers (<a href='https://github.com/mlverse/torch/issues/258'>#258</a>)</li>
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-    <div id="torch-002" class="section level1">
-<h1 class="page-header" data-toc-text="0.0.2">
-<a href="#torch-002" class="anchor"></a>torch 0.0.2<small> 2020-08-31 </small>
-</h1>
-<ul>
-<li>Added a <code>NEWS.md</code> file to track changes to the package.</li>
+<li>Added a few learning rate schedulers (<a href="https://github.com/mlverse/torch/issues/258" class="external-link">#258</a>)</li>
+</ul></div>
+    <div class="section level2">
+<h2 class="pkg-version" data-toc-text="0.0.2" id="torch-002">torch 0.0.2<a class="anchor" aria-label="anchor" href="#torch-002"></a></h2><p class="text-muted">CRAN release: 2020-08-31</p>
+<ul><li>Added a <code>NEWS.md</code> file to track changes to the package.</li>
 <li>Auto install when loading the package for the first time.</li>
-</ul>
-</div>
-  </div>
+</ul></div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
 
+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
 </div>
 
-
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-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
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-  <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p>
+<div class="pkgdown-footer-right">
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.2.</p>
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diff --git a/pkgdown.js b/pkgdown.js
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-    if (pos >= 0) {
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-      menu_anchor.parent().addClass("active");
-      menu_anchor.closest("li.dropdown").addClass("active");
-    }
-  });
-
-  function paths(pathname) {
-    var pieces = pathname.split("/");
-    pieces.shift(); // always starts with /
-
-    var end = pieces[pieces.length - 1];
-    if (end === "index.html" || end === "")
-      pieces.pop();
-    return(pieces);
-  }
-
-  // Returns -1 if not found
-  function prefix_length(needle, haystack) {
-    if (needle.length > haystack.length)
-      return(-1);
-
-    // Special case for length-0 haystack, since for loop won't run
-    if (haystack.length === 0) {
-      return(needle.length === 0 ? 0 : -1);
+    if ($('#toc').length) {
+      $('body').scrollspy({
+        target: '#toc',
+        offset: $("nav.navbar").outerHeight() + 1
+      });
     }
 
-    for (var i = 0; i < haystack.length; i++) {
-      if (needle[i] != haystack[i])
-        return(i);
-    }
+    // Activate popovers
+    $('[data-bs-toggle="popover"]').popover({
+      container: 'body',
+      html: true,
+      trigger: 'focus',
+      placement: "top",
+      sanitize: false,
+    });
 
-    return(haystack.length);
-  }
+    $('[data-bs-toggle="tooltip"]').tooltip();
 
   /* Clipboard --------------------------*/
 
@@ -78,9 +38,9 @@
 
   if(ClipboardJS.isSupported()) {
     $(document).ready(function() {
-      var copyButton = "<button type='button' class='btn btn-primary btn-copy-ex' type = 'submit' title='Copy to clipboard' aria-label='Copy to clipboard' data-toggle='tooltip' data-placement='left auto' data-trigger='hover' data-clipboard-copy><i class='fa fa-copy'></i></button>";
+      var copyButton = "<button type='button' class='btn btn-primary btn-copy-ex' title='Copy to clipboard' aria-label='Copy to clipboard' data-toggle='tooltip' data-placement='left' data-trigger='hover' data-clipboard-copy><i class='fa fa-copy'></i></button>";
 
-      $(".examples, div.sourceCode").addClass("hasCopyButton");
+      $("div.sourceCode").addClass("hasCopyButton");
 
       // Insert copy buttons:
       $(copyButton).prependTo(".hasCopyButton");
@@ -89,20 +49,108 @@
       $('.btn-copy-ex').tooltip({container: 'body'});
 
       // Initialize clipboard:
-      var clipboardBtnCopies = new ClipboardJS('[data-clipboard-copy]', {
+      var clipboard = new ClipboardJS('[data-clipboard-copy]', {
         text: function(trigger) {
-          return trigger.parentNode.textContent;
+          return trigger.parentNode.textContent.replace(/\n#>[^\n]*/g, "");
         }
       });
 
-      clipboardBtnCopies.on('success', function(e) {
+      clipboard.on('success', function(e) {
         changeTooltipMessage(e.trigger, 'Copied!');
         e.clearSelection();
       });
 
-      clipboardBtnCopies.on('error', function() {
+      clipboard.on('error', function() {
         changeTooltipMessage(e.trigger,'Press Ctrl+C or Command+C to copy');
       });
+
     });
   }
+
+    /* Search marking --------------------------*/
+    var url = new URL(window.location.href);
+    var toMark = url.searchParams.get("q");
+    var mark = new Mark("div.col-md-9");
+    if (toMark) {
+      mark.mark(toMark, {
+        accuracy: {
+          value: "complementary",
+          limiters: [",", ".", ":", "/"],
+        }
+      });
+    }
+
+  /* Search --------------------------*/
+  /* Adapted from https://github.com/rstudio/bookdown/blob/2d692ba4b61f1e466c92e78fd712b0ab08c11d31/inst/resources/bs4_book/bs4_book.js#L25 */
+    // Initialise search index on focus
+  var fuse;
+  $("#search-input").focus(async function(e) {
+    if (fuse) {
+      return;
+    }
+
+    $(e.target).addClass("loading");
+    var response = await fetch($("#search-input").data("search-index"));
+    var data = await response.json();
+
+    var options = {
+      keys: ["what", "text", "code"],
+      ignoreLocation: true,
+      threshold: 0.1,
+      includeMatches: true,
+      includeScore: true,
+    };
+    fuse = new Fuse(data, options);
+
+    $(e.target).removeClass("loading");
+  });
+
+  // Use algolia autocomplete
+  var options = {
+    autoselect: true,
+    debug: true,
+    hint: false,
+    minLength: 2,
+  };
+  var q;
+async function searchFuse(query, callback) {
+  await fuse;
+
+  var items;
+  if (!fuse) {
+    items = [];
+  } else {
+    q = query;
+    var results = fuse.search(query, { limit: 20 });
+    items = results
+      .filter((x) => x.score <= 0.75)
+      .map((x) => x.item);
+    if (items.length === 0) {
+      items = [{dir:"Sorry 😿",previous_headings:"",title:"No results found.",what:"No results found.",path:window.location.href}];
+    }
+  }
+  callback(items);
+}
+  $("#search-input").autocomplete(options, [
+    {
+      name: "content",
+      source: searchFuse,
+      templates: {
+        suggestion: (s) => {
+          if (s.title == s.what) {
+            return `${s.dir} >	<div class="search-details"> ${s.title}</div>`;
+          } else if (s.previous_headings == "") {
+            return `${s.dir} >	<div class="search-details"> ${s.title}</div> > ${s.what}`;
+          } else {
+            return `${s.dir} >	<div class="search-details"> ${s.title}</div> > ${s.previous_headings} > ${s.what}`;
+          }
+        },
+      },
+    },
+  ]).on('autocomplete:selected', function(event, s) {
+    window.location.href = s.path + "?q=" + q + "#" + s.id;
+  });
+  });
 })(window.jQuery || window.$)
+
+
diff --git a/pkgdown.yml b/pkgdown.yml
index 2e59daefb..f708ddb42 100644
--- a/pkgdown.yml
+++ b/pkgdown.yml
@@ -1,21 +1,24 @@
 pandoc: 2.7.3
-pkgdown: 1.6.1
+pkgdown: 2.0.2
 pkgdown_sha: ~
 articles:
   distributions: distributions.html
-  examples/basic-autograd: basic-autograd.html
-  examples/basic-nn-module: basic-nn-module.html
-  examples/dataset: dataset.html
-  examples/index: index.html
+  basic-autograd: examples/basic-autograd.html
+  basic-nn-module: examples/basic-nn-module.html
+  dataset: examples/dataset.html
+  index: examples/index.html
   extending-autograd: extending-autograd.html
   indexing: indexing.html
   installation: installation.html
   loading-data: loading-data.html
   python-to-r: python-to-r.html
   serialization: serialization.html
-  tensor/index: index.html
+  index: tensor/index.html
   tensor-creation: tensor-creation.html
   torchscript: torchscript.html
   using-autograd: using-autograd.html
-last_built: 2021-11-20T13:53Z
+last_built: 2022-02-18T11:53Z
+urls:
+  reference: https://torch.mlverse.org/docs/reference
+  article: https://torch.mlverse.org/docs/articles
 
diff --git a/reference/AutogradContext.html b/reference/AutogradContext.html
index a9c78b39f..9e3007cfe 100644
--- a/reference/AutogradContext.html
+++ b/reference/AutogradContext.html
@@ -1,264 +1,178 @@
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-
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-<link href="../pkgdown.css" rel="stylesheet">
-<script src="../pkgdown.js"></script>
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-  <link href="../extra.css" rel="stylesheet">
-  
-
-<meta property="og:title" content="Class representing the context. — AutogradContext" />
-<meta property="og:description" content="Class representing the context.
-Class representing the context." />
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-
-<!-- mathjax -->
-<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script>
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-
-<!--[if lt IE 9]>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Class representing the context.
+Class representing the context."><title>Class representing the context. — AutogradContext • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.1.0/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.1.0/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Class representing the context. — AutogradContext"><meta property="og:description" content="Class representing the context.
+Class representing the context."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+<![endif]--><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=UA-178883486-1"></script><script>
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-    <div class="container template-reference-topic">
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-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.6.1</span>
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+</script></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+    
 
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+    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary"><div class="container">
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+    <a class="navbar-brand me-2" href="../index.html">torch</a>
+
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+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
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+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="nav-item dropdown">
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+    <a class="dropdown-item" href="../articles/tensor-creation.html">Creating tensors</a>
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+    <a class="dropdown-item" href="../articles/serialization.html">Serialization</a>
+    <h6 class="dropdown-header" data-toc-skip>Datasets</h6>
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+      </ul><form class="form-inline my-2 my-lg-0" role="search">
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+    
+  </div>
+</nav><div class="container template-reference-topic">
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/autograd.R'><code>R/autograd.R</code></a></small>
-    <div class="hidden name"><code>AutogradContext.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Class representing the context.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/autograd.R" class="external-link"><code>R/autograd.R</code></a></small>
+      <div class="d-none name"><code>AutogradContext.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Class representing the context.</p>
 <p>Class representing the context.</p>
     </div>
 
 
+    <div class="section level2">
+    <h2 id="public-fields">Public fields<a class="anchor" aria-label="anchor" href="#public-fields"></a></h2>
+    <p></p><div class="r6-fields"><dl><dt><code>ptr</code></dt>
+<dd><p>(Dev related) pointer to the context c++ object.</p></dd>
 
-    <h2 class="hasAnchor" id="public-fields"><a class="anchor" href="#public-fields"></a>Public fields</h2>
 
-    <p><div class="r6-fields"></p><dl>
-<dt><code>ptr</code></dt><dd><p>(Dev related) pointer to the context c++ object.</p></dd>
-
-</dl><p></div></p>
-    <h2 class="hasAnchor" id="active-bindings"><a class="anchor" href="#active-bindings"></a>Active bindings</h2>
+</dl><p></p></div>
+    </div>
+    <div class="section level2">
+    <h2 id="active-bindings">Active bindings<a class="anchor" aria-label="anchor" href="#active-bindings"></a></h2>
+    <p></p><div class="r6-active-bindings"><dl><dt><code>needs_input_grad</code></dt>
+<dd><p>boolean listing arguments of <code>forward</code> and whether they require_grad.</p></dd>
 
-    <p><div class="r6-active-bindings"></p><dl>
-<dt><code>needs_input_grad</code></dt><dd><p>boolean listing arguments of <code>forward</code> and whether they require_grad.</p></dd>
 
-<dt><code>saved_variables</code></dt><dd><p>list of objects that were saved for backward via <code>save_for_backward</code>.</p></dd>
+<dt><code>saved_variables</code></dt>
+<dd><p>list of objects that were saved for backward via <code>save_for_backward</code>.</p></dd>
 
-</dl><p></div></p>
-    <h2 class="hasAnchor" id="methods"><a class="anchor" href="#methods"></a>Methods</h2>
 
+</dl><p></p></div>
+    </div>
+    <div class="section level2">
+    <h2 id="methods">Methods<a class="anchor" aria-label="anchor" href="#methods"></a></h2>
     
-<h3 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Public methods</h3>
-
-<ul>
-<li><p><a href='#method-new'><code>AutogradContext$new()</code></a></p></li>
-<li><p><a href='#method-save_for_backward'><code>AutogradContext$save_for_backward()</code></a></p></li>
-<li><p><a href='#method-mark_non_differentiable'><code>AutogradContext$mark_non_differentiable()</code></a></p></li>
-<li><p><a href='#method-mark_dirty'><code>AutogradContext$mark_dirty()</code></a></p></li>
-<li><p><a href='#method-clone'><code>AutogradContext$clone()</code></a></p></li>
-</ul>
-<p><hr>
-<a id="method-new"></a></p><h3 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Method <code>new()</code></h3>
-<p>(Dev related) Initializes the context. Not user related.</p><h4 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Usage</h4>
-<p><div class="r"></p><pre><span class='va'>AutogradContext</span><span class='op'>$</span><span class='fu'>new</span><span class='op'>(</span>
-  <span class='va'>ptr</span>,
-  <span class='va'>env</span>,
-  argument_names <span class='op'>=</span> <span class='cn'>NULL</span>,
-  argument_needs_grad <span class='op'>=</span> <span class='cn'>NULL</span>
-<span class='op'>)</span></pre><p></div></p>
-
-<h4 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Arguments</h4>
-<p><div class="arguments"></p><dl>
-<dt><code>ptr</code></dt><dd><p>pointer to the c++ object</p></dd>
-
-<dt><code>env</code></dt><dd><p>environment that encloses both forward and backward</p></dd>
-
-<dt><code>argument_names</code></dt><dd><p>names of forward arguments</p></dd>
-
-<dt><code>argument_needs_grad</code></dt><dd><p>whether each argument in forward needs grad.</p></dd>
-
-</dl><p></div></p>
-<p><hr>
-<a id="method-save_for_backward"></a></p><h3 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Method <code>save_for_backward()</code></h3>
+<div class="section">
+<h3 id="public-methods">Public methods<a class="anchor" aria-label="anchor" href="#public-methods"></a></h3>
+
+<ul><li><p><a href="#method-new"><code>AutogradContext$new()</code></a></p></li>
+<li><p><a href="#method-save_for_backward"><code>AutogradContext$save_for_backward()</code></a></p></li>
+<li><p><a href="#method-mark_non_differentiable"><code>AutogradContext$mark_non_differentiable()</code></a></p></li>
+<li><p><a href="#method-mark_dirty"><code>AutogradContext$mark_dirty()</code></a></p></li>
+<li><p><a href="#method-clone"><code>AutogradContext$clone()</code></a></p></li>
+</ul></div><p></p><hr><a id="method-new"></a><div class="section">
+<h3 id="method-new-">Method <code>new()</code><a class="anchor" aria-label="anchor" href="#method-new-"></a></h3>
+<p>(Dev related) Initializes the context. Not user related.</p><div class="section">
+<h4 id="usage">Usage<a class="anchor" aria-label="anchor" href="#usage"></a></h4>
+<p></p><div class="r"><div class="sourceCode"><pre><code><span class="va"><a href="../reference/AutogradContext.html">AutogradContext</a></span><span class="op">$</span><span class="fu">new</span><span class="op">(</span>
+  <span class="va">ptr</span>,
+  <span class="va">env</span>,
+  argument_names <span class="op">=</span> <span class="cn">NULL</span>,
+  argument_needs_grad <span class="op">=</span> <span class="cn">NULL</span>
+<span class="op">)</span></code></pre></div><p></p></div>
+</div>
+
+<div class="section">
+<h4 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h4>
+<p></p><div class="arguments"><dl><dt><code>ptr</code></dt>
+<dd><p>pointer to the c++ object</p></dd>
+
+
+<dt><code>env</code></dt>
+<dd><p>environment that encloses both forward and backward</p></dd>
+
+
+<dt><code>argument_names</code></dt>
+<dd><p>names of forward arguments</p></dd>
+
+
+<dt><code>argument_needs_grad</code></dt>
+<dd><p>whether each argument in forward needs grad.</p></dd>
+
+
+</dl><p></p></div>
+</div>
+
+</div><p></p><hr><a id="method-save_for_backward"></a><div class="section">
+<h3 id="method-save-for-backward-">Method <code>save_for_backward()</code><a class="anchor" aria-label="anchor" href="#method-save-for-backward-"></a></h3>
 <p>Saves given objects for a future call to backward().</p>
 <p>This should be called at most once, and only from inside the <code>forward()</code>
 method.</p>
 <p>Later, saved objects can be accessed through the <code>saved_variables</code> attribute.
 Before returning them to the user, a check is made to ensure they weren’t used
 in any in-place operation that modified their content.</p>
-<p>Arguments can also be any kind of R object.</p><h4 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Usage</h4>
-<p><div class="r"></p><pre><span class='va'>AutogradContext</span><span class='op'>$</span><span class='fu'>save_for_backward</span><span class='op'>(</span><span class='va'>...</span><span class='op'>)</span></pre><p></div></p>
+<p>Arguments can also be any kind of R object.</p><div class="section">
+<h4 id="usage-1">Usage<a class="anchor" aria-label="anchor" href="#usage-1"></a></h4>
+<p></p><div class="r"><div class="sourceCode"><pre><code><span class="va">AutogradContext</span><span class="op">$</span><span class="fu">save_for_backward</span><span class="op">(</span><span class="va">...</span><span class="op">)</span></code></pre></div><p></p></div>
+</div>
 
-<h4 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Arguments</h4>
-<p><div class="arguments"></p><dl>
-<dt><code>...</code></dt><dd><p>any kind of R object that will be saved for the backward pass.
+<div class="section">
+<h4 id="arguments-1">Arguments<a class="anchor" aria-label="anchor" href="#arguments-1"></a></h4>
+<p></p><div class="arguments"><dl><dt><code>...</code></dt>
+<dd><p>any kind of R object that will be saved for the backward pass.
 It's common to pass named arguments.</p></dd>
 
-</dl><p></div></p>
-<p><hr>
-<a id="method-mark_non_differentiable"></a></p><h3 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Method <code>mark_non_differentiable()</code></h3>
+
+</dl><p></p></div>
+</div>
+
+</div><p></p><hr><a id="method-mark_non_differentiable"></a><div class="section">
+<h3 id="method-mark-non-differentiable-">Method <code>mark_non_differentiable()</code><a class="anchor" aria-label="anchor" href="#method-mark-non-differentiable-"></a></h3>
 <p>Marks outputs as non-differentiable.</p>
 <p>This should be called at most once, only from inside the <code>forward()</code> method,
 and all arguments should be outputs.</p>
@@ -266,67 +180,78 @@ and all arguments should be outputs.</p>
 of backward computation. You still need to accept a gradient for each output
 in <code>backward()</code>, but it’s always going to be a zero tensor with the same
 shape as the shape of a corresponding output.</p>
-<p>This is used e.g. for indices returned from a max Function.</p><h4 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Usage</h4>
-<p><div class="r"></p><pre><span class='va'>AutogradContext</span><span class='op'>$</span><span class='fu'>mark_non_differentiable</span><span class='op'>(</span><span class='va'>...</span><span class='op'>)</span></pre><p></div></p>
+<p>This is used e.g. for indices returned from a max Function.</p><div class="section">
+<h4 id="usage-2">Usage<a class="anchor" aria-label="anchor" href="#usage-2"></a></h4>
+<p></p><div class="r"><div class="sourceCode"><pre><code><span class="va">AutogradContext</span><span class="op">$</span><span class="fu">mark_non_differentiable</span><span class="op">(</span><span class="va">...</span><span class="op">)</span></code></pre></div><p></p></div>
+</div>
+
+<div class="section">
+<h4 id="arguments-2">Arguments<a class="anchor" aria-label="anchor" href="#arguments-2"></a></h4>
+<p></p><div class="arguments"><dl><dt><code>...</code></dt>
+<dd><p>non-differentiable outputs.</p></dd>
 
-<h4 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Arguments</h4>
-<p><div class="arguments"></p><dl>
-<dt><code>...</code></dt><dd><p>non-differentiable outputs.</p></dd>
 
-</dl><p></div></p>
-<p><hr>
-<a id="method-mark_dirty"></a></p><h3 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Method <code>mark_dirty()</code></h3>
+</dl><p></p></div>
+</div>
+
+</div><p></p><hr><a id="method-mark_dirty"></a><div class="section">
+<h3 id="method-mark-dirty-">Method <code>mark_dirty()</code><a class="anchor" aria-label="anchor" href="#method-mark-dirty-"></a></h3>
 <p>Marks given tensors as modified in an in-place operation.</p>
 <p>This should be called at most once, only from inside the <code>forward()</code> method,
 and all arguments should be inputs.</p>
 <p>Every tensor that’s been modified in-place in a call to <code>forward()</code> should
 be given to this function, to ensure correctness of our checks. It doesn’t
-matter whether the function is called before or after modification.</p><h4 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Usage</h4>
-<p><div class="r"></p><pre><span class='va'>AutogradContext</span><span class='op'>$</span><span class='fu'>mark_dirty</span><span class='op'>(</span><span class='va'>...</span><span class='op'>)</span></pre><p></div></p>
+matter whether the function is called before or after modification.</p><div class="section">
+<h4 id="usage-3">Usage<a class="anchor" aria-label="anchor" href="#usage-3"></a></h4>
+<p></p><div class="r"><div class="sourceCode"><pre><code><span class="va">AutogradContext</span><span class="op">$</span><span class="fu">mark_dirty</span><span class="op">(</span><span class="va">...</span><span class="op">)</span></code></pre></div><p></p></div>
+</div>
 
-<h4 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Arguments</h4>
-<p><div class="arguments"></p><dl>
-<dt><code>...</code></dt><dd><p>tensors that are modified in-place.</p></dd>
+<div class="section">
+<h4 id="arguments-3">Arguments<a class="anchor" aria-label="anchor" href="#arguments-3"></a></h4>
+<p></p><div class="arguments"><dl><dt><code>...</code></dt>
+<dd><p>tensors that are modified in-place.</p></dd>
 
-</dl><p></div></p>
-<p><hr>
-<a id="method-clone"></a></p><h3 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Method <code>clone()</code></h3>
-<p>The objects of this class are cloneable with this method.</p><h4 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Usage</h4>
-<p><div class="r"></p><pre><span class='va'>AutogradContext</span><span class='op'>$</span><span class='fu'>clone</span><span class='op'>(</span>deep <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span></pre><p></div></p>
 
-<h4 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Arguments</h4>
-<p><div class="arguments"></p><dl>
-<dt><code>deep</code></dt><dd><p>Whether to make a deep clone.</p></dd>
+</dl><p></p></div>
+</div>
 
-</dl><p></div></p>
+</div><p></p><hr><a id="method-clone"></a><div class="section">
+<h3 id="method-clone-">Method <code>clone()</code><a class="anchor" aria-label="anchor" href="#method-clone-"></a></h3>
+<p>The objects of this class are cloneable with this method.</p><div class="section">
+<h4 id="usage-4">Usage<a class="anchor" aria-label="anchor" href="#usage-4"></a></h4>
+<p></p><div class="r"><div class="sourceCode"><pre><code><span class="va">AutogradContext</span><span class="op">$</span><span class="fu">clone</span><span class="op">(</span>deep <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div><p></p></div>
+</div>
 
+<div class="section">
+<h4 id="arguments-4">Arguments<a class="anchor" aria-label="anchor" href="#arguments-4"></a></h4>
+<p></p><div class="arguments"><dl><dt><code>deep</code></dt>
+<dd><p>Whether to make a deep clone.</p></dd>
 
 
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
+</dl><p></p></div>
 </div>
 
+</div>
+
+    </div>
 
-      <footer>
-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
 </div>
 
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+<div class="pkgdown-footer-right">
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diff --git a/reference/Constraint.html b/reference/Constraint.html
index 6b1da7b28..f93daab99 100644
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+++ b/reference/Constraint.html
@@ -1,266 +1,165 @@
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Abstract base class for constraints.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/distributions-constraints.R'><code>R/distributions-constraints.R</code></a></small>
-    <div class="hidden name"><code>Constraint.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Abstract base class for constraints.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/distributions-constraints.R" class="external-link"><code>R/distributions-constraints.R</code></a></small>
+      <div class="d-none name"><code>Constraint.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Abstract base class for constraints.</p>
 <p>Abstract base class for constraints.</p>
     </div>
 
 
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>A constraint object represents a region over which a variable is valid,
 e.g. within which a variable can be optimized.</p>
-    <h2 class="hasAnchor" id="methods"><a class="anchor" href="#methods"></a>Methods</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="methods">Methods<a class="anchor" aria-label="anchor" href="#methods"></a></h2>
     
-<h3 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Public methods</h3>
-
-<ul>
-<li><p><a href='#method-check'><code>Constraint$check()</code></a></p></li>
-<li><p><a href='#method-print'><code>Constraint$print()</code></a></p></li>
-<li><p><a href='#method-clone'><code>Constraint$clone()</code></a></p></li>
-</ul>
-<p><hr>
-<a id="method-check"></a></p><h3 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Method <code><a href='https://rdrr.io/r/utils/PkgUtils.html'>check()</a></code></h3>
+<div class="section">
+<h3 id="public-methods">Public methods<a class="anchor" aria-label="anchor" href="#public-methods"></a></h3>
+
+<ul><li><p><a href="#method-check"><code>Constraint$check()</code></a></p></li>
+<li><p><a href="#method-print"><code>Constraint$print()</code></a></p></li>
+<li><p><a href="#method-clone"><code>Constraint$clone()</code></a></p></li>
+</ul></div><p></p><hr><a id="method-check"></a><div class="section">
+<h3 id="method-check-">Method <code><a href="https://rdrr.io/r/utils/PkgUtils.html" class="external-link">check()</a></code><a class="anchor" aria-label="anchor" href="#method-check-"></a></h3>
 <p>Returns a byte tensor of <code>sample_shape + batch_shape</code> indicating
-whether each event in value satisfies this constraint.</p><h4 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Usage</h4>
-<p><div class="r"></p><pre><span class='va'>Constraint</span><span class='op'>$</span><span class='fu'>check</span><span class='op'>(</span><span class='va'>value</span><span class='op'>)</span></pre><p></div></p>
+whether each event in value satisfies this constraint.</p><div class="section">
+<h4 id="usage">Usage<a class="anchor" aria-label="anchor" href="#usage"></a></h4>
+<p></p><div class="r"><div class="sourceCode"><pre><code><span class="va">Constraint</span><span class="op">$</span><span class="fu">check</span><span class="op">(</span><span class="va">value</span><span class="op">)</span></code></pre></div><p></p></div>
+</div>
 
-<h4 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Arguments</h4>
-<p><div class="arguments"></p><dl>
-<dt><code>value</code></dt><dd><p>each event in value will be checked.</p></dd>
+<div class="section">
+<h4 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h4>
+<p></p><div class="arguments"><dl><dt><code>value</code></dt>
+<dd><p>each event in value will be checked.</p></dd>
 
-</dl><p></div></p>
-<p><hr>
-<a id="method-print"></a></p><h3 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Method <code><a href='https://rdrr.io/r/base/print.html'>print()</a></code></h3>
-<p>Define the print method for constraints,</p><h4 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Usage</h4>
-<p><div class="r"></p><pre><span class='va'>Constraint</span><span class='op'>$</span><span class='fu'>print</span><span class='op'>(</span><span class='op'>)</span></pre><p></div></p>
 
-<p><hr>
-<a id="method-clone"></a></p><h3 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Method <code>clone()</code></h3>
-<p>The objects of this class are cloneable with this method.</p><h4 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Usage</h4>
-<p><div class="r"></p><pre><span class='va'>Constraint</span><span class='op'>$</span><span class='fu'>clone</span><span class='op'>(</span>deep <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span></pre><p></div></p>
+</dl><p></p></div>
+</div>
+
+</div><p></p><hr><a id="method-print"></a><div class="section">
+<h3 id="method-print-">Method <code><a href="https://rdrr.io/r/base/print.html" class="external-link">print()</a></code><a class="anchor" aria-label="anchor" href="#method-print-"></a></h3>
+<p>Define the print method for constraints,</p><div class="section">
+<h4 id="usage-1">Usage<a class="anchor" aria-label="anchor" href="#usage-1"></a></h4>
+<p></p><div class="r"><div class="sourceCode"><pre><code><span class="va">Constraint</span><span class="op">$</span><span class="fu">print</span><span class="op">(</span><span class="op">)</span></code></pre></div><p></p></div>
+</div>
 
-<h4 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Arguments</h4>
-<p><div class="arguments"></p><dl>
-<dt><code>deep</code></dt><dd><p>Whether to make a deep clone.</p></dd>
 
-</dl><p></div></p>
+</div><p></p><hr><a id="method-clone"></a><div class="section">
+<h3 id="method-clone-">Method <code>clone()</code><a class="anchor" aria-label="anchor" href="#method-clone-"></a></h3>
+<p>The objects of this class are cloneable with this method.</p><div class="section">
+<h4 id="usage-2">Usage<a class="anchor" aria-label="anchor" href="#usage-2"></a></h4>
+<p></p><div class="r"><div class="sourceCode"><pre><code><span class="va">Constraint</span><span class="op">$</span><span class="fu">clone</span><span class="op">(</span>deep <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div><p></p></div>
+</div>
 
+<div class="section">
+<h4 id="arguments-1">Arguments<a class="anchor" aria-label="anchor" href="#arguments-1"></a></h4>
+<p></p><div class="arguments"><dl><dt><code>deep</code></dt>
+<dd><p>Whether to make a deep clone.</p></dd>
 
 
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+Try for example: torch.Size((2, 1)) + torch.Size((1,))"><title>Generic R6 class representing distributions — Distribution • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.1.0/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.1.0/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Generic R6 class representing distributions — Distribution"><meta property="og:description" content="Distribution is the abstract base class for probability distributions.
+Note: in Python, adding torch.Size objects works as concatenation
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Generic R6 class representing distributions</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/distributions.R'><code>R/distributions.R</code></a></small>
-    <div class="hidden name"><code>Distribution.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Generic R6 class representing distributions</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/distributions.R" class="external-link"><code>R/distributions.R</code></a></small>
+      <div class="d-none name"><code>Distribution.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Distribution is the abstract base class for probability distributions.
 Note: in Python, adding torch.Size objects works as concatenation
 Try for example: torch.Size((2, 1)) + torch.Size((1,))</p>
     </div>
 
 
+    <div class="section level2">
+    <h2 id="public-fields">Public fields<a class="anchor" aria-label="anchor" href="#public-fields"></a></h2>
+    <p></p><div class="r6-fields"><dl><dt><code>.validate_args</code></dt>
+<dd><p>whether to validate arguments</p></dd>
 
-    <h2 class="hasAnchor" id="public-fields"><a class="anchor" href="#public-fields"></a>Public fields</h2>
 
-    <p><div class="r6-fields"></p><dl>
-<dt><code>.validate_args</code></dt><dd><p>whether to validate arguments</p></dd>
+<dt><code>has_rsample</code></dt>
+<dd><p>whether has an rsample</p></dd>
 
-<dt><code>has_rsample</code></dt><dd><p>whether has an rsample</p></dd>
 
-<dt><code>has_enumerate_support</code></dt><dd><p>whether has enumerate support</p></dd>
+<dt><code>has_enumerate_support</code></dt>
+<dd><p>whether has enumerate support</p></dd>
 
-</dl><p></div></p>
-    <h2 class="hasAnchor" id="active-bindings"><a class="anchor" href="#active-bindings"></a>Active bindings</h2>
 
-    <p><div class="r6-active-bindings"></p><dl>
-<dt><code>batch_shape</code></dt><dd><p>Returns the shape over which parameters are batched.</p></dd>
+</dl><p></p></div>
+    </div>
+    <div class="section level2">
+    <h2 id="active-bindings">Active bindings<a class="anchor" aria-label="anchor" href="#active-bindings"></a></h2>
+    <p></p><div class="r6-active-bindings"><dl><dt><code>batch_shape</code></dt>
+<dd><p>Returns the shape over which parameters are batched.</p></dd>
+
 
-<dt><code>event_shape</code></dt><dd><p>Returns the shape of a single sample (without batching).
+<dt><code>event_shape</code></dt>
+<dd><p>Returns the shape of a single sample (without batching).
 Returns a dictionary from argument names to
 <code>torch_Constraint</code> objects that
 should be satisfied by each argument of this distribution. Args that
 are not tensors need not appear in this dict.</p></dd>
 
-<dt><code>support</code></dt><dd><p>Returns a <code>torch_Constraint</code> object representing this distribution's
+
+<dt><code>support</code></dt>
+<dd><p>Returns a <code>torch_Constraint</code> object representing this distribution's
 support.</p></dd>
 
-<dt><code>mean</code></dt><dd><p>Returns the mean on of the distribution</p></dd>
 
-<dt><code>variance</code></dt><dd><p>Returns the variance of the distribution</p></dd>
+<dt><code>mean</code></dt>
+<dd><p>Returns the mean on of the distribution</p></dd>
+
 
-<dt><code>stddev</code></dt><dd><p>Returns the standard deviation of the distribution
+<dt><code>variance</code></dt>
+<dd><p>Returns the variance of the distribution</p></dd>
+
+
+<dt><code>stddev</code></dt>
+<dd><p>Returns the standard deviation of the distribution
 TODO: consider different message</p></dd>
 
-</dl><p></div></p>
-    <h2 class="hasAnchor" id="methods"><a class="anchor" href="#methods"></a>Methods</h2>
 
+</dl><p></p></div>
+    </div>
+    <div class="section level2">
+    <h2 id="methods">Methods<a class="anchor" aria-label="anchor" href="#methods"></a></h2>
     
-<h3 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Public methods</h3>
-
-<ul>
-<li><p><a href='#method-new'><code>Distribution$new()</code></a></p></li>
-<li><p><a href='#method-expand'><code>Distribution$expand()</code></a></p></li>
-<li><p><a href='#method-sample'><code>Distribution$sample()</code></a></p></li>
-<li><p><a href='#method-rsample'><code>Distribution$rsample()</code></a></p></li>
-<li><p><a href='#method-log_prob'><code>Distribution$log_prob()</code></a></p></li>
-<li><p><a href='#method-cdf'><code>Distribution$cdf()</code></a></p></li>
-<li><p><a href='#method-icdf'><code>Distribution$icdf()</code></a></p></li>
-<li><p><a href='#method-enumerate_support'><code>Distribution$enumerate_support()</code></a></p></li>
-<li><p><a href='#method-entropy'><code>Distribution$entropy()</code></a></p></li>
-<li><p><a href='#method-perplexity'><code>Distribution$perplexity()</code></a></p></li>
-<li><p><a href='#method-.extended_shape'><code>Distribution$.extended_shape()</code></a></p></li>
-<li><p><a href='#method-.validate_sample'><code>Distribution$.validate_sample()</code></a></p></li>
-<li><p><a href='#method-print'><code>Distribution$print()</code></a></p></li>
-<li><p><a href='#method-clone'><code>Distribution$clone()</code></a></p></li>
-</ul>
-<p><hr>
-<a id="method-new"></a></p><h3 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Method <code>new()</code></h3>
-<p>Initializes a distribution class.</p><h4 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Usage</h4>
-<p><div class="r"></p><pre><span class='va'>Distribution</span><span class='op'>$</span><span class='fu'>new</span><span class='op'>(</span>batch_shape <span class='op'>=</span> <span class='cn'>NULL</span>, event_shape <span class='op'>=</span> <span class='cn'>NULL</span>, validate_args <span class='op'>=</span> <span class='cn'>NULL</span><span class='op'>)</span></pre><p></div></p>
-
-<h4 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Arguments</h4>
-<p><div class="arguments"></p><dl>
-<dt><code>batch_shape</code></dt><dd><p>the shape over which parameters are batched.</p></dd>
-
-<dt><code>event_shape</code></dt><dd><p>the shape of a single sample (without batching).</p></dd>
-
-<dt><code>validate_args</code></dt><dd><p>whether to validate the arguments or not. Validation
+<div class="section">
+<h3 id="public-methods">Public methods<a class="anchor" aria-label="anchor" href="#public-methods"></a></h3>
+
+<ul><li><p><a href="#method-new"><code>Distribution$new()</code></a></p></li>
+<li><p><a href="#method-expand"><code>Distribution$expand()</code></a></p></li>
+<li><p><a href="#method-sample"><code>Distribution$sample()</code></a></p></li>
+<li><p><a href="#method-rsample"><code>Distribution$rsample()</code></a></p></li>
+<li><p><a href="#method-log_prob"><code>Distribution$log_prob()</code></a></p></li>
+<li><p><a href="#method-cdf"><code>Distribution$cdf()</code></a></p></li>
+<li><p><a href="#method-icdf"><code>Distribution$icdf()</code></a></p></li>
+<li><p><a href="#method-enumerate_support"><code>Distribution$enumerate_support()</code></a></p></li>
+<li><p><a href="#method-entropy"><code>Distribution$entropy()</code></a></p></li>
+<li><p><a href="#method-perplexity"><code>Distribution$perplexity()</code></a></p></li>
+<li><p><a href="#method-.extended_shape"><code>Distribution$.extended_shape()</code></a></p></li>
+<li><p><a href="#method-.validate_sample"><code>Distribution$.validate_sample()</code></a></p></li>
+<li><p><a href="#method-print"><code>Distribution$print()</code></a></p></li>
+<li><p><a href="#method-clone"><code>Distribution$clone()</code></a></p></li>
+</ul></div><p></p><hr><a id="method-new"></a><div class="section">
+<h3 id="method-new-">Method <code>new()</code><a class="anchor" aria-label="anchor" href="#method-new-"></a></h3>
+<p>Initializes a distribution class.</p><div class="section">
+<h4 id="usage">Usage<a class="anchor" aria-label="anchor" href="#usage"></a></h4>
+<p></p><div class="r"><div class="sourceCode"><pre><code><span class="va"><a href="../reference/Distribution.html">Distribution</a></span><span class="op">$</span><span class="fu">new</span><span class="op">(</span>batch_shape <span class="op">=</span> <span class="cn">NULL</span>, event_shape <span class="op">=</span> <span class="cn">NULL</span>, validate_args <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span></code></pre></div><p></p></div>
+</div>
+
+<div class="section">
+<h4 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h4>
+<p></p><div class="arguments"><dl><dt><code>batch_shape</code></dt>
+<dd><p>the shape over which parameters are batched.</p></dd>
+
+
+<dt><code>event_shape</code></dt>
+<dd><p>the shape of a single sample (without batching).</p></dd>
+
+
+<dt><code>validate_args</code></dt>
+<dd><p>whether to validate the arguments or not. Validation
 can be time consuming so you might want to disable it.</p></dd>
 
-</dl><p></div></p>
-<p><hr>
-<a id="method-expand"></a></p><h3 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Method <code>expand()</code></h3>
+
+</dl><p></p></div>
+</div>
+
+</div><p></p><hr><a id="method-expand"></a><div class="section">
+<h3 id="method-expand-">Method <code>expand()</code><a class="anchor" aria-label="anchor" href="#method-expand-"></a></h3>
 <p>Returns a new distribution instance (or populates an existing instance
 provided by a derived class) with batch dimensions expanded to batch_shape.
 This method calls expand on the distribution’s parameters. As such, this
 does not allocate new memory for the expanded distribution instance.
 Additionally, this does not repeat any args checking or parameter
-broadcasting in <code>initialize</code>, when an instance is first created.</p><h4 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Usage</h4>
-<p><div class="r"></p><pre><span class='va'>Distribution</span><span class='op'>$</span><span class='fu'>expand</span><span class='op'>(</span><span class='va'>batch_shape</span>, .instance <span class='op'>=</span> <span class='cn'>NULL</span><span class='op'>)</span></pre><p></div></p>
+broadcasting in <code>initialize</code>, when an instance is first created.</p><div class="section">
+<h4 id="usage-1">Usage<a class="anchor" aria-label="anchor" href="#usage-1"></a></h4>
+<p></p><div class="r"><div class="sourceCode"><pre><code><span class="va">Distribution</span><span class="op">$</span><span class="fu">expand</span><span class="op">(</span><span class="va">batch_shape</span>, .instance <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span></code></pre></div><p></p></div>
+</div>
+
+<div class="section">
+<h4 id="arguments-1">Arguments<a class="anchor" aria-label="anchor" href="#arguments-1"></a></h4>
+<p></p><div class="arguments"><dl><dt><code>batch_shape</code></dt>
+<dd><p>the desired expanded size.</p></dd>
 
-<h4 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Arguments</h4>
-<p><div class="arguments"></p><dl>
-<dt><code>batch_shape</code></dt><dd><p>the desired expanded size.</p></dd>
 
-<dt><code>.instance</code></dt><dd><p>new instance provided by subclasses that need to
+<dt><code>.instance</code></dt>
+<dd><p>new instance provided by subclasses that need to
 override <code>expand</code>.</p></dd>
 
-</dl><p></div></p>
-<p><hr>
-<a id="method-sample"></a></p><h3 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Method <code><a href='https://rdrr.io/r/base/sample.html'>sample()</a></code></h3>
+
+</dl><p></p></div>
+</div>
+
+</div><p></p><hr><a id="method-sample"></a><div class="section">
+<h3 id="method-sample-">Method <code><a href="https://rdrr.io/r/base/sample.html" class="external-link">sample()</a></code><a class="anchor" aria-label="anchor" href="#method-sample-"></a></h3>
 <p>Generates a <code>sample_shape</code> shaped sample or <code>sample_shape</code> shaped batch of
-samples if the distribution parameters are batched.</p><h4 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Usage</h4>
-<p><div class="r"></p><pre><span class='va'>Distribution</span><span class='op'>$</span><span class='fu'>sample</span><span class='op'>(</span>sample_shape <span class='op'>=</span> <span class='cn'>NULL</span><span class='op'>)</span></pre><p></div></p>
+samples if the distribution parameters are batched.</p><div class="section">
+<h4 id="usage-2">Usage<a class="anchor" aria-label="anchor" href="#usage-2"></a></h4>
+<p></p><div class="r"><div class="sourceCode"><pre><code><span class="va">Distribution</span><span class="op">$</span><span class="fu">sample</span><span class="op">(</span>sample_shape <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span></code></pre></div><p></p></div>
+</div>
+
+<div class="section">
+<h4 id="arguments-2">Arguments<a class="anchor" aria-label="anchor" href="#arguments-2"></a></h4>
+<p></p><div class="arguments"><dl><dt><code>sample_shape</code></dt>
+<dd><p>the shape you want to sample.</p></dd>
 
-<h4 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Arguments</h4>
-<p><div class="arguments"></p><dl>
-<dt><code>sample_shape</code></dt><dd><p>the shape you want to sample.</p></dd>
 
-</dl><p></div></p>
-<p><hr>
-<a id="method-rsample"></a></p><h3 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Method <code>rsample()</code></h3>
+</dl><p></p></div>
+</div>
+
+</div><p></p><hr><a id="method-rsample"></a><div class="section">
+<h3 id="method-rsample-">Method <code>rsample()</code><a class="anchor" aria-label="anchor" href="#method-rsample-"></a></h3>
 <p>Generates a sample_shape shaped reparameterized sample or sample_shape
 shaped batch of reparameterized samples if the distribution parameters
-are batched.</p><h4 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Usage</h4>
-<p><div class="r"></p><pre><span class='va'>Distribution</span><span class='op'>$</span><span class='fu'>rsample</span><span class='op'>(</span>sample_shape <span class='op'>=</span> <span class='cn'>NULL</span><span class='op'>)</span></pre><p></div></p>
+are batched.</p><div class="section">
+<h4 id="usage-3">Usage<a class="anchor" aria-label="anchor" href="#usage-3"></a></h4>
+<p></p><div class="r"><div class="sourceCode"><pre><code><span class="va">Distribution</span><span class="op">$</span><span class="fu">rsample</span><span class="op">(</span>sample_shape <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span></code></pre></div><p></p></div>
+</div>
+
+<div class="section">
+<h4 id="arguments-3">Arguments<a class="anchor" aria-label="anchor" href="#arguments-3"></a></h4>
+<p></p><div class="arguments"><dl><dt><code>sample_shape</code></dt>
+<dd><p>the shape you want to sample.</p></dd>
+
 
-<h4 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Arguments</h4>
-<p><div class="arguments"></p><dl>
-<dt><code>sample_shape</code></dt><dd><p>the shape you want to sample.</p></dd>
+</dl><p></p></div>
+</div>
 
-</dl><p></div></p>
-<p><hr>
-<a id="method-log_prob"></a></p><h3 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Method <code>log_prob()</code></h3>
+</div><p></p><hr><a id="method-log_prob"></a><div class="section">
+<h3 id="method-log-prob-">Method <code>log_prob()</code><a class="anchor" aria-label="anchor" href="#method-log-prob-"></a></h3>
 <p>Returns the log of the probability density/mass function evaluated at
-<code>value</code>.</p><h4 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Usage</h4>
-<p><div class="r"></p><pre><span class='va'>Distribution</span><span class='op'>$</span><span class='fu'>log_prob</span><span class='op'>(</span><span class='va'>value</span><span class='op'>)</span></pre><p></div></p>
+<code>value</code>.</p><div class="section">
+<h4 id="usage-4">Usage<a class="anchor" aria-label="anchor" href="#usage-4"></a></h4>
+<p></p><div class="r"><div class="sourceCode"><pre><code><span class="va">Distribution</span><span class="op">$</span><span class="fu">log_prob</span><span class="op">(</span><span class="va">value</span><span class="op">)</span></code></pre></div><p></p></div>
+</div>
 
-<h4 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Arguments</h4>
-<p><div class="arguments"></p><dl>
-<dt><code>value</code></dt><dd><p>values to evaluate the density on.</p></dd>
+<div class="section">
+<h4 id="arguments-4">Arguments<a class="anchor" aria-label="anchor" href="#arguments-4"></a></h4>
+<p></p><div class="arguments"><dl><dt><code>value</code></dt>
+<dd><p>values to evaluate the density on.</p></dd>
 
-</dl><p></div></p>
-<p><hr>
-<a id="method-cdf"></a></p><h3 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Method <code>cdf()</code></h3>
+
+</dl><p></p></div>
+</div>
+
+</div><p></p><hr><a id="method-cdf"></a><div class="section">
+<h3 id="method-cdf-">Method <code>cdf()</code><a class="anchor" aria-label="anchor" href="#method-cdf-"></a></h3>
 <p>Returns the cumulative density/mass function evaluated at
-<code>value</code>.</p><h4 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Usage</h4>
-<p><div class="r"></p><pre><span class='va'>Distribution</span><span class='op'>$</span><span class='fu'>cdf</span><span class='op'>(</span><span class='va'>value</span><span class='op'>)</span></pre><p></div></p>
+<code>value</code>.</p><div class="section">
+<h4 id="usage-5">Usage<a class="anchor" aria-label="anchor" href="#usage-5"></a></h4>
+<p></p><div class="r"><div class="sourceCode"><pre><code><span class="va">Distribution</span><span class="op">$</span><span class="fu">cdf</span><span class="op">(</span><span class="va">value</span><span class="op">)</span></code></pre></div><p></p></div>
+</div>
+
+<div class="section">
+<h4 id="arguments-5">Arguments<a class="anchor" aria-label="anchor" href="#arguments-5"></a></h4>
+<p></p><div class="arguments"><dl><dt><code>value</code></dt>
+<dd><p>values to evaluate the density on.</p></dd>
 
-<h4 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Arguments</h4>
-<p><div class="arguments"></p><dl>
-<dt><code>value</code></dt><dd><p>values to evaluate the density on.</p></dd>
 
-</dl><p></div></p>
-<p><hr>
-<a id="method-icdf"></a></p><h3 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Method <code>icdf()</code></h3>
+</dl><p></p></div>
+</div>
+
+</div><p></p><hr><a id="method-icdf"></a><div class="section">
+<h3 id="method-icdf-">Method <code>icdf()</code><a class="anchor" aria-label="anchor" href="#method-icdf-"></a></h3>
 <p>Returns the inverse cumulative density/mass function evaluated at
 <code>value</code>.</p>
 <p>@description
 Returns tensor containing all values supported by a discrete
 distribution. The result will enumerate over dimension 0, so the shape
-of the result will be <code>(cardinality,) + batch_shape + event_shape (where </code>event_shape = ()<code>for univariate distributions). Note that this enumerates over all batched tensors in lock-step</code>list(c(0, 0), c(1, 1), ...)<code>. With </code>expand=FALSE<code>, enumeration happens along dim 0, but with the remaining batch dimensions being singleton dimensions, </code>list(c(0), c(1), ...)`.</p><h4 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Usage</h4>
-<p><div class="r"></p><pre><span class='va'>Distribution</span><span class='op'>$</span><span class='fu'>icdf</span><span class='op'>(</span><span class='va'>value</span><span class='op'>)</span></pre><p></div></p>
+of the result will be <code>(cardinality,) + batch_shape + event_shape (where </code>event_shape = ()<code>for univariate distributions). Note that this enumerates over all batched tensors in lock-step</code>list(c(0, 0), c(1, 1), ...)<code>. With </code>expand=FALSE<code>, enumeration happens along dim 0, but with the remaining batch dimensions being singleton dimensions, </code>list(c(0), c(1), ...)`.</p><div class="section">
+<h4 id="usage-6">Usage<a class="anchor" aria-label="anchor" href="#usage-6"></a></h4>
+<p></p><div class="r"><div class="sourceCode"><pre><code><span class="va">Distribution</span><span class="op">$</span><span class="fu">icdf</span><span class="op">(</span><span class="va">value</span><span class="op">)</span></code></pre></div><p></p></div>
+</div>
 
-<h4 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Arguments</h4>
-<p><div class="arguments"></p><dl>
-<dt><code>value</code></dt><dd><p>values to evaluate the density on.</p></dd>
+<div class="section">
+<h4 id="arguments-6">Arguments<a class="anchor" aria-label="anchor" href="#arguments-6"></a></h4>
+<p></p><div class="arguments"><dl><dt><code>value</code></dt>
+<dd><p>values to evaluate the density on.</p></dd>
 
-</dl><p></div></p>
-<p><hr>
-<a id="method-enumerate_support"></a></p><h3 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Method <code>enumerate_support()</code></h3>
 
-<h4 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Usage</h4>
-<p><div class="r"></p><pre><span class='va'>Distribution</span><span class='op'>$</span><span class='fu'>enumerate_support</span><span class='op'>(</span>expand <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span></pre><p></div></p>
+</dl><p></p></div>
+</div>
+
+</div><p></p><hr><a id="method-enumerate_support"></a><div class="section">
+<h3 id="method-enumerate-support-">Method <code>enumerate_support()</code><a class="anchor" aria-label="anchor" href="#method-enumerate-support-"></a></h3>
+
+<div class="section">
+<h4 id="usage-7">Usage<a class="anchor" aria-label="anchor" href="#usage-7"></a></h4>
+<p></p><div class="r"><div class="sourceCode"><pre><code><span class="va">Distribution</span><span class="op">$</span><span class="fu">enumerate_support</span><span class="op">(</span>expand <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></code></pre></div><p></p></div>
+</div>
 
-<h4 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Arguments</h4>
-<p><div class="arguments"></p><dl>
-<dt><code>expand</code></dt><dd><p>(bool): whether to expand the support over the
+<div class="section">
+<h4 id="arguments-7">Arguments<a class="anchor" aria-label="anchor" href="#arguments-7"></a></h4>
+<p></p><div class="arguments"><dl><dt><code>expand</code></dt>
+<dd><p>(bool): whether to expand the support over the
 batch dims to match the distribution's <code>batch_shape</code>.</p></dd>
 
-</dl><p></div></p>
-<h4 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Returns</h4>
+
+</dl><p></p></div>
+</div>
+<div class="section">
+<h4 id="returns">Returns<a class="anchor" aria-label="anchor" href="#returns"></a></h4>
 <p>Tensor iterating over dimension 0.</p>
-<p><hr>
-<a id="method-entropy"></a></p><h3 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Method <code>entropy()</code></h3>
-<p>Returns entropy of distribution, batched over batch_shape.</p><h4 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Usage</h4>
-<p><div class="r"></p><pre><span class='va'>Distribution</span><span class='op'>$</span><span class='fu'>entropy</span><span class='op'>(</span><span class='op'>)</span></pre><p></div></p>
+</div>
 
-<h4 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Returns</h4>
+</div><p></p><hr><a id="method-entropy"></a><div class="section">
+<h3 id="method-entropy-">Method <code>entropy()</code><a class="anchor" aria-label="anchor" href="#method-entropy-"></a></h3>
+<p>Returns entropy of distribution, batched over batch_shape.</p><div class="section">
+<h4 id="usage-8">Usage<a class="anchor" aria-label="anchor" href="#usage-8"></a></h4>
+<p></p><div class="r"><div class="sourceCode"><pre><code><span class="va">Distribution</span><span class="op">$</span><span class="fu">entropy</span><span class="op">(</span><span class="op">)</span></code></pre></div><p></p></div>
+</div>
+
+<div class="section">
+<h4 id="returns-1">Returns<a class="anchor" aria-label="anchor" href="#returns-1"></a></h4>
 <p>Tensor of shape batch_shape.</p>
-<p><hr>
-<a id="method-perplexity"></a></p><h3 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Method <code>perplexity()</code></h3>
-<p>Returns perplexity of distribution, batched over batch_shape.</p><h4 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Usage</h4>
-<p><div class="r"></p><pre><span class='va'>Distribution</span><span class='op'>$</span><span class='fu'>perplexity</span><span class='op'>(</span><span class='op'>)</span></pre><p></div></p>
+</div>
+
+</div><p></p><hr><a id="method-perplexity"></a><div class="section">
+<h3 id="method-perplexity-">Method <code>perplexity()</code><a class="anchor" aria-label="anchor" href="#method-perplexity-"></a></h3>
+<p>Returns perplexity of distribution, batched over batch_shape.</p><div class="section">
+<h4 id="usage-9">Usage<a class="anchor" aria-label="anchor" href="#usage-9"></a></h4>
+<p></p><div class="r"><div class="sourceCode"><pre><code><span class="va">Distribution</span><span class="op">$</span><span class="fu">perplexity</span><span class="op">(</span><span class="op">)</span></code></pre></div><p></p></div>
+</div>
 
-<h4 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Returns</h4>
+<div class="section">
+<h4 id="returns-2">Returns<a class="anchor" aria-label="anchor" href="#returns-2"></a></h4>
 <p>Tensor of shape batch_shape.</p>
-<p><hr>
-<a id="method-.extended_shape"></a></p><h3 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Method <code>.extended_shape()</code></h3>
+</div>
+
+</div><p></p><hr><a id="method-.extended_shape"></a><div class="section">
+<h3 id="method-extended-shape-">Method <code>.extended_shape()</code><a class="anchor" aria-label="anchor" href="#method-extended-shape-"></a></h3>
 <p>Returns the size of the sample returned by the distribution, given
 a <code>sample_shape</code>. Note, that the batch and event shapes of a distribution
 instance are fixed at the time of construction. If this is empty, the
-returned shape is upcast to (1,).</p><h4 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Usage</h4>
-<p><div class="r"></p><pre><span class='va'>Distribution</span><span class='op'>$</span><span class='fu'>.extended_shape</span><span class='op'>(</span>sample_shape <span class='op'>=</span> <span class='cn'>NULL</span><span class='op'>)</span></pre><p></div></p>
+returned shape is upcast to (1,).</p><div class="section">
+<h4 id="usage-10">Usage<a class="anchor" aria-label="anchor" href="#usage-10"></a></h4>
+<p></p><div class="r"><div class="sourceCode"><pre><code><span class="va">Distribution</span><span class="op">$</span><span class="fu">.extended_shape</span><span class="op">(</span>sample_shape <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span></code></pre></div><p></p></div>
+</div>
 
-<h4 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Arguments</h4>
-<p><div class="arguments"></p><dl>
-<dt><code>sample_shape</code></dt><dd><p>(torch_Size): the size of the sample to be drawn.</p></dd>
+<div class="section">
+<h4 id="arguments-8">Arguments<a class="anchor" aria-label="anchor" href="#arguments-8"></a></h4>
+<p></p><div class="arguments"><dl><dt><code>sample_shape</code></dt>
+<dd><p>(torch_Size): the size of the sample to be drawn.</p></dd>
+
+
+</dl><p></p></div>
+</div>
 
-</dl><p></div></p>
-<p><hr>
-<a id="method-.validate_sample"></a></p><h3 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Method <code>.validate_sample()</code></h3>
+</div><p></p><hr><a id="method-.validate_sample"></a><div class="section">
+<h3 id="method-validate-sample-">Method <code>.validate_sample()</code><a class="anchor" aria-label="anchor" href="#method-validate-sample-"></a></h3>
 <p>Argument validation for distribution methods such as <code>log_prob</code>,
 <code>cdf</code> and <code>icdf</code>. The rightmost dimensions of a value to be
 scored via these methods must agree with the distribution's batch
-and event shapes.</p><h4 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Usage</h4>
-<p><div class="r"></p><pre><span class='va'>Distribution</span><span class='op'>$</span><span class='fu'>.validate_sample</span><span class='op'>(</span><span class='va'>value</span><span class='op'>)</span></pre><p></div></p>
+and event shapes.</p><div class="section">
+<h4 id="usage-11">Usage<a class="anchor" aria-label="anchor" href="#usage-11"></a></h4>
+<p></p><div class="r"><div class="sourceCode"><pre><code><span class="va">Distribution</span><span class="op">$</span><span class="fu">.validate_sample</span><span class="op">(</span><span class="va">value</span><span class="op">)</span></code></pre></div><p></p></div>
+</div>
 
-<h4 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Arguments</h4>
-<p><div class="arguments"></p><dl>
-<dt><code>value</code></dt><dd><p>(Tensor): the tensor whose log probability is to be
+<div class="section">
+<h4 id="arguments-9">Arguments<a class="anchor" aria-label="anchor" href="#arguments-9"></a></h4>
+<p></p><div class="arguments"><dl><dt><code>value</code></dt>
+<dd><p>(Tensor): the tensor whose log probability is to be
 computed by the <code>log_prob</code> method.</p></dd>
 
-</dl><p></div></p>
-<p><hr>
-<a id="method-print"></a></p><h3 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Method <code><a href='https://rdrr.io/r/base/print.html'>print()</a></code></h3>
-<p>Prints the distribution instance.</p><h4 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Usage</h4>
-<p><div class="r"></p><pre><span class='va'>Distribution</span><span class='op'>$</span><span class='fu'>print</span><span class='op'>(</span><span class='op'>)</span></pre><p></div></p>
 
-<p><hr>
-<a id="method-clone"></a></p><h3 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Method <code>clone()</code></h3>
-<p>The objects of this class are cloneable with this method.</p><h4 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Usage</h4>
-<p><div class="r"></p><pre><span class='va'>Distribution</span><span class='op'>$</span><span class='fu'>clone</span><span class='op'>(</span>deep <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span></pre><p></div></p>
+</dl><p></p></div>
+</div>
 
-<h4 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Arguments</h4>
-<p><div class="arguments"></p><dl>
-<dt><code>deep</code></dt><dd><p>Whether to make a deep clone.</p></dd>
+</div><p></p><hr><a id="method-print"></a><div class="section">
+<h3 id="method-print-">Method <code><a href="https://rdrr.io/r/base/print.html" class="external-link">print()</a></code><a class="anchor" aria-label="anchor" href="#method-print-"></a></h3>
+<p>Prints the distribution instance.</p><div class="section">
+<h4 id="usage-12">Usage<a class="anchor" aria-label="anchor" href="#usage-12"></a></h4>
+<p></p><div class="r"><div class="sourceCode"><pre><code><span class="va">Distribution</span><span class="op">$</span><span class="fu">print</span><span class="op">(</span><span class="op">)</span></code></pre></div><p></p></div>
+</div>
 
-</dl><p></div></p>
 
+</div><p></p><hr><a id="method-clone"></a><div class="section">
+<h3 id="method-clone-">Method <code>clone()</code><a class="anchor" aria-label="anchor" href="#method-clone-"></a></h3>
+<p>The objects of this class are cloneable with this method.</p><div class="section">
+<h4 id="usage-13">Usage<a class="anchor" aria-label="anchor" href="#usage-13"></a></h4>
+<p></p><div class="r"><div class="sourceCode"><pre><code><span class="va">Distribution</span><span class="op">$</span><span class="fu">clone</span><span class="op">(</span>deep <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div><p></p></div>
+</div>
 
+<div class="section">
+<h4 id="arguments-10">Arguments<a class="anchor" aria-label="anchor" href="#arguments-10"></a></h4>
+<p></p><div class="arguments"><dl><dt><code>deep</code></dt>
+<dd><p>Whether to make a deep clone.</p></dd>
 
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+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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diff --git a/reference/as_array.html b/reference/as_array.html
index 704304feb..329a954f5 100644
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-    <div class="hidden name"><code>as_array.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Converts to array</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/tensor.R" class="external-link"><code>R/tensor.R</code></a></small>
+      <div class="d-none name"><code>as_array.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Converts to array</p>
     </div>
 
-    <pre class="usage"><span class='fu'>as_array</span><span class='op'>(</span><span class='va'>x</span><span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">as_array</span><span class="op">(</span><span class="va">x</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>x</th>
-      <td><p>object to be converted into an array</p></td>
-    </tr>
-    </table>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>x</dt>
+<dd><p>object to be converted into an array</p></dd>
+</dl></div>
 
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diff --git a/reference/autograd_backward.html b/reference/autograd_backward.html
index 9325d3504..fee0ec29c 100644
--- a/reference/autograd_backward.html
+++ b/reference/autograd_backward.html
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 then the Jacobian-vector product would be computed, in this case the function
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+then the Jacobian-vector product would be computed, in this case the function
+additionally requires specifying grad_tensors. It should be a sequence of
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Computes the sum of gradients of given tensors w.r.t. graph leaves.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/autograd.R'><code>R/autograd.R</code></a></small>
-    <div class="hidden name"><code>autograd_backward.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Computes the sum of gradients of given tensors w.r.t. graph leaves.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/autograd.R" class="external-link"><code>R/autograd.R</code></a></small>
+      <div class="d-none name"><code>autograd_backward.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>The graph is differentiated using the chain rule. If any of tensors are
 non-scalar (i.e. their data has more than one element) and require gradient,
 then the Jacobian-vector product would be computed, in this case the function
@@ -203,83 +101,70 @@ tensors (None is an acceptable value for all tensors that don’t need gradient
 tensors).</p>
     </div>
 
-    <pre class="usage"><span class='fu'>autograd_backward</span><span class='op'>(</span>
-  <span class='va'>tensors</span>,
-  grad_tensors <span class='op'>=</span> <span class='cn'>NULL</span>,
-  retain_graph <span class='op'>=</span> <span class='va'>create_graph</span>,
-  create_graph <span class='op'>=</span> <span class='cn'>FALSE</span>
-<span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">autograd_backward</span><span class="op">(</span>
+  <span class="va">tensors</span>,
+  grad_tensors <span class="op">=</span> <span class="cn">NULL</span>,
+  retain_graph <span class="op">=</span> <span class="va">create_graph</span>,
+  create_graph <span class="op">=</span> <span class="cn">FALSE</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>tensors</th>
-      <td><p>(list of Tensor) – Tensors of which the derivative will
-be computed.</p></td>
-    </tr>
-    <tr>
-      <th>grad_tensors</th>
-      <td><p>(list of (Tensor or <code>NULL)) – The “vector” in the  Jacobian-vector product, usually gradients w.r.t. each element of  corresponding tensors. </code>NULL<code>values can be specified for scalar Tensors or  ones that don’t require grad. If a</code>NULL` value would be acceptable for all
-grad_tensors, then this argument is optional.</p></td>
-    </tr>
-    <tr>
-      <th>retain_graph</th>
-      <td><p>(bool, optional) – If <code>FALSE</code>, the graph used to compute
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>tensors</dt>
+<dd><p>(list of Tensor) – Tensors of which the derivative will
+be computed.</p></dd>
+<dt>grad_tensors</dt>
+<dd><p>(list of (Tensor or <code>NULL)) – The “vector” in the Jacobian-vector product, usually gradients w.r.t. each element of corresponding tensors. </code>NULL<code>values can be specified for scalar Tensors or ones that don’t require grad. If a</code>NULL` value would be acceptable for all
+grad_tensors, then this argument is optional.</p></dd>
+<dt>retain_graph</dt>
+<dd><p>(bool, optional) – If <code>FALSE</code>, the graph used to compute
 the grad will be freed. Note that in nearly all cases setting this option to
 <code>TRUE</code> is not needed and often can be worked around in a much more efficient
-way. Defaults to the value of <code>create_graph</code>.</p></td>
-    </tr>
-    <tr>
-      <th>create_graph</th>
-      <td><p>(bool, optional) – If <code>TRUE</code>, graph of the derivative will
+way. Defaults to the value of <code>create_graph</code>.</p></dd>
+<dt>create_graph</dt>
+<dd><p>(bool, optional) – If <code>TRUE</code>, graph of the derivative will
 be constructed, allowing to compute higher order derivative products.
-Defaults to <code>FALSE</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
+Defaults to <code>FALSE</code>.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>This function accumulates gradients in the leaves - you might need to zero
 them before calling it.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>x</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fl'>1</span>, requires_grad <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>y</span> <span class='op'>&lt;-</span> <span class='fl'>2</span> <span class='op'>*</span> <span class='va'>x</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fl'>1</span>, requires_grad <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>b</span> <span class='op'>&lt;-</span> <span class='fl'>3</span> <span class='op'>*</span> <span class='va'>a</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='fu'>autograd_backward</span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span><span class='va'>y</span>, <span class='va'>b</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fl">1</span>, requires_grad <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">y</span> <span class="op">&lt;-</span> <span class="fl">2</span> <span class="op">*</span> <span class="va">x</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fl">1</span>, requires_grad <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">b</span> <span class="op">&lt;-</span> <span class="fl">3</span> <span class="op">*</span> <span class="va">a</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="fu">autograd_backward</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">y</span>, <span class="va">b</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer>
-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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+<div class="pkgdown-footer-right">
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diff --git a/reference/autograd_function.html b/reference/autograd_function.html
index 104dea0be..d1a34b967 100644
--- a/reference/autograd_function.html
+++ b/reference/autograd_function.html
@@ -1,196 +1,92 @@
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+retained in the form of a DAG of functions, with edges denoting data
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Records operation history and defines formulas for differentiating ops.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/autograd.R'><code>R/autograd.R</code></a></small>
-    <div class="hidden name"><code>autograd_function.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Records operation history and defines formulas for differentiating ops.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/autograd.R" class="external-link"><code>R/autograd.R</code></a></small>
+      <div class="d-none name"><code>autograd_function.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Every operation performed on Tensor's creates a new function object, that
 performs the computation, and records that it happened. The history is
 retained in the form of a DAG of functions, with edges denoting data
@@ -199,21 +95,20 @@ processed in the topological ordering, by calling <code>backward()</code> method
 Function object, and passing returned gradients on to next Function's.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>autograd_function</span><span class='op'>(</span><span class='va'>forward</span>, <span class='va'>backward</span><span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">autograd_function</span><span class="op">(</span><span class="va">forward</span>, <span class="va">backward</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>forward</th>
-      <td><p>Performs the operation. It must accept a context <code>ctx</code> as the first argument,
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>forward</dt>
+<dd><p>Performs the operation. It must accept a context <code>ctx</code> as the first argument,
 followed by any number of arguments (tensors or other types). The context can be
 used to store tensors that can be then retrieved during the backward pass.
-See <a href='AutogradContext.html'>AutogradContext</a> for more information about context methods.</p></td>
-    </tr>
-    <tr>
-      <th>backward</th>
-      <td><p>Defines a formula for differentiating the operation. It must accept
+See <a href="AutogradContext.html">AutogradContext</a> for more information about context methods.</p></dd>
+<dt>backward</dt>
+<dd><p>Defines a formula for differentiating the operation. It must accept
 a context <code>ctx</code> as the first argument, followed by as many outputs did <code>forward()</code>
 return, and it should return a named list. Each argument is the gradient w.r.t
 the given output, and each element in the returned list should be the gradient
@@ -222,52 +117,43 @@ during the forward pass. It also has an attribute <code>ctx$needs_input_grad</co
 named list of booleans representing whether each input needs gradient.
 E.g., <code>backward()</code> will have <code>ctx$needs_input_grad$input = TRUE</code> if the <code>input</code>
 argument to <code>forward()</code> needs gradient computated w.r.t. the output.
-See <a href='AutogradContext.html'>AutogradContext</a> for more information about context methods.</p></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>exp2</span> <span class='op'>&lt;-</span> <span class='fu'>autograd_function</span><span class='op'>(</span></span>
-<span class='r-in'>  forward <span class='op'>=</span> <span class='kw'>function</span><span class='op'>(</span><span class='va'>ctx</span>, <span class='va'>i</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'>    <span class='va'>result</span> <span class='op'>&lt;-</span> <span class='va'>i</span><span class='op'>$</span><span class='fu'>exp</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'>    <span class='va'>ctx</span><span class='op'>$</span><span class='fu'>save_for_backward</span><span class='op'>(</span>result <span class='op'>=</span> <span class='va'>result</span><span class='op'>)</span></span>
-<span class='r-in'>    <span class='va'>result</span></span>
-<span class='r-in'>  <span class='op'>}</span>,</span>
-<span class='r-in'>  backward <span class='op'>=</span> <span class='kw'>function</span><span class='op'>(</span><span class='va'>ctx</span>, <span class='va'>grad_output</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'>    <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span>i <span class='op'>=</span> <span class='va'>grad_output</span> <span class='op'>*</span> <span class='va'>ctx</span><span class='op'>$</span><span class='va'>saved_variable</span><span class='op'>$</span><span class='va'>result</span><span class='op'>)</span></span>
-<span class='r-in'>  <span class='op'>}</span></span>
-<span class='r-in'><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+See <a href="AutogradContext.html">AutogradContext</a> for more information about context methods.</p></dd>
+</dl></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">exp2</span> <span class="op">&lt;-</span> <span class="fu">autograd_function</span><span class="op">(</span></span>
+<span class="r-in">  forward <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="va">ctx</span>, <span class="va">i</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in">    <span class="va">result</span> <span class="op">&lt;-</span> <span class="va">i</span><span class="op">$</span><span class="fu">exp</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in">    <span class="va">ctx</span><span class="op">$</span><span class="fu">save_for_backward</span><span class="op">(</span>result <span class="op">=</span> <span class="va">result</span><span class="op">)</span></span>
+<span class="r-in">    <span class="va">result</span></span>
+<span class="r-in">  <span class="op">}</span>,</span>
+<span class="r-in">  backward <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="va">ctx</span>, <span class="va">grad_output</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in">    <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>i <span class="op">=</span> <span class="va">grad_output</span> <span class="op">*</span> <span class="va">ctx</span><span class="op">$</span><span class="va">saved_variable</span><span class="op">$</span><span class="va">result</span><span class="op">)</span></span>
+<span class="r-in">  <span class="op">}</span></span>
+<span class="r-in"><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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diff --git a/reference/autograd_grad.html b/reference/autograd_grad.html
index 40ee2ee8a..39d4f113c 100644
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="grad_outputs should be a list of length matching output containing the “vector”
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+the outputs. If an output doesn’t require_grad, then the gradient can be None)."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Computes and returns the sum of gradients of outputs w.r.t. the inputs.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/autograd.R'><code>R/autograd.R</code></a></small>
-    <div class="hidden name"><code>autograd_grad.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Computes and returns the sum of gradients of outputs w.r.t. the inputs.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/autograd.R" class="external-link"><code>R/autograd.R</code></a></small>
+      <div class="d-none name"><code>autograd_grad.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p><code>grad_outputs</code> should be a list of length matching output containing the “vector”
 in Jacobian-vector product, usually the pre-computed gradients w.r.t. each of
 the outputs. If an output doesn’t require_grad, then the gradient can be None).</p>
     </div>
 
-    <pre class="usage"><span class='fu'>autograd_grad</span><span class='op'>(</span>
-  <span class='va'>outputs</span>,
-  <span class='va'>inputs</span>,
-  grad_outputs <span class='op'>=</span> <span class='cn'>NULL</span>,
-  retain_graph <span class='op'>=</span> <span class='va'>create_graph</span>,
-  create_graph <span class='op'>=</span> <span class='cn'>FALSE</span>,
-  allow_unused <span class='op'>=</span> <span class='cn'>FALSE</span>
-<span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">autograd_grad</span><span class="op">(</span>
+  <span class="va">outputs</span>,
+  <span class="va">inputs</span>,
+  grad_outputs <span class="op">=</span> <span class="cn">NULL</span>,
+  retain_graph <span class="op">=</span> <span class="va">create_graph</span>,
+  create_graph <span class="op">=</span> <span class="cn">FALSE</span>,
+  allow_unused <span class="op">=</span> <span class="cn">FALSE</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>outputs</th>
-      <td><p>(sequence of Tensor) – outputs of the differentiated function.</p></td>
-    </tr>
-    <tr>
-      <th>inputs</th>
-      <td><p>(sequence of Tensor) – Inputs w.r.t. which the gradient will be
-returned (and not accumulated into .grad).</p></td>
-    </tr>
-    <tr>
-      <th>grad_outputs</th>
-      <td><p>(sequence of Tensor) – The “vector” in the Jacobian-vector
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>outputs</dt>
+<dd><p>(sequence of Tensor) – outputs of the differentiated function.</p></dd>
+<dt>inputs</dt>
+<dd><p>(sequence of Tensor) – Inputs w.r.t. which the gradient will be
+returned (and not accumulated into .grad).</p></dd>
+<dt>grad_outputs</dt>
+<dd><p>(sequence of Tensor) – The “vector” in the Jacobian-vector
 product. Usually gradients w.r.t. each output. None values can be specified for
 scalar Tensors or ones that don’t require grad. If a None value would be acceptable
-for all <code>grad_tensors</code>, then this argument is optional. Default: None.</p></td>
-    </tr>
-    <tr>
-      <th>retain_graph</th>
-      <td><p>(bool, optional) – If <code>FALSE</code>, the graph used to compute the
+for all <code>grad_tensors</code>, then this argument is optional. Default: None.</p></dd>
+<dt>retain_graph</dt>
+<dd><p>(bool, optional) – If <code>FALSE</code>, the graph used to compute the
 grad will be freed. Note that in nearly all cases setting this option to <code>TRUE</code> is
 not needed and often can be worked around in a much more efficient way.
-Defaults to the value of <code>create_graph</code>.</p></td>
-    </tr>
-    <tr>
-      <th>create_graph</th>
-      <td><p>(bool, optional) – If <code>TRUE, graph of the derivative will  be constructed, allowing to compute higher order derivative products.  Default: </code>FALSE`.</p></td>
-    </tr>
-    <tr>
-      <th>allow_unused</th>
-      <td><p>(bool, optional) – If <code>FALSE</code>, specifying inputs that were
+Defaults to the value of <code>create_graph</code>.</p></dd>
+<dt>create_graph</dt>
+<dd><p>(bool, optional) – If <code>TRUE, graph of the derivative will be constructed, allowing to compute higher order derivative products. Default: </code>FALSE`.</p></dd>
+<dt>allow_unused</dt>
+<dd><p>(bool, optional) – If <code>FALSE</code>, specifying inputs that were
 not used when computing outputs (and therefore their grad is always zero) is an
-error. Defaults to <code>FALSE</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
+error. Defaults to <code>FALSE</code></p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>If only_inputs is <code>TRUE</code>, the function will only return a list of gradients w.r.t
 the specified inputs. If it’s <code>FALSE</code>, then gradient w.r.t. all remaining leaves
 will still be computed, and will be accumulated into their <code>.grad</code> attribute.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>w</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fl'>0.5</span>, requires_grad <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>b</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fl'>0.9</span>, requires_grad <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>x</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/stats/Uniform.html'>runif</a></span><span class='op'>(</span><span class='fl'>100</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>y</span> <span class='op'>&lt;-</span> <span class='fl'>2</span> <span class='op'>*</span> <span class='va'>x</span> <span class='op'>+</span> <span class='fl'>1</span></span>
-<span class='r-in'><span class='va'>loss</span> <span class='op'>&lt;-</span> <span class='op'>(</span><span class='va'>y</span> <span class='op'>-</span> <span class='op'>(</span><span class='va'>w</span><span class='op'>*</span><span class='va'>x</span> <span class='op'>+</span> <span class='va'>b</span><span class='op'>)</span><span class='op'>)</span><span class='op'>^</span><span class='fl'>2</span></span>
-<span class='r-in'><span class='va'>loss</span> <span class='op'>&lt;-</span> <span class='va'>loss</span><span class='op'>$</span><span class='fu'>mean</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>o</span> <span class='op'>&lt;-</span> <span class='fu'>autograd_grad</span><span class='op'>(</span><span class='va'>loss</span>, <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span><span class='va'>w</span>, <span class='va'>b</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>o</span></span>
-<span class='r-in'> </span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [[1]]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.9935</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{1} ]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [[2]]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -1.6206</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{1} ]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">w</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fl">0.5</span>, requires_grad <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">b</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fl">0.9</span>, requires_grad <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/stats/Uniform.html" class="external-link">runif</a></span><span class="op">(</span><span class="fl">100</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">y</span> <span class="op">&lt;-</span> <span class="fl">2</span> <span class="op">*</span> <span class="va">x</span> <span class="op">+</span> <span class="fl">1</span></span>
+<span class="r-in"><span class="va">loss</span> <span class="op">&lt;-</span> <span class="op">(</span><span class="va">y</span> <span class="op">-</span> <span class="op">(</span><span class="va">w</span> <span class="op">*</span> <span class="va">x</span> <span class="op">+</span> <span class="va">b</span><span class="op">)</span><span class="op">)</span><span class="op">^</span><span class="fl">2</span></span>
+<span class="r-in"><span class="va">loss</span> <span class="op">&lt;-</span> <span class="va">loss</span><span class="op">$</span><span class="fu">mean</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">o</span> <span class="op">&lt;-</span> <span class="fu">autograd_grad</span><span class="op">(</span><span class="va">loss</span>, <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">w</span>, <span class="va">b</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">o</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [[1]]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -1.0233</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{1} ]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [[2]]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -1.6507</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{1} ]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+</code></pre></div>
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+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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diff --git a/reference/backends_cudnn_version.html b/reference/backends_cudnn_version.html
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+++ b/reference/backends_cudnn_version.html
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+<!DOCTYPE html>
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diff --git a/reference/backends_mkl_is_available.html b/reference/backends_mkl_is_available.html
index 9d2a45fa9..4f45a6e7a 100644
--- a/reference/backends_mkl_is_available.html
+++ b/reference/backends_mkl_is_available.html
@@ -1,226 +1,112 @@
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-    <h1>MKL is available</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/backends.R'><code>R/backends.R</code></a></small>
-    <div class="hidden name"><code>backends_mkl_is_available.Rd</code></div>
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+      <img src="" class="logo" alt=""><h1>MKL is available</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/backends.R" class="external-link"><code>R/backends.R</code></a></small>
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     <p>MKL is available</p>
     </div>
 
-    <pre class="usage"><span class='fu'>backends_mkl_is_available</span><span class='op'>(</span><span class='op'>)</span></pre>
-
-
-    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">backends_mkl_is_available</span><span class="op">(</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
     <p>Returns whether LibTorch is built with MKL support.</p>
+    </div>
 
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diff --git a/reference/backends_mkldnn_is_available.html b/reference/backends_mkldnn_is_available.html
index 98ac037e9..313bac847 100644
--- a/reference/backends_mkldnn_is_available.html
+++ b/reference/backends_mkldnn_is_available.html
@@ -1,226 +1,112 @@
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     <p>MKLDNN is available</p>
     </div>
 
-    <pre class="usage"><span class='fu'>backends_mkldnn_is_available</span><span class='op'>(</span><span class='op'>)</span></pre>
-
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-    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">backends_mkldnn_is_available</span><span class="op">(</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
     <p>Returns whether LibTorch is built with MKL-DNN support.</p>
+    </div>
 
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diff --git a/reference/backends_openmp_is_available.html b/reference/backends_openmp_is_available.html
index dcf23957e..b8b9d8440 100644
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+    
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Given a list of values (possibly containing numbers), returns a list where each
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Given a list of values (possibly containing numbers), returns a list where each
 value is broadcasted based on the following rules:</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/distributions-utils.R'><code>R/distributions-utils.R</code></a></small>
-    <div class="hidden name"><code>broadcast_all.Rd</code></div>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/distributions-utils.R" class="external-link"><code>R/distributions-utils.R</code></a></small>
+      <div class="d-none name"><code>broadcast_all.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Raises value_error: if any of the values is not a <code>numeric</code> instance,
 a <code>torch.*Tensor</code> instance, or an instance implementing <strong>torch_function</strong>
 TODO: add has_torch_function((v,))
 See: https://github.com/pytorch/pytorch/blob/master/torch/distributions/utils.py</p>
     </div>
 
-    <pre class="usage"><span class='fu'>broadcast_all</span><span class='op'>(</span><span class='va'>values</span><span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">broadcast_all</span><span class="op">(</span><span class="va">values</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>values</th>
-      <td><p>List of:</p><ul>
-<li><p><code>torch.*Tensor</code> instances are broadcasted as per <code>_broadcasting-semantics</code>.</p></li>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>values</dt>
+<dd><p>List of:</p><ul><li><p><code>torch.*Tensor</code> instances are broadcasted as per <code>_broadcasting-semantics</code>.</p></li>
 <li><p><code>numeric</code> instances (scalars) are upcast to tensors having
 the same size and type as the first tensor passed to <code>values</code>.  If all the
 values are scalars, then they are upcasted to scalar Tensors.
 values (list of <code>numeric</code>, <code>torch.*Tensor</code> or objects implementing <strong>torch_function</strong>)</p></li>
-</ul></td>
-    </tr>
-    </table>
+</ul></dd>
+</dl></div>
 
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diff --git a/reference/call_torch_function.html b/reference/call_torch_function.html
new file mode 100644
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+++ b/reference/call_torch_function.html
@@ -0,0 +1,165 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="This function allows calling a function prefixed with torch_, including unexported
+functions which could have potentially valuable uses but which do not yet have
+a user-friendly R wrapper function. Therefore, this function should be used with
+extreme caution. Make sure you understand what the function expects as input. It
+may be helpful to read the torch source code for help with this, as well as
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+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Call a (Potentially Unexported) Torch Function</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/call_torch_function.R" class="external-link"><code>R/call_torch_function.R</code></a></small>
+      <div class="d-none name"><code>call_torch_function.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>This function allows calling a function prefixed with <code>torch_</code>, including unexported
+functions which could have potentially valuable uses but which do not yet have
+a user-friendly R wrapper function. Therefore, this function should be used with
+extreme caution. Make sure you understand what the function expects as input. It
+may be helpful to read the <code>torch</code> source code for help with this, as well as
+the documentation for the corresponding function in the Pytorch C++ API. Generally
+for development and advanced use only.</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">call_torch_function</span><span class="op">(</span><span class="va">name</span>, <span class="va">...</span>, quiet <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>name</dt>
+<dd><p>Name of the function to call as a string. Should start with "torch_"</p></dd>
+<dt>...</dt>
+<dd><p>A list of arguments to pass to the function. Argument splicing with
+<code>!!!</code> is supported.</p></dd>
+<dt>quiet</dt>
+<dd><p>If TRUE, suppress warnings with valuable information about the dangers of
+this function.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>The return value from calling the function <code>name</code> with arguments <code>...</code></p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="co">## many unexported functions do 'backward' calculations (e.g. derivatives)</span></span>
+<span class="r-in"><span class="co">## These could be used as a part of custom autograd functions for example.</span></span>
+<span class="r-in"><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">10</span>, requires_grad <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">y</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tanh.html">torch_tanh</a></span><span class="op">(</span><span class="va">x</span><span class="op">)</span></span>
+<span class="r-in"><span class="co">## calculate backwards gradient using standard torch method</span></span>
+<span class="r-in"><span class="va">y</span><span class="op">$</span><span class="fu">backward</span><span class="op">(</span><span class="fu"><a href="torch_ones_like.html">torch_ones_like</a></span><span class="op">(</span><span class="va">x</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">x</span><span class="op">$</span><span class="va">grad</span></span>
+<span class="r-in"><span class="co">## we can get the same result by calling the unexported `torch_tanh_backward()`</span></span>
+<span class="r-in"><span class="co">## function. The first argument is 1 to setup the Jacobian-vector product.</span></span>
+<span class="r-in"><span class="co">## see https://pytorch.org/blog/overview-of-pytorch-autograd-engine/ for details.</span></span>
+<span class="r-in"><span class="fu">call_torch_function</span><span class="op">(</span><span class="st">"torch_tanh_backward"</span>, <span class="fl">1</span>, <span class="va">y</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/all.equal.html" class="external-link">all.equal</a></span><span class="op">(</span><span class="fu">call_torch_function</span><span class="op">(</span><span class="st">"torch_tanh_backward"</span>, <span class="fl">1</span>, <span class="va">y</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span>, <span class="va">x</span><span class="op">$</span><span class="va">grad</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>Because this function allows access to unexported functions, please use with caution, and</span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span>             only if you are sure know what you are doing. Unexported functions will expect inputs that</span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span>             are more C++-like than R-like. For example, they will expect all indexes to be 0-based instead</span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span>             of 1-based. In addition unexported functions may be subject to removal from the API without</span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span>             warning. Set quiet = TRUE to silence this warning.</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] TRUE</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
+</div>
+
+<div class="pkgdown-footer-right">
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diff --git a/reference/contrib_sort_vertices.html b/reference/contrib_sort_vertices.html
index 37fee7cd0..99c1eedab 100644
--- a/reference/contrib_sort_vertices.html
+++ b/reference/contrib_sort_vertices.html
@@ -1,257 +1,139 @@
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Contrib sort vertices</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/contrib.R'><code>R/contrib.R</code></a></small>
-    <div class="hidden name"><code>contrib_sort_vertices.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Contrib sort vertices</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/contrib.R" class="external-link"><code>R/contrib.R</code></a></small>
+      <div class="d-none name"><code>contrib_sort_vertices.Rd</code></div>
     </div>
 
-    <div class="ref-description">
-    <p>Based on the implementation from <a href='https://github.com/lilanxiao/Rotated_IoU'>Rotated_IoU</a></p>
+    <div class="ref-description section level2">
+    <p>Based on the implementation from <a href="https://github.com/lilanxiao/Rotated_IoU" class="external-link">Rotated_IoU</a></p>
     </div>
 
-    <pre class="usage"><span class='fu'>contrib_sort_vertices</span><span class='op'>(</span><span class='va'>vertices</span>, <span class='va'>mask</span>, <span class='va'>num_valid</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>vertices</th>
-      <td><p>A Tensor with the vertices.</p></td>
-    </tr>
-    <tr>
-      <th>mask</th>
-      <td><p>A tensors containing the masks.</p></td>
-    </tr>
-    <tr>
-      <th>num_valid</th>
-      <td><p>A integer tensors.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">contrib_sort_vertices</span><span class="op">(</span><span class="va">vertices</span>, <span class="va">mask</span>, <span class="va">num_valid</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>vertices</dt>
+<dd><p>A Tensor with the vertices.</p></dd>
+<dt>mask</dt>
+<dd><p>A tensors containing the masks.</p></dd>
+<dt>num_valid</dt>
+<dd><p>A integer tensors.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>All tensors should be on a CUDA device so this function can be used.</p>
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p>This function does not make part of the official torch API.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='cuda_is_available.html'>cuda_is_available</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>v</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>8</span>, <span class='fl'>1024</span>, <span class='fl'>24</span>, <span class='fl'>2</span><span class='op'>)</span><span class='op'>$</span><span class='fu'>cuda</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>mean</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_mean.html'>torch_mean</a></span><span class='op'>(</span><span class='va'>v</span>, dim<span class='op'>=</span><span class='fl'>2</span>, keepdim<span class='op'>=</span><span class='cn'>TRUE</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>v</span> <span class='op'>&lt;-</span> <span class='va'>v</span> <span class='op'>-</span> <span class='va'>mean</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>&lt;-</span> <span class='op'>(</span><span class='fu'><a href='torch_rand.html'>torch_rand</a></span><span class='op'>(</span><span class='fl'>8</span>, <span class='fl'>1024</span>, <span class='fl'>24</span><span class='op'>)</span> <span class='op'>&gt;</span> <span class='fl'>0.8</span><span class='op'>)</span><span class='op'>$</span><span class='fu'>cuda</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>nv</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_sum.html'>torch_sum</a></span><span class='op'>(</span><span class='va'>m</span><span class='op'>$</span><span class='fu'>to</span><span class='op'>(</span>dtype <span class='op'>=</span> <span class='fu'><a href='torch_dtype.html'>torch_int</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span>, dim<span class='op'>=</span><span class='op'>-</span><span class='fl'>1</span><span class='op'>)</span><span class='op'>$</span><span class='fu'>to</span><span class='op'>(</span>dtype <span class='op'>=</span> <span class='fu'><a href='torch_dtype.html'>torch_int</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span><span class='op'>$</span><span class='fu'>cuda</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>result</span> <span class='op'>&lt;-</span> <span class='fu'>contrib_sort_vertices</span><span class='op'>(</span><span class='va'>v</span>, <span class='va'>m</span>, <span class='va'>nv</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="cuda_is_available.html">cuda_is_available</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in">  <span class="va">v</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">8</span>, <span class="fl">1024</span>, <span class="fl">24</span>, <span class="fl">2</span><span class="op">)</span><span class="op">$</span><span class="fu">cuda</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in">  <span class="va">mean</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_mean.html">torch_mean</a></span><span class="op">(</span><span class="va">v</span>, dim <span class="op">=</span> <span class="fl">2</span>, keepdim <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
+<span class="r-in">  <span class="va">v</span> <span class="op">&lt;-</span> <span class="va">v</span> <span class="op">-</span> <span class="va">mean</span></span>
+<span class="r-in">  <span class="va">m</span> <span class="op">&lt;-</span> <span class="op">(</span><span class="fu"><a href="torch_rand.html">torch_rand</a></span><span class="op">(</span><span class="fl">8</span>, <span class="fl">1024</span>, <span class="fl">24</span><span class="op">)</span> <span class="op">&gt;</span> <span class="fl">0.8</span><span class="op">)</span><span class="op">$</span><span class="fu">cuda</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in">  <span class="va">nv</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_sum.html">torch_sum</a></span><span class="op">(</span><span class="va">m</span><span class="op">$</span><span class="fu">to</span><span class="op">(</span>dtype <span class="op">=</span> <span class="fu"><a href="torch_dtype.html">torch_int</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span>, dim <span class="op">=</span> <span class="op">-</span><span class="fl">1</span><span class="op">)</span><span class="op">$</span><span class="fu">to</span><span class="op">(</span>dtype <span class="op">=</span> <span class="fu"><a href="torch_dtype.html">torch_int</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span><span class="op">$</span><span class="fu">cuda</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in">  <span class="va">result</span> <span class="op">&lt;-</span> <span class="fu">contrib_sort_vertices</span><span class="op">(</span><span class="va">v</span>, <span class="va">m</span>, <span class="va">nv</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-in"><span class="op">}</span></span>
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-    <h1>Returns the index of a currently selected device.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/cuda.R'><code>R/cuda.R</code></a></small>
-    <div class="hidden name"><code>cuda_current_device.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Returns the index of a currently selected device.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/cuda.R" class="external-link"><code>R/cuda.R</code></a></small>
+      <div class="d-none name"><code>cuda_current_device.Rd</code></div>
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-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Returns the index of a currently selected device.</p>
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-    <pre class="usage"><span class='fu'>cuda_current_device</span><span class='op'>(</span><span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">cuda_current_device</span><span class="op">(</span><span class="op">)</span></code></pre></div>
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diff --git a/reference/cuda_device_count.html b/reference/cuda_device_count.html
index ce0ff3b4a..acaf7daf1 100644
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+      <img src="" class="logo" alt=""><h1>Returns the number of GPUs available.</h1>
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     <p>Returns the number of GPUs available.</p>
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-    <pre class="usage"><span class='fu'>cuda_device_count</span><span class='op'>(</span><span class='op'>)</span></pre>
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diff --git a/reference/cuda_get_device_capability.html b/reference/cuda_get_device_capability.html
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+      <img src="" class="logo" alt=""><h1>Returns the major and minor CUDA capability of <code>device</code></h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/cuda.R" class="external-link"><code>R/cuda.R</code></a></small>
+      <div class="d-none name"><code>cuda_get_device_capability.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>Returns the major and minor CUDA capability of <code>device</code></p>
+    </div>
+
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+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">cuda_get_device_capability</span><span class="op">(</span>device <span class="op">=</span> <span class="fu"><a href="cuda_current_device.html">cuda_current_device</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>device</dt>
+<dd><p>Integer value of the CUDA device to return capabilities of.</p></dd>
+</dl></div>
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-    <h1>Returns a bool indicating if CUDA is currently available.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/cuda.R'><code>R/cuda.R</code></a></small>
-    <div class="hidden name"><code>cuda_is_available.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Returns a bool indicating if CUDA is currently available.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/cuda.R" class="external-link"><code>R/cuda.R</code></a></small>
+      <div class="d-none name"><code>cuda_is_available.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Returns a bool indicating if CUDA is currently available.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>cuda_is_available</span><span class='op'>(</span><span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">cuda_is_available</span><span class="op">(</span><span class="op">)</span></code></pre></div>
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diff --git a/reference/cuda_memory_stats.html b/reference/cuda_memory_stats.html
new file mode 100644
index 000000000..d2c3751ed
--- /dev/null
+++ b/reference/cuda_memory_stats.html
@@ -0,0 +1,146 @@
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+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Returns a dictionary of CUDA memory allocator statistics for a given device.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/cuda.R" class="external-link"><code>R/cuda.R</code></a></small>
+      <div class="d-none name"><code>cuda_memory_stats.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>The return value of this function is a dictionary of statistics, each of which
+is a non-negative integer.</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">cuda_memory_stats</span><span class="op">(</span>device <span class="op">=</span> <span class="fu"><a href="cuda_current_device.html">cuda_current_device</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span>
+
+<span class="fu">cuda_memory_summary</span><span class="op">(</span>device <span class="op">=</span> <span class="fu"><a href="cuda_current_device.html">cuda_current_device</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>device</dt>
+<dd><p>Integer value of the CUDA device to return capabilities of.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="core-statistics">Core statistics<a class="anchor" aria-label="anchor" href="#core-statistics"></a></h2>
+    
+
+<ul><li><p>"allocated.all,large_pool,small_pool.current,peak,allocated,freed": number of allocation requests received by the memory allocator.</p></li>
+<li><p>"allocated_bytes.all,large_pool,small_pool.current,peak,allocated,freed": amount of allocated memory.</p></li>
+<li><p>"segment.all,large_pool,small_pool.current,peak,allocated,freed": number of reserved segments from cudaMalloc().</p></li>
+<li><p>"reserved_bytes.all,large_pool,small_pool.current,peak,allocated,freed": amount of reserved memory.</p></li>
+<li><p>"active.all,large_pool,small_pool.current,peak,allocated,freed": number of active memory blocks.</p></li>
+<li><p>"active_bytes.all,large_pool,small_pool.current,peak,allocated,freed": amount of active memory.</p></li>
+<li><p>"inactive_split.all,large_pool,small_pool.current,peak,allocated,freed": number of inactive, non-releasable memory blocks.</p></li>
+<li><p>"inactive_split_bytes.all,large_pool,small_pool.current,peak,allocated,freed": amount of inactive, non-releasable memory.</p></li>
+</ul><p>For these core statistics, values are broken down as follows.</p>
+<p>Pool type:</p><ul><li><p>all: combined statistics across all memory pools.</p></li>
+<li><p>large_pool: statistics for the large allocation pool (as of October 2019, for size &gt;= 1MB allocations).</p></li>
+<li><p>small_pool: statistics for the small allocation pool (as of October 2019, for size &lt; 1MB allocations).</p></li>
+</ul><p>Metric type:</p><ul><li><p>current: current value of this metric.</p></li>
+<li><p>peak: maximum value of this metric.</p></li>
+<li><p>allocated: historical total increase in this metric.</p></li>
+<li><p>freed: historical total decrease in this metric.</p></li>
+</ul></div>
+    <div class="section level2">
+    <h2 id="additional-metrics">Additional metrics<a class="anchor" aria-label="anchor" href="#additional-metrics"></a></h2>
+    
+
+<ul><li><p>"num_alloc_retries": number of failed cudaMalloc calls that result in a cache flush and retry.</p></li>
+<li><p>"num_ooms": number of out-of-memory errors thrown.</p></li>
+</ul></div>
+
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
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diff --git a/reference/dataloader.html b/reference/dataloader.html
index 52a94b7da..2f4b422ce 100644
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+        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
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+    
+  </div>
+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Data loader. Combines a dataset and a sampler, and provides
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Data loader. Combines a dataset and a sampler, and provides
 single- or multi-process iterators over the dataset.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/utils-data-dataloader.R'><code>R/utils-data-dataloader.R</code></a></small>
-    <div class="hidden name"><code>dataloader.Rd</code></div>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/utils-data-dataloader.R" class="external-link"><code>R/utils-data-dataloader.R</code></a></small>
+      <div class="d-none name"><code>dataloader.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Data loader. Combines a dataset and a sampler, and provides
 single- or multi-process iterators over the dataset.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>dataloader</span><span class='op'>(</span>
-  <span class='va'>dataset</span>,
-  batch_size <span class='op'>=</span> <span class='fl'>1</span>,
-  shuffle <span class='op'>=</span> <span class='cn'>FALSE</span>,
-  sampler <span class='op'>=</span> <span class='cn'>NULL</span>,
-  batch_sampler <span class='op'>=</span> <span class='cn'>NULL</span>,
-  num_workers <span class='op'>=</span> <span class='fl'>0</span>,
-  collate_fn <span class='op'>=</span> <span class='cn'>NULL</span>,
-  pin_memory <span class='op'>=</span> <span class='cn'>FALSE</span>,
-  drop_last <span class='op'>=</span> <span class='cn'>FALSE</span>,
-  timeout <span class='op'>=</span> <span class='op'>-</span><span class='fl'>1</span>,
-  worker_init_fn <span class='op'>=</span> <span class='cn'>NULL</span>,
-  worker_globals <span class='op'>=</span> <span class='cn'>NULL</span>,
-  worker_packages <span class='op'>=</span> <span class='cn'>NULL</span>
-<span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">dataloader</span><span class="op">(</span>
+  <span class="va">dataset</span>,
+  batch_size <span class="op">=</span> <span class="fl">1</span>,
+  shuffle <span class="op">=</span> <span class="cn">FALSE</span>,
+  sampler <span class="op">=</span> <span class="cn">NULL</span>,
+  batch_sampler <span class="op">=</span> <span class="cn">NULL</span>,
+  num_workers <span class="op">=</span> <span class="fl">0</span>,
+  collate_fn <span class="op">=</span> <span class="cn">NULL</span>,
+  pin_memory <span class="op">=</span> <span class="cn">FALSE</span>,
+  drop_last <span class="op">=</span> <span class="cn">FALSE</span>,
+  timeout <span class="op">=</span> <span class="op">-</span><span class="fl">1</span>,
+  worker_init_fn <span class="op">=</span> <span class="cn">NULL</span>,
+  worker_globals <span class="op">=</span> <span class="cn">NULL</span>,
+  worker_packages <span class="op">=</span> <span class="cn">NULL</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>dataset</th>
-      <td><p>(Dataset): dataset from which to load the data.</p></td>
-    </tr>
-    <tr>
-      <th>batch_size</th>
-      <td><p>(int, optional): how many samples per batch to load
-(default: <code>1</code>).</p></td>
-    </tr>
-    <tr>
-      <th>shuffle</th>
-      <td><p>(bool, optional): set to <code>TRUE</code> to have the data reshuffled
-at every epoch (default: <code>FALSE</code>).</p></td>
-    </tr>
-    <tr>
-      <th>sampler</th>
-      <td><p>(Sampler, optional): defines the strategy to draw samples from
-the dataset. If specified, <code>shuffle</code> must be False.</p></td>
-    </tr>
-    <tr>
-      <th>batch_sampler</th>
-      <td><p>(Sampler, optional): like sampler, but returns a batch of
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>dataset</dt>
+<dd><p>(Dataset): dataset from which to load the data.</p></dd>
+<dt>batch_size</dt>
+<dd><p>(int, optional): how many samples per batch to load
+(default: <code>1</code>).</p></dd>
+<dt>shuffle</dt>
+<dd><p>(bool, optional): set to <code>TRUE</code> to have the data reshuffled
+at every epoch (default: <code>FALSE</code>).</p></dd>
+<dt>sampler</dt>
+<dd><p>(Sampler, optional): defines the strategy to draw samples from
+the dataset. If specified, <code>shuffle</code> must be False.</p></dd>
+<dt>batch_sampler</dt>
+<dd><p>(Sampler, optional): like sampler, but returns a batch of
 indices at a time. Mutually exclusive with <code>batch_size</code>,
-<code>shuffle</code>, <code>sampler</code>, and <code>drop_last</code>.</p></td>
-    </tr>
-    <tr>
-      <th>num_workers</th>
-      <td><p>(int, optional): how many subprocesses to use for data
+<code>shuffle</code>, <code>sampler</code>, and <code>drop_last</code>.</p></dd>
+<dt>num_workers</dt>
+<dd><p>(int, optional): how many subprocesses to use for data
 loading. 0 means that the data will be loaded in the main process.
-(default: <code>0</code>)</p></td>
-    </tr>
-    <tr>
-      <th>collate_fn</th>
-      <td><p>(callable, optional): merges a list of samples to form a mini-batch.</p></td>
-    </tr>
-    <tr>
-      <th>pin_memory</th>
-      <td><p>(bool, optional): If <code>TRUE</code>, the data loader will copy tensors
+(default: <code>0</code>)</p></dd>
+<dt>collate_fn</dt>
+<dd><p>(callable, optional): merges a list of samples to form a mini-batch.</p></dd>
+<dt>pin_memory</dt>
+<dd><p>(bool, optional): If <code>TRUE</code>, the data loader will copy tensors
 into CUDA pinned memory before returning them.  If your data elements
 are a custom type, or your <code>collate_fn</code> returns a batch that is a custom type
-see the example below.</p></td>
-    </tr>
-    <tr>
-      <th>drop_last</th>
-      <td><p>(bool, optional): set to <code>TRUE</code> to drop the last incomplete batch,
+see the example below.</p></dd>
+<dt>drop_last</dt>
+<dd><p>(bool, optional): set to <code>TRUE</code> to drop the last incomplete batch,
 if the dataset size is not divisible by the batch size. If <code>FALSE</code> and
 the size of dataset is not divisible by the batch size, then the last batch
-will be smaller. (default: <code>FALSE</code>)</p></td>
-    </tr>
-    <tr>
-      <th>timeout</th>
-      <td><p>(numeric, optional): if positive, the timeout value for collecting a batch
-from workers. -1 means no timeout. (default: <code>-1</code>)</p></td>
-    </tr>
-    <tr>
-      <th>worker_init_fn</th>
-      <td><p>(callable, optional): If not <code>NULL</code>, this will be called on each
+will be smaller. (default: <code>FALSE</code>)</p></dd>
+<dt>timeout</dt>
+<dd><p>(numeric, optional): if positive, the timeout value for collecting a batch
+from workers. -1 means no timeout. (default: <code>-1</code>)</p></dd>
+<dt>worker_init_fn</dt>
+<dd><p>(callable, optional): If not <code>NULL</code>, this will be called on each
 worker subprocess with the worker id (an int in <code>[1, num_workers]</code>) as
-input, after seeding and before data loading. (default: <code>NULL</code>)</p></td>
-    </tr>
-    <tr>
-      <th>worker_globals</th>
-      <td><p>(list or character vector, optional) only used when
+input, after seeding and before data loading. (default: <code>NULL</code>)</p></dd>
+<dt>worker_globals</dt>
+<dd><p>(list or character vector, optional) only used when
 <code>num_workers &gt; 0</code>. If a character vector, then objects with those names are
 copied from the global environment to the workers. If a named list, then
 this list is copied and attached to the worker global environment. Notice
-that the objects are copied only once at the worker initialization.</p></td>
-    </tr>
-    <tr>
-      <th>worker_packages</th>
-      <td><p>(character vector, optional) Only used if <code>num_workers &gt; 0</code>
+that the objects are copied only once at the worker initialization.</p></dd>
+<dt>worker_packages</dt>
+<dd><p>(character vector, optional) Only used if <code>num_workers &gt; 0</code>
 optional character vector naming packages that should be loaded in
-each worker.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="parallel-data-loading"><a class="anchor" href="#parallel-data-loading"></a>Parallel data loading</h2>
-
+each worker.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="parallel-data-loading">Parallel data loading<a class="anchor" aria-label="anchor" href="#parallel-data-loading"></a></h2>
     
 
 
 <p>When using <code>num_workers &gt; 0</code> data loading will happen in parallel for each
 worker. Note that batches are taken in parallel and not observations.</p>
-<p>The worker initialization  process happens in the following order:</p><ul>
-<li><p><code>num_workers</code> R sessions are initialized.</p></li>
-</ul>
-
-<p>Then in each worker we perform the following actions:</p><ul>
-<li><p>the <code>torch</code> library is loaded.</p></li>
-<li><p>a random seed is set both using <code><a href='https://rdrr.io/r/base/Random.html'>set.seed()</a></code> and using <code>torch_manual_seed</code>.</p></li>
+<p>The worker initialization  process happens in the following order:</p><ul><li><p><code>num_workers</code> R sessions are initialized.</p></li>
+</ul><p>Then in each worker we perform the following actions:</p><ul><li><p>the <code>torch</code> library is loaded.</p></li>
+<li><p>a random seed is set both using <code><a href="https://rdrr.io/r/base/Random.html" class="external-link">set.seed()</a></code> and using <code>torch_manual_seed</code>.</p></li>
 <li><p>packages passed to the <code>worker_packages</code> argument are loaded.</p></li>
 <li><p>objects passed trough the <code>worker_globals</code> parameters are copied into the
 global environment.</p></li>
 <li><p>the <code>worker_init</code> function is ran with an <code>id</code> argument.</p></li>
 <li><p>the dataset fetcher is copied to the worker.</p></li>
-</ul>
+</ul></div>
 
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
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+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">dataloader_make_iter</span><span class="op">(</span><span class="va">dataloader</span><span class="op">)</span></code></pre></div>
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-    <table class="ref-arguments">
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-    <tr>
-      <th>dataloader</th>
-      <td><p>a dataloader object.</p></td>
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+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>dataloader</dt>
+<dd><p>a dataloader object.</p></dd>
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+    
+  </div>
+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Helper function to create an R6 class that inherits from the abstract <code>Dataset</code> class</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/utils-data.R'><code>R/utils-data.R</code></a></small>
-    <div class="hidden name"><code>dataset.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Helper function to create an function that generates R6 instances of class <code>dataset</code></h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/utils-data.R" class="external-link"><code>R/utils-data.R</code></a></small>
+      <div class="d-none name"><code>dataset.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>All datasets that represent a map from keys to data samples should subclass this
 class. All subclasses should overwrite the <code>.getitem()</code> method, which supports
 fetching a data sample for a given key. Subclasses could also optionally
 overwrite <code>.length()</code>, which is expected to return the size of the dataset
 (e.g. number of samples) used by many sampler implementations
-and the default options of <code><a href='dataloader.html'>dataloader()</a></code>.</p>
+and the default options of <code><a href="dataloader.html">dataloader()</a></code>.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>dataset</span><span class='op'>(</span>
-  name <span class='op'>=</span> <span class='cn'>NULL</span>,
-  inherit <span class='op'>=</span> <span class='va'>Dataset</span>,
-  <span class='va'>...</span>,
-  private <span class='op'>=</span> <span class='cn'>NULL</span>,
-  active <span class='op'>=</span> <span class='cn'>NULL</span>,
-  parent_env <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/sys.parent.html'>parent.frame</a></span><span class='op'>(</span><span class='op'>)</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>name</th>
-      <td><p>a name for the dataset. It it's also used as the class
-for it.</p></td>
-    </tr>
-    <tr>
-      <th>inherit</th>
-      <td><p>you can optionally inherit from a dataset when creating a
-new dataset.</p></td>
-    </tr>
-    <tr>
-      <th>...</th>
-      <td><p>public methods for the dataset class</p></td>
-    </tr>
-    <tr>
-      <th>private</th>
-      <td><p>passed to <code><a href='https://r6.r-lib.org/reference/R6Class.html'>R6::R6Class()</a></code>.</p></td>
-    </tr>
-    <tr>
-      <th>active</th>
-      <td><p>passed to <code><a href='https://r6.r-lib.org/reference/R6Class.html'>R6::R6Class()</a></code>.</p></td>
-    </tr>
-    <tr>
-      <th>parent_env</th>
-      <td><p>An environment to use as the parent of newly-created
-objects.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">dataset</span><span class="op">(</span>
+  name <span class="op">=</span> <span class="cn">NULL</span>,
+  inherit <span class="op">=</span> <span class="va">Dataset</span>,
+  <span class="va">...</span>,
+  private <span class="op">=</span> <span class="cn">NULL</span>,
+  active <span class="op">=</span> <span class="cn">NULL</span>,
+  parent_env <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sys.parent.html" class="external-link">parent.frame</a></span><span class="op">(</span><span class="op">)</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
-    <p><code><a href='dataloader.html'>dataloader()</a></code>  by default constructs a index
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>name</dt>
+<dd><p>a name for the dataset. It it's also used as the class
+for it.</p></dd>
+<dt>inherit</dt>
+<dd><p>you can optionally inherit from a dataset when creating a
+new dataset.</p></dd>
+<dt>...</dt>
+<dd><p>public methods for the dataset class</p></dd>
+<dt>private</dt>
+<dd><p>passed to <code><a href="https://r6.r-lib.org/reference/R6Class.html" class="external-link">R6::R6Class()</a></code>.</p></dd>
+<dt>active</dt>
+<dd><p>passed to <code><a href="https://r6.r-lib.org/reference/R6Class.html" class="external-link">R6::R6Class()</a></code>.</p></dd>
+<dt>parent_env</dt>
+<dd><p>An environment to use as the parent of newly-created
+objects.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>The output is a function <code>f</code> with class <code>dataset_generator</code>. Calling <code>f()</code>creates a new instance of the R6 class <code>dataset</code>. The R6 class is stored in the
+enclosing environment of <code>f</code> and can also be accessed through <code>f</code>s attribute
+<code>Dataset</code>.</p>
+    </div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
+    <p><code><a href="dataloader.html">dataloader()</a></code>  by default constructs a index
 sampler that yields integral indices.  To make it work with a map-style
 dataset with non-integral indices/keys, a custom sampler must be provided.</p>
-    <h2 class="hasAnchor" id="get-a-batch-of-observations"><a class="anchor" href="#get-a-batch-of-observations"></a>Get a batch of observations</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="get-a-batch-of-observations">Get a batch of observations<a class="anchor" aria-label="anchor" href="#get-a-batch-of-observations"></a></h2>
     
 
 
 <p>By default datasets are iterated by returning each observation/item individually.
-Sometimes it's possible to have an optimized implementation to take a batch
+Often it's possible to have an optimized implementation to take a batch
 of observations (eg, subsetting a tensor by multiple indexes at once is faster than
 subsetting once for each index), in this case you can implement a <code>.getbatch</code> method
 that will be used instead of <code>.getitem</code> when getting a batch of observations within
-the dataloader.</p>
+the dataloader. <code>.getbatch</code> must work for batches of size larger or equal to 1. For more
+on this see the the <code><a href="../articles/loading-data.html">vignette("loading-data")</a></code>.</p>
+    </div>
 
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+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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+      <img src="" class="logo" alt=""><h1>Gets and sets the default floating point dtype.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/dtype.R" class="external-link"><code>R/dtype.R</code></a></small>
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     <p>Gets and sets the default floating point dtype.</p>
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-    <pre class="usage"><span class='fu'>torch_set_default_dtype</span><span class='op'>(</span><span class='va'>d</span><span class='op'>)</span>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_set_default_dtype</span><span class="op">(</span><span class="va">d</span><span class="op">)</span>
 
-<span class='fu'>torch_get_default_dtype</span><span class='op'>(</span><span class='op'>)</span></pre>
+<span class="fu">torch_get_default_dtype</span><span class="op">(</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
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-      <th>d</th>
-      <td><p>The default floating point dtype to set. Initially set to
-<code><a href='torch_dtype.html'>torch_float()</a></code>.</p></td>
-    </tr>
-    </table>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>d</dt>
+<dd><p>The default floating point dtype to set. Initially set to
+<code><a href="torch_dtype.html">torch_float()</a></code>.</p></dd>
+</dl></div>
 
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index 5af1cfb77..53f2c9354 100644
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 Samples are binary (0 or 1). They take the value 1 with probability p
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Creates a Bernoulli distribution parameterized by <code>probs</code>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Creates a Bernoulli distribution parameterized by <code>probs</code>
 or <code>logits</code> (but not both).
 Samples are binary (0 or 1). They take the value <code>1</code> with probability <code>p</code>
 and <code>0</code> with probability <code>1 - p</code>.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/distributions-bernoulli.R'><code>R/distributions-bernoulli.R</code></a></small>
-    <div class="hidden name"><code>distr_bernoulli.Rd</code></div>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/distributions-bernoulli.R" class="external-link"><code>R/distributions-bernoulli.R</code></a></small>
+      <div class="d-none name"><code>distr_bernoulli.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Creates a Bernoulli distribution parameterized by <code>probs</code>
 or <code>logits</code> (but not both).
 Samples are binary (0 or 1). They take the value <code>1</code> with probability <code>p</code>
 and <code>0</code> with probability <code>1 - p</code>.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>distr_bernoulli</span><span class='op'>(</span>probs <span class='op'>=</span> <span class='cn'>NULL</span>, logits <span class='op'>=</span> <span class='cn'>NULL</span>, validate_args <span class='op'>=</span> <span class='cn'>NULL</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>probs</th>
-      <td><p>(numeric or torch_tensor): the probability of sampling <code>1</code></p></td>
-    </tr>
-    <tr>
-      <th>logits</th>
-      <td><p>(numeric or torch_tensor): the log-odds of sampling <code>1</code></p></td>
-    </tr>
-    <tr>
-      <th>validate_args</th>
-      <td><p>whether to validate arguments or not.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="see-also"><a class="anchor" href="#see-also"></a>See also</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">distr_bernoulli</span><span class="op">(</span>probs <span class="op">=</span> <span class="cn">NULL</span>, logits <span class="op">=</span> <span class="cn">NULL</span>, validate_args <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <div class='dont-index'><p><a href='Distribution.html'>Distribution</a> for details on the available methods.</p>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>probs</dt>
+<dd><p>(numeric or torch_tensor): the probability of sampling <code>1</code></p></dd>
+<dt>logits</dt>
+<dd><p>(numeric or torch_tensor): the log-odds of sampling <code>1</code></p></dd>
+<dt>validate_args</dt>
+<dd><p>whether to validate arguments or not.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
+    <div class="dont-index"><p><a href="Distribution.html">Distribution</a> for details on the available methods.</p>
 <p>Other distributions: 
-<code><a href='distr_chi2.html'>distr_chi2</a>()</code>,
-<code><a href='distr_gamma.html'>distr_gamma</a>()</code>,
-<code><a href='distr_multivariate_normal.html'>distr_multivariate_normal</a>()</code>,
-<code><a href='distr_normal.html'>distr_normal</a>()</code>,
-<code><a href='distr_poisson.html'>distr_poisson</a>()</code></p></div>
+<code><a href="distr_chi2.html">distr_chi2</a>()</code>,
+<code><a href="distr_gamma.html">distr_gamma</a>()</code>,
+<code><a href="distr_multivariate_normal.html">distr_multivariate_normal</a>()</code>,
+<code><a href="distr_normal.html">distr_normal</a>()</code>,
+<code><a href="distr_poisson.html">distr_poisson</a>()</code></p></div>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>&lt;-</span> <span class='fu'>distr_bernoulli</span><span class='op'>(</span><span class='fl'>0.3</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>m</span><span class='op'>$</span><span class='fu'>sample</span><span class='op'>(</span><span class='op'>)</span>  <span class='co'># 30% chance 1; 70% chance 0</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{1} ]</span>
-</pre>
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-      <h2 data-toc-skip>Contents</h2>
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+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">distr_bernoulli</span><span class="op">(</span><span class="fl">0.3</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">m</span><span class="op">$</span><span class="fu">sample</span><span class="op">(</span><span class="op">)</span> <span class="co"># 30% chance 1; 70% chance 0</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{1} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Creates a categorical distribution parameterized by either <code>probs</code> or
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Creates a categorical distribution parameterized by either <code>probs</code> or
 <code>logits</code> (but not both).</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/distributions-categorical.R'><code>R/distributions-categorical.R</code></a></small>
-    <div class="hidden name"><code>distr_categorical.Rd</code></div>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/distributions-categorical.R" class="external-link"><code>R/distributions-categorical.R</code></a></small>
+      <div class="d-none name"><code>distr_categorical.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Creates a categorical distribution parameterized by either <code>probs</code> or
 <code>logits</code> (but not both).</p>
     </div>
 
-    <pre class="usage"><span class='fu'>distr_categorical</span><span class='op'>(</span>probs <span class='op'>=</span> <span class='cn'>NULL</span>, logits <span class='op'>=</span> <span class='cn'>NULL</span>, validate_args <span class='op'>=</span> <span class='cn'>NULL</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>probs</th>
-      <td><p>(Tensor): event probabilities</p></td>
-    </tr>
-    <tr>
-      <th>logits</th>
-      <td><p>(Tensor): event log probabilities (unnormalized)</p></td>
-    </tr>
-    <tr>
-      <th>validate_args</th>
-      <td><p>Additional arguments</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">distr_categorical</span><span class="op">(</span>probs <span class="op">=</span> <span class="cn">NULL</span>, logits <span class="op">=</span> <span class="cn">NULL</span>, validate_args <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <p>It is equivalent to the distribution that <code><a href='torch_multinomial.html'>torch_multinomial()</a></code>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>probs</dt>
+<dd><p>(Tensor): event probabilities</p></dd>
+<dt>logits</dt>
+<dd><p>(Tensor): event log probabilities (unnormalized)</p></dd>
+<dt>validate_args</dt>
+<dd><p>Additional arguments</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
+    <p>It is equivalent to the distribution that <code><a href="torch_multinomial.html">torch_multinomial()</a></code>
 samples from.</p>
 <p>Samples are integers from \(\{0, \ldots, K-1\}\) where <code>K</code> is <code>probs$size(-1)</code>.</p>
 <p>If <code>probs</code> is 1-dimensional with length-<code>K</code>, each element is the relative probability
@@ -229,43 +116,37 @@ The <code>logits</code> argument will be interpreted as unnormalized log probabi
 and can therefore be any real number. It will likewise be normalized so that
 the resulting probabilities sum to 1 along the last dimension. attr:<code>logits</code>
 will return this normalized value.</p>
-<p>See also: <code><a href='torch_multinomial.html'>torch_multinomial()</a></code></p>
+<p>See also: <code><a href="torch_multinomial.html">torch_multinomial()</a></code></p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>&lt;-</span> <span class='fu'>distr_categorical</span><span class='op'>(</span><span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>0.25</span>, <span class='fl'>0.25</span>, <span class='fl'>0.25</span>, <span class='fl'>0.25</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>m</span><span class='op'>$</span><span class='fu'>sample</span><span class='op'>(</span><span class='op'>)</span>  <span class='co'># equal probability of 1,2,3,4</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> 3</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPULongType{} ]</span>
-</pre>
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-      <h2 data-toc-skip>Contents</h2>
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+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">distr_categorical</span><span class="op">(</span><span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.25</span>, <span class="fl">0.25</span>, <span class="fl">0.25</span>, <span class="fl">0.25</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">m</span><span class="op">$</span><span class="fu">sample</span><span class="op">(</span><span class="op">)</span> <span class="co"># equal probability of 1,2,3,4</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 3</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPULongType{} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Creates a Chi2 distribution parameterized by shape parameter <code>df</code>.
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Creates a Chi2 distribution parameterized by shape parameter <code>df</code>.
 This is exactly equivalent to <code>distr_gamma(alpha=0.5*df, beta=0.5)</code></h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/distributions-chi2.R'><code>R/distributions-chi2.R</code></a></small>
-    <div class="hidden name"><code>distr_chi2.Rd</code></div>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/distributions-chi2.R" class="external-link"><code>R/distributions-chi2.R</code></a></small>
+      <div class="d-none name"><code>distr_chi2.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Creates a Chi2 distribution parameterized by shape parameter <code>df</code>.
 This is exactly equivalent to <code>distr_gamma(alpha=0.5*df, beta=0.5)</code></p>
     </div>
 
-    <pre class="usage"><span class='fu'>distr_chi2</span><span class='op'>(</span><span class='va'>df</span>, validate_args <span class='op'>=</span> <span class='cn'>NULL</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>df</th>
-      <td><p>(float or torch_tensor): shape parameter of the distribution</p></td>
-    </tr>
-    <tr>
-      <th>validate_args</th>
-      <td><p>whether to validate arguments or not.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="see-also"><a class="anchor" href="#see-also"></a>See also</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">distr_chi2</span><span class="op">(</span><span class="va">df</span>, validate_args <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <div class='dont-index'><p><a href='Distribution.html'>Distribution</a> for details on the available methods.</p>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>df</dt>
+<dd><p>(float or torch_tensor): shape parameter of the distribution</p></dd>
+<dt>validate_args</dt>
+<dd><p>whether to validate arguments or not.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
+    <div class="dont-index"><p><a href="Distribution.html">Distribution</a> for details on the available methods.</p>
 <p>Other distributions: 
-<code><a href='distr_bernoulli.html'>distr_bernoulli</a>()</code>,
-<code><a href='distr_gamma.html'>distr_gamma</a>()</code>,
-<code><a href='distr_multivariate_normal.html'>distr_multivariate_normal</a>()</code>,
-<code><a href='distr_normal.html'>distr_normal</a>()</code>,
-<code><a href='distr_poisson.html'>distr_poisson</a>()</code></p></div>
+<code><a href="distr_bernoulli.html">distr_bernoulli</a>()</code>,
+<code><a href="distr_gamma.html">distr_gamma</a>()</code>,
+<code><a href="distr_multivariate_normal.html">distr_multivariate_normal</a>()</code>,
+<code><a href="distr_normal.html">distr_normal</a>()</code>,
+<code><a href="distr_poisson.html">distr_poisson</a>()</code></p></div>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>&lt;-</span> <span class='fu'>distr_chi2</span><span class='op'>(</span><span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fl'>1.0</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>m</span><span class='op'>$</span><span class='fu'>sample</span><span class='op'>(</span><span class='op'>)</span>  <span class='co'># Chi2 distributed with shape df=1</span></span>
-<span class='r-in'><span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fl'>0.1046</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.1046</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{1} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">distr_chi2</span><span class="op">(</span><span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fl">1.0</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">m</span><span class="op">$</span><span class="fu">sample</span><span class="op">(</span><span class="op">)</span> <span class="co"># Chi2 distributed with shape df=1</span></span>
+<span class="r-in"><span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fl">0.1046</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.1046</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{1} ]</span>
+</code></pre></div>
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Creates a Gamma distribution parameterized by shape <code>concentration</code> and <code>rate</code>.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/distributions-gamma.R'><code>R/distributions-gamma.R</code></a></small>
-    <div class="hidden name"><code>distr_gamma.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Creates a Gamma distribution parameterized by shape <code>concentration</code> and <code>rate</code>.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/distributions-gamma.R" class="external-link"><code>R/distributions-gamma.R</code></a></small>
+      <div class="d-none name"><code>distr_gamma.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Creates a Gamma distribution parameterized by shape <code>concentration</code> and <code>rate</code>.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>distr_gamma</span><span class='op'>(</span><span class='va'>concentration</span>, <span class='va'>rate</span>, validate_args <span class='op'>=</span> <span class='cn'>NULL</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>concentration</th>
-      <td><p>(float or Tensor): shape parameter of the distribution
-(often referred to as alpha)</p></td>
-    </tr>
-    <tr>
-      <th>rate</th>
-      <td><p>(float or Tensor): rate = 1 / scale of the distribution
-(often referred to as beta)</p></td>
-    </tr>
-    <tr>
-      <th>validate_args</th>
-      <td><p>whether to validate arguments or not.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="see-also"><a class="anchor" href="#see-also"></a>See also</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">distr_gamma</span><span class="op">(</span><span class="va">concentration</span>, <span class="va">rate</span>, validate_args <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <div class='dont-index'><p><a href='Distribution.html'>Distribution</a> for details on the available methods.</p>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>concentration</dt>
+<dd><p>(float or Tensor): shape parameter of the distribution
+(often referred to as alpha)</p></dd>
+<dt>rate</dt>
+<dd><p>(float or Tensor): rate = 1 / scale of the distribution
+(often referred to as beta)</p></dd>
+<dt>validate_args</dt>
+<dd><p>whether to validate arguments or not.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
+    <div class="dont-index"><p><a href="Distribution.html">Distribution</a> for details on the available methods.</p>
 <p>Other distributions: 
-<code><a href='distr_bernoulli.html'>distr_bernoulli</a>()</code>,
-<code><a href='distr_chi2.html'>distr_chi2</a>()</code>,
-<code><a href='distr_multivariate_normal.html'>distr_multivariate_normal</a>()</code>,
-<code><a href='distr_normal.html'>distr_normal</a>()</code>,
-<code><a href='distr_poisson.html'>distr_poisson</a>()</code></p></div>
+<code><a href="distr_bernoulli.html">distr_bernoulli</a>()</code>,
+<code><a href="distr_chi2.html">distr_chi2</a>()</code>,
+<code><a href="distr_multivariate_normal.html">distr_multivariate_normal</a>()</code>,
+<code><a href="distr_normal.html">distr_normal</a>()</code>,
+<code><a href="distr_poisson.html">distr_poisson</a>()</code></p></div>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>&lt;-</span> <span class='fu'>distr_gamma</span><span class='op'>(</span><span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fl'>1.0</span><span class='op'>)</span>, <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fl'>1.0</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>m</span><span class='op'>$</span><span class='fu'>sample</span><span class='op'>(</span><span class='op'>)</span>  <span class='co'># Gamma distributed with concentration=1 and rate=1</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> 0.01 *</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  2.1107</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{1} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">distr_gamma</span><span class="op">(</span><span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fl">1.0</span><span class="op">)</span>, <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fl">1.0</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">m</span><span class="op">$</span><span class="fu">sample</span><span class="op">(</span><span class="op">)</span> <span class="co"># Gamma distributed with concentration=1 and rate=1</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2.2154</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{1} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer>
-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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diff --git a/reference/distr_mixture_same_family.html b/reference/distr_mixture_same_family.html
index c73e90030..429dddea7 100644
--- a/reference/distr_mixture_same_family.html
+++ b/reference/distr_mixture_same_family.html
@@ -1,196 +1,92 @@
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content='The MixtureSameFamily distribution implements a (batch of) mixture
 distribution where all component are from different parameterizations of
 the same distribution type. It is parameterized by a Categorical
-selecting distribution&quot; (over k component) and a component
+selecting distribution" (over k component) and a component
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+distribution where all component are from different parameterizations of
+the same distribution type. It is parameterized by a Categorical
+selecting distribution" (over k component) and a component
+distribution, i.e., a Distribution with a rightmost batch shape
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Mixture of components in the same family</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/distributions-mixture_same_family.R'><code>R/distributions-mixture_same_family.R</code></a></small>
-    <div class="hidden name"><code>distr_mixture_same_family.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Mixture of components in the same family</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/distributions-mixture_same_family.R" class="external-link"><code>R/distributions-mixture_same_family.R</code></a></small>
+      <div class="d-none name"><code>distr_mixture_same_family.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>The <code>MixtureSameFamily</code> distribution implements a (batch of) mixture
 distribution where all component are from different parameterizations of
 the same distribution type. It is parameterized by a <code>Categorical</code>
@@ -199,71 +95,59 @@ distribution, i.e., a <code>Distribution</code> with a rightmost batch shape
 (equal to <code>[k]</code>) which indexes each (batch of) component.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>distr_mixture_same_family</span><span class='op'>(</span>
-  <span class='va'>mixture_distribution</span>,
-  <span class='va'>component_distribution</span>,
-  validate_args <span class='op'>=</span> <span class='cn'>NULL</span>
-<span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">distr_mixture_same_family</span><span class="op">(</span>
+  <span class="va">mixture_distribution</span>,
+  <span class="va">component_distribution</span>,
+  validate_args <span class="op">=</span> <span class="cn">NULL</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>mixture_distribution</th>
-      <td><p><code>torch_distributions.Categorical</code>-like
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>mixture_distribution</dt>
+<dd><p><code>torch_distributions.Categorical</code>-like
 instance. Manages the probability of selecting component.
 The number of categories must match the rightmost batch
 dimension of the <code>component_distribution</code>. Must have either
 scalar <code>batch_shape</code> or <code>batch_shape</code> matching
-<code>component_distribution.batch_shape[:-1]</code></p></td>
-    </tr>
-    <tr>
-      <th>component_distribution</th>
-      <td><p><code>torch_distributions.Distribution</code>-like
-instance. Right-most batch dimension indexes component.</p></td>
-    </tr>
-    <tr>
-      <th>validate_args</th>
-      <td><p>Additional arguments</p></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='co'># Construct Gaussian Mixture Model in 1D consisting of 5 equally</span></span>
-<span class='r-in'><span class='co'># weighted normal distributions</span></span>
-<span class='r-in'><span class='va'>mix</span> <span class='op'>&lt;-</span> <span class='fu'><a href='distr_categorical.html'>distr_categorical</a></span><span class='op'>(</span><span class='fu'><a href='torch_ones.html'>torch_ones</a></span><span class='op'>(</span><span class='fl'>5</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>comp</span> <span class='op'>&lt;-</span> <span class='fu'><a href='distr_normal.html'>distr_normal</a></span><span class='op'>(</span><span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>5</span><span class='op'>)</span>, <span class='fu'><a href='torch_rand.html'>torch_rand</a></span><span class='op'>(</span><span class='fl'>5</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>gmm</span> <span class='op'>&lt;-</span> <span class='fu'>distr_mixture_same_family</span><span class='op'>(</span><span class='va'>mix</span>, <span class='va'>comp</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+<code>component_distribution.batch_shape[:-1]</code></p></dd>
+<dt>component_distribution</dt>
+<dd><p><code>torch_distributions.Distribution</code>-like
+instance. Right-most batch dimension indexes component.</p></dd>
+<dt>validate_args</dt>
+<dd><p>Additional arguments</p></dd>
+</dl></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="co"># Construct Gaussian Mixture Model in 1D consisting of 5 equally</span></span>
+<span class="r-in"><span class="co"># weighted normal distributions</span></span>
+<span class="r-in"><span class="va">mix</span> <span class="op">&lt;-</span> <span class="fu"><a href="distr_categorical.html">distr_categorical</a></span><span class="op">(</span><span class="fu"><a href="torch_ones.html">torch_ones</a></span><span class="op">(</span><span class="fl">5</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">comp</span> <span class="op">&lt;-</span> <span class="fu"><a href="distr_normal.html">distr_normal</a></span><span class="op">(</span><span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">5</span><span class="op">)</span>, <span class="fu"><a href="torch_rand.html">torch_rand</a></span><span class="op">(</span><span class="fl">5</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">gmm</span> <span class="op">&lt;-</span> <span class="fu">distr_mixture_same_family</span><span class="op">(</span><span class="va">mix</span>, <span class="va">comp</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer>
-      <div class="copyright">
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+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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+<div class="pkgdown-footer-right">
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 <div class="row">
-  <div class="col-md-9 contents">
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-    <h1>Gaussian distribution</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/distributions-multivariate_normal.R'><code>R/distributions-multivariate_normal.R</code></a></small>
-    <div class="hidden name"><code>distr_multivariate_normal.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Gaussian distribution</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/distributions-multivariate_normal.R" class="external-link"><code>R/distributions-multivariate_normal.R</code></a></small>
+      <div class="d-none name"><code>distr_multivariate_normal.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Creates a multivariate normal (also called Gaussian) distribution
 parameterized by a mean vector and a covariance matrix.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>distr_multivariate_normal</span><span class='op'>(</span>
-  <span class='va'>loc</span>,
-  covariance_matrix <span class='op'>=</span> <span class='cn'>NULL</span>,
-  precision_matrix <span class='op'>=</span> <span class='cn'>NULL</span>,
-  scale_tril <span class='op'>=</span> <span class='cn'>NULL</span>,
-  validate_args <span class='op'>=</span> <span class='cn'>NULL</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>loc</th>
-      <td><p>(Tensor): mean of the distribution</p></td>
-    </tr>
-    <tr>
-      <th>covariance_matrix</th>
-      <td><p>(Tensor): positive-definite covariance matrix</p></td>
-    </tr>
-    <tr>
-      <th>precision_matrix</th>
-      <td><p>(Tensor): positive-definite precision matrix</p></td>
-    </tr>
-    <tr>
-      <th>scale_tril</th>
-      <td><p>(Tensor): lower-triangular factor of covariance, with positive-valued diagonal</p></td>
-    </tr>
-    <tr>
-      <th>validate_args</th>
-      <td><p>Bool wether to validate the arguments or not.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">distr_multivariate_normal</span><span class="op">(</span>
+  <span class="va">loc</span>,
+  covariance_matrix <span class="op">=</span> <span class="cn">NULL</span>,
+  precision_matrix <span class="op">=</span> <span class="cn">NULL</span>,
+  scale_tril <span class="op">=</span> <span class="cn">NULL</span>,
+  validate_args <span class="op">=</span> <span class="cn">NULL</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>loc</dt>
+<dd><p>(Tensor): mean of the distribution</p></dd>
+<dt>covariance_matrix</dt>
+<dd><p>(Tensor): positive-definite covariance matrix</p></dd>
+<dt>precision_matrix</dt>
+<dd><p>(Tensor): positive-definite precision matrix</p></dd>
+<dt>scale_tril</dt>
+<dd><p>(Tensor): lower-triangular factor of covariance, with positive-valued diagonal</p></dd>
+<dt>validate_args</dt>
+<dd><p>Bool wether to validate the arguments or not.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>The multivariate normal distribution can be parameterized either
 in terms of a positive definite covariance matrix \(\mathbf{\Sigma}\)
 or a positive definite precision matrix \(\mathbf{\Sigma}^{-1}\)
@@ -233,62 +116,56 @@ or a lower-triangular matrix \(\mathbf{L}\) with positive-valued
 diagonal entries, such that
 \(\mathbf{\Sigma} = \mathbf{L}\mathbf{L}^\top\). This triangular matrix
 can be obtained via e.g. Cholesky decomposition of the covariance.</p>
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p>Only one of <code>covariance_matrix</code> or <code>precision_matrix</code> or
 <code>scale_tril</code> can be specified.
 Using <code>scale_tril</code> will be more efficient: all computations internally
 are based on <code>scale_tril</code>. If <code>covariance_matrix</code> or
 <code>precision_matrix</code> is passed instead, it is only used to compute
 the corresponding lower triangular matrices using a Cholesky decomposition.</p>
-    <h2 class="hasAnchor" id="see-also"><a class="anchor" href="#see-also"></a>See also</h2>
-
-    <div class='dont-index'><p><a href='Distribution.html'>Distribution</a> for details on the available methods.</p>
+    </div>
+    <div class="section level2">
+    <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
+    <div class="dont-index"><p><a href="Distribution.html">Distribution</a> for details on the available methods.</p>
 <p>Other distributions: 
-<code><a href='distr_bernoulli.html'>distr_bernoulli</a>()</code>,
-<code><a href='distr_chi2.html'>distr_chi2</a>()</code>,
-<code><a href='distr_gamma.html'>distr_gamma</a>()</code>,
-<code><a href='distr_normal.html'>distr_normal</a>()</code>,
-<code><a href='distr_poisson.html'>distr_poisson</a>()</code></p></div>
+<code><a href="distr_bernoulli.html">distr_bernoulli</a>()</code>,
+<code><a href="distr_chi2.html">distr_chi2</a>()</code>,
+<code><a href="distr_gamma.html">distr_gamma</a>()</code>,
+<code><a href="distr_normal.html">distr_normal</a>()</code>,
+<code><a href="distr_poisson.html">distr_poisson</a>()</code></p></div>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>&lt;-</span> <span class='fu'>distr_multivariate_normal</span><span class='op'>(</span><span class='fu'><a href='torch_zeros.html'>torch_zeros</a></span><span class='op'>(</span><span class='fl'>2</span><span class='op'>)</span>, <span class='fu'><a href='torch_eye.html'>torch_eye</a></span><span class='op'>(</span><span class='fl'>2</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>m</span><span class='op'>$</span><span class='fu'>sample</span><span class='op'>(</span><span class='op'>)</span>  <span class='co'># normally distributed with mean=`[0,0]` and covariance_matrix=`I`</span></span>
-<span class='r-in'></span>
-<span class='r-in'></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.1308</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.5401</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{2} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">distr_multivariate_normal</span><span class="op">(</span><span class="fu"><a href="torch_zeros.html">torch_zeros</a></span><span class="op">(</span><span class="fl">2</span><span class="op">)</span>, <span class="fu"><a href="torch_eye.html">torch_eye</a></span><span class="op">(</span><span class="fl">2</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">m</span><span class="op">$</span><span class="fu">sample</span><span class="op">(</span><span class="op">)</span> <span class="co"># normally distributed with mean=`[0,0]` and covariance_matrix=`I`</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.3365</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.0608</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer>
-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
 </div>
 
-<div class="pkgdown">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p>
+<div class="pkgdown-footer-right">
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index 22660f37a..42e5540d5 100644
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+    <a class="dropdown-item" href="../articles/examples/basic-autograd.html">basic-autograd</a>
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Creates a normal (also called Gaussian) distribution parameterized by
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Creates a normal (also called Gaussian) distribution parameterized by
 <code>loc</code> and <code>scale</code>.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/distributions-normal.R'><code>R/distributions-normal.R</code></a></small>
-    <div class="hidden name"><code>distr_normal.Rd</code></div>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/distributions-normal.R" class="external-link"><code>R/distributions-normal.R</code></a></small>
+      <div class="d-none name"><code>distr_normal.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Creates a normal (also called Gaussian) distribution parameterized by
 <code>loc</code> and <code>scale</code>.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>distr_normal</span><span class='op'>(</span><span class='va'>loc</span>, <span class='va'>scale</span>, validate_args <span class='op'>=</span> <span class='cn'>NULL</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>loc</th>
-      <td><p>(float or Tensor): mean of the distribution (often referred to as mu)</p></td>
-    </tr>
-    <tr>
-      <th>scale</th>
-      <td><p>(float or Tensor): standard deviation of the distribution (often referred to as sigma)</p></td>
-    </tr>
-    <tr>
-      <th>validate_args</th>
-      <td><p>Additional arguments</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">distr_normal</span><span class="op">(</span><span class="va">loc</span>, <span class="va">scale</span>, validate_args <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>loc</dt>
+<dd><p>(float or Tensor): mean of the distribution (often referred to as mu)</p></dd>
+<dt>scale</dt>
+<dd><p>(float or Tensor): standard deviation of the distribution (often referred to as sigma)</p></dd>
+<dt>validate_args</dt>
+<dd><p>Additional arguments</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
     <p>Object of <code>torch_Normal</code> class</p>
-    <h2 class="hasAnchor" id="see-also"><a class="anchor" href="#see-also"></a>See also</h2>
-
-    <div class='dont-index'><p><a href='Distribution.html'>Distribution</a> for details on the available methods.</p>
+    </div>
+    <div class="section level2">
+    <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
+    <div class="dont-index"><p><a href="Distribution.html">Distribution</a> for details on the available methods.</p>
 <p>Other distributions: 
-<code><a href='distr_bernoulli.html'>distr_bernoulli</a>()</code>,
-<code><a href='distr_chi2.html'>distr_chi2</a>()</code>,
-<code><a href='distr_gamma.html'>distr_gamma</a>()</code>,
-<code><a href='distr_multivariate_normal.html'>distr_multivariate_normal</a>()</code>,
-<code><a href='distr_poisson.html'>distr_poisson</a>()</code></p></div>
+<code><a href="distr_bernoulli.html">distr_bernoulli</a>()</code>,
+<code><a href="distr_chi2.html">distr_chi2</a>()</code>,
+<code><a href="distr_gamma.html">distr_gamma</a>()</code>,
+<code><a href="distr_multivariate_normal.html">distr_multivariate_normal</a>()</code>,
+<code><a href="distr_poisson.html">distr_poisson</a>()</code></p></div>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>&lt;-</span> <span class='fu'>distr_normal</span><span class='op'>(</span>loc <span class='op'>=</span> <span class='fl'>0</span>, scale <span class='op'>=</span> <span class='fl'>1</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>m</span><span class='op'>$</span><span class='fu'>sample</span><span class='op'>(</span><span class='op'>)</span>  <span class='co'># normally distributed with loc=0 and scale=1</span></span>
-<span class='r-in'></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -1.0996</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{1} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">distr_normal</span><span class="op">(</span>loc <span class="op">=</span> <span class="fl">0</span>, scale <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">m</span><span class="op">$</span><span class="fu">sample</span><span class="op">(</span><span class="op">)</span> <span class="co"># normally distributed with loc=0 and scale=1</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.2166</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{1} ]</span>
+</code></pre></div>
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-    <div class="page-header">
-    <h1>Creates a Poisson distribution parameterized by <code>rate</code>, the rate parameter.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/distributions-poisson.R'><code>R/distributions-poisson.R</code></a></small>
-    <div class="hidden name"><code>distr_poisson.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Creates a Poisson distribution parameterized by <code>rate</code>, the rate parameter.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/distributions-poisson.R" class="external-link"><code>R/distributions-poisson.R</code></a></small>
+      <div class="d-none name"><code>distr_poisson.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Samples are nonnegative integers, with a pmf given by
 $$
 \mbox{rate}^{k} \frac{e^{-\mbox{rate}}}{k!}
 $$</p>
     </div>
 
-    <pre class="usage"><span class='fu'>distr_poisson</span><span class='op'>(</span><span class='va'>rate</span>, validate_args <span class='op'>=</span> <span class='cn'>NULL</span><span class='op'>)</span></pre>
-
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-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>rate</th>
-      <td><p>(numeric, torch_tensor): the rate parameter</p></td>
-    </tr>
-    <tr>
-      <th>validate_args</th>
-      <td><p>whether to validate arguments or not.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="see-also"><a class="anchor" href="#see-also"></a>See also</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">distr_poisson</span><span class="op">(</span><span class="va">rate</span>, validate_args <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <div class='dont-index'><p><a href='Distribution.html'>Distribution</a> for details on the available methods.</p>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>rate</dt>
+<dd><p>(numeric, torch_tensor): the rate parameter</p></dd>
+<dt>validate_args</dt>
+<dd><p>whether to validate arguments or not.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
+    <div class="dont-index"><p><a href="Distribution.html">Distribution</a> for details on the available methods.</p>
 <p>Other distributions: 
-<code><a href='distr_bernoulli.html'>distr_bernoulli</a>()</code>,
-<code><a href='distr_chi2.html'>distr_chi2</a>()</code>,
-<code><a href='distr_gamma.html'>distr_gamma</a>()</code>,
-<code><a href='distr_multivariate_normal.html'>distr_multivariate_normal</a>()</code>,
-<code><a href='distr_normal.html'>distr_normal</a>()</code></p></div>
+<code><a href="distr_bernoulli.html">distr_bernoulli</a>()</code>,
+<code><a href="distr_chi2.html">distr_chi2</a>()</code>,
+<code><a href="distr_gamma.html">distr_gamma</a>()</code>,
+<code><a href="distr_multivariate_normal.html">distr_multivariate_normal</a>()</code>,
+<code><a href="distr_normal.html">distr_normal</a>()</code></p></div>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>&lt;-</span> <span class='fu'>distr_poisson</span><span class='op'>(</span><span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>m</span><span class='op'>$</span><span class='fu'>sample</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  8</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{1} ]</span>
-</pre>
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+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">distr_poisson</span><span class="op">(</span><span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fl">4</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">m</span><span class="op">$</span><span class="fu">sample</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  4</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{1} ]</span>
+</code></pre></div>
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+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Enumerate an iterator</h1>
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-    <pre class="usage"><span class='co'># S3 method for dataloader</span>
-<span class='fu'><a href='enumerate.html'>enumerate</a></span><span class='op'>(</span><span class='va'>x</span>, max_len <span class='op'>=</span> <span class='fl'>1e+06</span>, <span class='va'>...</span><span class='op'>)</span></pre>
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-    <table class="ref-arguments">
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-    <tr>
-      <th>x</th>
-      <td><p>the generator to enumerate.</p></td>
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-      <th>max_len</th>
-      <td><p>maximum number of iterations.</p></td>
-    </tr>
-    <tr>
-      <th>...</th>
-      <td><p>passed to specific methods.</p></td>
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+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="co"># S3 method for dataloader</span>
+<span class="fu"><a href="enumerate.html">enumerate</a></span><span class="op">(</span><span class="va">x</span>, max_len <span class="op">=</span> <span class="fl">1e+06</span>, <span class="va">...</span><span class="op">)</span></code></pre></div>
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+<dd><p>the generator to enumerate.</p></dd>
+<dt>max_len</dt>
+<dd><p>maximum number of iterations.</p></dd>
+<dt>...</dt>
+<dd><p>passed to specific methods.</p></dd>
+</dl></div>
 
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-    <div class="hidden name"><code>enumerate.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Enumerate an iterator</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/utils-data-enum.R" class="external-link"><code>R/utils-data-enum.R</code></a></small>
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     <p>Enumerate an iterator</p>
     </div>
 
-    <pre class="usage"><span class='fu'>enumerate</span><span class='op'>(</span><span class='va'>x</span>, <span class='va'>...</span><span class='op'>)</span></pre>
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+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">enumerate</span><span class="op">(</span><span class="va">x</span>, <span class="va">...</span><span class="op">)</span></code></pre></div>
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-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
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-      <th>x</th>
-      <td><p>the generator to enumerate.</p></td>
-    </tr>
-    <tr>
-      <th>...</th>
-      <td><p>passed to specific methods.</p></td>
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-    </table>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>x</dt>
+<dd><p>the generator to enumerate.</p></dd>
+<dt>...</dt>
+<dd><p>passed to specific methods.</p></dd>
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diff --git a/reference/get_install_libs_url.html b/reference/get_install_libs_url.html
index f3052a764..529b4a9ed 100644
--- a/reference/get_install_libs_url.html
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+</nav><div class="container template-reference-topic">
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-  <div class="col-md-9 contents">
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-    <h1>List of files to download</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/install.R'><code>R/install.R</code></a></small>
-    <div class="hidden name"><code>get_install_libs_url.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>List of files to download</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/install.R" class="external-link"><code>R/install.R</code></a></small>
+      <div class="d-none name"><code>get_install_libs_url.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>List the Torch and Lantern files to download as local files in order to proceed with install_torch_from_file().</p>
     </div>
 
-    <pre class="usage"><span class='fu'>get_install_libs_url</span><span class='op'>(</span>version <span class='op'>=</span> <span class='st'>"1.9.1"</span>, type <span class='op'>=</span> <span class='fu'>install_type</span><span class='op'>(</span>version <span class='op'>=</span> <span class='va'>version</span><span class='op'>)</span><span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">get_install_libs_url</span><span class="op">(</span>
+  version <span class="op">=</span> <span class="st">"1.10.2"</span>,
+  type <span class="op">=</span> <span class="fu">install_type</span><span class="op">(</span>version <span class="op">=</span> <span class="va">version</span><span class="op">)</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>version</th>
-      <td><p>The Torch version to install.</p></td>
-    </tr>
-    <tr>
-      <th>type</th>
-      <td><p>The installation type for Torch. Valid values are <code>"cpu"</code> or the 'CUDA' version.</p></td>
-    </tr>
-    </table>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>version</dt>
+<dd><p>The Torch version to install.</p></dd>
+<dt>type</dt>
+<dd><p>The installation type for Torch. Valid values are <code>"cpu"</code> or the 'CUDA' version.</p></dd>
+</dl></div>
 
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diff --git a/reference/index.html b/reference/index.html
index 27ceaff49..b842914f5 100644
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+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Function reference</h1>
+    </div>
+
+    <div class="section level2">
+      <h2 id="tensor-creation-utilities">Tensor creation utilities<a class="anchor" aria-label="anchor" href="#tensor-creation-utilities"></a></h2>
+      
       
-    </div><!--/.nav-collapse -->
-  </div><!--/.container -->
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+    </div><div id="" class="section level2">
+      
+      
+      
 
-      </header>
+      <dl><dt>
+          
+          <code><a href="torch_empty.html">torch_empty()</a></code> 
+        </dt>
+        <dd>Empty</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_arange.html">torch_arange()</a></code> 
+        </dt>
+        <dd>Arange</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_eye.html">torch_eye()</a></code> 
+        </dt>
+        <dd>Eye</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_full.html">torch_full()</a></code> 
+        </dt>
+        <dd>Full</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_linspace.html">torch_linspace()</a></code> 
+        </dt>
+        <dd>Linspace</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_logspace.html">torch_logspace()</a></code> 
+        </dt>
+        <dd>Logspace</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_ones.html">torch_ones()</a></code> 
+        </dt>
+        <dd>Ones</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_rand.html">torch_rand()</a></code> 
+        </dt>
+        <dd>Rand</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_randint.html">torch_randint()</a></code> 
+        </dt>
+        <dd>Randint</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_randn.html">torch_randn()</a></code> 
+        </dt>
+        <dd>Randn</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_randperm.html">torch_randperm()</a></code> 
+        </dt>
+        <dd>Randperm</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_zeros.html">torch_zeros()</a></code> 
+        </dt>
+        <dd>Zeros</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_empty_like.html">torch_empty_like()</a></code> 
+        </dt>
+        <dd>Empty_like</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_full_like.html">torch_full_like()</a></code> 
+        </dt>
+        <dd>Full_like</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_ones_like.html">torch_ones_like()</a></code> 
+        </dt>
+        <dd>Ones_like</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_rand_like.html">torch_rand_like()</a></code> 
+        </dt>
+        <dd>Rand_like</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_randint_like.html">torch_randint_like()</a></code> 
+        </dt>
+        <dd>Randint_like</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_randn_like.html">torch_randn_like()</a></code> 
+        </dt>
+        <dd>Randn_like</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_zeros_like.html">torch_zeros_like()</a></code> 
+        </dt>
+        <dd>Zeros_like</dd>
+      </dl><dl><dt>
+          
+          <code><a href="as_array.html">as_array()</a></code> 
+        </dt>
+        <dd>Converts to array</dd>
+      </dl></div><div class="section level2">
+      <h2 id="tensor-attributes">Tensor attributes<a class="anchor" aria-label="anchor" href="#tensor-attributes"></a></h2>
+      
+      
 
-<div class="row">
-  <div class="contents col-md-9">
-    <div class="page-header">
-      <h1>Reference</h1>
-    </div>
+      
+    </div><div id="" class="section level2">
+      
+      
+      
 
-    <table class="ref-index">
+      <dl><dt>
+          
+          <code><a href="default_dtype.html">torch_set_default_dtype()</a></code> <code><a href="default_dtype.html">torch_get_default_dtype()</a></code> 
+        </dt>
+        <dd>Gets and sets the default floating point dtype.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="is_torch_device.html">is_torch_device()</a></code> 
+        </dt>
+        <dd>Checks if object is a device</dd>
+      </dl><dl><dt>
+          
+          <code><a href="is_torch_dtype.html">is_torch_dtype()</a></code> 
+        </dt>
+        <dd>Check if object is a torch data type</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_dtype.html">torch_float32()</a></code> <code><a href="torch_dtype.html">torch_float()</a></code> <code><a href="torch_dtype.html">torch_float64()</a></code> <code><a href="torch_dtype.html">torch_double()</a></code> <code><a href="torch_dtype.html">torch_float16()</a></code> <code><a href="torch_dtype.html">torch_half()</a></code> <code><a href="torch_dtype.html">torch_uint8()</a></code> <code><a href="torch_dtype.html">torch_int8()</a></code> <code><a href="torch_dtype.html">torch_int16()</a></code> <code><a href="torch_dtype.html">torch_short()</a></code> <code><a href="torch_dtype.html">torch_int32()</a></code> <code><a href="torch_dtype.html">torch_int()</a></code> <code><a href="torch_dtype.html">torch_int64()</a></code> <code><a href="torch_dtype.html">torch_long()</a></code> <code><a href="torch_dtype.html">torch_bool()</a></code> <code><a href="torch_dtype.html">torch_quint8()</a></code> <code><a href="torch_dtype.html">torch_qint8()</a></code> <code><a href="torch_dtype.html">torch_qint32()</a></code> 
+        </dt>
+        <dd>Torch data types</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_finfo.html">torch_finfo()</a></code> 
+        </dt>
+        <dd>Floating point type info</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_iinfo.html">torch_iinfo()</a></code> 
+        </dt>
+        <dd>Integer type info</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_qscheme.html">torch_per_channel_affine()</a></code> <code><a href="torch_qscheme.html">torch_per_tensor_affine()</a></code> <code><a href="torch_qscheme.html">torch_per_channel_symmetric()</a></code> <code><a href="torch_qscheme.html">torch_per_tensor_symmetric()</a></code> 
+        </dt>
+        <dd>Creates the corresponding Scheme object</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_reduction.html">torch_reduction_sum()</a></code> <code><a href="torch_reduction.html">torch_reduction_mean()</a></code> <code><a href="torch_reduction.html">torch_reduction_none()</a></code> 
+        </dt>
+        <dd>Creates the reduction objet</dd>
+      </dl><dl><dt>
+          
+          <code><a href="is_torch_layout.html">is_torch_layout()</a></code> 
+        </dt>
+        <dd>Check if an object is a torch layout.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="is_torch_memory_format.html">is_torch_memory_format()</a></code> 
+        </dt>
+        <dd>Check if an object is a memory format</dd>
+      </dl><dl><dt>
+          
+          <code><a href="is_torch_qscheme.html">is_torch_qscheme()</a></code> 
+        </dt>
+        <dd>Checks if an object is a QScheme</dd>
+      </dl><dl><dt>
+          
+          <code><a href="is_undefined_tensor.html">is_undefined_tensor()</a></code> 
+        </dt>
+        <dd>Checks if a tensor is undefined</dd>
+      </dl></div><div class="section level2">
+      <h2 id="serialization">Serialization<a class="anchor" aria-label="anchor" href="#serialization"></a></h2>
+      
+      
 
-    <colgroup>
       
-      <col class="alias" />
-      <col class="title" />
-    </colgroup>
+    </div><div id="" class="section level2">
+      
+      
+      
 
-    <tbody>
-      <tr>
-        <th colspan="2">
-          <h2 id="section-tensor-creation-utilities" class="hasAnchor"><a href="#section-tensor-creation-utilities" class="anchor"></a>Tensor creation utilities</h2>
-          <p class="section-desc"></p>
-        </th>
-      </tr>
+      <dl><dt>
+          
+          <code><a href="load_state_dict.html">load_state_dict()</a></code> 
+        </dt>
+        <dd>Load a state dict file</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_load.html">torch_load()</a></code> 
+        </dt>
+        <dd>Loads a saved object</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_save.html">torch_save()</a></code> 
+        </dt>
+        <dd>Saves an object to a disk file.</dd>
+      </dl></div><div class="section level2">
+      <h2 id="mathematical-operations-on-tensors">Mathematical operations on tensors<a class="anchor" aria-label="anchor" href="#mathematical-operations-on-tensors"></a></h2>
       
       
-    </tbody><tbody>
+
       
+    </div><div id="" class="section level2">
       
-      <tr>
-        
-        <td>
-          <p><code><a href="torch_empty.html">torch_empty()</a></code> </p>
-        </td>
-        <td><p>Empty</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_arange.html">torch_arange()</a></code> </p>
-        </td>
-        <td><p>Arange</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_eye.html">torch_eye()</a></code> </p>
-        </td>
-        <td><p>Eye</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_full.html">torch_full()</a></code> </p>
-        </td>
-        <td><p>Full</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_linspace.html">torch_linspace()</a></code> </p>
-        </td>
-        <td><p>Linspace</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_logspace.html">torch_logspace()</a></code> </p>
-        </td>
-        <td><p>Logspace</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_ones.html">torch_ones()</a></code> </p>
-        </td>
-        <td><p>Ones</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_rand.html">torch_rand()</a></code> </p>
-        </td>
-        <td><p>Rand</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_randint.html">torch_randint()</a></code> </p>
-        </td>
-        <td><p>Randint</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_randn.html">torch_randn()</a></code> </p>
-        </td>
-        <td><p>Randn</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_randperm.html">torch_randperm()</a></code> </p>
-        </td>
-        <td><p>Randperm</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_zeros.html">torch_zeros()</a></code> </p>
-        </td>
-        <td><p>Zeros</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_empty_like.html">torch_empty_like()</a></code> </p>
-        </td>
-        <td><p>Empty_like</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_full_like.html">torch_full_like()</a></code> </p>
-        </td>
-        <td><p>Full_like</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_ones_like.html">torch_ones_like()</a></code> </p>
-        </td>
-        <td><p>Ones_like</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_rand_like.html">torch_rand_like()</a></code> </p>
-        </td>
-        <td><p>Rand_like</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_randint_like.html">torch_randint_like()</a></code> </p>
-        </td>
-        <td><p>Randint_like</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_randn_like.html">torch_randn_like()</a></code> </p>
-        </td>
-        <td><p>Randn_like</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_zeros_like.html">torch_zeros_like()</a></code> </p>
-        </td>
-        <td><p>Zeros_like</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="as_array.html">as_array()</a></code> </p>
-        </td>
-        <td><p>Converts to array</p></td>
-      </tr>
-    </tbody><tbody>
-      <tr>
-        <th colspan="2">
-          <h2 id="section-tensor-attributes" class="hasAnchor"><a href="#section-tensor-attributes" class="anchor"></a>Tensor attributes</h2>
-          <p class="section-desc"></p>
-        </th>
-      </tr>
       
       
-    </tbody><tbody>
+
+      <dl><dt>
+          
+          <code><a href="default_dtype.html">torch_set_default_dtype()</a></code> <code><a href="default_dtype.html">torch_get_default_dtype()</a></code> 
+        </dt>
+        <dd>Gets and sets the default floating point dtype.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="threads.html">torch_set_num_threads()</a></code> <code><a href="threads.html">torch_set_num_interop_threads()</a></code> <code><a href="threads.html">torch_get_num_interop_threads()</a></code> <code><a href="threads.html">torch_get_num_threads()</a></code> 
+        </dt>
+        <dd>Number of threads</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_abs.html">torch_abs()</a></code> 
+        </dt>
+        <dd>Abs</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_absolute.html">torch_absolute()</a></code> 
+        </dt>
+        <dd>Absolute</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_acos.html">torch_acos()</a></code> 
+        </dt>
+        <dd>Acos</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_acosh.html">torch_acosh()</a></code> 
+        </dt>
+        <dd>Acosh</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_adaptive_avg_pool1d.html">torch_adaptive_avg_pool1d()</a></code> 
+        </dt>
+        <dd>Adaptive_avg_pool1d</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_add.html">torch_add()</a></code> 
+        </dt>
+        <dd>Add</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_addbmm.html">torch_addbmm()</a></code> 
+        </dt>
+        <dd>Addbmm</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_addcdiv.html">torch_addcdiv()</a></code> 
+        </dt>
+        <dd>Addcdiv</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_addcmul.html">torch_addcmul()</a></code> 
+        </dt>
+        <dd>Addcmul</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_addmm.html">torch_addmm()</a></code> 
+        </dt>
+        <dd>Addmm</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_addmv.html">torch_addmv()</a></code> 
+        </dt>
+        <dd>Addmv</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_addr.html">torch_addr()</a></code> 
+        </dt>
+        <dd>Addr</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_allclose.html">torch_allclose()</a></code> 
+        </dt>
+        <dd>Allclose</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_amax.html">torch_amax()</a></code> 
+        </dt>
+        <dd>Amax</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_amin.html">torch_amin()</a></code> 
+        </dt>
+        <dd>Amin</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_angle.html">torch_angle()</a></code> 
+        </dt>
+        <dd>Angle</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_arange.html">torch_arange()</a></code> 
+        </dt>
+        <dd>Arange</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_arccos.html">torch_arccos()</a></code> 
+        </dt>
+        <dd>Arccos</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_arccosh.html">torch_arccosh()</a></code> 
+        </dt>
+        <dd>Arccosh</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_arcsin.html">torch_arcsin()</a></code> 
+        </dt>
+        <dd>Arcsin</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_arcsinh.html">torch_arcsinh()</a></code> 
+        </dt>
+        <dd>Arcsinh</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_arctan.html">torch_arctan()</a></code> 
+        </dt>
+        <dd>Arctan</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_arctanh.html">torch_arctanh()</a></code> 
+        </dt>
+        <dd>Arctanh</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_argmax.html">torch_argmax</a></code> 
+        </dt>
+        <dd>Argmax</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_argmin.html">torch_argmin</a></code> 
+        </dt>
+        <dd>Argmin</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_argsort.html">torch_argsort()</a></code> 
+        </dt>
+        <dd>Argsort</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_as_strided.html">torch_as_strided()</a></code> 
+        </dt>
+        <dd>As_strided</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_asin.html">torch_asin()</a></code> 
+        </dt>
+        <dd>Asin</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_asinh.html">torch_asinh()</a></code> 
+        </dt>
+        <dd>Asinh</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_atan.html">torch_atan()</a></code> 
+        </dt>
+        <dd>Atan</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_atan2.html">torch_atan2()</a></code> 
+        </dt>
+        <dd>Atan2</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_atanh.html">torch_atanh()</a></code> 
+        </dt>
+        <dd>Atanh</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_atleast_1d.html">torch_atleast_1d()</a></code> 
+        </dt>
+        <dd>Atleast_1d</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_atleast_2d.html">torch_atleast_2d()</a></code> 
+        </dt>
+        <dd>Atleast_2d</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_atleast_3d.html">torch_atleast_3d()</a></code> 
+        </dt>
+        <dd>Atleast_3d</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_avg_pool1d.html">torch_avg_pool1d()</a></code> 
+        </dt>
+        <dd>Avg_pool1d</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_baddbmm.html">torch_baddbmm()</a></code> 
+        </dt>
+        <dd>Baddbmm</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_bartlett_window.html">torch_bartlett_window()</a></code> 
+        </dt>
+        <dd>Bartlett_window</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_bernoulli.html">torch_bernoulli()</a></code> 
+        </dt>
+        <dd>Bernoulli</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_bincount.html">torch_bincount()</a></code> 
+        </dt>
+        <dd>Bincount</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_bitwise_and.html">torch_bitwise_and()</a></code> 
+        </dt>
+        <dd>Bitwise_and</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_bitwise_not.html">torch_bitwise_not()</a></code> 
+        </dt>
+        <dd>Bitwise_not</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_bitwise_or.html">torch_bitwise_or()</a></code> 
+        </dt>
+        <dd>Bitwise_or</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_bitwise_xor.html">torch_bitwise_xor()</a></code> 
+        </dt>
+        <dd>Bitwise_xor</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_blackman_window.html">torch_blackman_window()</a></code> 
+        </dt>
+        <dd>Blackman_window</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_block_diag.html">torch_block_diag()</a></code> 
+        </dt>
+        <dd>Block_diag</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_bmm.html">torch_bmm()</a></code> 
+        </dt>
+        <dd>Bmm</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_broadcast_tensors.html">torch_broadcast_tensors()</a></code> 
+        </dt>
+        <dd>Broadcast_tensors</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_bucketize.html">torch_bucketize()</a></code> 
+        </dt>
+        <dd>Bucketize</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_can_cast.html">torch_can_cast()</a></code> 
+        </dt>
+        <dd>Can_cast</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_cartesian_prod.html">torch_cartesian_prod()</a></code> 
+        </dt>
+        <dd>Cartesian_prod</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_cat.html">torch_cat()</a></code> 
+        </dt>
+        <dd>Cat</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_cdist.html">torch_cdist()</a></code> 
+        </dt>
+        <dd>Cdist</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_ceil.html">torch_ceil()</a></code> 
+        </dt>
+        <dd>Ceil</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_celu.html">torch_celu()</a></code> 
+        </dt>
+        <dd>Celu</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_celu_.html">torch_celu_()</a></code> 
+        </dt>
+        <dd>Celu_</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_chain_matmul.html">torch_chain_matmul()</a></code> 
+        </dt>
+        <dd>Chain_matmul</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_channel_shuffle.html">torch_channel_shuffle()</a></code> 
+        </dt>
+        <dd>Channel_shuffle</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_cholesky.html">torch_cholesky()</a></code> 
+        </dt>
+        <dd>Cholesky</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_cholesky_inverse.html">torch_cholesky_inverse()</a></code> 
+        </dt>
+        <dd>Cholesky_inverse</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_cholesky_solve.html">torch_cholesky_solve()</a></code> 
+        </dt>
+        <dd>Cholesky_solve</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_chunk.html">torch_chunk()</a></code> 
+        </dt>
+        <dd>Chunk</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_clamp.html">torch_clamp()</a></code> 
+        </dt>
+        <dd>Clamp</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_clip.html">torch_clip()</a></code> 
+        </dt>
+        <dd>Clip</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_clone.html">torch_clone()</a></code> 
+        </dt>
+        <dd>Clone</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_combinations.html">torch_combinations()</a></code> 
+        </dt>
+        <dd>Combinations</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_complex.html">torch_complex()</a></code> 
+        </dt>
+        <dd>Complex</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_conj.html">torch_conj()</a></code> 
+        </dt>
+        <dd>Conj</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_conv1d.html">torch_conv1d()</a></code> 
+        </dt>
+        <dd>Conv1d</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_conv2d.html">torch_conv2d()</a></code> 
+        </dt>
+        <dd>Conv2d</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_conv3d.html">torch_conv3d()</a></code> 
+        </dt>
+        <dd>Conv3d</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_conv_tbc.html">torch_conv_tbc()</a></code> 
+        </dt>
+        <dd>Conv_tbc</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_conv_transpose1d.html">torch_conv_transpose1d()</a></code> 
+        </dt>
+        <dd>Conv_transpose1d</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_conv_transpose2d.html">torch_conv_transpose2d()</a></code> 
+        </dt>
+        <dd>Conv_transpose2d</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_conv_transpose3d.html">torch_conv_transpose3d()</a></code> 
+        </dt>
+        <dd>Conv_transpose3d</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_cos.html">torch_cos()</a></code> 
+        </dt>
+        <dd>Cos</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_cosh.html">torch_cosh()</a></code> 
+        </dt>
+        <dd>Cosh</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_cosine_similarity.html">torch_cosine_similarity()</a></code> 
+        </dt>
+        <dd>Cosine_similarity</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_count_nonzero.html">torch_count_nonzero()</a></code> 
+        </dt>
+        <dd>Count_nonzero</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_cross.html">torch_cross()</a></code> 
+        </dt>
+        <dd>Cross</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_cummax.html">torch_cummax()</a></code> 
+        </dt>
+        <dd>Cummax</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_cummin.html">torch_cummin()</a></code> 
+        </dt>
+        <dd>Cummin</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_cumprod.html">torch_cumprod()</a></code> 
+        </dt>
+        <dd>Cumprod</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_cumsum.html">torch_cumsum()</a></code> 
+        </dt>
+        <dd>Cumsum</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_deg2rad.html">torch_deg2rad()</a></code> 
+        </dt>
+        <dd>Deg2rad</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_dequantize.html">torch_dequantize()</a></code> 
+        </dt>
+        <dd>Dequantize</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_det.html">torch_det()</a></code> 
+        </dt>
+        <dd>Det</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_device.html">torch_device()</a></code> 
+        </dt>
+        <dd>Create a Device object</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_diag.html">torch_diag()</a></code> 
+        </dt>
+        <dd>Diag</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_diag_embed.html">torch_diag_embed()</a></code> 
+        </dt>
+        <dd>Diag_embed</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_diagflat.html">torch_diagflat()</a></code> 
+        </dt>
+        <dd>Diagflat</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_diagonal.html">torch_diagonal()</a></code> 
+        </dt>
+        <dd>Diagonal</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_diff.html">torch_diff()</a></code> 
+        </dt>
+        <dd>Computes the n-th forward difference along the given dimension.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_digamma.html">torch_digamma()</a></code> 
+        </dt>
+        <dd>Digamma</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_dist.html">torch_dist()</a></code> 
+        </dt>
+        <dd>Dist</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_div.html">torch_div()</a></code> 
+        </dt>
+        <dd>Div</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_divide.html">torch_divide()</a></code> 
+        </dt>
+        <dd>Divide</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_dot.html">torch_dot()</a></code> 
+        </dt>
+        <dd>Dot</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_dstack.html">torch_dstack()</a></code> 
+        </dt>
+        <dd>Dstack</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_dtype.html">torch_float32()</a></code> <code><a href="torch_dtype.html">torch_float()</a></code> <code><a href="torch_dtype.html">torch_float64()</a></code> <code><a href="torch_dtype.html">torch_double()</a></code> <code><a href="torch_dtype.html">torch_float16()</a></code> <code><a href="torch_dtype.html">torch_half()</a></code> <code><a href="torch_dtype.html">torch_uint8()</a></code> <code><a href="torch_dtype.html">torch_int8()</a></code> <code><a href="torch_dtype.html">torch_int16()</a></code> <code><a href="torch_dtype.html">torch_short()</a></code> <code><a href="torch_dtype.html">torch_int32()</a></code> <code><a href="torch_dtype.html">torch_int()</a></code> <code><a href="torch_dtype.html">torch_int64()</a></code> <code><a href="torch_dtype.html">torch_long()</a></code> <code><a href="torch_dtype.html">torch_bool()</a></code> <code><a href="torch_dtype.html">torch_quint8()</a></code> <code><a href="torch_dtype.html">torch_qint8()</a></code> <code><a href="torch_dtype.html">torch_qint32()</a></code> 
+        </dt>
+        <dd>Torch data types</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_eig.html">torch_eig()</a></code> 
+        </dt>
+        <dd>Eig</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_einsum.html">torch_einsum()</a></code> 
+        </dt>
+        <dd>Einsum</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_empty.html">torch_empty()</a></code> 
+        </dt>
+        <dd>Empty</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_empty_like.html">torch_empty_like()</a></code> 
+        </dt>
+        <dd>Empty_like</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_empty_strided.html">torch_empty_strided()</a></code> 
+        </dt>
+        <dd>Empty_strided</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_eq.html">torch_eq()</a></code> 
+        </dt>
+        <dd>Eq</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_equal.html">torch_equal()</a></code> 
+        </dt>
+        <dd>Equal</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_erf.html">torch_erf()</a></code> 
+        </dt>
+        <dd>Erf</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_erfc.html">torch_erfc()</a></code> 
+        </dt>
+        <dd>Erfc</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_erfinv.html">torch_erfinv()</a></code> 
+        </dt>
+        <dd>Erfinv</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_exp.html">torch_exp()</a></code> 
+        </dt>
+        <dd>Exp</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_exp2.html">torch_exp2()</a></code> 
+        </dt>
+        <dd>Exp2</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_expm1.html">torch_expm1()</a></code> 
+        </dt>
+        <dd>Expm1</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_eye.html">torch_eye()</a></code> 
+        </dt>
+        <dd>Eye</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_fft_fft.html">torch_fft_fft()</a></code> 
+        </dt>
+        <dd>Fft</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_fft_ifft.html">torch_fft_ifft()</a></code> 
+        </dt>
+        <dd>Ifft</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_fft_irfft.html">torch_fft_irfft()</a></code> 
+        </dt>
+        <dd>Irfft</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_fft_rfft.html">torch_fft_rfft()</a></code> 
+        </dt>
+        <dd>Rfft</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_finfo.html">torch_finfo()</a></code> 
+        </dt>
+        <dd>Floating point type info</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_fix.html">torch_fix()</a></code> 
+        </dt>
+        <dd>Fix</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_flatten.html">torch_flatten()</a></code> 
+        </dt>
+        <dd>Flatten</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_flip.html">torch_flip()</a></code> 
+        </dt>
+        <dd>Flip</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_fliplr.html">torch_fliplr()</a></code> 
+        </dt>
+        <dd>Fliplr</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_flipud.html">torch_flipud()</a></code> 
+        </dt>
+        <dd>Flipud</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_floor.html">torch_floor()</a></code> 
+        </dt>
+        <dd>Floor</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_floor_divide.html">torch_floor_divide()</a></code> 
+        </dt>
+        <dd>Floor_divide</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_fmod.html">torch_fmod()</a></code> 
+        </dt>
+        <dd>Fmod</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_frac.html">torch_frac()</a></code> 
+        </dt>
+        <dd>Frac</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_full.html">torch_full()</a></code> 
+        </dt>
+        <dd>Full</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_full_like.html">torch_full_like()</a></code> 
+        </dt>
+        <dd>Full_like</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_gather.html">torch_gather()</a></code> 
+        </dt>
+        <dd>Gather</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_gcd.html">torch_gcd()</a></code> 
+        </dt>
+        <dd>Gcd</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_ge.html">torch_ge()</a></code> 
+        </dt>
+        <dd>Ge</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_generator.html">torch_generator()</a></code> 
+        </dt>
+        <dd>Create a Generator object</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_geqrf.html">torch_geqrf()</a></code> 
+        </dt>
+        <dd>Geqrf</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_ger.html">torch_ger()</a></code> 
+        </dt>
+        <dd>Ger</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_greater.html">torch_greater()</a></code> 
+        </dt>
+        <dd>Greater</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_greater_equal.html">torch_greater_equal()</a></code> 
+        </dt>
+        <dd>Greater_equal</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_gt.html">torch_gt()</a></code> 
+        </dt>
+        <dd>Gt</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_hamming_window.html">torch_hamming_window()</a></code> 
+        </dt>
+        <dd>Hamming_window</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_hann_window.html">torch_hann_window()</a></code> 
+        </dt>
+        <dd>Hann_window</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_heaviside.html">torch_heaviside()</a></code> 
+        </dt>
+        <dd>Heaviside</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_histc.html">torch_histc()</a></code> 
+        </dt>
+        <dd>Histc</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_hstack.html">torch_hstack()</a></code> 
+        </dt>
+        <dd>Hstack</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_hypot.html">torch_hypot()</a></code> 
+        </dt>
+        <dd>Hypot</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_i0.html">torch_i0()</a></code> 
+        </dt>
+        <dd>I0</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_iinfo.html">torch_iinfo()</a></code> 
+        </dt>
+        <dd>Integer type info</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_imag.html">torch_imag()</a></code> 
+        </dt>
+        <dd>Imag</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_index.html">torch_index()</a></code> 
+        </dt>
+        <dd>Index torch tensors</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_index_put.html">torch_index_put()</a></code> 
+        </dt>
+        <dd>Modify values selected by <code>indices</code>.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_index_put_.html">torch_index_put_()</a></code> 
+        </dt>
+        <dd>In-place version of <code>torch_index_put</code>.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_index_select.html">torch_index_select()</a></code> 
+        </dt>
+        <dd>Index_select</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_install_path.html">torch_install_path()</a></code> 
+        </dt>
+        <dd>A simple exported version of install_path
+Returns the torch installation path.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_inverse.html">torch_inverse()</a></code> 
+        </dt>
+        <dd>Inverse</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_is_complex.html">torch_is_complex()</a></code> 
+        </dt>
+        <dd>Is_complex</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_is_floating_point.html">torch_is_floating_point()</a></code> 
+        </dt>
+        <dd>Is_floating_point</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_is_installed.html">torch_is_installed()</a></code> 
+        </dt>
+        <dd>Verifies if torch is installed</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_is_nonzero.html">torch_is_nonzero()</a></code> 
+        </dt>
+        <dd>Is_nonzero</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_isclose.html">torch_isclose()</a></code> 
+        </dt>
+        <dd>Isclose</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_isfinite.html">torch_isfinite()</a></code> 
+        </dt>
+        <dd>Isfinite</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_isinf.html">torch_isinf()</a></code> 
+        </dt>
+        <dd>Isinf</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_isnan.html">torch_isnan()</a></code> 
+        </dt>
+        <dd>Isnan</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_isneginf.html">torch_isneginf()</a></code> 
+        </dt>
+        <dd>Isneginf</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_isposinf.html">torch_isposinf()</a></code> 
+        </dt>
+        <dd>Isposinf</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_isreal.html">torch_isreal()</a></code> 
+        </dt>
+        <dd>Isreal</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_istft.html">torch_istft()</a></code> 
+        </dt>
+        <dd>Istft</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_kaiser_window.html">torch_kaiser_window()</a></code> 
+        </dt>
+        <dd>Kaiser_window</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_kthvalue.html">torch_kthvalue()</a></code> 
+        </dt>
+        <dd>Kthvalue</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_layout.html">torch_strided()</a></code> <code><a href="torch_layout.html">torch_sparse_coo()</a></code> 
+        </dt>
+        <dd>Creates the corresponding layout</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_lcm.html">torch_lcm()</a></code> 
+        </dt>
+        <dd>Lcm</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_le.html">torch_le()</a></code> 
+        </dt>
+        <dd>Le</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_lerp.html">torch_lerp()</a></code> 
+        </dt>
+        <dd>Lerp</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_less.html">torch_less()</a></code> 
+        </dt>
+        <dd>Less</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_less_equal.html">torch_less_equal()</a></code> 
+        </dt>
+        <dd>Less_equal</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_lgamma.html">torch_lgamma()</a></code> 
+        </dt>
+        <dd>Lgamma</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_linspace.html">torch_linspace()</a></code> 
+        </dt>
+        <dd>Linspace</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_load.html">torch_load()</a></code> 
+        </dt>
+        <dd>Loads a saved object</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_log.html">torch_log()</a></code> 
+        </dt>
+        <dd>Log</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_log10.html">torch_log10()</a></code> 
+        </dt>
+        <dd>Log10</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_log1p.html">torch_log1p()</a></code> 
+        </dt>
+        <dd>Log1p</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_log2.html">torch_log2()</a></code> 
+        </dt>
+        <dd>Log2</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_logaddexp.html">torch_logaddexp()</a></code> 
+        </dt>
+        <dd>Logaddexp</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_logaddexp2.html">torch_logaddexp2()</a></code> 
+        </dt>
+        <dd>Logaddexp2</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_logcumsumexp.html">torch_logcumsumexp()</a></code> 
+        </dt>
+        <dd>Logcumsumexp</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_logdet.html">torch_logdet()</a></code> 
+        </dt>
+        <dd>Logdet</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_logical_and.html">torch_logical_and()</a></code> 
+        </dt>
+        <dd>Logical_and</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_logical_not.html">torch_logical_not</a></code> 
+        </dt>
+        <dd>Logical_not</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_logical_or.html">torch_logical_or()</a></code> 
+        </dt>
+        <dd>Logical_or</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_logical_xor.html">torch_logical_xor()</a></code> 
+        </dt>
+        <dd>Logical_xor</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_logit.html">torch_logit()</a></code> 
+        </dt>
+        <dd>Logit</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_logspace.html">torch_logspace()</a></code> 
+        </dt>
+        <dd>Logspace</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_logsumexp.html">torch_logsumexp()</a></code> 
+        </dt>
+        <dd>Logsumexp</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_lstsq.html">torch_lstsq()</a></code> 
+        </dt>
+        <dd>Lstsq</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_lt.html">torch_lt()</a></code> 
+        </dt>
+        <dd>Lt</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_lu.html">torch_lu()</a></code> 
+        </dt>
+        <dd>LU</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_lu_solve.html">torch_lu_solve()</a></code> 
+        </dt>
+        <dd>Lu_solve</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_manual_seed.html">torch_manual_seed()</a></code> 
+        </dt>
+        <dd>Sets the seed for generating random numbers.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_masked_select.html">torch_masked_select()</a></code> 
+        </dt>
+        <dd>Masked_select</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_matmul.html">torch_matmul()</a></code> 
+        </dt>
+        <dd>Matmul</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_matrix_exp.html">torch_matrix_exp()</a></code> 
+        </dt>
+        <dd>Matrix_exp</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_matrix_power.html">torch_matrix_power()</a></code> 
+        </dt>
+        <dd>Matrix_power</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_matrix_rank.html">torch_matrix_rank()</a></code> 
+        </dt>
+        <dd>Matrix_rank</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_max.html">torch_max</a></code> 
+        </dt>
+        <dd>Max</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_maximum.html">torch_maximum()</a></code> 
+        </dt>
+        <dd>Maximum</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_mean.html">torch_mean()</a></code> 
+        </dt>
+        <dd>Mean</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_median.html">torch_median()</a></code> 
+        </dt>
+        <dd>Median</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_memory_format.html">torch_contiguous_format()</a></code> <code><a href="torch_memory_format.html">torch_preserve_format()</a></code> <code><a href="torch_memory_format.html">torch_channels_last_format()</a></code> 
+        </dt>
+        <dd>Memory format</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_meshgrid.html">torch_meshgrid()</a></code> 
+        </dt>
+        <dd>Meshgrid</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_min.html">torch_min</a></code> 
+        </dt>
+        <dd>Min</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_minimum.html">torch_minimum()</a></code> 
+        </dt>
+        <dd>Minimum</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_mm.html">torch_mm()</a></code> 
+        </dt>
+        <dd>Mm</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_mode.html">torch_mode()</a></code> 
+        </dt>
+        <dd>Mode</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_movedim.html">torch_movedim()</a></code> 
+        </dt>
+        <dd>Movedim</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_mul.html">torch_mul()</a></code> 
+        </dt>
+        <dd>Mul</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_multinomial.html">torch_multinomial()</a></code> 
+        </dt>
+        <dd>Multinomial</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_multiply.html">torch_multiply()</a></code> 
+        </dt>
+        <dd>Multiply</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_mv.html">torch_mv()</a></code> 
+        </dt>
+        <dd>Mv</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_mvlgamma.html">torch_mvlgamma()</a></code> 
+        </dt>
+        <dd>Mvlgamma</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_nanquantile.html">torch_nanquantile()</a></code> 
+        </dt>
+        <dd>Nanquantile</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_nansum.html">torch_nansum()</a></code> 
+        </dt>
+        <dd>Nansum</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_narrow.html">torch_narrow()</a></code> 
+        </dt>
+        <dd>Narrow</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_ne.html">torch_ne()</a></code> 
+        </dt>
+        <dd>Ne</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_neg.html">torch_neg()</a></code> 
+        </dt>
+        <dd>Neg</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_negative.html">torch_negative()</a></code> 
+        </dt>
+        <dd>Negative</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_nextafter.html">torch_nextafter()</a></code> 
+        </dt>
+        <dd>Nextafter</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_nonzero.html">torch_nonzero()</a></code> 
+        </dt>
+        <dd>Nonzero</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_norm.html">torch_norm()</a></code> 
+        </dt>
+        <dd>Norm</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_normal.html">torch_normal()</a></code> 
+        </dt>
+        <dd>Normal</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_not_equal.html">torch_not_equal()</a></code> 
+        </dt>
+        <dd>Not_equal</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_ones.html">torch_ones()</a></code> 
+        </dt>
+        <dd>Ones</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_ones_like.html">torch_ones_like()</a></code> 
+        </dt>
+        <dd>Ones_like</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_orgqr.html">torch_orgqr()</a></code> 
+        </dt>
+        <dd>Orgqr</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_ormqr.html">torch_ormqr()</a></code> 
+        </dt>
+        <dd>Ormqr</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_outer.html">torch_outer()</a></code> 
+        </dt>
+        <dd>Outer</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_pdist.html">torch_pdist()</a></code> 
+        </dt>
+        <dd>Pdist</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_pinverse.html">torch_pinverse()</a></code> 
+        </dt>
+        <dd>Pinverse</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_pixel_shuffle.html">torch_pixel_shuffle()</a></code> 
+        </dt>
+        <dd>Pixel_shuffle</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_poisson.html">torch_poisson()</a></code> 
+        </dt>
+        <dd>Poisson</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_polar.html">torch_polar()</a></code> 
+        </dt>
+        <dd>Polar</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_polygamma.html">torch_polygamma()</a></code> 
+        </dt>
+        <dd>Polygamma</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_pow.html">torch_pow()</a></code> 
+        </dt>
+        <dd>Pow</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_prod.html">torch_prod()</a></code> 
+        </dt>
+        <dd>Prod</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_promote_types.html">torch_promote_types()</a></code> 
+        </dt>
+        <dd>Promote_types</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_qr.html">torch_qr()</a></code> 
+        </dt>
+        <dd>Qr</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_qscheme.html">torch_per_channel_affine()</a></code> <code><a href="torch_qscheme.html">torch_per_tensor_affine()</a></code> <code><a href="torch_qscheme.html">torch_per_channel_symmetric()</a></code> <code><a href="torch_qscheme.html">torch_per_tensor_symmetric()</a></code> 
+        </dt>
+        <dd>Creates the corresponding Scheme object</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_quantile.html">torch_quantile()</a></code> 
+        </dt>
+        <dd>Quantile</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_quantize_per_channel.html">torch_quantize_per_channel()</a></code> 
+        </dt>
+        <dd>Quantize_per_channel</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_quantize_per_tensor.html">torch_quantize_per_tensor()</a></code> 
+        </dt>
+        <dd>Quantize_per_tensor</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_rad2deg.html">torch_rad2deg()</a></code> 
+        </dt>
+        <dd>Rad2deg</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_rand.html">torch_rand()</a></code> 
+        </dt>
+        <dd>Rand</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_rand_like.html">torch_rand_like()</a></code> 
+        </dt>
+        <dd>Rand_like</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_randint.html">torch_randint()</a></code> 
+        </dt>
+        <dd>Randint</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_randint_like.html">torch_randint_like()</a></code> 
+        </dt>
+        <dd>Randint_like</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_randn.html">torch_randn()</a></code> 
+        </dt>
+        <dd>Randn</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_randn_like.html">torch_randn_like()</a></code> 
+        </dt>
+        <dd>Randn_like</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_randperm.html">torch_randperm()</a></code> 
+        </dt>
+        <dd>Randperm</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_range.html">torch_range()</a></code> 
+        </dt>
+        <dd>Range</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_real.html">torch_real()</a></code> 
+        </dt>
+        <dd>Real</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_reciprocal.html">torch_reciprocal()</a></code> 
+        </dt>
+        <dd>Reciprocal</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_reduction.html">torch_reduction_sum()</a></code> <code><a href="torch_reduction.html">torch_reduction_mean()</a></code> <code><a href="torch_reduction.html">torch_reduction_none()</a></code> 
+        </dt>
+        <dd>Creates the reduction objet</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_relu.html">torch_relu()</a></code> 
+        </dt>
+        <dd>Relu</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_relu_.html">torch_relu_()</a></code> 
+        </dt>
+        <dd>Relu_</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_remainder.html">torch_remainder()</a></code> 
+        </dt>
+        <dd>Remainder</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_renorm.html">torch_renorm()</a></code> 
+        </dt>
+        <dd>Renorm</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_repeat_interleave.html">torch_repeat_interleave()</a></code> 
+        </dt>
+        <dd>Repeat_interleave</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_reshape.html">torch_reshape()</a></code> 
+        </dt>
+        <dd>Reshape</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_result_type.html">torch_result_type()</a></code> 
+        </dt>
+        <dd>Result_type</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_roll.html">torch_roll()</a></code> 
+        </dt>
+        <dd>Roll</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_rot90.html">torch_rot90()</a></code> 
+        </dt>
+        <dd>Rot90</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_round.html">torch_round()</a></code> 
+        </dt>
+        <dd>Round</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_rrelu_.html">torch_rrelu_()</a></code> 
+        </dt>
+        <dd>Rrelu_</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_rsqrt.html">torch_rsqrt()</a></code> 
+        </dt>
+        <dd>Rsqrt</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_save.html">torch_save()</a></code> 
+        </dt>
+        <dd>Saves an object to a disk file.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_scalar_tensor.html">torch_scalar_tensor()</a></code> 
+        </dt>
+        <dd>Scalar tensor</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_searchsorted.html">torch_searchsorted()</a></code> 
+        </dt>
+        <dd>Searchsorted</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_selu.html">torch_selu()</a></code> 
+        </dt>
+        <dd>Selu</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_selu_.html">torch_selu_()</a></code> 
+        </dt>
+        <dd>Selu_</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_sgn.html">torch_sgn()</a></code> 
+        </dt>
+        <dd>Sgn</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_sigmoid.html">torch_sigmoid()</a></code> 
+        </dt>
+        <dd>Sigmoid</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_sign.html">torch_sign()</a></code> 
+        </dt>
+        <dd>Sign</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_signbit.html">torch_signbit()</a></code> 
+        </dt>
+        <dd>Signbit</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_sin.html">torch_sin()</a></code> 
+        </dt>
+        <dd>Sin</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_sinh.html">torch_sinh()</a></code> 
+        </dt>
+        <dd>Sinh</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_slogdet.html">torch_slogdet()</a></code> 
+        </dt>
+        <dd>Slogdet</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_solve.html">torch_solve()</a></code> 
+        </dt>
+        <dd>Solve</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_sort.html">torch_sort</a></code> 
+        </dt>
+        <dd>Sort</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_sparse_coo_tensor.html">torch_sparse_coo_tensor()</a></code> 
+        </dt>
+        <dd>Sparse_coo_tensor</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_split.html">torch_split()</a></code> 
+        </dt>
+        <dd>Split</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_sqrt.html">torch_sqrt()</a></code> 
+        </dt>
+        <dd>Sqrt</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_square.html">torch_square()</a></code> 
+        </dt>
+        <dd>Square</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_squeeze.html">torch_squeeze()</a></code> 
+        </dt>
+        <dd>Squeeze</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_stack.html">torch_stack()</a></code> 
+        </dt>
+        <dd>Stack</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_std.html">torch_std()</a></code> 
+        </dt>
+        <dd>Std</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_std_mean.html">torch_std_mean()</a></code> 
+        </dt>
+        <dd>Std_mean</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_stft.html">torch_stft()</a></code> 
+        </dt>
+        <dd>Stft</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_sub.html">torch_sub()</a></code> 
+        </dt>
+        <dd>Sub</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_subtract.html">torch_subtract()</a></code> 
+        </dt>
+        <dd>Subtract</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_sum.html">torch_sum()</a></code> 
+        </dt>
+        <dd>Sum</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_svd.html">torch_svd()</a></code> 
+        </dt>
+        <dd>Svd</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_symeig.html">torch_symeig()</a></code> 
+        </dt>
+        <dd>Symeig</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_t.html">torch_t()</a></code> 
+        </dt>
+        <dd>T</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_take.html">torch_take()</a></code> 
+        </dt>
+        <dd>Take</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_tan.html">torch_tan()</a></code> 
+        </dt>
+        <dd>Tan</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_tanh.html">torch_tanh()</a></code> 
+        </dt>
+        <dd>Tanh</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_tensor.html">torch_tensor()</a></code> 
+        </dt>
+        <dd>Converts R objects to a torch tensor</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_tensordot.html">torch_tensordot()</a></code> 
+        </dt>
+        <dd>Tensordot</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_threshold_.html">torch_threshold_()</a></code> 
+        </dt>
+        <dd>Threshold_</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_topk.html">torch_topk()</a></code> 
+        </dt>
+        <dd>Topk</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_trace.html">torch_trace()</a></code> 
+        </dt>
+        <dd>Trace</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_transpose.html">torch_transpose()</a></code> 
+        </dt>
+        <dd>Transpose</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_trapz.html">torch_trapz()</a></code> 
+        </dt>
+        <dd>Trapz</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_triangular_solve.html">torch_triangular_solve()</a></code> 
+        </dt>
+        <dd>Triangular_solve</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_tril.html">torch_tril()</a></code> 
+        </dt>
+        <dd>Tril</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_tril_indices.html">torch_tril_indices()</a></code> 
+        </dt>
+        <dd>Tril_indices</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_triu.html">torch_triu()</a></code> 
+        </dt>
+        <dd>Triu</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_triu_indices.html">torch_triu_indices()</a></code> 
+        </dt>
+        <dd>Triu_indices</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_true_divide.html">torch_true_divide()</a></code> 
+        </dt>
+        <dd>TRUE_divide</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_trunc.html">torch_trunc()</a></code> 
+        </dt>
+        <dd>Trunc</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_unbind.html">torch_unbind()</a></code> 
+        </dt>
+        <dd>Unbind</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_unique_consecutive.html">torch_unique_consecutive()</a></code> 
+        </dt>
+        <dd>Unique_consecutive</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_unsafe_chunk.html">torch_unsafe_chunk()</a></code> 
+        </dt>
+        <dd>Unsafe_chunk</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_unsafe_split.html">torch_unsafe_split()</a></code> 
+        </dt>
+        <dd>Unsafe_split</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_unsqueeze.html">torch_unsqueeze()</a></code> 
+        </dt>
+        <dd>Unsqueeze</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_vander.html">torch_vander()</a></code> 
+        </dt>
+        <dd>Vander</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_var.html">torch_var()</a></code> 
+        </dt>
+        <dd>Var</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_var_mean.html">torch_var_mean()</a></code> 
+        </dt>
+        <dd>Var_mean</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_vdot.html">torch_vdot()</a></code> 
+        </dt>
+        <dd>Vdot</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_view_as_complex.html">torch_view_as_complex()</a></code> 
+        </dt>
+        <dd>View_as_complex</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_view_as_real.html">torch_view_as_real()</a></code> 
+        </dt>
+        <dd>View_as_real</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_vstack.html">torch_vstack()</a></code> 
+        </dt>
+        <dd>Vstack</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_where.html">torch_where()</a></code> 
+        </dt>
+        <dd>Where</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_zeros.html">torch_zeros()</a></code> 
+        </dt>
+        <dd>Zeros</dd>
+      </dl><dl><dt>
+          
+          <code><a href="torch_zeros_like.html">torch_zeros_like()</a></code> 
+        </dt>
+        <dd>Zeros_like</dd>
+      </dl><dl><dt>
+          
+          <code><a href="AutogradContext.html">AutogradContext</a></code> 
+        </dt>
+        <dd>Class representing the context.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="Constraint.html">Constraint</a></code> 
+        </dt>
+        <dd>Abstract base class for constraints.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="Distribution.html">Distribution</a></code> 
+        </dt>
+        <dd>Generic R6 class representing distributions</dd>
+      </dl><dl><dt>
+          
+          <code><a href="as_array.html">as_array()</a></code> 
+        </dt>
+        <dd>Converts to array</dd>
+      </dl><dl><dt>
+          
+          <code><a href="autograd_backward.html">autograd_backward()</a></code> 
+        </dt>
+        <dd>Computes the sum of gradients of given tensors w.r.t. graph leaves.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="autograd_function.html">autograd_function()</a></code> 
+        </dt>
+        <dd>Records operation history and defines formulas for differentiating ops.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="autograd_grad.html">autograd_grad()</a></code> 
+        </dt>
+        <dd>Computes and returns the sum of gradients of outputs w.r.t. the inputs.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="autograd_set_grad_mode.html">autograd_set_grad_mode()</a></code> 
+        </dt>
+        <dd>Set grad mode</dd>
+      </dl><dl><dt>
+          
+          <code><a href="backends_cudnn_is_available.html">backends_cudnn_is_available()</a></code> 
+        </dt>
+        <dd>CuDNN is available</dd>
+      </dl><dl><dt>
+          
+          <code><a href="backends_cudnn_version.html">backends_cudnn_version()</a></code> 
+        </dt>
+        <dd>CuDNN version</dd>
+      </dl><dl><dt>
+          
+          <code><a href="backends_mkl_is_available.html">backends_mkl_is_available()</a></code> 
+        </dt>
+        <dd>MKL is available</dd>
+      </dl><dl><dt>
+          
+          <code><a href="backends_mkldnn_is_available.html">backends_mkldnn_is_available()</a></code> 
+        </dt>
+        <dd>MKLDNN is available</dd>
+      </dl><dl><dt>
+          
+          <code><a href="backends_openmp_is_available.html">backends_openmp_is_available()</a></code> 
+        </dt>
+        <dd>OpenMP is available</dd>
+      </dl><dl><dt>
+          
+          <code><a href="broadcast_all.html">broadcast_all()</a></code> 
+        </dt>
+        <dd>Given a list of values (possibly containing numbers), returns a list where each
+value is broadcasted based on the following rules:</dd>
+      </dl><dl><dt>
+          
+          <code><a href="call_torch_function.html">call_torch_function()</a></code> 
+        </dt>
+        <dd>Call a (Potentially Unexported) Torch Function</dd>
+      </dl><dl><dt>
+          
+          <code><a href="contrib_sort_vertices.html">contrib_sort_vertices()</a></code> 
+        </dt>
+        <dd>Contrib sort vertices</dd>
+      </dl><dl><dt>
+          
+          <code><a href="cuda_current_device.html">cuda_current_device()</a></code> 
+        </dt>
+        <dd>Returns the index of a currently selected device.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="cuda_device_count.html">cuda_device_count()</a></code> 
+        </dt>
+        <dd>Returns the number of GPUs available.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="cuda_get_device_capability.html">cuda_get_device_capability()</a></code> 
+        </dt>
+        <dd>Returns the major and minor CUDA capability of <code>device</code></dd>
+      </dl><dl><dt>
+          
+          <code><a href="cuda_is_available.html">cuda_is_available()</a></code> 
+        </dt>
+        <dd>Returns a bool indicating if CUDA is currently available.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="cuda_memory_stats.html">cuda_memory_stats()</a></code> <code><a href="cuda_memory_stats.html">cuda_memory_summary()</a></code> 
+        </dt>
+        <dd>Returns a dictionary of CUDA memory allocator statistics for a given device.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="dataloader.html">dataloader()</a></code> 
+        </dt>
+        <dd>Data loader. Combines a dataset and a sampler, and provides
+single- or multi-process iterators over the dataset.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="dataloader_make_iter.html">dataloader_make_iter()</a></code> 
+        </dt>
+        <dd>Creates an iterator from a DataLoader</dd>
+      </dl><dl><dt>
+          
+          <code><a href="dataloader_next.html">dataloader_next()</a></code> 
+        </dt>
+        <dd>Get the next element of a dataloader iterator</dd>
+      </dl><dl><dt>
+          
+          <code><a href="dataset.html">dataset()</a></code> 
+        </dt>
+        <dd>Helper function to create an function that generates R6 instances of class <code>dataset</code></dd>
+      </dl><dl><dt>
+          
+          <code><a href="dataset_subset.html">dataset_subset()</a></code> 
+        </dt>
+        <dd>Dataset Subset</dd>
+      </dl><dl><dt>
+          
+          <code><a href="distr_bernoulli.html">distr_bernoulli()</a></code> 
+        </dt>
+        <dd>Creates a Bernoulli distribution parameterized by <code>probs</code>
+or <code>logits</code> (but not both).
+Samples are binary (0 or 1). They take the value <code>1</code> with probability <code>p</code>
+and <code>0</code> with probability <code>1 - p</code>.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="distr_categorical.html">distr_categorical()</a></code> 
+        </dt>
+        <dd>Creates a categorical distribution parameterized by either <code>probs</code> or
+<code>logits</code> (but not both).</dd>
+      </dl><dl><dt>
+          
+          <code><a href="distr_chi2.html">distr_chi2()</a></code> 
+        </dt>
+        <dd>Creates a Chi2 distribution parameterized by shape parameter <code>df</code>.
+This is exactly equivalent to <code>distr_gamma(alpha=0.5*df, beta=0.5)</code></dd>
+      </dl><dl><dt>
+          
+          <code><a href="distr_gamma.html">distr_gamma()</a></code> 
+        </dt>
+        <dd>Creates a Gamma distribution parameterized by shape <code>concentration</code> and <code>rate</code>.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="distr_mixture_same_family.html">distr_mixture_same_family()</a></code> 
+        </dt>
+        <dd>Mixture of components in the same family</dd>
+      </dl><dl><dt>
+          
+          <code><a href="distr_multivariate_normal.html">distr_multivariate_normal()</a></code> 
+        </dt>
+        <dd>Gaussian distribution</dd>
+      </dl><dl><dt>
+          
+          <code><a href="distr_normal.html">distr_normal()</a></code> 
+        </dt>
+        <dd>Creates a normal (also called Gaussian) distribution parameterized by
+<code>loc</code> and <code>scale</code>.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="distr_poisson.html">distr_poisson()</a></code> 
+        </dt>
+        <dd>Creates a Poisson distribution parameterized by <code>rate</code>, the rate parameter.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="enumerate.html">enumerate()</a></code> 
+        </dt>
+        <dd>Enumerate an iterator</dd>
+      </dl><dl><dt>
+          
+          <code><a href="enumerate.dataloader.html">enumerate(<i>&lt;dataloader&gt;</i>)</a></code> 
+        </dt>
+        <dd>Enumerate an iterator</dd>
+      </dl><dl><dt>
+          
+          <code><a href="get_install_libs_url.html">get_install_libs_url()</a></code> 
+        </dt>
+        <dd>List of files to download</dd>
+      </dl><dl><dt>
+          
+          <code><a href="install_torch.html">install_torch()</a></code> 
+        </dt>
+        <dd>Install Torch</dd>
+      </dl><dl><dt>
+          
+          <code><a href="install_torch_from_file.html">install_torch_from_file()</a></code> 
+        </dt>
+        <dd>Install Torch from files</dd>
+      </dl><dl><dt>
+          
+          <code><a href="is_dataloader.html">is_dataloader()</a></code> 
+        </dt>
+        <dd>Checks if the object is a dataloader</dd>
+      </dl><dl><dt>
+          
+          <code><a href="is_nn_buffer.html">is_nn_buffer()</a></code> 
+        </dt>
+        <dd>Checks if the object is a nn_buffer</dd>
+      </dl><dl><dt>
+          
+          <code><a href="is_nn_module.html">is_nn_module()</a></code> 
+        </dt>
+        <dd>Checks if the object is an nn_module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="is_nn_parameter.html">is_nn_parameter()</a></code> 
+        </dt>
+        <dd>Checks if an object is a nn_parameter</dd>
+      </dl><dl><dt>
+          
+          <code><a href="is_optimizer.html">is_optimizer()</a></code> 
+        </dt>
+        <dd>Checks if the object is a torch optimizer</dd>
+      </dl><dl><dt>
+          
+          <code><a href="is_torch_device.html">is_torch_device()</a></code> 
+        </dt>
+        <dd>Checks if object is a device</dd>
+      </dl><dl><dt>
+          
+          <code><a href="is_torch_dtype.html">is_torch_dtype()</a></code> 
+        </dt>
+        <dd>Check if object is a torch data type</dd>
+      </dl><dl><dt>
+          
+          <code><a href="is_torch_layout.html">is_torch_layout()</a></code> 
+        </dt>
+        <dd>Check if an object is a torch layout.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="is_torch_memory_format.html">is_torch_memory_format()</a></code> 
+        </dt>
+        <dd>Check if an object is a memory format</dd>
+      </dl><dl><dt>
+          
+          <code><a href="is_torch_qscheme.html">is_torch_qscheme()</a></code> 
+        </dt>
+        <dd>Checks if an object is a QScheme</dd>
+      </dl><dl><dt>
+          
+          <code><a href="is_undefined_tensor.html">is_undefined_tensor()</a></code> 
+        </dt>
+        <dd>Checks if a tensor is undefined</dd>
+      </dl><dl><dt>
+          
+          <code><a href="jit_compile.html">jit_compile()</a></code> 
+        </dt>
+        <dd>Compile TorchScript code into a graph</dd>
+      </dl><dl><dt>
+          
+          <code><a href="jit_load.html">jit_load()</a></code> 
+        </dt>
+        <dd>Loads a <code>script_function</code> or <code>script_module</code> previously saved with <code>jit_save</code></dd>
+      </dl><dl><dt>
+          
+          <code><a href="jit_save.html">jit_save()</a></code> 
+        </dt>
+        <dd>Saves a <code>script_function</code> to a path</dd>
+      </dl><dl><dt>
+          
+          <code><a href="jit_save_for_mobile.html">jit_save_for_mobile()</a></code> 
+        </dt>
+        <dd>Saves a <code>script_function</code> or <code>script_module</code> in bytecode form,
+to be loaded on a mobile device</dd>
+      </dl><dl><dt>
+          
+          <code><a href="jit_scalar.html">jit_scalar()</a></code> 
+        </dt>
+        <dd>Adds the 'jit_scalar' class to the input</dd>
+      </dl><dl><dt>
+          
+          <code><a href="jit_trace.html">jit_trace()</a></code> 
+        </dt>
+        <dd>Trace a function and return an executable <code>script_function</code>.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="jit_trace_module.html">jit_trace_module()</a></code> 
+        </dt>
+        <dd>Trace a module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="jit_tuple.html">jit_tuple()</a></code> 
+        </dt>
+        <dd>Adds the 'jit_tuple' class to the input</dd>
+      </dl><dl><dt>
+          
+          <code><a href="linalg_cholesky.html">linalg_cholesky()</a></code> 
+        </dt>
+        <dd>Computes the Cholesky decomposition of a complex Hermitian or real symmetric positive-definite matrix.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="linalg_cholesky_ex.html">linalg_cholesky_ex()</a></code> 
+        </dt>
+        <dd>Computes the Cholesky decomposition of a complex Hermitian or real
+symmetric positive-definite matrix.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="linalg_cond.html">linalg_cond()</a></code> 
+        </dt>
+        <dd>Computes the condition number of a matrix with respect to a matrix norm.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="linalg_det.html">linalg_det()</a></code> 
+        </dt>
+        <dd>Computes the determinant of a square matrix.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="linalg_eig.html">linalg_eig()</a></code> 
+        </dt>
+        <dd>Computes the eigenvalue decomposition of a square matrix if it exists.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="linalg_eigh.html">linalg_eigh()</a></code> 
+        </dt>
+        <dd>Computes the eigenvalue decomposition of a complex Hermitian or real symmetric matrix.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="linalg_eigvals.html">linalg_eigvals()</a></code> 
+        </dt>
+        <dd>Computes the eigenvalues of a square matrix.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="linalg_eigvalsh.html">linalg_eigvalsh()</a></code> 
+        </dt>
+        <dd>Computes the eigenvalues of a complex Hermitian or real symmetric matrix.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="linalg_householder_product.html">linalg_householder_product()</a></code> 
+        </dt>
+        <dd>Computes the first <code>n</code> columns of a product of Householder matrices.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="linalg_inv.html">linalg_inv()</a></code> 
+        </dt>
+        <dd>Computes the inverse of a square matrix if it exists.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="linalg_inv_ex.html">linalg_inv_ex()</a></code> 
+        </dt>
+        <dd>Computes the inverse of a square matrix if it is invertible.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="linalg_lstsq.html">linalg_lstsq()</a></code> 
+        </dt>
+        <dd>Computes a solution to the least squares problem of a system of linear equations.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="linalg_matrix_norm.html">linalg_matrix_norm()</a></code> 
+        </dt>
+        <dd>Computes a matrix norm.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="linalg_matrix_power.html">linalg_matrix_power()</a></code> 
+        </dt>
+        <dd>Computes the <code>n</code>-th power of a square matrix for an integer <code>n</code>.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="linalg_matrix_rank.html">linalg_matrix_rank()</a></code> 
+        </dt>
+        <dd>Computes the numerical rank of a matrix.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="linalg_multi_dot.html">linalg_multi_dot()</a></code> 
+        </dt>
+        <dd>Efficiently multiplies two or more matrices</dd>
+      </dl><dl><dt>
+          
+          <code><a href="linalg_norm.html">linalg_norm()</a></code> 
+        </dt>
+        <dd>Computes a vector or matrix norm.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="linalg_pinv.html">linalg_pinv()</a></code> 
+        </dt>
+        <dd>Computes the pseudoinverse (Moore-Penrose inverse) of a matrix.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="linalg_qr.html">linalg_qr()</a></code> 
+        </dt>
+        <dd>Computes the QR decomposition of a matrix.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="linalg_slogdet.html">linalg_slogdet()</a></code> 
+        </dt>
+        <dd>Computes the sign and natural logarithm of the absolute value of the determinant of a square matrix.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="linalg_solve.html">linalg_solve()</a></code> 
+        </dt>
+        <dd>Computes the solution of a square system of linear equations with a unique solution.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="linalg_svd.html">linalg_svd()</a></code> 
+        </dt>
+        <dd>Computes the singular value decomposition (SVD) of a matrix.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="linalg_svdvals.html">linalg_svdvals()</a></code> 
+        </dt>
+        <dd>Computes the singular values of a matrix.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="linalg_tensorinv.html">linalg_tensorinv()</a></code> 
+        </dt>
+        <dd>Computes the multiplicative inverse of <code><a href="../reference/torch_tensordot.html">torch_tensordot()</a></code></dd>
+      </dl><dl><dt>
+          
+          <code><a href="linalg_tensorsolve.html">linalg_tensorsolve()</a></code> 
+        </dt>
+        <dd>Computes the solution <code>X</code> to the system <code>torch_tensordot(A, X) = B</code>.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="linalg_vector_norm.html">linalg_vector_norm()</a></code> 
+        </dt>
+        <dd>Computes a vector norm.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="load_state_dict.html">load_state_dict()</a></code> 
+        </dt>
+        <dd>Load a state dict file</dd>
+      </dl><dl><dt>
+          
+          <code><a href="lr_lambda.html">lr_lambda()</a></code> 
+        </dt>
+        <dd>Sets the learning rate of each parameter group to the initial lr
+times a given function. When last_epoch=-1, sets initial lr as lr.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="lr_multiplicative.html">lr_multiplicative()</a></code> 
+        </dt>
+        <dd>Multiply the learning rate of each parameter group by the factor given
+in the specified function. When last_epoch=-1, sets initial lr as lr.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="lr_one_cycle.html">lr_one_cycle()</a></code> 
+        </dt>
+        <dd>Once cycle learning rate</dd>
+      </dl><dl><dt>
+          
+          <code><a href="lr_scheduler.html">lr_scheduler()</a></code> 
+        </dt>
+        <dd>Creates learning rate schedulers</dd>
+      </dl><dl><dt>
+          
+          <code><a href="lr_step.html">lr_step()</a></code> 
+        </dt>
+        <dd>Step learning rate decay</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_adaptive_avg_pool1d.html">nn_adaptive_avg_pool1d()</a></code> 
+        </dt>
+        <dd>Applies a 1D adaptive average pooling over an input signal composed of several input planes.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_adaptive_avg_pool2d.html">nn_adaptive_avg_pool2d()</a></code> 
+        </dt>
+        <dd>Applies a 2D adaptive average pooling over an input signal composed of several input planes.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_adaptive_avg_pool3d.html">nn_adaptive_avg_pool3d()</a></code> 
+        </dt>
+        <dd>Applies a 3D adaptive average pooling over an input signal composed of several input planes.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_adaptive_log_softmax_with_loss.html">nn_adaptive_log_softmax_with_loss()</a></code> 
+        </dt>
+        <dd>AdaptiveLogSoftmaxWithLoss module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_adaptive_max_pool1d.html">nn_adaptive_max_pool1d()</a></code> 
+        </dt>
+        <dd>Applies a 1D adaptive max pooling over an input signal composed of several input planes.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_adaptive_max_pool2d.html">nn_adaptive_max_pool2d()</a></code> 
+        </dt>
+        <dd>Applies a 2D adaptive max pooling over an input signal composed of several input planes.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_adaptive_max_pool3d.html">nn_adaptive_max_pool3d()</a></code> 
+        </dt>
+        <dd>Applies a 3D adaptive max pooling over an input signal composed of several input planes.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_avg_pool1d.html">nn_avg_pool1d()</a></code> 
+        </dt>
+        <dd>Applies a 1D average pooling over an input signal composed of several
+input planes.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_avg_pool2d.html">nn_avg_pool2d()</a></code> 
+        </dt>
+        <dd>Applies a 2D average pooling over an input signal composed of several input
+planes.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_avg_pool3d.html">nn_avg_pool3d()</a></code> 
+        </dt>
+        <dd>Applies a 3D average pooling over an input signal composed of several input
+planes.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_batch_norm1d.html">nn_batch_norm1d()</a></code> 
+        </dt>
+        <dd>BatchNorm1D module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_batch_norm2d.html">nn_batch_norm2d()</a></code> 
+        </dt>
+        <dd>BatchNorm2D</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_batch_norm3d.html">nn_batch_norm3d()</a></code> 
+        </dt>
+        <dd>BatchNorm3D</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_bce_loss.html">nn_bce_loss()</a></code> 
+        </dt>
+        <dd>Binary cross entropy loss</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_bce_with_logits_loss.html">nn_bce_with_logits_loss()</a></code> 
+        </dt>
+        <dd>BCE with logits loss</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_bilinear.html">nn_bilinear()</a></code> 
+        </dt>
+        <dd>Bilinear module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_buffer.html">nn_buffer()</a></code> 
+        </dt>
+        <dd>Creates a nn_buffer</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_celu.html">nn_celu()</a></code> 
+        </dt>
+        <dd>CELU module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_contrib_sparsemax.html">nn_contrib_sparsemax()</a></code> 
+        </dt>
+        <dd>Sparsemax activation</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_conv1d.html">nn_conv1d()</a></code> 
+        </dt>
+        <dd>Conv1D module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_conv2d.html">nn_conv2d()</a></code> 
+        </dt>
+        <dd>Conv2D module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_conv3d.html">nn_conv3d()</a></code> 
+        </dt>
+        <dd>Conv3D module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_conv_transpose1d.html">nn_conv_transpose1d()</a></code> 
+        </dt>
+        <dd>ConvTranspose1D</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_conv_transpose2d.html">nn_conv_transpose2d()</a></code> 
+        </dt>
+        <dd>ConvTranpose2D module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_conv_transpose3d.html">nn_conv_transpose3d()</a></code> 
+        </dt>
+        <dd>ConvTranpose3D module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_cosine_embedding_loss.html">nn_cosine_embedding_loss()</a></code> 
+        </dt>
+        <dd>Cosine embedding loss</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_cross_entropy_loss.html">nn_cross_entropy_loss()</a></code> 
+        </dt>
+        <dd>CrossEntropyLoss module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_ctc_loss.html">nn_ctc_loss()</a></code> 
+        </dt>
+        <dd>The Connectionist Temporal Classification loss.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_dropout.html">nn_dropout()</a></code> 
+        </dt>
+        <dd>Dropout module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_dropout2d.html">nn_dropout2d()</a></code> 
+        </dt>
+        <dd>Dropout2D module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_dropout3d.html">nn_dropout3d()</a></code> 
+        </dt>
+        <dd>Dropout3D module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_elu.html">nn_elu()</a></code> 
+        </dt>
+        <dd>ELU module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_embedding.html">nn_embedding()</a></code> 
+        </dt>
+        <dd>Embedding module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_flatten.html">nn_flatten()</a></code> 
+        </dt>
+        <dd>Flattens a contiguous range of dims into a tensor.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_fractional_max_pool2d.html">nn_fractional_max_pool2d()</a></code> 
+        </dt>
+        <dd>Applies a 2D fractional max pooling over an input signal composed of several input planes.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_fractional_max_pool3d.html">nn_fractional_max_pool3d()</a></code> 
+        </dt>
+        <dd>Applies a 3D fractional max pooling over an input signal composed of several input planes.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_gelu.html">nn_gelu()</a></code> 
+        </dt>
+        <dd>GELU module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_glu.html">nn_glu()</a></code> 
+        </dt>
+        <dd>GLU module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_group_norm.html">nn_group_norm()</a></code> 
+        </dt>
+        <dd>Group normalization</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_gru.html">nn_gru()</a></code> 
+        </dt>
+        <dd>Applies a multi-layer gated recurrent unit (GRU) RNN to an input sequence.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_hardshrink.html">nn_hardshrink()</a></code> 
+        </dt>
+        <dd>Hardshwink module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_hardsigmoid.html">nn_hardsigmoid()</a></code> 
+        </dt>
+        <dd>Hardsigmoid module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_hardswish.html">nn_hardswish()</a></code> 
+        </dt>
+        <dd>Hardswish module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_hardtanh.html">nn_hardtanh()</a></code> 
+        </dt>
+        <dd>Hardtanh module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_hinge_embedding_loss.html">nn_hinge_embedding_loss()</a></code> 
+        </dt>
+        <dd>Hinge embedding loss</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_identity.html">nn_identity()</a></code> 
+        </dt>
+        <dd>Identity module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_init_calculate_gain.html">nn_init_calculate_gain()</a></code> 
+        </dt>
+        <dd>Calculate gain</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_init_constant_.html">nn_init_constant_()</a></code> 
+        </dt>
+        <dd>Constant initialization</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_init_dirac_.html">nn_init_dirac_()</a></code> 
+        </dt>
+        <dd>Dirac initialization</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_init_eye_.html">nn_init_eye_()</a></code> 
+        </dt>
+        <dd>Eye initialization</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_init_kaiming_normal_.html">nn_init_kaiming_normal_()</a></code> 
+        </dt>
+        <dd>Kaiming normal initialization</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_init_kaiming_uniform_.html">nn_init_kaiming_uniform_()</a></code> 
+        </dt>
+        <dd>Kaiming uniform initialization</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_init_normal_.html">nn_init_normal_()</a></code> 
+        </dt>
+        <dd>Normal initialization</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_init_ones_.html">nn_init_ones_()</a></code> 
+        </dt>
+        <dd>Ones initialization</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_init_orthogonal_.html">nn_init_orthogonal_()</a></code> 
+        </dt>
+        <dd>Orthogonal initialization</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_init_sparse_.html">nn_init_sparse_()</a></code> 
+        </dt>
+        <dd>Sparse initialization</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_init_trunc_normal_.html">nn_init_trunc_normal_()</a></code> 
+        </dt>
+        <dd>Truncated normal initialization</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_init_uniform_.html">nn_init_uniform_()</a></code> 
+        </dt>
+        <dd>Uniform initialization</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_init_xavier_normal_.html">nn_init_xavier_normal_()</a></code> 
+        </dt>
+        <dd>Xavier normal initialization</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_init_xavier_uniform_.html">nn_init_xavier_uniform_()</a></code> 
+        </dt>
+        <dd>Xavier uniform initialization</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_init_zeros_.html">nn_init_zeros_()</a></code> 
+        </dt>
+        <dd>Zeros initialization</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_kl_div_loss.html">nn_kl_div_loss()</a></code> 
+        </dt>
+        <dd>Kullback-Leibler divergence loss</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_l1_loss.html">nn_l1_loss()</a></code> 
+        </dt>
+        <dd>L1 loss</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_layer_norm.html">nn_layer_norm()</a></code> 
+        </dt>
+        <dd>Layer normalization</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_leaky_relu.html">nn_leaky_relu()</a></code> 
+        </dt>
+        <dd>LeakyReLU module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_linear.html">nn_linear()</a></code> 
+        </dt>
+        <dd>Linear module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_log_sigmoid.html">nn_log_sigmoid()</a></code> 
+        </dt>
+        <dd>LogSigmoid module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_log_softmax.html">nn_log_softmax()</a></code> 
+        </dt>
+        <dd>LogSoftmax module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_lp_pool1d.html">nn_lp_pool1d()</a></code> 
+        </dt>
+        <dd>Applies a 1D power-average pooling over an input signal composed of several input
+planes.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_lp_pool2d.html">nn_lp_pool2d()</a></code> 
+        </dt>
+        <dd>Applies a 2D power-average pooling over an input signal composed of several input
+planes.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_lstm.html">nn_lstm()</a></code> 
+        </dt>
+        <dd>Applies a multi-layer long short-term memory (LSTM) RNN to an input
+sequence.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_margin_ranking_loss.html">nn_margin_ranking_loss()</a></code> 
+        </dt>
+        <dd>Margin ranking loss</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_max_pool1d.html">nn_max_pool1d()</a></code> 
+        </dt>
+        <dd>MaxPool1D module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_max_pool2d.html">nn_max_pool2d()</a></code> 
+        </dt>
+        <dd>MaxPool2D module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_max_pool3d.html">nn_max_pool3d()</a></code> 
+        </dt>
+        <dd>Applies a 3D max pooling over an input signal composed of several input
+planes.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_max_unpool1d.html">nn_max_unpool1d()</a></code> 
+        </dt>
+        <dd>Computes a partial inverse of <code>MaxPool1d</code>.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_max_unpool2d.html">nn_max_unpool2d()</a></code> 
+        </dt>
+        <dd>Computes a partial inverse of <code>MaxPool2d</code>.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_max_unpool3d.html">nn_max_unpool3d()</a></code> 
+        </dt>
+        <dd>Computes a partial inverse of <code>MaxPool3d</code>.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_module.html">nn_module()</a></code> 
+        </dt>
+        <dd>Base class for all neural network modules.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_module_list.html">nn_module_list()</a></code> 
+        </dt>
+        <dd>Holds submodules in a list.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_mse_loss.html">nn_mse_loss()</a></code> 
+        </dt>
+        <dd>MSE loss</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_multi_margin_loss.html">nn_multi_margin_loss()</a></code> 
+        </dt>
+        <dd>Multi margin loss</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_multihead_attention.html">nn_multihead_attention()</a></code> 
+        </dt>
+        <dd>MultiHead attention</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_multilabel_margin_loss.html">nn_multilabel_margin_loss()</a></code> 
+        </dt>
+        <dd>Multilabel margin loss</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_multilabel_soft_margin_loss.html">nn_multilabel_soft_margin_loss()</a></code> 
+        </dt>
+        <dd>Multi label soft margin loss</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_nll_loss.html">nn_nll_loss()</a></code> 
+        </dt>
+        <dd>Nll loss</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_pairwise_distance.html">nn_pairwise_distance()</a></code> 
+        </dt>
+        <dd>Pairwise distance</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_parameter.html">nn_parameter()</a></code> 
+        </dt>
+        <dd>Creates an <code>nn_parameter</code></dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_poisson_nll_loss.html">nn_poisson_nll_loss()</a></code> 
+        </dt>
+        <dd>Poisson NLL loss</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_prelu.html">nn_prelu()</a></code> 
+        </dt>
+        <dd>PReLU module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_relu.html">nn_relu()</a></code> 
+        </dt>
+        <dd>ReLU module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_relu6.html">nn_relu6()</a></code> 
+        </dt>
+        <dd>ReLu6 module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_rnn.html">nn_rnn()</a></code> 
+        </dt>
+        <dd>RNN module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_rrelu.html">nn_rrelu()</a></code> 
+        </dt>
+        <dd>RReLU module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_selu.html">nn_selu()</a></code> 
+        </dt>
+        <dd>SELU module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_sequential.html">nn_sequential()</a></code> 
+        </dt>
+        <dd>A sequential container</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_sigmoid.html">nn_sigmoid()</a></code> 
+        </dt>
+        <dd>Sigmoid module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_smooth_l1_loss.html">nn_smooth_l1_loss()</a></code> 
+        </dt>
+        <dd>Smooth L1 loss</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_soft_margin_loss.html">nn_soft_margin_loss()</a></code> 
+        </dt>
+        <dd>Soft margin loss</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_softmax.html">nn_softmax()</a></code> 
+        </dt>
+        <dd>Softmax module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_softmax2d.html">nn_softmax2d()</a></code> 
+        </dt>
+        <dd>Softmax2d module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_softmin.html">nn_softmin()</a></code> 
+        </dt>
+        <dd>Softmin</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_softplus.html">nn_softplus()</a></code> 
+        </dt>
+        <dd>Softplus module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_softshrink.html">nn_softshrink()</a></code> 
+        </dt>
+        <dd>Softshrink module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_softsign.html">nn_softsign()</a></code> 
+        </dt>
+        <dd>Softsign module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_tanh.html">nn_tanh()</a></code> 
+        </dt>
+        <dd>Tanh module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_tanhshrink.html">nn_tanhshrink()</a></code> 
+        </dt>
+        <dd>Tanhshrink module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_threshold.html">nn_threshold()</a></code> 
+        </dt>
+        <dd>Threshoold module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_triplet_margin_loss.html">nn_triplet_margin_loss()</a></code> 
+        </dt>
+        <dd>Triplet margin loss</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_triplet_margin_with_distance_loss.html">nn_triplet_margin_with_distance_loss()</a></code> 
+        </dt>
+        <dd>Triplet margin with distance loss</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_unflatten.html">nn_unflatten()</a></code> 
+        </dt>
+        <dd>Unflattens a tensor dim expanding it to a desired shape.
+For use with [nn_sequential.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_upsample.html">nn_upsample()</a></code> 
+        </dt>
+        <dd>Upsample module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_utils_clip_grad_norm_.html">nn_utils_clip_grad_norm_()</a></code> 
+        </dt>
+        <dd>Clips gradient norm of an iterable of parameters.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_utils_clip_grad_value_.html">nn_utils_clip_grad_value_()</a></code> 
+        </dt>
+        <dd>Clips gradient of an iterable of parameters at specified value.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_utils_rnn_pack_padded_sequence.html">nn_utils_rnn_pack_padded_sequence()</a></code> 
+        </dt>
+        <dd>Packs a Tensor containing padded sequences of variable length.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_utils_rnn_pack_sequence.html">nn_utils_rnn_pack_sequence()</a></code> 
+        </dt>
+        <dd>Packs a list of variable length Tensors</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_utils_rnn_pad_packed_sequence.html">nn_utils_rnn_pad_packed_sequence()</a></code> 
+        </dt>
+        <dd>Pads a packed batch of variable length sequences.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_utils_rnn_pad_sequence.html">nn_utils_rnn_pad_sequence()</a></code> 
+        </dt>
+        <dd>Pad a list of variable length Tensors with <code>padding_value</code></dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_adaptive_avg_pool1d.html">nnf_adaptive_avg_pool1d()</a></code> 
+        </dt>
+        <dd>Adaptive_avg_pool1d</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_adaptive_avg_pool2d.html">nnf_adaptive_avg_pool2d()</a></code> 
+        </dt>
+        <dd>Adaptive_avg_pool2d</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_adaptive_avg_pool3d.html">nnf_adaptive_avg_pool3d()</a></code> 
+        </dt>
+        <dd>Adaptive_avg_pool3d</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_adaptive_max_pool1d.html">nnf_adaptive_max_pool1d()</a></code> 
+        </dt>
+        <dd>Adaptive_max_pool1d</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_adaptive_max_pool2d.html">nnf_adaptive_max_pool2d()</a></code> 
+        </dt>
+        <dd>Adaptive_max_pool2d</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_adaptive_max_pool3d.html">nnf_adaptive_max_pool3d()</a></code> 
+        </dt>
+        <dd>Adaptive_max_pool3d</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_affine_grid.html">nnf_affine_grid()</a></code> 
+        </dt>
+        <dd>Affine_grid</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_alpha_dropout.html">nnf_alpha_dropout()</a></code> 
+        </dt>
+        <dd>Alpha_dropout</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_avg_pool1d.html">nnf_avg_pool1d()</a></code> 
+        </dt>
+        <dd>Avg_pool1d</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_avg_pool2d.html">nnf_avg_pool2d()</a></code> 
+        </dt>
+        <dd>Avg_pool2d</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_avg_pool3d.html">nnf_avg_pool3d()</a></code> 
+        </dt>
+        <dd>Avg_pool3d</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_batch_norm.html">nnf_batch_norm()</a></code> 
+        </dt>
+        <dd>Batch_norm</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_bilinear.html">nnf_bilinear()</a></code> 
+        </dt>
+        <dd>Bilinear</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_binary_cross_entropy.html">nnf_binary_cross_entropy()</a></code> 
+        </dt>
+        <dd>Binary_cross_entropy</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_binary_cross_entropy_with_logits.html">nnf_binary_cross_entropy_with_logits()</a></code> 
+        </dt>
+        <dd>Binary_cross_entropy_with_logits</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_celu.html">nnf_celu()</a></code> <code><a href="nnf_celu.html">nnf_celu_()</a></code> 
+        </dt>
+        <dd>Celu</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_contrib_sparsemax.html">nnf_contrib_sparsemax()</a></code> 
+        </dt>
+        <dd>Sparsemax</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_conv1d.html">nnf_conv1d()</a></code> 
+        </dt>
+        <dd>Conv1d</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_conv2d.html">nnf_conv2d()</a></code> 
+        </dt>
+        <dd>Conv2d</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_conv3d.html">nnf_conv3d()</a></code> 
+        </dt>
+        <dd>Conv3d</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_conv_tbc.html">nnf_conv_tbc()</a></code> 
+        </dt>
+        <dd>Conv_tbc</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_conv_transpose1d.html">nnf_conv_transpose1d()</a></code> 
+        </dt>
+        <dd>Conv_transpose1d</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_conv_transpose2d.html">nnf_conv_transpose2d()</a></code> 
+        </dt>
+        <dd>Conv_transpose2d</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_conv_transpose3d.html">nnf_conv_transpose3d()</a></code> 
+        </dt>
+        <dd>Conv_transpose3d</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_cosine_embedding_loss.html">nnf_cosine_embedding_loss()</a></code> 
+        </dt>
+        <dd>Cosine_embedding_loss</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_cosine_similarity.html">nnf_cosine_similarity()</a></code> 
+        </dt>
+        <dd>Cosine_similarity</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_cross_entropy.html">nnf_cross_entropy()</a></code> 
+        </dt>
+        <dd>Cross_entropy</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_ctc_loss.html">nnf_ctc_loss()</a></code> 
+        </dt>
+        <dd>Ctc_loss</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_dropout.html">nnf_dropout()</a></code> 
+        </dt>
+        <dd>Dropout</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_dropout2d.html">nnf_dropout2d()</a></code> 
+        </dt>
+        <dd>Dropout2d</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_dropout3d.html">nnf_dropout3d()</a></code> 
+        </dt>
+        <dd>Dropout3d</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_elu.html">nnf_elu()</a></code> <code><a href="nnf_elu.html">nnf_elu_()</a></code> 
+        </dt>
+        <dd>Elu</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_embedding.html">nnf_embedding()</a></code> 
+        </dt>
+        <dd>Embedding</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_embedding_bag.html">nnf_embedding_bag()</a></code> 
+        </dt>
+        <dd>Embedding_bag</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_fold.html">nnf_fold()</a></code> 
+        </dt>
+        <dd>Fold</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_fractional_max_pool2d.html">nnf_fractional_max_pool2d()</a></code> 
+        </dt>
+        <dd>Fractional_max_pool2d</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_fractional_max_pool3d.html">nnf_fractional_max_pool3d()</a></code> 
+        </dt>
+        <dd>Fractional_max_pool3d</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_gelu.html">nnf_gelu()</a></code> 
+        </dt>
+        <dd>Gelu</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_glu.html">nnf_glu()</a></code> 
+        </dt>
+        <dd>Glu</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_grid_sample.html">nnf_grid_sample()</a></code> 
+        </dt>
+        <dd>Grid_sample</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_group_norm.html">nnf_group_norm()</a></code> 
+        </dt>
+        <dd>Group_norm</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_gumbel_softmax.html">nnf_gumbel_softmax()</a></code> 
+        </dt>
+        <dd>Gumbel_softmax</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_hardshrink.html">nnf_hardshrink()</a></code> 
+        </dt>
+        <dd>Hardshrink</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_hardsigmoid.html">nnf_hardsigmoid()</a></code> 
+        </dt>
+        <dd>Hardsigmoid</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_hardswish.html">nnf_hardswish()</a></code> 
+        </dt>
+        <dd>Hardswish</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_hardtanh.html">nnf_hardtanh()</a></code> <code><a href="nnf_hardtanh.html">nnf_hardtanh_()</a></code> 
+        </dt>
+        <dd>Hardtanh</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_hinge_embedding_loss.html">nnf_hinge_embedding_loss()</a></code> 
+        </dt>
+        <dd>Hinge_embedding_loss</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_instance_norm.html">nnf_instance_norm()</a></code> 
+        </dt>
+        <dd>Instance_norm</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_interpolate.html">nnf_interpolate()</a></code> 
+        </dt>
+        <dd>Interpolate</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_kl_div.html">nnf_kl_div()</a></code> 
+        </dt>
+        <dd>Kl_div</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_l1_loss.html">nnf_l1_loss()</a></code> 
+        </dt>
+        <dd>L1_loss</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_layer_norm.html">nnf_layer_norm()</a></code> 
+        </dt>
+        <dd>Layer_norm</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_leaky_relu.html">nnf_leaky_relu()</a></code> 
+        </dt>
+        <dd>Leaky_relu</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_linear.html">nnf_linear()</a></code> 
+        </dt>
+        <dd>Linear</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_local_response_norm.html">nnf_local_response_norm()</a></code> 
+        </dt>
+        <dd>Local_response_norm</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_log_softmax.html">nnf_log_softmax()</a></code> 
+        </dt>
+        <dd>Log_softmax</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_logsigmoid.html">nnf_logsigmoid()</a></code> 
+        </dt>
+        <dd>Logsigmoid</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_lp_pool1d.html">nnf_lp_pool1d()</a></code> 
+        </dt>
+        <dd>Lp_pool1d</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_lp_pool2d.html">nnf_lp_pool2d()</a></code> 
+        </dt>
+        <dd>Lp_pool2d</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_margin_ranking_loss.html">nnf_margin_ranking_loss()</a></code> 
+        </dt>
+        <dd>Margin_ranking_loss</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_max_pool1d.html">nnf_max_pool1d()</a></code> 
+        </dt>
+        <dd>Max_pool1d</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_max_pool2d.html">nnf_max_pool2d()</a></code> 
+        </dt>
+        <dd>Max_pool2d</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_max_pool3d.html">nnf_max_pool3d()</a></code> 
+        </dt>
+        <dd>Max_pool3d</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_max_unpool1d.html">nnf_max_unpool1d()</a></code> 
+        </dt>
+        <dd>Max_unpool1d</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_max_unpool2d.html">nnf_max_unpool2d()</a></code> 
+        </dt>
+        <dd>Max_unpool2d</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_max_unpool3d.html">nnf_max_unpool3d()</a></code> 
+        </dt>
+        <dd>Max_unpool3d</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_mse_loss.html">nnf_mse_loss()</a></code> 
+        </dt>
+        <dd>Mse_loss</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_multi_head_attention_forward.html">nnf_multi_head_attention_forward()</a></code> 
+        </dt>
+        <dd>Multi head attention forward</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_multi_margin_loss.html">nnf_multi_margin_loss()</a></code> 
+        </dt>
+        <dd>Multi_margin_loss</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_multilabel_margin_loss.html">nnf_multilabel_margin_loss()</a></code> 
+        </dt>
+        <dd>Multilabel_margin_loss</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_multilabel_soft_margin_loss.html">nnf_multilabel_soft_margin_loss()</a></code> 
+        </dt>
+        <dd>Multilabel_soft_margin_loss</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_nll_loss.html">nnf_nll_loss()</a></code> 
+        </dt>
+        <dd>Nll_loss</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_normalize.html">nnf_normalize()</a></code> 
+        </dt>
+        <dd>Normalize</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_one_hot.html">nnf_one_hot()</a></code> 
+        </dt>
+        <dd>One_hot</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_pad.html">nnf_pad()</a></code> 
+        </dt>
+        <dd>Pad</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_pairwise_distance.html">nnf_pairwise_distance()</a></code> 
+        </dt>
+        <dd>Pairwise_distance</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_pdist.html">nnf_pdist()</a></code> 
+        </dt>
+        <dd>Pdist</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_pixel_shuffle.html">nnf_pixel_shuffle()</a></code> 
+        </dt>
+        <dd>Pixel_shuffle</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_poisson_nll_loss.html">nnf_poisson_nll_loss()</a></code> 
+        </dt>
+        <dd>Poisson_nll_loss</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_prelu.html">nnf_prelu()</a></code> 
+        </dt>
+        <dd>Prelu</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_relu.html">nnf_relu()</a></code> <code><a href="nnf_relu.html">nnf_relu_()</a></code> 
+        </dt>
+        <dd>Relu</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_relu6.html">nnf_relu6()</a></code> 
+        </dt>
+        <dd>Relu6</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_rrelu.html">nnf_rrelu()</a></code> <code><a href="nnf_rrelu.html">nnf_rrelu_()</a></code> 
+        </dt>
+        <dd>Rrelu</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_selu.html">nnf_selu()</a></code> <code><a href="nnf_selu.html">nnf_selu_()</a></code> 
+        </dt>
+        <dd>Selu</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_sigmoid.html">nnf_sigmoid()</a></code> 
+        </dt>
+        <dd>Sigmoid</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_smooth_l1_loss.html">nnf_smooth_l1_loss()</a></code> 
+        </dt>
+        <dd>Smooth_l1_loss</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_soft_margin_loss.html">nnf_soft_margin_loss()</a></code> 
+        </dt>
+        <dd>Soft_margin_loss</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_softmax.html">nnf_softmax()</a></code> 
+        </dt>
+        <dd>Softmax</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_softmin.html">nnf_softmin()</a></code> 
+        </dt>
+        <dd>Softmin</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_softplus.html">nnf_softplus()</a></code> 
+        </dt>
+        <dd>Softplus</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_softshrink.html">nnf_softshrink()</a></code> 
+        </dt>
+        <dd>Softshrink</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_softsign.html">nnf_softsign()</a></code> 
+        </dt>
+        <dd>Softsign</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_tanhshrink.html">nnf_tanhshrink()</a></code> 
+        </dt>
+        <dd>Tanhshrink</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_threshold.html">nnf_threshold()</a></code> <code><a href="nnf_threshold.html">nnf_threshold_()</a></code> 
+        </dt>
+        <dd>Threshold</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_triplet_margin_loss.html">nnf_triplet_margin_loss()</a></code> 
+        </dt>
+        <dd>Triplet_margin_loss</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_triplet_margin_with_distance_loss.html">nnf_triplet_margin_with_distance_loss()</a></code> 
+        </dt>
+        <dd>Triplet margin with distance loss</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_unfold.html">nnf_unfold()</a></code> 
+        </dt>
+        <dd>Unfold</dd>
+      </dl><dl><dt>
+          
+          <code><a href="optim_adadelta.html">optim_adadelta()</a></code> 
+        </dt>
+        <dd>Adadelta optimizer</dd>
+      </dl><dl><dt>
+          
+          <code><a href="optim_adagrad.html">optim_adagrad()</a></code> 
+        </dt>
+        <dd>Adagrad optimizer</dd>
+      </dl><dl><dt>
+          
+          <code><a href="optim_adam.html">optim_adam()</a></code> 
+        </dt>
+        <dd>Implements Adam algorithm.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="optim_asgd.html">optim_asgd()</a></code> 
+        </dt>
+        <dd>Averaged Stochastic Gradient Descent optimizer</dd>
+      </dl><dl><dt>
+          
+          <code><a href="optim_lbfgs.html">optim_lbfgs()</a></code> 
+        </dt>
+        <dd>LBFGS optimizer</dd>
+      </dl><dl><dt>
+          
+          <code><a href="optim_required.html">optim_required()</a></code> 
+        </dt>
+        <dd>Dummy value indicating a required value.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="optim_rmsprop.html">optim_rmsprop()</a></code> 
+        </dt>
+        <dd>RMSprop optimizer</dd>
+      </dl><dl><dt>
+          
+          <code><a href="optim_rprop.html">optim_rprop()</a></code> 
+        </dt>
+        <dd>Implements the resilient backpropagation algorithm.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="optim_sgd.html">optim_sgd()</a></code> 
+        </dt>
+        <dd>SGD optimizer</dd>
+      </dl><dl><dt>
+          
+          <code><a href="optimizer.html">optimizer()</a></code> 
+        </dt>
+        <dd>Creates a custom optimizer</dd>
+      </dl><dl><dt>
+          
+          <code><a href="slc.html">slc()</a></code> 
+        </dt>
+        <dd>Creates a slice</dd>
+      </dl><dl><dt>
+          
+          <code><a href="tensor_dataset.html">tensor_dataset()</a></code> 
+        </dt>
+        <dd>Dataset wrapping tensors.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="with_detect_anomaly.html">with_detect_anomaly()</a></code> 
+        </dt>
+        <dd>Context-manager that enable anomaly detection for the autograd engine.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="with_enable_grad.html">with_enable_grad()</a></code> 
+        </dt>
+        <dd>Enable grad</dd>
+      </dl><dl><dt>
+          
+          <code><a href="with_no_grad.html">with_no_grad()</a></code> 
+        </dt>
+        <dd>Temporarily modify gradient recording.</dd>
+      </dl></div><div class="section level2">
+      <h2 id="neural-network-modules">Neural network modules<a class="anchor" aria-label="anchor" href="#neural-network-modules"></a></h2>
       
       
-      <tr>
-        
-        <td>
-          <p><code><a href="default_dtype.html">torch_set_default_dtype()</a></code> <code><a href="default_dtype.html">torch_get_default_dtype()</a></code> </p>
-        </td>
-        <td><p>Gets and sets the default floating point dtype.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="is_torch_device.html">is_torch_device()</a></code> </p>
-        </td>
-        <td><p>Checks if object is a device</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="is_torch_dtype.html">is_torch_dtype()</a></code> </p>
-        </td>
-        <td><p>Check if object is a torch data type</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_dtype.html">torch_float32()</a></code> <code><a href="torch_dtype.html">torch_float()</a></code> <code><a href="torch_dtype.html">torch_float64()</a></code> <code><a href="torch_dtype.html">torch_double()</a></code> <code><a href="torch_dtype.html">torch_float16()</a></code> <code><a href="torch_dtype.html">torch_half()</a></code> <code><a href="torch_dtype.html">torch_uint8()</a></code> <code><a href="torch_dtype.html">torch_int8()</a></code> <code><a href="torch_dtype.html">torch_int16()</a></code> <code><a href="torch_dtype.html">torch_short()</a></code> <code><a href="torch_dtype.html">torch_int32()</a></code> <code><a href="torch_dtype.html">torch_int()</a></code> <code><a href="torch_dtype.html">torch_int64()</a></code> <code><a href="torch_dtype.html">torch_long()</a></code> <code><a href="torch_dtype.html">torch_bool()</a></code> <code><a href="torch_dtype.html">torch_quint8()</a></code> <code><a href="torch_dtype.html">torch_qint8()</a></code> <code><a href="torch_dtype.html">torch_qint32()</a></code> </p>
-        </td>
-        <td><p>Torch data types</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_finfo.html">torch_finfo()</a></code> </p>
-        </td>
-        <td><p>Floating point type info</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_iinfo.html">torch_iinfo()</a></code> </p>
-        </td>
-        <td><p>Integer type info</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_qscheme.html">torch_per_channel_affine()</a></code> <code><a href="torch_qscheme.html">torch_per_tensor_affine()</a></code> <code><a href="torch_qscheme.html">torch_per_channel_symmetric()</a></code> <code><a href="torch_qscheme.html">torch_per_tensor_symmetric()</a></code> </p>
-        </td>
-        <td><p>Creates the corresponding Scheme object</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_reduction.html">torch_reduction_sum()</a></code> <code><a href="torch_reduction.html">torch_reduction_mean()</a></code> <code><a href="torch_reduction.html">torch_reduction_none()</a></code> </p>
-        </td>
-        <td><p>Creates the reduction objet</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="is_torch_layout.html">is_torch_layout()</a></code> </p>
-        </td>
-        <td><p>Check if an object is a torch layout.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="is_torch_memory_format.html">is_torch_memory_format()</a></code> </p>
-        </td>
-        <td><p>Check if an object is a memory format</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="is_torch_qscheme.html">is_torch_qscheme()</a></code> </p>
-        </td>
-        <td><p>Checks if an object is a QScheme</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="is_undefined_tensor.html">is_undefined_tensor()</a></code> </p>
-        </td>
-        <td><p>Checks if a tensor is undefined</p></td>
-      </tr>
-    </tbody><tbody>
-      <tr>
-        <th colspan="2">
-          <h2 id="section-serialization" class="hasAnchor"><a href="#section-serialization" class="anchor"></a>Serialization</h2>
-          <p class="section-desc"></p>
-        </th>
-      </tr>
+
       
+    </div><div id="" class="section level2">
       
-    </tbody><tbody>
       
       
-      <tr>
-        
-        <td>
-          <p><code><a href="load_state_dict.html">load_state_dict()</a></code> </p>
-        </td>
-        <td><p>Load a state dict file</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_load.html">torch_load()</a></code> </p>
-        </td>
-        <td><p>Loads a saved object</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_save.html">torch_save()</a></code> </p>
-        </td>
-        <td><p>Saves an object to a disk file.</p></td>
-      </tr>
-    </tbody><tbody>
-      <tr>
-        <th colspan="2">
-          <h2 id="section-mathematical-operations-on-tensors" class="hasAnchor"><a href="#section-mathematical-operations-on-tensors" class="anchor"></a>Mathematical operations on tensors</h2>
-          <p class="section-desc"></p>
-        </th>
-      </tr>
+
+      <dl><dt>
+          
+          <code><a href="nn_adaptive_avg_pool1d.html">nn_adaptive_avg_pool1d()</a></code> 
+        </dt>
+        <dd>Applies a 1D adaptive average pooling over an input signal composed of several input planes.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_adaptive_avg_pool2d.html">nn_adaptive_avg_pool2d()</a></code> 
+        </dt>
+        <dd>Applies a 2D adaptive average pooling over an input signal composed of several input planes.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_adaptive_avg_pool3d.html">nn_adaptive_avg_pool3d()</a></code> 
+        </dt>
+        <dd>Applies a 3D adaptive average pooling over an input signal composed of several input planes.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_adaptive_log_softmax_with_loss.html">nn_adaptive_log_softmax_with_loss()</a></code> 
+        </dt>
+        <dd>AdaptiveLogSoftmaxWithLoss module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_adaptive_max_pool1d.html">nn_adaptive_max_pool1d()</a></code> 
+        </dt>
+        <dd>Applies a 1D adaptive max pooling over an input signal composed of several input planes.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_adaptive_max_pool2d.html">nn_adaptive_max_pool2d()</a></code> 
+        </dt>
+        <dd>Applies a 2D adaptive max pooling over an input signal composed of several input planes.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_adaptive_max_pool3d.html">nn_adaptive_max_pool3d()</a></code> 
+        </dt>
+        <dd>Applies a 3D adaptive max pooling over an input signal composed of several input planes.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_avg_pool1d.html">nn_avg_pool1d()</a></code> 
+        </dt>
+        <dd>Applies a 1D average pooling over an input signal composed of several
+input planes.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_avg_pool2d.html">nn_avg_pool2d()</a></code> 
+        </dt>
+        <dd>Applies a 2D average pooling over an input signal composed of several input
+planes.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_avg_pool3d.html">nn_avg_pool3d()</a></code> 
+        </dt>
+        <dd>Applies a 3D average pooling over an input signal composed of several input
+planes.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_batch_norm1d.html">nn_batch_norm1d()</a></code> 
+        </dt>
+        <dd>BatchNorm1D module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_batch_norm2d.html">nn_batch_norm2d()</a></code> 
+        </dt>
+        <dd>BatchNorm2D</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_batch_norm3d.html">nn_batch_norm3d()</a></code> 
+        </dt>
+        <dd>BatchNorm3D</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_bce_loss.html">nn_bce_loss()</a></code> 
+        </dt>
+        <dd>Binary cross entropy loss</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_bce_with_logits_loss.html">nn_bce_with_logits_loss()</a></code> 
+        </dt>
+        <dd>BCE with logits loss</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_bilinear.html">nn_bilinear()</a></code> 
+        </dt>
+        <dd>Bilinear module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_buffer.html">nn_buffer()</a></code> 
+        </dt>
+        <dd>Creates a nn_buffer</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_celu.html">nn_celu()</a></code> 
+        </dt>
+        <dd>CELU module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_contrib_sparsemax.html">nn_contrib_sparsemax()</a></code> 
+        </dt>
+        <dd>Sparsemax activation</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_conv1d.html">nn_conv1d()</a></code> 
+        </dt>
+        <dd>Conv1D module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_conv2d.html">nn_conv2d()</a></code> 
+        </dt>
+        <dd>Conv2D module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_conv3d.html">nn_conv3d()</a></code> 
+        </dt>
+        <dd>Conv3D module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_conv_transpose1d.html">nn_conv_transpose1d()</a></code> 
+        </dt>
+        <dd>ConvTranspose1D</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_conv_transpose2d.html">nn_conv_transpose2d()</a></code> 
+        </dt>
+        <dd>ConvTranpose2D module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_conv_transpose3d.html">nn_conv_transpose3d()</a></code> 
+        </dt>
+        <dd>ConvTranpose3D module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_cosine_embedding_loss.html">nn_cosine_embedding_loss()</a></code> 
+        </dt>
+        <dd>Cosine embedding loss</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_cross_entropy_loss.html">nn_cross_entropy_loss()</a></code> 
+        </dt>
+        <dd>CrossEntropyLoss module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_ctc_loss.html">nn_ctc_loss()</a></code> 
+        </dt>
+        <dd>The Connectionist Temporal Classification loss.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_dropout.html">nn_dropout()</a></code> 
+        </dt>
+        <dd>Dropout module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_dropout2d.html">nn_dropout2d()</a></code> 
+        </dt>
+        <dd>Dropout2D module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_dropout3d.html">nn_dropout3d()</a></code> 
+        </dt>
+        <dd>Dropout3D module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_elu.html">nn_elu()</a></code> 
+        </dt>
+        <dd>ELU module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_embedding.html">nn_embedding()</a></code> 
+        </dt>
+        <dd>Embedding module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_flatten.html">nn_flatten()</a></code> 
+        </dt>
+        <dd>Flattens a contiguous range of dims into a tensor.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_fractional_max_pool2d.html">nn_fractional_max_pool2d()</a></code> 
+        </dt>
+        <dd>Applies a 2D fractional max pooling over an input signal composed of several input planes.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_fractional_max_pool3d.html">nn_fractional_max_pool3d()</a></code> 
+        </dt>
+        <dd>Applies a 3D fractional max pooling over an input signal composed of several input planes.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_gelu.html">nn_gelu()</a></code> 
+        </dt>
+        <dd>GELU module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_glu.html">nn_glu()</a></code> 
+        </dt>
+        <dd>GLU module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_group_norm.html">nn_group_norm()</a></code> 
+        </dt>
+        <dd>Group normalization</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_gru.html">nn_gru()</a></code> 
+        </dt>
+        <dd>Applies a multi-layer gated recurrent unit (GRU) RNN to an input sequence.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_hardshrink.html">nn_hardshrink()</a></code> 
+        </dt>
+        <dd>Hardshwink module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_hardsigmoid.html">nn_hardsigmoid()</a></code> 
+        </dt>
+        <dd>Hardsigmoid module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_hardswish.html">nn_hardswish()</a></code> 
+        </dt>
+        <dd>Hardswish module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_hardtanh.html">nn_hardtanh()</a></code> 
+        </dt>
+        <dd>Hardtanh module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_hinge_embedding_loss.html">nn_hinge_embedding_loss()</a></code> 
+        </dt>
+        <dd>Hinge embedding loss</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_identity.html">nn_identity()</a></code> 
+        </dt>
+        <dd>Identity module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_init_calculate_gain.html">nn_init_calculate_gain()</a></code> 
+        </dt>
+        <dd>Calculate gain</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_init_constant_.html">nn_init_constant_()</a></code> 
+        </dt>
+        <dd>Constant initialization</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_init_dirac_.html">nn_init_dirac_()</a></code> 
+        </dt>
+        <dd>Dirac initialization</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_init_eye_.html">nn_init_eye_()</a></code> 
+        </dt>
+        <dd>Eye initialization</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_init_kaiming_normal_.html">nn_init_kaiming_normal_()</a></code> 
+        </dt>
+        <dd>Kaiming normal initialization</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_init_kaiming_uniform_.html">nn_init_kaiming_uniform_()</a></code> 
+        </dt>
+        <dd>Kaiming uniform initialization</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_init_normal_.html">nn_init_normal_()</a></code> 
+        </dt>
+        <dd>Normal initialization</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_init_ones_.html">nn_init_ones_()</a></code> 
+        </dt>
+        <dd>Ones initialization</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_init_orthogonal_.html">nn_init_orthogonal_()</a></code> 
+        </dt>
+        <dd>Orthogonal initialization</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_init_sparse_.html">nn_init_sparse_()</a></code> 
+        </dt>
+        <dd>Sparse initialization</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_init_trunc_normal_.html">nn_init_trunc_normal_()</a></code> 
+        </dt>
+        <dd>Truncated normal initialization</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_init_uniform_.html">nn_init_uniform_()</a></code> 
+        </dt>
+        <dd>Uniform initialization</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_init_xavier_normal_.html">nn_init_xavier_normal_()</a></code> 
+        </dt>
+        <dd>Xavier normal initialization</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_init_xavier_uniform_.html">nn_init_xavier_uniform_()</a></code> 
+        </dt>
+        <dd>Xavier uniform initialization</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_init_zeros_.html">nn_init_zeros_()</a></code> 
+        </dt>
+        <dd>Zeros initialization</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_kl_div_loss.html">nn_kl_div_loss()</a></code> 
+        </dt>
+        <dd>Kullback-Leibler divergence loss</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_l1_loss.html">nn_l1_loss()</a></code> 
+        </dt>
+        <dd>L1 loss</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_layer_norm.html">nn_layer_norm()</a></code> 
+        </dt>
+        <dd>Layer normalization</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_leaky_relu.html">nn_leaky_relu()</a></code> 
+        </dt>
+        <dd>LeakyReLU module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_linear.html">nn_linear()</a></code> 
+        </dt>
+        <dd>Linear module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_log_sigmoid.html">nn_log_sigmoid()</a></code> 
+        </dt>
+        <dd>LogSigmoid module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_log_softmax.html">nn_log_softmax()</a></code> 
+        </dt>
+        <dd>LogSoftmax module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_lp_pool1d.html">nn_lp_pool1d()</a></code> 
+        </dt>
+        <dd>Applies a 1D power-average pooling over an input signal composed of several input
+planes.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_lp_pool2d.html">nn_lp_pool2d()</a></code> 
+        </dt>
+        <dd>Applies a 2D power-average pooling over an input signal composed of several input
+planes.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_lstm.html">nn_lstm()</a></code> 
+        </dt>
+        <dd>Applies a multi-layer long short-term memory (LSTM) RNN to an input
+sequence.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_margin_ranking_loss.html">nn_margin_ranking_loss()</a></code> 
+        </dt>
+        <dd>Margin ranking loss</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_max_pool1d.html">nn_max_pool1d()</a></code> 
+        </dt>
+        <dd>MaxPool1D module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_max_pool2d.html">nn_max_pool2d()</a></code> 
+        </dt>
+        <dd>MaxPool2D module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_max_pool3d.html">nn_max_pool3d()</a></code> 
+        </dt>
+        <dd>Applies a 3D max pooling over an input signal composed of several input
+planes.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_max_unpool1d.html">nn_max_unpool1d()</a></code> 
+        </dt>
+        <dd>Computes a partial inverse of <code>MaxPool1d</code>.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_max_unpool2d.html">nn_max_unpool2d()</a></code> 
+        </dt>
+        <dd>Computes a partial inverse of <code>MaxPool2d</code>.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_max_unpool3d.html">nn_max_unpool3d()</a></code> 
+        </dt>
+        <dd>Computes a partial inverse of <code>MaxPool3d</code>.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_module.html">nn_module()</a></code> 
+        </dt>
+        <dd>Base class for all neural network modules.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_module_list.html">nn_module_list()</a></code> 
+        </dt>
+        <dd>Holds submodules in a list.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_mse_loss.html">nn_mse_loss()</a></code> 
+        </dt>
+        <dd>MSE loss</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_multi_margin_loss.html">nn_multi_margin_loss()</a></code> 
+        </dt>
+        <dd>Multi margin loss</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_multihead_attention.html">nn_multihead_attention()</a></code> 
+        </dt>
+        <dd>MultiHead attention</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_multilabel_margin_loss.html">nn_multilabel_margin_loss()</a></code> 
+        </dt>
+        <dd>Multilabel margin loss</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_multilabel_soft_margin_loss.html">nn_multilabel_soft_margin_loss()</a></code> 
+        </dt>
+        <dd>Multi label soft margin loss</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_nll_loss.html">nn_nll_loss()</a></code> 
+        </dt>
+        <dd>Nll loss</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_pairwise_distance.html">nn_pairwise_distance()</a></code> 
+        </dt>
+        <dd>Pairwise distance</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_parameter.html">nn_parameter()</a></code> 
+        </dt>
+        <dd>Creates an <code>nn_parameter</code></dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_poisson_nll_loss.html">nn_poisson_nll_loss()</a></code> 
+        </dt>
+        <dd>Poisson NLL loss</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_prelu.html">nn_prelu()</a></code> 
+        </dt>
+        <dd>PReLU module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_relu.html">nn_relu()</a></code> 
+        </dt>
+        <dd>ReLU module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_relu6.html">nn_relu6()</a></code> 
+        </dt>
+        <dd>ReLu6 module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_rnn.html">nn_rnn()</a></code> 
+        </dt>
+        <dd>RNN module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_rrelu.html">nn_rrelu()</a></code> 
+        </dt>
+        <dd>RReLU module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_selu.html">nn_selu()</a></code> 
+        </dt>
+        <dd>SELU module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_sequential.html">nn_sequential()</a></code> 
+        </dt>
+        <dd>A sequential container</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_sigmoid.html">nn_sigmoid()</a></code> 
+        </dt>
+        <dd>Sigmoid module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_smooth_l1_loss.html">nn_smooth_l1_loss()</a></code> 
+        </dt>
+        <dd>Smooth L1 loss</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_soft_margin_loss.html">nn_soft_margin_loss()</a></code> 
+        </dt>
+        <dd>Soft margin loss</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_softmax.html">nn_softmax()</a></code> 
+        </dt>
+        <dd>Softmax module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_softmax2d.html">nn_softmax2d()</a></code> 
+        </dt>
+        <dd>Softmax2d module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_softmin.html">nn_softmin()</a></code> 
+        </dt>
+        <dd>Softmin</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_softplus.html">nn_softplus()</a></code> 
+        </dt>
+        <dd>Softplus module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_softshrink.html">nn_softshrink()</a></code> 
+        </dt>
+        <dd>Softshrink module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_softsign.html">nn_softsign()</a></code> 
+        </dt>
+        <dd>Softsign module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_tanh.html">nn_tanh()</a></code> 
+        </dt>
+        <dd>Tanh module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_tanhshrink.html">nn_tanhshrink()</a></code> 
+        </dt>
+        <dd>Tanhshrink module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_threshold.html">nn_threshold()</a></code> 
+        </dt>
+        <dd>Threshoold module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_triplet_margin_loss.html">nn_triplet_margin_loss()</a></code> 
+        </dt>
+        <dd>Triplet margin loss</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_triplet_margin_with_distance_loss.html">nn_triplet_margin_with_distance_loss()</a></code> 
+        </dt>
+        <dd>Triplet margin with distance loss</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_unflatten.html">nn_unflatten()</a></code> 
+        </dt>
+        <dd>Unflattens a tensor dim expanding it to a desired shape.
+For use with [nn_sequential.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_upsample.html">nn_upsample()</a></code> 
+        </dt>
+        <dd>Upsample module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_utils_clip_grad_norm_.html">nn_utils_clip_grad_norm_()</a></code> 
+        </dt>
+        <dd>Clips gradient norm of an iterable of parameters.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_utils_clip_grad_value_.html">nn_utils_clip_grad_value_()</a></code> 
+        </dt>
+        <dd>Clips gradient of an iterable of parameters at specified value.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_utils_rnn_pack_padded_sequence.html">nn_utils_rnn_pack_padded_sequence()</a></code> 
+        </dt>
+        <dd>Packs a Tensor containing padded sequences of variable length.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_utils_rnn_pack_sequence.html">nn_utils_rnn_pack_sequence()</a></code> 
+        </dt>
+        <dd>Packs a list of variable length Tensors</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_utils_rnn_pad_packed_sequence.html">nn_utils_rnn_pad_packed_sequence()</a></code> 
+        </dt>
+        <dd>Pads a packed batch of variable length sequences.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nn_utils_rnn_pad_sequence.html">nn_utils_rnn_pad_sequence()</a></code> 
+        </dt>
+        <dd>Pad a list of variable length Tensors with <code>padding_value</code></dd>
+      </dl><dl><dt>
+          
+          <code><a href="is_nn_module.html">is_nn_module()</a></code> 
+        </dt>
+        <dd>Checks if the object is an nn_module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="is_nn_parameter.html">is_nn_parameter()</a></code> 
+        </dt>
+        <dd>Checks if an object is a nn_parameter</dd>
+      </dl><dl><dt>
+          
+          <code><a href="is_nn_buffer.html">is_nn_buffer()</a></code> 
+        </dt>
+        <dd>Checks if the object is a nn_buffer</dd>
+      </dl></div><div class="section level2">
+      <h2 id="neural-networks-functional-module">Neural networks functional module<a class="anchor" aria-label="anchor" href="#neural-networks-functional-module"></a></h2>
       
       
-    </tbody><tbody>
+
       
+    </div><div id="" class="section level2">
       
-      <tr>
-        
-        <td>
-          <p><code><a href="threads.html">torch_set_num_threads()</a></code> <code><a href="threads.html">torch_set_num_interop_threads()</a></code> <code><a href="threads.html">torch_get_num_interop_threads()</a></code> <code><a href="threads.html">torch_get_num_threads()</a></code> </p>
-        </td>
-        <td><p>Number of threads</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_abs.html">torch_abs()</a></code> </p>
-        </td>
-        <td><p>Abs</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_absolute.html">torch_absolute()</a></code> </p>
-        </td>
-        <td><p>Absolute</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_acos.html">torch_acos()</a></code> </p>
-        </td>
-        <td><p>Acos</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_acosh.html">torch_acosh()</a></code> </p>
-        </td>
-        <td><p>Acosh</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_adaptive_avg_pool1d.html">torch_adaptive_avg_pool1d()</a></code> </p>
-        </td>
-        <td><p>Adaptive_avg_pool1d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_add.html">torch_add()</a></code> </p>
-        </td>
-        <td><p>Add</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_addbmm.html">torch_addbmm()</a></code> </p>
-        </td>
-        <td><p>Addbmm</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_addcdiv.html">torch_addcdiv()</a></code> </p>
-        </td>
-        <td><p>Addcdiv</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_addcmul.html">torch_addcmul()</a></code> </p>
-        </td>
-        <td><p>Addcmul</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_addmm.html">torch_addmm()</a></code> </p>
-        </td>
-        <td><p>Addmm</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_addmv.html">torch_addmv()</a></code> </p>
-        </td>
-        <td><p>Addmv</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_addr.html">torch_addr()</a></code> </p>
-        </td>
-        <td><p>Addr</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_allclose.html">torch_allclose()</a></code> </p>
-        </td>
-        <td><p>Allclose</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_amax.html">torch_amax()</a></code> </p>
-        </td>
-        <td><p>Amax</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_amin.html">torch_amin()</a></code> </p>
-        </td>
-        <td><p>Amin</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_angle.html">torch_angle()</a></code> </p>
-        </td>
-        <td><p>Angle</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_arccos.html">torch_arccos()</a></code> </p>
-        </td>
-        <td><p>Arccos</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_arccosh.html">torch_arccosh()</a></code> </p>
-        </td>
-        <td><p>Arccosh</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_arcsin.html">torch_arcsin()</a></code> </p>
-        </td>
-        <td><p>Arcsin</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_arcsinh.html">torch_arcsinh()</a></code> </p>
-        </td>
-        <td><p>Arcsinh</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_arctan.html">torch_arctan()</a></code> </p>
-        </td>
-        <td><p>Arctan</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_arctanh.html">torch_arctanh()</a></code> </p>
-        </td>
-        <td><p>Arctanh</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_argmax.html">torch_argmax</a></code> </p>
-        </td>
-        <td><p>Argmax</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_argmin.html">torch_argmin</a></code> </p>
-        </td>
-        <td><p>Argmin</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_argsort.html">torch_argsort()</a></code> </p>
-        </td>
-        <td><p>Argsort</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_as_strided.html">torch_as_strided()</a></code> </p>
-        </td>
-        <td><p>As_strided</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_asin.html">torch_asin()</a></code> </p>
-        </td>
-        <td><p>Asin</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_asinh.html">torch_asinh()</a></code> </p>
-        </td>
-        <td><p>Asinh</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_atan.html">torch_atan()</a></code> </p>
-        </td>
-        <td><p>Atan</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_atan2.html">torch_atan2()</a></code> </p>
-        </td>
-        <td><p>Atan2</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_atanh.html">torch_atanh()</a></code> </p>
-        </td>
-        <td><p>Atanh</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_atleast_1d.html">torch_atleast_1d()</a></code> </p>
-        </td>
-        <td><p>Atleast_1d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_atleast_2d.html">torch_atleast_2d()</a></code> </p>
-        </td>
-        <td><p>Atleast_2d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_atleast_3d.html">torch_atleast_3d()</a></code> </p>
-        </td>
-        <td><p>Atleast_3d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_avg_pool1d.html">torch_avg_pool1d()</a></code> </p>
-        </td>
-        <td><p>Avg_pool1d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_baddbmm.html">torch_baddbmm()</a></code> </p>
-        </td>
-        <td><p>Baddbmm</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_bartlett_window.html">torch_bartlett_window()</a></code> </p>
-        </td>
-        <td><p>Bartlett_window</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_bernoulli.html">torch_bernoulli()</a></code> </p>
-        </td>
-        <td><p>Bernoulli</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_bincount.html">torch_bincount()</a></code> </p>
-        </td>
-        <td><p>Bincount</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_bitwise_and.html">torch_bitwise_and()</a></code> </p>
-        </td>
-        <td><p>Bitwise_and</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_bitwise_not.html">torch_bitwise_not()</a></code> </p>
-        </td>
-        <td><p>Bitwise_not</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_bitwise_or.html">torch_bitwise_or()</a></code> </p>
-        </td>
-        <td><p>Bitwise_or</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_bitwise_xor.html">torch_bitwise_xor()</a></code> </p>
-        </td>
-        <td><p>Bitwise_xor</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_blackman_window.html">torch_blackman_window()</a></code> </p>
-        </td>
-        <td><p>Blackman_window</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_block_diag.html">torch_block_diag()</a></code> </p>
-        </td>
-        <td><p>Block_diag</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_bmm.html">torch_bmm()</a></code> </p>
-        </td>
-        <td><p>Bmm</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_broadcast_tensors.html">torch_broadcast_tensors()</a></code> </p>
-        </td>
-        <td><p>Broadcast_tensors</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_bucketize.html">torch_bucketize()</a></code> </p>
-        </td>
-        <td><p>Bucketize</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_can_cast.html">torch_can_cast()</a></code> </p>
-        </td>
-        <td><p>Can_cast</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_cartesian_prod.html">torch_cartesian_prod()</a></code> </p>
-        </td>
-        <td><p>Cartesian_prod</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_cat.html">torch_cat()</a></code> </p>
-        </td>
-        <td><p>Cat</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_cdist.html">torch_cdist()</a></code> </p>
-        </td>
-        <td><p>Cdist</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_ceil.html">torch_ceil()</a></code> </p>
-        </td>
-        <td><p>Ceil</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_celu.html">torch_celu()</a></code> </p>
-        </td>
-        <td><p>Celu</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_celu_.html">torch_celu_()</a></code> </p>
-        </td>
-        <td><p>Celu_</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_chain_matmul.html">torch_chain_matmul()</a></code> </p>
-        </td>
-        <td><p>Chain_matmul</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_channel_shuffle.html">torch_channel_shuffle()</a></code> </p>
-        </td>
-        <td><p>Channel_shuffle</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_cholesky.html">torch_cholesky()</a></code> </p>
-        </td>
-        <td><p>Cholesky</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_cholesky_inverse.html">torch_cholesky_inverse()</a></code> </p>
-        </td>
-        <td><p>Cholesky_inverse</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_cholesky_solve.html">torch_cholesky_solve()</a></code> </p>
-        </td>
-        <td><p>Cholesky_solve</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_chunk.html">torch_chunk()</a></code> </p>
-        </td>
-        <td><p>Chunk</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_clamp.html">torch_clamp()</a></code> </p>
-        </td>
-        <td><p>Clamp</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_clip.html">torch_clip()</a></code> </p>
-        </td>
-        <td><p>Clip</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_clone.html">torch_clone()</a></code> </p>
-        </td>
-        <td><p>Clone</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_combinations.html">torch_combinations()</a></code> </p>
-        </td>
-        <td><p>Combinations</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_complex.html">torch_complex()</a></code> </p>
-        </td>
-        <td><p>Complex</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_conj.html">torch_conj()</a></code> </p>
-        </td>
-        <td><p>Conj</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_conv1d.html">torch_conv1d()</a></code> </p>
-        </td>
-        <td><p>Conv1d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_conv2d.html">torch_conv2d()</a></code> </p>
-        </td>
-        <td><p>Conv2d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_conv3d.html">torch_conv3d()</a></code> </p>
-        </td>
-        <td><p>Conv3d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_conv_tbc.html">torch_conv_tbc()</a></code> </p>
-        </td>
-        <td><p>Conv_tbc</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_conv_transpose1d.html">torch_conv_transpose1d()</a></code> </p>
-        </td>
-        <td><p>Conv_transpose1d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_conv_transpose2d.html">torch_conv_transpose2d()</a></code> </p>
-        </td>
-        <td><p>Conv_transpose2d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_conv_transpose3d.html">torch_conv_transpose3d()</a></code> </p>
-        </td>
-        <td><p>Conv_transpose3d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_cos.html">torch_cos()</a></code> </p>
-        </td>
-        <td><p>Cos</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_cosh.html">torch_cosh()</a></code> </p>
-        </td>
-        <td><p>Cosh</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_cosine_similarity.html">torch_cosine_similarity()</a></code> </p>
-        </td>
-        <td><p>Cosine_similarity</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_count_nonzero.html">torch_count_nonzero()</a></code> </p>
-        </td>
-        <td><p>Count_nonzero</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_cross.html">torch_cross()</a></code> </p>
-        </td>
-        <td><p>Cross</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_cummax.html">torch_cummax()</a></code> </p>
-        </td>
-        <td><p>Cummax</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_cummin.html">torch_cummin()</a></code> </p>
-        </td>
-        <td><p>Cummin</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_cumprod.html">torch_cumprod()</a></code> </p>
-        </td>
-        <td><p>Cumprod</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_cumsum.html">torch_cumsum()</a></code> </p>
-        </td>
-        <td><p>Cumsum</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_deg2rad.html">torch_deg2rad()</a></code> </p>
-        </td>
-        <td><p>Deg2rad</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_dequantize.html">torch_dequantize()</a></code> </p>
-        </td>
-        <td><p>Dequantize</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_det.html">torch_det()</a></code> </p>
-        </td>
-        <td><p>Det</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_device.html">torch_device()</a></code> </p>
-        </td>
-        <td><p>Create a Device object</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_diag.html">torch_diag()</a></code> </p>
-        </td>
-        <td><p>Diag</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_diag_embed.html">torch_diag_embed()</a></code> </p>
-        </td>
-        <td><p>Diag_embed</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_diagflat.html">torch_diagflat()</a></code> </p>
-        </td>
-        <td><p>Diagflat</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_diagonal.html">torch_diagonal()</a></code> </p>
-        </td>
-        <td><p>Diagonal</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_diff.html">torch_diff()</a></code> </p>
-        </td>
-        <td><p>Computes the n-th forward difference along the given dimension.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_digamma.html">torch_digamma()</a></code> </p>
-        </td>
-        <td><p>Digamma</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_dist.html">torch_dist()</a></code> </p>
-        </td>
-        <td><p>Dist</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_div.html">torch_div()</a></code> </p>
-        </td>
-        <td><p>Div</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_divide.html">torch_divide()</a></code> </p>
-        </td>
-        <td><p>Divide</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_dot.html">torch_dot()</a></code> </p>
-        </td>
-        <td><p>Dot</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_dstack.html">torch_dstack()</a></code> </p>
-        </td>
-        <td><p>Dstack</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_eig.html">torch_eig()</a></code> </p>
-        </td>
-        <td><p>Eig</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_einsum.html">torch_einsum()</a></code> </p>
-        </td>
-        <td><p>Einsum</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_empty_strided.html">torch_empty_strided()</a></code> </p>
-        </td>
-        <td><p>Empty_strided</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_eq.html">torch_eq()</a></code> </p>
-        </td>
-        <td><p>Eq</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_equal.html">torch_equal()</a></code> </p>
-        </td>
-        <td><p>Equal</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_erf.html">torch_erf()</a></code> </p>
-        </td>
-        <td><p>Erf</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_erfc.html">torch_erfc()</a></code> </p>
-        </td>
-        <td><p>Erfc</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_erfinv.html">torch_erfinv()</a></code> </p>
-        </td>
-        <td><p>Erfinv</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_exp.html">torch_exp()</a></code> </p>
-        </td>
-        <td><p>Exp</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_exp2.html">torch_exp2()</a></code> </p>
-        </td>
-        <td><p>Exp2</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_expm1.html">torch_expm1()</a></code> </p>
-        </td>
-        <td><p>Expm1</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_fft_fft.html">torch_fft_fft()</a></code> </p>
-        </td>
-        <td><p>Fft</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_fft_ifft.html">torch_fft_ifft()</a></code> </p>
-        </td>
-        <td><p>Ifft</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_fft_irfft.html">torch_fft_irfft()</a></code> </p>
-        </td>
-        <td><p>Irfft</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_fft_rfft.html">torch_fft_rfft()</a></code> </p>
-        </td>
-        <td><p>Rfft</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_fix.html">torch_fix()</a></code> </p>
-        </td>
-        <td><p>Fix</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_flatten.html">torch_flatten()</a></code> </p>
-        </td>
-        <td><p>Flatten</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_flip.html">torch_flip()</a></code> </p>
-        </td>
-        <td><p>Flip</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_fliplr.html">torch_fliplr()</a></code> </p>
-        </td>
-        <td><p>Fliplr</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_flipud.html">torch_flipud()</a></code> </p>
-        </td>
-        <td><p>Flipud</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_floor.html">torch_floor()</a></code> </p>
-        </td>
-        <td><p>Floor</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_floor_divide.html">torch_floor_divide()</a></code> </p>
-        </td>
-        <td><p>Floor_divide</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_fmod.html">torch_fmod()</a></code> </p>
-        </td>
-        <td><p>Fmod</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_frac.html">torch_frac()</a></code> </p>
-        </td>
-        <td><p>Frac</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_gather.html">torch_gather()</a></code> </p>
-        </td>
-        <td><p>Gather</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_gcd.html">torch_gcd()</a></code> </p>
-        </td>
-        <td><p>Gcd</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_ge.html">torch_ge()</a></code> </p>
-        </td>
-        <td><p>Ge</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_generator.html">torch_generator()</a></code> </p>
-        </td>
-        <td><p>Create a Generator object</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_geqrf.html">torch_geqrf()</a></code> </p>
-        </td>
-        <td><p>Geqrf</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_ger.html">torch_ger()</a></code> </p>
-        </td>
-        <td><p>Ger</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_greater.html">torch_greater()</a></code> </p>
-        </td>
-        <td><p>Greater</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_greater_equal.html">torch_greater_equal()</a></code> </p>
-        </td>
-        <td><p>Greater_equal</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_gt.html">torch_gt()</a></code> </p>
-        </td>
-        <td><p>Gt</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_hamming_window.html">torch_hamming_window()</a></code> </p>
-        </td>
-        <td><p>Hamming_window</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_hann_window.html">torch_hann_window()</a></code> </p>
-        </td>
-        <td><p>Hann_window</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_heaviside.html">torch_heaviside()</a></code> </p>
-        </td>
-        <td><p>Heaviside</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_histc.html">torch_histc()</a></code> </p>
-        </td>
-        <td><p>Histc</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_hstack.html">torch_hstack()</a></code> </p>
-        </td>
-        <td><p>Hstack</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_hypot.html">torch_hypot()</a></code> </p>
-        </td>
-        <td><p>Hypot</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_i0.html">torch_i0()</a></code> </p>
-        </td>
-        <td><p>I0</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_imag.html">torch_imag()</a></code> </p>
-        </td>
-        <td><p>Imag</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_index.html">torch_index()</a></code> </p>
-        </td>
-        <td><p>Index torch tensors</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_index_put.html">torch_index_put()</a></code> </p>
-        </td>
-        <td><p>Modify values selected by <code>indices</code>.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_index_put_.html">torch_index_put_()</a></code> </p>
-        </td>
-        <td><p>In-place version of <code>torch_index_put</code>.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_index_select.html">torch_index_select()</a></code> </p>
-        </td>
-        <td><p>Index_select</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_inverse.html">torch_inverse()</a></code> </p>
-        </td>
-        <td><p>Inverse</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_is_complex.html">torch_is_complex()</a></code> </p>
-        </td>
-        <td><p>Is_complex</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_is_floating_point.html">torch_is_floating_point()</a></code> </p>
-        </td>
-        <td><p>Is_floating_point</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_is_installed.html">torch_is_installed()</a></code> </p>
-        </td>
-        <td><p>Verifies if torch is installed</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_is_nonzero.html">torch_is_nonzero()</a></code> </p>
-        </td>
-        <td><p>Is_nonzero</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_isclose.html">torch_isclose()</a></code> </p>
-        </td>
-        <td><p>Isclose</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_isfinite.html">torch_isfinite()</a></code> </p>
-        </td>
-        <td><p>Isfinite</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_isinf.html">torch_isinf()</a></code> </p>
-        </td>
-        <td><p>Isinf</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_isnan.html">torch_isnan()</a></code> </p>
-        </td>
-        <td><p>Isnan</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_isneginf.html">torch_isneginf()</a></code> </p>
-        </td>
-        <td><p>Isneginf</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_isposinf.html">torch_isposinf()</a></code> </p>
-        </td>
-        <td><p>Isposinf</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_isreal.html">torch_isreal()</a></code> </p>
-        </td>
-        <td><p>Isreal</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_istft.html">torch_istft()</a></code> </p>
-        </td>
-        <td><p>Istft</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_kaiser_window.html">torch_kaiser_window()</a></code> </p>
-        </td>
-        <td><p>Kaiser_window</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_kthvalue.html">torch_kthvalue()</a></code> </p>
-        </td>
-        <td><p>Kthvalue</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_layout.html">torch_strided()</a></code> <code><a href="torch_layout.html">torch_sparse_coo()</a></code> </p>
-        </td>
-        <td><p>Creates the corresponding layout</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_lcm.html">torch_lcm()</a></code> </p>
-        </td>
-        <td><p>Lcm</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_le.html">torch_le()</a></code> </p>
-        </td>
-        <td><p>Le</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_lerp.html">torch_lerp()</a></code> </p>
-        </td>
-        <td><p>Lerp</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_less.html">torch_less()</a></code> </p>
-        </td>
-        <td><p>Less</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_less_equal.html">torch_less_equal()</a></code> </p>
-        </td>
-        <td><p>Less_equal</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_lgamma.html">torch_lgamma()</a></code> </p>
-        </td>
-        <td><p>Lgamma</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_log.html">torch_log()</a></code> </p>
-        </td>
-        <td><p>Log</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_log10.html">torch_log10()</a></code> </p>
-        </td>
-        <td><p>Log10</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_log1p.html">torch_log1p()</a></code> </p>
-        </td>
-        <td><p>Log1p</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_log2.html">torch_log2()</a></code> </p>
-        </td>
-        <td><p>Log2</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_logaddexp.html">torch_logaddexp()</a></code> </p>
-        </td>
-        <td><p>Logaddexp</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_logaddexp2.html">torch_logaddexp2()</a></code> </p>
-        </td>
-        <td><p>Logaddexp2</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_logcumsumexp.html">torch_logcumsumexp()</a></code> </p>
-        </td>
-        <td><p>Logcumsumexp</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_logdet.html">torch_logdet()</a></code> </p>
-        </td>
-        <td><p>Logdet</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_logical_and.html">torch_logical_and()</a></code> </p>
-        </td>
-        <td><p>Logical_and</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_logical_not.html">torch_logical_not</a></code> </p>
-        </td>
-        <td><p>Logical_not</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_logical_or.html">torch_logical_or()</a></code> </p>
-        </td>
-        <td><p>Logical_or</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_logical_xor.html">torch_logical_xor()</a></code> </p>
-        </td>
-        <td><p>Logical_xor</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_logit.html">torch_logit()</a></code> </p>
-        </td>
-        <td><p>Logit</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_logsumexp.html">torch_logsumexp()</a></code> </p>
-        </td>
-        <td><p>Logsumexp</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_lstsq.html">torch_lstsq()</a></code> </p>
-        </td>
-        <td><p>Lstsq</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_lt.html">torch_lt()</a></code> </p>
-        </td>
-        <td><p>Lt</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_lu.html">torch_lu()</a></code> </p>
-        </td>
-        <td><p>LU</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_lu_solve.html">torch_lu_solve()</a></code> </p>
-        </td>
-        <td><p>Lu_solve</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_manual_seed.html">torch_manual_seed()</a></code> </p>
-        </td>
-        <td><p>Sets the seed for generating random numbers.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_masked_select.html">torch_masked_select()</a></code> </p>
-        </td>
-        <td><p>Masked_select</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_matmul.html">torch_matmul()</a></code> </p>
-        </td>
-        <td><p>Matmul</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_matrix_exp.html">torch_matrix_exp()</a></code> </p>
-        </td>
-        <td><p>Matrix_exp</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_matrix_power.html">torch_matrix_power()</a></code> </p>
-        </td>
-        <td><p>Matrix_power</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_matrix_rank.html">torch_matrix_rank()</a></code> </p>
-        </td>
-        <td><p>Matrix_rank</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_max.html">torch_max</a></code> </p>
-        </td>
-        <td><p>Max</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_maximum.html">torch_maximum()</a></code> </p>
-        </td>
-        <td><p>Maximum</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_mean.html">torch_mean()</a></code> </p>
-        </td>
-        <td><p>Mean</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_median.html">torch_median()</a></code> </p>
-        </td>
-        <td><p>Median</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_memory_format.html">torch_contiguous_format()</a></code> <code><a href="torch_memory_format.html">torch_preserve_format()</a></code> <code><a href="torch_memory_format.html">torch_channels_last_format()</a></code> </p>
-        </td>
-        <td><p>Memory format</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_meshgrid.html">torch_meshgrid()</a></code> </p>
-        </td>
-        <td><p>Meshgrid</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_min.html">torch_min</a></code> </p>
-        </td>
-        <td><p>Min</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_minimum.html">torch_minimum()</a></code> </p>
-        </td>
-        <td><p>Minimum</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_mm.html">torch_mm()</a></code> </p>
-        </td>
-        <td><p>Mm</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_mode.html">torch_mode()</a></code> </p>
-        </td>
-        <td><p>Mode</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_movedim.html">torch_movedim()</a></code> </p>
-        </td>
-        <td><p>Movedim</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_mul.html">torch_mul()</a></code> </p>
-        </td>
-        <td><p>Mul</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_multinomial.html">torch_multinomial()</a></code> </p>
-        </td>
-        <td><p>Multinomial</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_multiply.html">torch_multiply()</a></code> </p>
-        </td>
-        <td><p>Multiply</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_mv.html">torch_mv()</a></code> </p>
-        </td>
-        <td><p>Mv</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_mvlgamma.html">torch_mvlgamma()</a></code> </p>
-        </td>
-        <td><p>Mvlgamma</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_nanquantile.html">torch_nanquantile()</a></code> </p>
-        </td>
-        <td><p>Nanquantile</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_nansum.html">torch_nansum()</a></code> </p>
-        </td>
-        <td><p>Nansum</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_narrow.html">torch_narrow()</a></code> </p>
-        </td>
-        <td><p>Narrow</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_ne.html">torch_ne()</a></code> </p>
-        </td>
-        <td><p>Ne</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_neg.html">torch_neg()</a></code> </p>
-        </td>
-        <td><p>Neg</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_negative.html">torch_negative()</a></code> </p>
-        </td>
-        <td><p>Negative</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_nextafter.html">torch_nextafter()</a></code> </p>
-        </td>
-        <td><p>Nextafter</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_nonzero.html">torch_nonzero()</a></code> </p>
-        </td>
-        <td><p>Nonzero</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_norm.html">torch_norm()</a></code> </p>
-        </td>
-        <td><p>Norm</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_normal.html">torch_normal()</a></code> </p>
-        </td>
-        <td><p>Normal</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_not_equal.html">torch_not_equal()</a></code> </p>
-        </td>
-        <td><p>Not_equal</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_orgqr.html">torch_orgqr()</a></code> </p>
-        </td>
-        <td><p>Orgqr</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_ormqr.html">torch_ormqr()</a></code> </p>
-        </td>
-        <td><p>Ormqr</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_outer.html">torch_outer()</a></code> </p>
-        </td>
-        <td><p>Outer</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_pdist.html">torch_pdist()</a></code> </p>
-        </td>
-        <td><p>Pdist</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_pinverse.html">torch_pinverse()</a></code> </p>
-        </td>
-        <td><p>Pinverse</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_pixel_shuffle.html">torch_pixel_shuffle()</a></code> </p>
-        </td>
-        <td><p>Pixel_shuffle</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_poisson.html">torch_poisson()</a></code> </p>
-        </td>
-        <td><p>Poisson</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_polar.html">torch_polar()</a></code> </p>
-        </td>
-        <td><p>Polar</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_polygamma.html">torch_polygamma()</a></code> </p>
-        </td>
-        <td><p>Polygamma</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_pow.html">torch_pow()</a></code> </p>
-        </td>
-        <td><p>Pow</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_prod.html">torch_prod()</a></code> </p>
-        </td>
-        <td><p>Prod</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_promote_types.html">torch_promote_types()</a></code> </p>
-        </td>
-        <td><p>Promote_types</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_qr.html">torch_qr()</a></code> </p>
-        </td>
-        <td><p>Qr</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_quantile.html">torch_quantile()</a></code> </p>
-        </td>
-        <td><p>Quantile</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_quantize_per_channel.html">torch_quantize_per_channel()</a></code> </p>
-        </td>
-        <td><p>Quantize_per_channel</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_quantize_per_tensor.html">torch_quantize_per_tensor()</a></code> </p>
-        </td>
-        <td><p>Quantize_per_tensor</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_rad2deg.html">torch_rad2deg()</a></code> </p>
-        </td>
-        <td><p>Rad2deg</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_range.html">torch_range()</a></code> </p>
-        </td>
-        <td><p>Range</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_real.html">torch_real()</a></code> </p>
-        </td>
-        <td><p>Real</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_reciprocal.html">torch_reciprocal()</a></code> </p>
-        </td>
-        <td><p>Reciprocal</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_relu.html">torch_relu()</a></code> </p>
-        </td>
-        <td><p>Relu</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_relu_.html">torch_relu_()</a></code> </p>
-        </td>
-        <td><p>Relu_</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_remainder.html">torch_remainder()</a></code> </p>
-        </td>
-        <td><p>Remainder</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_renorm.html">torch_renorm()</a></code> </p>
-        </td>
-        <td><p>Renorm</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_repeat_interleave.html">torch_repeat_interleave()</a></code> </p>
-        </td>
-        <td><p>Repeat_interleave</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_reshape.html">torch_reshape()</a></code> </p>
-        </td>
-        <td><p>Reshape</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_result_type.html">torch_result_type()</a></code> </p>
-        </td>
-        <td><p>Result_type</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_roll.html">torch_roll()</a></code> </p>
-        </td>
-        <td><p>Roll</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_rot90.html">torch_rot90()</a></code> </p>
-        </td>
-        <td><p>Rot90</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_round.html">torch_round()</a></code> </p>
-        </td>
-        <td><p>Round</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_rrelu_.html">torch_rrelu_()</a></code> </p>
-        </td>
-        <td><p>Rrelu_</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_rsqrt.html">torch_rsqrt()</a></code> </p>
-        </td>
-        <td><p>Rsqrt</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_scalar_tensor.html">torch_scalar_tensor()</a></code> </p>
-        </td>
-        <td><p>Scalar tensor</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_searchsorted.html">torch_searchsorted()</a></code> </p>
-        </td>
-        <td><p>Searchsorted</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_selu.html">torch_selu()</a></code> </p>
-        </td>
-        <td><p>Selu</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_selu_.html">torch_selu_()</a></code> </p>
-        </td>
-        <td><p>Selu_</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_sgn.html">torch_sgn()</a></code> </p>
-        </td>
-        <td><p>Sgn</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_sigmoid.html">torch_sigmoid()</a></code> </p>
-        </td>
-        <td><p>Sigmoid</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_sign.html">torch_sign()</a></code> </p>
-        </td>
-        <td><p>Sign</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_signbit.html">torch_signbit()</a></code> </p>
-        </td>
-        <td><p>Signbit</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_sin.html">torch_sin()</a></code> </p>
-        </td>
-        <td><p>Sin</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_sinh.html">torch_sinh()</a></code> </p>
-        </td>
-        <td><p>Sinh</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_slogdet.html">torch_slogdet()</a></code> </p>
-        </td>
-        <td><p>Slogdet</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_solve.html">torch_solve()</a></code> </p>
-        </td>
-        <td><p>Solve</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_sort.html">torch_sort()</a></code> </p>
-        </td>
-        <td><p>Sort</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_sparse_coo_tensor.html">torch_sparse_coo_tensor()</a></code> </p>
-        </td>
-        <td><p>Sparse_coo_tensor</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_split.html">torch_split()</a></code> </p>
-        </td>
-        <td><p>Split</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_sqrt.html">torch_sqrt()</a></code> </p>
-        </td>
-        <td><p>Sqrt</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_square.html">torch_square()</a></code> </p>
-        </td>
-        <td><p>Square</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_squeeze.html">torch_squeeze()</a></code> </p>
-        </td>
-        <td><p>Squeeze</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_stack.html">torch_stack()</a></code> </p>
-        </td>
-        <td><p>Stack</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_std.html">torch_std()</a></code> </p>
-        </td>
-        <td><p>Std</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_std_mean.html">torch_std_mean()</a></code> </p>
-        </td>
-        <td><p>Std_mean</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_stft.html">torch_stft()</a></code> </p>
-        </td>
-        <td><p>Stft</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_sub.html">torch_sub()</a></code> </p>
-        </td>
-        <td><p>Sub</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_subtract.html">torch_subtract()</a></code> </p>
-        </td>
-        <td><p>Subtract</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_sum.html">torch_sum()</a></code> </p>
-        </td>
-        <td><p>Sum</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_svd.html">torch_svd()</a></code> </p>
-        </td>
-        <td><p>Svd</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_symeig.html">torch_symeig()</a></code> </p>
-        </td>
-        <td><p>Symeig</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_t.html">torch_t()</a></code> </p>
-        </td>
-        <td><p>T</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_take.html">torch_take()</a></code> </p>
-        </td>
-        <td><p>Take</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_tan.html">torch_tan()</a></code> </p>
-        </td>
-        <td><p>Tan</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_tanh.html">torch_tanh()</a></code> </p>
-        </td>
-        <td><p>Tanh</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_tensor.html">torch_tensor()</a></code> </p>
-        </td>
-        <td><p>Converts R objects to a torch tensor</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_tensordot.html">torch_tensordot()</a></code> </p>
-        </td>
-        <td><p>Tensordot</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_threshold_.html">torch_threshold_()</a></code> </p>
-        </td>
-        <td><p>Threshold_</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_topk.html">torch_topk()</a></code> </p>
-        </td>
-        <td><p>Topk</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_trace.html">torch_trace()</a></code> </p>
-        </td>
-        <td><p>Trace</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_transpose.html">torch_transpose()</a></code> </p>
-        </td>
-        <td><p>Transpose</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_trapz.html">torch_trapz()</a></code> </p>
-        </td>
-        <td><p>Trapz</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_triangular_solve.html">torch_triangular_solve()</a></code> </p>
-        </td>
-        <td><p>Triangular_solve</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_tril.html">torch_tril()</a></code> </p>
-        </td>
-        <td><p>Tril</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_tril_indices.html">torch_tril_indices()</a></code> </p>
-        </td>
-        <td><p>Tril_indices</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_triu.html">torch_triu()</a></code> </p>
-        </td>
-        <td><p>Triu</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_triu_indices.html">torch_triu_indices()</a></code> </p>
-        </td>
-        <td><p>Triu_indices</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_true_divide.html">torch_true_divide()</a></code> </p>
-        </td>
-        <td><p>TRUE_divide</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_trunc.html">torch_trunc()</a></code> </p>
-        </td>
-        <td><p>Trunc</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_unbind.html">torch_unbind()</a></code> </p>
-        </td>
-        <td><p>Unbind</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_unique_consecutive.html">torch_unique_consecutive()</a></code> </p>
-        </td>
-        <td><p>Unique_consecutive</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_unsafe_chunk.html">torch_unsafe_chunk()</a></code> </p>
-        </td>
-        <td><p>Unsafe_chunk</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_unsafe_split.html">torch_unsafe_split()</a></code> </p>
-        </td>
-        <td><p>Unsafe_split</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_unsqueeze.html">torch_unsqueeze()</a></code> </p>
-        </td>
-        <td><p>Unsqueeze</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_vander.html">torch_vander()</a></code> </p>
-        </td>
-        <td><p>Vander</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_var.html">torch_var()</a></code> </p>
-        </td>
-        <td><p>Var</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_var_mean.html">torch_var_mean()</a></code> </p>
-        </td>
-        <td><p>Var_mean</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_vdot.html">torch_vdot()</a></code> </p>
-        </td>
-        <td><p>Vdot</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_view_as_complex.html">torch_view_as_complex()</a></code> </p>
-        </td>
-        <td><p>View_as_complex</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_view_as_real.html">torch_view_as_real()</a></code> </p>
-        </td>
-        <td><p>View_as_real</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_vstack.html">torch_vstack()</a></code> </p>
-        </td>
-        <td><p>Vstack</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="torch_where.html">torch_where()</a></code> </p>
-        </td>
-        <td><p>Where</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="broadcast_all.html">broadcast_all()</a></code> </p>
-        </td>
-        <td><p>Given a list of values (possibly containing numbers), returns a list where each
-value is broadcasted based on the following rules:</p></td>
-      </tr>
-    </tbody><tbody>
-      <tr>
-        <th colspan="2">
-          <h2 id="section-neural-network-modules" class="hasAnchor"><a href="#section-neural-network-modules" class="anchor"></a>Neural network modules</h2>
-          <p class="section-desc"></p>
-        </th>
-      </tr>
       
       
-    </tbody><tbody>
+
+      <dl><dt>
+          
+          <code><a href="nnf_adaptive_avg_pool1d.html">nnf_adaptive_avg_pool1d()</a></code> 
+        </dt>
+        <dd>Adaptive_avg_pool1d</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_adaptive_avg_pool2d.html">nnf_adaptive_avg_pool2d()</a></code> 
+        </dt>
+        <dd>Adaptive_avg_pool2d</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_adaptive_avg_pool3d.html">nnf_adaptive_avg_pool3d()</a></code> 
+        </dt>
+        <dd>Adaptive_avg_pool3d</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_adaptive_max_pool1d.html">nnf_adaptive_max_pool1d()</a></code> 
+        </dt>
+        <dd>Adaptive_max_pool1d</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_adaptive_max_pool2d.html">nnf_adaptive_max_pool2d()</a></code> 
+        </dt>
+        <dd>Adaptive_max_pool2d</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_adaptive_max_pool3d.html">nnf_adaptive_max_pool3d()</a></code> 
+        </dt>
+        <dd>Adaptive_max_pool3d</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_affine_grid.html">nnf_affine_grid()</a></code> 
+        </dt>
+        <dd>Affine_grid</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_alpha_dropout.html">nnf_alpha_dropout()</a></code> 
+        </dt>
+        <dd>Alpha_dropout</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_avg_pool1d.html">nnf_avg_pool1d()</a></code> 
+        </dt>
+        <dd>Avg_pool1d</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_avg_pool2d.html">nnf_avg_pool2d()</a></code> 
+        </dt>
+        <dd>Avg_pool2d</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_avg_pool3d.html">nnf_avg_pool3d()</a></code> 
+        </dt>
+        <dd>Avg_pool3d</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_batch_norm.html">nnf_batch_norm()</a></code> 
+        </dt>
+        <dd>Batch_norm</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_bilinear.html">nnf_bilinear()</a></code> 
+        </dt>
+        <dd>Bilinear</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_binary_cross_entropy.html">nnf_binary_cross_entropy()</a></code> 
+        </dt>
+        <dd>Binary_cross_entropy</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_binary_cross_entropy_with_logits.html">nnf_binary_cross_entropy_with_logits()</a></code> 
+        </dt>
+        <dd>Binary_cross_entropy_with_logits</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_celu.html">nnf_celu()</a></code> <code><a href="nnf_celu.html">nnf_celu_()</a></code> 
+        </dt>
+        <dd>Celu</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_contrib_sparsemax.html">nnf_contrib_sparsemax()</a></code> 
+        </dt>
+        <dd>Sparsemax</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_conv1d.html">nnf_conv1d()</a></code> 
+        </dt>
+        <dd>Conv1d</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_conv2d.html">nnf_conv2d()</a></code> 
+        </dt>
+        <dd>Conv2d</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_conv3d.html">nnf_conv3d()</a></code> 
+        </dt>
+        <dd>Conv3d</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_conv_tbc.html">nnf_conv_tbc()</a></code> 
+        </dt>
+        <dd>Conv_tbc</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_conv_transpose1d.html">nnf_conv_transpose1d()</a></code> 
+        </dt>
+        <dd>Conv_transpose1d</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_conv_transpose2d.html">nnf_conv_transpose2d()</a></code> 
+        </dt>
+        <dd>Conv_transpose2d</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_conv_transpose3d.html">nnf_conv_transpose3d()</a></code> 
+        </dt>
+        <dd>Conv_transpose3d</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_cosine_embedding_loss.html">nnf_cosine_embedding_loss()</a></code> 
+        </dt>
+        <dd>Cosine_embedding_loss</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_cosine_similarity.html">nnf_cosine_similarity()</a></code> 
+        </dt>
+        <dd>Cosine_similarity</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_cross_entropy.html">nnf_cross_entropy()</a></code> 
+        </dt>
+        <dd>Cross_entropy</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_ctc_loss.html">nnf_ctc_loss()</a></code> 
+        </dt>
+        <dd>Ctc_loss</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_dropout.html">nnf_dropout()</a></code> 
+        </dt>
+        <dd>Dropout</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_dropout2d.html">nnf_dropout2d()</a></code> 
+        </dt>
+        <dd>Dropout2d</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_dropout3d.html">nnf_dropout3d()</a></code> 
+        </dt>
+        <dd>Dropout3d</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_elu.html">nnf_elu()</a></code> <code><a href="nnf_elu.html">nnf_elu_()</a></code> 
+        </dt>
+        <dd>Elu</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_embedding.html">nnf_embedding()</a></code> 
+        </dt>
+        <dd>Embedding</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_embedding_bag.html">nnf_embedding_bag()</a></code> 
+        </dt>
+        <dd>Embedding_bag</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_fold.html">nnf_fold()</a></code> 
+        </dt>
+        <dd>Fold</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_fractional_max_pool2d.html">nnf_fractional_max_pool2d()</a></code> 
+        </dt>
+        <dd>Fractional_max_pool2d</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_fractional_max_pool3d.html">nnf_fractional_max_pool3d()</a></code> 
+        </dt>
+        <dd>Fractional_max_pool3d</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_gelu.html">nnf_gelu()</a></code> 
+        </dt>
+        <dd>Gelu</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_glu.html">nnf_glu()</a></code> 
+        </dt>
+        <dd>Glu</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_grid_sample.html">nnf_grid_sample()</a></code> 
+        </dt>
+        <dd>Grid_sample</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_group_norm.html">nnf_group_norm()</a></code> 
+        </dt>
+        <dd>Group_norm</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_gumbel_softmax.html">nnf_gumbel_softmax()</a></code> 
+        </dt>
+        <dd>Gumbel_softmax</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_hardshrink.html">nnf_hardshrink()</a></code> 
+        </dt>
+        <dd>Hardshrink</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_hardsigmoid.html">nnf_hardsigmoid()</a></code> 
+        </dt>
+        <dd>Hardsigmoid</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_hardswish.html">nnf_hardswish()</a></code> 
+        </dt>
+        <dd>Hardswish</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_hardtanh.html">nnf_hardtanh()</a></code> <code><a href="nnf_hardtanh.html">nnf_hardtanh_()</a></code> 
+        </dt>
+        <dd>Hardtanh</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_hinge_embedding_loss.html">nnf_hinge_embedding_loss()</a></code> 
+        </dt>
+        <dd>Hinge_embedding_loss</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_instance_norm.html">nnf_instance_norm()</a></code> 
+        </dt>
+        <dd>Instance_norm</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_interpolate.html">nnf_interpolate()</a></code> 
+        </dt>
+        <dd>Interpolate</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_kl_div.html">nnf_kl_div()</a></code> 
+        </dt>
+        <dd>Kl_div</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_l1_loss.html">nnf_l1_loss()</a></code> 
+        </dt>
+        <dd>L1_loss</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_layer_norm.html">nnf_layer_norm()</a></code> 
+        </dt>
+        <dd>Layer_norm</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_leaky_relu.html">nnf_leaky_relu()</a></code> 
+        </dt>
+        <dd>Leaky_relu</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_linear.html">nnf_linear()</a></code> 
+        </dt>
+        <dd>Linear</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_local_response_norm.html">nnf_local_response_norm()</a></code> 
+        </dt>
+        <dd>Local_response_norm</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_log_softmax.html">nnf_log_softmax()</a></code> 
+        </dt>
+        <dd>Log_softmax</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_logsigmoid.html">nnf_logsigmoid()</a></code> 
+        </dt>
+        <dd>Logsigmoid</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_lp_pool1d.html">nnf_lp_pool1d()</a></code> 
+        </dt>
+        <dd>Lp_pool1d</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_lp_pool2d.html">nnf_lp_pool2d()</a></code> 
+        </dt>
+        <dd>Lp_pool2d</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_margin_ranking_loss.html">nnf_margin_ranking_loss()</a></code> 
+        </dt>
+        <dd>Margin_ranking_loss</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_max_pool1d.html">nnf_max_pool1d()</a></code> 
+        </dt>
+        <dd>Max_pool1d</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_max_pool2d.html">nnf_max_pool2d()</a></code> 
+        </dt>
+        <dd>Max_pool2d</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_max_pool3d.html">nnf_max_pool3d()</a></code> 
+        </dt>
+        <dd>Max_pool3d</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_max_unpool1d.html">nnf_max_unpool1d()</a></code> 
+        </dt>
+        <dd>Max_unpool1d</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_max_unpool2d.html">nnf_max_unpool2d()</a></code> 
+        </dt>
+        <dd>Max_unpool2d</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_max_unpool3d.html">nnf_max_unpool3d()</a></code> 
+        </dt>
+        <dd>Max_unpool3d</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_mse_loss.html">nnf_mse_loss()</a></code> 
+        </dt>
+        <dd>Mse_loss</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_multi_head_attention_forward.html">nnf_multi_head_attention_forward()</a></code> 
+        </dt>
+        <dd>Multi head attention forward</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_multi_margin_loss.html">nnf_multi_margin_loss()</a></code> 
+        </dt>
+        <dd>Multi_margin_loss</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_multilabel_margin_loss.html">nnf_multilabel_margin_loss()</a></code> 
+        </dt>
+        <dd>Multilabel_margin_loss</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_multilabel_soft_margin_loss.html">nnf_multilabel_soft_margin_loss()</a></code> 
+        </dt>
+        <dd>Multilabel_soft_margin_loss</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_nll_loss.html">nnf_nll_loss()</a></code> 
+        </dt>
+        <dd>Nll_loss</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_normalize.html">nnf_normalize()</a></code> 
+        </dt>
+        <dd>Normalize</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_one_hot.html">nnf_one_hot()</a></code> 
+        </dt>
+        <dd>One_hot</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_pad.html">nnf_pad()</a></code> 
+        </dt>
+        <dd>Pad</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_pairwise_distance.html">nnf_pairwise_distance()</a></code> 
+        </dt>
+        <dd>Pairwise_distance</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_pdist.html">nnf_pdist()</a></code> 
+        </dt>
+        <dd>Pdist</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_pixel_shuffle.html">nnf_pixel_shuffle()</a></code> 
+        </dt>
+        <dd>Pixel_shuffle</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_poisson_nll_loss.html">nnf_poisson_nll_loss()</a></code> 
+        </dt>
+        <dd>Poisson_nll_loss</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_prelu.html">nnf_prelu()</a></code> 
+        </dt>
+        <dd>Prelu</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_relu.html">nnf_relu()</a></code> <code><a href="nnf_relu.html">nnf_relu_()</a></code> 
+        </dt>
+        <dd>Relu</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_relu6.html">nnf_relu6()</a></code> 
+        </dt>
+        <dd>Relu6</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_rrelu.html">nnf_rrelu()</a></code> <code><a href="nnf_rrelu.html">nnf_rrelu_()</a></code> 
+        </dt>
+        <dd>Rrelu</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_selu.html">nnf_selu()</a></code> <code><a href="nnf_selu.html">nnf_selu_()</a></code> 
+        </dt>
+        <dd>Selu</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_sigmoid.html">nnf_sigmoid()</a></code> 
+        </dt>
+        <dd>Sigmoid</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_smooth_l1_loss.html">nnf_smooth_l1_loss()</a></code> 
+        </dt>
+        <dd>Smooth_l1_loss</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_soft_margin_loss.html">nnf_soft_margin_loss()</a></code> 
+        </dt>
+        <dd>Soft_margin_loss</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_softmax.html">nnf_softmax()</a></code> 
+        </dt>
+        <dd>Softmax</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_softmin.html">nnf_softmin()</a></code> 
+        </dt>
+        <dd>Softmin</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_softplus.html">nnf_softplus()</a></code> 
+        </dt>
+        <dd>Softplus</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_softshrink.html">nnf_softshrink()</a></code> 
+        </dt>
+        <dd>Softshrink</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_softsign.html">nnf_softsign()</a></code> 
+        </dt>
+        <dd>Softsign</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_tanhshrink.html">nnf_tanhshrink()</a></code> 
+        </dt>
+        <dd>Tanhshrink</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_threshold.html">nnf_threshold()</a></code> <code><a href="nnf_threshold.html">nnf_threshold_()</a></code> 
+        </dt>
+        <dd>Threshold</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_triplet_margin_loss.html">nnf_triplet_margin_loss()</a></code> 
+        </dt>
+        <dd>Triplet_margin_loss</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_triplet_margin_with_distance_loss.html">nnf_triplet_margin_with_distance_loss()</a></code> 
+        </dt>
+        <dd>Triplet margin with distance loss</dd>
+      </dl><dl><dt>
+          
+          <code><a href="nnf_unfold.html">nnf_unfold()</a></code> 
+        </dt>
+        <dd>Unfold</dd>
+      </dl></div><div class="section level2">
+      <h2 id="optimizers">Optimizers<a class="anchor" aria-label="anchor" href="#optimizers"></a></h2>
       
       
-      <tr>
-        
-        <td>
-          <p><code><a href="nn_adaptive_avg_pool1d.html">nn_adaptive_avg_pool1d()</a></code> </p>
-        </td>
-        <td><p>Applies a 1D adaptive average pooling over an input signal composed of several input planes.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_adaptive_avg_pool2d.html">nn_adaptive_avg_pool2d()</a></code> </p>
-        </td>
-        <td><p>Applies a 2D adaptive average pooling over an input signal composed of several input planes.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_adaptive_avg_pool3d.html">nn_adaptive_avg_pool3d()</a></code> </p>
-        </td>
-        <td><p>Applies a 3D adaptive average pooling over an input signal composed of several input planes.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_adaptive_log_softmax_with_loss.html">nn_adaptive_log_softmax_with_loss()</a></code> </p>
-        </td>
-        <td><p>AdaptiveLogSoftmaxWithLoss module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_adaptive_max_pool1d.html">nn_adaptive_max_pool1d()</a></code> </p>
-        </td>
-        <td><p>Applies a 1D adaptive max pooling over an input signal composed of several input planes.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_adaptive_max_pool2d.html">nn_adaptive_max_pool2d()</a></code> </p>
-        </td>
-        <td><p>Applies a 2D adaptive max pooling over an input signal composed of several input planes.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_adaptive_max_pool3d.html">nn_adaptive_max_pool3d()</a></code> </p>
-        </td>
-        <td><p>Applies a 3D adaptive max pooling over an input signal composed of several input planes.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_avg_pool1d.html">nn_avg_pool1d()</a></code> </p>
-        </td>
-        <td><p>Applies a 1D average pooling over an input signal composed of several
-input planes.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_avg_pool2d.html">nn_avg_pool2d()</a></code> </p>
-        </td>
-        <td><p>Applies a 2D average pooling over an input signal composed of several input
-planes.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_avg_pool3d.html">nn_avg_pool3d()</a></code> </p>
-        </td>
-        <td><p>Applies a 3D average pooling over an input signal composed of several input
-planes.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_batch_norm1d.html">nn_batch_norm1d()</a></code> </p>
-        </td>
-        <td><p>BatchNorm1D module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_batch_norm2d.html">nn_batch_norm2d()</a></code> </p>
-        </td>
-        <td><p>BatchNorm2D</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_batch_norm3d.html">nn_batch_norm3d()</a></code> </p>
-        </td>
-        <td><p>BatchNorm3D</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_bce_loss.html">nn_bce_loss()</a></code> </p>
-        </td>
-        <td><p>Binary cross entropy loss</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_bce_with_logits_loss.html">nn_bce_with_logits_loss()</a></code> </p>
-        </td>
-        <td><p>BCE with logits loss</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_bilinear.html">nn_bilinear()</a></code> </p>
-        </td>
-        <td><p>Bilinear module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_buffer.html">nn_buffer()</a></code> </p>
-        </td>
-        <td><p>Creates a nn_buffer</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_celu.html">nn_celu()</a></code> </p>
-        </td>
-        <td><p>CELU module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_contrib_sparsemax.html">nn_contrib_sparsemax()</a></code> </p>
-        </td>
-        <td><p>Sparsemax activation</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_conv1d.html">nn_conv1d()</a></code> </p>
-        </td>
-        <td><p>Conv1D module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_conv2d.html">nn_conv2d()</a></code> </p>
-        </td>
-        <td><p>Conv2D module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_conv3d.html">nn_conv3d()</a></code> </p>
-        </td>
-        <td><p>Conv3D module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_conv_transpose1d.html">nn_conv_transpose1d()</a></code> </p>
-        </td>
-        <td><p>ConvTranspose1D</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_conv_transpose2d.html">nn_conv_transpose2d()</a></code> </p>
-        </td>
-        <td><p>ConvTranpose2D module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_conv_transpose3d.html">nn_conv_transpose3d()</a></code> </p>
-        </td>
-        <td><p>ConvTranpose3D module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_cosine_embedding_loss.html">nn_cosine_embedding_loss()</a></code> </p>
-        </td>
-        <td><p>Cosine embedding loss</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_cross_entropy_loss.html">nn_cross_entropy_loss()</a></code> </p>
-        </td>
-        <td><p>CrossEntropyLoss module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_ctc_loss.html">nn_ctc_loss()</a></code> </p>
-        </td>
-        <td><p>The Connectionist Temporal Classification loss.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_dropout.html">nn_dropout()</a></code> </p>
-        </td>
-        <td><p>Dropout module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_dropout2d.html">nn_dropout2d()</a></code> </p>
-        </td>
-        <td><p>Dropout2D module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_dropout3d.html">nn_dropout3d()</a></code> </p>
-        </td>
-        <td><p>Dropout3D module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_elu.html">nn_elu()</a></code> </p>
-        </td>
-        <td><p>ELU module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_embedding.html">nn_embedding()</a></code> </p>
-        </td>
-        <td><p>Embedding module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_fractional_max_pool2d.html">nn_fractional_max_pool2d()</a></code> </p>
-        </td>
-        <td><p>Applies a 2D fractional max pooling over an input signal composed of several input planes.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_fractional_max_pool3d.html">nn_fractional_max_pool3d()</a></code> </p>
-        </td>
-        <td><p>Applies a 3D fractional max pooling over an input signal composed of several input planes.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_gelu.html">nn_gelu()</a></code> </p>
-        </td>
-        <td><p>GELU module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_glu.html">nn_glu()</a></code> </p>
-        </td>
-        <td><p>GLU module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_group_norm.html">nn_group_norm()</a></code> </p>
-        </td>
-        <td><p>Group normalization</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_gru.html">nn_gru()</a></code> </p>
-        </td>
-        <td><p>Applies a multi-layer gated recurrent unit (GRU) RNN to an input sequence.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_hardshrink.html">nn_hardshrink()</a></code> </p>
-        </td>
-        <td><p>Hardshwink module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_hardsigmoid.html">nn_hardsigmoid()</a></code> </p>
-        </td>
-        <td><p>Hardsigmoid module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_hardswish.html">nn_hardswish()</a></code> </p>
-        </td>
-        <td><p>Hardswish module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_hardtanh.html">nn_hardtanh()</a></code> </p>
-        </td>
-        <td><p>Hardtanh module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_hinge_embedding_loss.html">nn_hinge_embedding_loss()</a></code> </p>
-        </td>
-        <td><p>Hinge embedding loss</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_identity.html">nn_identity()</a></code> </p>
-        </td>
-        <td><p>Identity module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_init_calculate_gain.html">nn_init_calculate_gain()</a></code> </p>
-        </td>
-        <td><p>Calculate gain</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_init_constant_.html">nn_init_constant_()</a></code> </p>
-        </td>
-        <td><p>Constant initialization</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_init_dirac_.html">nn_init_dirac_()</a></code> </p>
-        </td>
-        <td><p>Dirac initialization</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_init_eye_.html">nn_init_eye_()</a></code> </p>
-        </td>
-        <td><p>Eye initialization</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_init_kaiming_normal_.html">nn_init_kaiming_normal_()</a></code> </p>
-        </td>
-        <td><p>Kaiming normal initialization</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_init_kaiming_uniform_.html">nn_init_kaiming_uniform_()</a></code> </p>
-        </td>
-        <td><p>Kaiming uniform initialization</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_init_normal_.html">nn_init_normal_()</a></code> </p>
-        </td>
-        <td><p>Normal initialization</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_init_ones_.html">nn_init_ones_()</a></code> </p>
-        </td>
-        <td><p>Ones initialization</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_init_orthogonal_.html">nn_init_orthogonal_()</a></code> </p>
-        </td>
-        <td><p>Orthogonal initialization</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_init_sparse_.html">nn_init_sparse_()</a></code> </p>
-        </td>
-        <td><p>Sparse initialization</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_init_trunc_normal_.html">nn_init_trunc_normal_()</a></code> </p>
-        </td>
-        <td><p>Truncated normal initialization</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_init_uniform_.html">nn_init_uniform_()</a></code> </p>
-        </td>
-        <td><p>Uniform initialization</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_init_xavier_normal_.html">nn_init_xavier_normal_()</a></code> </p>
-        </td>
-        <td><p>Xavier normal initialization</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_init_xavier_uniform_.html">nn_init_xavier_uniform_()</a></code> </p>
-        </td>
-        <td><p>Xavier uniform initialization</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_init_zeros_.html">nn_init_zeros_()</a></code> </p>
-        </td>
-        <td><p>Zeros initialization</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_kl_div_loss.html">nn_kl_div_loss()</a></code> </p>
-        </td>
-        <td><p>Kullback-Leibler divergence loss</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_l1_loss.html">nn_l1_loss()</a></code> </p>
-        </td>
-        <td><p>L1 loss</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_layer_norm.html">nn_layer_norm()</a></code> </p>
-        </td>
-        <td><p>Layer normalization</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_leaky_relu.html">nn_leaky_relu()</a></code> </p>
-        </td>
-        <td><p>LeakyReLU module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_linear.html">nn_linear()</a></code> </p>
-        </td>
-        <td><p>Linear module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_log_sigmoid.html">nn_log_sigmoid()</a></code> </p>
-        </td>
-        <td><p>LogSigmoid module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_log_softmax.html">nn_log_softmax()</a></code> </p>
-        </td>
-        <td><p>LogSoftmax module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_lp_pool1d.html">nn_lp_pool1d()</a></code> </p>
-        </td>
-        <td><p>Applies a 1D power-average pooling over an input signal composed of several input
-planes.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_lp_pool2d.html">nn_lp_pool2d()</a></code> </p>
-        </td>
-        <td><p>Applies a 2D power-average pooling over an input signal composed of several input
-planes.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_lstm.html">nn_lstm()</a></code> </p>
-        </td>
-        <td><p>Applies a multi-layer long short-term memory (LSTM) RNN to an input
-sequence.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_margin_ranking_loss.html">nn_margin_ranking_loss()</a></code> </p>
-        </td>
-        <td><p>Margin ranking loss</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_max_pool1d.html">nn_max_pool1d()</a></code> </p>
-        </td>
-        <td><p>MaxPool1D module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_max_pool2d.html">nn_max_pool2d()</a></code> </p>
-        </td>
-        <td><p>MaxPool2D module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_max_pool3d.html">nn_max_pool3d()</a></code> </p>
-        </td>
-        <td><p>Applies a 3D max pooling over an input signal composed of several input
-planes.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_max_unpool1d.html">nn_max_unpool1d()</a></code> </p>
-        </td>
-        <td><p>Computes a partial inverse of <code>MaxPool1d</code>.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_max_unpool2d.html">nn_max_unpool2d()</a></code> </p>
-        </td>
-        <td><p>Computes a partial inverse of <code>MaxPool2d</code>.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_max_unpool3d.html">nn_max_unpool3d()</a></code> </p>
-        </td>
-        <td><p>Computes a partial inverse of <code>MaxPool3d</code>.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_module.html">nn_module()</a></code> </p>
-        </td>
-        <td><p>Base class for all neural network modules.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_module_list.html">nn_module_list()</a></code> </p>
-        </td>
-        <td><p>Holds submodules in a list.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_mse_loss.html">nn_mse_loss()</a></code> </p>
-        </td>
-        <td><p>MSE loss</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_multi_margin_loss.html">nn_multi_margin_loss()</a></code> </p>
-        </td>
-        <td><p>Multi margin loss</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_multihead_attention.html">nn_multihead_attention()</a></code> </p>
-        </td>
-        <td><p>MultiHead attention</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_multilabel_margin_loss.html">nn_multilabel_margin_loss()</a></code> </p>
-        </td>
-        <td><p>Multilabel margin loss</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_multilabel_soft_margin_loss.html">nn_multilabel_soft_margin_loss()</a></code> </p>
-        </td>
-        <td><p>Multi label soft margin loss</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_nll_loss.html">nn_nll_loss()</a></code> </p>
-        </td>
-        <td><p>Nll loss</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_pairwise_distance.html">nn_pairwise_distance()</a></code> </p>
-        </td>
-        <td><p>Pairwise distance</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_parameter.html">nn_parameter()</a></code> </p>
-        </td>
-        <td><p>Creates an <code>nn_parameter</code></p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_poisson_nll_loss.html">nn_poisson_nll_loss()</a></code> </p>
-        </td>
-        <td><p>Poisson NLL loss</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_prelu.html">nn_prelu()</a></code> </p>
-        </td>
-        <td><p>PReLU module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_relu.html">nn_relu()</a></code> </p>
-        </td>
-        <td><p>ReLU module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_relu6.html">nn_relu6()</a></code> </p>
-        </td>
-        <td><p>ReLu6 module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_rnn.html">nn_rnn()</a></code> </p>
-        </td>
-        <td><p>RNN module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_rrelu.html">nn_rrelu()</a></code> </p>
-        </td>
-        <td><p>RReLU module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_selu.html">nn_selu()</a></code> </p>
-        </td>
-        <td><p>SELU module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_sequential.html">nn_sequential()</a></code> </p>
-        </td>
-        <td><p>A sequential container</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_sigmoid.html">nn_sigmoid()</a></code> </p>
-        </td>
-        <td><p>Sigmoid module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_smooth_l1_loss.html">nn_smooth_l1_loss()</a></code> </p>
-        </td>
-        <td><p>Smooth L1 loss</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_soft_margin_loss.html">nn_soft_margin_loss()</a></code> </p>
-        </td>
-        <td><p>Soft margin loss</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_softmax.html">nn_softmax()</a></code> </p>
-        </td>
-        <td><p>Softmax module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_softmax2d.html">nn_softmax2d()</a></code> </p>
-        </td>
-        <td><p>Softmax2d module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_softmin.html">nn_softmin()</a></code> </p>
-        </td>
-        <td><p>Softmin</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_softplus.html">nn_softplus()</a></code> </p>
-        </td>
-        <td><p>Softplus module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_softshrink.html">nn_softshrink()</a></code> </p>
-        </td>
-        <td><p>Softshrink module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_softsign.html">nn_softsign()</a></code> </p>
-        </td>
-        <td><p>Softsign module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_tanh.html">nn_tanh()</a></code> </p>
-        </td>
-        <td><p>Tanh module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_tanhshrink.html">nn_tanhshrink()</a></code> </p>
-        </td>
-        <td><p>Tanhshrink module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_threshold.html">nn_threshold()</a></code> </p>
-        </td>
-        <td><p>Threshoold module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_triplet_margin_loss.html">nn_triplet_margin_loss()</a></code> </p>
-        </td>
-        <td><p>Triplet margin loss</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_triplet_margin_with_distance_loss.html">nn_triplet_margin_with_distance_loss()</a></code> </p>
-        </td>
-        <td><p>Triplet margin with distance loss</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_utils_clip_grad_norm_.html">nn_utils_clip_grad_norm_()</a></code> </p>
-        </td>
-        <td><p>Clips gradient norm of an iterable of parameters.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_utils_clip_grad_value_.html">nn_utils_clip_grad_value_()</a></code> </p>
-        </td>
-        <td><p>Clips gradient of an iterable of parameters at specified value.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_utils_rnn_pack_padded_sequence.html">nn_utils_rnn_pack_padded_sequence()</a></code> </p>
-        </td>
-        <td><p>Packs a Tensor containing padded sequences of variable length.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_utils_rnn_pack_sequence.html">nn_utils_rnn_pack_sequence()</a></code> </p>
-        </td>
-        <td><p>Packs a list of variable length Tensors</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_utils_rnn_pad_packed_sequence.html">nn_utils_rnn_pad_packed_sequence()</a></code> </p>
-        </td>
-        <td><p>Pads a packed batch of variable length sequences.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nn_utils_rnn_pad_sequence.html">nn_utils_rnn_pad_sequence()</a></code> </p>
-        </td>
-        <td><p>Pad a list of variable length Tensors with <code>padding_value</code></p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="is_nn_module.html">is_nn_module()</a></code> </p>
-        </td>
-        <td><p>Checks if the object is an nn_module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="is_nn_parameter.html">is_nn_parameter()</a></code> </p>
-        </td>
-        <td><p>Checks if an object is a nn_parameter</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="is_nn_buffer.html">is_nn_buffer()</a></code> </p>
-        </td>
-        <td><p>Checks if the object is a nn_buffer</p></td>
-      </tr>
-    </tbody><tbody>
-      <tr>
-        <th colspan="2">
-          <h2 id="section-neural-networks-functional-module" class="hasAnchor"><a href="#section-neural-networks-functional-module" class="anchor"></a>Neural networks functional module</h2>
-          <p class="section-desc"></p>
-        </th>
-      </tr>
+
       
+    </div><div id="" class="section level2">
       
-    </tbody><tbody>
       
       
-      <tr>
-        
-        <td>
-          <p><code><a href="nnf_adaptive_avg_pool1d.html">nnf_adaptive_avg_pool1d()</a></code> </p>
-        </td>
-        <td><p>Adaptive_avg_pool1d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_adaptive_avg_pool2d.html">nnf_adaptive_avg_pool2d()</a></code> </p>
-        </td>
-        <td><p>Adaptive_avg_pool2d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_adaptive_avg_pool3d.html">nnf_adaptive_avg_pool3d()</a></code> </p>
-        </td>
-        <td><p>Adaptive_avg_pool3d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_adaptive_max_pool1d.html">nnf_adaptive_max_pool1d()</a></code> </p>
-        </td>
-        <td><p>Adaptive_max_pool1d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_adaptive_max_pool2d.html">nnf_adaptive_max_pool2d()</a></code> </p>
-        </td>
-        <td><p>Adaptive_max_pool2d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_adaptive_max_pool3d.html">nnf_adaptive_max_pool3d()</a></code> </p>
-        </td>
-        <td><p>Adaptive_max_pool3d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_affine_grid.html">nnf_affine_grid()</a></code> </p>
-        </td>
-        <td><p>Affine_grid</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_alpha_dropout.html">nnf_alpha_dropout()</a></code> </p>
-        </td>
-        <td><p>Alpha_dropout</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_avg_pool1d.html">nnf_avg_pool1d()</a></code> </p>
-        </td>
-        <td><p>Avg_pool1d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_avg_pool2d.html">nnf_avg_pool2d()</a></code> </p>
-        </td>
-        <td><p>Avg_pool2d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_avg_pool3d.html">nnf_avg_pool3d()</a></code> </p>
-        </td>
-        <td><p>Avg_pool3d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_batch_norm.html">nnf_batch_norm()</a></code> </p>
-        </td>
-        <td><p>Batch_norm</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_bilinear.html">nnf_bilinear()</a></code> </p>
-        </td>
-        <td><p>Bilinear</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_binary_cross_entropy.html">nnf_binary_cross_entropy()</a></code> </p>
-        </td>
-        <td><p>Binary_cross_entropy</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_binary_cross_entropy_with_logits.html">nnf_binary_cross_entropy_with_logits()</a></code> </p>
-        </td>
-        <td><p>Binary_cross_entropy_with_logits</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_celu.html">nnf_celu()</a></code> <code><a href="nnf_celu.html">nnf_celu_()</a></code> </p>
-        </td>
-        <td><p>Celu</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_contrib_sparsemax.html">nnf_contrib_sparsemax()</a></code> </p>
-        </td>
-        <td><p>Sparsemax</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_conv1d.html">nnf_conv1d()</a></code> </p>
-        </td>
-        <td><p>Conv1d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_conv2d.html">nnf_conv2d()</a></code> </p>
-        </td>
-        <td><p>Conv2d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_conv3d.html">nnf_conv3d()</a></code> </p>
-        </td>
-        <td><p>Conv3d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_conv_tbc.html">nnf_conv_tbc()</a></code> </p>
-        </td>
-        <td><p>Conv_tbc</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_conv_transpose1d.html">nnf_conv_transpose1d()</a></code> </p>
-        </td>
-        <td><p>Conv_transpose1d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_conv_transpose2d.html">nnf_conv_transpose2d()</a></code> </p>
-        </td>
-        <td><p>Conv_transpose2d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_conv_transpose3d.html">nnf_conv_transpose3d()</a></code> </p>
-        </td>
-        <td><p>Conv_transpose3d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_cosine_embedding_loss.html">nnf_cosine_embedding_loss()</a></code> </p>
-        </td>
-        <td><p>Cosine_embedding_loss</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_cosine_similarity.html">nnf_cosine_similarity()</a></code> </p>
-        </td>
-        <td><p>Cosine_similarity</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_cross_entropy.html">nnf_cross_entropy()</a></code> </p>
-        </td>
-        <td><p>Cross_entropy</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_ctc_loss.html">nnf_ctc_loss()</a></code> </p>
-        </td>
-        <td><p>Ctc_loss</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_dropout.html">nnf_dropout()</a></code> </p>
-        </td>
-        <td><p>Dropout</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_dropout2d.html">nnf_dropout2d()</a></code> </p>
-        </td>
-        <td><p>Dropout2d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_dropout3d.html">nnf_dropout3d()</a></code> </p>
-        </td>
-        <td><p>Dropout3d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_elu.html">nnf_elu()</a></code> <code><a href="nnf_elu.html">nnf_elu_()</a></code> </p>
-        </td>
-        <td><p>Elu</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_embedding.html">nnf_embedding()</a></code> </p>
-        </td>
-        <td><p>Embedding</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_embedding_bag.html">nnf_embedding_bag()</a></code> </p>
-        </td>
-        <td><p>Embedding_bag</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_fold.html">nnf_fold()</a></code> </p>
-        </td>
-        <td><p>Fold</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_fractional_max_pool2d.html">nnf_fractional_max_pool2d()</a></code> </p>
-        </td>
-        <td><p>Fractional_max_pool2d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_fractional_max_pool3d.html">nnf_fractional_max_pool3d()</a></code> </p>
-        </td>
-        <td><p>Fractional_max_pool3d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_gelu.html">nnf_gelu()</a></code> </p>
-        </td>
-        <td><p>Gelu</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_glu.html">nnf_glu()</a></code> </p>
-        </td>
-        <td><p>Glu</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_grid_sample.html">nnf_grid_sample()</a></code> </p>
-        </td>
-        <td><p>Grid_sample</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_group_norm.html">nnf_group_norm()</a></code> </p>
-        </td>
-        <td><p>Group_norm</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_gumbel_softmax.html">nnf_gumbel_softmax()</a></code> </p>
-        </td>
-        <td><p>Gumbel_softmax</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_hardshrink.html">nnf_hardshrink()</a></code> </p>
-        </td>
-        <td><p>Hardshrink</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_hardsigmoid.html">nnf_hardsigmoid()</a></code> </p>
-        </td>
-        <td><p>Hardsigmoid</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_hardswish.html">nnf_hardswish()</a></code> </p>
-        </td>
-        <td><p>Hardswish</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_hardtanh.html">nnf_hardtanh()</a></code> <code><a href="nnf_hardtanh.html">nnf_hardtanh_()</a></code> </p>
-        </td>
-        <td><p>Hardtanh</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_hinge_embedding_loss.html">nnf_hinge_embedding_loss()</a></code> </p>
-        </td>
-        <td><p>Hinge_embedding_loss</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_instance_norm.html">nnf_instance_norm()</a></code> </p>
-        </td>
-        <td><p>Instance_norm</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_interpolate.html">nnf_interpolate()</a></code> </p>
-        </td>
-        <td><p>Interpolate</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_kl_div.html">nnf_kl_div()</a></code> </p>
-        </td>
-        <td><p>Kl_div</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_l1_loss.html">nnf_l1_loss()</a></code> </p>
-        </td>
-        <td><p>L1_loss</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_layer_norm.html">nnf_layer_norm()</a></code> </p>
-        </td>
-        <td><p>Layer_norm</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_leaky_relu.html">nnf_leaky_relu()</a></code> </p>
-        </td>
-        <td><p>Leaky_relu</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_linear.html">nnf_linear()</a></code> </p>
-        </td>
-        <td><p>Linear</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_local_response_norm.html">nnf_local_response_norm()</a></code> </p>
-        </td>
-        <td><p>Local_response_norm</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_log_softmax.html">nnf_log_softmax()</a></code> </p>
-        </td>
-        <td><p>Log_softmax</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_logsigmoid.html">nnf_logsigmoid()</a></code> </p>
-        </td>
-        <td><p>Logsigmoid</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_lp_pool1d.html">nnf_lp_pool1d()</a></code> </p>
-        </td>
-        <td><p>Lp_pool1d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_lp_pool2d.html">nnf_lp_pool2d()</a></code> </p>
-        </td>
-        <td><p>Lp_pool2d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_margin_ranking_loss.html">nnf_margin_ranking_loss()</a></code> </p>
-        </td>
-        <td><p>Margin_ranking_loss</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_max_pool1d.html">nnf_max_pool1d()</a></code> </p>
-        </td>
-        <td><p>Max_pool1d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_max_pool2d.html">nnf_max_pool2d()</a></code> </p>
-        </td>
-        <td><p>Max_pool2d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_max_pool3d.html">nnf_max_pool3d()</a></code> </p>
-        </td>
-        <td><p>Max_pool3d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_max_unpool1d.html">nnf_max_unpool1d()</a></code> </p>
-        </td>
-        <td><p>Max_unpool1d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_max_unpool2d.html">nnf_max_unpool2d()</a></code> </p>
-        </td>
-        <td><p>Max_unpool2d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_max_unpool3d.html">nnf_max_unpool3d()</a></code> </p>
-        </td>
-        <td><p>Max_unpool3d</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_mse_loss.html">nnf_mse_loss()</a></code> </p>
-        </td>
-        <td><p>Mse_loss</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_multi_head_attention_forward.html">nnf_multi_head_attention_forward()</a></code> </p>
-        </td>
-        <td><p>Multi head attention forward</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_multi_margin_loss.html">nnf_multi_margin_loss()</a></code> </p>
-        </td>
-        <td><p>Multi_margin_loss</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_multilabel_margin_loss.html">nnf_multilabel_margin_loss()</a></code> </p>
-        </td>
-        <td><p>Multilabel_margin_loss</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_multilabel_soft_margin_loss.html">nnf_multilabel_soft_margin_loss()</a></code> </p>
-        </td>
-        <td><p>Multilabel_soft_margin_loss</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_nll_loss.html">nnf_nll_loss()</a></code> </p>
-        </td>
-        <td><p>Nll_loss</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_normalize.html">nnf_normalize()</a></code> </p>
-        </td>
-        <td><p>Normalize</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_one_hot.html">nnf_one_hot()</a></code> </p>
-        </td>
-        <td><p>One_hot</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_pad.html">nnf_pad()</a></code> </p>
-        </td>
-        <td><p>Pad</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_pairwise_distance.html">nnf_pairwise_distance()</a></code> </p>
-        </td>
-        <td><p>Pairwise_distance</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_pdist.html">nnf_pdist()</a></code> </p>
-        </td>
-        <td><p>Pdist</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_pixel_shuffle.html">nnf_pixel_shuffle()</a></code> </p>
-        </td>
-        <td><p>Pixel_shuffle</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_poisson_nll_loss.html">nnf_poisson_nll_loss()</a></code> </p>
-        </td>
-        <td><p>Poisson_nll_loss</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_prelu.html">nnf_prelu()</a></code> </p>
-        </td>
-        <td><p>Prelu</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_relu.html">nnf_relu()</a></code> <code><a href="nnf_relu.html">nnf_relu_()</a></code> </p>
-        </td>
-        <td><p>Relu</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_relu6.html">nnf_relu6()</a></code> </p>
-        </td>
-        <td><p>Relu6</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_rrelu.html">nnf_rrelu()</a></code> <code><a href="nnf_rrelu.html">nnf_rrelu_()</a></code> </p>
-        </td>
-        <td><p>Rrelu</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_selu.html">nnf_selu()</a></code> <code><a href="nnf_selu.html">nnf_selu_()</a></code> </p>
-        </td>
-        <td><p>Selu</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_sigmoid.html">nnf_sigmoid()</a></code> </p>
-        </td>
-        <td><p>Sigmoid</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_smooth_l1_loss.html">nnf_smooth_l1_loss()</a></code> </p>
-        </td>
-        <td><p>Smooth_l1_loss</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_soft_margin_loss.html">nnf_soft_margin_loss()</a></code> </p>
-        </td>
-        <td><p>Soft_margin_loss</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_softmax.html">nnf_softmax()</a></code> </p>
-        </td>
-        <td><p>Softmax</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_softmin.html">nnf_softmin()</a></code> </p>
-        </td>
-        <td><p>Softmin</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_softplus.html">nnf_softplus()</a></code> </p>
-        </td>
-        <td><p>Softplus</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_softshrink.html">nnf_softshrink()</a></code> </p>
-        </td>
-        <td><p>Softshrink</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_softsign.html">nnf_softsign()</a></code> </p>
-        </td>
-        <td><p>Softsign</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_tanhshrink.html">nnf_tanhshrink()</a></code> </p>
-        </td>
-        <td><p>Tanhshrink</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_threshold.html">nnf_threshold()</a></code> <code><a href="nnf_threshold.html">nnf_threshold_()</a></code> </p>
-        </td>
-        <td><p>Threshold</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_triplet_margin_loss.html">nnf_triplet_margin_loss()</a></code> </p>
-        </td>
-        <td><p>Triplet_margin_loss</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_triplet_margin_with_distance_loss.html">nnf_triplet_margin_with_distance_loss()</a></code> </p>
-        </td>
-        <td><p>Triplet margin with distance loss</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="nnf_unfold.html">nnf_unfold()</a></code> </p>
-        </td>
-        <td><p>Unfold</p></td>
-      </tr>
-    </tbody><tbody>
-      <tr>
-        <th colspan="2">
-          <h2 id="section-optimizers" class="hasAnchor"><a href="#section-optimizers" class="anchor"></a>Optimizers</h2>
-          <p class="section-desc"></p>
-        </th>
-      </tr>
+
+      <dl><dt>
+          
+          <code><a href="optimizer.html">optimizer()</a></code> 
+        </dt>
+        <dd>Creates a custom optimizer</dd>
+      </dl><dl><dt>
+          
+          <code><a href="optim_adadelta.html">optim_adadelta()</a></code> 
+        </dt>
+        <dd>Adadelta optimizer</dd>
+      </dl><dl><dt>
+          
+          <code><a href="optim_adagrad.html">optim_adagrad()</a></code> 
+        </dt>
+        <dd>Adagrad optimizer</dd>
+      </dl><dl><dt>
+          
+          <code><a href="optim_adam.html">optim_adam()</a></code> 
+        </dt>
+        <dd>Implements Adam algorithm.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="optim_asgd.html">optim_asgd()</a></code> 
+        </dt>
+        <dd>Averaged Stochastic Gradient Descent optimizer</dd>
+      </dl><dl><dt>
+          
+          <code><a href="optim_lbfgs.html">optim_lbfgs()</a></code> 
+        </dt>
+        <dd>LBFGS optimizer</dd>
+      </dl><dl><dt>
+          
+          <code><a href="optim_required.html">optim_required()</a></code> 
+        </dt>
+        <dd>Dummy value indicating a required value.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="optim_rmsprop.html">optim_rmsprop()</a></code> 
+        </dt>
+        <dd>RMSprop optimizer</dd>
+      </dl><dl><dt>
+          
+          <code><a href="optim_rprop.html">optim_rprop()</a></code> 
+        </dt>
+        <dd>Implements the resilient backpropagation algorithm.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="optim_sgd.html">optim_sgd()</a></code> 
+        </dt>
+        <dd>SGD optimizer</dd>
+      </dl><dl><dt>
+          
+          <code><a href="is_optimizer.html">is_optimizer()</a></code> 
+        </dt>
+        <dd>Checks if the object is a torch optimizer</dd>
+      </dl></div><div class="section level2">
+      <h2 id="learning-rate-schedulers">Learning rate schedulers<a class="anchor" aria-label="anchor" href="#learning-rate-schedulers"></a></h2>
+      
+      
+
       
+    </div><div id="" class="section level2">
       
-    </tbody><tbody>
       
       
-      <tr>
-        
-        <td>
-          <p><code><a href="optimizer.html">optimizer()</a></code> </p>
-        </td>
-        <td><p>Creates a custom optimizer</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="optim_adadelta.html">optim_adadelta()</a></code> </p>
-        </td>
-        <td><p>Adadelta optimizer</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="optim_adagrad.html">optim_adagrad()</a></code> </p>
-        </td>
-        <td><p>Adagrad optimizer</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="optim_adam.html">optim_adam()</a></code> </p>
-        </td>
-        <td><p>Implements Adam algorithm.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="optim_asgd.html">optim_asgd()</a></code> </p>
-        </td>
-        <td><p>Averaged Stochastic Gradient Descent optimizer</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="optim_lbfgs.html">optim_lbfgs()</a></code> </p>
-        </td>
-        <td><p>LBFGS optimizer</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="optim_required.html">optim_required()</a></code> </p>
-        </td>
-        <td><p>Dummy value indicating a required value.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="optim_rmsprop.html">optim_rmsprop()</a></code> </p>
-        </td>
-        <td><p>RMSprop optimizer</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="optim_rprop.html">optim_rprop()</a></code> </p>
-        </td>
-        <td><p>Implements the resilient backpropagation algorithm.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="optim_sgd.html">optim_sgd()</a></code> </p>
-        </td>
-        <td><p>SGD optimizer</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="is_optimizer.html">is_optimizer()</a></code> </p>
-        </td>
-        <td><p>Checks if the object is a torch optimizer</p></td>
-      </tr>
-    </tbody><tbody>
-      <tr>
-        <th colspan="2">
-          <h2 id="section-learning-rate-schedulers" class="hasAnchor"><a href="#section-learning-rate-schedulers" class="anchor"></a>Learning rate schedulers</h2>
-          <p class="section-desc"></p>
-        </th>
-      </tr>
+
+      <dl><dt>
+          
+          <code><a href="lr_lambda.html">lr_lambda()</a></code> 
+        </dt>
+        <dd>Sets the learning rate of each parameter group to the initial lr
+times a given function. When last_epoch=-1, sets initial lr as lr.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="lr_multiplicative.html">lr_multiplicative()</a></code> 
+        </dt>
+        <dd>Multiply the learning rate of each parameter group by the factor given
+in the specified function. When last_epoch=-1, sets initial lr as lr.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="lr_one_cycle.html">lr_one_cycle()</a></code> 
+        </dt>
+        <dd>Once cycle learning rate</dd>
+      </dl><dl><dt>
+          
+          <code><a href="lr_scheduler.html">lr_scheduler()</a></code> 
+        </dt>
+        <dd>Creates learning rate schedulers</dd>
+      </dl><dl><dt>
+          
+          <code><a href="lr_step.html">lr_step()</a></code> 
+        </dt>
+        <dd>Step learning rate decay</dd>
+      </dl></div><div class="section level2">
+      <h2 id="datasets">Datasets<a class="anchor" aria-label="anchor" href="#datasets"></a></h2>
       
       
-    </tbody><tbody>
+
       
+    </div><div id="" class="section level2">
       
-      <tr>
-        
-        <td>
-          <p><code><a href="lr_lambda.html">lr_lambda()</a></code> </p>
-        </td>
-        <td><p>Sets the learning rate of each parameter group to the initial lr
-times a given function. When last_epoch=-1, sets initial lr as lr.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="lr_multiplicative.html">lr_multiplicative()</a></code> </p>
-        </td>
-        <td><p>Multiply the learning rate of each parameter group by the factor given
-in the specified function. When last_epoch=-1, sets initial lr as lr.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="lr_one_cycle.html">lr_one_cycle()</a></code> </p>
-        </td>
-        <td><p>Once cycle learning rate</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="lr_scheduler.html">lr_scheduler()</a></code> </p>
-        </td>
-        <td><p>Creates learning rate schedulers</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="lr_step.html">lr_step()</a></code> </p>
-        </td>
-        <td><p>Step learning rate decay</p></td>
-      </tr>
-    </tbody><tbody>
-      <tr>
-        <th colspan="2">
-          <h2 id="section-datasets" class="hasAnchor"><a href="#section-datasets" class="anchor"></a>Datasets</h2>
-          <p class="section-desc"></p>
-        </th>
-      </tr>
       
       
-    </tbody><tbody>
+
+      <dl><dt>
+          
+          <code><a href="dataset.html">dataset()</a></code> 
+        </dt>
+        <dd>Helper function to create an function that generates R6 instances of class <code>dataset</code></dd>
+      </dl><dl><dt>
+          
+          <code><a href="dataset_subset.html">dataset_subset()</a></code> 
+        </dt>
+        <dd>Dataset Subset</dd>
+      </dl><dl><dt>
+          
+          <code><a href="dataloader.html">dataloader()</a></code> 
+        </dt>
+        <dd>Data loader. Combines a dataset and a sampler, and provides
+single- or multi-process iterators over the dataset.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="dataloader_make_iter.html">dataloader_make_iter()</a></code> 
+        </dt>
+        <dd>Creates an iterator from a DataLoader</dd>
+      </dl><dl><dt>
+          
+          <code><a href="dataloader_next.html">dataloader_next()</a></code> 
+        </dt>
+        <dd>Get the next element of a dataloader iterator</dd>
+      </dl><dl><dt>
+          
+          <code><a href="enumerate.html">enumerate()</a></code> 
+        </dt>
+        <dd>Enumerate an iterator</dd>
+      </dl><dl><dt>
+          
+          <code><a href="enumerate.dataloader.html">enumerate(<i>&lt;dataloader&gt;</i>)</a></code> 
+        </dt>
+        <dd>Enumerate an iterator</dd>
+      </dl><dl><dt>
+          
+          <code><a href="tensor_dataset.html">tensor_dataset()</a></code> 
+        </dt>
+        <dd>Dataset wrapping tensors.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="is_dataloader.html">is_dataloader()</a></code> 
+        </dt>
+        <dd>Checks if the object is a dataloader</dd>
+      </dl></div><div class="section level2">
+      <h2 id="distributions">Distributions<a class="anchor" aria-label="anchor" href="#distributions"></a></h2>
       
       
-      <tr>
-        
-        <td>
-          <p><code><a href="dataset.html">dataset()</a></code> </p>
-        </td>
-        <td><p>Helper function to create an R6 class that inherits from the abstract <code>Dataset</code> class</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="dataset_subset.html">dataset_subset()</a></code> </p>
-        </td>
-        <td><p>Dataset Subset</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="dataloader.html">dataloader()</a></code> </p>
-        </td>
-        <td><p>Data loader. Combines a dataset and a sampler, and provides
-single- or multi-process iterators over the dataset.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="dataloader_make_iter.html">dataloader_make_iter()</a></code> </p>
-        </td>
-        <td><p>Creates an iterator from a DataLoader</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="dataloader_next.html">dataloader_next()</a></code> </p>
-        </td>
-        <td><p>Get the next element of a dataloader iterator</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="enumerate.html">enumerate()</a></code> </p>
-        </td>
-        <td><p>Enumerate an iterator</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="enumerate.dataloader.html">enumerate(<i>&lt;dataloader&gt;</i>)</a></code> </p>
-        </td>
-        <td><p>Enumerate an iterator</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="tensor_dataset.html">tensor_dataset()</a></code> </p>
-        </td>
-        <td><p>Dataset wrapping tensors.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="is_dataloader.html">is_dataloader()</a></code> </p>
-        </td>
-        <td><p>Checks if the object is a dataloader</p></td>
-      </tr>
-    </tbody><tbody>
-      <tr>
-        <th colspan="2">
-          <h2 id="section-distributions" class="hasAnchor"><a href="#section-distributions" class="anchor"></a>Distributions</h2>
-          <p class="section-desc"></p>
-        </th>
-      </tr>
+
       
+    </div><div id="" class="section level2">
       
-    </tbody><tbody>
       
       
-      <tr>
-        
-        <td>
-          <p><code><a href="Distribution.html">Distribution</a></code> </p>
-        </td>
-        <td><p>Generic R6 class representing distributions</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="distr_bernoulli.html">distr_bernoulli()</a></code> </p>
-        </td>
-        <td><p>Creates a Bernoulli distribution parameterized by <code>probs</code>
+
+      <dl><dt>
+          
+          <code><a href="Distribution.html">Distribution</a></code> 
+        </dt>
+        <dd>Generic R6 class representing distributions</dd>
+      </dl><dl><dt>
+          
+          <code><a href="distr_bernoulli.html">distr_bernoulli()</a></code> 
+        </dt>
+        <dd>Creates a Bernoulli distribution parameterized by <code>probs</code>
 or <code>logits</code> (but not both).
 Samples are binary (0 or 1). They take the value <code>1</code> with probability <code>p</code>
-and <code>0</code> with probability <code>1 - p</code>.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="distr_categorical.html">distr_categorical()</a></code> </p>
-        </td>
-        <td><p>Creates a categorical distribution parameterized by either <code>probs</code> or
-<code>logits</code> (but not both).</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="distr_chi2.html">distr_chi2()</a></code> </p>
-        </td>
-        <td><p>Creates a Chi2 distribution parameterized by shape parameter <code>df</code>.
-This is exactly equivalent to <code>distr_gamma(alpha=0.5*df, beta=0.5)</code></p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="distr_gamma.html">distr_gamma()</a></code> </p>
-        </td>
-        <td><p>Creates a Gamma distribution parameterized by shape <code>concentration</code> and <code>rate</code>.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="distr_mixture_same_family.html">distr_mixture_same_family()</a></code> </p>
-        </td>
-        <td><p>Mixture of components in the same family</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="distr_multivariate_normal.html">distr_multivariate_normal()</a></code> </p>
-        </td>
-        <td><p>Gaussian distribution</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="distr_normal.html">distr_normal()</a></code> </p>
-        </td>
-        <td><p>Creates a normal (also called Gaussian) distribution parameterized by
-<code>loc</code> and <code>scale</code>.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="distr_poisson.html">distr_poisson()</a></code> </p>
-        </td>
-        <td><p>Creates a Poisson distribution parameterized by <code>rate</code>, the rate parameter.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="Constraint.html">Constraint</a></code> </p>
-        </td>
-        <td><p>Abstract base class for constraints.</p></td>
-      </tr>
-    </tbody><tbody>
-      <tr>
-        <th colspan="2">
-          <h2 id="section-autograd" class="hasAnchor"><a href="#section-autograd" class="anchor"></a>Autograd</h2>
-          <p class="section-desc"></p>
-        </th>
-      </tr>
+and <code>0</code> with probability <code>1 - p</code>.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="distr_categorical.html">distr_categorical()</a></code> 
+        </dt>
+        <dd>Creates a categorical distribution parameterized by either <code>probs</code> or
+<code>logits</code> (but not both).</dd>
+      </dl><dl><dt>
+          
+          <code><a href="distr_chi2.html">distr_chi2()</a></code> 
+        </dt>
+        <dd>Creates a Chi2 distribution parameterized by shape parameter <code>df</code>.
+This is exactly equivalent to <code>distr_gamma(alpha=0.5*df, beta=0.5)</code></dd>
+      </dl><dl><dt>
+          
+          <code><a href="distr_gamma.html">distr_gamma()</a></code> 
+        </dt>
+        <dd>Creates a Gamma distribution parameterized by shape <code>concentration</code> and <code>rate</code>.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="distr_mixture_same_family.html">distr_mixture_same_family()</a></code> 
+        </dt>
+        <dd>Mixture of components in the same family</dd>
+      </dl><dl><dt>
+          
+          <code><a href="distr_multivariate_normal.html">distr_multivariate_normal()</a></code> 
+        </dt>
+        <dd>Gaussian distribution</dd>
+      </dl><dl><dt>
+          
+          <code><a href="distr_normal.html">distr_normal()</a></code> 
+        </dt>
+        <dd>Creates a normal (also called Gaussian) distribution parameterized by
+<code>loc</code> and <code>scale</code>.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="distr_poisson.html">distr_poisson()</a></code> 
+        </dt>
+        <dd>Creates a Poisson distribution parameterized by <code>rate</code>, the rate parameter.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="Constraint.html">Constraint</a></code> 
+        </dt>
+        <dd>Abstract base class for constraints.</dd>
+      </dl></div><div class="section level2">
+      <h2 id="autograd">Autograd<a class="anchor" aria-label="anchor" href="#autograd"></a></h2>
+      
+      
+
       
+    </div><div id="" class="section level2">
       
-    </tbody><tbody>
       
       
-      <tr>
-        
-        <td>
-          <p><code><a href="autograd_backward.html">autograd_backward()</a></code> </p>
-        </td>
-        <td><p>Computes the sum of gradients of given tensors w.r.t. graph leaves.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="autograd_function.html">autograd_function()</a></code> </p>
-        </td>
-        <td><p>Records operation history and defines formulas for differentiating ops.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="autograd_grad.html">autograd_grad()</a></code> </p>
-        </td>
-        <td><p>Computes and returns the sum of gradients of outputs w.r.t. the inputs.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="autograd_set_grad_mode.html">autograd_set_grad_mode()</a></code> </p>
-        </td>
-        <td><p>Set grad mode</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="with_no_grad.html">with_no_grad()</a></code> </p>
-        </td>
-        <td><p>Temporarily modify gradient recording.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="with_enable_grad.html">with_enable_grad()</a></code> </p>
-        </td>
-        <td><p>Enable grad</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="AutogradContext.html">AutogradContext</a></code> </p>
-        </td>
-        <td><p>Class representing the context.</p></td>
-      </tr>
-    </tbody><tbody>
-      <tr>
-        <th colspan="2">
-          <h2 id="section-linear-algebra" class="hasAnchor"><a href="#section-linear-algebra" class="anchor"></a>Linear Algebra</h2>
-          <p class="section-desc"></p>
-        </th>
-      </tr>
+
+      <dl><dt>
+          
+          <code><a href="autograd_backward.html">autograd_backward()</a></code> 
+        </dt>
+        <dd>Computes the sum of gradients of given tensors w.r.t. graph leaves.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="autograd_function.html">autograd_function()</a></code> 
+        </dt>
+        <dd>Records operation history and defines formulas for differentiating ops.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="autograd_grad.html">autograd_grad()</a></code> 
+        </dt>
+        <dd>Computes and returns the sum of gradients of outputs w.r.t. the inputs.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="autograd_set_grad_mode.html">autograd_set_grad_mode()</a></code> 
+        </dt>
+        <dd>Set grad mode</dd>
+      </dl><dl><dt>
+          
+          <code><a href="with_no_grad.html">with_no_grad()</a></code> 
+        </dt>
+        <dd>Temporarily modify gradient recording.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="with_enable_grad.html">with_enable_grad()</a></code> 
+        </dt>
+        <dd>Enable grad</dd>
+      </dl><dl><dt>
+          
+          <code><a href="AutogradContext.html">AutogradContext</a></code> 
+        </dt>
+        <dd>Class representing the context.</dd>
+      </dl></div><div class="section level2">
+      <h2 id="linear-algebra">Linear Algebra<a class="anchor" aria-label="anchor" href="#linear-algebra"></a></h2>
       
       
-    </tbody><tbody>
+
       
+    </div><div id="" class="section level2">
       
-      <tr>
-        
-        <td>
-          <p><code><a href="linalg_cholesky.html">linalg_cholesky()</a></code> </p>
-        </td>
-        <td><p>Computes the Cholesky decomposition of a complex Hermitian or real symmetric positive-definite matrix.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="linalg_cholesky_ex.html">linalg_cholesky_ex()</a></code> </p>
-        </td>
-        <td><p>Computes the Cholesky decomposition of a complex Hermitian or real
-symmetric positive-definite matrix.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="linalg_cond.html">linalg_cond()</a></code> </p>
-        </td>
-        <td><p>Computes the condition number of a matrix with respect to a matrix norm.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="linalg_det.html">linalg_det()</a></code> </p>
-        </td>
-        <td><p>Computes the determinant of a square matrix.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="linalg_eig.html">linalg_eig()</a></code> </p>
-        </td>
-        <td><p>Computes the eigenvalue decomposition of a square matrix if it exists.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="linalg_eigh.html">linalg_eigh()</a></code> </p>
-        </td>
-        <td><p>Computes the eigenvalue decomposition of a complex Hermitian or real symmetric matrix.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="linalg_eigvals.html">linalg_eigvals()</a></code> </p>
-        </td>
-        <td><p>Computes the eigenvalues of a square matrix.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="linalg_eigvalsh.html">linalg_eigvalsh()</a></code> </p>
-        </td>
-        <td><p>Computes the eigenvalues of a complex Hermitian or real symmetric matrix.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="linalg_householder_product.html">linalg_householder_product()</a></code> </p>
-        </td>
-        <td><p>Computes the first <code>n</code> columns of a product of Householder matrices.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="linalg_inv.html">linalg_inv()</a></code> </p>
-        </td>
-        <td><p>Computes the inverse of a square matrix if it exists.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="linalg_inv_ex.html">linalg_inv_ex()</a></code> </p>
-        </td>
-        <td><p>Computes the inverse of a square matrix if it is invertible.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="linalg_lstsq.html">linalg_lstsq()</a></code> </p>
-        </td>
-        <td><p>Computes a solution to the least squares problem of a system of linear equations.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="linalg_matrix_norm.html">linalg_matrix_norm()</a></code> </p>
-        </td>
-        <td><p>Computes a matrix norm.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="linalg_matrix_power.html">linalg_matrix_power()</a></code> </p>
-        </td>
-        <td><p>Computes the <code>n</code>-th power of a square matrix for an integer <code>n</code>.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="linalg_matrix_rank.html">linalg_matrix_rank()</a></code> </p>
-        </td>
-        <td><p>Computes the numerical rank of a matrix.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="linalg_multi_dot.html">linalg_multi_dot()</a></code> </p>
-        </td>
-        <td><p>Efficiently multiplies two or more matrices</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="linalg_norm.html">linalg_norm()</a></code> </p>
-        </td>
-        <td><p>Computes a vector or matrix norm.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="linalg_pinv.html">linalg_pinv()</a></code> </p>
-        </td>
-        <td><p>Computes the pseudoinverse (Moore-Penrose inverse) of a matrix.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="linalg_qr.html">linalg_qr()</a></code> </p>
-        </td>
-        <td><p>Computes the QR decomposition of a matrix.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="linalg_slogdet.html">linalg_slogdet()</a></code> </p>
-        </td>
-        <td><p>Computes the sign and natural logarithm of the absolute value of the determinant of a square matrix.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="linalg_solve.html">linalg_solve()</a></code> </p>
-        </td>
-        <td><p>Computes the solution of a square system of linear equations with a unique solution.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="linalg_svd.html">linalg_svd()</a></code> </p>
-        </td>
-        <td><p>Computes the singular value decomposition (SVD) of a matrix.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="linalg_svdvals.html">linalg_svdvals()</a></code> </p>
-        </td>
-        <td><p>Computes the singular values of a matrix.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="linalg_tensorinv.html">linalg_tensorinv()</a></code> </p>
-        </td>
-        <td><p>Computes the multiplicative inverse of <code>torch_tensordot()</code></p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="linalg_tensorsolve.html">linalg_tensorsolve()</a></code> </p>
-        </td>
-        <td><p>Computes the solution <code>X</code> to the system <code>torch_tensordot(A, X) = B</code>.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="linalg_vector_norm.html">linalg_vector_norm()</a></code> </p>
-        </td>
-        <td><p>Computes a vector norm.</p></td>
-      </tr>
-    </tbody><tbody>
-      <tr>
-        <th colspan="2">
-          <h2 id="section-cuda-utilities" class="hasAnchor"><a href="#section-cuda-utilities" class="anchor"></a>Cuda utilities</h2>
-          <p class="section-desc"></p>
-        </th>
-      </tr>
       
       
-    </tbody><tbody>
+
+      <dl><dt>
+          
+          <code><a href="linalg_cholesky.html">linalg_cholesky()</a></code> 
+        </dt>
+        <dd>Computes the Cholesky decomposition of a complex Hermitian or real symmetric positive-definite matrix.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="linalg_cholesky_ex.html">linalg_cholesky_ex()</a></code> 
+        </dt>
+        <dd>Computes the Cholesky decomposition of a complex Hermitian or real
+symmetric positive-definite matrix.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="linalg_cond.html">linalg_cond()</a></code> 
+        </dt>
+        <dd>Computes the condition number of a matrix with respect to a matrix norm.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="linalg_det.html">linalg_det()</a></code> 
+        </dt>
+        <dd>Computes the determinant of a square matrix.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="linalg_eig.html">linalg_eig()</a></code> 
+        </dt>
+        <dd>Computes the eigenvalue decomposition of a square matrix if it exists.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="linalg_eigh.html">linalg_eigh()</a></code> 
+        </dt>
+        <dd>Computes the eigenvalue decomposition of a complex Hermitian or real symmetric matrix.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="linalg_eigvals.html">linalg_eigvals()</a></code> 
+        </dt>
+        <dd>Computes the eigenvalues of a square matrix.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="linalg_eigvalsh.html">linalg_eigvalsh()</a></code> 
+        </dt>
+        <dd>Computes the eigenvalues of a complex Hermitian or real symmetric matrix.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="linalg_householder_product.html">linalg_householder_product()</a></code> 
+        </dt>
+        <dd>Computes the first <code>n</code> columns of a product of Householder matrices.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="linalg_inv.html">linalg_inv()</a></code> 
+        </dt>
+        <dd>Computes the inverse of a square matrix if it exists.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="linalg_inv_ex.html">linalg_inv_ex()</a></code> 
+        </dt>
+        <dd>Computes the inverse of a square matrix if it is invertible.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="linalg_lstsq.html">linalg_lstsq()</a></code> 
+        </dt>
+        <dd>Computes a solution to the least squares problem of a system of linear equations.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="linalg_matrix_norm.html">linalg_matrix_norm()</a></code> 
+        </dt>
+        <dd>Computes a matrix norm.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="linalg_matrix_power.html">linalg_matrix_power()</a></code> 
+        </dt>
+        <dd>Computes the <code>n</code>-th power of a square matrix for an integer <code>n</code>.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="linalg_matrix_rank.html">linalg_matrix_rank()</a></code> 
+        </dt>
+        <dd>Computes the numerical rank of a matrix.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="linalg_multi_dot.html">linalg_multi_dot()</a></code> 
+        </dt>
+        <dd>Efficiently multiplies two or more matrices</dd>
+      </dl><dl><dt>
+          
+          <code><a href="linalg_norm.html">linalg_norm()</a></code> 
+        </dt>
+        <dd>Computes a vector or matrix norm.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="linalg_pinv.html">linalg_pinv()</a></code> 
+        </dt>
+        <dd>Computes the pseudoinverse (Moore-Penrose inverse) of a matrix.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="linalg_qr.html">linalg_qr()</a></code> 
+        </dt>
+        <dd>Computes the QR decomposition of a matrix.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="linalg_slogdet.html">linalg_slogdet()</a></code> 
+        </dt>
+        <dd>Computes the sign and natural logarithm of the absolute value of the determinant of a square matrix.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="linalg_solve.html">linalg_solve()</a></code> 
+        </dt>
+        <dd>Computes the solution of a square system of linear equations with a unique solution.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="linalg_svd.html">linalg_svd()</a></code> 
+        </dt>
+        <dd>Computes the singular value decomposition (SVD) of a matrix.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="linalg_svdvals.html">linalg_svdvals()</a></code> 
+        </dt>
+        <dd>Computes the singular values of a matrix.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="linalg_tensorinv.html">linalg_tensorinv()</a></code> 
+        </dt>
+        <dd>Computes the multiplicative inverse of <code><a href="../reference/torch_tensordot.html">torch_tensordot()</a></code></dd>
+      </dl><dl><dt>
+          
+          <code><a href="linalg_tensorsolve.html">linalg_tensorsolve()</a></code> 
+        </dt>
+        <dd>Computes the solution <code>X</code> to the system <code>torch_tensordot(A, X) = B</code>.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="linalg_vector_norm.html">linalg_vector_norm()</a></code> 
+        </dt>
+        <dd>Computes a vector norm.</dd>
+      </dl></div><div class="section level2">
+      <h2 id="cuda-utilities">Cuda utilities<a class="anchor" aria-label="anchor" href="#cuda-utilities"></a></h2>
       
       
-      <tr>
-        
-        <td>
-          <p><code><a href="cuda_current_device.html">cuda_current_device()</a></code> </p>
-        </td>
-        <td><p>Returns the index of a currently selected device.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="cuda_device_count.html">cuda_device_count()</a></code> </p>
-        </td>
-        <td><p>Returns the number of GPUs available.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="cuda_is_available.html">cuda_is_available()</a></code> </p>
-        </td>
-        <td><p>Returns a bool indicating if CUDA is currently available.</p></td>
-      </tr>
-    </tbody><tbody>
-      <tr>
-        <th colspan="2">
-          <h2 id="section-jit" class="hasAnchor"><a href="#section-jit" class="anchor"></a>JIT</h2>
-          <p class="section-desc"></p>
-        </th>
-      </tr>
+
       
+    </div><div id="" class="section level2">
       
-    </tbody><tbody>
       
       
-      <tr>
-        
-        <td>
-          <p><code><a href="jit_compile.html">jit_compile()</a></code> </p>
-        </td>
-        <td><p>Compile TorchScript code into a graph</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="jit_load.html">jit_load()</a></code> </p>
-        </td>
-        <td><p>Loads a <code>script_function</code> or <code>script_module</code> previously saved with <code>jit_save</code></p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="jit_save.html">jit_save()</a></code> </p>
-        </td>
-        <td><p>Saves a <code>script_function</code> to a path</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="jit_scalar.html">jit_scalar()</a></code> </p>
-        </td>
-        <td><p>Adds the 'jit_scalar' class to the input</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="jit_trace.html">jit_trace()</a></code> </p>
-        </td>
-        <td><p>Trace a function and return an executable <code>script_function</code>.</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="jit_trace_module.html">jit_trace_module()</a></code> </p>
-        </td>
-        <td><p>Trace a module</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="jit_tuple.html">jit_tuple()</a></code> </p>
-        </td>
-        <td><p>Adds the 'jit_tuple' class to the input</p></td>
-      </tr>
-    </tbody><tbody>
-      <tr>
-        <th colspan="2">
-          <h2 id="section-backends" class="hasAnchor"><a href="#section-backends" class="anchor"></a>Backends</h2>
-          <p class="section-desc"></p>
-        </th>
-      </tr>
+
+      <dl><dt>
+          
+          <code><a href="cuda_current_device.html">cuda_current_device()</a></code> 
+        </dt>
+        <dd>Returns the index of a currently selected device.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="cuda_device_count.html">cuda_device_count()</a></code> 
+        </dt>
+        <dd>Returns the number of GPUs available.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="cuda_get_device_capability.html">cuda_get_device_capability()</a></code> 
+        </dt>
+        <dd>Returns the major and minor CUDA capability of <code>device</code></dd>
+      </dl><dl><dt>
+          
+          <code><a href="cuda_is_available.html">cuda_is_available()</a></code> 
+        </dt>
+        <dd>Returns a bool indicating if CUDA is currently available.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="cuda_memory_stats.html">cuda_memory_stats()</a></code> <code><a href="cuda_memory_stats.html">cuda_memory_summary()</a></code> 
+        </dt>
+        <dd>Returns a dictionary of CUDA memory allocator statistics for a given device.</dd>
+      </dl></div><div class="section level2">
+      <h2 id="jit">JIT<a class="anchor" aria-label="anchor" href="#jit"></a></h2>
       
       
-    </tbody><tbody>
+
       
+    </div><div id="" class="section level2">
       
-      <tr>
-        
-        <td>
-          <p><code><a href="backends_mkl_is_available.html">backends_mkl_is_available()</a></code> </p>
-        </td>
-        <td><p>MKL is available</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="backends_mkldnn_is_available.html">backends_mkldnn_is_available()</a></code> </p>
-        </td>
-        <td><p>MKLDNN is available</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="backends_openmp_is_available.html">backends_openmp_is_available()</a></code> </p>
-        </td>
-        <td><p>OpenMP is available</p></td>
-      </tr>
-    </tbody><tbody>
-      <tr>
-        <th colspan="2">
-          <h2 id="section-installation" class="hasAnchor"><a href="#section-installation" class="anchor"></a>Installation</h2>
-          <p class="section-desc"></p>
-        </th>
-      </tr>
       
       
-    </tbody><tbody>
+
+      <dl><dt>
+          
+          <code><a href="jit_compile.html">jit_compile()</a></code> 
+        </dt>
+        <dd>Compile TorchScript code into a graph</dd>
+      </dl><dl><dt>
+          
+          <code><a href="jit_load.html">jit_load()</a></code> 
+        </dt>
+        <dd>Loads a <code>script_function</code> or <code>script_module</code> previously saved with <code>jit_save</code></dd>
+      </dl><dl><dt>
+          
+          <code><a href="jit_save.html">jit_save()</a></code> 
+        </dt>
+        <dd>Saves a <code>script_function</code> to a path</dd>
+      </dl><dl><dt>
+          
+          <code><a href="jit_save_for_mobile.html">jit_save_for_mobile()</a></code> 
+        </dt>
+        <dd>Saves a <code>script_function</code> or <code>script_module</code> in bytecode form,
+to be loaded on a mobile device</dd>
+      </dl><dl><dt>
+          
+          <code><a href="jit_scalar.html">jit_scalar()</a></code> 
+        </dt>
+        <dd>Adds the 'jit_scalar' class to the input</dd>
+      </dl><dl><dt>
+          
+          <code><a href="jit_trace.html">jit_trace()</a></code> 
+        </dt>
+        <dd>Trace a function and return an executable <code>script_function</code>.</dd>
+      </dl><dl><dt>
+          
+          <code><a href="jit_trace_module.html">jit_trace_module()</a></code> 
+        </dt>
+        <dd>Trace a module</dd>
+      </dl><dl><dt>
+          
+          <code><a href="jit_tuple.html">jit_tuple()</a></code> 
+        </dt>
+        <dd>Adds the 'jit_tuple' class to the input</dd>
+      </dl></div><div class="section level2">
+      <h2 id="backends">Backends<a class="anchor" aria-label="anchor" href="#backends"></a></h2>
       
       
-      <tr>
-        
-        <td>
-          <p><code><a href="install_torch.html">install_torch()</a></code> </p>
-        </td>
-        <td><p>Install Torch</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="install_torch_from_file.html">install_torch_from_file()</a></code> </p>
-        </td>
-        <td><p>Install Torch from files</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="get_install_libs_url.html">get_install_libs_url()</a></code> </p>
-        </td>
-        <td><p>List of files to download</p></td>
-      </tr>
-    </tbody>
-    </table>
-  </div>
 
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+      
+    </div><div id="" class="section level2">
+      
+      
+      
+
+      <dl><dt>
+          
+          <code><a href="backends_cudnn_is_available.html">backends_cudnn_is_available()</a></code> 
+        </dt>
+        <dd>CuDNN is available</dd>
+      </dl><dl><dt>
+          
+          <code><a href="backends_cudnn_version.html">backends_cudnn_version()</a></code> 
+        </dt>
+        <dd>CuDNN version</dd>
+      </dl><dl><dt>
+          
+          <code><a href="backends_mkl_is_available.html">backends_mkl_is_available()</a></code> 
+        </dt>
+        <dd>MKL is available</dd>
+      </dl><dl><dt>
+          
+          <code><a href="backends_mkldnn_is_available.html">backends_mkldnn_is_available()</a></code> 
+        </dt>
+        <dd>MKLDNN is available</dd>
+      </dl><dl><dt>
+          
+          <code><a href="backends_openmp_is_available.html">backends_openmp_is_available()</a></code> 
+        </dt>
+        <dd>OpenMP is available</dd>
+      </dl></div><div class="section level2">
+      <h2 id="installation">Installation<a class="anchor" aria-label="anchor" href="#installation"></a></h2>
+      
+      
+
+      
+    </div><div id="" class="section level2">
+      
+      
+      
 
+      <dl><dt>
+          
+          <code><a href="install_torch.html">install_torch()</a></code> 
+        </dt>
+        <dd>Install Torch</dd>
+      </dl><dl><dt>
+          
+          <code><a href="install_torch_from_file.html">install_torch_from_file()</a></code> 
+        </dt>
+        <dd>Install Torch from files</dd>
+      </dl><dl><dt>
+          
+          <code><a href="get_install_libs_url.html">get_install_libs_url()</a></code> 
+        </dt>
+        <dd>List of files to download</dd>
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+        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
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+    
+  </div>
+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Install Torch</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/install.R'><code>R/install.R</code></a></small>
-    <div class="hidden name"><code>install_torch.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Install Torch</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/install.R" class="external-link"><code>R/install.R</code></a></small>
+      <div class="d-none name"><code>install_torch.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Installs Torch and its dependencies.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>install_torch</span><span class='op'>(</span>
-  version <span class='op'>=</span> <span class='st'>"1.9.1"</span>,
-  type <span class='op'>=</span> <span class='fu'>install_type</span><span class='op'>(</span>version <span class='op'>=</span> <span class='va'>version</span><span class='op'>)</span>,
-  reinstall <span class='op'>=</span> <span class='cn'>FALSE</span>,
-  path <span class='op'>=</span> <span class='fu'>install_path</span><span class='op'>(</span><span class='op'>)</span>,
-  timeout <span class='op'>=</span> <span class='fl'>360</span>,
-  <span class='va'>...</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>version</th>
-      <td><p>The Torch version to install.</p></td>
-    </tr>
-    <tr>
-      <th>type</th>
-      <td><p>The installation type for Torch. Valid values are <code>"cpu"</code> or the 'CUDA' version.</p></td>
-    </tr>
-    <tr>
-      <th>reinstall</th>
-      <td><p>Re-install Torch even if its already installed?</p></td>
-    </tr>
-    <tr>
-      <th>path</th>
-      <td><p>Optional path to install or check for an already existing installation.</p></td>
-    </tr>
-    <tr>
-      <th>timeout</th>
-      <td><p>Optional timeout in seconds for large file download.</p></td>
-    </tr>
-    <tr>
-      <th>...</th>
-      <td><p>other optional arguments (like <code>`load`</code> for manual installation).</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">install_torch</span><span class="op">(</span>
+  version <span class="op">=</span> <span class="st">"1.10.2"</span>,
+  type <span class="op">=</span> <span class="fu">install_type</span><span class="op">(</span>version <span class="op">=</span> <span class="va">version</span><span class="op">)</span>,
+  reinstall <span class="op">=</span> <span class="cn">FALSE</span>,
+  path <span class="op">=</span> <span class="fu">install_path</span><span class="op">(</span><span class="op">)</span>,
+  timeout <span class="op">=</span> <span class="fl">360</span>,
+  <span class="va">...</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>version</dt>
+<dd><p>The Torch version to install.</p></dd>
+<dt>type</dt>
+<dd><p>The installation type for Torch. Valid values are <code>"cpu"</code> or the 'CUDA' version.</p></dd>
+<dt>reinstall</dt>
+<dd><p>Re-install Torch even if its already installed?</p></dd>
+<dt>path</dt>
+<dd><p>Optional path to install or check for an already existing installation.</p></dd>
+<dt>timeout</dt>
+<dd><p>Optional timeout in seconds for large file download.</p></dd>
+<dt>...</dt>
+<dd><p>other optional arguments (like <code>`load`</code> for manual installation).</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>When using <code>path</code> to install in a specific location, make sure the <code>TORCH_HOME</code> environment
 variable is set to this same path to reuse this installation. The <code>TORCH_INSTALL</code> environment
 variable can be set to <code>0</code> to prevent auto-installing torch and <code>TORCH_LOAD</code> set to <code>0</code>
@@ -236,32 +116,25 @@ to avoid loading dependencies automatically. These environment variables are mea
 cases and troubleshooting only.
 When timeout error occurs during library archive download, or length of downloaded files differ from
 reported length, an increase of the <code>timeout</code> value should help.</p>
+    </div>
 
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+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Install Torch from files</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/install.R'><code>R/install.R</code></a></small>
-    <div class="hidden name"><code>install_torch_from_file.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Install Torch from files</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/install.R" class="external-link"><code>R/install.R</code></a></small>
+      <div class="d-none name"><code>install_torch_from_file.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Installs Torch and its dependencies from files.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>install_torch_from_file</span><span class='op'>(</span>
-  version <span class='op'>=</span> <span class='st'>"1.9.1"</span>,
-  type <span class='op'>=</span> <span class='fu'>install_type</span><span class='op'>(</span>version <span class='op'>=</span> <span class='va'>version</span><span class='op'>)</span>,
-  <span class='va'>libtorch</span>,
-  <span class='va'>liblantern</span>,
-  <span class='va'>...</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>version</th>
-      <td><p>The Torch version to install.</p></td>
-    </tr>
-    <tr>
-      <th>type</th>
-      <td><p>The installation type for Torch. Valid values are <code>"cpu"</code> or the 'CUDA' version.</p></td>
-    </tr>
-    <tr>
-      <th>libtorch</th>
-      <td><p>The installation archive file to use for Torch. Shall be a <code>"file://"</code> URL scheme.</p></td>
-    </tr>
-    <tr>
-      <th>liblantern</th>
-      <td><p>The installation archive file to use for Lantern. Shall be a <code>"file://"</code> URL scheme.</p></td>
-    </tr>
-    <tr>
-      <th>...</th>
-      <td><p>other parameters to be passed to <code>"install_torch()"</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">install_torch_from_file</span><span class="op">(</span>
+  version <span class="op">=</span> <span class="st">"1.10.2"</span>,
+  type <span class="op">=</span> <span class="fu">install_type</span><span class="op">(</span>version <span class="op">=</span> <span class="va">version</span><span class="op">)</span>,
+  <span class="va">libtorch</span>,
+  <span class="va">liblantern</span>,
+  <span class="va">...</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>version</dt>
+<dd><p>The Torch version to install.</p></dd>
+<dt>type</dt>
+<dd><p>The installation type for Torch. Valid values are <code>"cpu"</code> or the 'CUDA' version.</p></dd>
+<dt>libtorch</dt>
+<dd><p>The installation archive file to use for Torch. Shall be a <code>"file://"</code> URL scheme.</p></dd>
+<dt>liblantern</dt>
+<dd><p>The installation archive file to use for Lantern. Shall be a <code>"file://"</code> URL scheme.</p></dd>
+<dt>...</dt>
+<dd><p>other parameters to be passed to <code>"install_torch()"</code></p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>When <code>"install_torch()"</code> initiated download is not possible, but installation archive files are
 present on local filesystem, <code>"install_torch_from_file()"</code> can be used as a workaround to installation issue.
 <code>"libtorch"</code> is the archive containing all torch modules, and <code>"liblantern"</code> is the C interface to libtorch
 that is used for the R package. Both are highly dependent, and should be checked through <code>"get_install_libs_url()"</code></p>
+    </div>
 
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diff --git a/reference/is_dataloader.html b/reference/is_dataloader.html
index 4edbe05a6..e07337a4e 100644
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-    <h1>Checks if the object is a dataloader</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/utils-data-dataloader.R'><code>R/utils-data-dataloader.R</code></a></small>
-    <div class="hidden name"><code>is_dataloader.Rd</code></div>
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+      <img src="" class="logo" alt=""><h1>Checks if the object is a dataloader</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/utils-data-dataloader.R" class="external-link"><code>R/utils-data-dataloader.R</code></a></small>
+      <div class="d-none name"><code>is_dataloader.Rd</code></div>
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-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Checks if the object is a dataloader</p>
     </div>
 
-    <pre class="usage"><span class='fu'>is_dataloader</span><span class='op'>(</span><span class='va'>x</span><span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">is_dataloader</span><span class="op">(</span><span class="va">x</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>x</th>
-      <td><p>object to check</p></td>
-    </tr>
-    </table>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>x</dt>
+<dd><p>object to check</p></dd>
+</dl></div>
 
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-    <h1>Checks if the object is a nn_buffer</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn.R'><code>R/nn.R</code></a></small>
-    <div class="hidden name"><code>is_nn_buffer.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Checks if the object is a nn_buffer</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn.R" class="external-link"><code>R/nn.R</code></a></small>
+      <div class="d-none name"><code>is_nn_buffer.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Checks if the object is a nn_buffer</p>
     </div>
 
-    <pre class="usage"><span class='fu'>is_nn_buffer</span><span class='op'>(</span><span class='va'>x</span><span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">is_nn_buffer</span><span class="op">(</span><span class="va">x</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>x</th>
-      <td><p>object to check</p></td>
-    </tr>
-    </table>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>x</dt>
+<dd><p>object to check</p></dd>
+</dl></div>
 
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn.R'><code>R/nn.R</code></a></small>
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+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Checks if the object is an nn_module</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn.R" class="external-link"><code>R/nn.R</code></a></small>
+      <div class="d-none name"><code>is_nn_module.Rd</code></div>
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-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Checks if the object is an nn_module</p>
     </div>
 
-    <pre class="usage"><span class='fu'>is_nn_module</span><span class='op'>(</span><span class='va'>x</span><span class='op'>)</span></pre>
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+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">is_nn_module</span><span class="op">(</span><span class="va">x</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>x</th>
-      <td><p>object to check</p></td>
-    </tr>
-    </table>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>x</dt>
+<dd><p>object to check</p></dd>
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+  <main id="main" class="col-md-9"><div class="page-header">
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-    <table class="ref-arguments">
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+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
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-    <h1>Checks if the object is a torch optimizer</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/optim.R'><code>R/optim.R</code></a></small>
-    <div class="hidden name"><code>is_optimizer.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Checks if the object is a torch optimizer</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/optim.R" class="external-link"><code>R/optim.R</code></a></small>
+      <div class="d-none name"><code>is_optimizer.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Checks if the object is a torch optimizer</p>
     </div>
 
-    <pre class="usage"><span class='fu'>is_optimizer</span><span class='op'>(</span><span class='va'>x</span><span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">is_optimizer</span><span class="op">(</span><span class="va">x</span><span class="op">)</span></code></pre></div>
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-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
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+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>x</dt>
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-    <h1>Checks if object is a device</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/device.R'><code>R/device.R</code></a></small>
-    <div class="hidden name"><code>is_torch_device.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Checks if object is a device</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/device.R" class="external-link"><code>R/device.R</code></a></small>
+      <div class="d-none name"><code>is_torch_device.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Checks if object is a device</p>
     </div>
 
-    <pre class="usage"><span class='fu'>is_torch_device</span><span class='op'>(</span><span class='va'>x</span><span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">is_torch_device</span><span class="op">(</span><span class="va">x</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>x</th>
-      <td><p>object to check</p></td>
-    </tr>
-    </table>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>x</dt>
+<dd><p>object to check</p></dd>
+</dl></div>
 
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index c0ea5d288..eb2ea40b5 100644
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/dtype.R'><code>R/dtype.R</code></a></small>
-    <div class="hidden name"><code>is_torch_dtype.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Check if object is a torch data type</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/dtype.R" class="external-link"><code>R/dtype.R</code></a></small>
+      <div class="d-none name"><code>is_torch_dtype.Rd</code></div>
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-    <div class="ref-description">
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     <p>Check if object is a torch data type</p>
     </div>
 
-    <pre class="usage"><span class='fu'>is_torch_dtype</span><span class='op'>(</span><span class='va'>x</span><span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">is_torch_dtype</span><span class="op">(</span><span class="va">x</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>x</th>
-      <td><p>object to check.</p></td>
-    </tr>
-    </table>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>x</dt>
+<dd><p>object to check.</p></dd>
+</dl></div>
 
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     <p>Check if an object is a torch layout.</p>
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-    <pre class="usage"><span class='fu'>is_torch_layout</span><span class='op'>(</span><span class='va'>x</span><span class='op'>)</span></pre>
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-    <table class="ref-arguments">
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+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
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     <p>Checks if an object is a QScheme</p>
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-    <pre class="usage"><span class='fu'>is_torch_qscheme</span><span class='op'>(</span><span class='va'>x</span><span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
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-    <h1>Checks if a tensor is undefined</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/tensor.R'><code>R/tensor.R</code></a></small>
-    <div class="hidden name"><code>is_undefined_tensor.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Checks if a tensor is undefined</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/tensor.R" class="external-link"><code>R/tensor.R</code></a></small>
+      <div class="d-none name"><code>is_undefined_tensor.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Checks if a tensor is undefined</p>
     </div>
 
-    <pre class="usage"><span class='fu'>is_undefined_tensor</span><span class='op'>(</span><span class='va'>x</span><span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">is_undefined_tensor</span><span class="op">(</span><span class="va">x</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>x</th>
-      <td><p>tensor to check</p></td>
-    </tr>
-    </table>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>x</dt>
+<dd><p>tensor to check</p></dd>
+</dl></div>
 
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index ec1b86503..86244f86c 100644
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Compile TorchScript code into a graph</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/jit-compile.R'><code>R/jit-compile.R</code></a></small>
-    <div class="hidden name"><code>jit_compile.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Compile TorchScript code into a graph</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/jit-compile.R" class="external-link"><code>R/jit-compile.R</code></a></small>
+      <div class="d-none name"><code>jit_compile.Rd</code></div>
     </div>
 
-    <div class="ref-description">
-    <p>See the <a href='https://pytorch.org/docs/stable/jit_language_reference.html#language-reference'>TorchScript language reference</a> for
+    <div class="ref-description section level2">
+    <p>See the <a href="https://pytorch.org/docs/stable/jit_language_reference.html#language-reference" class="external-link">TorchScript language reference</a> for
 documentation on how to write TorchScript code.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>jit_compile</span><span class='op'>(</span><span class='va'>source</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>source</th>
-      <td><p>valid TorchScript source code.</p></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>comp</span> <span class='op'>&lt;-</span> <span class='fu'>jit_compile</span><span class='op'>(</span><span class='st'>"</span>
-<span class='r-in'>def fn (x):</span>
-<span class='r-in'>  return torch.abs(x)</span>
-<span class='r-in'>  </span>
-<span class='r-in'>def foo (x):</span>
-<span class='r-in'>  return torch.sum(x)</span>
-<span class='r-in'></span>
-<span class='r-in'>"</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>comp</span><span class='op'>$</span><span class='fu'>fn</span><span class='op'>(</span><span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='op'>-</span><span class='fl'>1</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>comp</span><span class='op'>$</span><span class='fu'>foo</span><span class='op'>(</span><span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>10</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> 0.0337583</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">jit_compile</span><span class="op">(</span><span class="va">source</span><span class="op">)</span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>source</dt>
+<dd><p>valid TorchScript source code.</p></dd>
+</dl></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">comp</span> <span class="op">&lt;-</span> <span class="fu">jit_compile</span><span class="op">(</span><span class="st">"</span>
+<span class="r-in">def fn (x):</span>
+<span class="r-in">  return torch.abs(x)</span>
+<span class="r-in"></span>
+<span class="r-in">def foo (x):</span>
+<span class="r-in">  return torch.sum(x)</span>
+<span class="r-in"></span>
+<span class="r-in">"</span><span class="op">)</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">comp</span><span class="op">$</span><span class="fu">fn</span><span class="op">(</span><span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="op">-</span><span class="fl">1</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">comp</span><span class="op">$</span><span class="fu">foo</span><span class="op">(</span><span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">10</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.467084</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{} ]</span>
+</code></pre></div>
+    </div>
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Loads a <code>script_function</code> or <code>script_module</code> previously saved with <code>jit_save</code></h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/trace.R'><code>R/trace.R</code></a></small>
-    <div class="hidden name"><code>jit_load.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Loads a <code>script_function</code> or <code>script_module</code> previously saved with <code>jit_save</code></h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/trace.R" class="external-link"><code>R/trace.R</code></a></small>
+      <div class="d-none name"><code>jit_load.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Loads a <code>script_function</code> or <code>script_module</code> previously saved with <code>jit_save</code></p>
     </div>
 
-    <pre class="usage"><span class='fu'>jit_load</span><span class='op'>(</span><span class='va'>path</span>, <span class='va'>...</span><span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">jit_load</span><span class="op">(</span><span class="va">path</span>, <span class="va">...</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>path</th>
-      <td><p>a path to a <code>script_function</code> or <code>script_module</code> serialized with
-<code><a href='jit_save.html'>jit_save()</a></code>.</p></td>
-    </tr>
-    <tr>
-      <th>...</th>
-      <td><p>currently unused.</p></td>
-    </tr>
-    </table>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>path</dt>
+<dd><p>a path to a <code>script_function</code> or <code>script_module</code> serialized with
+<code><a href="jit_save.html">jit_save()</a></code>.</p></dd>
+<dt>...</dt>
+<dd><p>currently unused.</p></dd>
+</dl></div>
 
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diff --git a/reference/jit_save.html b/reference/jit_save.html
index 99accd903..d968e2547 100644
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Saves a <code>script_function</code> to a path</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/trace.R'><code>R/trace.R</code></a></small>
-    <div class="hidden name"><code>jit_save.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Saves a <code>script_function</code> to a path</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/trace.R" class="external-link"><code>R/trace.R</code></a></small>
+      <div class="d-none name"><code>jit_save.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Saves a <code>script_function</code> to a path</p>
     </div>
 
-    <pre class="usage"><span class='fu'>jit_save</span><span class='op'>(</span><span class='va'>obj</span>, <span class='va'>path</span>, <span class='va'>...</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>obj</th>
-      <td><p>An <code>script_function</code> to save</p></td>
-    </tr>
-    <tr>
-      <th>path</th>
-      <td><p>The path to save the serialized function.</p></td>
-    </tr>
-    <tr>
-      <th>...</th>
-      <td><p>currently unused</p></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>fn</span> <span class='op'>&lt;-</span> <span class='kw'>function</span><span class='op'>(</span><span class='va'>x</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'>  <span class='fu'><a href='torch_relu.html'>torch_relu</a></span><span class='op'>(</span><span class='va'>x</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='op'>-</span><span class='fl'>1</span>, <span class='fl'>0</span>, <span class='fl'>1</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>tr_fn</span> <span class='op'>&lt;-</span> <span class='fu'><a href='jit_trace.html'>jit_trace</a></span><span class='op'>(</span><span class='va'>fn</span>, <span class='va'>input</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>tmp</span> <span class='op'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/r/base/tempfile.html'>tempfile</a></span><span class='op'>(</span><span class='st'>"tst"</span>, fileext <span class='op'>=</span> <span class='st'>"pt"</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>jit_save</span><span class='op'>(</span><span class='va'>tr_fn</span>, <span class='va'>tmp</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">jit_save</span><span class="op">(</span><span class="va">obj</span>, <span class="va">path</span>, <span class="va">...</span><span class="op">)</span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>obj</dt>
+<dd><p>An <code>script_function</code> to save</p></dd>
+<dt>path</dt>
+<dd><p>The path to save the serialized function.</p></dd>
+<dt>...</dt>
+<dd><p>currently unused</p></dd>
+</dl></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">fn</span> <span class="op">&lt;-</span> <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in">  <span class="fu"><a href="torch_relu.html">torch_relu</a></span><span class="op">(</span><span class="va">x</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="op">-</span><span class="fl">1</span>, <span class="fl">0</span>, <span class="fl">1</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">tr_fn</span> <span class="op">&lt;-</span> <span class="fu"><a href="jit_trace.html">jit_trace</a></span><span class="op">(</span><span class="va">fn</span>, <span class="va">input</span><span class="op">)</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">tmp</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/tempfile.html" class="external-link">tempfile</a></span><span class="op">(</span><span class="st">"tst"</span>, fileext <span class="op">=</span> <span class="st">"pt"</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">jit_save</span><span class="op">(</span><span class="va">tr_fn</span>, <span class="va">tmp</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
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+<li class="active nav-item">
+  <a class="nav-link" href="../reference/index.html">Reference</a>
+</li>
+<li class="nav-item">
+  <a class="nav-link" href="../news/index.html">Changelog</a>
+</li>
+      </ul><form class="form-inline my-2 my-lg-0" role="search">
+        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
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+      </ul></div>
+
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+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Saves a <code>script_function</code> or <code>script_module</code> in bytecode form,
+to be loaded on a mobile device</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/trace.R" class="external-link"><code>R/trace.R</code></a></small>
+      <div class="d-none name"><code>jit_save_for_mobile.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>Saves a <code>script_function</code> or <code>script_module</code> in bytecode form,
+to be loaded on a mobile device</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">jit_save_for_mobile</span><span class="op">(</span><span class="va">obj</span>, <span class="va">path</span>, <span class="va">...</span><span class="op">)</span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>obj</dt>
+<dd><p>An <code>script_function</code> or <code>script_module</code> to save</p></dd>
+<dt>path</dt>
+<dd><p>The path to save the serialized function.</p></dd>
+<dt>...</dt>
+<dd><p>currently unused</p></dd>
+</dl></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">fn</span> <span class="op">&lt;-</span> <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in">  <span class="fu"><a href="torch_relu.html">torch_relu</a></span><span class="op">(</span><span class="va">x</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="op">-</span><span class="fl">1</span>, <span class="fl">0</span>, <span class="fl">1</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">tr_fn</span> <span class="op">&lt;-</span> <span class="fu"><a href="jit_trace.html">jit_trace</a></span><span class="op">(</span><span class="va">fn</span>, <span class="va">input</span><span class="op">)</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">tmp</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/tempfile.html" class="external-link">tempfile</a></span><span class="op">(</span><span class="st">"tst"</span>, fileext <span class="op">=</span> <span class="st">"pt"</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">jit_save_for_mobile</span><span class="op">(</span><span class="va">tr_fn</span>, <span class="va">tmp</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
+
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diff --git a/reference/jit_scalar.html b/reference/jit_scalar.html
index db915d788..db21bc65a 100644
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Adds the 'jit_scalar' class to the input</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/ivalue.R'><code>R/ivalue.R</code></a></small>
-    <div class="hidden name"><code>jit_scalar.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Adds the 'jit_scalar' class to the input</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/ivalue.R" class="external-link"><code>R/ivalue.R</code></a></small>
+      <div class="d-none name"><code>jit_scalar.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Allows disambiguating length 1 vectors from scalars when passing
 them to the jit.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>jit_scalar</span><span class='op'>(</span><span class='va'>x</span><span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">jit_scalar</span><span class="op">(</span><span class="va">x</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>x</th>
-      <td><p>a length 1 R vector.</p></td>
-    </tr>
-    </table>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>x</dt>
+<dd><p>a length 1 R vector.</p></dd>
+</dl></div>
 
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index 8666ba76c..b8775dcd8 100644
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Trace a function and return an executable <code>script_function</code>.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/trace.R'><code>R/trace.R</code></a></small>
-    <div class="hidden name"><code>jit_trace.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Trace a function and return an executable <code>script_function</code>.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/trace.R" class="external-link"><code>R/trace.R</code></a></small>
+      <div class="d-none name"><code>jit_trace.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Using <code>jit_trace</code>, you can turn an existing R function into a TorchScript
 <code>script_function</code>. You must provide example inputs, and we run the function,
 recording the operations performed on all the tensors.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>jit_trace</span><span class='op'>(</span><span class='va'>func</span>, <span class='va'>...</span>, strict <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">jit_trace</span><span class="op">(</span><span class="va">func</span>, <span class="va">...</span>, strict <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>func</th>
-      <td><p>An R function that will be run with <code>example_inputs</code>. func arguments
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>func</dt>
+<dd><p>An R function that will be run with <code>example_inputs</code>. func arguments
 and return values must be tensors or (possibly nested) lists that contain tensors.
-Can also be a <code><a href='nn_module.html'>nn_module()</a></code>, in such case <code><a href='jit_trace_module.html'>jit_trace_module()</a></code> is used to trace
-that module.</p></td>
-    </tr>
-    <tr>
-      <th>...</th>
-      <td><p>example inputs that will be passed to the function while
+Can also be a <code><a href="nn_module.html">nn_module()</a></code>, in such case <code><a href="jit_trace_module.html">jit_trace_module()</a></code> is used to trace
+that module.</p></dd>
+<dt>...</dt>
+<dd><p>example inputs that will be passed to the function while
 tracing. The resulting trace can be run with inputs of different types and
 shapes assuming the traced operations support those types and shapes.
 <code>example_inputs</code> may also be a single Tensor in which case it is automatically
 wrapped in a list. Note that <code>...</code> <strong>can not</strong> be named, and the order is
-respected.</p></td>
-    </tr>
-    <tr>
-      <th>strict</th>
-      <td><p>run the tracer in a strict mode or not (default: <code>TRUE</code>). Only
+respected.</p></dd>
+<dt>strict</dt>
+<dd><p>run the tracer in a strict mode or not (default: <code>TRUE</code>). Only
 turn this off when you want the tracer to record your mutable container types
 (currently list/dict) and you are sure that the container you are using in
 your problem is a constant structure and does not get used as control flow
-(<code>if</code>, <code>for</code>) conditions.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
-
+(<code>if</code>, <code>for</code>) conditions.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
     <p>An <code>script_function</code> if <code>func</code> is a function and <code>script_module</code> if
-<code>func</code> is a <code><a href='nn_module.html'>nn_module()</a></code>.</p>
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
+<code>func</code> is a <code><a href="nn_module.html">nn_module()</a></code>.</p>
+    </div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>The resulting recording of a standalone function produces a <code>script_function</code>.
 In the future we will also support tracing <code>nn_modules</code>.</p>
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p>Scripting is not yet supported in R.</p>
-    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></a></h2>
     
 
 
@@ -247,8 +138,7 @@ Tracing only records operations done when the given function is run on the given
 tensors. Therefore, the returned <code>script_function</code> will always run the same traced
 graph on any input. This has some important implications when your module is
 expected to run different sets of operations, depending on the input and/or the
-module state. For example,</p><ul>
-<li><p>Tracing will not record any control-flow like if-statements or loops. When
+module state. For example,</p><ul><li><p>Tracing will not record any control-flow like if-statements or loops. When
 this control-flow is constant across your module, this is fine and it often
 inlines the control-flow decisions. But sometimes the control-flow is actually
 part of the model itself. For instance, a recurrent network is a loop over
@@ -256,54 +146,46 @@ the (possibly dynamic) length of an input sequence.</p></li>
 <li><p>In the returned <code>script_function</code>, operations that have different behaviors
 in training and eval modes will always behave as if it is in the mode it was
 in during tracing, no matter which mode the <code>script_function</code> is in.</p></li>
-</ul>
-
-<p>In cases like these, tracing would not be appropriate and scripting is a better
+</ul><p>In cases like these, tracing would not be appropriate and scripting is a better
 choice. If you trace such models, you may silently get incorrect results on
 subsequent invocations of the model. The tracer will try to emit warnings when
 doing something that may cause an incorrect trace to be produced.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>fn</span> <span class='op'>&lt;-</span> <span class='kw'>function</span><span class='op'>(</span><span class='va'>x</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'> <span class='fu'><a href='torch_relu.html'>torch_relu</a></span><span class='op'>(</span><span class='va'>x</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='op'>-</span><span class='fl'>1</span>, <span class='fl'>0</span>, <span class='fl'>1</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>tr_fn</span> <span class='op'>&lt;-</span> <span class='fu'>jit_trace</span><span class='op'>(</span><span class='va'>fn</span>, <span class='va'>input</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>tr_fn</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{3} ]</span>
-</pre>
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">fn</span> <span class="op">&lt;-</span> <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in">  <span class="fu"><a href="torch_relu.html">torch_relu</a></span><span class="op">(</span><span class="va">x</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-in"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="op">-</span><span class="fl">1</span>, <span class="fl">0</span>, <span class="fl">1</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">tr_fn</span> <span class="op">&lt;-</span> <span class="fu">jit_trace</span><span class="op">(</span><span class="va">fn</span>, <span class="va">input</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">tr_fn</span><span class="op">(</span><span class="va">input</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Trace a module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/trace.R'><code>R/trace.R</code></a></small>
-    <div class="hidden name"><code>jit_trace_module.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Trace a module</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/trace.R" class="external-link"><code>R/trace.R</code></a></small>
+      <div class="d-none name"><code>jit_trace_module.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Trace a module and return an executable ScriptModule that will be optimized
-using just-in-time compilation. When a module is passed to <code><a href='jit_trace.html'>jit_trace()</a></code>, only
+using just-in-time compilation. When a module is passed to <code><a href="jit_trace.html">jit_trace()</a></code>, only
 the forward method is run and traced. With <code>jit_trace_module()</code>, you can specify
 a named list of method names to example inputs to trace (see the inputs)
 argument below.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>jit_trace_module</span><span class='op'>(</span><span class='va'>mod</span>, <span class='va'>...</span>, strict <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">jit_trace_module</span><span class="op">(</span><span class="va">mod</span>, <span class="va">...</span>, strict <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>mod</th>
-      <td><p>A torch <code><a href='nn_module.html'>nn_module()</a></code>  containing methods whose names are specified
-in inputs. The given methods will be compiled as a part of a single ScriptModule.</p></td>
-    </tr>
-    <tr>
-      <th>...</th>
-      <td><p>A named list containing sample inputs indexed by method names
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>mod</dt>
+<dd><p>A torch <code><a href="nn_module.html">nn_module()</a></code>  containing methods whose names are specified
+in inputs. The given methods will be compiled as a part of a single ScriptModule.</p></dd>
+<dt>...</dt>
+<dd><p>A named list containing sample inputs indexed by method names
 in mod. The inputs will be passed to methods whose names correspond to inputs
-keys while tracing. <code>list('forward'=example_forward_input, 'method2'=example_method2_input)</code>.</p></td>
-    </tr>
-    <tr>
-      <th>strict</th>
-      <td><p>run the tracer in a strict mode or not (default: <code>TRUE</code>). Only
+keys while tracing. <code>list('forward'=example_forward_input, 'method2'=example_method2_input)</code>.</p></dd>
+<dt>strict</dt>
+<dd><p>run the tracer in a strict mode or not (default: <code>TRUE</code>). Only
 turn this off when you want the tracer to record your mutable container types
 (currently list/dict) and you are sure that the container you are using in
 your problem is a constant structure and does not get used as control flow
-(<code>if</code>, <code>for</code>) conditions.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>See <a href='jit_trace.html'>jit_trace</a> for more information on tracing.</p>
+(<code>if</code>, <code>for</code>) conditions.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
+    <p>See <a href="jit_trace.html">jit_trace</a> for more information on tracing.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>linear</span> <span class='op'>&lt;-</span> <span class='fu'><a href='nn_linear.html'>nn_linear</a></span><span class='op'>(</span><span class='fl'>10</span>, <span class='fl'>1</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>tr_linear</span> <span class='op'>&lt;-</span> <span class='fu'>jit_trace_module</span><span class='op'>(</span><span class='va'>linear</span>, forward <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span><span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>10</span>, <span class='fl'>10</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>x</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>10</span>, <span class='fl'>10</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'><a href='torch_allclose.html'>torch_allclose</a></span><span class='op'>(</span><span class='fu'>linear</span><span class='op'>(</span><span class='va'>x</span><span class='op'>)</span>, <span class='fu'>tr_linear</span><span class='op'>(</span><span class='va'>x</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [1] TRUE</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">linear</span> <span class="op">&lt;-</span> <span class="fu"><a href="nn_linear.html">nn_linear</a></span><span class="op">(</span><span class="fl">10</span>, <span class="fl">1</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">tr_linear</span> <span class="op">&lt;-</span> <span class="fu">jit_trace_module</span><span class="op">(</span><span class="va">linear</span>, forward <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">10</span>, <span class="fl">10</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">10</span>, <span class="fl">10</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu"><a href="torch_allclose.html">torch_allclose</a></span><span class="op">(</span><span class="fu">linear</span><span class="op">(</span><span class="va">x</span><span class="op">)</span>, <span class="fu">tr_linear</span><span class="op">(</span><span class="va">x</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] TRUE</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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     <p>Allows specifying that an output or input must be considered a jit
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+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Computes the Cholesky decomposition of a complex Hermitian or real symmetric positive-definite matrix.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/linalg.R'><code>R/linalg.R</code></a></small>
-    <div class="hidden name"><code>linalg_cholesky.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Computes the Cholesky decomposition of a complex Hermitian or real symmetric positive-definite matrix.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/linalg.R" class="external-link"><code>R/linalg.R</code></a></small>
+      <div class="d-none name"><code>linalg_cholesky.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Letting  be  or ,
 the <strong>Cholesky decomposition</strong> of a complex Hermitian or real symmetric positive-definite matrix
  is defined as</p>
     </div>
 
-    <pre class="usage"><span class='fu'>linalg_cholesky</span><span class='op'>(</span><span class='va'>A</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>A</th>
-      <td><p>(Tensor): tensor of shape <code>(*, n, n)</code> where <code>*</code> is zero or more batch dimensions
-consisting of symmetric or Hermitian positive-definite matrices.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">linalg_cholesky</span><span class="op">(</span><span class="va">A</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <p><link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/npm/katex@0.13.11/dist/katex.min.css" data-external="1">
-<span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mi>A</mi><mo>=</mo><mi>L</mi><msup><mi>L</mi><mi>H</mi></msup><mpadded width="0px"><mrow><mspace width="2em"/><mi>L</mi><mo>∈</mo><msup><mi mathvariant="double-struck">K</mi><mrow><mi>n</mi><mo>×</mo><mi>n</mi></mrow></msup></mrow></mpadded></mrow><annotation encoding="application/x-tex">
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>A</dt>
+<dd><p>(Tensor): tensor of shape <code>(*, n, n)</code> where <code>*</code> is zero or more batch dimensions
+consisting of symmetric or Hermitian positive-definite matrices.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
+    <p><link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/npm/katex@0.15.2/dist/katex.min.css" data-external="1"><span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mi>A</mi><mo>=</mo><mi>L</mi><msup><mi>L</mi><mi>H</mi></msup><mpadded width="0px"><mrow><mspace width="2em"></mspace><mi>L</mi><mo>∈</mo><msup><mi mathvariant="double-struck">K</mi><mrow><mi>n</mi><mo>×</mo><mi>n</mi></mrow></msup></mrow></mpadded></mrow><annotation encoding="application/x-tex">
 A = LL^{H}\mathrlap{\qquad L \in \mathbb{K}^{n \times n}}
-</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.68333em;vertical-align:0em;"></span><span class="mord mathnormal">A</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span></span><span class="base"><span class="strut" style="height:0.9304309999999999em;vertical-align:-0.0391em;"></span><span class="mord mathnormal">L</span><span class="mord"><span class="mord mathnormal">L</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8913309999999999em;"><span style="top:-3.113em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight" style="margin-right:0.08125em;">H</span></span></span></span></span></span></span></span></span><span class="mord vbox"><span class="thinbox"><span class="rlap"><span class="strut" style="height:0.8604310000000001em;vertical-align:-0.0391em;"></span><span class="inner"><span class="mord"><span class="mspace" style="margin-right:2em;"></span><span class="mord mathnormal">L</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span><span class="mrel">∈</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span><span class="mord"><span class="mord mathbb">K</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.821331em;"><span style="top:-3.113em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">n</span><span class="mbin mtight">×</span><span class="mord mathnormal mtight">n</span></span></span></span></span></span></span></span></span></span></span><span class="fix"></span></span></span></span></span></span></span></span></p>
+</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6833em;"></span><span class="mord mathnormal">A</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:0.9304em;vertical-align:-0.0391em;"></span><span class="mord mathnormal">L</span><span class="mord"><span class="mord mathnormal">L</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8913em;"><span style="top:-3.113em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight" style="margin-right:0.08125em;">H</span></span></span></span></span></span></span></span></span><span class="mord vbox"><span class="thinbox"><span class="rlap"><span class="strut" style="height:0.8604em;vertical-align:-0.0391em;"></span><span class="inner"><span class="mord"><span class="mspace" style="margin-right:2em;"></span><span class="mord mathnormal">L</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">∈</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mord"><span class="mord mathbb">K</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8213em;"><span style="top:-3.113em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">n</span><span class="mbin mtight">×</span><span class="mord mathnormal mtight">n</span></span></span></span></span></span></span></span></span></span></span><span class="fix"></span></span></span></span></span></span></span></span></p>
 <p>where  is a lower triangular matrix and
  is the conjugate transpose when  is complex, and the
 transpose when  is real-valued.</p>
 <p>Supports input of float, double, cfloat and cdouble dtypes.
 Also supports batches of matrices, and if <code>A</code> is a batch of matrices then
 the output has the same batch dimensions.</p>
-    <h2 class="hasAnchor" id="see-also"><a class="anchor" href="#see-also"></a>See also</h2>
-
-    <div class='dont-index'>
-<ul>
-<li><p><code><a href='linalg_cholesky_ex.html'>linalg_cholesky_ex()</a></code> for a version of this operation that
+    </div>
+    <div class="section level2">
+    <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
+    <div class="dont-index">
+<ul><li><p><code><a href="linalg_cholesky_ex.html">linalg_cholesky_ex()</a></code> for a version of this operation that
 skips the (slow) error checking by default and instead returns the debug
 information. This makes it a faster way to check if a matrix is
 positive-definite.
-<code><a href='linalg_eigh.html'>linalg_eigh()</a></code> for a different decomposition of a Hermitian matrix.
+<code><a href="linalg_eigh.html">linalg_eigh()</a></code> for a different decomposition of a Hermitian matrix.
 The eigenvalue decomposition gives more information about the matrix but it
 slower to compute than the Cholesky decomposition.</p></li>
-</ul>
-
-<p>Other linalg: 
-<code><a href='linalg_cholesky_ex.html'>linalg_cholesky_ex</a>()</code>,
-<code><a href='linalg_det.html'>linalg_det</a>()</code>,
-<code><a href='linalg_eigh.html'>linalg_eigh</a>()</code>,
-<code><a href='linalg_eigvalsh.html'>linalg_eigvalsh</a>()</code>,
-<code><a href='linalg_eigvals.html'>linalg_eigvals</a>()</code>,
-<code><a href='linalg_eig.html'>linalg_eig</a>()</code>,
-<code><a href='linalg_householder_product.html'>linalg_householder_product</a>()</code>,
-<code><a href='linalg_inv_ex.html'>linalg_inv_ex</a>()</code>,
-<code><a href='linalg_inv.html'>linalg_inv</a>()</code>,
-<code><a href='linalg_lstsq.html'>linalg_lstsq</a>()</code>,
-<code><a href='linalg_matrix_norm.html'>linalg_matrix_norm</a>()</code>,
-<code><a href='linalg_matrix_power.html'>linalg_matrix_power</a>()</code>,
-<code><a href='linalg_matrix_rank.html'>linalg_matrix_rank</a>()</code>,
-<code><a href='linalg_multi_dot.html'>linalg_multi_dot</a>()</code>,
-<code><a href='linalg_norm.html'>linalg_norm</a>()</code>,
-<code><a href='linalg_pinv.html'>linalg_pinv</a>()</code>,
-<code><a href='linalg_qr.html'>linalg_qr</a>()</code>,
-<code><a href='linalg_slogdet.html'>linalg_slogdet</a>()</code>,
-<code><a href='linalg_solve.html'>linalg_solve</a>()</code>,
-<code><a href='linalg_svdvals.html'>linalg_svdvals</a>()</code>,
-<code><a href='linalg_svd.html'>linalg_svd</a>()</code>,
-<code><a href='linalg_tensorinv.html'>linalg_tensorinv</a>()</code>,
-<code><a href='linalg_tensorsolve.html'>linalg_tensorsolve</a>()</code>,
-<code><a href='linalg_vector_norm.html'>linalg_vector_norm</a>()</code></p></div>
+</ul><p>Other linalg: 
+<code><a href="linalg_cholesky_ex.html">linalg_cholesky_ex</a>()</code>,
+<code><a href="linalg_det.html">linalg_det</a>()</code>,
+<code><a href="linalg_eigh.html">linalg_eigh</a>()</code>,
+<code><a href="linalg_eigvalsh.html">linalg_eigvalsh</a>()</code>,
+<code><a href="linalg_eigvals.html">linalg_eigvals</a>()</code>,
+<code><a href="linalg_eig.html">linalg_eig</a>()</code>,
+<code><a href="linalg_householder_product.html">linalg_householder_product</a>()</code>,
+<code><a href="linalg_inv_ex.html">linalg_inv_ex</a>()</code>,
+<code><a href="linalg_inv.html">linalg_inv</a>()</code>,
+<code><a href="linalg_lstsq.html">linalg_lstsq</a>()</code>,
+<code><a href="linalg_matrix_norm.html">linalg_matrix_norm</a>()</code>,
+<code><a href="linalg_matrix_power.html">linalg_matrix_power</a>()</code>,
+<code><a href="linalg_matrix_rank.html">linalg_matrix_rank</a>()</code>,
+<code><a href="linalg_multi_dot.html">linalg_multi_dot</a>()</code>,
+<code><a href="linalg_norm.html">linalg_norm</a>()</code>,
+<code><a href="linalg_pinv.html">linalg_pinv</a>()</code>,
+<code><a href="linalg_qr.html">linalg_qr</a>()</code>,
+<code><a href="linalg_slogdet.html">linalg_slogdet</a>()</code>,
+<code><a href="linalg_solve.html">linalg_solve</a>()</code>,
+<code><a href="linalg_svdvals.html">linalg_svdvals</a>()</code>,
+<code><a href="linalg_svd.html">linalg_svd</a>()</code>,
+<code><a href="linalg_tensorinv.html">linalg_tensorinv</a>()</code>,
+<code><a href="linalg_tensorsolve.html">linalg_tensorsolve</a>()</code>,
+<code><a href="linalg_vector_norm.html">linalg_vector_norm</a>()</code></p></div>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_eye.html'>torch_eye</a></span><span class='op'>(</span><span class='fl'>10</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>linalg_cholesky</span><span class='op'>(</span><span class='va'>a</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1  0  0  0  0  0  0  0  0  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0  1  0  0  0  0  0  0  0  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0  0  1  0  0  0  0  0  0  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0  0  0  1  0  0  0  0  0  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0  0  0  0  1  0  0  0  0  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0  0  0  0  0  1  0  0  0  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0  0  0  0  0  0  1  0  0  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0  0  0  0  0  0  0  1  0  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0  0  0  0  0  0  0  0  1  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0  0  0  0  0  0  0  0  0  1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{10,10} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_eye.html">torch_eye</a></span><span class="op">(</span><span class="fl">10</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">linalg_cholesky</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1  0  0  0  0  0  0  0  0  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0  1  0  0  0  0  0  0  0  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0  0  1  0  0  0  0  0  0  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0  0  0  1  0  0  0  0  0  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0  0  0  0  1  0  0  0  0  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0  0  0  0  0  1  0  0  0  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0  0  0  0  0  0  1  0  0  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0  0  0  0  0  0  0  1  0  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0  0  0  0  0  0  0  0  1  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0  0  0  0  0  0  0  0  0  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{10,10} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer>
-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
 </div>
 
-<div class="pkgdown">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p>
+<div class="pkgdown-footer-right">
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diff --git a/reference/linalg_cholesky_ex.html b/reference/linalg_cholesky_ex.html
index 4f34a9da3..482e02355 100644
--- a/reference/linalg_cholesky_ex.html
+++ b/reference/linalg_cholesky_ex.html
@@ -1,50 +1,5 @@
-<!-- Generated by pkgdown: do not edit by hand -->
 <!DOCTYPE html>
-<html lang="en">
-  <head>
-  <meta charset="utf-8">
-<meta http-equiv="X-UA-Compatible" content="IE=edge">
-<meta name="viewport" content="width=device-width, initial-scale=1.0">
-
-<title>Computes the Cholesky decomposition of a complex Hermitian or real
-symmetric positive-definite matrix. — linalg_cholesky_ex • torch</title>
-
-
-<!-- jquery -->
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-<meta property="og:title" content="Computes the Cholesky decomposition of a complex Hermitian or real
-symmetric positive-definite matrix. — linalg_cholesky_ex" />
-<meta property="og:description" content="This function skips the (slow) error checking and error message construction
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="This function skips the (slow) error checking and error message construction
 of linalg_cholesky(), instead directly returning the LAPACK
 error codes as part of a named tuple (L, info). This makes this function
 a faster way to check if a matrix is positive-definite, and it provides an
@@ -59,157 +14,108 @@ then info stores a positive integer for the corresponding matrix.
 The positive integer indicates the order of the leading minor that is not positive-definite,
 and the decomposition could not be completed.
 info filled with zeros indicates that the decomposition was successful.
-If check_errors=TRUE and info contains positive integers, then a RuntimeError is thrown." />
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-
-<!--[if lt IE 9]>
+If check_errors=TRUE and info contains positive integers, then a RuntimeError is thrown."><title>Computes the Cholesky decomposition of a complex Hermitian or real
+symmetric positive-definite matrix. — linalg_cholesky_ex • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.1.0/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.1.0/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Computes the Cholesky decomposition of a complex Hermitian or real
+symmetric positive-definite matrix. — linalg_cholesky_ex"><meta property="og:description" content="This function skips the (slow) error checking and error message construction
+of linalg_cholesky(), instead directly returning the LAPACK
+error codes as part of a named tuple (L, info). This makes this function
+a faster way to check if a matrix is positive-definite, and it provides an
+opportunity to handle decomposition errors more gracefully or performantly
+than linalg_cholesky() does.
+Supports input of float, double, cfloat and cdouble dtypes.
+Also supports batches of matrices, and if A is a batch of matrices then
+the output has the same batch dimensions.
+If A is not a Hermitian positive-definite matrix, or if it's a batch of matrices
+and one or more of them is not a Hermitian positive-definite matrix,
+then info stores a positive integer for the corresponding matrix.
+The positive integer indicates the order of the leading minor that is not positive-definite,
+and the decomposition could not be completed.
+info filled with zeros indicates that the decomposition was successful.
+If check_errors=TRUE and info contains positive integers, then a RuntimeError is thrown."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Computes the Cholesky decomposition of a complex Hermitian or real
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Computes the Cholesky decomposition of a complex Hermitian or real
 symmetric positive-definite matrix.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/linalg.R'><code>R/linalg.R</code></a></small>
-    <div class="hidden name"><code>linalg_cholesky_ex.Rd</code></div>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/linalg.R" class="external-link"><code>R/linalg.R</code></a></small>
+      <div class="d-none name"><code>linalg_cholesky_ex.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>This function skips the (slow) error checking and error message construction
-of <code><a href='linalg_cholesky.html'>linalg_cholesky()</a></code>, instead directly returning the LAPACK
+of <code><a href="linalg_cholesky.html">linalg_cholesky()</a></code>, instead directly returning the LAPACK
 error codes as part of a named tuple <code>(L, info)</code>. This makes this function
 a faster way to check if a matrix is positive-definite, and it provides an
 opportunity to handle decomposition errors more gracefully or performantly
-than <code><a href='linalg_cholesky.html'>linalg_cholesky()</a></code> does.
+than <code><a href="linalg_cholesky.html">linalg_cholesky()</a></code> does.
 Supports input of float, double, cfloat and cdouble dtypes.
 Also supports batches of matrices, and if <code>A</code> is a batch of matrices then
 the output has the same batch dimensions.
@@ -222,99 +128,91 @@ and the decomposition could not be completed.
 If <code>check_errors=TRUE</code> and <code>info</code> contains positive integers, then a RuntimeError is thrown.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>linalg_cholesky_ex</span><span class='op'>(</span><span class='va'>A</span>, check_errors <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>A</th>
-      <td><p>(Tensor): the Hermitian <code>n \times n</code> matrix or the batch of such matrices of size
-<code>(*, n, n)</code> where <code>*</code> is one or more batch dimensions.</p></td>
-    </tr>
-    <tr>
-      <th>check_errors</th>
-      <td><p>(bool, optional): controls whether to check the content of <code>infos</code>. Default: <code>FALSE</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">linalg_cholesky_ex</span><span class="op">(</span><span class="va">A</span>, check_errors <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>A</dt>
+<dd><p>(Tensor): the Hermitian <code>n \times n</code> matrix or the batch of such matrices of size
+<code>(*, n, n)</code> where <code>*</code> is one or more batch dimensions.</p></dd>
+<dt>check_errors</dt>
+<dd><p>(bool, optional): controls whether to check the content of <code>infos</code>. Default: <code>FALSE</code>.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p>If <code>A</code> is on a CUDA device, this function may synchronize that device with the CPU.</p>
 <p>This function is "experimental" and it may change in a future PyTorch release.</p>
-    <h2 class="hasAnchor" id="see-also"><a class="anchor" href="#see-also"></a>See also</h2>
-
-    <div class='dont-index'><p><code><a href='linalg_cholesky.html'>linalg_cholesky()</a></code> is a NumPy compatible variant that always checks for errors.</p>
+    </div>
+    <div class="section level2">
+    <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
+    <div class="dont-index"><p><code><a href="linalg_cholesky.html">linalg_cholesky()</a></code> is a NumPy compatible variant that always checks for errors.</p>
 <p>Other linalg: 
-<code><a href='linalg_cholesky.html'>linalg_cholesky</a>()</code>,
-<code><a href='linalg_det.html'>linalg_det</a>()</code>,
-<code><a href='linalg_eigh.html'>linalg_eigh</a>()</code>,
-<code><a href='linalg_eigvalsh.html'>linalg_eigvalsh</a>()</code>,
-<code><a href='linalg_eigvals.html'>linalg_eigvals</a>()</code>,
-<code><a href='linalg_eig.html'>linalg_eig</a>()</code>,
-<code><a href='linalg_householder_product.html'>linalg_householder_product</a>()</code>,
-<code><a href='linalg_inv_ex.html'>linalg_inv_ex</a>()</code>,
-<code><a href='linalg_inv.html'>linalg_inv</a>()</code>,
-<code><a href='linalg_lstsq.html'>linalg_lstsq</a>()</code>,
-<code><a href='linalg_matrix_norm.html'>linalg_matrix_norm</a>()</code>,
-<code><a href='linalg_matrix_power.html'>linalg_matrix_power</a>()</code>,
-<code><a href='linalg_matrix_rank.html'>linalg_matrix_rank</a>()</code>,
-<code><a href='linalg_multi_dot.html'>linalg_multi_dot</a>()</code>,
-<code><a href='linalg_norm.html'>linalg_norm</a>()</code>,
-<code><a href='linalg_pinv.html'>linalg_pinv</a>()</code>,
-<code><a href='linalg_qr.html'>linalg_qr</a>()</code>,
-<code><a href='linalg_slogdet.html'>linalg_slogdet</a>()</code>,
-<code><a href='linalg_solve.html'>linalg_solve</a>()</code>,
-<code><a href='linalg_svdvals.html'>linalg_svdvals</a>()</code>,
-<code><a href='linalg_svd.html'>linalg_svd</a>()</code>,
-<code><a href='linalg_tensorinv.html'>linalg_tensorinv</a>()</code>,
-<code><a href='linalg_tensorsolve.html'>linalg_tensorsolve</a>()</code>,
-<code><a href='linalg_vector_norm.html'>linalg_vector_norm</a>()</code></p></div>
+<code><a href="linalg_cholesky.html">linalg_cholesky</a>()</code>,
+<code><a href="linalg_det.html">linalg_det</a>()</code>,
+<code><a href="linalg_eigh.html">linalg_eigh</a>()</code>,
+<code><a href="linalg_eigvalsh.html">linalg_eigvalsh</a>()</code>,
+<code><a href="linalg_eigvals.html">linalg_eigvals</a>()</code>,
+<code><a href="linalg_eig.html">linalg_eig</a>()</code>,
+<code><a href="linalg_householder_product.html">linalg_householder_product</a>()</code>,
+<code><a href="linalg_inv_ex.html">linalg_inv_ex</a>()</code>,
+<code><a href="linalg_inv.html">linalg_inv</a>()</code>,
+<code><a href="linalg_lstsq.html">linalg_lstsq</a>()</code>,
+<code><a href="linalg_matrix_norm.html">linalg_matrix_norm</a>()</code>,
+<code><a href="linalg_matrix_power.html">linalg_matrix_power</a>()</code>,
+<code><a href="linalg_matrix_rank.html">linalg_matrix_rank</a>()</code>,
+<code><a href="linalg_multi_dot.html">linalg_multi_dot</a>()</code>,
+<code><a href="linalg_norm.html">linalg_norm</a>()</code>,
+<code><a href="linalg_pinv.html">linalg_pinv</a>()</code>,
+<code><a href="linalg_qr.html">linalg_qr</a>()</code>,
+<code><a href="linalg_slogdet.html">linalg_slogdet</a>()</code>,
+<code><a href="linalg_solve.html">linalg_solve</a>()</code>,
+<code><a href="linalg_svdvals.html">linalg_svdvals</a>()</code>,
+<code><a href="linalg_svd.html">linalg_svd</a>()</code>,
+<code><a href="linalg_tensorinv.html">linalg_tensorinv</a>()</code>,
+<code><a href="linalg_tensorsolve.html">linalg_tensorsolve</a>()</code>,
+<code><a href="linalg_vector_norm.html">linalg_vector_norm</a>()</code></p></div>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>A</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>out</span> <span class='op'>=</span> <span class='fu'>linalg_cholesky_ex</span><span class='op'>(</span><span class='va'>A</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>out</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> $L</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.4019  0.0000</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -1.8442 -0.6523</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{2,2} ]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> $info</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> 1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUIntType{} ]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-</pre>
-  </div>
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+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">A</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">out</span> <span class="op">&lt;-</span> <span class="fu">linalg_cholesky_ex</span><span class="op">(</span><span class="va">A</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">out</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> $L</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -1.0269  0.0000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.2623 -0.0618</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2,2} ]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> $info</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUIntType{} ]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+</code></pre></div>
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Computes the condition number of a matrix with respect to a matrix norm.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/linalg.R'><code>R/linalg.R</code></a></small>
-    <div class="hidden name"><code>linalg_cond.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Computes the condition number of a matrix with respect to a matrix norm.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/linalg.R" class="external-link"><code>R/linalg.R</code></a></small>
+      <div class="d-none name"><code>linalg_cond.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Letting  be  or ,
 the <strong>condition number</strong>  of a matrix
  is defined as</p>
     </div>
 
-    <pre class="usage"><span class='fu'>linalg_cond</span><span class='op'>(</span><span class='va'>A</span>, p <span class='op'>=</span> <span class='cn'>NULL</span><span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">linalg_cond</span><span class="op">(</span><span class="va">A</span>, p <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>A</th>
-      <td><p>(Tensor): tensor of shape <code>(*, m, n)</code> where <code>*</code> is zero or more batch dimensions
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>A</dt>
+<dd><p>(Tensor): tensor of shape <code>(*, m, n)</code> where <code>*</code> is zero or more batch dimensions
 for <code>p</code> in <code>(2, -2)</code>, and of shape <code>(*, n, n)</code> where every matrix
-is invertible for <code>p</code> in <code>('fro', 'nuc', inf, -inf, 1, -1)</code>.</p></td>
-    </tr>
-    <tr>
-      <th>p</th>
-      <td><p>(int, inf, -inf, 'fro', 'nuc', optional):
-the type of the matrix norm to use in the computations (see above). Default: <code>NULL</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
-
+is invertible for <code>p</code> in <code>('fro', 'nuc', inf, -inf, 1, -1)</code>.</p></dd>
+<dt>p</dt>
+<dd><p>(int, inf, -inf, 'fro', 'nuc', optional):
+the type of the matrix norm to use in the computations (see above). Default: <code>NULL</code></p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
     <p>A real-valued tensor, even when <code>A</code> is complex.</p>
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p><link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/npm/katex@0.13.11/dist/katex.min.css" data-external="1">
-<span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mi>κ</mi><mo stretchy="false">(</mo><mi>A</mi><mo stretchy="false">)</mo><mo>=</mo><mi mathvariant="normal">∥</mi><mi>A</mi><msub><mi mathvariant="normal">∥</mi><mi>p</mi></msub><mi mathvariant="normal">∥</mi><msup><mi>A</mi><mrow><mo>−</mo><mn>1</mn></mrow></msup><msub><mi mathvariant="normal">∥</mi><mi>p</mi></msub></mrow><annotation encoding="application/x-tex">\kappa(A) = \|A\|_p\|A^{-1}\|_p</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord mathnormal">κ</span><span class="mopen">(</span><span class="mord mathnormal">A</span><span class="mclose">)</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span></span><span class="base"><span class="strut" style="height:1.150216em;vertical-align:-0.286108em;"></span><span class="mord">∥</span><span class="mord mathnormal">A</span><span class="mord"><span class="mord">∥</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.15139200000000003em;"><span style="top:-2.5500000000000003em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight">p</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.286108em;"><span></span></span></span></span></span></span><span class="mord">∥</span><span class="mord"><span class="mord mathnormal">A</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.864108em;"><span style="top:-3.113em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mtight">−</span><span class="mord mtight">1</span></span></span></span></span></span></span></span></span><span class="mord"><span class="mord">∥</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.15139200000000003em;"><span style="top:-2.5500000000000003em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight">p</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.286108em;"><span></span></span></span></span></span></span></span></span></span></span></p>
+    </div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
+    <p><link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/npm/katex@0.15.2/dist/katex.min.css" data-external="1"><span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mi>κ</mi><mo stretchy="false">(</mo><mi>A</mi><mo stretchy="false">)</mo><mo>=</mo><mi mathvariant="normal">∥</mi><mi>A</mi><msub><mi mathvariant="normal">∥</mi><mi>p</mi></msub><mi mathvariant="normal">∥</mi><msup><mi>A</mi><mrow><mo>−</mo><mn>1</mn></mrow></msup><msub><mi mathvariant="normal">∥</mi><mi>p</mi></msub></mrow><annotation encoding="application/x-tex">\kappa(A) = \|A\|_p\|A^{-1}\|_p</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord mathnormal">κ</span><span class="mopen">(</span><span class="mord mathnormal">A</span><span class="mclose">)</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:1.1502em;vertical-align:-0.2861em;"></span><span class="mord">∥</span><span class="mord mathnormal">A</span><span class="mord"><span class="mord">∥</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.1514em;"><span style="top:-2.55em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight">p</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.2861em;"><span></span></span></span></span></span></span><span class="mord">∥</span><span class="mord"><span class="mord mathnormal">A</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8641em;"><span style="top:-3.113em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mtight">−</span><span class="mord mtight">1</span></span></span></span></span></span></span></span></span><span class="mord"><span class="mord">∥</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.1514em;"><span style="top:-2.55em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight">p</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.2861em;"><span></span></span></span></span></span></span></span></span></span></span></p>
 <p>The condition number of <code>A</code> measures the numerical stability of the linear system <code>AX = B</code>
 with respect to a matrix norm.</p>
 <p>Supports input of float, double, cfloat and cdouble dtypes.
@@ -226,69 +116,49 @@ the output has the same batch dimensions.</p>
 <p><code>p</code> defines the matrix norm that is computed. See the table in 'Details' to
 find the supported norms.</p>
 <p>For <code>p</code> is one of <code>('fro', 'nuc', inf, -inf, 1, -1)</code>, this function uses
-<code><a href='linalg_norm.html'>linalg_norm()</a></code> and <code><a href='linalg_inv.html'>linalg_inv()</a></code>.</p>
+<code><a href="linalg_norm.html">linalg_norm()</a></code> and <code><a href="linalg_inv.html">linalg_inv()</a></code>.</p>
 <p>As such, in this case, the matrix (or every matrix in the batch) <code>A</code> has to be square
 and invertible.</p>
 <p>For <code>p</code> in <code>(2, -2)</code>, this function can be computed in terms of the singular values</p>
-<p><link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/npm/katex@0.13.11/dist/katex.min.css" data-external="1">
-<span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><msub><mi>κ</mi><mn>2</mn></msub><mo stretchy="false">(</mo><mi>A</mi><mo stretchy="false">)</mo><mo>=</mo><mfrac><msub><mi>σ</mi><mn>1</mn></msub><msub><mi>σ</mi><mi>n</mi></msub></mfrac><mspace width="2em"/><msub><mi>κ</mi><mrow><mo>−</mo><mn>2</mn></mrow></msub><mo stretchy="false">(</mo><mi>A</mi><mo stretchy="false">)</mo><mo>=</mo><mfrac><msub><mi>σ</mi><mi>n</mi></msub><msub><mi>σ</mi><mn>1</mn></msub></mfrac></mrow><annotation encoding="application/x-tex">\kappa_2(A) = \frac{\sigma_1}{\sigma_n}\qquad \kappa_{-2}(A) = \frac{\sigma_n}{\sigma_1}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord"><span class="mord mathnormal">κ</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.30110799999999993em;"><span style="top:-2.5500000000000003em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight">2</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mopen">(</span><span class="mord mathnormal">A</span><span class="mclose">)</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span></span><span class="base"><span class="strut" style="height:1.94356em;vertical-align:-0.8360000000000001em;"></span><span class="mord"><span class="mopen nulldelimiter"></span><span class="mfrac"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:1.1075599999999999em;"><span style="top:-2.3139999999999996em;"><span class="pstrut" style="height:3em;"></span><span class="mord"><span class="mord"><span class="mord mathnormal" style="margin-right:0.03588em;">σ</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.151392em;"><span style="top:-2.5500000000000003em;margin-left:-0.03588em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight">n</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span></span></span><span style="top:-3.23em;"><span class="pstrut" style="height:3em;"></span><span class="frac-line" style="border-bottom-width:0.04em;"></span></span><span style="top:-3.677em;"><span class="pstrut" style="height:3em;"></span><span class="mord"><span class="mord"><span class="mord mathnormal" style="margin-right:0.03588em;">σ</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.30110799999999993em;"><span style="top:-2.5500000000000003em;margin-left:-0.03588em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight">1</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.8360000000000001em;"><span></span></span></span></span></span><span class="mclose nulldelimiter"></span></span><span class="mspace" style="margin-right:2em;"></span><span class="mord"><span class="mord mathnormal">κ</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.301108em;"><span style="top:-2.5500000000000003em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mtight">−</span><span class="mord mtight">2</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.208331em;"><span></span></span></span></span></span></span><span class="mopen">(</span><span class="mord mathnormal">A</span><span class="mclose">)</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span></span><span class="base"><span class="strut" style="height:1.94356em;vertical-align:-0.8360000000000001em;"></span><span class="mord"><span class="mopen nulldelimiter"></span><span class="mfrac"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:1.1075599999999999em;"><span style="top:-2.3139999999999996em;"><span class="pstrut" style="height:3em;"></span><span class="mord"><span class="mord"><span class="mord mathnormal" style="margin-right:0.03588em;">σ</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.30110799999999993em;"><span style="top:-2.5500000000000003em;margin-left:-0.03588em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight">1</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span></span></span><span style="top:-3.23em;"><span class="pstrut" style="height:3em;"></span><span class="frac-line" style="border-bottom-width:0.04em;"></span></span><span style="top:-3.677em;"><span class="pstrut" style="height:3em;"></span><span class="mord"><span class="mord"><span class="mord mathnormal" style="margin-right:0.03588em;">σ</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.151392em;"><span style="top:-2.5500000000000003em;margin-left:-0.03588em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight">n</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.8360000000000001em;"><span></span></span></span></span></span><span class="mclose nulldelimiter"></span></span></span></span></span></span></p>
-<p>In these cases, it is computed using <code><a href='linalg_svd.html'>linalg_svd()</a></code>. For these norms, the matrix
+<p><link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/npm/katex@0.15.2/dist/katex.min.css" data-external="1"><span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><msub><mi>κ</mi><mn>2</mn></msub><mo stretchy="false">(</mo><mi>A</mi><mo stretchy="false">)</mo><mo>=</mo><mfrac><msub><mi>σ</mi><mn>1</mn></msub><msub><mi>σ</mi><mi>n</mi></msub></mfrac><mspace width="2em"></mspace><msub><mi>κ</mi><mrow><mo>−</mo><mn>2</mn></mrow></msub><mo stretchy="false">(</mo><mi>A</mi><mo stretchy="false">)</mo><mo>=</mo><mfrac><msub><mi>σ</mi><mi>n</mi></msub><msub><mi>σ</mi><mn>1</mn></msub></mfrac></mrow><annotation encoding="application/x-tex">\kappa_2(A) = \frac{\sigma_1}{\sigma_n}\qquad \kappa_{-2}(A) = \frac{\sigma_n}{\sigma_1}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord"><span class="mord mathnormal">κ</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.3011em;"><span style="top:-2.55em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight">2</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mopen">(</span><span class="mord mathnormal">A</span><span class="mclose">)</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:1.9436em;vertical-align:-0.836em;"></span><span class="mord"><span class="mopen nulldelimiter"></span><span class="mfrac"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:1.1076em;"><span style="top:-2.314em;"><span class="pstrut" style="height:3em;"></span><span class="mord"><span class="mord"><span class="mord mathnormal" style="margin-right:0.03588em;">σ</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.1514em;"><span style="top:-2.55em;margin-left:-0.0359em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight">n</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span></span></span><span style="top:-3.23em;"><span class="pstrut" style="height:3em;"></span><span class="frac-line" style="border-bottom-width:0.04em;"></span></span><span style="top:-3.677em;"><span class="pstrut" style="height:3em;"></span><span class="mord"><span class="mord"><span class="mord mathnormal" style="margin-right:0.03588em;">σ</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.3011em;"><span style="top:-2.55em;margin-left:-0.0359em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight">1</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.836em;"><span></span></span></span></span></span><span class="mclose nulldelimiter"></span></span><span class="mspace" style="margin-right:2em;"></span><span class="mord"><span class="mord mathnormal">κ</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.3011em;"><span style="top:-2.55em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mtight">−</span><span class="mord mtight">2</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.2083em;"><span></span></span></span></span></span></span><span class="mopen">(</span><span class="mord mathnormal">A</span><span class="mclose">)</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:1.9436em;vertical-align:-0.836em;"></span><span class="mord"><span class="mopen nulldelimiter"></span><span class="mfrac"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:1.1076em;"><span style="top:-2.314em;"><span class="pstrut" style="height:3em;"></span><span class="mord"><span class="mord"><span class="mord mathnormal" style="margin-right:0.03588em;">σ</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.3011em;"><span style="top:-2.55em;margin-left:-0.0359em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight">1</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span></span></span><span style="top:-3.23em;"><span class="pstrut" style="height:3em;"></span><span class="frac-line" style="border-bottom-width:0.04em;"></span></span><span style="top:-3.677em;"><span class="pstrut" style="height:3em;"></span><span class="mord"><span class="mord"><span class="mord mathnormal" style="margin-right:0.03588em;">σ</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.1514em;"><span style="top:-2.55em;margin-left:-0.0359em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight">n</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.836em;"><span></span></span></span></span></span><span class="mclose nulldelimiter"></span></span></span></span></span></span></p>
+<p>In these cases, it is computed using <code><a href="linalg_svd.html">linalg_svd()</a></code>. For these norms, the matrix
 (or every matrix in the batch) <code>A</code> may have any shape.</p>
-<table class='table'>
-<tr><td><code>p</code></td><td>matrix norm</td></tr>
-<tr><td><code>NULL</code></td><td><code>2</code>-norm (largest singular value)</td></tr>
-<tr><td><code>'fro'</code></td><td>Frobenius norm</td></tr>
-<tr><td><code>'nuc'</code></td><td>nuclear norm</td></tr>
-<tr><td><code>Inf</code></td><td><code>max(sum(abs(x), dim=2))</code></td></tr>
-<tr><td><code>-Inf</code></td><td><code>min(sum(abs(x), dim=2))</code></td></tr>
-<tr><td><code>1</code></td><td><code>max(sum(abs(x), dim=1))</code></td></tr>
-<tr><td><code>-1</code></td><td><code>min(sum(abs(x), dim=1))</code></td></tr>
-<tr><td><code>2</code></td><td>largest singular value</td></tr>
-<tr><td><code>-2</code></td><td>smallest singular value</td></tr>
-</table>
-
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
+<table class="table table"><tr><td><code>p</code></td><td>matrix norm</td></tr><tr><td><code>NULL</code></td><td><code>2</code>-norm (largest singular value)</td></tr><tr><td><code>'fro'</code></td><td>Frobenius norm</td></tr><tr><td><code>'nuc'</code></td><td>nuclear norm</td></tr><tr><td><code>Inf</code></td><td><code>max(sum(abs(x), dim=2))</code></td></tr><tr><td><code>-Inf</code></td><td><code>min(sum(abs(x), dim=2))</code></td></tr><tr><td><code>1</code></td><td><code>max(sum(abs(x), dim=1))</code></td></tr><tr><td><code>-1</code></td><td><code>min(sum(abs(x), dim=1))</code></td></tr><tr><td><code>2</code></td><td>largest singular value</td></tr><tr><td><code>-2</code></td><td>smallest singular value</td></tr></table></div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p>When inputs are on a CUDA device, this function synchronizes that device with the CPU if
 if <code>p</code> is one of <code>('fro', 'nuc', inf, -inf, 1, -1)</code>.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/cbind.html'>rbind</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1.</span>, <span class='fl'>0</span>, <span class='op'>-</span><span class='fl'>1</span><span class='op'>)</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>0</span>, <span class='fl'>1</span>, <span class='fl'>0</span><span class='op'>)</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>0</span>, <span class='fl'>1</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>linalg_cond</span><span class='op'>(</span><span class='va'>a</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>linalg_cond</span><span class='op'>(</span><span class='va'>a</span>, <span class='st'>"fro"</span><span class='op'>)</span></span>
-<span class='r-in'> </span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> 3.16228</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/cbind.html" class="external-link">rbind</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1.</span>, <span class="fl">0</span>, <span class="op">-</span><span class="fl">1</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span>, <span class="fl">0</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">0</span>, <span class="fl">1</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">linalg_cond</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">linalg_cond</span><span class="op">(</span><span class="va">a</span>, <span class="st">"fro"</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 3.16228</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{} ]</span>
+</code></pre></div>
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+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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-    <h1>Computes the determinant of a square matrix.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/linalg.R'><code>R/linalg.R</code></a></small>
-    <div class="hidden name"><code>linalg_det.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Computes the determinant of a square matrix.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/linalg.R" class="external-link"><code>R/linalg.R</code></a></small>
+      <div class="d-none name"><code>linalg_det.Rd</code></div>
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-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Supports input of float, double, cfloat and cdouble dtypes.
 Also supports batches of matrices, and if <code>A</code> is a batch of matrices then
 the output has the same batch dimensions.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>linalg_det</span><span class='op'>(</span><span class='va'>A</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>A</th>
-      <td><p>(Tensor): tensor of shape <code>(*, n, n)</code> where <code>*</code> is zero or more batch dimensions.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="see-also"><a class="anchor" href="#see-also"></a>See also</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">linalg_det</span><span class="op">(</span><span class="va">A</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <div class='dont-index'><p>Other linalg: 
-<code><a href='linalg_cholesky_ex.html'>linalg_cholesky_ex</a>()</code>,
-<code><a href='linalg_cholesky.html'>linalg_cholesky</a>()</code>,
-<code><a href='linalg_eigh.html'>linalg_eigh</a>()</code>,
-<code><a href='linalg_eigvalsh.html'>linalg_eigvalsh</a>()</code>,
-<code><a href='linalg_eigvals.html'>linalg_eigvals</a>()</code>,
-<code><a href='linalg_eig.html'>linalg_eig</a>()</code>,
-<code><a href='linalg_householder_product.html'>linalg_householder_product</a>()</code>,
-<code><a href='linalg_inv_ex.html'>linalg_inv_ex</a>()</code>,
-<code><a href='linalg_inv.html'>linalg_inv</a>()</code>,
-<code><a href='linalg_lstsq.html'>linalg_lstsq</a>()</code>,
-<code><a href='linalg_matrix_norm.html'>linalg_matrix_norm</a>()</code>,
-<code><a href='linalg_matrix_power.html'>linalg_matrix_power</a>()</code>,
-<code><a href='linalg_matrix_rank.html'>linalg_matrix_rank</a>()</code>,
-<code><a href='linalg_multi_dot.html'>linalg_multi_dot</a>()</code>,
-<code><a href='linalg_norm.html'>linalg_norm</a>()</code>,
-<code><a href='linalg_pinv.html'>linalg_pinv</a>()</code>,
-<code><a href='linalg_qr.html'>linalg_qr</a>()</code>,
-<code><a href='linalg_slogdet.html'>linalg_slogdet</a>()</code>,
-<code><a href='linalg_solve.html'>linalg_solve</a>()</code>,
-<code><a href='linalg_svdvals.html'>linalg_svdvals</a>()</code>,
-<code><a href='linalg_svd.html'>linalg_svd</a>()</code>,
-<code><a href='linalg_tensorinv.html'>linalg_tensorinv</a>()</code>,
-<code><a href='linalg_tensorsolve.html'>linalg_tensorsolve</a>()</code>,
-<code><a href='linalg_vector_norm.html'>linalg_vector_norm</a>()</code></p></div>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>A</dt>
+<dd><p>(Tensor): tensor of shape <code>(*, n, n)</code> where <code>*</code> is zero or more batch dimensions.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
+    <div class="dont-index"><p>Other linalg: 
+<code><a href="linalg_cholesky_ex.html">linalg_cholesky_ex</a>()</code>,
+<code><a href="linalg_cholesky.html">linalg_cholesky</a>()</code>,
+<code><a href="linalg_eigh.html">linalg_eigh</a>()</code>,
+<code><a href="linalg_eigvalsh.html">linalg_eigvalsh</a>()</code>,
+<code><a href="linalg_eigvals.html">linalg_eigvals</a>()</code>,
+<code><a href="linalg_eig.html">linalg_eig</a>()</code>,
+<code><a href="linalg_householder_product.html">linalg_householder_product</a>()</code>,
+<code><a href="linalg_inv_ex.html">linalg_inv_ex</a>()</code>,
+<code><a href="linalg_inv.html">linalg_inv</a>()</code>,
+<code><a href="linalg_lstsq.html">linalg_lstsq</a>()</code>,
+<code><a href="linalg_matrix_norm.html">linalg_matrix_norm</a>()</code>,
+<code><a href="linalg_matrix_power.html">linalg_matrix_power</a>()</code>,
+<code><a href="linalg_matrix_rank.html">linalg_matrix_rank</a>()</code>,
+<code><a href="linalg_multi_dot.html">linalg_multi_dot</a>()</code>,
+<code><a href="linalg_norm.html">linalg_norm</a>()</code>,
+<code><a href="linalg_pinv.html">linalg_pinv</a>()</code>,
+<code><a href="linalg_qr.html">linalg_qr</a>()</code>,
+<code><a href="linalg_slogdet.html">linalg_slogdet</a>()</code>,
+<code><a href="linalg_solve.html">linalg_solve</a>()</code>,
+<code><a href="linalg_svdvals.html">linalg_svdvals</a>()</code>,
+<code><a href="linalg_svd.html">linalg_svd</a>()</code>,
+<code><a href="linalg_tensorinv.html">linalg_tensorinv</a>()</code>,
+<code><a href="linalg_tensorsolve.html">linalg_tensorsolve</a>()</code>,
+<code><a href="linalg_vector_norm.html">linalg_vector_norm</a>()</code></p></div>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>3</span>,<span class='fl'>3</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>linalg_det</span><span class='op'>(</span><span class='va'>a</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>3</span>,<span class='fl'>3</span>,<span class='fl'>3</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>linalg_det</span><span class='op'>(</span><span class='va'>a</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.8822</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -1.0529</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.8622</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{3} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">3</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">linalg_det</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">3</span>, <span class="fl">3</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">linalg_det</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.3038</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.0061</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.4470</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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+    
+  </div>
+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Computes the eigenvalue decomposition of a square matrix if it exists.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/linalg.R'><code>R/linalg.R</code></a></small>
-    <div class="hidden name"><code>linalg_eig.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Computes the eigenvalue decomposition of a square matrix if it exists.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/linalg.R" class="external-link"><code>R/linalg.R</code></a></small>
+      <div class="d-none name"><code>linalg_eig.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Letting  be  or ,
 the <strong>eigenvalue decomposition</strong> of a square matrix
  (if it exists) is defined as</p>
     </div>
 
-    <pre class="usage"><span class='fu'>linalg_eig</span><span class='op'>(</span><span class='va'>A</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>A</th>
-      <td><p>(Tensor): tensor of shape <code>(*, n, n)</code> where <code>*</code> is zero or more batch dimensions
-consisting of diagonalizable matrices.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">linalg_eig</span><span class="op">(</span><span class="va">A</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>A</dt>
+<dd><p>(Tensor): tensor of shape <code>(*, n, n)</code> where <code>*</code> is zero or more batch dimensions
+consisting of diagonalizable matrices.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
     <p>A list <code>(eigenvalues, eigenvectors)</code> which corresponds to  and  above.
 <code>eigenvalues</code> and <code>eigenvectors</code> will always be complex-valued, even when <code>A</code> is real. The eigenvectors
 will be given by the columns of <code>eigenvectors</code>.</p>
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p><link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/npm/katex@0.13.11/dist/katex.min.css" data-external="1">
-<span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mi>A</mi><mo>=</mo><mi>V</mi><mi mathvariant="normal">diag</mi><mo>⁡</mo><mo stretchy="false">(</mo><mi mathvariant="normal">Λ</mi><mo stretchy="false">)</mo><msup><mi>V</mi><mrow><mo>−</mo><mn>1</mn></mrow></msup><mpadded width="0px"><mrow><mspace width="2em"/><mi>V</mi><mo>∈</mo><msup><mi mathvariant="double-struck">C</mi><mrow><mi>n</mi><mo>×</mo><mi>n</mi></mrow></msup><mo separator="true">,</mo><mi mathvariant="normal">Λ</mi><mo>∈</mo><msup><mi mathvariant="double-struck">C</mi><mi>n</mi></msup></mrow></mpadded></mrow><annotation encoding="application/x-tex">
+    </div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
+    <p><link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/npm/katex@0.15.2/dist/katex.min.css" data-external="1"><span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mi>A</mi><mo>=</mo><mi>V</mi><mi mathvariant="normal">diag</mi><mo>⁡</mo><mo stretchy="false">(</mo><mi mathvariant="normal">Λ</mi><mo stretchy="false">)</mo><msup><mi>V</mi><mrow><mo>−</mo><mn>1</mn></mrow></msup><mpadded width="0px"><mrow><mspace width="2em"></mspace><mi>V</mi><mo>∈</mo><msup><mi mathvariant="double-struck">C</mi><mrow><mi>n</mi><mo>×</mo><mi>n</mi></mrow></msup><mo separator="true">,</mo><mi mathvariant="normal">Λ</mi><mo>∈</mo><msup><mi mathvariant="double-struck">C</mi><mi>n</mi></msup></mrow></mpadded></mrow><annotation encoding="application/x-tex">
 A = V \operatorname{diag}(\Lambda) V^{-1}\mathrlap{\qquad V \in \mathbb{C}^{n \times n}, \Lambda \in \mathbb{C}^n}
-</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.68333em;vertical-align:0em;"></span><span class="mord mathnormal">A</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span></span><span class="base"><span class="strut" style="height:1.1141079999999999em;vertical-align:-0.25em;"></span><span class="mord mathnormal" style="margin-right:0.22222em;">V</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mop"><span class="mord mathrm" style="margin-right:0.01389em;">diag</span></span><span class="mopen">(</span><span class="mord">Λ</span><span class="mclose">)</span><span class="mord"><span class="mord mathnormal" style="margin-right:0.22222em;">V</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.864108em;"><span style="top:-3.113em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mtight">−</span><span class="mord mtight">1</span></span></span></span></span></span></span></span></span><span class="mord vbox"><span class="thinbox"><span class="rlap"><span class="strut" style="height:1.015771em;vertical-align:-0.19444em;"></span><span class="inner"><span class="mord"><span class="mspace" style="margin-right:2em;"></span><span class="mord mathnormal" style="margin-right:0.22222em;">V</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span><span class="mrel">∈</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span><span class="mord"><span class="mord mathbb">C</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.821331em;"><span style="top:-3.113em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">n</span><span class="mbin mtight">×</span><span class="mord mathnormal mtight">n</span></span></span></span></span></span></span></span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord">Λ</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span><span class="mrel">∈</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span><span class="mord"><span class="mord mathbb">C</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.7143919999999999em;"><span style="top:-3.113em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight">n</span></span></span></span></span></span></span></span></span></span><span class="fix"></span></span></span></span></span></span></span></span></p>
+</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6833em;"></span><span class="mord mathnormal">A</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:1.1141em;vertical-align:-0.25em;"></span><span class="mord mathnormal" style="margin-right:0.22222em;">V</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mop"><span class="mord mathrm" style="margin-right:0.01389em;">diag</span></span><span class="mopen">(</span><span class="mord">Λ</span><span class="mclose">)</span><span class="mord"><span class="mord mathnormal" style="margin-right:0.22222em;">V</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8641em;"><span style="top:-3.113em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mtight">−</span><span class="mord mtight">1</span></span></span></span></span></span></span></span></span><span class="mord vbox"><span class="thinbox"><span class="rlap"><span class="strut" style="height:1.0158em;vertical-align:-0.1944em;"></span><span class="inner"><span class="mord"><span class="mspace" style="margin-right:2em;"></span><span class="mord mathnormal" style="margin-right:0.22222em;">V</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">∈</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mord"><span class="mord mathbb">C</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8213em;"><span style="top:-3.113em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">n</span><span class="mbin mtight">×</span><span class="mord mathnormal mtight">n</span></span></span></span></span></span></span></span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord">Λ</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">∈</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mord"><span class="mord mathbb">C</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.7144em;"><span style="top:-3.113em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight">n</span></span></span></span></span></span></span></span></span></span><span class="fix"></span></span></span></span></span></span></span></span></p>
 <p>This decomposition exists if and only if  is <code>diagonalizable</code>_.
 This is the case when all its eigenvalues are different.
 Supports input of float, double, cfloat and cdouble dtypes.
 Also supports batches of matrices, and if <code>A</code> is a batch of matrices then
 the output has the same batch dimensions.</p>
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p>The eigenvalues and eigenvectors of a real matrix may be complex.</p>
-    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></a></h2>
     
 
-<ul>
-<li><p>This function assumes that <code>A</code> is <code>diagonalizable</code>_ (for example, when all the
+<ul><li><p>This function assumes that <code>A</code> is <code>diagonalizable</code>_ (for example, when all the
 eigenvalues are different). If it is not diagonalizable, the returned
 eigenvalues will be correct but .</p></li>
 <li><p>The eigenvectors of a matrix are not unique, nor are they continuous with respect to
@@ -244,80 +137,70 @@ Furthermore, if the distance between any two eigenvalues is close to zero,
 the gradient will be numerically unstable, as it depends on the eigenvalues
  through the computation of
 .</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="see-also"><a class="anchor" href="#see-also"></a>See also</h2>
-
-    <div class='dont-index'>
-<ul>
-<li><p><code><a href='linalg_eigvals.html'>linalg_eigvals()</a></code> computes only the eigenvalues. Unlike <code>linalg_eig()</code>, the gradients of
-<code><a href='linalg_eigvals.html'>linalg_eigvals()</a></code> are always numerically stable.</p></li>
-<li><p><code><a href='linalg_eigh.html'>linalg_eigh()</a></code> for a (faster) function that computes the eigenvalue decomposition
+</ul></div>
+    <div class="section level2">
+    <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
+    <div class="dont-index">
+<ul><li><p><code><a href="linalg_eigvals.html">linalg_eigvals()</a></code> computes only the eigenvalues. Unlike <code>linalg_eig()</code>, the gradients of
+<code><a href="linalg_eigvals.html">linalg_eigvals()</a></code> are always numerically stable.</p></li>
+<li><p><code><a href="linalg_eigh.html">linalg_eigh()</a></code> for a (faster) function that computes the eigenvalue decomposition
 for Hermitian and symmetric matrices.</p></li>
-<li><p><code><a href='linalg_svd.html'>linalg_svd()</a></code> for a function that computes another type of spectral
+<li><p><code><a href="linalg_svd.html">linalg_svd()</a></code> for a function that computes another type of spectral
 decomposition that works on matrices of any shape.</p></li>
-<li><p><code><a href='linalg_qr.html'>linalg_qr()</a></code> for another (much faster) decomposition that works on matrices of
+<li><p><code><a href="linalg_qr.html">linalg_qr()</a></code> for another (much faster) decomposition that works on matrices of
 any shape.</p></li>
-</ul>
-
-<p>Other linalg: 
-<code><a href='linalg_cholesky_ex.html'>linalg_cholesky_ex</a>()</code>,
-<code><a href='linalg_cholesky.html'>linalg_cholesky</a>()</code>,
-<code><a href='linalg_det.html'>linalg_det</a>()</code>,
-<code><a href='linalg_eigh.html'>linalg_eigh</a>()</code>,
-<code><a href='linalg_eigvalsh.html'>linalg_eigvalsh</a>()</code>,
-<code><a href='linalg_eigvals.html'>linalg_eigvals</a>()</code>,
-<code><a href='linalg_householder_product.html'>linalg_householder_product</a>()</code>,
-<code><a href='linalg_inv_ex.html'>linalg_inv_ex</a>()</code>,
-<code><a href='linalg_inv.html'>linalg_inv</a>()</code>,
-<code><a href='linalg_lstsq.html'>linalg_lstsq</a>()</code>,
-<code><a href='linalg_matrix_norm.html'>linalg_matrix_norm</a>()</code>,
-<code><a href='linalg_matrix_power.html'>linalg_matrix_power</a>()</code>,
-<code><a href='linalg_matrix_rank.html'>linalg_matrix_rank</a>()</code>,
-<code><a href='linalg_multi_dot.html'>linalg_multi_dot</a>()</code>,
-<code><a href='linalg_norm.html'>linalg_norm</a>()</code>,
-<code><a href='linalg_pinv.html'>linalg_pinv</a>()</code>,
-<code><a href='linalg_qr.html'>linalg_qr</a>()</code>,
-<code><a href='linalg_slogdet.html'>linalg_slogdet</a>()</code>,
-<code><a href='linalg_solve.html'>linalg_solve</a>()</code>,
-<code><a href='linalg_svdvals.html'>linalg_svdvals</a>()</code>,
-<code><a href='linalg_svd.html'>linalg_svd</a>()</code>,
-<code><a href='linalg_tensorinv.html'>linalg_tensorinv</a>()</code>,
-<code><a href='linalg_tensorsolve.html'>linalg_tensorsolve</a>()</code>,
-<code><a href='linalg_vector_norm.html'>linalg_vector_norm</a>()</code></p></div>
+</ul><p>Other linalg: 
+<code><a href="linalg_cholesky_ex.html">linalg_cholesky_ex</a>()</code>,
+<code><a href="linalg_cholesky.html">linalg_cholesky</a>()</code>,
+<code><a href="linalg_det.html">linalg_det</a>()</code>,
+<code><a href="linalg_eigh.html">linalg_eigh</a>()</code>,
+<code><a href="linalg_eigvalsh.html">linalg_eigvalsh</a>()</code>,
+<code><a href="linalg_eigvals.html">linalg_eigvals</a>()</code>,
+<code><a href="linalg_householder_product.html">linalg_householder_product</a>()</code>,
+<code><a href="linalg_inv_ex.html">linalg_inv_ex</a>()</code>,
+<code><a href="linalg_inv.html">linalg_inv</a>()</code>,
+<code><a href="linalg_lstsq.html">linalg_lstsq</a>()</code>,
+<code><a href="linalg_matrix_norm.html">linalg_matrix_norm</a>()</code>,
+<code><a href="linalg_matrix_power.html">linalg_matrix_power</a>()</code>,
+<code><a href="linalg_matrix_rank.html">linalg_matrix_rank</a>()</code>,
+<code><a href="linalg_multi_dot.html">linalg_multi_dot</a>()</code>,
+<code><a href="linalg_norm.html">linalg_norm</a>()</code>,
+<code><a href="linalg_pinv.html">linalg_pinv</a>()</code>,
+<code><a href="linalg_qr.html">linalg_qr</a>()</code>,
+<code><a href="linalg_slogdet.html">linalg_slogdet</a>()</code>,
+<code><a href="linalg_solve.html">linalg_solve</a>()</code>,
+<code><a href="linalg_svdvals.html">linalg_svdvals</a>()</code>,
+<code><a href="linalg_svd.html">linalg_svd</a>()</code>,
+<code><a href="linalg_tensorinv.html">linalg_tensorinv</a>()</code>,
+<code><a href="linalg_tensorsolve.html">linalg_tensorsolve</a>()</code>,
+<code><a href="linalg_vector_norm.html">linalg_vector_norm</a>()</code></p></div>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>wv</span> <span class='op'>=</span> <span class='fu'>linalg_eig</span><span class='op'>(</span><span class='va'>a</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">wv</span> <span class="op">&lt;-</span> <span class="fu">linalg_eig</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
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-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Computes the eigenvalue decomposition of a complex Hermitian or real symmetric matrix.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/linalg.R'><code>R/linalg.R</code></a></small>
-    <div class="hidden name"><code>linalg_eigh.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Computes the eigenvalue decomposition of a complex Hermitian or real symmetric matrix.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/linalg.R" class="external-link"><code>R/linalg.R</code></a></small>
+      <div class="d-none name"><code>linalg_eigh.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Letting  be  or ,
 the <strong>eigenvalue decomposition</strong> of a complex Hermitian or real symmetric matrix
  is defined as</p>
     </div>
 
-    <pre class="usage"><span class='fu'>linalg_eigh</span><span class='op'>(</span><span class='va'>A</span>, UPLO <span class='op'>=</span> <span class='st'>"L"</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>A</th>
-      <td><p>(Tensor): tensor of shape <code>(*, n, n)</code> where <code>*</code> is zero or more batch dimensions
-consisting of symmetric or Hermitian matrices.</p></td>
-    </tr>
-    <tr>
-      <th>UPLO</th>
-      <td><p>('L', 'U', optional): controls whether to use the upper or lower triangular part
-of <code>A</code> in the computations. Default: <code>'L'</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">linalg_eigh</span><span class="op">(</span><span class="va">A</span>, UPLO <span class="op">=</span> <span class="st">"L"</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>A</dt>
+<dd><p>(Tensor): tensor of shape <code>(*, n, n)</code> where <code>*</code> is zero or more batch dimensions
+consisting of symmetric or Hermitian matrices.</p></dd>
+<dt>UPLO</dt>
+<dd><p>('L', 'U', optional): controls whether to use the upper or lower triangular part
+of <code>A</code> in the computations. Default: <code>'L'</code>.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
     <p>A list <code>(eigenvalues, eigenvectors)</code> which corresponds to  and  above.
-<code>eigenvalues</code> will always be real-valued, even when <code>A</code> is complex.</p>
-<p>It will also be ordered in ascending order.
+<code>eigenvalues</code> will always be real-valued, even when <code>A</code> is complex.
+It will also be ordered in ascending order.
 <code>eigenvectors</code> will have the same dtype as <code>A</code> and will contain the eigenvectors as its columns.</p>
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p><link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/npm/katex@0.13.11/dist/katex.min.css" data-external="1">
-<span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mi>A</mi><mo>=</mo><mi>Q</mi><mi mathvariant="normal">diag</mi><mo>⁡</mo><mo stretchy="false">(</mo><mi mathvariant="normal">Λ</mi><mo stretchy="false">)</mo><msup><mi>Q</mi><mi>H</mi></msup><mpadded width="0px"><mrow><mspace width="2em"/><mi>Q</mi><mo>∈</mo><msup><mi mathvariant="double-struck">K</mi><mrow><mi>n</mi><mo>×</mo><mi>n</mi></mrow></msup><mo separator="true">,</mo><mi mathvariant="normal">Λ</mi><mo>∈</mo><msup><mi mathvariant="double-struck">R</mi><mi>n</mi></msup></mrow></mpadded></mrow><annotation encoding="application/x-tex">
+    </div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
+    <p><link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/npm/katex@0.15.2/dist/katex.min.css" data-external="1"><span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mi>A</mi><mo>=</mo><mi>Q</mi><mi mathvariant="normal">diag</mi><mo>⁡</mo><mo stretchy="false">(</mo><mi mathvariant="normal">Λ</mi><mo stretchy="false">)</mo><msup><mi>Q</mi><mi>H</mi></msup><mpadded width="0px"><mrow><mspace width="2em"></mspace><mi>Q</mi><mo>∈</mo><msup><mi mathvariant="double-struck">K</mi><mrow><mi>n</mi><mo>×</mo><mi>n</mi></mrow></msup><mo separator="true">,</mo><mi mathvariant="normal">Λ</mi><mo>∈</mo><msup><mi mathvariant="double-struck">R</mi><mi>n</mi></msup></mrow></mpadded></mrow><annotation encoding="application/x-tex">
 A = Q \operatorname{diag}(\Lambda) Q^{H}\mathrlap{\qquad Q \in \mathbb{K}^{n \times n}, \Lambda \in \mathbb{R}^n}
-</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.68333em;vertical-align:0em;"></span><span class="mord mathnormal">A</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span></span><span class="base"><span class="strut" style="height:1.1413309999999999em;vertical-align:-0.25em;"></span><span class="mord mathnormal">Q</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mop"><span class="mord mathrm" style="margin-right:0.01389em;">diag</span></span><span class="mopen">(</span><span class="mord">Λ</span><span class="mclose">)</span><span class="mord"><span class="mord mathnormal">Q</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8913309999999999em;"><span style="top:-3.113em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight" style="margin-right:0.08125em;">H</span></span></span></span></span></span></span></span></span><span class="mord vbox"><span class="thinbox"><span class="rlap"><span class="strut" style="height:1.015771em;vertical-align:-0.19444em;"></span><span class="inner"><span class="mord"><span class="mspace" style="margin-right:2em;"></span><span class="mord mathnormal">Q</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span><span class="mrel">∈</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span><span class="mord"><span class="mord mathbb">K</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.821331em;"><span style="top:-3.113em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">n</span><span class="mbin mtight">×</span><span class="mord mathnormal mtight">n</span></span></span></span></span></span></span></span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord">Λ</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span><span class="mrel">∈</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span><span class="mord"><span class="mord mathbb">R</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.7143919999999999em;"><span style="top:-3.113em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight">n</span></span></span></span></span></span></span></span></span></span><span class="fix"></span></span></span></span></span></span></span></span></p>
+</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6833em;"></span><span class="mord mathnormal">A</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:1.1413em;vertical-align:-0.25em;"></span><span class="mord mathnormal">Q</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mop"><span class="mord mathrm" style="margin-right:0.01389em;">diag</span></span><span class="mopen">(</span><span class="mord">Λ</span><span class="mclose">)</span><span class="mord"><span class="mord mathnormal">Q</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8913em;"><span style="top:-3.113em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight" style="margin-right:0.08125em;">H</span></span></span></span></span></span></span></span></span><span class="mord vbox"><span class="thinbox"><span class="rlap"><span class="strut" style="height:1.0158em;vertical-align:-0.1944em;"></span><span class="inner"><span class="mord"><span class="mspace" style="margin-right:2em;"></span><span class="mord mathnormal">Q</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">∈</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mord"><span class="mord mathbb">K</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8213em;"><span style="top:-3.113em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">n</span><span class="mbin mtight">×</span><span class="mord mathnormal mtight">n</span></span></span></span></span></span></span></span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord">Λ</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">∈</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mord"><span class="mord mathbb">R</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.7144em;"><span style="top:-3.113em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight">n</span></span></span></span></span></span></span></span></span></span><span class="fix"></span></span></span></span></span></span></span></span></p>
 <p>where  is the conjugate transpose when  is complex, and the transpose when  is real-valued.
  is orthogonal in the real case and unitary in the complex case.</p>
 <p>Supports input of float, double, cfloat and cdouble dtypes.
 Also supports batches of matrices, and if <code>A</code> is a batch of matrices then
 the output has the same batch dimensions.</p>
-<p><code>A</code> is assumed to be Hermitian (resp. symmetric), but this is not checked internally, instead:</p><ul>
-<li><p>If <code>UPLO</code>\ <code>= 'L'</code> (default), only the lower triangular part of the matrix is used in the computation.</p></li>
+<p><code>A</code> is assumed to be Hermitian (resp. symmetric), but this is not checked internally, instead:</p><ul><li><p>If <code>UPLO</code>\ <code>= 'L'</code> (default), only the lower triangular part of the matrix is used in the computation.</p></li>
 <li><p>If <code>UPLO</code>\ <code>= 'U'</code>, only the upper triangular part of the matrix is used.
 The eigenvalues are returned in ascending order.</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
+</ul></div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p>The eigenvalues of real symmetric or complex Hermitian matrices are always real.</p>
-    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></a></h2>
     
 
-<ul>
-<li><p>The eigenvectors of a symmetric matrix are not unique, nor are they continuous with
+<ul><li><p>The eigenvectors of a symmetric matrix are not unique, nor are they continuous with
 respect to <code>A</code>. Due to this lack of uniqueness, different hardware and
 software may compute different eigenvectors.
 This non-uniqueness is caused by the fact that multiplying an eigenvector by
@@ -257,96 +145,86 @@ Furthermore, if the distance between any two eigvalues is close to zero,
 the gradient will be numerically unstable, as it depends on the eigenvalues
  through the computation of
 .</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="see-also"><a class="anchor" href="#see-also"></a>See also</h2>
-
-    <div class='dont-index'>
-<ul>
-<li><p><code><a href='linalg_eigvalsh.html'>linalg_eigvalsh()</a></code> computes only the eigenvalues values of a Hermitian matrix.
-Unlike <code>linalg_eigh()</code>, the gradients of <code><a href='linalg_eigvalsh.html'>linalg_eigvalsh()</a></code> are always
+</ul></div>
+    <div class="section level2">
+    <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
+    <div class="dont-index">
+<ul><li><p><code><a href="linalg_eigvalsh.html">linalg_eigvalsh()</a></code> computes only the eigenvalues values of a Hermitian matrix.
+Unlike <code>linalg_eigh()</code>, the gradients of <code><a href="linalg_eigvalsh.html">linalg_eigvalsh()</a></code> are always
 numerically stable.</p></li>
-<li><p><code><a href='linalg_cholesky.html'>linalg_cholesky()</a></code> for a different decomposition of a Hermitian matrix.
+<li><p><code><a href="linalg_cholesky.html">linalg_cholesky()</a></code> for a different decomposition of a Hermitian matrix.
 The Cholesky decomposition gives less information about the matrix but is much faster
 to compute than the eigenvalue decomposition.</p></li>
-<li><p><code><a href='linalg_eig.html'>linalg_eig()</a></code> for a (slower) function that computes the eigenvalue decomposition
+<li><p><code><a href="linalg_eig.html">linalg_eig()</a></code> for a (slower) function that computes the eigenvalue decomposition
 of a not necessarily Hermitian square matrix.</p></li>
-<li><p><code><a href='linalg_svd.html'>linalg_svd()</a></code> for a (slower) function that computes the more general SVD
+<li><p><code><a href="linalg_svd.html">linalg_svd()</a></code> for a (slower) function that computes the more general SVD
 decomposition of matrices of any shape.</p></li>
-<li><p><code><a href='linalg_qr.html'>linalg_qr()</a></code> for another (much faster) decomposition that works on general
+<li><p><code><a href="linalg_qr.html">linalg_qr()</a></code> for another (much faster) decomposition that works on general
 matrices.</p></li>
-</ul>
-
-<p>Other linalg: 
-<code><a href='linalg_cholesky_ex.html'>linalg_cholesky_ex</a>()</code>,
-<code><a href='linalg_cholesky.html'>linalg_cholesky</a>()</code>,
-<code><a href='linalg_det.html'>linalg_det</a>()</code>,
-<code><a href='linalg_eigvalsh.html'>linalg_eigvalsh</a>()</code>,
-<code><a href='linalg_eigvals.html'>linalg_eigvals</a>()</code>,
-<code><a href='linalg_eig.html'>linalg_eig</a>()</code>,
-<code><a href='linalg_householder_product.html'>linalg_householder_product</a>()</code>,
-<code><a href='linalg_inv_ex.html'>linalg_inv_ex</a>()</code>,
-<code><a href='linalg_inv.html'>linalg_inv</a>()</code>,
-<code><a href='linalg_lstsq.html'>linalg_lstsq</a>()</code>,
-<code><a href='linalg_matrix_norm.html'>linalg_matrix_norm</a>()</code>,
-<code><a href='linalg_matrix_power.html'>linalg_matrix_power</a>()</code>,
-<code><a href='linalg_matrix_rank.html'>linalg_matrix_rank</a>()</code>,
-<code><a href='linalg_multi_dot.html'>linalg_multi_dot</a>()</code>,
-<code><a href='linalg_norm.html'>linalg_norm</a>()</code>,
-<code><a href='linalg_pinv.html'>linalg_pinv</a>()</code>,
-<code><a href='linalg_qr.html'>linalg_qr</a>()</code>,
-<code><a href='linalg_slogdet.html'>linalg_slogdet</a>()</code>,
-<code><a href='linalg_solve.html'>linalg_solve</a>()</code>,
-<code><a href='linalg_svdvals.html'>linalg_svdvals</a>()</code>,
-<code><a href='linalg_svd.html'>linalg_svd</a>()</code>,
-<code><a href='linalg_tensorinv.html'>linalg_tensorinv</a>()</code>,
-<code><a href='linalg_tensorsolve.html'>linalg_tensorsolve</a>()</code>,
-<code><a href='linalg_vector_norm.html'>linalg_vector_norm</a>()</code></p></div>
+</ul><p>Other linalg: 
+<code><a href="linalg_cholesky_ex.html">linalg_cholesky_ex</a>()</code>,
+<code><a href="linalg_cholesky.html">linalg_cholesky</a>()</code>,
+<code><a href="linalg_det.html">linalg_det</a>()</code>,
+<code><a href="linalg_eigvalsh.html">linalg_eigvalsh</a>()</code>,
+<code><a href="linalg_eigvals.html">linalg_eigvals</a>()</code>,
+<code><a href="linalg_eig.html">linalg_eig</a>()</code>,
+<code><a href="linalg_householder_product.html">linalg_householder_product</a>()</code>,
+<code><a href="linalg_inv_ex.html">linalg_inv_ex</a>()</code>,
+<code><a href="linalg_inv.html">linalg_inv</a>()</code>,
+<code><a href="linalg_lstsq.html">linalg_lstsq</a>()</code>,
+<code><a href="linalg_matrix_norm.html">linalg_matrix_norm</a>()</code>,
+<code><a href="linalg_matrix_power.html">linalg_matrix_power</a>()</code>,
+<code><a href="linalg_matrix_rank.html">linalg_matrix_rank</a>()</code>,
+<code><a href="linalg_multi_dot.html">linalg_multi_dot</a>()</code>,
+<code><a href="linalg_norm.html">linalg_norm</a>()</code>,
+<code><a href="linalg_pinv.html">linalg_pinv</a>()</code>,
+<code><a href="linalg_qr.html">linalg_qr</a>()</code>,
+<code><a href="linalg_slogdet.html">linalg_slogdet</a>()</code>,
+<code><a href="linalg_solve.html">linalg_solve</a>()</code>,
+<code><a href="linalg_svdvals.html">linalg_svdvals</a>()</code>,
+<code><a href="linalg_svd.html">linalg_svd</a>()</code>,
+<code><a href="linalg_tensorinv.html">linalg_tensorinv</a>()</code>,
+<code><a href="linalg_tensorsolve.html">linalg_tensorsolve</a>()</code>,
+<code><a href="linalg_vector_norm.html">linalg_vector_norm</a>()</code></p></div>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>linalg_eigh</span><span class='op'>(</span><span class='va'>a</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [[1]]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.9422</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.9135</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{2} ]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [[2]]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.5668 -0.8239</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.8239  0.5668</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{2,2} ]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">linalg_eigh</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [[1]]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -1.0987</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.0196</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2} ]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [[2]]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.6044 -0.7967</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.7967  0.6044</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2,2} ]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer>
-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
 </div>
 
-<div class="pkgdown">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p>
+<div class="pkgdown-footer-right">
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diff --git a/reference/linalg_eigvals.html b/reference/linalg_eigvals.html
index add4185fe..f3d9d5d7e 100644
--- a/reference/linalg_eigvals.html
+++ b/reference/linalg_eigvals.html
@@ -1,284 +1,171 @@
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-    <h1>Computes the eigenvalues of a square matrix.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/linalg.R'><code>R/linalg.R</code></a></small>
-    <div class="hidden name"><code>linalg_eigvals.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Computes the eigenvalues of a square matrix.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/linalg.R" class="external-link"><code>R/linalg.R</code></a></small>
+      <div class="d-none name"><code>linalg_eigvals.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Letting  be  or ,
 the <strong>eigenvalues</strong> of a square matrix  are defined
 as the roots (counted with multiplicity) of the polynomial <code>p</code> of degree <code>n</code> given by</p>
     </div>
 
-    <pre class="usage"><span class='fu'>linalg_eigvals</span><span class='op'>(</span><span class='va'>A</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>A</th>
-      <td><p>(Tensor): tensor of shape <code>(*, n, n)</code> where <code>*</code> is zero or more batch dimensions.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">linalg_eigvals</span><span class="op">(</span><span class="va">A</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <p><link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/npm/katex@0.13.11/dist/katex.min.css" data-external="1">
-<span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mi>p</mi><mo stretchy="false">(</mo><mi>λ</mi><mo stretchy="false">)</mo><mo>=</mo><mi mathvariant="normal">det</mi><mo>⁡</mo><mo stretchy="false">(</mo><mi>A</mi><mo>−</mo><mi>λ</mi><msub><mi mathvariant="normal">I</mi><mi>n</mi></msub><mo stretchy="false">)</mo><mpadded width="0px"><mrow><mspace width="2em"/><mi>λ</mi><mo>∈</mo><mi mathvariant="double-struck">C</mi></mrow></mpadded></mrow><annotation encoding="application/x-tex">
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>A</dt>
+<dd><p>(Tensor): tensor of shape <code>(*, n, n)</code> where <code>*</code> is zero or more batch dimensions.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
+    <p><link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/npm/katex@0.15.2/dist/katex.min.css" data-external="1"><span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mi>p</mi><mo stretchy="false">(</mo><mi>λ</mi><mo stretchy="false">)</mo><mo>=</mo><mi mathvariant="normal">det</mi><mo>⁡</mo><mo stretchy="false">(</mo><mi>A</mi><mo>−</mo><mi>λ</mi><msub><mi mathvariant="normal">I</mi><mi>n</mi></msub><mo stretchy="false">)</mo><mpadded width="0px"><mrow><mspace width="2em"></mspace><mi>λ</mi><mo>∈</mo><mi mathvariant="double-struck">C</mi></mrow></mpadded></mrow><annotation encoding="application/x-tex">
 p(\lambda) = \operatorname{det}(A - \lambda \mathrm{I}_n)\mathrlap{\qquad \lambda \in \mathbb{C}}
-</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord mathnormal">p</span><span class="mopen">(</span><span class="mord mathnormal">λ</span><span class="mclose">)</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mop"><span class="mord mathrm">det</span></span><span class="mopen">(</span><span class="mord mathnormal">A</span><span class="mspace" style="margin-right:0.2222222222222222em;"></span><span class="mbin">−</span><span class="mspace" style="margin-right:0.2222222222222222em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord mathnormal">λ</span><span class="mord"><span class="mord mathrm">I</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.151392em;"><span style="top:-2.5500000000000003em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight">n</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mclose">)</span><span class="mord vbox"><span class="thinbox"><span class="rlap"><span class="strut" style="height:0.73354em;vertical-align:-0.0391em;"></span><span class="inner"><span class="mord"><span class="mspace" style="margin-right:2em;"></span><span class="mord mathnormal">λ</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span><span class="mrel">∈</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span><span class="mord mathbb">C</span></span></span><span class="fix"></span></span></span></span></span></span></span></span></p>
+</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord mathnormal">p</span><span class="mopen">(</span><span class="mord mathnormal">λ</span><span class="mclose">)</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mop"><span class="mord mathrm">det</span></span><span class="mopen">(</span><span class="mord mathnormal">A</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">−</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord mathnormal">λ</span><span class="mord"><span class="mord mathrm">I</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.1514em;"><span style="top:-2.55em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight">n</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mclose">)</span><span class="mord vbox"><span class="thinbox"><span class="rlap"><span class="strut" style="height:0.7335em;vertical-align:-0.0391em;"></span><span class="inner"><span class="mord"><span class="mspace" style="margin-right:2em;"></span><span class="mord mathnormal">λ</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">∈</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mord mathbb">C</span></span></span><span class="fix"></span></span></span></span></span></span></span></span></p>
 <p>where  is the <code>n</code>-dimensional identity matrix.
 Supports input of float, double, cfloat and cdouble dtypes.
 Also supports batches of matrices, and if <code>A</code> is a batch of matrices then
 the output has the same batch dimensions.</p>
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p>The eigenvalues of a real matrix may be complex, as the roots of a real polynomial may be complex.
 The eigenvalues of a matrix are always well-defined, even when the matrix is not diagonalizable.</p>
-    <h2 class="hasAnchor" id="see-also"><a class="anchor" href="#see-also"></a>See also</h2>
-
-    <div class='dont-index'><p><code><a href='linalg_eig.html'>linalg_eig()</a></code> computes the full eigenvalue decomposition.</p>
+    </div>
+    <div class="section level2">
+    <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
+    <div class="dont-index"><p><code><a href="linalg_eig.html">linalg_eig()</a></code> computes the full eigenvalue decomposition.</p>
 <p>Other linalg: 
-<code><a href='linalg_cholesky_ex.html'>linalg_cholesky_ex</a>()</code>,
-<code><a href='linalg_cholesky.html'>linalg_cholesky</a>()</code>,
-<code><a href='linalg_det.html'>linalg_det</a>()</code>,
-<code><a href='linalg_eigh.html'>linalg_eigh</a>()</code>,
-<code><a href='linalg_eigvalsh.html'>linalg_eigvalsh</a>()</code>,
-<code><a href='linalg_eig.html'>linalg_eig</a>()</code>,
-<code><a href='linalg_householder_product.html'>linalg_householder_product</a>()</code>,
-<code><a href='linalg_inv_ex.html'>linalg_inv_ex</a>()</code>,
-<code><a href='linalg_inv.html'>linalg_inv</a>()</code>,
-<code><a href='linalg_lstsq.html'>linalg_lstsq</a>()</code>,
-<code><a href='linalg_matrix_norm.html'>linalg_matrix_norm</a>()</code>,
-<code><a href='linalg_matrix_power.html'>linalg_matrix_power</a>()</code>,
-<code><a href='linalg_matrix_rank.html'>linalg_matrix_rank</a>()</code>,
-<code><a href='linalg_multi_dot.html'>linalg_multi_dot</a>()</code>,
-<code><a href='linalg_norm.html'>linalg_norm</a>()</code>,
-<code><a href='linalg_pinv.html'>linalg_pinv</a>()</code>,
-<code><a href='linalg_qr.html'>linalg_qr</a>()</code>,
-<code><a href='linalg_slogdet.html'>linalg_slogdet</a>()</code>,
-<code><a href='linalg_solve.html'>linalg_solve</a>()</code>,
-<code><a href='linalg_svdvals.html'>linalg_svdvals</a>()</code>,
-<code><a href='linalg_svd.html'>linalg_svd</a>()</code>,
-<code><a href='linalg_tensorinv.html'>linalg_tensorinv</a>()</code>,
-<code><a href='linalg_tensorsolve.html'>linalg_tensorsolve</a>()</code>,
-<code><a href='linalg_vector_norm.html'>linalg_vector_norm</a>()</code></p></div>
+<code><a href="linalg_cholesky_ex.html">linalg_cholesky_ex</a>()</code>,
+<code><a href="linalg_cholesky.html">linalg_cholesky</a>()</code>,
+<code><a href="linalg_det.html">linalg_det</a>()</code>,
+<code><a href="linalg_eigh.html">linalg_eigh</a>()</code>,
+<code><a href="linalg_eigvalsh.html">linalg_eigvalsh</a>()</code>,
+<code><a href="linalg_eig.html">linalg_eig</a>()</code>,
+<code><a href="linalg_householder_product.html">linalg_householder_product</a>()</code>,
+<code><a href="linalg_inv_ex.html">linalg_inv_ex</a>()</code>,
+<code><a href="linalg_inv.html">linalg_inv</a>()</code>,
+<code><a href="linalg_lstsq.html">linalg_lstsq</a>()</code>,
+<code><a href="linalg_matrix_norm.html">linalg_matrix_norm</a>()</code>,
+<code><a href="linalg_matrix_power.html">linalg_matrix_power</a>()</code>,
+<code><a href="linalg_matrix_rank.html">linalg_matrix_rank</a>()</code>,
+<code><a href="linalg_multi_dot.html">linalg_multi_dot</a>()</code>,
+<code><a href="linalg_norm.html">linalg_norm</a>()</code>,
+<code><a href="linalg_pinv.html">linalg_pinv</a>()</code>,
+<code><a href="linalg_qr.html">linalg_qr</a>()</code>,
+<code><a href="linalg_slogdet.html">linalg_slogdet</a>()</code>,
+<code><a href="linalg_solve.html">linalg_solve</a>()</code>,
+<code><a href="linalg_svdvals.html">linalg_svdvals</a>()</code>,
+<code><a href="linalg_svd.html">linalg_svd</a>()</code>,
+<code><a href="linalg_tensorinv.html">linalg_tensorinv</a>()</code>,
+<code><a href="linalg_tensorsolve.html">linalg_tensorsolve</a>()</code>,
+<code><a href="linalg_vector_norm.html">linalg_vector_norm</a>()</code></p></div>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>w</span> <span class='op'>&lt;-</span> <span class='fu'>linalg_eigvals</span><span class='op'>(</span><span class='va'>a</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
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+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">w</span> <span class="op">&lt;-</span> <span class="fu">linalg_eigvals</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
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-    <h1>Computes the eigenvalues of a complex Hermitian or real symmetric matrix.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/linalg.R'><code>R/linalg.R</code></a></small>
-    <div class="hidden name"><code>linalg_eigvalsh.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Computes the eigenvalues of a complex Hermitian or real symmetric matrix.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/linalg.R" class="external-link"><code>R/linalg.R</code></a></small>
+      <div class="d-none name"><code>linalg_eigvalsh.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Letting  be  or ,
 the <strong>eigenvalues</strong> of a complex Hermitian or real symmetric  matrix 
 are defined as the roots (counted with multiplicity) of the polynomial <code>p</code> of degree <code>n</code> given by</p>
     </div>
 
-    <pre class="usage"><span class='fu'>linalg_eigvalsh</span><span class='op'>(</span><span class='va'>A</span>, UPLO <span class='op'>=</span> <span class='st'>"L"</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>A</th>
-      <td><p>(Tensor): tensor of shape <code>(*, n, n)</code> where <code>*</code> is zero or more batch dimensions
-consisting of symmetric or Hermitian matrices.</p></td>
-    </tr>
-    <tr>
-      <th>UPLO</th>
-      <td><p>('L', 'U', optional): controls whether to use the upper or lower triangular part
-of <code>A</code> in the computations. Default: <code>'L'</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">linalg_eigvalsh</span><span class="op">(</span><span class="va">A</span>, UPLO <span class="op">=</span> <span class="st">"L"</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>A</dt>
+<dd><p>(Tensor): tensor of shape <code>(*, n, n)</code> where <code>*</code> is zero or more batch dimensions
+consisting of symmetric or Hermitian matrices.</p></dd>
+<dt>UPLO</dt>
+<dd><p>('L', 'U', optional): controls whether to use the upper or lower triangular part
+of <code>A</code> in the computations. Default: <code>'L'</code>.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
     <p>A real-valued tensor cointaining the eigenvalues even when <code>A</code> is complex.
 The eigenvalues are returned in ascending order.</p>
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p><link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/npm/katex@0.13.11/dist/katex.min.css" data-external="1">
-<span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mi>p</mi><mo stretchy="false">(</mo><mi>λ</mi><mo stretchy="false">)</mo><mo>=</mo><mi mathvariant="normal">det</mi><mo>⁡</mo><mo stretchy="false">(</mo><mi>A</mi><mo>−</mo><mi>λ</mi><msub><mi mathvariant="normal">I</mi><mi>n</mi></msub><mo stretchy="false">)</mo><mpadded width="0px"><mrow><mspace width="2em"/><mi>λ</mi><mo>∈</mo><mi mathvariant="double-struck">R</mi></mrow></mpadded></mrow><annotation encoding="application/x-tex">
+    </div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
+    <p><link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/npm/katex@0.15.2/dist/katex.min.css" data-external="1"><span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mi>p</mi><mo stretchy="false">(</mo><mi>λ</mi><mo stretchy="false">)</mo><mo>=</mo><mi mathvariant="normal">det</mi><mo>⁡</mo><mo stretchy="false">(</mo><mi>A</mi><mo>−</mo><mi>λ</mi><msub><mi mathvariant="normal">I</mi><mi>n</mi></msub><mo stretchy="false">)</mo><mpadded width="0px"><mrow><mspace width="2em"></mspace><mi>λ</mi><mo>∈</mo><mi mathvariant="double-struck">R</mi></mrow></mpadded></mrow><annotation encoding="application/x-tex">
 p(\lambda) = \operatorname{det}(A - \lambda \mathrm{I}_n)\mathrlap{\qquad \lambda \in \mathbb{R}}
-</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord mathnormal">p</span><span class="mopen">(</span><span class="mord mathnormal">λ</span><span class="mclose">)</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mop"><span class="mord mathrm">det</span></span><span class="mopen">(</span><span class="mord mathnormal">A</span><span class="mspace" style="margin-right:0.2222222222222222em;"></span><span class="mbin">−</span><span class="mspace" style="margin-right:0.2222222222222222em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord mathnormal">λ</span><span class="mord"><span class="mord mathrm">I</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.151392em;"><span style="top:-2.5500000000000003em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight">n</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mclose">)</span><span class="mord vbox"><span class="thinbox"><span class="rlap"><span class="strut" style="height:0.73354em;vertical-align:-0.0391em;"></span><span class="inner"><span class="mord"><span class="mspace" style="margin-right:2em;"></span><span class="mord mathnormal">λ</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span><span class="mrel">∈</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span><span class="mord mathbb">R</span></span></span><span class="fix"></span></span></span></span></span></span></span></span></p>
+</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord mathnormal">p</span><span class="mopen">(</span><span class="mord mathnormal">λ</span><span class="mclose">)</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mop"><span class="mord mathrm">det</span></span><span class="mopen">(</span><span class="mord mathnormal">A</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">−</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord mathnormal">λ</span><span class="mord"><span class="mord mathrm">I</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.1514em;"><span style="top:-2.55em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight">n</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mclose">)</span><span class="mord vbox"><span class="thinbox"><span class="rlap"><span class="strut" style="height:0.7335em;vertical-align:-0.0391em;"></span><span class="inner"><span class="mord"><span class="mspace" style="margin-right:2em;"></span><span class="mord mathnormal">λ</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">∈</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mord mathbb">R</span></span></span><span class="fix"></span></span></span></span></span></span></span></span></p>
 <p>where  is the <code>n</code>-dimensional identity matrix.</p>
 <p>The eigenvalues of a real symmetric or complex Hermitian matrix are always real.
 Supports input of float, double, cfloat and cdouble dtypes.
 Also supports batches of matrices, and if <code>A</code> is a batch of matrices then
 the output has the same batch dimensions.
 The eigenvalues are returned in ascending order.</p>
-<p><code>A</code> is assumed to be Hermitian (resp. symmetric), but this is not checked internally, instead:</p><ul>
-<li><p>If <code>UPLO</code>\ <code>= 'L'</code> (default), only the lower triangular part of the matrix is used in the computation.</p></li>
+<p><code>A</code> is assumed to be Hermitian (resp. symmetric), but this is not checked internally, instead:</p><ul><li><p>If <code>UPLO</code>\ <code>= 'L'</code> (default), only the lower triangular part of the matrix is used in the computation.</p></li>
 <li><p>If <code>UPLO</code>\ <code>= 'U'</code>, only the upper triangular part of the matrix is used.</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="see-also"><a class="anchor" href="#see-also"></a>See also</h2>
-
-    <div class='dont-index'>
-<ul>
-<li><p><code><a href='linalg_eigh.html'>linalg_eigh()</a></code> computes the full eigenvalue decomposition.</p></li>
-</ul>
-
-<p>Other linalg: 
-<code><a href='linalg_cholesky_ex.html'>linalg_cholesky_ex</a>()</code>,
-<code><a href='linalg_cholesky.html'>linalg_cholesky</a>()</code>,
-<code><a href='linalg_det.html'>linalg_det</a>()</code>,
-<code><a href='linalg_eigh.html'>linalg_eigh</a>()</code>,
-<code><a href='linalg_eigvals.html'>linalg_eigvals</a>()</code>,
-<code><a href='linalg_eig.html'>linalg_eig</a>()</code>,
-<code><a href='linalg_householder_product.html'>linalg_householder_product</a>()</code>,
-<code><a href='linalg_inv_ex.html'>linalg_inv_ex</a>()</code>,
-<code><a href='linalg_inv.html'>linalg_inv</a>()</code>,
-<code><a href='linalg_lstsq.html'>linalg_lstsq</a>()</code>,
-<code><a href='linalg_matrix_norm.html'>linalg_matrix_norm</a>()</code>,
-<code><a href='linalg_matrix_power.html'>linalg_matrix_power</a>()</code>,
-<code><a href='linalg_matrix_rank.html'>linalg_matrix_rank</a>()</code>,
-<code><a href='linalg_multi_dot.html'>linalg_multi_dot</a>()</code>,
-<code><a href='linalg_norm.html'>linalg_norm</a>()</code>,
-<code><a href='linalg_pinv.html'>linalg_pinv</a>()</code>,
-<code><a href='linalg_qr.html'>linalg_qr</a>()</code>,
-<code><a href='linalg_slogdet.html'>linalg_slogdet</a>()</code>,
-<code><a href='linalg_solve.html'>linalg_solve</a>()</code>,
-<code><a href='linalg_svdvals.html'>linalg_svdvals</a>()</code>,
-<code><a href='linalg_svd.html'>linalg_svd</a>()</code>,
-<code><a href='linalg_tensorinv.html'>linalg_tensorinv</a>()</code>,
-<code><a href='linalg_tensorsolve.html'>linalg_tensorsolve</a>()</code>,
-<code><a href='linalg_vector_norm.html'>linalg_vector_norm</a>()</code></p></div>
+</ul></div>
+    <div class="section level2">
+    <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
+    <div class="dont-index">
+<ul><li><p><code><a href="linalg_eigh.html">linalg_eigh()</a></code> computes the full eigenvalue decomposition.</p></li>
+</ul><p>Other linalg: 
+<code><a href="linalg_cholesky_ex.html">linalg_cholesky_ex</a>()</code>,
+<code><a href="linalg_cholesky.html">linalg_cholesky</a>()</code>,
+<code><a href="linalg_det.html">linalg_det</a>()</code>,
+<code><a href="linalg_eigh.html">linalg_eigh</a>()</code>,
+<code><a href="linalg_eigvals.html">linalg_eigvals</a>()</code>,
+<code><a href="linalg_eig.html">linalg_eig</a>()</code>,
+<code><a href="linalg_householder_product.html">linalg_householder_product</a>()</code>,
+<code><a href="linalg_inv_ex.html">linalg_inv_ex</a>()</code>,
+<code><a href="linalg_inv.html">linalg_inv</a>()</code>,
+<code><a href="linalg_lstsq.html">linalg_lstsq</a>()</code>,
+<code><a href="linalg_matrix_norm.html">linalg_matrix_norm</a>()</code>,
+<code><a href="linalg_matrix_power.html">linalg_matrix_power</a>()</code>,
+<code><a href="linalg_matrix_rank.html">linalg_matrix_rank</a>()</code>,
+<code><a href="linalg_multi_dot.html">linalg_multi_dot</a>()</code>,
+<code><a href="linalg_norm.html">linalg_norm</a>()</code>,
+<code><a href="linalg_pinv.html">linalg_pinv</a>()</code>,
+<code><a href="linalg_qr.html">linalg_qr</a>()</code>,
+<code><a href="linalg_slogdet.html">linalg_slogdet</a>()</code>,
+<code><a href="linalg_solve.html">linalg_solve</a>()</code>,
+<code><a href="linalg_svdvals.html">linalg_svdvals</a>()</code>,
+<code><a href="linalg_svd.html">linalg_svd</a>()</code>,
+<code><a href="linalg_tensorinv.html">linalg_tensorinv</a>()</code>,
+<code><a href="linalg_tensorsolve.html">linalg_tensorsolve</a>()</code>,
+<code><a href="linalg_vector_norm.html">linalg_vector_norm</a>()</code></p></div>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>linalg_eigvalsh</span><span class='op'>(</span><span class='va'>a</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -1.7057</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1.4003</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{2} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">linalg_eigvalsh</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -1.0867</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2.0278</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer>
-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
 </div>
 
-<div class="pkgdown">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p>
+<div class="pkgdown-footer-right">
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.2.</p>
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diff --git a/reference/linalg_householder_product.html b/reference/linalg_householder_product.html
index 227be227e..95c386e14 100644
--- a/reference/linalg_householder_product.html
+++ b/reference/linalg_householder_product.html
@@ -1,304 +1,187 @@
-<!-- Generated by pkgdown: do not edit by hand -->
 <!DOCTYPE html>
-<html lang="en">
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-  <meta charset="utf-8">
-<meta http-equiv="X-UA-Compatible" content="IE=edge">
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-<title>Computes the first n columns of a product of Householder matrices. — linalg_householder_product • torch</title>
-
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-<!-- jquery -->
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-<meta property="og:description" content="Letting  be  or ,
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Letting  be  or ,
 for a matrix  with columns 
 with  and a vector  with ,
-this function computes the first  columns of the matrix" />
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+this function computes the first  columns of the matrix"><title>Computes the first n columns of a product of Householder matrices. — linalg_householder_product • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.1.0/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.1.0/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Computes the first n columns of a product of Householder matrices. — linalg_householder_product"><meta property="og:description" content="Letting  be  or ,
+for a matrix  with columns 
+with  and a vector  with ,
+this function computes the first  columns of the matrix"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Computes the first <code>n</code> columns of a product of Householder matrices.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/linalg.R'><code>R/linalg.R</code></a></small>
-    <div class="hidden name"><code>linalg_householder_product.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Computes the first <code>n</code> columns of a product of Householder matrices.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/linalg.R" class="external-link"><code>R/linalg.R</code></a></small>
+      <div class="d-none name"><code>linalg_householder_product.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Letting  be  or ,
 for a matrix  with columns 
 with  and a vector  with ,
 this function computes the first  columns of the matrix</p>
     </div>
 
-    <pre class="usage"><span class='fu'>linalg_householder_product</span><span class='op'>(</span><span class='va'>A</span>, <span class='va'>tau</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>A</th>
-      <td><p>(Tensor): tensor of shape <code>(*, m, n)</code> where <code>*</code> is zero or more batch dimensions.</p></td>
-    </tr>
-    <tr>
-      <th>tau</th>
-      <td><p>(Tensor): tensor of shape <code>(*, k)</code> where <code>*</code> is zero or more batch dimensions.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">linalg_householder_product</span><span class="op">(</span><span class="va">A</span>, <span class="va">tau</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <p><link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/npm/katex@0.13.11/dist/katex.min.css" data-external="1">
-<span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><msub><mi>H</mi><mn>1</mn></msub><msub><mi>H</mi><mn>2</mn></msub><mi mathvariant="normal">.</mi><mi mathvariant="normal">.</mi><mi mathvariant="normal">.</mi><msub><mi>H</mi><mi>k</mi></msub><mspace width="2em"/><mi>w</mi><mi>i</mi><mi>t</mi><mi>h</mi><mspace width="2em"/><msub><mi>H</mi><mi>i</mi></msub><mo>=</mo><msub><mi mathvariant="normal">I</mi><mi>m</mi></msub><mo>−</mo><msub><mi>τ</mi><mi>i</mi></msub><msub><mi>v</mi><mi>i</mi></msub><msubsup><mi>v</mi><mi>i</mi><mi>H</mi></msubsup></mrow><annotation encoding="application/x-tex">
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>A</dt>
+<dd><p>(Tensor): tensor of shape <code>(*, m, n)</code> where <code>*</code> is zero or more batch dimensions.</p></dd>
+<dt>tau</dt>
+<dd><p>(Tensor): tensor of shape <code>(*, k)</code> where <code>*</code> is zero or more batch dimensions.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
+    <p><link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/npm/katex@0.15.2/dist/katex.min.css" data-external="1"><span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><msub><mi>H</mi><mn>1</mn></msub><msub><mi>H</mi><mn>2</mn></msub><mi mathvariant="normal">.</mi><mi mathvariant="normal">.</mi><mi mathvariant="normal">.</mi><msub><mi>H</mi><mi>k</mi></msub><mspace width="2em"></mspace><mi>w</mi><mi>i</mi><mi>t</mi><mi>h</mi><mspace width="2em"></mspace><msub><mi>H</mi><mi>i</mi></msub><mo>=</mo><msub><mi mathvariant="normal">I</mi><mi>m</mi></msub><mo>−</mo><msub><mi>τ</mi><mi>i</mi></msub><msub><mi>v</mi><mi>i</mi></msub><msubsup><mi>v</mi><mi>i</mi><mi>H</mi></msubsup></mrow><annotation encoding="application/x-tex">
 H_1H_2 ... H_k \qquad with \qquad H_i = \mathrm{I}_m - \tau_i v_i v_i^{H}
-</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.84444em;vertical-align:-0.15em;"></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.08125em;">H</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.30110799999999993em;"><span style="top:-2.5500000000000003em;margin-left:-0.08125em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight">1</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.08125em;">H</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.30110799999999993em;"><span style="top:-2.5500000000000003em;margin-left:-0.08125em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight">2</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mord">...</span><span class="mord"><span class="mord mathnormal" style="margin-right:0.08125em;">H</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.33610799999999996em;"><span style="top:-2.5500000000000003em;margin-left:-0.08125em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight" style="margin-right:0.03148em;">k</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mspace" style="margin-right:2em;"></span><span class="mord mathnormal" style="margin-right:0.02691em;">w</span><span class="mord mathnormal">i</span><span class="mord mathnormal">t</span><span class="mord mathnormal">h</span><span class="mspace" style="margin-right:2em;"></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.08125em;">H</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.31166399999999994em;"><span style="top:-2.5500000000000003em;margin-left:-0.08125em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight">i</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mspace" style="margin-right:0.2777777777777778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span></span><span class="base"><span class="strut" style="height:0.83333em;vertical-align:-0.15em;"></span><span class="mord"><span class="mord mathrm">I</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.151392em;"><span style="top:-2.5500000000000003em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight">m</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mspace" style="margin-right:0.2222222222222222em;"></span><span class="mbin">−</span><span class="mspace" style="margin-right:0.2222222222222222em;"></span></span><span class="base"><span class="strut" style="height:1.138331em;vertical-align:-0.247em;"></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.1132em;">τ</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.31166399999999994em;"><span style="top:-2.5500000000000003em;margin-left:-0.1132em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight">i</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.03588em;">v</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.31166399999999994em;"><span style="top:-2.5500000000000003em;margin-left:-0.03588em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight">i</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.03588em;">v</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.8913309999999999em;"><span style="top:-2.4530000000000003em;margin-left:-0.03588em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight">i</span></span></span><span style="top:-3.113em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight" style="margin-right:0.08125em;">H</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.247em;"><span></span></span></span></span></span></span></span></span></span></span></p>
+</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.8444em;vertical-align:-0.15em;"></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.08125em;">H</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.3011em;"><span style="top:-2.55em;margin-left:-0.0813em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight">1</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.08125em;">H</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.3011em;"><span style="top:-2.55em;margin-left:-0.0813em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight">2</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mord">...</span><span class="mord"><span class="mord mathnormal" style="margin-right:0.08125em;">H</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.3361em;"><span style="top:-2.55em;margin-left:-0.0813em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight" style="margin-right:0.03148em;">k</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mspace" style="margin-right:2em;"></span><span class="mord mathnormal" style="margin-right:0.02691em;">w</span><span class="mord mathnormal">i</span><span class="mord mathnormal">t</span><span class="mord mathnormal">h</span><span class="mspace" style="margin-right:2em;"></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.08125em;">H</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.3117em;"><span style="top:-2.55em;margin-left:-0.0813em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight">i</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:0.8333em;vertical-align:-0.15em;"></span><span class="mord"><span class="mord mathrm">I</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.1514em;"><span style="top:-2.55em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight">m</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">−</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:1.1383em;vertical-align:-0.247em;"></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.1132em;">τ</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.3117em;"><span style="top:-2.55em;margin-left:-0.1132em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight">i</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.03588em;">v</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.3117em;"><span style="top:-2.55em;margin-left:-0.0359em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight">i</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.03588em;">v</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.8913em;"><span style="top:-2.453em;margin-left:-0.0359em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight">i</span></span></span><span style="top:-3.113em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight" style="margin-right:0.08125em;">H</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.247em;"><span></span></span></span></span></span></span></span></span></span></span></p>
 <p>where  is the <code>m</code>-dimensional identity matrix and
  is the conjugate transpose when  is complex, and the transpose when  is real-valued.
-See <a href='https://www.netlib.org/lapack/lug/node128.html'>Representation of Orthogonal or Unitary Matrices</a> for
+See <a href="https://www.netlib.org/lapack/lug/node128.html" class="external-link">Representation of Orthogonal or Unitary Matrices</a> for
 further details.</p>
 <p>Supports inputs of float, double, cfloat and cdouble dtypes.
 Also supports batches of matrices, and if the inputs are batches of matrices then
 the output has the same batch dimensions.</p>
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p>This function only uses the values strictly below the main diagonal of <code>A</code>.
 The other values are ignored.</p>
-    <h2 class="hasAnchor" id="see-also"><a class="anchor" href="#see-also"></a>See also</h2>
-
-    <div class='dont-index'>
-<ul>
-<li><p><code><a href='torch_geqrf.html'>torch_geqrf()</a></code> can be used together with this function to form the <code>Q</code> from the
-<code><a href='linalg_qr.html'>linalg_qr()</a></code> decomposition.</p></li>
-<li><p><code><a href='torch_ormqr.html'>torch_ormqr()</a></code> is a related function that computes the matrix multiplication
+    </div>
+    <div class="section level2">
+    <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
+    <div class="dont-index">
+<ul><li><p><code><a href="torch_geqrf.html">torch_geqrf()</a></code> can be used together with this function to form the <code>Q</code> from the
+<code><a href="linalg_qr.html">linalg_qr()</a></code> decomposition.</p></li>
+<li><p><code><a href="torch_ormqr.html">torch_ormqr()</a></code> is a related function that computes the matrix multiplication
 of a product of Householder matrices with another matrix.
 However, that function is not supported by autograd.</p></li>
-</ul>
-
-<p>Other linalg: 
-<code><a href='linalg_cholesky_ex.html'>linalg_cholesky_ex</a>()</code>,
-<code><a href='linalg_cholesky.html'>linalg_cholesky</a>()</code>,
-<code><a href='linalg_det.html'>linalg_det</a>()</code>,
-<code><a href='linalg_eigh.html'>linalg_eigh</a>()</code>,
-<code><a href='linalg_eigvalsh.html'>linalg_eigvalsh</a>()</code>,
-<code><a href='linalg_eigvals.html'>linalg_eigvals</a>()</code>,
-<code><a href='linalg_eig.html'>linalg_eig</a>()</code>,
-<code><a href='linalg_inv_ex.html'>linalg_inv_ex</a>()</code>,
-<code><a href='linalg_inv.html'>linalg_inv</a>()</code>,
-<code><a href='linalg_lstsq.html'>linalg_lstsq</a>()</code>,
-<code><a href='linalg_matrix_norm.html'>linalg_matrix_norm</a>()</code>,
-<code><a href='linalg_matrix_power.html'>linalg_matrix_power</a>()</code>,
-<code><a href='linalg_matrix_rank.html'>linalg_matrix_rank</a>()</code>,
-<code><a href='linalg_multi_dot.html'>linalg_multi_dot</a>()</code>,
-<code><a href='linalg_norm.html'>linalg_norm</a>()</code>,
-<code><a href='linalg_pinv.html'>linalg_pinv</a>()</code>,
-<code><a href='linalg_qr.html'>linalg_qr</a>()</code>,
-<code><a href='linalg_slogdet.html'>linalg_slogdet</a>()</code>,
-<code><a href='linalg_solve.html'>linalg_solve</a>()</code>,
-<code><a href='linalg_svdvals.html'>linalg_svdvals</a>()</code>,
-<code><a href='linalg_svd.html'>linalg_svd</a>()</code>,
-<code><a href='linalg_tensorinv.html'>linalg_tensorinv</a>()</code>,
-<code><a href='linalg_tensorsolve.html'>linalg_tensorsolve</a>()</code>,
-<code><a href='linalg_vector_norm.html'>linalg_vector_norm</a>()</code></p></div>
+</ul><p>Other linalg: 
+<code><a href="linalg_cholesky_ex.html">linalg_cholesky_ex</a>()</code>,
+<code><a href="linalg_cholesky.html">linalg_cholesky</a>()</code>,
+<code><a href="linalg_det.html">linalg_det</a>()</code>,
+<code><a href="linalg_eigh.html">linalg_eigh</a>()</code>,
+<code><a href="linalg_eigvalsh.html">linalg_eigvalsh</a>()</code>,
+<code><a href="linalg_eigvals.html">linalg_eigvals</a>()</code>,
+<code><a href="linalg_eig.html">linalg_eig</a>()</code>,
+<code><a href="linalg_inv_ex.html">linalg_inv_ex</a>()</code>,
+<code><a href="linalg_inv.html">linalg_inv</a>()</code>,
+<code><a href="linalg_lstsq.html">linalg_lstsq</a>()</code>,
+<code><a href="linalg_matrix_norm.html">linalg_matrix_norm</a>()</code>,
+<code><a href="linalg_matrix_power.html">linalg_matrix_power</a>()</code>,
+<code><a href="linalg_matrix_rank.html">linalg_matrix_rank</a>()</code>,
+<code><a href="linalg_multi_dot.html">linalg_multi_dot</a>()</code>,
+<code><a href="linalg_norm.html">linalg_norm</a>()</code>,
+<code><a href="linalg_pinv.html">linalg_pinv</a>()</code>,
+<code><a href="linalg_qr.html">linalg_qr</a>()</code>,
+<code><a href="linalg_slogdet.html">linalg_slogdet</a>()</code>,
+<code><a href="linalg_solve.html">linalg_solve</a>()</code>,
+<code><a href="linalg_svdvals.html">linalg_svdvals</a>()</code>,
+<code><a href="linalg_svd.html">linalg_svd</a>()</code>,
+<code><a href="linalg_tensorinv.html">linalg_tensorinv</a>()</code>,
+<code><a href="linalg_tensorsolve.html">linalg_tensorsolve</a>()</code>,
+<code><a href="linalg_vector_norm.html">linalg_vector_norm</a>()</code></p></div>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>A</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>h_tau</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_geqrf.html'>torch_geqrf</a></span><span class='op'>(</span><span class='va'>A</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>Q</span> <span class='op'>&lt;-</span> <span class='fu'>linalg_householder_product</span><span class='op'>(</span><span class='va'>h_tau</span><span class='op'>[[</span><span class='fl'>1</span><span class='op'>]</span><span class='op'>]</span>, <span class='va'>h_tau</span><span class='op'>[[</span><span class='fl'>2</span><span class='op'>]</span><span class='op'>]</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'><a href='torch_allclose.html'>torch_allclose</a></span><span class='op'>(</span><span class='va'>Q</span>, <span class='fu'><a href='linalg_qr.html'>linalg_qr</a></span><span class='op'>(</span><span class='va'>A</span><span class='op'>)</span><span class='op'>[[</span><span class='fl'>1</span><span class='op'>]</span><span class='op'>]</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [1] TRUE</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">A</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">h_tau</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_geqrf.html">torch_geqrf</a></span><span class="op">(</span><span class="va">A</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">Q</span> <span class="op">&lt;-</span> <span class="fu">linalg_householder_product</span><span class="op">(</span><span class="va">h_tau</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span>, <span class="va">h_tau</span><span class="op">[[</span><span class="fl">2</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu"><a href="torch_allclose.html">torch_allclose</a></span><span class="op">(</span><span class="va">Q</span>, <span class="fu"><a href="linalg_qr.html">linalg_qr</a></span><span class="op">(</span><span class="va">A</span><span class="op">)</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] TRUE</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
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+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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-    <h1>Computes the inverse of a square matrix if it exists.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/linalg.R'><code>R/linalg.R</code></a></small>
-    <div class="hidden name"><code>linalg_inv.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Computes the inverse of a square matrix if it exists.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/linalg.R" class="external-link"><code>R/linalg.R</code></a></small>
+      <div class="d-none name"><code>linalg_inv.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Throws a <code>runtime_error</code> if the matrix is not invertible.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>linalg_inv</span><span class='op'>(</span><span class='va'>A</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>A</th>
-      <td><p>(Tensor): tensor of shape <code>(*, n, n)</code> where <code>*</code> is zero or more batch dimensions
-consisting of invertible matrices.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">linalg_inv</span><span class="op">(</span><span class="va">A</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>A</dt>
+<dd><p>(Tensor): tensor of shape <code>(*, n, n)</code> where <code>*</code> is zero or more batch dimensions
+consisting of invertible matrices.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>Letting  be  or ,
 for a matrix ,
 its <strong>inverse matrix</strong>  (if it exists) is defined as</p>
-<p><link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/npm/katex@0.13.11/dist/katex.min.css" data-external="1">
-<span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><msup><mi>A</mi><mrow><mo>−</mo><mn>1</mn></mrow></msup><mi>A</mi><mo>=</mo><mi>A</mi><msup><mi>A</mi><mrow><mo>−</mo><mn>1</mn></mrow></msup><mo>=</mo><msub><mi mathvariant="normal">I</mi><mi>n</mi></msub></mrow><annotation encoding="application/x-tex">
+<p><link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/npm/katex@0.15.2/dist/katex.min.css" data-external="1"><span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><msup><mi>A</mi><mrow><mo>−</mo><mn>1</mn></mrow></msup><mi>A</mi><mo>=</mo><mi>A</mi><msup><mi>A</mi><mrow><mo>−</mo><mn>1</mn></mrow></msup><mo>=</mo><msub><mi mathvariant="normal">I</mi><mi>n</mi></msub></mrow><annotation encoding="application/x-tex">
 A^{-1}A = AA^{-1} = \mathrm{I}_n
-</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.864108em;vertical-align:0em;"></span><span class="mord"><span class="mord mathnormal">A</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.864108em;"><span style="top:-3.113em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mtight">−</span><span class="mord mtight">1</span></span></span></span></span></span></span></span></span><span class="mord mathnormal">A</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span></span><span class="base"><span class="strut" style="height:0.864108em;vertical-align:0em;"></span><span class="mord mathnormal">A</span><span class="mord"><span class="mord mathnormal">A</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.864108em;"><span style="top:-3.113em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mtight">−</span><span class="mord mtight">1</span></span></span></span></span></span></span></span></span><span class="mspace" style="margin-right:0.2777777777777778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span></span><span class="base"><span class="strut" style="height:0.83333em;vertical-align:-0.15em;"></span><span class="mord"><span class="mord mathrm">I</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.151392em;"><span style="top:-2.5500000000000003em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight">n</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span></span></span></span></span>
+</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.8641em;"></span><span class="mord"><span class="mord mathnormal">A</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8641em;"><span style="top:-3.113em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mtight">−</span><span class="mord mtight">1</span></span></span></span></span></span></span></span></span><span class="mord mathnormal">A</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:0.8641em;"></span><span class="mord mathnormal">A</span><span class="mord"><span class="mord mathnormal">A</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8641em;"><span style="top:-3.113em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mtight">−</span><span class="mord mtight">1</span></span></span></span></span></span></span></span></span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:0.8333em;vertical-align:-0.15em;"></span><span class="mord"><span class="mord mathrm">I</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.1514em;"><span style="top:-2.55em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight">n</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span></span></span></span></span>
 where  is the <code>n</code>-dimensional identity matrix.</p>
 <p>The inverse matrix exists if and only if  is invertible. In this case,
 the inverse is unique.
 Supports input of float, double, cfloat and cdouble dtypes.
 Also supports batches of matrices, and if <code>A</code> is a batch of matrices
 then the output has the same batch dimensions.</p>
-<p>Consider using <code><a href='linalg_solve.html'>linalg_solve()</a></code> if possible for multiplying a matrix on the left by
+<p>Consider using <code><a href="linalg_solve.html">linalg_solve()</a></code> if possible for multiplying a matrix on the left by
 the inverse, as <code>linalg_solve(A, B) == A$inv() %*% B</code>
-It is always prefered to use <code><a href='linalg_solve.html'>linalg_solve()</a></code> when possible, as it is faster and more
+It is always prefered to use <code><a href="linalg_solve.html">linalg_solve()</a></code> when possible, as it is faster and more
 numerically stable than computing the inverse explicitly.</p>
-    <h2 class="hasAnchor" id="see-also"><a class="anchor" href="#see-also"></a>See also</h2>
-
-    <div class='dont-index'><p><code><a href='linalg_pinv.html'>linalg_pinv()</a></code> computes the pseudoinverse (Moore-Penrose inverse) of matrices
+    </div>
+    <div class="section level2">
+    <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
+    <div class="dont-index"><p><code><a href="linalg_pinv.html">linalg_pinv()</a></code> computes the pseudoinverse (Moore-Penrose inverse) of matrices
 of any shape.
-<code><a href='linalg_solve.html'>linalg_solve()</a></code> computes <code>A$inv() %*% B</code> with a
+<code><a href="linalg_solve.html">linalg_solve()</a></code> computes <code>A$inv() %*% B</code> with a
 numerically stable algorithm.</p>
 <p>Other linalg: 
-<code><a href='linalg_cholesky_ex.html'>linalg_cholesky_ex</a>()</code>,
-<code><a href='linalg_cholesky.html'>linalg_cholesky</a>()</code>,
-<code><a href='linalg_det.html'>linalg_det</a>()</code>,
-<code><a href='linalg_eigh.html'>linalg_eigh</a>()</code>,
-<code><a href='linalg_eigvalsh.html'>linalg_eigvalsh</a>()</code>,
-<code><a href='linalg_eigvals.html'>linalg_eigvals</a>()</code>,
-<code><a href='linalg_eig.html'>linalg_eig</a>()</code>,
-<code><a href='linalg_householder_product.html'>linalg_householder_product</a>()</code>,
-<code><a href='linalg_inv_ex.html'>linalg_inv_ex</a>()</code>,
-<code><a href='linalg_lstsq.html'>linalg_lstsq</a>()</code>,
-<code><a href='linalg_matrix_norm.html'>linalg_matrix_norm</a>()</code>,
-<code><a href='linalg_matrix_power.html'>linalg_matrix_power</a>()</code>,
-<code><a href='linalg_matrix_rank.html'>linalg_matrix_rank</a>()</code>,
-<code><a href='linalg_multi_dot.html'>linalg_multi_dot</a>()</code>,
-<code><a href='linalg_norm.html'>linalg_norm</a>()</code>,
-<code><a href='linalg_pinv.html'>linalg_pinv</a>()</code>,
-<code><a href='linalg_qr.html'>linalg_qr</a>()</code>,
-<code><a href='linalg_slogdet.html'>linalg_slogdet</a>()</code>,
-<code><a href='linalg_solve.html'>linalg_solve</a>()</code>,
-<code><a href='linalg_svdvals.html'>linalg_svdvals</a>()</code>,
-<code><a href='linalg_svd.html'>linalg_svd</a>()</code>,
-<code><a href='linalg_tensorinv.html'>linalg_tensorinv</a>()</code>,
-<code><a href='linalg_tensorsolve.html'>linalg_tensorsolve</a>()</code>,
-<code><a href='linalg_vector_norm.html'>linalg_vector_norm</a>()</code></p></div>
+<code><a href="linalg_cholesky_ex.html">linalg_cholesky_ex</a>()</code>,
+<code><a href="linalg_cholesky.html">linalg_cholesky</a>()</code>,
+<code><a href="linalg_det.html">linalg_det</a>()</code>,
+<code><a href="linalg_eigh.html">linalg_eigh</a>()</code>,
+<code><a href="linalg_eigvalsh.html">linalg_eigvalsh</a>()</code>,
+<code><a href="linalg_eigvals.html">linalg_eigvals</a>()</code>,
+<code><a href="linalg_eig.html">linalg_eig</a>()</code>,
+<code><a href="linalg_householder_product.html">linalg_householder_product</a>()</code>,
+<code><a href="linalg_inv_ex.html">linalg_inv_ex</a>()</code>,
+<code><a href="linalg_lstsq.html">linalg_lstsq</a>()</code>,
+<code><a href="linalg_matrix_norm.html">linalg_matrix_norm</a>()</code>,
+<code><a href="linalg_matrix_power.html">linalg_matrix_power</a>()</code>,
+<code><a href="linalg_matrix_rank.html">linalg_matrix_rank</a>()</code>,
+<code><a href="linalg_multi_dot.html">linalg_multi_dot</a>()</code>,
+<code><a href="linalg_norm.html">linalg_norm</a>()</code>,
+<code><a href="linalg_pinv.html">linalg_pinv</a>()</code>,
+<code><a href="linalg_qr.html">linalg_qr</a>()</code>,
+<code><a href="linalg_slogdet.html">linalg_slogdet</a>()</code>,
+<code><a href="linalg_solve.html">linalg_solve</a>()</code>,
+<code><a href="linalg_svdvals.html">linalg_svdvals</a>()</code>,
+<code><a href="linalg_svd.html">linalg_svd</a>()</code>,
+<code><a href="linalg_tensorinv.html">linalg_tensorinv</a>()</code>,
+<code><a href="linalg_tensorsolve.html">linalg_tensorsolve</a>()</code>,
+<code><a href="linalg_vector_norm.html">linalg_vector_norm</a>()</code></p></div>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>A</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>4</span>, <span class='fl'>4</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>linalg_inv</span><span class='op'>(</span><span class='va'>A</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -2.3029 -1.1064  6.8285  0.0669</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  2.7416  1.2385 -6.2944 -0.2016</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1.3283  1.1579 -4.0119  0.1803</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  4.9961  2.7458 -13.7660 -2.8242</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{4,4} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">A</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">4</span>, <span class="fl">4</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">linalg_inv</span><span class="op">(</span><span class="va">A</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.3617  0.3020  0.0682 -1.6960</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -2.0115 -0.5945  0.3367 -4.5855</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -1.4055  0.3159 -0.2382 -2.5192</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.4039 -0.4235 -0.6591  2.8453</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4,4} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer>
-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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index 56c63ac2a..41a27f3e4 100644
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-<meta property="og:description" content="Returns a namedtuple (inverse, info). inverse contains the result of
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Returns a namedtuple (inverse, info). inverse contains the result of
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 If A is not an invertible matrix, or if it's a batch of matrices
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@@ -53,150 +10,95 @@ info filled with zeros indicates that the inversion was successful.
 If check_errors=TRUE and info contains positive integers, then a RuntimeError is thrown.
 Supports input of float, double, cfloat and cdouble dtypes.
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+the output has the same batch dimensions."><title>Computes the inverse of a square matrix if it is invertible. — linalg_inv_ex • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.1.0/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.1.0/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Computes the inverse of a square matrix if it is invertible. — linalg_inv_ex"><meta property="og:description" content="Returns a namedtuple (inverse, info). inverse contains the result of
+inverting A and info stores the LAPACK error codes.
+If A is not an invertible matrix, or if it's a batch of matrices
+and one or more of them is not an invertible matrix,
+then info stores a positive integer for the corresponding matrix.
+The positive integer indicates the diagonal element of the LU decomposition of
+the input matrix that is exactly zero.
+info filled with zeros indicates that the inversion was successful.
+If check_errors=TRUE and info contains positive integers, then a RuntimeError is thrown.
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+  <a class="external-link nav-link" href="https://github.com/mlverse/torch/" aria-label="github">
+    <span class="fab fa fab fa-github fa-lg"></span>
      
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+      </ul></div>
 
+    
+  </div>
+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Computes the inverse of a square matrix if it is invertible.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/linalg.R'><code>R/linalg.R</code></a></small>
-    <div class="hidden name"><code>linalg_inv_ex.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Computes the inverse of a square matrix if it is invertible.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/linalg.R" class="external-link"><code>R/linalg.R</code></a></small>
+      <div class="d-none name"><code>linalg_inv_ex.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Returns a namedtuple <code>(inverse, info)</code>. <code>inverse</code> contains the result of
 inverting <code>A</code> and <code>info</code> stores the LAPACK error codes.
 If <code>A</code> is not an invertible matrix, or if it's a batch of matrices
@@ -211,88 +113,80 @@ Also supports batches of matrices, and if <code>A</code> is a batch of matrices
 the output has the same batch dimensions.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>linalg_inv_ex</span><span class='op'>(</span><span class='va'>A</span>, check_errors <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>A</th>
-      <td><p>(Tensor): tensor of shape <code>(*, n, n)</code> where <code>*</code> is zero or more batch dimensions
-consisting of square matrices.</p></td>
-    </tr>
-    <tr>
-      <th>check_errors</th>
-      <td><p>(bool, optional): controls whether to check the content of <code>info</code>. Default: <code>FALSE</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">linalg_inv_ex</span><span class="op">(</span><span class="va">A</span>, check_errors <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>A</dt>
+<dd><p>(Tensor): tensor of shape <code>(*, n, n)</code> where <code>*</code> is zero or more batch dimensions
+consisting of square matrices.</p></dd>
+<dt>check_errors</dt>
+<dd><p>(bool, optional): controls whether to check the content of <code>info</code>. Default: <code>FALSE</code>.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p>If <code>A</code> is on a CUDA device then this function may synchronize
 that device with the CPU.</p>
 <p>This function is "experimental" and it may change in a future PyTorch release.</p>
-    <h2 class="hasAnchor" id="see-also"><a class="anchor" href="#see-also"></a>See also</h2>
-
-    <div class='dont-index'><p><code><a href='linalg_inv.html'>linalg_inv()</a></code> is a NumPy compatible variant that always checks for errors.</p>
+    </div>
+    <div class="section level2">
+    <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
+    <div class="dont-index"><p><code><a href="linalg_inv.html">linalg_inv()</a></code> is a NumPy compatible variant that always checks for errors.</p>
 <p>Other linalg: 
-<code><a href='linalg_cholesky_ex.html'>linalg_cholesky_ex</a>()</code>,
-<code><a href='linalg_cholesky.html'>linalg_cholesky</a>()</code>,
-<code><a href='linalg_det.html'>linalg_det</a>()</code>,
-<code><a href='linalg_eigh.html'>linalg_eigh</a>()</code>,
-<code><a href='linalg_eigvalsh.html'>linalg_eigvalsh</a>()</code>,
-<code><a href='linalg_eigvals.html'>linalg_eigvals</a>()</code>,
-<code><a href='linalg_eig.html'>linalg_eig</a>()</code>,
-<code><a href='linalg_householder_product.html'>linalg_householder_product</a>()</code>,
-<code><a href='linalg_inv.html'>linalg_inv</a>()</code>,
-<code><a href='linalg_lstsq.html'>linalg_lstsq</a>()</code>,
-<code><a href='linalg_matrix_norm.html'>linalg_matrix_norm</a>()</code>,
-<code><a href='linalg_matrix_power.html'>linalg_matrix_power</a>()</code>,
-<code><a href='linalg_matrix_rank.html'>linalg_matrix_rank</a>()</code>,
-<code><a href='linalg_multi_dot.html'>linalg_multi_dot</a>()</code>,
-<code><a href='linalg_norm.html'>linalg_norm</a>()</code>,
-<code><a href='linalg_pinv.html'>linalg_pinv</a>()</code>,
-<code><a href='linalg_qr.html'>linalg_qr</a>()</code>,
-<code><a href='linalg_slogdet.html'>linalg_slogdet</a>()</code>,
-<code><a href='linalg_solve.html'>linalg_solve</a>()</code>,
-<code><a href='linalg_svdvals.html'>linalg_svdvals</a>()</code>,
-<code><a href='linalg_svd.html'>linalg_svd</a>()</code>,
-<code><a href='linalg_tensorinv.html'>linalg_tensorinv</a>()</code>,
-<code><a href='linalg_tensorsolve.html'>linalg_tensorsolve</a>()</code>,
-<code><a href='linalg_vector_norm.html'>linalg_vector_norm</a>()</code></p></div>
+<code><a href="linalg_cholesky_ex.html">linalg_cholesky_ex</a>()</code>,
+<code><a href="linalg_cholesky.html">linalg_cholesky</a>()</code>,
+<code><a href="linalg_det.html">linalg_det</a>()</code>,
+<code><a href="linalg_eigh.html">linalg_eigh</a>()</code>,
+<code><a href="linalg_eigvalsh.html">linalg_eigvalsh</a>()</code>,
+<code><a href="linalg_eigvals.html">linalg_eigvals</a>()</code>,
+<code><a href="linalg_eig.html">linalg_eig</a>()</code>,
+<code><a href="linalg_householder_product.html">linalg_householder_product</a>()</code>,
+<code><a href="linalg_inv.html">linalg_inv</a>()</code>,
+<code><a href="linalg_lstsq.html">linalg_lstsq</a>()</code>,
+<code><a href="linalg_matrix_norm.html">linalg_matrix_norm</a>()</code>,
+<code><a href="linalg_matrix_power.html">linalg_matrix_power</a>()</code>,
+<code><a href="linalg_matrix_rank.html">linalg_matrix_rank</a>()</code>,
+<code><a href="linalg_multi_dot.html">linalg_multi_dot</a>()</code>,
+<code><a href="linalg_norm.html">linalg_norm</a>()</code>,
+<code><a href="linalg_pinv.html">linalg_pinv</a>()</code>,
+<code><a href="linalg_qr.html">linalg_qr</a>()</code>,
+<code><a href="linalg_slogdet.html">linalg_slogdet</a>()</code>,
+<code><a href="linalg_solve.html">linalg_solve</a>()</code>,
+<code><a href="linalg_svdvals.html">linalg_svdvals</a>()</code>,
+<code><a href="linalg_svd.html">linalg_svd</a>()</code>,
+<code><a href="linalg_tensorinv.html">linalg_tensorinv</a>()</code>,
+<code><a href="linalg_tensorsolve.html">linalg_tensorsolve</a>()</code>,
+<code><a href="linalg_vector_norm.html">linalg_vector_norm</a>()</code></p></div>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>A</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>3</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>out</span> <span class='op'>&lt;-</span> <span class='fu'>linalg_inv_ex</span><span class='op'>(</span><span class='va'>A</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">A</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">3</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">out</span> <span class="op">&lt;-</span> <span class="fu">linalg_inv_ex</span><span class="op">(</span><span class="va">A</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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+    
+  </div>
+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Computes a solution to the least squares problem of a system of linear equations.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/linalg.R'><code>R/linalg.R</code></a></small>
-    <div class="hidden name"><code>linalg_lstsq.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Computes a solution to the least squares problem of a system of linear equations.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/linalg.R" class="external-link"><code>R/linalg.R</code></a></small>
+      <div class="d-none name"><code>linalg_lstsq.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Letting  be  or ,
 the <strong>least squares problem</strong> for a linear system  with
  is defined as</p>
     </div>
 
-    <pre class="usage"><span class='fu'>linalg_lstsq</span><span class='op'>(</span><span class='va'>A</span>, <span class='va'>B</span>, rcond <span class='op'>=</span> <span class='cn'>NULL</span>, <span class='va'>...</span>, driver <span class='op'>=</span> <span class='cn'>NULL</span><span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">linalg_lstsq</span><span class="op">(</span><span class="va">A</span>, <span class="va">B</span>, rcond <span class="op">=</span> <span class="cn">NULL</span>, <span class="va">...</span>, driver <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>A</th>
-      <td><p>(Tensor): lhs tensor of shape <code>(*, m, n)</code> where <code>*</code> is zero or more batch dimensions.</p></td>
-    </tr>
-    <tr>
-      <th>B</th>
-      <td><p>(Tensor): rhs tensor of shape <code>(*, m, k)</code> where <code>*</code> is zero or more batch dimensions.</p></td>
-    </tr>
-    <tr>
-      <th>rcond</th>
-      <td><p>(float, optional): used to determine the effective rank of <code>A</code>.
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>A</dt>
+<dd><p>(Tensor): lhs tensor of shape <code>(*, m, n)</code> where <code>*</code> is zero or more batch dimensions.</p></dd>
+<dt>B</dt>
+<dd><p>(Tensor): rhs tensor of shape <code>(*, m, k)</code> where <code>*</code> is zero or more batch dimensions.</p></dd>
+<dt>rcond</dt>
+<dd><p>(float, optional): used to determine the effective rank of <code>A</code>.
 If <code>rcond = NULL</code>, <code>rcond</code> is set to the machine
-precision of the dtype of <code>A</code> times <code>max(m, n)</code>. Default: <code>NULL</code>.</p></td>
-    </tr>
-    <tr>
-      <th>...</th>
-      <td><p>currently unused.</p></td>
-    </tr>
-    <tr>
-      <th>driver</th>
-      <td><p>(str, optional): name of the LAPACK/MAGMA method to be used.
+precision of the dtype of <code>A</code> times <code>max(m, n)</code>. Default: <code>NULL</code>.</p></dd>
+<dt>...</dt>
+<dd><p>currently unused.</p></dd>
+<dt>driver</dt>
+<dd><p>(str, optional): name of the LAPACK/MAGMA method to be used.
 If <code>NULL</code>, <code>'gelsy'</code> is used for CPU inputs and <code>'gels'</code> for CUDA inputs.
-Default: <code>NULL</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
-
+Default: <code>NULL</code>.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
     <p>A list <code>(solution, residuals, rank, singular_values)</code>.</p>
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p><link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/npm/katex@0.13.11/dist/katex.min.css" data-external="1">
-<span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><munder><mrow><mi>min</mi><mo>⁡</mo></mrow><mrow><mi>X</mi><mo>∈</mo><msup><mi mathvariant="double-struck">K</mi><mrow><mi>n</mi><mo>×</mo><mi>k</mi></mrow></msup></mrow></munder><mi mathvariant="normal">∥</mi><mi>A</mi><mi>X</mi><mo>−</mo><mi>B</mi><msub><mi mathvariant="normal">∥</mi><mi>F</mi></msub></mrow><annotation encoding="application/x-tex">
+    </div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
+    <p><link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/npm/katex@0.15.2/dist/katex.min.css" data-external="1"><span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><munder><mrow><mi>min</mi><mo>⁡</mo></mrow><mrow><mi>X</mi><mo>∈</mo><msup><mi mathvariant="double-struck">K</mi><mrow><mi>n</mi><mo>×</mo><mi>k</mi></mrow></msup></mrow></munder><mi mathvariant="normal">∥</mi><mi>A</mi><mi>X</mi><mo>−</mo><mi>B</mi><msub><mi mathvariant="normal">∥</mi><mi>F</mi></msub></mrow><annotation encoding="application/x-tex">
 \min_{X \in \mathbb{K}^{n \times k}} \|AX - B\|_F
-</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1.59079em;vertical-align:-0.8407899999999999em;"></span><span class="mop op-limits"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.66786em;"><span style="top:-2.28658em;margin-left:0em;"><span class="pstrut" style="height:3em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight" style="margin-right:0.07847em;">X</span><span class="mrel mtight">∈</span><span class="mord mtight"><span class="mord mathbb mtight">K</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.7820285714285714em;"><span style="top:-2.786em;margin-right:0.07142857142857144em;"><span class="pstrut" style="height:2.5em;"></span><span class="sizing reset-size3 size1 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">n</span><span class="mbin mtight">×</span><span class="mord mathnormal mtight" style="margin-right:0.03148em;">k</span></span></span></span></span></span></span></span></span></span></span></span><span style="top:-3em;"><span class="pstrut" style="height:3em;"></span><span><span class="mop">min</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.8407899999999999em;"><span></span></span></span></span></span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord">∥</span><span class="mord mathnormal">A</span><span class="mord mathnormal" style="margin-right:0.07847em;">X</span><span class="mspace" style="margin-right:0.2222222222222222em;"></span><span class="mbin">−</span><span class="mspace" style="margin-right:0.2222222222222222em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord mathnormal" style="margin-right:0.05017em;">B</span><span class="mord"><span class="mord">∥</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.32833099999999993em;"><span style="top:-2.5500000000000003em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight" style="margin-right:0.13889em;">F</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span></span></span></span></span></p>
+</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1.5908em;vertical-align:-0.8408em;"></span><span class="mop op-limits"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.6679em;"><span style="top:-2.2866em;margin-left:0em;"><span class="pstrut" style="height:3em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight" style="margin-right:0.07847em;">X</span><span class="mrel mtight">∈</span><span class="mord mtight"><span class="mord mathbb mtight">K</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.782em;"><span style="top:-2.786em;margin-right:0.0714em;"><span class="pstrut" style="height:2.5em;"></span><span class="sizing reset-size3 size1 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">n</span><span class="mbin mtight">×</span><span class="mord mathnormal mtight" style="margin-right:0.03148em;">k</span></span></span></span></span></span></span></span></span></span></span></span><span style="top:-3em;"><span class="pstrut" style="height:3em;"></span><span><span class="mop">min</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.8408em;"><span></span></span></span></span></span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord">∥</span><span class="mord mathnormal">A</span><span class="mord mathnormal" style="margin-right:0.07847em;">X</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">−</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord mathnormal" style="margin-right:0.05017em;">B</span><span class="mord"><span class="mord">∥</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.3283em;"><span style="top:-2.55em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight" style="margin-right:0.13889em;">F</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span></span></span></span></span></p>
 <p>where  denotes the Frobenius norm.
 Supports inputs of float, double, cfloat and cdouble dtypes.</p>
 <p>Also supports batches of matrices, and if the inputs are batches of matrices then
@@ -240,16 +124,13 @@ the output has the same batch dimensions.
 <code>driver</code> chooses the LAPACK/MAGMA function that will be used.</p>
 <p>For CPU inputs the valid values are <code>'gels'</code>, <code>'gelsy'</code>, <code>'gelsd</code>, <code>'gelss'</code>.
 For CUDA input, the only valid driver is <code>'gels'</code>, which assumes that <code>A</code> is full-rank.</p>
-<p>To choose the best driver on CPU consider:</p><ul>
-<li><p>If <code>A</code> is well-conditioned (its <a href='https://pytorch.org/docs/master/linalg.html#torch.linalg.cond'>condition number</a> is not too large), or you do not mind some precision loss.</p></li>
+<p>To choose the best driver on CPU consider:</p><ul><li><p>If <code>A</code> is well-conditioned (its <a href="https://pytorch.org/docs/master/linalg.html#torch.linalg.cond" class="external-link">condition number</a> is not too large), or you do not mind some precision loss.</p></li>
 <li><p>For a general matrix: <code>'gelsy'</code> (QR with pivoting) (default)</p></li>
 <li><p>If <code>A</code> is full-rank: <code>'gels'</code> (QR)</p></li>
 <li><p>If <code>A</code> is not well-conditioned.</p></li>
 <li><p><code>'gelsd'</code> (tridiagonal reduction and SVD)</p></li>
 <li><p>But if you run into memory issues: <code>'gelss'</code> (full SVD).</p></li>
-</ul>
-
-<p>See also the <a href='https://www.netlib.org/lapack/lug/node27.html'>full description of these drivers</a></p>
+</ul><p>See also the <a href="https://www.netlib.org/lapack/lug/node27.html" class="external-link">full description of these drivers</a></p>
 <p><code>rcond</code> is used to determine the effective rank of the matrices in <code>A</code>
 when <code>driver</code> is one of (<code>'gelsy'</code>, <code>'gelsd'</code>, <code>'gelss'</code>).
 In this case, if  are the singular values of <code>A</code> in decreasing order,
@@ -257,8 +138,7 @@ In this case, if  are the singular values of <code>A</code> in decreasing order,
 If <code>rcond = NULL</code> (default), <code>rcond</code> is set to the machine precision of the dtype of <code>A</code>.</p>
 <p>This function returns the solution to the problem and some extra information in a list of
 four tensors <code>(solution, residuals, rank, singular_values)</code>. For inputs <code>A</code>, <code>B</code>
-of shape <code>(*, m, n)</code>, <code>(*, m, k)</code> respectively, it cointains</p><ul>
-<li><p><code>solution</code>: the least squares solution. It has shape <code>(*, n, k)</code>.</p></li>
+of shape <code>(*, m, n)</code>, <code>(*, m, k)</code> respectively, it cointains</p><ul><li><p><code>solution</code>: the least squares solution. It has shape <code>(*, n, k)</code>.</p></li>
 <li><p><code>residuals</code>: the squared residuals of the solutions, that is, .
 It has shape equal to the batch dimensions of <code>A</code>.
 It is computed when <code>m &gt; n</code> and every matrix in <code>A</code> is full-rank,
@@ -273,81 +153,78 @@ otherwise it is an empty tensor.</p></li>
 It has shape <code>(*, min(m, n))</code>.
 It is computed when <code>driver</code> is one of (<code>'gelsd'</code>, <code>'gelss'</code>),
 otherwise it is an empty tensor.</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
+</ul></div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p>This function computes <code>X = A$pinverse() %*% B</code> in a faster and
 more numerically stable way than performing the computations separately.</p>
-    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></a></h2>
     
 
 <p>The default value of <code>rcond</code> may change in a future PyTorch release.
 It is therefore recommended to use a fixed value to avoid potential
 breaking changes.</p>
-    <h2 class="hasAnchor" id="see-also"><a class="anchor" href="#see-also"></a>See also</h2>
-
-    <div class='dont-index'><p>Other linalg: 
-<code><a href='linalg_cholesky_ex.html'>linalg_cholesky_ex</a>()</code>,
-<code><a href='linalg_cholesky.html'>linalg_cholesky</a>()</code>,
-<code><a href='linalg_det.html'>linalg_det</a>()</code>,
-<code><a href='linalg_eigh.html'>linalg_eigh</a>()</code>,
-<code><a href='linalg_eigvalsh.html'>linalg_eigvalsh</a>()</code>,
-<code><a href='linalg_eigvals.html'>linalg_eigvals</a>()</code>,
-<code><a href='linalg_eig.html'>linalg_eig</a>()</code>,
-<code><a href='linalg_householder_product.html'>linalg_householder_product</a>()</code>,
-<code><a href='linalg_inv_ex.html'>linalg_inv_ex</a>()</code>,
-<code><a href='linalg_inv.html'>linalg_inv</a>()</code>,
-<code><a href='linalg_matrix_norm.html'>linalg_matrix_norm</a>()</code>,
-<code><a href='linalg_matrix_power.html'>linalg_matrix_power</a>()</code>,
-<code><a href='linalg_matrix_rank.html'>linalg_matrix_rank</a>()</code>,
-<code><a href='linalg_multi_dot.html'>linalg_multi_dot</a>()</code>,
-<code><a href='linalg_norm.html'>linalg_norm</a>()</code>,
-<code><a href='linalg_pinv.html'>linalg_pinv</a>()</code>,
-<code><a href='linalg_qr.html'>linalg_qr</a>()</code>,
-<code><a href='linalg_slogdet.html'>linalg_slogdet</a>()</code>,
-<code><a href='linalg_solve.html'>linalg_solve</a>()</code>,
-<code><a href='linalg_svdvals.html'>linalg_svdvals</a>()</code>,
-<code><a href='linalg_svd.html'>linalg_svd</a>()</code>,
-<code><a href='linalg_tensorinv.html'>linalg_tensorinv</a>()</code>,
-<code><a href='linalg_tensorsolve.html'>linalg_tensorsolve</a>()</code>,
-<code><a href='linalg_vector_norm.html'>linalg_vector_norm</a>()</code></p></div>
+    </div>
+    <div class="section level2">
+    <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
+    <div class="dont-index"><p>Other linalg: 
+<code><a href="linalg_cholesky_ex.html">linalg_cholesky_ex</a>()</code>,
+<code><a href="linalg_cholesky.html">linalg_cholesky</a>()</code>,
+<code><a href="linalg_det.html">linalg_det</a>()</code>,
+<code><a href="linalg_eigh.html">linalg_eigh</a>()</code>,
+<code><a href="linalg_eigvalsh.html">linalg_eigvalsh</a>()</code>,
+<code><a href="linalg_eigvals.html">linalg_eigvals</a>()</code>,
+<code><a href="linalg_eig.html">linalg_eig</a>()</code>,
+<code><a href="linalg_householder_product.html">linalg_householder_product</a>()</code>,
+<code><a href="linalg_inv_ex.html">linalg_inv_ex</a>()</code>,
+<code><a href="linalg_inv.html">linalg_inv</a>()</code>,
+<code><a href="linalg_matrix_norm.html">linalg_matrix_norm</a>()</code>,
+<code><a href="linalg_matrix_power.html">linalg_matrix_power</a>()</code>,
+<code><a href="linalg_matrix_rank.html">linalg_matrix_rank</a>()</code>,
+<code><a href="linalg_multi_dot.html">linalg_multi_dot</a>()</code>,
+<code><a href="linalg_norm.html">linalg_norm</a>()</code>,
+<code><a href="linalg_pinv.html">linalg_pinv</a>()</code>,
+<code><a href="linalg_qr.html">linalg_qr</a>()</code>,
+<code><a href="linalg_slogdet.html">linalg_slogdet</a>()</code>,
+<code><a href="linalg_solve.html">linalg_solve</a>()</code>,
+<code><a href="linalg_svdvals.html">linalg_svdvals</a>()</code>,
+<code><a href="linalg_svd.html">linalg_svd</a>()</code>,
+<code><a href="linalg_tensorinv.html">linalg_tensorinv</a>()</code>,
+<code><a href="linalg_tensorsolve.html">linalg_tensorsolve</a>()</code>,
+<code><a href="linalg_vector_norm.html">linalg_vector_norm</a>()</code></p></div>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>A</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/cbind.html'>rbind</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>10</span>, <span class='fl'>2</span>, <span class='fl'>3</span><span class='op'>)</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>10</span>, <span class='fl'>5</span><span class='op'>)</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>5</span>, <span class='fl'>6</span>, <span class='fl'>12</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span><span class='op'>$</span><span class='fu'>unsqueeze</span><span class='op'>(</span><span class='fl'>1</span><span class='op'>)</span> <span class='co'># shape (1, 3, 3)</span></span>
-<span class='r-in'><span class='va'>B</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_stack.html'>torch_stack</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/cbind.html'>rbind</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>5</span>, <span class='fl'>1</span><span class='op'>)</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>2</span>, <span class='fl'>1</span><span class='op'>)</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>5</span>, <span class='fl'>1</span>, <span class='fl'>9</span><span class='op'>)</span><span class='op'>)</span>,</span>
-<span class='r-in'>                      <span class='fu'><a href='https://rdrr.io/r/base/cbind.html'>rbind</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span>, <span class='fl'>2</span>, <span class='fl'>9</span><span class='op'>)</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>0</span>, <span class='fl'>3</span><span class='op'>)</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>5</span>, <span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span>, dim <span class='op'>=</span> <span class='fl'>1</span><span class='op'>)</span> <span class='co'># shape (2, 3, 3)</span></span>
-<span class='r-in'><span class='va'>X</span> <span class='op'>&lt;-</span> <span class='fu'>linalg_lstsq</span><span class='op'>(</span><span class='va'>A</span>, <span class='va'>B</span><span class='op'>)</span><span class='op'>$</span><span class='va'>solution</span> <span class='co'># A is broadcasted to shape (2, 3, 3)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">A</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/cbind.html" class="external-link">rbind</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">10</span>, <span class="fl">2</span>, <span class="fl">3</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">10</span>, <span class="fl">5</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">5</span>, <span class="fl">6</span>, <span class="fl">12</span><span class="op">)</span><span class="op">)</span><span class="op">)</span><span class="op">$</span><span class="fu">unsqueeze</span><span class="op">(</span><span class="fl">1</span><span class="op">)</span> <span class="co"># shape (1, 3, 3)</span></span>
+<span class="r-in"><span class="va">B</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_stack.html">torch_stack</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span></span>
+<span class="r-in">  <span class="fu"><a href="https://rdrr.io/r/base/cbind.html" class="external-link">rbind</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">5</span>, <span class="fl">1</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">2</span>, <span class="fl">1</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">5</span>, <span class="fl">1</span>, <span class="fl">9</span><span class="op">)</span><span class="op">)</span>,</span>
+<span class="r-in">  <span class="fu"><a href="https://rdrr.io/r/base/cbind.html" class="external-link">rbind</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span>, <span class="fl">2</span>, <span class="fl">9</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">0</span>, <span class="fl">3</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">5</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">)</span>, dim <span class="op">=</span> <span class="fl">1</span><span class="op">)</span> <span class="co"># shape (2, 3, 3)</span></span>
+<span class="r-in"><span class="va">X</span> <span class="op">&lt;-</span> <span class="fu">linalg_lstsq</span><span class="op">(</span><span class="va">A</span>, <span class="va">B</span><span class="op">)</span><span class="op">$</span><span class="va">solution</span> <span class="co"># A is broadcasted to shape (2, 3, 3)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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diff --git a/reference/linalg_matrix_norm.html b/reference/linalg_matrix_norm.html
index 032723934..da563cc41 100644
--- a/reference/linalg_matrix_norm.html
+++ b/reference/linalg_matrix_norm.html
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-<meta property="og:description" content="If A is complex valued, it computes the norm of A$abs()
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="If A is complex valued, it computes the norm of A$abs()
 Support input of float, double, cfloat and cdouble dtypes.
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+be treated as batch dimensions. The output will have the same batch dimensions."><title>Computes a matrix norm. — linalg_matrix_norm • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.1.0/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.1.0/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Computes a matrix norm. — linalg_matrix_norm"><meta property="og:description" content="If A is complex valued, it computes the norm of A$abs()
+Support input of float, double, cfloat and cdouble dtypes.
+Also supports batches of matrices: the norm will be computed over the
+dimensions specified by the 2-tuple dim and the other dimensions will
+be treated as batch dimensions. The output will have the same batch dimensions."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+    <a class="dropdown-item" href="../articles/installation.html">Installation</a>
+    <h6 class="dropdown-header" data-toc-skip>Tensors</h6>
+    <a class="dropdown-item" href="../articles/tensor-creation.html">Creating tensors</a>
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+    <a class="dropdown-item" href="../articles/extending-autograd.html">Extending autograd</a>
+    <a class="dropdown-item" href="../articles/python-to-r.html">Python models</a>
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 </li>
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+  <a class="nav-link" href="../news/index.html">Changelog</a>
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-        <li>
-  <a href="https://github.com/mlverse/torch/">
-    <span class="fab fa-github fa-lg"></span>
+      </ul><form class="form-inline my-2 my-lg-0" role="search">
+        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
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+      </ul></div>
 
+    
+  </div>
+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Computes a matrix norm.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/linalg.R'><code>R/linalg.R</code></a></small>
-    <div class="hidden name"><code>linalg_matrix_norm.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Computes a matrix norm.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/linalg.R" class="external-link"><code>R/linalg.R</code></a></small>
+      <div class="d-none name"><code>linalg_matrix_norm.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>If <code>A</code> is complex valued, it computes the norm of <code>A$abs()</code>
 Support input of float, double, cfloat and cdouble dtypes.
 Also supports batches of matrices: the norm will be computed over the
@@ -197,134 +92,104 @@ dimensions specified by the 2-tuple <code>dim</code> and the other dimensions wi
 be treated as batch dimensions. The output will have the same batch dimensions.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>linalg_matrix_norm</span><span class='op'>(</span>
-  <span class='va'>A</span>,
-  ord <span class='op'>=</span> <span class='st'>"fro"</span>,
-  dim <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='op'>-</span><span class='fl'>2</span>, <span class='op'>-</span><span class='fl'>1</span><span class='op'>)</span>,
-  keepdim <span class='op'>=</span> <span class='cn'>FALSE</span>,
-  dtype <span class='op'>=</span> <span class='cn'>NULL</span>
-<span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">linalg_matrix_norm</span><span class="op">(</span>
+  <span class="va">A</span>,
+  ord <span class="op">=</span> <span class="st">"fro"</span>,
+  dim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="op">-</span><span class="fl">2</span>, <span class="op">-</span><span class="fl">1</span><span class="op">)</span>,
+  keepdim <span class="op">=</span> <span class="cn">FALSE</span>,
+  dtype <span class="op">=</span> <span class="cn">NULL</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>A</th>
-      <td><p>(Tensor): tensor with two or more dimensions. By default its
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>A</dt>
+<dd><p>(Tensor): tensor with two or more dimensions. By default its
 shape is interpreted as <code>(*, m, n)</code> where <code>*</code> is zero or more
-batch dimensions, but this behavior can be controlled using <code>dim</code>.</p></td>
-    </tr>
-    <tr>
-      <th>ord</th>
-      <td><p>(int, inf, -inf, 'fro', 'nuc', optional): order of norm. Default: <code>'fro'</code></p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int, Tupleint, optional): dimensions over which to compute
+batch dimensions, but this behavior can be controlled using <code>dim</code>.</p></dd>
+<dt>ord</dt>
+<dd><p>(int, inf, -inf, 'fro', 'nuc', optional): order of norm. Default: <code>'fro'</code></p></dd>
+<dt>dim</dt>
+<dd><p>(int, Tupleint, optional): dimensions over which to compute
 the vector or matrix norm. See above for the behavior when <code>dim=NULL</code>.
-Default: <code>NULL</code></p></td>
-    </tr>
-    <tr>
-      <th>keepdim</th>
-      <td><p>(bool, optional): If set to <code>TRUE</code>, the reduced dimensions are retained
-in the result as dimensions with size one. Default: <code>FALSE</code></p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>dtype (<code>torch_dtype</code>, optional): If specified, the input tensor is cast to
+Default: <code>NULL</code></p></dd>
+<dt>keepdim</dt>
+<dd><p>(bool, optional): If set to <code>TRUE</code>, the reduced dimensions are retained
+in the result as dimensions with size one. Default: <code>FALSE</code></p></dd>
+<dt>dtype</dt>
+<dd><p>dtype (<code>torch_dtype</code>, optional): If specified, the input tensor is cast to
 <code>dtype</code> before performing the operation, and the returned tensor's type
-will be <code>dtype</code>. Default: <code>NULL</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
+will be <code>dtype</code>. Default: <code>NULL</code></p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p><code>ord</code> defines the norm that is computed. The following norms are
-supported:</p><table class='table'>
-<tr><td><code>ord</code></td><td>norm for matrices</td><td>norm for vectors</td></tr>
-<tr><td><code>NULL</code> (default)</td><td>Frobenius norm</td><td><code>2</code>-norm (see below)</td></tr>
-<tr><td><code>"fro"</code></td><td>Frobenius norm</td><td>– not supported –</td></tr>
-<tr><td><code>"nuc"</code></td><td>nuclear norm</td><td>– not supported –</td></tr>
-<tr><td><code>Inf</code></td><td><code>max(sum(abs(x), dim=2))</code></td><td><code>max(abs(x))</code></td></tr>
-<tr><td><code>-Inf</code></td><td><code>min(sum(abs(x), dim=2))</code></td><td><code>min(abs(x))</code></td></tr>
-<tr><td><code>0</code></td><td>– not supported –</td><td><code>sum(x != 0)</code></td></tr>
-<tr><td><code>1</code></td><td><code>max(sum(abs(x), dim=1))</code></td><td>as below</td></tr>
-<tr><td><code>-1</code></td><td><code>min(sum(abs(x), dim=1))</code></td><td>as below</td></tr>
-<tr><td><code>2</code></td><td>largest singular value</td><td>as below</td></tr>
-<tr><td><code>-2</code></td><td>smallest singular value</td><td>as below</td></tr>
-<tr><td>other <code>int</code> or <code>float</code></td><td>– not supported –</td><td><code>sum(abs(x)^{ord})^{(1 / ord)}</code></td></tr>
-</table>
-
-
-    <h2 class="hasAnchor" id="see-also"><a class="anchor" href="#see-also"></a>See also</h2>
-
-    <div class='dont-index'><p>Other linalg: 
-<code><a href='linalg_cholesky_ex.html'>linalg_cholesky_ex</a>()</code>,
-<code><a href='linalg_cholesky.html'>linalg_cholesky</a>()</code>,
-<code><a href='linalg_det.html'>linalg_det</a>()</code>,
-<code><a href='linalg_eigh.html'>linalg_eigh</a>()</code>,
-<code><a href='linalg_eigvalsh.html'>linalg_eigvalsh</a>()</code>,
-<code><a href='linalg_eigvals.html'>linalg_eigvals</a>()</code>,
-<code><a href='linalg_eig.html'>linalg_eig</a>()</code>,
-<code><a href='linalg_householder_product.html'>linalg_householder_product</a>()</code>,
-<code><a href='linalg_inv_ex.html'>linalg_inv_ex</a>()</code>,
-<code><a href='linalg_inv.html'>linalg_inv</a>()</code>,
-<code><a href='linalg_lstsq.html'>linalg_lstsq</a>()</code>,
-<code><a href='linalg_matrix_power.html'>linalg_matrix_power</a>()</code>,
-<code><a href='linalg_matrix_rank.html'>linalg_matrix_rank</a>()</code>,
-<code><a href='linalg_multi_dot.html'>linalg_multi_dot</a>()</code>,
-<code><a href='linalg_norm.html'>linalg_norm</a>()</code>,
-<code><a href='linalg_pinv.html'>linalg_pinv</a>()</code>,
-<code><a href='linalg_qr.html'>linalg_qr</a>()</code>,
-<code><a href='linalg_slogdet.html'>linalg_slogdet</a>()</code>,
-<code><a href='linalg_solve.html'>linalg_solve</a>()</code>,
-<code><a href='linalg_svdvals.html'>linalg_svdvals</a>()</code>,
-<code><a href='linalg_svd.html'>linalg_svd</a>()</code>,
-<code><a href='linalg_tensorinv.html'>linalg_tensorinv</a>()</code>,
-<code><a href='linalg_tensorsolve.html'>linalg_tensorsolve</a>()</code>,
-<code><a href='linalg_vector_norm.html'>linalg_vector_norm</a>()</code></p></div>
+supported:</p><table class="table table"><tr><td><code>ord</code></td><td>norm for matrices</td><td>norm for vectors</td></tr><tr><td><code>NULL</code> (default)</td><td>Frobenius norm</td><td><code>2</code>-norm (see below)</td></tr><tr><td><code>"fro"</code></td><td>Frobenius norm</td><td>– not supported –</td></tr><tr><td><code>"nuc"</code></td><td>nuclear norm</td><td>– not supported –</td></tr><tr><td><code>Inf</code></td><td><code>max(sum(abs(x), dim=2))</code></td><td><code>max(abs(x))</code></td></tr><tr><td><code>-Inf</code></td><td><code>min(sum(abs(x), dim=2))</code></td><td><code>min(abs(x))</code></td></tr><tr><td><code>0</code></td><td>– not supported –</td><td><code>sum(x != 0)</code></td></tr><tr><td><code>1</code></td><td><code>max(sum(abs(x), dim=1))</code></td><td>as below</td></tr><tr><td><code>-1</code></td><td><code>min(sum(abs(x), dim=1))</code></td><td>as below</td></tr><tr><td><code>2</code></td><td>largest singular value</td><td>as below</td></tr><tr><td><code>-2</code></td><td>smallest singular value</td><td>as below</td></tr><tr><td>other <code>int</code> or <code>float</code></td><td>– not supported –</td><td><code>sum(abs(x)^{ord})^{(1 / ord)}</code></td></tr></table></div>
+    <div class="section level2">
+    <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
+    <div class="dont-index"><p>Other linalg: 
+<code><a href="linalg_cholesky_ex.html">linalg_cholesky_ex</a>()</code>,
+<code><a href="linalg_cholesky.html">linalg_cholesky</a>()</code>,
+<code><a href="linalg_det.html">linalg_det</a>()</code>,
+<code><a href="linalg_eigh.html">linalg_eigh</a>()</code>,
+<code><a href="linalg_eigvalsh.html">linalg_eigvalsh</a>()</code>,
+<code><a href="linalg_eigvals.html">linalg_eigvals</a>()</code>,
+<code><a href="linalg_eig.html">linalg_eig</a>()</code>,
+<code><a href="linalg_householder_product.html">linalg_householder_product</a>()</code>,
+<code><a href="linalg_inv_ex.html">linalg_inv_ex</a>()</code>,
+<code><a href="linalg_inv.html">linalg_inv</a>()</code>,
+<code><a href="linalg_lstsq.html">linalg_lstsq</a>()</code>,
+<code><a href="linalg_matrix_power.html">linalg_matrix_power</a>()</code>,
+<code><a href="linalg_matrix_rank.html">linalg_matrix_rank</a>()</code>,
+<code><a href="linalg_multi_dot.html">linalg_multi_dot</a>()</code>,
+<code><a href="linalg_norm.html">linalg_norm</a>()</code>,
+<code><a href="linalg_pinv.html">linalg_pinv</a>()</code>,
+<code><a href="linalg_qr.html">linalg_qr</a>()</code>,
+<code><a href="linalg_slogdet.html">linalg_slogdet</a>()</code>,
+<code><a href="linalg_solve.html">linalg_solve</a>()</code>,
+<code><a href="linalg_svdvals.html">linalg_svdvals</a>()</code>,
+<code><a href="linalg_svd.html">linalg_svd</a>()</code>,
+<code><a href="linalg_tensorinv.html">linalg_tensorinv</a>()</code>,
+<code><a href="linalg_tensorsolve.html">linalg_tensorsolve</a>()</code>,
+<code><a href="linalg_vector_norm.html">linalg_vector_norm</a>()</code></p></div>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span><span class='op'>(</span><span class='fl'>0</span>, <span class='fl'>8</span>, dtype<span class='op'>=</span><span class='fu'><a href='torch_dtype.html'>torch_float</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span><span class='op'>$</span><span class='fu'>reshape</span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3</span>,<span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>linalg_matrix_norm</span><span class='op'>(</span><span class='va'>a</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>linalg_matrix_norm</span><span class='op'>(</span><span class='va'>a</span>, ord <span class='op'>=</span> <span class='op'>-</span><span class='fl'>1</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>b</span> <span class='op'>&lt;-</span> <span class='va'>a</span><span class='op'>$</span><span class='fu'>expand</span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='op'>-</span><span class='fl'>1</span>, <span class='op'>-</span><span class='fl'>1</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>linalg_matrix_norm</span><span class='op'>(</span><span class='va'>b</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>linalg_matrix_norm</span><span class='op'>(</span><span class='va'>b</span>, dim <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   3.1623</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  10.0000</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  17.2627</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{3} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_arange.html">torch_arange</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">8</span>, dtype <span class="op">=</span> <span class="fu"><a href="torch_dtype.html">torch_float</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span><span class="op">$</span><span class="fu">reshape</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">linalg_matrix_norm</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">linalg_matrix_norm</span><span class="op">(</span><span class="va">a</span>, ord <span class="op">=</span> <span class="op">-</span><span class="fl">1</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">b</span> <span class="op">&lt;-</span> <span class="va">a</span><span class="op">$</span><span class="fu">expand</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="op">-</span><span class="fl">1</span>, <span class="op">-</span><span class="fl">1</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">linalg_matrix_norm</span><span class="op">(</span><span class="va">b</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">linalg_matrix_norm</span><span class="op">(</span><span class="va">b</span>, dim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   3.1623</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  10.0000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  17.2627</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3} ]</span>
+</code></pre></div>
+    </div>
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Computes the <code>n</code>-th power of a square matrix for an integer <code>n</code>.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/linalg.R'><code>R/linalg.R</code></a></small>
-    <div class="hidden name"><code>linalg_matrix_power.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Computes the <code>n</code>-th power of a square matrix for an integer <code>n</code>.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/linalg.R" class="external-link"><code>R/linalg.R</code></a></small>
+      <div class="d-none name"><code>linalg_matrix_power.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Supports input of float, double, cfloat and cdouble dtypes.
 Also supports batches of matrices, and if <code>A</code> is a batch of matrices then
 the output has the same batch dimensions.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>linalg_matrix_power</span><span class='op'>(</span><span class='va'>A</span>, <span class='va'>n</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>A</th>
-      <td><p>(Tensor): tensor of shape <code>(*, m, m)</code> where <code>*</code> is zero or more batch dimensions.</p></td>
-    </tr>
-    <tr>
-      <th>n</th>
-      <td><p>(int): the exponent.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">linalg_matrix_power</span><span class="op">(</span><span class="va">A</span>, <span class="va">n</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>A</dt>
+<dd><p>(Tensor): tensor of shape <code>(*, m, m)</code> where <code>*</code> is zero or more batch dimensions.</p></dd>
+<dt>n</dt>
+<dd><p>(int): the exponent.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>If <code>n=0</code>, it returns the identity matrix (or batch) of the same shape
 as <code>A</code>. If <code>n</code> is negative, it returns the inverse of each matrix
 (if invertible) raised to the power of <code>abs(n)</code>.</p>
-    <h2 class="hasAnchor" id="see-also"><a class="anchor" href="#see-also"></a>See also</h2>
-
-    <div class='dont-index'><p><code><a href='linalg_solve.html'>linalg_solve()</a></code> computes <code>A$inverse() %*% B</code> with a
+    </div>
+    <div class="section level2">
+    <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
+    <div class="dont-index"><p><code><a href="linalg_solve.html">linalg_solve()</a></code> computes <code>A$inverse() %*% B</code> with a
 numerically stable algorithm.</p>
 <p>Other linalg: 
-<code><a href='linalg_cholesky_ex.html'>linalg_cholesky_ex</a>()</code>,
-<code><a href='linalg_cholesky.html'>linalg_cholesky</a>()</code>,
-<code><a href='linalg_det.html'>linalg_det</a>()</code>,
-<code><a href='linalg_eigh.html'>linalg_eigh</a>()</code>,
-<code><a href='linalg_eigvalsh.html'>linalg_eigvalsh</a>()</code>,
-<code><a href='linalg_eigvals.html'>linalg_eigvals</a>()</code>,
-<code><a href='linalg_eig.html'>linalg_eig</a>()</code>,
-<code><a href='linalg_householder_product.html'>linalg_householder_product</a>()</code>,
-<code><a href='linalg_inv_ex.html'>linalg_inv_ex</a>()</code>,
-<code><a href='linalg_inv.html'>linalg_inv</a>()</code>,
-<code><a href='linalg_lstsq.html'>linalg_lstsq</a>()</code>,
-<code><a href='linalg_matrix_norm.html'>linalg_matrix_norm</a>()</code>,
-<code><a href='linalg_matrix_rank.html'>linalg_matrix_rank</a>()</code>,
-<code><a href='linalg_multi_dot.html'>linalg_multi_dot</a>()</code>,
-<code><a href='linalg_norm.html'>linalg_norm</a>()</code>,
-<code><a href='linalg_pinv.html'>linalg_pinv</a>()</code>,
-<code><a href='linalg_qr.html'>linalg_qr</a>()</code>,
-<code><a href='linalg_slogdet.html'>linalg_slogdet</a>()</code>,
-<code><a href='linalg_solve.html'>linalg_solve</a>()</code>,
-<code><a href='linalg_svdvals.html'>linalg_svdvals</a>()</code>,
-<code><a href='linalg_svd.html'>linalg_svd</a>()</code>,
-<code><a href='linalg_tensorinv.html'>linalg_tensorinv</a>()</code>,
-<code><a href='linalg_tensorsolve.html'>linalg_tensorsolve</a>()</code>,
-<code><a href='linalg_vector_norm.html'>linalg_vector_norm</a>()</code></p></div>
+<code><a href="linalg_cholesky_ex.html">linalg_cholesky_ex</a>()</code>,
+<code><a href="linalg_cholesky.html">linalg_cholesky</a>()</code>,
+<code><a href="linalg_det.html">linalg_det</a>()</code>,
+<code><a href="linalg_eigh.html">linalg_eigh</a>()</code>,
+<code><a href="linalg_eigvalsh.html">linalg_eigvalsh</a>()</code>,
+<code><a href="linalg_eigvals.html">linalg_eigvals</a>()</code>,
+<code><a href="linalg_eig.html">linalg_eig</a>()</code>,
+<code><a href="linalg_householder_product.html">linalg_householder_product</a>()</code>,
+<code><a href="linalg_inv_ex.html">linalg_inv_ex</a>()</code>,
+<code><a href="linalg_inv.html">linalg_inv</a>()</code>,
+<code><a href="linalg_lstsq.html">linalg_lstsq</a>()</code>,
+<code><a href="linalg_matrix_norm.html">linalg_matrix_norm</a>()</code>,
+<code><a href="linalg_matrix_rank.html">linalg_matrix_rank</a>()</code>,
+<code><a href="linalg_multi_dot.html">linalg_multi_dot</a>()</code>,
+<code><a href="linalg_norm.html">linalg_norm</a>()</code>,
+<code><a href="linalg_pinv.html">linalg_pinv</a>()</code>,
+<code><a href="linalg_qr.html">linalg_qr</a>()</code>,
+<code><a href="linalg_slogdet.html">linalg_slogdet</a>()</code>,
+<code><a href="linalg_solve.html">linalg_solve</a>()</code>,
+<code><a href="linalg_svdvals.html">linalg_svdvals</a>()</code>,
+<code><a href="linalg_svd.html">linalg_svd</a>()</code>,
+<code><a href="linalg_tensorinv.html">linalg_tensorinv</a>()</code>,
+<code><a href="linalg_tensorsolve.html">linalg_tensorsolve</a>()</code>,
+<code><a href="linalg_vector_norm.html">linalg_vector_norm</a>()</code></p></div>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>A</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>3</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>linalg_matrix_power</span><span class='op'>(</span><span class='va'>A</span>, <span class='fl'>0</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1  0  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0  1  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0  0  1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{3,3} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">A</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">3</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">linalg_matrix_power</span><span class="op">(</span><span class="va">A</span>, <span class="fl">0</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1  0  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0  1  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0  0  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3,3} ]</span>
+</code></pre></div>
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+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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index 579917082..6e6725f44 100644
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Computes the numerical rank of a matrix.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/linalg.R'><code>R/linalg.R</code></a></small>
-    <div class="hidden name"><code>linalg_matrix_rank.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Computes the numerical rank of a matrix.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/linalg.R" class="external-link"><code>R/linalg.R</code></a></small>
+      <div class="d-none name"><code>linalg_matrix_rank.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>The matrix rank is computed as the number of singular values
 (or eigenvalues in absolute value when <code>hermitian = TRUE</code>)
 that are greater than the specified <code>tol</code> threshold.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>linalg_matrix_rank</span><span class='op'>(</span><span class='va'>A</span>, tol <span class='op'>=</span> <span class='cn'>NULL</span>, hermitian <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>A</th>
-      <td><p>(Tensor): tensor of shape <code>(*, m, n)</code> where <code>*</code> is zero or more
-batch dimensions.</p></td>
-    </tr>
-    <tr>
-      <th>tol</th>
-      <td><p>(float, Tensor, optional): the tolerance value. See above for
-the value it takes when <code>NULL</code>. Default: <code>NULL</code>.</p></td>
-    </tr>
-    <tr>
-      <th>hermitian</th>
-      <td><p>(bool, optional): indicates whether <code>A</code> is Hermitian if complex
-or symmetric if real. Default: <code>FALSE</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">linalg_matrix_rank</span><span class="op">(</span><span class="va">A</span>, tol <span class="op">=</span> <span class="cn">NULL</span>, hermitian <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>A</dt>
+<dd><p>(Tensor): tensor of shape <code>(*, m, n)</code> where <code>*</code> is zero or more
+batch dimensions.</p></dd>
+<dt>tol</dt>
+<dd><p>(float, Tensor, optional): the tolerance value. See above for
+the value it takes when <code>NULL</code>. Default: <code>NULL</code>.</p></dd>
+<dt>hermitian</dt>
+<dd><p>(bool, optional): indicates whether <code>A</code> is Hermitian if complex
+or symmetric if real. Default: <code>FALSE</code>.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>Supports input of float, double, cfloat and cdouble dtypes.
 Also supports batches of matrices, and if <code>A</code> is a batch of matrices then
 the output has the same batch dimensions.</p>
@@ -225,78 +113,72 @@ symmetric if real, but this is not checked internally. Instead, just the lower
 triangular part of the matrix is used in the computations.</p>
 <p>If <code>tol</code> is not specified and <code>A</code> is a matrix of dimensions <code>(m, n)</code>,
 the tolerance is set to be</p>
-<p><link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/npm/katex@0.13.11/dist/katex.min.css" data-external="1">
-<span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mi>t</mi><mi>o</mi><mi>l</mi><mo>=</mo><msub><mi>σ</mi><mn>1</mn></msub><mi>max</mi><mo>⁡</mo><mo stretchy="false">(</mo><mi>m</mi><mo separator="true">,</mo><mi>n</mi><mo stretchy="false">)</mo><mi>ε</mi></mrow><annotation encoding="application/x-tex">
+<p><link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/npm/katex@0.15.2/dist/katex.min.css" data-external="1"><span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mi>t</mi><mi>o</mi><mi>l</mi><mo>=</mo><msub><mi>σ</mi><mn>1</mn></msub><mi>max</mi><mo>⁡</mo><mo stretchy="false">(</mo><mi>m</mi><mo separator="true">,</mo><mi>n</mi><mo stretchy="false">)</mo><mi>ε</mi></mrow><annotation encoding="application/x-tex">
 tol = \sigma_1 \max(m, n) \varepsilon
-</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.69444em;vertical-align:0em;"></span><span class="mord mathnormal">t</span><span class="mord mathnormal">o</span><span class="mord mathnormal" style="margin-right:0.01968em;">l</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.03588em;">σ</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.30110799999999993em;"><span style="top:-2.5500000000000003em;margin-left:-0.03588em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight">1</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mop">max</span><span class="mopen">(</span><span class="mord mathnormal">m</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord mathnormal">n</span><span class="mclose">)</span><span class="mord mathnormal">ε</span></span></span></span></span></p>
+</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6944em;"></span><span class="mord mathnormal">t</span><span class="mord mathnormal">o</span><span class="mord mathnormal" style="margin-right:0.01968em;">l</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.03588em;">σ</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.3011em;"><span style="top:-2.55em;margin-left:-0.0359em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight">1</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mop">max</span><span class="mopen">(</span><span class="mord mathnormal">m</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal">n</span><span class="mclose">)</span><span class="mord mathnormal">ε</span></span></span></span></span></p>
 <p>where  is the largest singular value
 (or eigenvalue in absolute value when <code>hermitian = TRUE</code>), and
- is the epsilon value for the dtype of <code>A</code> (see <code><a href='torch_finfo.html'>torch_finfo()</a></code>).</p>
+ is the epsilon value for the dtype of <code>A</code> (see <code><a href="torch_finfo.html">torch_finfo()</a></code>).</p>
 <p>If <code>A</code> is a batch of matrices, <code>tol</code> is computed this way for every element of
 the batch.</p>
-    <h2 class="hasAnchor" id="see-also"><a class="anchor" href="#see-also"></a>See also</h2>
-
-    <div class='dont-index'><p>Other linalg: 
-<code><a href='linalg_cholesky_ex.html'>linalg_cholesky_ex</a>()</code>,
-<code><a href='linalg_cholesky.html'>linalg_cholesky</a>()</code>,
-<code><a href='linalg_det.html'>linalg_det</a>()</code>,
-<code><a href='linalg_eigh.html'>linalg_eigh</a>()</code>,
-<code><a href='linalg_eigvalsh.html'>linalg_eigvalsh</a>()</code>,
-<code><a href='linalg_eigvals.html'>linalg_eigvals</a>()</code>,
-<code><a href='linalg_eig.html'>linalg_eig</a>()</code>,
-<code><a href='linalg_householder_product.html'>linalg_householder_product</a>()</code>,
-<code><a href='linalg_inv_ex.html'>linalg_inv_ex</a>()</code>,
-<code><a href='linalg_inv.html'>linalg_inv</a>()</code>,
-<code><a href='linalg_lstsq.html'>linalg_lstsq</a>()</code>,
-<code><a href='linalg_matrix_norm.html'>linalg_matrix_norm</a>()</code>,
-<code><a href='linalg_matrix_power.html'>linalg_matrix_power</a>()</code>,
-<code><a href='linalg_multi_dot.html'>linalg_multi_dot</a>()</code>,
-<code><a href='linalg_norm.html'>linalg_norm</a>()</code>,
-<code><a href='linalg_pinv.html'>linalg_pinv</a>()</code>,
-<code><a href='linalg_qr.html'>linalg_qr</a>()</code>,
-<code><a href='linalg_slogdet.html'>linalg_slogdet</a>()</code>,
-<code><a href='linalg_solve.html'>linalg_solve</a>()</code>,
-<code><a href='linalg_svdvals.html'>linalg_svdvals</a>()</code>,
-<code><a href='linalg_svd.html'>linalg_svd</a>()</code>,
-<code><a href='linalg_tensorinv.html'>linalg_tensorinv</a>()</code>,
-<code><a href='linalg_tensorsolve.html'>linalg_tensorsolve</a>()</code>,
-<code><a href='linalg_vector_norm.html'>linalg_vector_norm</a>()</code></p></div>
+    </div>
+    <div class="section level2">
+    <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
+    <div class="dont-index"><p>Other linalg: 
+<code><a href="linalg_cholesky_ex.html">linalg_cholesky_ex</a>()</code>,
+<code><a href="linalg_cholesky.html">linalg_cholesky</a>()</code>,
+<code><a href="linalg_det.html">linalg_det</a>()</code>,
+<code><a href="linalg_eigh.html">linalg_eigh</a>()</code>,
+<code><a href="linalg_eigvalsh.html">linalg_eigvalsh</a>()</code>,
+<code><a href="linalg_eigvals.html">linalg_eigvals</a>()</code>,
+<code><a href="linalg_eig.html">linalg_eig</a>()</code>,
+<code><a href="linalg_householder_product.html">linalg_householder_product</a>()</code>,
+<code><a href="linalg_inv_ex.html">linalg_inv_ex</a>()</code>,
+<code><a href="linalg_inv.html">linalg_inv</a>()</code>,
+<code><a href="linalg_lstsq.html">linalg_lstsq</a>()</code>,
+<code><a href="linalg_matrix_norm.html">linalg_matrix_norm</a>()</code>,
+<code><a href="linalg_matrix_power.html">linalg_matrix_power</a>()</code>,
+<code><a href="linalg_multi_dot.html">linalg_multi_dot</a>()</code>,
+<code><a href="linalg_norm.html">linalg_norm</a>()</code>,
+<code><a href="linalg_pinv.html">linalg_pinv</a>()</code>,
+<code><a href="linalg_qr.html">linalg_qr</a>()</code>,
+<code><a href="linalg_slogdet.html">linalg_slogdet</a>()</code>,
+<code><a href="linalg_solve.html">linalg_solve</a>()</code>,
+<code><a href="linalg_svdvals.html">linalg_svdvals</a>()</code>,
+<code><a href="linalg_svd.html">linalg_svd</a>()</code>,
+<code><a href="linalg_tensorinv.html">linalg_tensorinv</a>()</code>,
+<code><a href="linalg_tensorsolve.html">linalg_tensorsolve</a>()</code>,
+<code><a href="linalg_vector_norm.html">linalg_vector_norm</a>()</code></p></div>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_eye.html'>torch_eye</a></span><span class='op'>(</span><span class='fl'>10</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>linalg_matrix_rank</span><span class='op'>(</span><span class='va'>a</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> 10</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPULongType{} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_eye.html">torch_eye</a></span><span class="op">(</span><span class="fl">10</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">linalg_matrix_rank</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 10</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPULongType{} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer>
-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
 </div>
 
-<div class="pkgdown">
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+<div class="pkgdown-footer-right">
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diff --git a/reference/linalg_multi_dot.html b/reference/linalg_multi_dot.html
index a82bf8846..9dc6ee53f 100644
--- a/reference/linalg_multi_dot.html
+++ b/reference/linalg_multi_dot.html
@@ -1,210 +1,101 @@
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Efficiently multiplies two or more matrices</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/linalg.R'><code>R/linalg.R</code></a></small>
-    <div class="hidden name"><code>linalg_multi_dot.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Efficiently multiplies two or more matrices</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/linalg.R" class="external-link"><code>R/linalg.R</code></a></small>
+      <div class="d-none name"><code>linalg_multi_dot.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Efficiently multiplies two or more matrices by reordering the multiplications so that
 the fewest arithmetic operations are performed.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>linalg_multi_dot</span><span class='op'>(</span><span class='va'>tensors</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>tensors</th>
-      <td><p>(<code>Sequence[Tensor]</code>): two or more tensors to multiply. The first and last
-tensors may be 1D or 2D. Every other tensor must be 2D.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">linalg_multi_dot</span><span class="op">(</span><span class="va">tensors</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>tensors</dt>
+<dd><p>(<code>Sequence[Tensor]</code>): two or more tensors to multiply. The first and last
+tensors may be 1D or 2D. Every other tensor must be 2D.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>Supports inputs of <code>float</code>, <code>double</code>, <code>cfloat</code> and <code>cdouble</code> dtypes.
 This function does not support batched inputs.</p>
 <p>Every tensor in <code>tensors</code> must be 2D, except for the first and last which
@@ -213,85 +104,80 @@ of shape <code>(1, n)</code>, similarly if the last tensor is a 1D vector of sha
 as a column vector of shape <code>(n, 1)</code>.</p>
 <p>If the first and last tensors are matrices, the output will be a matrix.
 However, if either is a 1D vector, then the output will be a 1D vector.</p>
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    <p>This function is implemented by chaining <code><a href='torch_mm.html'>torch_mm()</a></code> calls after
+    </div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
+    <p>This function is implemented by chaining <code><a href="torch_mm.html">torch_mm()</a></code> calls after
 computing the optimal matrix multiplication order.</p>
 <p>The cost of multiplying two matrices with shapes <code>(a, b)</code> and <code>(b, c)</code> is
 <code>a * b * c</code>. Given matrices <code>A</code>, <code>B</code>, <code>C</code> with shapes <code>(10, 100)</code>,
 <code>(100, 5)</code>, <code>(5, 50)</code> respectively, we can calculate the cost of different
 multiplication orders as follows:</p>
-<p><link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/npm/katex@0.13.11/dist/katex.min.css" data-external="1">
-<span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mtable rowspacing="0.2500em" columnalign="right left right" columnspacing="0em 1em"><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mi mathvariant="normal">cost</mi><mo>⁡</mo><mo stretchy="false">(</mo><mo stretchy="false">(</mo><mi>A</mi><mi>B</mi><mo stretchy="false">)</mo><mi>C</mi><mo stretchy="false">)</mo></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mo>=</mo><mn>10</mn><mo>×</mo><mn>100</mn><mo>×</mo><mn>5</mn><mo>+</mo><mn>10</mn><mo>×</mo><mn>5</mn><mo>×</mo><mn>50</mn><mo>=</mo><mn>7500</mn><mtext> </mtext><mi mathvariant="normal">cost</mi><mo>⁡</mo><mo stretchy="false">(</mo><mi>A</mi><mo stretchy="false">(</mo><mi>B</mi><mi>C</mi><mo stretchy="false">)</mo><mo stretchy="false">)</mo></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mo>=</mo><mn>10</mn><mo>×</mo><mn>100</mn><mo>×</mo><mn>50</mn><mo>+</mo><mn>100</mn><mo>×</mo><mn>5</mn><mo>×</mo><mn>50</mn><mo>=</mo><mn>75000</mn></mrow></mstyle></mtd></mtr></mtable><annotation encoding="application/x-tex">
+<p><link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/npm/katex@0.15.2/dist/katex.min.css" data-external="1"><span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mtable rowspacing="0.25em" columnalign="right left right" columnspacing="0em 1em"><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mi mathvariant="normal">cost</mi><mo>⁡</mo><mo stretchy="false">(</mo><mo stretchy="false">(</mo><mi>A</mi><mi>B</mi><mo stretchy="false">)</mo><mi>C</mi><mo stretchy="false">)</mo></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mo>=</mo><mn>10</mn><mo>×</mo><mn>100</mn><mo>×</mo><mn>5</mn><mo>+</mo><mn>10</mn><mo>×</mo><mn>5</mn><mo>×</mo><mn>50</mn><mo>=</mo><mn>7500</mn><mtext> </mtext><mi mathvariant="normal">cost</mi><mo>⁡</mo><mo stretchy="false">(</mo><mi>A</mi><mo stretchy="false">(</mo><mi>B</mi><mi>C</mi><mo stretchy="false">)</mo><mo stretchy="false">)</mo></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mo>=</mo><mn>10</mn><mo>×</mo><mn>100</mn><mo>×</mo><mn>50</mn><mo>+</mo><mn>100</mn><mo>×</mo><mn>5</mn><mo>×</mo><mn>50</mn><mo>=</mo><mn>75000</mn></mrow></mstyle></mtd></mtr></mtable><annotation encoding="application/x-tex">
 \begin{align*}
 \operatorname{cost}((AB)C) &amp;= 10 \times 100 \times 5 + 10 \times 5 \times 50 = 7500 \
 \operatorname{cost}(A(BC)) &amp;= 10 \times 100 \times 50 + 100 \times 5 \times 50 = 75000
 \end{align*}
-</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1.5000000000000002em;vertical-align:-0.5000000000000002em;"></span><span class="mord"><span class="mtable"><span class="col-align-r"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:1em;"><span style="top:-3.16em;"><span class="pstrut" style="height:3em;"></span><span class="mord"><span class="mop"><span class="mord mathrm">cost</span></span><span class="mopen">((</span><span class="mord mathnormal">A</span><span class="mord mathnormal" style="margin-right:0.05017em;">B</span><span class="mclose">)</span><span class="mord mathnormal" style="margin-right:0.07153em;">C</span><span class="mclose">)</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.5000000000000002em;"><span></span></span></span></span></span><span class="col-align-l"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:1em;"><span style="top:-3.16em;"><span class="pstrut" style="height:3em;"></span><span class="mord"><span class="mord"></span><span class="mspace" style="margin-right:0.2777777777777778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span><span class="mord">10</span><span class="mspace" style="margin-right:0.2222222222222222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222222222222222em;"></span><span class="mord">100</span><span class="mspace" style="margin-right:0.2222222222222222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222222222222222em;"></span><span class="mord">5</span><span class="mspace" style="margin-right:0.2222222222222222em;"></span><span class="mbin">+</span><span class="mspace" style="margin-right:0.2222222222222222em;"></span><span class="mord">10</span><span class="mspace" style="margin-right:0.2222222222222222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222222222222222em;"></span><span class="mord">5</span><span class="mspace" style="margin-right:0.2222222222222222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222222222222222em;"></span><span class="mord">50</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span><span class="mord">7500</span><span class="mspace"> </span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mop"><span class="mord mathrm">cost</span></span><span class="mopen">(</span><span class="mord mathnormal">A</span><span class="mopen">(</span><span class="mord mathnormal" style="margin-right:0.07153em;">BC</span><span class="mclose">))</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.5000000000000002em;"><span></span></span></span></span></span><span class="arraycolsep" style="width:1em;"></span><span class="col-align-r"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:1em;"><span style="top:-3.16em;"><span class="pstrut" style="height:3em;"></span><span class="mord"><span class="mrel">=</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span><span class="mord">10</span><span class="mspace" style="margin-right:0.2222222222222222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222222222222222em;"></span><span class="mord">100</span><span class="mspace" style="margin-right:0.2222222222222222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222222222222222em;"></span><span class="mord">50</span><span class="mspace" style="margin-right:0.2222222222222222em;"></span><span class="mbin">+</span><span class="mspace" style="margin-right:0.2222222222222222em;"></span><span class="mord">100</span><span class="mspace" style="margin-right:0.2222222222222222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222222222222222em;"></span><span class="mord">5</span><span class="mspace" style="margin-right:0.2222222222222222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222222222222222em;"></span><span class="mord">50</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span><span class="mord">75000</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.5000000000000002em;"><span></span></span></span></span></span></span></span></span></span></span></span></p>
+</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1.5em;vertical-align:-0.5em;"></span><span class="mord"><span class="mtable"><span class="col-align-r"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:1em;"><span style="top:-3.16em;"><span class="pstrut" style="height:3em;"></span><span class="mord"><span class="mop"><span class="mord mathrm">cost</span></span><span class="mopen">((</span><span class="mord mathnormal">A</span><span class="mord mathnormal" style="margin-right:0.05017em;">B</span><span class="mclose">)</span><span class="mord mathnormal" style="margin-right:0.07153em;">C</span><span class="mclose">)</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.5em;"><span></span></span></span></span></span><span class="col-align-l"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:1em;"><span style="top:-3.16em;"><span class="pstrut" style="height:3em;"></span><span class="mord"><span class="mord"></span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mord">10</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mord">100</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mord">5</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">+</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mord">10</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mord">5</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mord">50</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mord">7500</span><span class="mspace"> </span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mop"><span class="mord mathrm">cost</span></span><span class="mopen">(</span><span class="mord mathnormal">A</span><span class="mopen">(</span><span class="mord mathnormal" style="margin-right:0.07153em;">BC</span><span class="mclose">))</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.5em;"><span></span></span></span></span></span><span class="arraycolsep" style="width:1em;"></span><span class="col-align-r"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:1em;"><span style="top:-3.16em;"><span class="pstrut" style="height:3em;"></span><span class="mord"><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mord">10</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mord">100</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mord">50</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">+</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mord">100</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mord">5</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mord">50</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mord">75000</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.5em;"><span></span></span></span></span></span></span></span></span></span></span></span></p>
 <p>In this case, multiplying <code>A</code> and <code>B</code> first followed by <code>C</code> is 10 times faster.</p>
-    <h2 class="hasAnchor" id="see-also"><a class="anchor" href="#see-also"></a>See also</h2>
-
-    <div class='dont-index'><p>Other linalg: 
-<code><a href='linalg_cholesky_ex.html'>linalg_cholesky_ex</a>()</code>,
-<code><a href='linalg_cholesky.html'>linalg_cholesky</a>()</code>,
-<code><a href='linalg_det.html'>linalg_det</a>()</code>,
-<code><a href='linalg_eigh.html'>linalg_eigh</a>()</code>,
-<code><a href='linalg_eigvalsh.html'>linalg_eigvalsh</a>()</code>,
-<code><a href='linalg_eigvals.html'>linalg_eigvals</a>()</code>,
-<code><a href='linalg_eig.html'>linalg_eig</a>()</code>,
-<code><a href='linalg_householder_product.html'>linalg_householder_product</a>()</code>,
-<code><a href='linalg_inv_ex.html'>linalg_inv_ex</a>()</code>,
-<code><a href='linalg_inv.html'>linalg_inv</a>()</code>,
-<code><a href='linalg_lstsq.html'>linalg_lstsq</a>()</code>,
-<code><a href='linalg_matrix_norm.html'>linalg_matrix_norm</a>()</code>,
-<code><a href='linalg_matrix_power.html'>linalg_matrix_power</a>()</code>,
-<code><a href='linalg_matrix_rank.html'>linalg_matrix_rank</a>()</code>,
-<code><a href='linalg_norm.html'>linalg_norm</a>()</code>,
-<code><a href='linalg_pinv.html'>linalg_pinv</a>()</code>,
-<code><a href='linalg_qr.html'>linalg_qr</a>()</code>,
-<code><a href='linalg_slogdet.html'>linalg_slogdet</a>()</code>,
-<code><a href='linalg_solve.html'>linalg_solve</a>()</code>,
-<code><a href='linalg_svdvals.html'>linalg_svdvals</a>()</code>,
-<code><a href='linalg_svd.html'>linalg_svd</a>()</code>,
-<code><a href='linalg_tensorinv.html'>linalg_tensorinv</a>()</code>,
-<code><a href='linalg_tensorsolve.html'>linalg_tensorsolve</a>()</code>,
-<code><a href='linalg_vector_norm.html'>linalg_vector_norm</a>()</code></p></div>
+    </div>
+    <div class="section level2">
+    <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
+    <div class="dont-index"><p>Other linalg: 
+<code><a href="linalg_cholesky_ex.html">linalg_cholesky_ex</a>()</code>,
+<code><a href="linalg_cholesky.html">linalg_cholesky</a>()</code>,
+<code><a href="linalg_det.html">linalg_det</a>()</code>,
+<code><a href="linalg_eigh.html">linalg_eigh</a>()</code>,
+<code><a href="linalg_eigvalsh.html">linalg_eigvalsh</a>()</code>,
+<code><a href="linalg_eigvals.html">linalg_eigvals</a>()</code>,
+<code><a href="linalg_eig.html">linalg_eig</a>()</code>,
+<code><a href="linalg_householder_product.html">linalg_householder_product</a>()</code>,
+<code><a href="linalg_inv_ex.html">linalg_inv_ex</a>()</code>,
+<code><a href="linalg_inv.html">linalg_inv</a>()</code>,
+<code><a href="linalg_lstsq.html">linalg_lstsq</a>()</code>,
+<code><a href="linalg_matrix_norm.html">linalg_matrix_norm</a>()</code>,
+<code><a href="linalg_matrix_power.html">linalg_matrix_power</a>()</code>,
+<code><a href="linalg_matrix_rank.html">linalg_matrix_rank</a>()</code>,
+<code><a href="linalg_norm.html">linalg_norm</a>()</code>,
+<code><a href="linalg_pinv.html">linalg_pinv</a>()</code>,
+<code><a href="linalg_qr.html">linalg_qr</a>()</code>,
+<code><a href="linalg_slogdet.html">linalg_slogdet</a>()</code>,
+<code><a href="linalg_solve.html">linalg_solve</a>()</code>,
+<code><a href="linalg_svdvals.html">linalg_svdvals</a>()</code>,
+<code><a href="linalg_svd.html">linalg_svd</a>()</code>,
+<code><a href="linalg_tensorinv.html">linalg_tensorinv</a>()</code>,
+<code><a href="linalg_tensorsolve.html">linalg_tensorsolve</a>()</code>,
+<code><a href="linalg_vector_norm.html">linalg_vector_norm</a>()</code></p></div>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='fu'>linalg_multi_dot</span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span><span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>,<span class='fl'>2</span><span class='op'>)</span><span class='op'>)</span>, <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>2</span>,<span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> 8</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="fu">linalg_multi_dot</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">2</span><span class="op">)</span><span class="op">)</span>, <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 8</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer>
-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
 </div>
 
-<div class="pkgdown">
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+<div class="pkgdown-footer-right">
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-<meta property="og:description" content="If A is complex valued, it computes the norm of A$abs()
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="If A is complex valued, it computes the norm of A$abs()
 Supports input of float, double, cfloat and cdouble dtypes.
-Whether this function computes a vector or matrix norm is determined as follows:
-If dim is an int, the vector norm will be computed.
-If dim is a 2-tuple, the matrix norm will be computed.
-If dim=NULL and ord=NULL, A will be flattened to 1D and the 2-norm of the resulting vector will be computed.
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+Whether this function computes a vector or matrix norm is determined as follows:"><title>Computes a vector or matrix norm. — linalg_norm • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.1.0/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.1.0/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Computes a vector or matrix norm. — linalg_norm"><meta property="og:description" content="If A is complex valued, it computes the norm of A$abs()
+Supports input of float, double, cfloat and cdouble dtypes.
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Computes a vector or matrix norm.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/linalg.R'><code>R/linalg.R</code></a></small>
-    <div class="hidden name"><code>linalg_norm.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Computes a vector or matrix norm.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/linalg.R" class="external-link"><code>R/linalg.R</code></a></small>
+      <div class="d-none name"><code>linalg_norm.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>If <code>A</code> is complex valued, it computes the norm of <code>A$abs()</code>
 Supports input of float, double, cfloat and cdouble dtypes.
-Whether this function computes a vector or matrix norm is determined as follows:</p><ul>
-<li><p>If <code>dim</code> is an int, the vector norm will be computed.</p></li>
-<li><p>If <code>dim</code> is a 2-tuple, the matrix norm will be computed.</p></li>
-<li><p>If <code>dim=NULL</code> and <code>ord=NULL</code>, A will be flattened to 1D and the 2-norm of the resulting vector will be computed.</p></li>
-<li><p>If <code>dim=NULL</code> and <code>ord!=NULL</code>, A must be 1D or 2D.</p></li>
-</ul>
-
+Whether this function computes a vector or matrix norm is determined as follows:</p>
     </div>
 
-    <pre class="usage"><span class='fu'>linalg_norm</span><span class='op'>(</span><span class='va'>A</span>, ord <span class='op'>=</span> <span class='cn'>NULL</span>, dim <span class='op'>=</span> <span class='cn'>NULL</span>, keepdim <span class='op'>=</span> <span class='cn'>FALSE</span>, dtype <span class='op'>=</span> <span class='cn'>NULL</span><span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">linalg_norm</span><span class="op">(</span><span class="va">A</span>, ord <span class="op">=</span> <span class="cn">NULL</span>, dim <span class="op">=</span> <span class="cn">NULL</span>, keepdim <span class="op">=</span> <span class="cn">FALSE</span>, dtype <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>A</th>
-      <td><p>(Tensor): tensor of shape <code>(*, n)</code> or <code>(*, m, n)</code> where <code>*</code> is zero or more batch dimensions</p></td>
-    </tr>
-    <tr>
-      <th>ord</th>
-      <td><p>(int, float, inf, -inf, 'fro', 'nuc', optional): order of norm. Default: <code>NULL</code></p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int, Tupleint, optional): dimensions over which to compute
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>A</dt>
+<dd><p>(Tensor): tensor of shape <code>(*, n)</code> or <code>(*, m, n)</code> where <code>*</code> is zero or more batch dimensions</p></dd>
+<dt>ord</dt>
+<dd><p>(int, float, inf, -inf, 'fro', 'nuc', optional): order of norm. Default: <code>NULL</code></p></dd>
+<dt>dim</dt>
+<dd><p>(int, Tupleint, optional): dimensions over which to compute
 the vector or matrix norm. See above for the behavior when <code>dim=NULL</code>.
-Default: <code>NULL</code></p></td>
-    </tr>
-    <tr>
-      <th>keepdim</th>
-      <td><p>(bool, optional): If set to <code>TRUE</code>, the reduced dimensions are retained
-in the result as dimensions with size one. Default: <code>FALSE</code></p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>dtype (<code>torch_dtype</code>, optional): If specified, the input tensor is cast to
+Default: <code>NULL</code></p></dd>
+<dt>keepdim</dt>
+<dd><p>(bool, optional): If set to <code>TRUE</code>, the reduced dimensions are retained
+in the result as dimensions with size one. Default: <code>FALSE</code></p></dd>
+<dt>dtype</dt>
+<dd><p>dtype (<code>torch_dtype</code>, optional): If specified, the input tensor is cast to
 <code>dtype</code> before performing the operation, and the returned tensor's type
-will be <code>dtype</code>. Default: <code>NULL</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p><code>ord</code> defines the norm that is computed. The following norms are
-supported:</p><table class='table'>
-<tr><td><code>ord</code></td><td>norm for matrices</td><td>norm for vectors</td></tr>
-<tr><td><code>NULL</code> (default)</td><td>Frobenius norm</td><td><code>2</code>-norm (see below)</td></tr>
-<tr><td><code>"fro"</code></td><td>Frobenius norm</td><td>– not supported –</td></tr>
-<tr><td><code>"nuc"</code></td><td>nuclear norm</td><td>– not supported –</td></tr>
-<tr><td><code>Inf</code></td><td><code>max(sum(abs(x), dim=2))</code></td><td><code>max(abs(x))</code></td></tr>
-<tr><td><code>-Inf</code></td><td><code>min(sum(abs(x), dim=2))</code></td><td><code>min(abs(x))</code></td></tr>
-<tr><td><code>0</code></td><td>– not supported –</td><td><code>sum(x != 0)</code></td></tr>
-<tr><td><code>1</code></td><td><code>max(sum(abs(x), dim=1))</code></td><td>as below</td></tr>
-<tr><td><code>-1</code></td><td><code>min(sum(abs(x), dim=1))</code></td><td>as below</td></tr>
-<tr><td><code>2</code></td><td>largest singular value</td><td>as below</td></tr>
-<tr><td><code>-2</code></td><td>smallest singular value</td><td>as below</td></tr>
-<tr><td>other <code>int</code> or <code>float</code></td><td>– not supported –</td><td><code>sum(abs(x)^{ord})^{(1 / ord)}</code></td></tr>
-</table>
-
-
-    <h2 class="hasAnchor" id="see-also"><a class="anchor" href="#see-also"></a>See also</h2>
-
-    <div class='dont-index'><p>Other linalg: 
-<code><a href='linalg_cholesky_ex.html'>linalg_cholesky_ex</a>()</code>,
-<code><a href='linalg_cholesky.html'>linalg_cholesky</a>()</code>,
-<code><a href='linalg_det.html'>linalg_det</a>()</code>,
-<code><a href='linalg_eigh.html'>linalg_eigh</a>()</code>,
-<code><a href='linalg_eigvalsh.html'>linalg_eigvalsh</a>()</code>,
-<code><a href='linalg_eigvals.html'>linalg_eigvals</a>()</code>,
-<code><a href='linalg_eig.html'>linalg_eig</a>()</code>,
-<code><a href='linalg_householder_product.html'>linalg_householder_product</a>()</code>,
-<code><a href='linalg_inv_ex.html'>linalg_inv_ex</a>()</code>,
-<code><a href='linalg_inv.html'>linalg_inv</a>()</code>,
-<code><a href='linalg_lstsq.html'>linalg_lstsq</a>()</code>,
-<code><a href='linalg_matrix_norm.html'>linalg_matrix_norm</a>()</code>,
-<code><a href='linalg_matrix_power.html'>linalg_matrix_power</a>()</code>,
-<code><a href='linalg_matrix_rank.html'>linalg_matrix_rank</a>()</code>,
-<code><a href='linalg_multi_dot.html'>linalg_multi_dot</a>()</code>,
-<code><a href='linalg_pinv.html'>linalg_pinv</a>()</code>,
-<code><a href='linalg_qr.html'>linalg_qr</a>()</code>,
-<code><a href='linalg_slogdet.html'>linalg_slogdet</a>()</code>,
-<code><a href='linalg_solve.html'>linalg_solve</a>()</code>,
-<code><a href='linalg_svdvals.html'>linalg_svdvals</a>()</code>,
-<code><a href='linalg_svd.html'>linalg_svd</a>()</code>,
-<code><a href='linalg_tensorinv.html'>linalg_tensorinv</a>()</code>,
-<code><a href='linalg_tensorsolve.html'>linalg_tensorsolve</a>()</code>,
-<code><a href='linalg_vector_norm.html'>linalg_vector_norm</a>()</code></p></div>
+will be <code>dtype</code>. Default: <code>NULL</code></p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
+    
+<ul><li><p>If <code>dim</code> is an int, the vector norm will be computed.</p></li>
+<li><p>If <code>dim</code> is a 2-tuple, the matrix norm will be computed.</p></li>
+<li><p>If <code>dim=NULL</code> and <code>ord=NULL</code>, A will be flattened to 1D and the 2-norm of the resulting vector will be computed.</p></li>
+<li><p>If <code>dim=NULL</code> and <code>ord!=NULL</code>, A must be 1D or 2D.</p></li>
+</ul><p><code>ord</code> defines the norm that is computed. The following norms are
+supported:</p><table class="table table"><tr><td><code>ord</code></td><td>norm for matrices</td><td>norm for vectors</td></tr><tr><td><code>NULL</code> (default)</td><td>Frobenius norm</td><td><code>2</code>-norm (see below)</td></tr><tr><td><code>"fro"</code></td><td>Frobenius norm</td><td>– not supported –</td></tr><tr><td><code>"nuc"</code></td><td>nuclear norm</td><td>– not supported –</td></tr><tr><td><code>Inf</code></td><td><code>max(sum(abs(x), dim=2))</code></td><td><code>max(abs(x))</code></td></tr><tr><td><code>-Inf</code></td><td><code>min(sum(abs(x), dim=2))</code></td><td><code>min(abs(x))</code></td></tr><tr><td><code>0</code></td><td>– not supported –</td><td><code>sum(x != 0)</code></td></tr><tr><td><code>1</code></td><td><code>max(sum(abs(x), dim=1))</code></td><td>as below</td></tr><tr><td><code>-1</code></td><td><code>min(sum(abs(x), dim=1))</code></td><td>as below</td></tr><tr><td><code>2</code></td><td>largest singular value</td><td>as below</td></tr><tr><td><code>-2</code></td><td>smallest singular value</td><td>as below</td></tr><tr><td>other <code>int</code> or <code>float</code></td><td>– not supported –</td><td><code>sum(abs(x)^{ord})^{(1 / ord)}</code></td></tr></table></div>
+    <div class="section level2">
+    <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
+    <div class="dont-index"><p>Other linalg: 
+<code><a href="linalg_cholesky_ex.html">linalg_cholesky_ex</a>()</code>,
+<code><a href="linalg_cholesky.html">linalg_cholesky</a>()</code>,
+<code><a href="linalg_det.html">linalg_det</a>()</code>,
+<code><a href="linalg_eigh.html">linalg_eigh</a>()</code>,
+<code><a href="linalg_eigvalsh.html">linalg_eigvalsh</a>()</code>,
+<code><a href="linalg_eigvals.html">linalg_eigvals</a>()</code>,
+<code><a href="linalg_eig.html">linalg_eig</a>()</code>,
+<code><a href="linalg_householder_product.html">linalg_householder_product</a>()</code>,
+<code><a href="linalg_inv_ex.html">linalg_inv_ex</a>()</code>,
+<code><a href="linalg_inv.html">linalg_inv</a>()</code>,
+<code><a href="linalg_lstsq.html">linalg_lstsq</a>()</code>,
+<code><a href="linalg_matrix_norm.html">linalg_matrix_norm</a>()</code>,
+<code><a href="linalg_matrix_power.html">linalg_matrix_power</a>()</code>,
+<code><a href="linalg_matrix_rank.html">linalg_matrix_rank</a>()</code>,
+<code><a href="linalg_multi_dot.html">linalg_multi_dot</a>()</code>,
+<code><a href="linalg_pinv.html">linalg_pinv</a>()</code>,
+<code><a href="linalg_qr.html">linalg_qr</a>()</code>,
+<code><a href="linalg_slogdet.html">linalg_slogdet</a>()</code>,
+<code><a href="linalg_solve.html">linalg_solve</a>()</code>,
+<code><a href="linalg_svdvals.html">linalg_svdvals</a>()</code>,
+<code><a href="linalg_svd.html">linalg_svd</a>()</code>,
+<code><a href="linalg_tensorinv.html">linalg_tensorinv</a>()</code>,
+<code><a href="linalg_tensorsolve.html">linalg_tensorsolve</a>()</code>,
+<code><a href="linalg_vector_norm.html">linalg_vector_norm</a>()</code></p></div>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span><span class='op'>(</span><span class='fl'>0</span>, <span class='fl'>8</span>, dtype<span class='op'>=</span><span class='fu'><a href='torch_dtype.html'>torch_float</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>-</span> <span class='fl'>4</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='va'>b</span> <span class='op'>&lt;-</span> <span class='va'>a</span><span class='op'>$</span><span class='fu'>reshape</span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>b</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='fu'>linalg_norm</span><span class='op'>(</span><span class='va'>a</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>linalg_norm</span><span class='op'>(</span><span class='va'>b</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> 7.74597</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_arange.html">torch_arange</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">8</span>, dtype <span class="op">=</span> <span class="fu"><a href="torch_dtype.html">torch_float</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">-</span> <span class="fl">4</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="va">b</span> <span class="op">&lt;-</span> <span class="va">a</span><span class="op">$</span><span class="fu">reshape</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">b</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="fu">linalg_norm</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">linalg_norm</span><span class="op">(</span><span class="va">b</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 7.74597</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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-    <h1>Computes the pseudoinverse (Moore-Penrose inverse) of a matrix.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/linalg.R'><code>R/linalg.R</code></a></small>
-    <div class="hidden name"><code>linalg_pinv.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Computes the pseudoinverse (Moore-Penrose inverse) of a matrix.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/linalg.R" class="external-link"><code>R/linalg.R</code></a></small>
+      <div class="d-none name"><code>linalg_pinv.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>The pseudoinverse may be <code>defined algebraically</code>_
 but it is more computationally convenient to understand it <code>through the SVD</code>_
 Supports input of float, double, cfloat and cdouble dtypes.
@@ -197,121 +92,109 @@ Also supports batches of matrices, and if <code>A</code> is a batch of matrices
 the output has the same batch dimensions.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>linalg_pinv</span><span class='op'>(</span><span class='va'>A</span>, rcond <span class='op'>=</span> <span class='fl'>1e-15</span>, hermitian <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">linalg_pinv</span><span class="op">(</span><span class="va">A</span>, rcond <span class="op">=</span> <span class="fl">1e-15</span>, hermitian <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>A</th>
-      <td><p>(Tensor): tensor of shape <code>(*, m, n)</code> where <code>*</code> is zero or more batch dimensions.</p></td>
-    </tr>
-    <tr>
-      <th>rcond</th>
-      <td><p>(float or Tensor, optional): the tolerance value to determine when is a singular value zero
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>A</dt>
+<dd><p>(Tensor): tensor of shape <code>(*, m, n)</code> where <code>*</code> is zero or more batch dimensions.</p></dd>
+<dt>rcond</dt>
+<dd><p>(float or Tensor, optional): the tolerance value to determine when is a singular value zero
 If it is a <code>torch_Tensor</code>, its shape must be
 broadcastable to that of the singular values of
-<code>A</code> as returned by <code><a href='linalg_svd.html'>linalg_svd()</a></code>.
-Default: <code>1e-15</code>.</p></td>
-    </tr>
-    <tr>
-      <th>hermitian</th>
-      <td><p>(bool, optional): indicates whether <code>A</code> is Hermitian if complex
-or symmetric if real. Default: <code>FALSE</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
+<code>A</code> as returned by <code><a href="linalg_svd.html">linalg_svd()</a></code>.
+Default: <code>1e-15</code>.</p></dd>
+<dt>hermitian</dt>
+<dd><p>(bool, optional): indicates whether <code>A</code> is Hermitian if complex
+or symmetric if real. Default: <code>FALSE</code>.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>If <code>hermitian= TRUE</code>, <code>A</code> is assumed to be Hermitian if complex or
 symmetric if real, but this is not checked internally. Instead, just the lower
 triangular part of the matrix is used in the computations.
 The singular values (or the norm of the eigenvalues when <code>hermitian= TRUE</code>)
 that are below the specified <code>rcond</code> threshold are treated as zero and discarded
 in the computation.</p>
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    <p>This function uses <code><a href='linalg_svd.html'>linalg_svd()</a></code> if <code>hermitian= FALSE</code> and
-<code><a href='linalg_eigh.html'>linalg_eigh()</a></code> if <code>hermitian= TRUE</code>.
+    </div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
+    <p>This function uses <code><a href="linalg_svd.html">linalg_svd()</a></code> if <code>hermitian= FALSE</code> and
+<code><a href="linalg_eigh.html">linalg_eigh()</a></code> if <code>hermitian= TRUE</code>.
 For CUDA inputs, this function synchronizes that device with the CPU.</p>
-<p>Consider using <code><a href='linalg_lstsq.html'>linalg_lstsq()</a></code> if possible for multiplying a matrix on the left by
+<p>Consider using <code><a href="linalg_lstsq.html">linalg_lstsq()</a></code> if possible for multiplying a matrix on the left by
 the pseudoinverse, as <code>linalg_lstsq(A, B)$solution == A$pinv() %*% B</code></p>
-<p>It is always prefered to use <code><a href='linalg_lstsq.html'>linalg_lstsq()</a></code> when possible, as it is faster and more
+<p>It is always prefered to use <code><a href="linalg_lstsq.html">linalg_lstsq()</a></code> when possible, as it is faster and more
 numerically stable than computing the pseudoinverse explicitly.</p>
-    <h2 class="hasAnchor" id="see-also"><a class="anchor" href="#see-also"></a>See also</h2>
-
-    <div class='dont-index'>
-<ul>
-<li><p><code><a href='linalg_inv.html'>linalg_inv()</a></code> computes the inverse of a square matrix.</p></li>
-<li><p><code><a href='linalg_lstsq.html'>linalg_lstsq()</a></code> computes <code>A$pinv() %*% B</code> with a
+    </div>
+    <div class="section level2">
+    <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
+    <div class="dont-index">
+<ul><li><p><code><a href="linalg_inv.html">linalg_inv()</a></code> computes the inverse of a square matrix.</p></li>
+<li><p><code><a href="linalg_lstsq.html">linalg_lstsq()</a></code> computes <code>A$pinv() %*% B</code> with a
 numerically stable algorithm.</p></li>
-</ul>
-
-<p>Other linalg: 
-<code><a href='linalg_cholesky_ex.html'>linalg_cholesky_ex</a>()</code>,
-<code><a href='linalg_cholesky.html'>linalg_cholesky</a>()</code>,
-<code><a href='linalg_det.html'>linalg_det</a>()</code>,
-<code><a href='linalg_eigh.html'>linalg_eigh</a>()</code>,
-<code><a href='linalg_eigvalsh.html'>linalg_eigvalsh</a>()</code>,
-<code><a href='linalg_eigvals.html'>linalg_eigvals</a>()</code>,
-<code><a href='linalg_eig.html'>linalg_eig</a>()</code>,
-<code><a href='linalg_householder_product.html'>linalg_householder_product</a>()</code>,
-<code><a href='linalg_inv_ex.html'>linalg_inv_ex</a>()</code>,
-<code><a href='linalg_inv.html'>linalg_inv</a>()</code>,
-<code><a href='linalg_lstsq.html'>linalg_lstsq</a>()</code>,
-<code><a href='linalg_matrix_norm.html'>linalg_matrix_norm</a>()</code>,
-<code><a href='linalg_matrix_power.html'>linalg_matrix_power</a>()</code>,
-<code><a href='linalg_matrix_rank.html'>linalg_matrix_rank</a>()</code>,
-<code><a href='linalg_multi_dot.html'>linalg_multi_dot</a>()</code>,
-<code><a href='linalg_norm.html'>linalg_norm</a>()</code>,
-<code><a href='linalg_qr.html'>linalg_qr</a>()</code>,
-<code><a href='linalg_slogdet.html'>linalg_slogdet</a>()</code>,
-<code><a href='linalg_solve.html'>linalg_solve</a>()</code>,
-<code><a href='linalg_svdvals.html'>linalg_svdvals</a>()</code>,
-<code><a href='linalg_svd.html'>linalg_svd</a>()</code>,
-<code><a href='linalg_tensorinv.html'>linalg_tensorinv</a>()</code>,
-<code><a href='linalg_tensorsolve.html'>linalg_tensorsolve</a>()</code>,
-<code><a href='linalg_vector_norm.html'>linalg_vector_norm</a>()</code></p></div>
+</ul><p>Other linalg: 
+<code><a href="linalg_cholesky_ex.html">linalg_cholesky_ex</a>()</code>,
+<code><a href="linalg_cholesky.html">linalg_cholesky</a>()</code>,
+<code><a href="linalg_det.html">linalg_det</a>()</code>,
+<code><a href="linalg_eigh.html">linalg_eigh</a>()</code>,
+<code><a href="linalg_eigvalsh.html">linalg_eigvalsh</a>()</code>,
+<code><a href="linalg_eigvals.html">linalg_eigvals</a>()</code>,
+<code><a href="linalg_eig.html">linalg_eig</a>()</code>,
+<code><a href="linalg_householder_product.html">linalg_householder_product</a>()</code>,
+<code><a href="linalg_inv_ex.html">linalg_inv_ex</a>()</code>,
+<code><a href="linalg_inv.html">linalg_inv</a>()</code>,
+<code><a href="linalg_lstsq.html">linalg_lstsq</a>()</code>,
+<code><a href="linalg_matrix_norm.html">linalg_matrix_norm</a>()</code>,
+<code><a href="linalg_matrix_power.html">linalg_matrix_power</a>()</code>,
+<code><a href="linalg_matrix_rank.html">linalg_matrix_rank</a>()</code>,
+<code><a href="linalg_multi_dot.html">linalg_multi_dot</a>()</code>,
+<code><a href="linalg_norm.html">linalg_norm</a>()</code>,
+<code><a href="linalg_qr.html">linalg_qr</a>()</code>,
+<code><a href="linalg_slogdet.html">linalg_slogdet</a>()</code>,
+<code><a href="linalg_solve.html">linalg_solve</a>()</code>,
+<code><a href="linalg_svdvals.html">linalg_svdvals</a>()</code>,
+<code><a href="linalg_svd.html">linalg_svd</a>()</code>,
+<code><a href="linalg_tensorinv.html">linalg_tensorinv</a>()</code>,
+<code><a href="linalg_tensorsolve.html">linalg_tensorsolve</a>()</code>,
+<code><a href="linalg_vector_norm.html">linalg_vector_norm</a>()</code></p></div>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>A</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>5</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>linalg_pinv</span><span class='op'>(</span><span class='va'>A</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.0610 -0.0677 -0.4113</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.3452 -0.4032  0.2598</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.4719 -0.1896 -0.4721</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.7374  0.5149  0.4570</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  3.1385  1.4408  1.1257</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{5,3} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">A</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">5</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">linalg_pinv</span><span class="op">(</span><span class="va">A</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.0634  0.2726  0.5803</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.1160  0.0856  0.0541</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.5057  0.6576  0.1978</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.1240  0.1646 -0.2429</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.2866 -0.1429 -0.2102</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{5,3} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer>
-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
 </div>
 
-<div class="pkgdown">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p>
+<div class="pkgdown-footer-right">
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.2.</p>
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diff --git a/reference/linalg_qr.html b/reference/linalg_qr.html
index e6c860a47..aba7f85e0 100644
--- a/reference/linalg_qr.html
+++ b/reference/linalg_qr.html
@@ -1,310 +1,191 @@
-<!-- Generated by pkgdown: do not edit by hand -->
 <!DOCTYPE html>
-<html lang="en">
-  <head>
-  <meta charset="utf-8">
-<meta http-equiv="X-UA-Compatible" content="IE=edge">
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+the full QR decomposition of a matrix
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Computes the QR decomposition of a matrix.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/linalg.R'><code>R/linalg.R</code></a></small>
-    <div class="hidden name"><code>linalg_qr.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Computes the QR decomposition of a matrix.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/linalg.R" class="external-link"><code>R/linalg.R</code></a></small>
+      <div class="d-none name"><code>linalg_qr.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Letting  be  or ,
 the <strong>full QR decomposition</strong> of a matrix
  is defined as</p>
     </div>
 
-    <pre class="usage"><span class='fu'>linalg_qr</span><span class='op'>(</span><span class='va'>A</span>, mode <span class='op'>=</span> <span class='st'>"reduced"</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>A</th>
-      <td><p>(Tensor): tensor of shape <code>(*, m, n)</code> where <code>*</code> is zero or more batch dimensions.</p></td>
-    </tr>
-    <tr>
-      <th>mode</th>
-      <td><p>(str, optional): one of <code>'reduced'</code>, <code>'complete'</code>, <code>'r'</code>.
-Controls the shape of the returned tensors. Default: <code>'reduced'</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">linalg_qr</span><span class="op">(</span><span class="va">A</span>, mode <span class="op">=</span> <span class="st">"reduced"</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>A</dt>
+<dd><p>(Tensor): tensor of shape <code>(*, m, n)</code> where <code>*</code> is zero or more batch dimensions.</p></dd>
+<dt>mode</dt>
+<dd><p>(str, optional): one of <code>'reduced'</code>, <code>'complete'</code>, <code>'r'</code>.
+Controls the shape of the returned tensors. Default: <code>'reduced'</code>.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
     <p>A list <code>(Q, R)</code>.</p>
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p><link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/npm/katex@0.13.11/dist/katex.min.css" data-external="1">
-<span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mi>A</mi><mo>=</mo><mi>Q</mi><mi>R</mi><mpadded width="0px"><mrow><mspace width="2em"/><mi>Q</mi><mo>∈</mo><msup><mi mathvariant="double-struck">K</mi><mrow><mi>m</mi><mo>×</mo><mi>m</mi></mrow></msup><mo separator="true">,</mo><mi>R</mi><mo>∈</mo><msup><mi mathvariant="double-struck">K</mi><mrow><mi>m</mi><mo>×</mo><mi>n</mi></mrow></msup></mrow></mpadded></mrow><annotation encoding="application/x-tex">
+    </div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
+    <p><link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/npm/katex@0.15.2/dist/katex.min.css" data-external="1"><span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mi>A</mi><mo>=</mo><mi>Q</mi><mi>R</mi><mpadded width="0px"><mrow><mspace width="2em"></mspace><mi>Q</mi><mo>∈</mo><msup><mi mathvariant="double-struck">K</mi><mrow><mi>m</mi><mo>×</mo><mi>m</mi></mrow></msup><mo separator="true">,</mo><mi>R</mi><mo>∈</mo><msup><mi mathvariant="double-struck">K</mi><mrow><mi>m</mi><mo>×</mo><mi>n</mi></mrow></msup></mrow></mpadded></mrow><annotation encoding="application/x-tex">
 A = QR\mathrlap{\qquad Q \in \mathbb{K}^{m \times m}, R \in \mathbb{K}^{m \times n}}
-</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.68333em;vertical-align:0em;"></span><span class="mord mathnormal">A</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span></span><span class="base"><span class="strut" style="height:1.015771em;vertical-align:-0.19444em;"></span><span class="mord mathnormal" style="margin-right:0.00773em;">QR</span><span class="mord vbox"><span class="thinbox"><span class="rlap"><span class="strut" style="height:1.015771em;vertical-align:-0.19444em;"></span><span class="inner"><span class="mord"><span class="mspace" style="margin-right:2em;"></span><span class="mord mathnormal">Q</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span><span class="mrel">∈</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span><span class="mord"><span class="mord mathbb">K</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.821331em;"><span style="top:-3.113em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">m</span><span class="mbin mtight">×</span><span class="mord mathnormal mtight">m</span></span></span></span></span></span></span></span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord mathnormal" style="margin-right:0.00773em;">R</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span><span class="mrel">∈</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span><span class="mord"><span class="mord mathbb">K</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.821331em;"><span style="top:-3.113em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">m</span><span class="mbin mtight">×</span><span class="mord mathnormal mtight">n</span></span></span></span></span></span></span></span></span></span></span><span class="fix"></span></span></span></span></span></span></span></span></p>
+</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6833em;"></span><span class="mord mathnormal">A</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:1.0158em;vertical-align:-0.1944em;"></span><span class="mord mathnormal" style="margin-right:0.00773em;">QR</span><span class="mord vbox"><span class="thinbox"><span class="rlap"><span class="strut" style="height:1.0158em;vertical-align:-0.1944em;"></span><span class="inner"><span class="mord"><span class="mspace" style="margin-right:2em;"></span><span class="mord mathnormal">Q</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">∈</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mord"><span class="mord mathbb">K</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8213em;"><span style="top:-3.113em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">m</span><span class="mbin mtight">×</span><span class="mord mathnormal mtight">m</span></span></span></span></span></span></span></span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.00773em;">R</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">∈</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mord"><span class="mord mathbb">K</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8213em;"><span style="top:-3.113em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">m</span><span class="mbin mtight">×</span><span class="mord mathnormal mtight">n</span></span></span></span></span></span></span></span></span></span></span><span class="fix"></span></span></span></span></span></span></span></span></p>
 <p>where  is orthogonal in the real case and unitary in the complex case, and  is upper triangular.
 When <code>m &gt; n</code> (tall matrix), as <code>R</code> is upper triangular, its last <code>m - n</code> rows are zero.
 In this case, we can drop the last <code>m - n</code> columns of <code>Q</code> to form the
 <strong>reduced QR decomposition</strong>:</p>
-<p><link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/npm/katex@0.13.11/dist/katex.min.css" data-external="1">
-<span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mi>A</mi><mo>=</mo><mi>Q</mi><mi>R</mi><mpadded width="0px"><mrow><mspace width="2em"/><mi>Q</mi><mo>∈</mo><msup><mi mathvariant="double-struck">K</mi><mrow><mi>m</mi><mo>×</mo><mi>n</mi></mrow></msup><mo separator="true">,</mo><mi>R</mi><mo>∈</mo><msup><mi mathvariant="double-struck">K</mi><mrow><mi>n</mi><mo>×</mo><mi>n</mi></mrow></msup></mrow></mpadded></mrow><annotation encoding="application/x-tex">
+<p><link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/npm/katex@0.15.2/dist/katex.min.css" data-external="1"><span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mi>A</mi><mo>=</mo><mi>Q</mi><mi>R</mi><mpadded width="0px"><mrow><mspace width="2em"></mspace><mi>Q</mi><mo>∈</mo><msup><mi mathvariant="double-struck">K</mi><mrow><mi>m</mi><mo>×</mo><mi>n</mi></mrow></msup><mo separator="true">,</mo><mi>R</mi><mo>∈</mo><msup><mi mathvariant="double-struck">K</mi><mrow><mi>n</mi><mo>×</mo><mi>n</mi></mrow></msup></mrow></mpadded></mrow><annotation encoding="application/x-tex">
 A = QR\mathrlap{\qquad Q \in \mathbb{K}^{m \times n}, R \in \mathbb{K}^{n \times n}}
-</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.68333em;vertical-align:0em;"></span><span class="mord mathnormal">A</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span></span><span class="base"><span class="strut" style="height:1.015771em;vertical-align:-0.19444em;"></span><span class="mord mathnormal" style="margin-right:0.00773em;">QR</span><span class="mord vbox"><span class="thinbox"><span class="rlap"><span class="strut" style="height:1.015771em;vertical-align:-0.19444em;"></span><span class="inner"><span class="mord"><span class="mspace" style="margin-right:2em;"></span><span class="mord mathnormal">Q</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span><span class="mrel">∈</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span><span class="mord"><span class="mord mathbb">K</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.821331em;"><span style="top:-3.113em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">m</span><span class="mbin mtight">×</span><span class="mord mathnormal mtight">n</span></span></span></span></span></span></span></span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord mathnormal" style="margin-right:0.00773em;">R</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span><span class="mrel">∈</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span><span class="mord"><span class="mord mathbb">K</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.821331em;"><span style="top:-3.113em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">n</span><span class="mbin mtight">×</span><span class="mord mathnormal mtight">n</span></span></span></span></span></span></span></span></span></span></span><span class="fix"></span></span></span></span></span></span></span></span></p>
+</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6833em;"></span><span class="mord mathnormal">A</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:1.0158em;vertical-align:-0.1944em;"></span><span class="mord mathnormal" style="margin-right:0.00773em;">QR</span><span class="mord vbox"><span class="thinbox"><span class="rlap"><span class="strut" style="height:1.0158em;vertical-align:-0.1944em;"></span><span class="inner"><span class="mord"><span class="mspace" style="margin-right:2em;"></span><span class="mord mathnormal">Q</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">∈</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mord"><span class="mord mathbb">K</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8213em;"><span style="top:-3.113em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">m</span><span class="mbin mtight">×</span><span class="mord mathnormal mtight">n</span></span></span></span></span></span></span></span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.00773em;">R</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">∈</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mord"><span class="mord mathbb">K</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8213em;"><span style="top:-3.113em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">n</span><span class="mbin mtight">×</span><span class="mord mathnormal mtight">n</span></span></span></span></span></span></span></span></span></span></span><span class="fix"></span></span></span></span></span></span></span></span></p>
 <p>The reduced QR decomposition agrees with the full QR decomposition when <code>n &gt;= m</code> (wide matrix).
 Supports input of float, double, cfloat and cdouble dtypes.
 Also supports batches of matrices, and if <code>A</code> is a batch of matrices then
 the output has the same batch dimensions.
 The parameter <code>mode</code> chooses between the full and reduced QR decomposition.</p>
-<p>If <code>A</code> has shape <code>(*, m, n)</code>, denoting <code>k = min(m, n)</code></p><ul>
-<li><p><code>mode = 'reduced'</code> (default): Returns <code>(Q, R)</code> of shapes <code>(*, m, k)</code>, <code>(*, k, n)</code> respectively.</p></li>
+<p>If <code>A</code> has shape <code>(*, m, n)</code>, denoting <code>k = min(m, n)</code></p><ul><li><p><code>mode = 'reduced'</code> (default): Returns <code>(Q, R)</code> of shapes <code>(*, m, k)</code>, <code>(*, k, n)</code> respectively.</p></li>
 <li><p><code>mode = 'complete'</code>: Returns <code>(Q, R)</code> of shapes <code>(*, m, m)</code>, <code>(*, m, n)</code> respectively.</p></li>
 <li><p><code>mode = 'r'</code>: Computes only the reduced <code>R</code>. Returns <code>(Q, R)</code> with <code>Q</code> empty and <code>R</code> of shape <code>(*, k, n)</code>.</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="see-also"><a class="anchor" href="#see-also"></a>See also</h2>
-
-    <div class='dont-index'><p>Other linalg: 
-<code><a href='linalg_cholesky_ex.html'>linalg_cholesky_ex</a>()</code>,
-<code><a href='linalg_cholesky.html'>linalg_cholesky</a>()</code>,
-<code><a href='linalg_det.html'>linalg_det</a>()</code>,
-<code><a href='linalg_eigh.html'>linalg_eigh</a>()</code>,
-<code><a href='linalg_eigvalsh.html'>linalg_eigvalsh</a>()</code>,
-<code><a href='linalg_eigvals.html'>linalg_eigvals</a>()</code>,
-<code><a href='linalg_eig.html'>linalg_eig</a>()</code>,
-<code><a href='linalg_householder_product.html'>linalg_householder_product</a>()</code>,
-<code><a href='linalg_inv_ex.html'>linalg_inv_ex</a>()</code>,
-<code><a href='linalg_inv.html'>linalg_inv</a>()</code>,
-<code><a href='linalg_lstsq.html'>linalg_lstsq</a>()</code>,
-<code><a href='linalg_matrix_norm.html'>linalg_matrix_norm</a>()</code>,
-<code><a href='linalg_matrix_power.html'>linalg_matrix_power</a>()</code>,
-<code><a href='linalg_matrix_rank.html'>linalg_matrix_rank</a>()</code>,
-<code><a href='linalg_multi_dot.html'>linalg_multi_dot</a>()</code>,
-<code><a href='linalg_norm.html'>linalg_norm</a>()</code>,
-<code><a href='linalg_pinv.html'>linalg_pinv</a>()</code>,
-<code><a href='linalg_slogdet.html'>linalg_slogdet</a>()</code>,
-<code><a href='linalg_solve.html'>linalg_solve</a>()</code>,
-<code><a href='linalg_svdvals.html'>linalg_svdvals</a>()</code>,
-<code><a href='linalg_svd.html'>linalg_svd</a>()</code>,
-<code><a href='linalg_tensorinv.html'>linalg_tensorinv</a>()</code>,
-<code><a href='linalg_tensorsolve.html'>linalg_tensorsolve</a>()</code>,
-<code><a href='linalg_vector_norm.html'>linalg_vector_norm</a>()</code></p></div>
+</ul></div>
+    <div class="section level2">
+    <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
+    <div class="dont-index"><p>Other linalg: 
+<code><a href="linalg_cholesky_ex.html">linalg_cholesky_ex</a>()</code>,
+<code><a href="linalg_cholesky.html">linalg_cholesky</a>()</code>,
+<code><a href="linalg_det.html">linalg_det</a>()</code>,
+<code><a href="linalg_eigh.html">linalg_eigh</a>()</code>,
+<code><a href="linalg_eigvalsh.html">linalg_eigvalsh</a>()</code>,
+<code><a href="linalg_eigvals.html">linalg_eigvals</a>()</code>,
+<code><a href="linalg_eig.html">linalg_eig</a>()</code>,
+<code><a href="linalg_householder_product.html">linalg_householder_product</a>()</code>,
+<code><a href="linalg_inv_ex.html">linalg_inv_ex</a>()</code>,
+<code><a href="linalg_inv.html">linalg_inv</a>()</code>,
+<code><a href="linalg_lstsq.html">linalg_lstsq</a>()</code>,
+<code><a href="linalg_matrix_norm.html">linalg_matrix_norm</a>()</code>,
+<code><a href="linalg_matrix_power.html">linalg_matrix_power</a>()</code>,
+<code><a href="linalg_matrix_rank.html">linalg_matrix_rank</a>()</code>,
+<code><a href="linalg_multi_dot.html">linalg_multi_dot</a>()</code>,
+<code><a href="linalg_norm.html">linalg_norm</a>()</code>,
+<code><a href="linalg_pinv.html">linalg_pinv</a>()</code>,
+<code><a href="linalg_slogdet.html">linalg_slogdet</a>()</code>,
+<code><a href="linalg_solve.html">linalg_solve</a>()</code>,
+<code><a href="linalg_svdvals.html">linalg_svdvals</a>()</code>,
+<code><a href="linalg_svd.html">linalg_svd</a>()</code>,
+<code><a href="linalg_tensorinv.html">linalg_tensorinv</a>()</code>,
+<code><a href="linalg_tensorsolve.html">linalg_tensorsolve</a>()</code>,
+<code><a href="linalg_vector_norm.html">linalg_vector_norm</a>()</code></p></div>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/cbind.html'>rbind</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>12.</span>, <span class='op'>-</span><span class='fl'>51</span>, <span class='fl'>4</span><span class='op'>)</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>6</span>, <span class='fl'>167</span>, <span class='op'>-</span><span class='fl'>68</span><span class='op'>)</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='op'>-</span><span class='fl'>4</span>, <span class='fl'>24</span>, <span class='op'>-</span><span class='fl'>41</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>qr</span> <span class='op'>&lt;-</span> <span class='fu'>linalg_qr</span><span class='op'>(</span><span class='va'>a</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='fu'><a href='torch_mm.html'>torch_mm</a></span><span class='op'>(</span><span class='va'>qr</span><span class='op'>[[</span><span class='fl'>1</span><span class='op'>]</span><span class='op'>]</span>, <span class='va'>qr</span><span class='op'>[[</span><span class='fl'>2</span><span class='op'>]</span><span class='op'>]</span><span class='op'>)</span><span class='op'>$</span><span class='fu'>round</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'><a href='torch_mm.html'>torch_mm</a></span><span class='op'>(</span><span class='va'>qr</span><span class='op'>[[</span><span class='fl'>1</span><span class='op'>]</span><span class='op'>]</span><span class='op'>$</span><span class='fu'>t</span><span class='op'>(</span><span class='op'>)</span>, <span class='va'>qr</span><span class='op'>[[</span><span class='fl'>1</span><span class='op'>]</span><span class='op'>]</span><span class='op'>)</span><span class='op'>$</span><span class='fu'>round</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1  0  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0  1  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0  0  1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{3,3} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/cbind.html" class="external-link">rbind</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">12.</span>, <span class="op">-</span><span class="fl">51</span>, <span class="fl">4</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">6</span>, <span class="fl">167</span>, <span class="op">-</span><span class="fl">68</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="op">-</span><span class="fl">4</span>, <span class="fl">24</span>, <span class="op">-</span><span class="fl">41</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">qr</span> <span class="op">&lt;-</span> <span class="fu">linalg_qr</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="fu"><a href="torch_mm.html">torch_mm</a></span><span class="op">(</span><span class="va">qr</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span>, <span class="va">qr</span><span class="op">[[</span><span class="fl">2</span><span class="op">]</span><span class="op">]</span><span class="op">)</span><span class="op">$</span><span class="fu">round</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu"><a href="torch_mm.html">torch_mm</a></span><span class="op">(</span><span class="va">qr</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="fu">t</span><span class="op">(</span><span class="op">)</span>, <span class="va">qr</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span><span class="op">)</span><span class="op">$</span><span class="fu">round</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1  0  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0  1  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0  0  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3,3} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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diff --git a/reference/linalg_slogdet.html b/reference/linalg_slogdet.html
index d2dc6c9d8..57ea5d69b 100644
--- a/reference/linalg_slogdet.html
+++ b/reference/linalg_slogdet.html
@@ -1,195 +1,90 @@
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="For complex A, it returns the angle and the natural logarithm of the modulus of the
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+the output has the same batch dimensions."><title>Computes the sign and natural logarithm of the absolute value of the determinant of a square matrix. — linalg_slogdet • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.1.0/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.1.0/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Computes the sign and natural logarithm of the absolute value of the determinant of a square matrix. — linalg_slogdet"><meta property="og:description" content="For complex A, it returns the angle and the natural logarithm of the modulus of the
+determinant, that is, a logarithmic polar decomposition of the determinant.
+Supports input of float, double, cfloat and cdouble dtypes.
+Also supports batches of matrices, and if A is a batch of matrices then
+the output has the same batch dimensions."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Computes the sign and natural logarithm of the absolute value of the determinant of a square matrix.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/linalg.R'><code>R/linalg.R</code></a></small>
-    <div class="hidden name"><code>linalg_slogdet.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Computes the sign and natural logarithm of the absolute value of the determinant of a square matrix.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/linalg.R" class="external-link"><code>R/linalg.R</code></a></small>
+      <div class="d-none name"><code>linalg_slogdet.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>For complex <code>A</code>, it returns the angle and the natural logarithm of the modulus of the
 determinant, that is, a logarithmic polar decomposition of the determinant.
 Supports input of float, double, cfloat and cdouble dtypes.
@@ -197,101 +92,93 @@ Also supports batches of matrices, and if <code>A</code> is a batch of matrices
 the output has the same batch dimensions.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>linalg_slogdet</span><span class='op'>(</span><span class='va'>A</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>A</th>
-      <td><p>(Tensor): tensor of shape <code>(*, n, n)</code> where <code>*</code> is zero or more batch dimensions.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">linalg_slogdet</span><span class="op">(</span><span class="va">A</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>A</dt>
+<dd><p>(Tensor): tensor of shape <code>(*, n, n)</code> where <code>*</code> is zero or more batch dimensions.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
     <p>A list <code>(sign, logabsdet)</code>.
 <code>logabsdet</code> will always be real-valued, even when <code>A</code> is complex.
 <code>sign</code> will have the same dtype as <code>A</code>.</p>
-    <h2 class="hasAnchor" id="notes"><a class="anchor" href="#notes"></a>Notes</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="notes">Notes<a class="anchor" aria-label="anchor" href="#notes"></a></h2>
     
 
-<ul>
-<li><p>The determinant can be recovered as <code>sign * exp(logabsdet)</code>.</p></li>
+<ul><li><p>The determinant can be recovered as <code>sign * exp(logabsdet)</code>.</p></li>
 <li><p>When a matrix has a determinant of zero, it returns <code>(0, -Inf)</code>.</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="see-also"><a class="anchor" href="#see-also"></a>See also</h2>
-
-    <div class='dont-index'><p>Other linalg: 
-<code><a href='linalg_cholesky_ex.html'>linalg_cholesky_ex</a>()</code>,
-<code><a href='linalg_cholesky.html'>linalg_cholesky</a>()</code>,
-<code><a href='linalg_det.html'>linalg_det</a>()</code>,
-<code><a href='linalg_eigh.html'>linalg_eigh</a>()</code>,
-<code><a href='linalg_eigvalsh.html'>linalg_eigvalsh</a>()</code>,
-<code><a href='linalg_eigvals.html'>linalg_eigvals</a>()</code>,
-<code><a href='linalg_eig.html'>linalg_eig</a>()</code>,
-<code><a href='linalg_householder_product.html'>linalg_householder_product</a>()</code>,
-<code><a href='linalg_inv_ex.html'>linalg_inv_ex</a>()</code>,
-<code><a href='linalg_inv.html'>linalg_inv</a>()</code>,
-<code><a href='linalg_lstsq.html'>linalg_lstsq</a>()</code>,
-<code><a href='linalg_matrix_norm.html'>linalg_matrix_norm</a>()</code>,
-<code><a href='linalg_matrix_power.html'>linalg_matrix_power</a>()</code>,
-<code><a href='linalg_matrix_rank.html'>linalg_matrix_rank</a>()</code>,
-<code><a href='linalg_multi_dot.html'>linalg_multi_dot</a>()</code>,
-<code><a href='linalg_norm.html'>linalg_norm</a>()</code>,
-<code><a href='linalg_pinv.html'>linalg_pinv</a>()</code>,
-<code><a href='linalg_qr.html'>linalg_qr</a>()</code>,
-<code><a href='linalg_solve.html'>linalg_solve</a>()</code>,
-<code><a href='linalg_svdvals.html'>linalg_svdvals</a>()</code>,
-<code><a href='linalg_svd.html'>linalg_svd</a>()</code>,
-<code><a href='linalg_tensorinv.html'>linalg_tensorinv</a>()</code>,
-<code><a href='linalg_tensorsolve.html'>linalg_tensorsolve</a>()</code>,
-<code><a href='linalg_vector_norm.html'>linalg_vector_norm</a>()</code></p></div>
+</ul></div>
+    <div class="section level2">
+    <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
+    <div class="dont-index"><p>Other linalg: 
+<code><a href="linalg_cholesky_ex.html">linalg_cholesky_ex</a>()</code>,
+<code><a href="linalg_cholesky.html">linalg_cholesky</a>()</code>,
+<code><a href="linalg_det.html">linalg_det</a>()</code>,
+<code><a href="linalg_eigh.html">linalg_eigh</a>()</code>,
+<code><a href="linalg_eigvalsh.html">linalg_eigvalsh</a>()</code>,
+<code><a href="linalg_eigvals.html">linalg_eigvals</a>()</code>,
+<code><a href="linalg_eig.html">linalg_eig</a>()</code>,
+<code><a href="linalg_householder_product.html">linalg_householder_product</a>()</code>,
+<code><a href="linalg_inv_ex.html">linalg_inv_ex</a>()</code>,
+<code><a href="linalg_inv.html">linalg_inv</a>()</code>,
+<code><a href="linalg_lstsq.html">linalg_lstsq</a>()</code>,
+<code><a href="linalg_matrix_norm.html">linalg_matrix_norm</a>()</code>,
+<code><a href="linalg_matrix_power.html">linalg_matrix_power</a>()</code>,
+<code><a href="linalg_matrix_rank.html">linalg_matrix_rank</a>()</code>,
+<code><a href="linalg_multi_dot.html">linalg_multi_dot</a>()</code>,
+<code><a href="linalg_norm.html">linalg_norm</a>()</code>,
+<code><a href="linalg_pinv.html">linalg_pinv</a>()</code>,
+<code><a href="linalg_qr.html">linalg_qr</a>()</code>,
+<code><a href="linalg_solve.html">linalg_solve</a>()</code>,
+<code><a href="linalg_svdvals.html">linalg_svdvals</a>()</code>,
+<code><a href="linalg_svd.html">linalg_svd</a>()</code>,
+<code><a href="linalg_tensorinv.html">linalg_tensorinv</a>()</code>,
+<code><a href="linalg_tensorsolve.html">linalg_tensorsolve</a>()</code>,
+<code><a href="linalg_vector_norm.html">linalg_vector_norm</a>()</code></p></div>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>3</span>,<span class='fl'>3</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>linalg_slogdet</span><span class='op'>(</span><span class='va'>a</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [[1]]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{} ]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [[2]]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> 1.50507</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{} ]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">3</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">linalg_slogdet</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [[1]]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{} ]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [[2]]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -1.90759</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{} ]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+</code></pre></div>
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Computes the solution of a square system of linear equations with a unique solution.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/linalg.R'><code>R/linalg.R</code></a></small>
-    <div class="hidden name"><code>linalg_solve.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Computes the solution of a square system of linear equations with a unique solution.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/linalg.R" class="external-link"><code>R/linalg.R</code></a></small>
+      <div class="d-none name"><code>linalg_solve.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Letting  be  or ,
 this function computes the solution  of the <strong>linear system</strong> associated to
 , which is defined as</p>
     </div>
 
-    <pre class="usage"><span class='fu'>linalg_solve</span><span class='op'>(</span><span class='va'>A</span>, <span class='va'>B</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>A</th>
-      <td><p>(Tensor): tensor of shape <code>(*, n, n)</code> where <code>*</code> is zero or more batch dimensions.</p></td>
-    </tr>
-    <tr>
-      <th>B</th>
-      <td><p>(Tensor): right-hand side tensor of shape <code>(*, n)</code> or  <code>(*, n, k)</code> or <code>(n,)</code> or <code>(n, k)</code>
-according to the rules described above</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">linalg_solve</span><span class="op">(</span><span class="va">A</span>, <span class="va">B</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>A</dt>
+<dd><p>(Tensor): tensor of shape <code>(*, n, n)</code> where <code>*</code> is zero or more batch dimensions.</p></dd>
+<dt>B</dt>
+<dd><p>(Tensor): right-hand side tensor of shape <code>(*, n)</code> or  <code>(*, n, k)</code> or <code>(n,)</code> or <code>(n, k)</code>
+according to the rules described above</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>$$
   AX = B
 $$</p>
@@ -219,82 +109,75 @@ This function assumes that  is invertible.
 Supports inputs of float, double, cfloat and cdouble dtypes.
 Also supports batches of matrices, and if the inputs are batches of matrices then
 the output has the same batch dimensions.</p>
-<p>Letting <code>*</code> be zero or more batch dimensions,</p><ul>
-<li><p>If <code>A</code> has shape <code>(*, n, n)</code> and <code>B</code> has shape <code>(*, n)</code> (a batch of vectors) or shape
+<p>Letting <code>*</code> be zero or more batch dimensions,</p><ul><li><p>If <code>A</code> has shape <code>(*, n, n)</code> and <code>B</code> has shape <code>(*, n)</code> (a batch of vectors) or shape
 <code>(*, n, k)</code> (a batch of matrices or "multiple right-hand sides"), this function returns <code>X</code> of shape
 <code>(*, n)</code> or <code>(*, n, k)</code> respectively.</p></li>
 <li><p>Otherwise, if <code>A</code> has shape <code>(*, n, n)</code> and  <code>B</code> has shape <code>(n,)</code>  or <code>(n, k)</code>, <code>B</code>
 is broadcasted to have shape <code>(*, n)</code> or <code>(*, n, k)</code> respectively.</p></li>
-</ul>
-
-<p>This function then returns the solution of the resulting batch of systems of linear equations.</p>
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
+</ul><p>This function then returns the solution of the resulting batch of systems of linear equations.</p>
+    </div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p>This function computes <code>X = A$inverse() @ B</code> in a faster and
 more numerically stable way than performing the computations separately.</p>
-    <h2 class="hasAnchor" id="see-also"><a class="anchor" href="#see-also"></a>See also</h2>
-
-    <div class='dont-index'><p>Other linalg: 
-<code><a href='linalg_cholesky_ex.html'>linalg_cholesky_ex</a>()</code>,
-<code><a href='linalg_cholesky.html'>linalg_cholesky</a>()</code>,
-<code><a href='linalg_det.html'>linalg_det</a>()</code>,
-<code><a href='linalg_eigh.html'>linalg_eigh</a>()</code>,
-<code><a href='linalg_eigvalsh.html'>linalg_eigvalsh</a>()</code>,
-<code><a href='linalg_eigvals.html'>linalg_eigvals</a>()</code>,
-<code><a href='linalg_eig.html'>linalg_eig</a>()</code>,
-<code><a href='linalg_householder_product.html'>linalg_householder_product</a>()</code>,
-<code><a href='linalg_inv_ex.html'>linalg_inv_ex</a>()</code>,
-<code><a href='linalg_inv.html'>linalg_inv</a>()</code>,
-<code><a href='linalg_lstsq.html'>linalg_lstsq</a>()</code>,
-<code><a href='linalg_matrix_norm.html'>linalg_matrix_norm</a>()</code>,
-<code><a href='linalg_matrix_power.html'>linalg_matrix_power</a>()</code>,
-<code><a href='linalg_matrix_rank.html'>linalg_matrix_rank</a>()</code>,
-<code><a href='linalg_multi_dot.html'>linalg_multi_dot</a>()</code>,
-<code><a href='linalg_norm.html'>linalg_norm</a>()</code>,
-<code><a href='linalg_pinv.html'>linalg_pinv</a>()</code>,
-<code><a href='linalg_qr.html'>linalg_qr</a>()</code>,
-<code><a href='linalg_slogdet.html'>linalg_slogdet</a>()</code>,
-<code><a href='linalg_svdvals.html'>linalg_svdvals</a>()</code>,
-<code><a href='linalg_svd.html'>linalg_svd</a>()</code>,
-<code><a href='linalg_tensorinv.html'>linalg_tensorinv</a>()</code>,
-<code><a href='linalg_tensorsolve.html'>linalg_tensorsolve</a>()</code>,
-<code><a href='linalg_vector_norm.html'>linalg_vector_norm</a>()</code></p></div>
+    </div>
+    <div class="section level2">
+    <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
+    <div class="dont-index"><p>Other linalg: 
+<code><a href="linalg_cholesky_ex.html">linalg_cholesky_ex</a>()</code>,
+<code><a href="linalg_cholesky.html">linalg_cholesky</a>()</code>,
+<code><a href="linalg_det.html">linalg_det</a>()</code>,
+<code><a href="linalg_eigh.html">linalg_eigh</a>()</code>,
+<code><a href="linalg_eigvalsh.html">linalg_eigvalsh</a>()</code>,
+<code><a href="linalg_eigvals.html">linalg_eigvals</a>()</code>,
+<code><a href="linalg_eig.html">linalg_eig</a>()</code>,
+<code><a href="linalg_householder_product.html">linalg_householder_product</a>()</code>,
+<code><a href="linalg_inv_ex.html">linalg_inv_ex</a>()</code>,
+<code><a href="linalg_inv.html">linalg_inv</a>()</code>,
+<code><a href="linalg_lstsq.html">linalg_lstsq</a>()</code>,
+<code><a href="linalg_matrix_norm.html">linalg_matrix_norm</a>()</code>,
+<code><a href="linalg_matrix_power.html">linalg_matrix_power</a>()</code>,
+<code><a href="linalg_matrix_rank.html">linalg_matrix_rank</a>()</code>,
+<code><a href="linalg_multi_dot.html">linalg_multi_dot</a>()</code>,
+<code><a href="linalg_norm.html">linalg_norm</a>()</code>,
+<code><a href="linalg_pinv.html">linalg_pinv</a>()</code>,
+<code><a href="linalg_qr.html">linalg_qr</a>()</code>,
+<code><a href="linalg_slogdet.html">linalg_slogdet</a>()</code>,
+<code><a href="linalg_svdvals.html">linalg_svdvals</a>()</code>,
+<code><a href="linalg_svd.html">linalg_svd</a>()</code>,
+<code><a href="linalg_tensorinv.html">linalg_tensorinv</a>()</code>,
+<code><a href="linalg_tensorsolve.html">linalg_tensorsolve</a>()</code>,
+<code><a href="linalg_vector_norm.html">linalg_vector_norm</a>()</code></p></div>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>A</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>3</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>b</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>3</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>x</span> <span class='op'>&lt;-</span> <span class='fu'>linalg_solve</span><span class='op'>(</span><span class='va'>A</span>, <span class='va'>b</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'><a href='torch_allclose.html'>torch_allclose</a></span><span class='op'>(</span><span class='fu'><a href='torch_matmul.html'>torch_matmul</a></span><span class='op'>(</span><span class='va'>A</span>, <span class='va'>x</span><span class='op'>)</span>, <span class='va'>b</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [1] TRUE</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">A</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">3</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">b</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">3</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu">linalg_solve</span><span class="op">(</span><span class="va">A</span>, <span class="va">b</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu"><a href="torch_allclose.html">torch_allclose</a></span><span class="op">(</span><span class="fu"><a href="torch_matmul.html">torch_matmul</a></span><span class="op">(</span><span class="va">A</span>, <span class="va">x</span><span class="op">)</span>, <span class="va">b</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] TRUE</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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-    <h1>Computes the singular value decomposition (SVD) of a matrix.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/linalg.R'><code>R/linalg.R</code></a></small>
-    <div class="hidden name"><code>linalg_svd.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Computes the singular value decomposition (SVD) of a matrix.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/linalg.R" class="external-link"><code>R/linalg.R</code></a></small>
+      <div class="d-none name"><code>linalg_svd.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Letting  be  or ,
 the <strong>full SVD</strong> of a matrix
 , if <code>k = min(m,n)</code>, is defined as</p>
     </div>
 
-    <pre class="usage"><span class='fu'>linalg_svd</span><span class='op'>(</span><span class='va'>A</span>, full_matrices <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>A</th>
-      <td><p>(Tensor): tensor of shape <code>(*, m, n)</code> where <code>*</code> is zero or more batch dimensions.</p></td>
-    </tr>
-    <tr>
-      <th>full_matrices</th>
-      <td><p>(bool, optional): controls whether to compute the full or reduced
-SVD, and consequently, the shape of the returned tensors <code>U</code> and <code>V</code>. Default: <code>TRUE</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">linalg_svd</span><span class="op">(</span><span class="va">A</span>, full_matrices <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>A</dt>
+<dd><p>(Tensor): tensor of shape <code>(*, m, n)</code> where <code>*</code> is zero or more batch dimensions.</p></dd>
+<dt>full_matrices</dt>
+<dd><p>(bool, optional): controls whether to compute the full or reduced
+SVD, and consequently, the shape of the returned tensors <code>U</code> and <code>V</code>. Default: <code>TRUE</code>.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
     <p>A list <code>(U, S, V)</code> which corresponds to , ,  above.
 <code>S</code> will always be real-valued, even when <code>A</code> is complex.
 It will also be ordered in descending order.
 <code>U</code> and <code>V</code> will have the same dtype as <code>A</code>. The left / right singular vectors will be given by
 the columns of <code>U</code> and the rows of <code>V</code> respectively.</p>
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p><link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/npm/katex@0.13.11/dist/katex.min.css" data-external="1">
-<span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mi>A</mi><mo>=</mo><mi>U</mi><mi mathvariant="normal">diag</mi><mo>⁡</mo><mo stretchy="false">(</mo><mi>S</mi><mo stretchy="false">)</mo><msup><mi>V</mi><mi>H</mi></msup><mpadded width="0px"><mrow><mspace width="2em"/><mi>U</mi><mo>∈</mo><msup><mi mathvariant="double-struck">K</mi><mrow><mi>m</mi><mo>×</mo><mi>m</mi></mrow></msup><mo separator="true">,</mo><mi>S</mi><mo>∈</mo><msup><mi mathvariant="double-struck">R</mi><mi>k</mi></msup><mo separator="true">,</mo><mi>V</mi><mo>∈</mo><msup><mi mathvariant="double-struck">K</mi><mrow><mi>n</mi><mo>×</mo><mi>n</mi></mrow></msup></mrow></mpadded></mrow><annotation encoding="application/x-tex">
+    </div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
+    <p><link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/npm/katex@0.15.2/dist/katex.min.css" data-external="1"><span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mi>A</mi><mo>=</mo><mi>U</mi><mi mathvariant="normal">diag</mi><mo>⁡</mo><mo stretchy="false">(</mo><mi>S</mi><mo stretchy="false">)</mo><msup><mi>V</mi><mi>H</mi></msup><mpadded width="0px"><mrow><mspace width="2em"></mspace><mi>U</mi><mo>∈</mo><msup><mi mathvariant="double-struck">K</mi><mrow><mi>m</mi><mo>×</mo><mi>m</mi></mrow></msup><mo separator="true">,</mo><mi>S</mi><mo>∈</mo><msup><mi mathvariant="double-struck">R</mi><mi>k</mi></msup><mo separator="true">,</mo><mi>V</mi><mo>∈</mo><msup><mi mathvariant="double-struck">K</mi><mrow><mi>n</mi><mo>×</mo><mi>n</mi></mrow></msup></mrow></mpadded></mrow><annotation encoding="application/x-tex">
 A = U \operatorname{diag}(S) V^{H} \mathrlap{\qquad U \in \mathbb{K}^{m \times m}, S \in \mathbb{R}^k, V \in \mathbb{K}^{n \times n}}
-</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.68333em;vertical-align:0em;"></span><span class="mord mathnormal">A</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span></span><span class="base"><span class="strut" style="height:1.149108em;vertical-align:-0.25em;"></span><span class="mord mathnormal" style="margin-right:0.10903em;">U</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mop"><span class="mord mathrm" style="margin-right:0.01389em;">diag</span></span><span class="mopen">(</span><span class="mord mathnormal" style="margin-right:0.05764em;">S</span><span class="mclose">)</span><span class="mord"><span class="mord mathnormal" style="margin-right:0.22222em;">V</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8913309999999999em;"><span style="top:-3.113em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight" style="margin-right:0.08125em;">H</span></span></span></span></span></span></span></span></span><span class="mord vbox"><span class="thinbox"><span class="rlap"><span class="strut" style="height:1.093548em;vertical-align:-0.19444em;"></span><span class="inner"><span class="mord"><span class="mspace" style="margin-right:2em;"></span><span class="mord mathnormal" style="margin-right:0.10903em;">U</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span><span class="mrel">∈</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span><span class="mord"><span class="mord mathbb">K</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.821331em;"><span style="top:-3.113em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">m</span><span class="mbin mtight">×</span><span class="mord mathnormal mtight">m</span></span></span></span></span></span></span></span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord mathnormal" style="margin-right:0.05764em;">S</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span><span class="mrel">∈</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span><span class="mord"><span class="mord mathbb">R</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8991079999999999em;"><span style="top:-3.113em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight" style="margin-right:0.03148em;">k</span></span></span></span></span></span></span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord mathnormal" style="margin-right:0.22222em;">V</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span><span class="mrel">∈</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span><span class="mord"><span class="mord mathbb">K</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.821331em;"><span style="top:-3.113em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">n</span><span class="mbin mtight">×</span><span class="mord mathnormal mtight">n</span></span></span></span></span></span></span></span></span></span></span><span class="fix"></span></span></span></span></span></span></span></span></p>
+</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6833em;"></span><span class="mord mathnormal">A</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:1.1491em;vertical-align:-0.25em;"></span><span class="mord mathnormal" style="margin-right:0.10903em;">U</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mop"><span class="mord mathrm" style="margin-right:0.01389em;">diag</span></span><span class="mopen">(</span><span class="mord mathnormal" style="margin-right:0.05764em;">S</span><span class="mclose">)</span><span class="mord"><span class="mord mathnormal" style="margin-right:0.22222em;">V</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8913em;"><span style="top:-3.113em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight" style="margin-right:0.08125em;">H</span></span></span></span></span></span></span></span></span><span class="mord vbox"><span class="thinbox"><span class="rlap"><span class="strut" style="height:1.0935em;vertical-align:-0.1944em;"></span><span class="inner"><span class="mord"><span class="mspace" style="margin-right:2em;"></span><span class="mord mathnormal" style="margin-right:0.10903em;">U</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">∈</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mord"><span class="mord mathbb">K</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8213em;"><span style="top:-3.113em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">m</span><span class="mbin mtight">×</span><span class="mord mathnormal mtight">m</span></span></span></span></span></span></span></span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.05764em;">S</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">∈</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mord"><span class="mord mathbb">R</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8991em;"><span style="top:-3.113em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight" style="margin-right:0.03148em;">k</span></span></span></span></span></span></span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.22222em;">V</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">∈</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mord"><span class="mord mathbb">K</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8213em;"><span style="top:-3.113em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">n</span><span class="mbin mtight">×</span><span class="mord mathnormal mtight">n</span></span></span></span></span></span></span></span></span></span></span><span class="fix"></span></span></span></span></span></span></span></span></p>
 <p>where ,
  is the conjugate transpose when  is complex, and the transpose when  is real-valued.</p>
 <p>The matrices  ,  (and thus ) are orthogonal in the real case, and unitary in the complex case.
 When <code>m &gt; n</code> (resp. <code>m &lt; n</code>) we can drop the last <code>m - n</code> (resp. <code>n - m</code>) columns of <code>U</code> (resp. <code>V</code>) to form the <strong>reduced SVD</strong>:</p>
-<p><link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/npm/katex@0.13.11/dist/katex.min.css" data-external="1">
-<span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mi>A</mi><mo>=</mo><mi>U</mi><mi mathvariant="normal">diag</mi><mo>⁡</mo><mo stretchy="false">(</mo><mi>S</mi><mo stretchy="false">)</mo><msup><mi>V</mi><mi>H</mi></msup><mpadded width="0px"><mrow><mspace width="2em"/><mi>U</mi><mo>∈</mo><msup><mi mathvariant="double-struck">K</mi><mrow><mi>m</mi><mo>×</mo><mi>k</mi></mrow></msup><mo separator="true">,</mo><mi>S</mi><mo>∈</mo><msup><mi mathvariant="double-struck">R</mi><mi>k</mi></msup><mo separator="true">,</mo><mi>V</mi><mo>∈</mo><msup><mi mathvariant="double-struck">K</mi><mrow><mi>k</mi><mo>×</mo><mi>n</mi></mrow></msup></mrow></mpadded></mrow><annotation encoding="application/x-tex">
+<p><link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/npm/katex@0.15.2/dist/katex.min.css" data-external="1"><span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mi>A</mi><mo>=</mo><mi>U</mi><mi mathvariant="normal">diag</mi><mo>⁡</mo><mo stretchy="false">(</mo><mi>S</mi><mo stretchy="false">)</mo><msup><mi>V</mi><mi>H</mi></msup><mpadded width="0px"><mrow><mspace width="2em"></mspace><mi>U</mi><mo>∈</mo><msup><mi mathvariant="double-struck">K</mi><mrow><mi>m</mi><mo>×</mo><mi>k</mi></mrow></msup><mo separator="true">,</mo><mi>S</mi><mo>∈</mo><msup><mi mathvariant="double-struck">R</mi><mi>k</mi></msup><mo separator="true">,</mo><mi>V</mi><mo>∈</mo><msup><mi mathvariant="double-struck">K</mi><mrow><mi>k</mi><mo>×</mo><mi>n</mi></mrow></msup></mrow></mpadded></mrow><annotation encoding="application/x-tex">
 A = U \operatorname{diag}(S) V^{H} \mathrlap{\qquad U \in \mathbb{K}^{m \times k}, S \in \mathbb{R}^k, V \in \mathbb{K}^{k \times n}}
-</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.68333em;vertical-align:0em;"></span><span class="mord mathnormal">A</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span></span><span class="base"><span class="strut" style="height:1.149108em;vertical-align:-0.25em;"></span><span class="mord mathnormal" style="margin-right:0.10903em;">U</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mop"><span class="mord mathrm" style="margin-right:0.01389em;">diag</span></span><span class="mopen">(</span><span class="mord mathnormal" style="margin-right:0.05764em;">S</span><span class="mclose">)</span><span class="mord"><span class="mord mathnormal" style="margin-right:0.22222em;">V</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8913309999999999em;"><span style="top:-3.113em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight" style="margin-right:0.08125em;">H</span></span></span></span></span></span></span></span></span><span class="mord vbox"><span class="thinbox"><span class="rlap"><span class="strut" style="height:1.093548em;vertical-align:-0.19444em;"></span><span class="inner"><span class="mord"><span class="mspace" style="margin-right:2em;"></span><span class="mord mathnormal" style="margin-right:0.10903em;">U</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span><span class="mrel">∈</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span><span class="mord"><span class="mord mathbb">K</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8991079999999999em;"><span style="top:-3.113em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">m</span><span class="mbin mtight">×</span><span class="mord mathnormal mtight" style="margin-right:0.03148em;">k</span></span></span></span></span></span></span></span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord mathnormal" style="margin-right:0.05764em;">S</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span><span class="mrel">∈</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span><span class="mord"><span class="mord mathbb">R</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8991079999999999em;"><span style="top:-3.113em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight" style="margin-right:0.03148em;">k</span></span></span></span></span></span></span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord mathnormal" style="margin-right:0.22222em;">V</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span><span class="mrel">∈</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span><span class="mord"><span class="mord mathbb">K</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8991079999999999em;"><span style="top:-3.113em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight" style="margin-right:0.03148em;">k</span><span class="mbin mtight">×</span><span class="mord mathnormal mtight">n</span></span></span></span></span></span></span></span></span></span></span><span class="fix"></span></span></span></span></span></span></span></span></p>
+</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6833em;"></span><span class="mord mathnormal">A</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:1.1491em;vertical-align:-0.25em;"></span><span class="mord mathnormal" style="margin-right:0.10903em;">U</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mop"><span class="mord mathrm" style="margin-right:0.01389em;">diag</span></span><span class="mopen">(</span><span class="mord mathnormal" style="margin-right:0.05764em;">S</span><span class="mclose">)</span><span class="mord"><span class="mord mathnormal" style="margin-right:0.22222em;">V</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8913em;"><span style="top:-3.113em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight" style="margin-right:0.08125em;">H</span></span></span></span></span></span></span></span></span><span class="mord vbox"><span class="thinbox"><span class="rlap"><span class="strut" style="height:1.0935em;vertical-align:-0.1944em;"></span><span class="inner"><span class="mord"><span class="mspace" style="margin-right:2em;"></span><span class="mord mathnormal" style="margin-right:0.10903em;">U</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">∈</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mord"><span class="mord mathbb">K</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8991em;"><span style="top:-3.113em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">m</span><span class="mbin mtight">×</span><span class="mord mathnormal mtight" style="margin-right:0.03148em;">k</span></span></span></span></span></span></span></span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.05764em;">S</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">∈</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mord"><span class="mord mathbb">R</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8991em;"><span style="top:-3.113em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight" style="margin-right:0.03148em;">k</span></span></span></span></span></span></span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.22222em;">V</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">∈</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mord"><span class="mord mathbb">K</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8991em;"><span style="top:-3.113em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight" style="margin-right:0.03148em;">k</span><span class="mbin mtight">×</span><span class="mord mathnormal mtight">n</span></span></span></span></span></span></span></span></span></span></span><span class="fix"></span></span></span></span></span></span></span></span></p>
 <p>where .</p>
 <p>In this case,  and  also have orthonormal columns.
 Supports input of float, double, cfloat and cdouble dtypes.</p>
@@ -239,13 +128,15 @@ the output has the same batch dimensions.</p>
 which corresponds to , ,  above.</p>
 <p>The singular values are returned in descending order.
 The parameter <code>full_matrices</code> chooses between the full (default) and reduced SVD.</p>
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p>When <code>full_matrices=TRUE</code>, the gradients with respect to <code>U[..., :, min(m, n):]</code>
 and <code>Vh[..., min(m, n):, :]</code> will be ignored, as those vectors can be arbitrary bases
 of the corresponding subspaces.</p>
-    <h2 class="hasAnchor" id="warnings"><a class="anchor" href="#warnings"></a>Warnings</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="warnings">Warnings<a class="anchor" aria-label="anchor" href="#warnings"></a></h2>
     
 
 <p>The returned tensors <code>U</code> and <code>V</code> are not unique, nor are they continuous with
@@ -267,103 +158,95 @@ the gradient will be numerically unstable, as it depends on the singular values
 .
 The gradient will also be numerically unstable when <code>A</code> has small singular
 values, as it also depends on the computaiton of .</p>
-    <h2 class="hasAnchor" id="see-also"><a class="anchor" href="#see-also"></a>See also</h2>
-
-    <div class='dont-index'>
-<ul>
-<li><p><code><a href='linalg_svdvals.html'>linalg_svdvals()</a></code> computes only the singular values.
-Unlike <code>linalg_svd()</code>, the gradients of <code><a href='linalg_svdvals.html'>linalg_svdvals()</a></code> are always
+    </div>
+    <div class="section level2">
+    <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
+    <div class="dont-index">
+<ul><li><p><code><a href="linalg_svdvals.html">linalg_svdvals()</a></code> computes only the singular values.
+Unlike <code>linalg_svd()</code>, the gradients of <code><a href="linalg_svdvals.html">linalg_svdvals()</a></code> are always
 numerically stable.</p></li>
-<li><p><code><a href='linalg_eig.html'>linalg_eig()</a></code> for a function that computes another type of spectral
+<li><p><code><a href="linalg_eig.html">linalg_eig()</a></code> for a function that computes another type of spectral
 decomposition of a matrix. The eigendecomposition works just on on square matrices.</p></li>
-<li><p><code><a href='linalg_eigh.html'>linalg_eigh()</a></code> for a (faster) function that computes the eigenvalue decomposition
+<li><p><code><a href="linalg_eigh.html">linalg_eigh()</a></code> for a (faster) function that computes the eigenvalue decomposition
 for Hermitian and symmetric matrices.</p></li>
-<li><p><code><a href='linalg_qr.html'>linalg_qr()</a></code> for another (much faster) decomposition that works on general
+<li><p><code><a href="linalg_qr.html">linalg_qr()</a></code> for another (much faster) decomposition that works on general
 matrices.</p></li>
-</ul>
-
-<p>Other linalg: 
-<code><a href='linalg_cholesky_ex.html'>linalg_cholesky_ex</a>()</code>,
-<code><a href='linalg_cholesky.html'>linalg_cholesky</a>()</code>,
-<code><a href='linalg_det.html'>linalg_det</a>()</code>,
-<code><a href='linalg_eigh.html'>linalg_eigh</a>()</code>,
-<code><a href='linalg_eigvalsh.html'>linalg_eigvalsh</a>()</code>,
-<code><a href='linalg_eigvals.html'>linalg_eigvals</a>()</code>,
-<code><a href='linalg_eig.html'>linalg_eig</a>()</code>,
-<code><a href='linalg_householder_product.html'>linalg_householder_product</a>()</code>,
-<code><a href='linalg_inv_ex.html'>linalg_inv_ex</a>()</code>,
-<code><a href='linalg_inv.html'>linalg_inv</a>()</code>,
-<code><a href='linalg_lstsq.html'>linalg_lstsq</a>()</code>,
-<code><a href='linalg_matrix_norm.html'>linalg_matrix_norm</a>()</code>,
-<code><a href='linalg_matrix_power.html'>linalg_matrix_power</a>()</code>,
-<code><a href='linalg_matrix_rank.html'>linalg_matrix_rank</a>()</code>,
-<code><a href='linalg_multi_dot.html'>linalg_multi_dot</a>()</code>,
-<code><a href='linalg_norm.html'>linalg_norm</a>()</code>,
-<code><a href='linalg_pinv.html'>linalg_pinv</a>()</code>,
-<code><a href='linalg_qr.html'>linalg_qr</a>()</code>,
-<code><a href='linalg_slogdet.html'>linalg_slogdet</a>()</code>,
-<code><a href='linalg_solve.html'>linalg_solve</a>()</code>,
-<code><a href='linalg_svdvals.html'>linalg_svdvals</a>()</code>,
-<code><a href='linalg_tensorinv.html'>linalg_tensorinv</a>()</code>,
-<code><a href='linalg_tensorsolve.html'>linalg_tensorsolve</a>()</code>,
-<code><a href='linalg_vector_norm.html'>linalg_vector_norm</a>()</code></p></div>
+</ul><p>Other linalg: 
+<code><a href="linalg_cholesky_ex.html">linalg_cholesky_ex</a>()</code>,
+<code><a href="linalg_cholesky.html">linalg_cholesky</a>()</code>,
+<code><a href="linalg_det.html">linalg_det</a>()</code>,
+<code><a href="linalg_eigh.html">linalg_eigh</a>()</code>,
+<code><a href="linalg_eigvalsh.html">linalg_eigvalsh</a>()</code>,
+<code><a href="linalg_eigvals.html">linalg_eigvals</a>()</code>,
+<code><a href="linalg_eig.html">linalg_eig</a>()</code>,
+<code><a href="linalg_householder_product.html">linalg_householder_product</a>()</code>,
+<code><a href="linalg_inv_ex.html">linalg_inv_ex</a>()</code>,
+<code><a href="linalg_inv.html">linalg_inv</a>()</code>,
+<code><a href="linalg_lstsq.html">linalg_lstsq</a>()</code>,
+<code><a href="linalg_matrix_norm.html">linalg_matrix_norm</a>()</code>,
+<code><a href="linalg_matrix_power.html">linalg_matrix_power</a>()</code>,
+<code><a href="linalg_matrix_rank.html">linalg_matrix_rank</a>()</code>,
+<code><a href="linalg_multi_dot.html">linalg_multi_dot</a>()</code>,
+<code><a href="linalg_norm.html">linalg_norm</a>()</code>,
+<code><a href="linalg_pinv.html">linalg_pinv</a>()</code>,
+<code><a href="linalg_qr.html">linalg_qr</a>()</code>,
+<code><a href="linalg_slogdet.html">linalg_slogdet</a>()</code>,
+<code><a href="linalg_solve.html">linalg_solve</a>()</code>,
+<code><a href="linalg_svdvals.html">linalg_svdvals</a>()</code>,
+<code><a href="linalg_tensorinv.html">linalg_tensorinv</a>()</code>,
+<code><a href="linalg_tensorsolve.html">linalg_tensorsolve</a>()</code>,
+<code><a href="linalg_vector_norm.html">linalg_vector_norm</a>()</code></p></div>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>5</span>, <span class='fl'>3</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>linalg_svd</span><span class='op'>(</span><span class='va'>a</span>, full_matrices<span class='op'>=</span><span class='cn'>FALSE</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [[1]]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.5399 -0.1517  0.4495</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.7209  0.2894  0.4771</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.3557  0.9138 -0.1021</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.1677  0.2393  0.0040</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.1847  0.0301  0.7483</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{5,3} ]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [[2]]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  3.2561</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  2.3965</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1.1569</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{3} ]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [[3]]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.7258  0.1916  0.6607</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.3087  0.9490  0.0639</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.6147  0.2504 -0.7479</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{3,3} ]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">5</span>, <span class="fl">3</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">linalg_svd</span><span class="op">(</span><span class="va">a</span>, full_matrices <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [[1]]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.5040  0.5134  0.0992</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.6428  0.1399  0.3054</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.2819  0.5506 -0.5038</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.2576  0.2464 -0.5827</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.4325 -0.5941 -0.5509</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{5,3} ]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [[2]]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2.2731</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.4690</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.5087</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3} ]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [[3]]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.6751 -0.7345 -0.0694</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.5571  0.5692 -0.6047</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.4836 -0.3695 -0.7935</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3,3} ]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Computes the singular values of a matrix.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/linalg.R'><code>R/linalg.R</code></a></small>
-    <div class="hidden name"><code>linalg_svdvals.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Computes the singular values of a matrix.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/linalg.R" class="external-link"><code>R/linalg.R</code></a></small>
+      <div class="d-none name"><code>linalg_svdvals.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Supports input of float, double, cfloat and cdouble dtypes.
 Also supports batches of matrices, and if <code>A</code> is a batch of matrices then
 the output has the same batch dimensions.
 The singular values are returned in descending order.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>linalg_svdvals</span><span class='op'>(</span><span class='va'>A</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>A</th>
-      <td><p>(Tensor): tensor of shape <code>(*, m, n)</code> where <code>*</code> is zero or more batch dimensions.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">linalg_svdvals</span><span class="op">(</span><span class="va">A</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>A</dt>
+<dd><p>(Tensor): tensor of shape <code>(*, m, n)</code> where <code>*</code> is zero or more batch dimensions.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
     <p>A real-valued tensor, even when <code>A</code> is complex.</p>
-    <h2 class="hasAnchor" id="see-also"><a class="anchor" href="#see-also"></a>See also</h2>
-
-    <div class='dont-index'><p><code><a href='linalg_svd.html'>linalg_svd()</a></code> computes the full singular value decomposition.</p>
+    </div>
+    <div class="section level2">
+    <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
+    <div class="dont-index"><p><code><a href="linalg_svd.html">linalg_svd()</a></code> computes the full singular value decomposition.</p>
 <p>Other linalg: 
-<code><a href='linalg_cholesky_ex.html'>linalg_cholesky_ex</a>()</code>,
-<code><a href='linalg_cholesky.html'>linalg_cholesky</a>()</code>,
-<code><a href='linalg_det.html'>linalg_det</a>()</code>,
-<code><a href='linalg_eigh.html'>linalg_eigh</a>()</code>,
-<code><a href='linalg_eigvalsh.html'>linalg_eigvalsh</a>()</code>,
-<code><a href='linalg_eigvals.html'>linalg_eigvals</a>()</code>,
-<code><a href='linalg_eig.html'>linalg_eig</a>()</code>,
-<code><a href='linalg_householder_product.html'>linalg_householder_product</a>()</code>,
-<code><a href='linalg_inv_ex.html'>linalg_inv_ex</a>()</code>,
-<code><a href='linalg_inv.html'>linalg_inv</a>()</code>,
-<code><a href='linalg_lstsq.html'>linalg_lstsq</a>()</code>,
-<code><a href='linalg_matrix_norm.html'>linalg_matrix_norm</a>()</code>,
-<code><a href='linalg_matrix_power.html'>linalg_matrix_power</a>()</code>,
-<code><a href='linalg_matrix_rank.html'>linalg_matrix_rank</a>()</code>,
-<code><a href='linalg_multi_dot.html'>linalg_multi_dot</a>()</code>,
-<code><a href='linalg_norm.html'>linalg_norm</a>()</code>,
-<code><a href='linalg_pinv.html'>linalg_pinv</a>()</code>,
-<code><a href='linalg_qr.html'>linalg_qr</a>()</code>,
-<code><a href='linalg_slogdet.html'>linalg_slogdet</a>()</code>,
-<code><a href='linalg_solve.html'>linalg_solve</a>()</code>,
-<code><a href='linalg_svd.html'>linalg_svd</a>()</code>,
-<code><a href='linalg_tensorinv.html'>linalg_tensorinv</a>()</code>,
-<code><a href='linalg_tensorsolve.html'>linalg_tensorsolve</a>()</code>,
-<code><a href='linalg_vector_norm.html'>linalg_vector_norm</a>()</code></p></div>
+<code><a href="linalg_cholesky_ex.html">linalg_cholesky_ex</a>()</code>,
+<code><a href="linalg_cholesky.html">linalg_cholesky</a>()</code>,
+<code><a href="linalg_det.html">linalg_det</a>()</code>,
+<code><a href="linalg_eigh.html">linalg_eigh</a>()</code>,
+<code><a href="linalg_eigvalsh.html">linalg_eigvalsh</a>()</code>,
+<code><a href="linalg_eigvals.html">linalg_eigvals</a>()</code>,
+<code><a href="linalg_eig.html">linalg_eig</a>()</code>,
+<code><a href="linalg_householder_product.html">linalg_householder_product</a>()</code>,
+<code><a href="linalg_inv_ex.html">linalg_inv_ex</a>()</code>,
+<code><a href="linalg_inv.html">linalg_inv</a>()</code>,
+<code><a href="linalg_lstsq.html">linalg_lstsq</a>()</code>,
+<code><a href="linalg_matrix_norm.html">linalg_matrix_norm</a>()</code>,
+<code><a href="linalg_matrix_power.html">linalg_matrix_power</a>()</code>,
+<code><a href="linalg_matrix_rank.html">linalg_matrix_rank</a>()</code>,
+<code><a href="linalg_multi_dot.html">linalg_multi_dot</a>()</code>,
+<code><a href="linalg_norm.html">linalg_norm</a>()</code>,
+<code><a href="linalg_pinv.html">linalg_pinv</a>()</code>,
+<code><a href="linalg_qr.html">linalg_qr</a>()</code>,
+<code><a href="linalg_slogdet.html">linalg_slogdet</a>()</code>,
+<code><a href="linalg_solve.html">linalg_solve</a>()</code>,
+<code><a href="linalg_svd.html">linalg_svd</a>()</code>,
+<code><a href="linalg_tensorinv.html">linalg_tensorinv</a>()</code>,
+<code><a href="linalg_tensorsolve.html">linalg_tensorsolve</a>()</code>,
+<code><a href="linalg_vector_norm.html">linalg_vector_norm</a>()</code></p></div>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>A</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>5</span>, <span class='fl'>3</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>S</span> <span class='op'>&lt;-</span> <span class='fu'>linalg_svdvals</span><span class='op'>(</span><span class='va'>A</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>S</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  2.4415</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1.9280</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1.1917</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{3} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">A</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">5</span>, <span class="fl">3</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">S</span> <span class="op">&lt;-</span> <span class="fu">linalg_svdvals</span><span class="op">(</span><span class="va">A</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">S</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2.9854</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.3161</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.4644</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer>
-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
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+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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index a3f53b1eb..8928a12c5 100644
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Computes the multiplicative inverse of <code><a href='torch_tensordot.html'>torch_tensordot()</a></code></h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/linalg.R'><code>R/linalg.R</code></a></small>
-    <div class="hidden name"><code>linalg_tensorinv.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Computes the multiplicative inverse of <code><a href="torch_tensordot.html">torch_tensordot()</a></code></h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/linalg.R" class="external-link"><code>R/linalg.R</code></a></small>
+      <div class="d-none name"><code>linalg_tensorinv.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>If <code>m</code> is the product of the first <code>ind</code> dimensions of <code>A</code> and <code>n</code> is the product of
 the rest of the dimensions, this function expects <code>m</code> and <code>n</code> to be equal.
 If this is the case, it computes a tensor <code>X</code> such that
 <code>tensordot(A, X, ind)</code> is the identity matrix in dimension <code>m</code>.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>linalg_tensorinv</span><span class='op'>(</span><span class='va'>A</span>, ind <span class='op'>=</span> <span class='fl'>3L</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>A</th>
-      <td><p>(Tensor): tensor to invert.</p></td>
-    </tr>
-    <tr>
-      <th>ind</th>
-      <td><p>(int): index at which to compute the inverse of <code><a href='torch_tensordot.html'>torch_tensordot()</a></code>. Default: <code>3</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">linalg_tensorinv</span><span class="op">(</span><span class="va">A</span>, ind <span class="op">=</span> <span class="fl">3L</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>A</dt>
+<dd><p>(Tensor): tensor to invert.</p></dd>
+<dt>ind</dt>
+<dd><p>(int): index at which to compute the inverse of <code><a href="torch_tensordot.html">torch_tensordot()</a></code>. Default: <code>3</code>.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>Supports input of float, double, cfloat and cdouble dtypes.</p>
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    <p>Consider using <code><a href='linalg_tensorsolve.html'>linalg_tensorsolve()</a></code> if possible for multiplying a tensor on the left
+    </div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
+    <p>Consider using <code><a href="linalg_tensorsolve.html">linalg_tensorsolve()</a></code> if possible for multiplying a tensor on the left
 by the tensor inverse as <code>linalg_tensorsolve(A, B) == torch_tensordot(linalg_tensorinv(A), B))</code></p>
-<p>It is always prefered to use <code><a href='linalg_tensorsolve.html'>linalg_tensorsolve()</a></code> when possible, as it is faster and more
+<p>It is always prefered to use <code><a href="linalg_tensorsolve.html">linalg_tensorsolve()</a></code> when possible, as it is faster and more
 numerically stable than computing the pseudoinverse explicitly.</p>
-    <h2 class="hasAnchor" id="see-also"><a class="anchor" href="#see-also"></a>See also</h2>
-
-    <div class='dont-index'>
-<ul>
-<li><p><code><a href='linalg_tensorsolve.html'>linalg_tensorsolve()</a></code> computes <code>torch_tensordot(linalg_tensorinv(A), B))</code>.</p></li>
-</ul>
-
-<p>Other linalg: 
-<code><a href='linalg_cholesky_ex.html'>linalg_cholesky_ex</a>()</code>,
-<code><a href='linalg_cholesky.html'>linalg_cholesky</a>()</code>,
-<code><a href='linalg_det.html'>linalg_det</a>()</code>,
-<code><a href='linalg_eigh.html'>linalg_eigh</a>()</code>,
-<code><a href='linalg_eigvalsh.html'>linalg_eigvalsh</a>()</code>,
-<code><a href='linalg_eigvals.html'>linalg_eigvals</a>()</code>,
-<code><a href='linalg_eig.html'>linalg_eig</a>()</code>,
-<code><a href='linalg_householder_product.html'>linalg_householder_product</a>()</code>,
-<code><a href='linalg_inv_ex.html'>linalg_inv_ex</a>()</code>,
-<code><a href='linalg_inv.html'>linalg_inv</a>()</code>,
-<code><a href='linalg_lstsq.html'>linalg_lstsq</a>()</code>,
-<code><a href='linalg_matrix_norm.html'>linalg_matrix_norm</a>()</code>,
-<code><a href='linalg_matrix_power.html'>linalg_matrix_power</a>()</code>,
-<code><a href='linalg_matrix_rank.html'>linalg_matrix_rank</a>()</code>,
-<code><a href='linalg_multi_dot.html'>linalg_multi_dot</a>()</code>,
-<code><a href='linalg_norm.html'>linalg_norm</a>()</code>,
-<code><a href='linalg_pinv.html'>linalg_pinv</a>()</code>,
-<code><a href='linalg_qr.html'>linalg_qr</a>()</code>,
-<code><a href='linalg_slogdet.html'>linalg_slogdet</a>()</code>,
-<code><a href='linalg_solve.html'>linalg_solve</a>()</code>,
-<code><a href='linalg_svdvals.html'>linalg_svdvals</a>()</code>,
-<code><a href='linalg_svd.html'>linalg_svd</a>()</code>,
-<code><a href='linalg_tensorsolve.html'>linalg_tensorsolve</a>()</code>,
-<code><a href='linalg_vector_norm.html'>linalg_vector_norm</a>()</code></p></div>
+    </div>
+    <div class="section level2">
+    <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
+    <div class="dont-index">
+<ul><li><p><code><a href="linalg_tensorsolve.html">linalg_tensorsolve()</a></code> computes <code>torch_tensordot(linalg_tensorinv(A), B))</code>.</p></li>
+</ul><p>Other linalg: 
+<code><a href="linalg_cholesky_ex.html">linalg_cholesky_ex</a>()</code>,
+<code><a href="linalg_cholesky.html">linalg_cholesky</a>()</code>,
+<code><a href="linalg_det.html">linalg_det</a>()</code>,
+<code><a href="linalg_eigh.html">linalg_eigh</a>()</code>,
+<code><a href="linalg_eigvalsh.html">linalg_eigvalsh</a>()</code>,
+<code><a href="linalg_eigvals.html">linalg_eigvals</a>()</code>,
+<code><a href="linalg_eig.html">linalg_eig</a>()</code>,
+<code><a href="linalg_householder_product.html">linalg_householder_product</a>()</code>,
+<code><a href="linalg_inv_ex.html">linalg_inv_ex</a>()</code>,
+<code><a href="linalg_inv.html">linalg_inv</a>()</code>,
+<code><a href="linalg_lstsq.html">linalg_lstsq</a>()</code>,
+<code><a href="linalg_matrix_norm.html">linalg_matrix_norm</a>()</code>,
+<code><a href="linalg_matrix_power.html">linalg_matrix_power</a>()</code>,
+<code><a href="linalg_matrix_rank.html">linalg_matrix_rank</a>()</code>,
+<code><a href="linalg_multi_dot.html">linalg_multi_dot</a>()</code>,
+<code><a href="linalg_norm.html">linalg_norm</a>()</code>,
+<code><a href="linalg_pinv.html">linalg_pinv</a>()</code>,
+<code><a href="linalg_qr.html">linalg_qr</a>()</code>,
+<code><a href="linalg_slogdet.html">linalg_slogdet</a>()</code>,
+<code><a href="linalg_solve.html">linalg_solve</a>()</code>,
+<code><a href="linalg_svdvals.html">linalg_svdvals</a>()</code>,
+<code><a href="linalg_svd.html">linalg_svd</a>()</code>,
+<code><a href="linalg_tensorsolve.html">linalg_tensorsolve</a>()</code>,
+<code><a href="linalg_vector_norm.html">linalg_vector_norm</a>()</code></p></div>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>A</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_eye.html'>torch_eye</a></span><span class='op'>(</span><span class='fl'>4</span> <span class='op'>*</span> <span class='fl'>6</span><span class='op'>)</span><span class='op'>$</span><span class='fu'>reshape</span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span>, <span class='fl'>6</span>, <span class='fl'>8</span>, <span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>Ainv</span> <span class='op'>&lt;-</span> <span class='fu'>linalg_tensorinv</span><span class='op'>(</span><span class='va'>A</span>, ind<span class='op'>=</span><span class='fl'>3</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>Ainv</span><span class='op'>$</span><span class='va'>shape</span></span>
-<span class='r-in'><span class='va'>B</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>4</span>, <span class='fl'>6</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'><a href='torch_allclose.html'>torch_allclose</a></span><span class='op'>(</span><span class='fu'><a href='torch_tensordot.html'>torch_tensordot</a></span><span class='op'>(</span><span class='va'>Ainv</span>, <span class='va'>B</span><span class='op'>)</span>, <span class='fu'><a href='linalg_tensorsolve.html'>linalg_tensorsolve</a></span><span class='op'>(</span><span class='va'>A</span>, <span class='va'>B</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>A</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>4</span>, <span class='fl'>4</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>Atensorinv</span><span class='op'>&lt;-</span> <span class='fu'>linalg_tensorinv</span><span class='op'>(</span><span class='va'>A</span>, <span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>Ainv</span> <span class='op'>&lt;-</span> <span class='fu'><a href='linalg_inv.html'>linalg_inv</a></span><span class='op'>(</span><span class='va'>A</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'><a href='torch_allclose.html'>torch_allclose</a></span><span class='op'>(</span><span class='va'>Atensorinv</span>, <span class='va'>Ainv</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [1] TRUE</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">A</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_eye.html">torch_eye</a></span><span class="op">(</span><span class="fl">4</span> <span class="op">*</span> <span class="fl">6</span><span class="op">)</span><span class="op">$</span><span class="fu">reshape</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span>, <span class="fl">6</span>, <span class="fl">8</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">Ainv</span> <span class="op">&lt;-</span> <span class="fu">linalg_tensorinv</span><span class="op">(</span><span class="va">A</span>, ind <span class="op">=</span> <span class="fl">3</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">Ainv</span><span class="op">$</span><span class="va">shape</span></span>
+<span class="r-in"><span class="va">B</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">4</span>, <span class="fl">6</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu"><a href="torch_allclose.html">torch_allclose</a></span><span class="op">(</span><span class="fu"><a href="torch_tensordot.html">torch_tensordot</a></span><span class="op">(</span><span class="va">Ainv</span>, <span class="va">B</span><span class="op">)</span>, <span class="fu"><a href="linalg_tensorsolve.html">linalg_tensorsolve</a></span><span class="op">(</span><span class="va">A</span>, <span class="va">B</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">A</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">4</span>, <span class="fl">4</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">Atensorinv</span> <span class="op">&lt;-</span> <span class="fu">linalg_tensorinv</span><span class="op">(</span><span class="va">A</span>, <span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">Ainv</span> <span class="op">&lt;-</span> <span class="fu"><a href="linalg_inv.html">linalg_inv</a></span><span class="op">(</span><span class="va">A</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu"><a href="torch_allclose.html">torch_allclose</a></span><span class="op">(</span><span class="va">Atensorinv</span>, <span class="va">Ainv</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] TRUE</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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-      <div class="copyright">
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+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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index 4e98549d2..c4d7775b6 100644
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+    
+  </div>
+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Computes the solution <code>X</code> to the system <code>torch_tensordot(A, X) = B</code>.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/linalg.R'><code>R/linalg.R</code></a></small>
-    <div class="hidden name"><code>linalg_tensorsolve.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Computes the solution <code>X</code> to the system <code>torch_tensordot(A, X) = B</code>.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/linalg.R" class="external-link"><code>R/linalg.R</code></a></small>
+      <div class="d-none name"><code>linalg_tensorsolve.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>If <code>m</code> is the product of the first <code>B</code>\ <code>.ndim</code>  dimensions of <code>A</code> and
 <code>n</code> is the product of the rest of the dimensions, this function expects <code>m</code> and <code>n</code> to be equal.
 The returned tensor <code>x</code> satisfies
 <code>tensordot(A, x, dims=x$ndim) == B</code>.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>linalg_tensorsolve</span><span class='op'>(</span><span class='va'>A</span>, <span class='va'>B</span>, dims <span class='op'>=</span> <span class='cn'>NULL</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>A</th>
-      <td><p>(Tensor): tensor to solve for.</p></td>
-    </tr>
-    <tr>
-      <th>B</th>
-      <td><p>(Tensor): the solution</p></td>
-    </tr>
-    <tr>
-      <th>dims</th>
-      <td><p>(Tupleint, optional): dimensions of <code>A</code> to be moved.
-If <code>NULL</code>, no dimensions are moved. Default: <code>NULL</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">linalg_tensorsolve</span><span class="op">(</span><span class="va">A</span>, <span class="va">B</span>, dims <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>A</dt>
+<dd><p>(Tensor): tensor to solve for.</p></dd>
+<dt>B</dt>
+<dd><p>(Tensor): the solution</p></dd>
+<dt>dims</dt>
+<dd><p>(Tupleint, optional): dimensions of <code>A</code> to be moved.
+If <code>NULL</code>, no dimensions are moved. Default: <code>NULL</code>.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>If <code>dims</code> is specified, <code>A</code> will be reshaped as
 <code>A = movedim(A, dims, seq(len(dims) - A$ndim + 1, 0))</code></p>
 <p>Supports inputs of float, double, cfloat and cdouble dtypes.</p>
-    <h2 class="hasAnchor" id="see-also"><a class="anchor" href="#see-also"></a>See also</h2>
-
-    <div class='dont-index'>
-<ul>
-<li><p><code><a href='linalg_tensorinv.html'>linalg_tensorinv()</a></code> computes the multiplicative inverse of
-<code><a href='torch_tensordot.html'>torch_tensordot()</a></code>.</p></li>
-</ul>
-
-<p>Other linalg: 
-<code><a href='linalg_cholesky_ex.html'>linalg_cholesky_ex</a>()</code>,
-<code><a href='linalg_cholesky.html'>linalg_cholesky</a>()</code>,
-<code><a href='linalg_det.html'>linalg_det</a>()</code>,
-<code><a href='linalg_eigh.html'>linalg_eigh</a>()</code>,
-<code><a href='linalg_eigvalsh.html'>linalg_eigvalsh</a>()</code>,
-<code><a href='linalg_eigvals.html'>linalg_eigvals</a>()</code>,
-<code><a href='linalg_eig.html'>linalg_eig</a>()</code>,
-<code><a href='linalg_householder_product.html'>linalg_householder_product</a>()</code>,
-<code><a href='linalg_inv_ex.html'>linalg_inv_ex</a>()</code>,
-<code><a href='linalg_inv.html'>linalg_inv</a>()</code>,
-<code><a href='linalg_lstsq.html'>linalg_lstsq</a>()</code>,
-<code><a href='linalg_matrix_norm.html'>linalg_matrix_norm</a>()</code>,
-<code><a href='linalg_matrix_power.html'>linalg_matrix_power</a>()</code>,
-<code><a href='linalg_matrix_rank.html'>linalg_matrix_rank</a>()</code>,
-<code><a href='linalg_multi_dot.html'>linalg_multi_dot</a>()</code>,
-<code><a href='linalg_norm.html'>linalg_norm</a>()</code>,
-<code><a href='linalg_pinv.html'>linalg_pinv</a>()</code>,
-<code><a href='linalg_qr.html'>linalg_qr</a>()</code>,
-<code><a href='linalg_slogdet.html'>linalg_slogdet</a>()</code>,
-<code><a href='linalg_solve.html'>linalg_solve</a>()</code>,
-<code><a href='linalg_svdvals.html'>linalg_svdvals</a>()</code>,
-<code><a href='linalg_svd.html'>linalg_svd</a>()</code>,
-<code><a href='linalg_tensorinv.html'>linalg_tensorinv</a>()</code>,
-<code><a href='linalg_vector_norm.html'>linalg_vector_norm</a>()</code></p></div>
+    </div>
+    <div class="section level2">
+    <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
+    <div class="dont-index">
+<ul><li><p><code><a href="linalg_tensorinv.html">linalg_tensorinv()</a></code> computes the multiplicative inverse of
+<code><a href="torch_tensordot.html">torch_tensordot()</a></code>.</p></li>
+</ul><p>Other linalg: 
+<code><a href="linalg_cholesky_ex.html">linalg_cholesky_ex</a>()</code>,
+<code><a href="linalg_cholesky.html">linalg_cholesky</a>()</code>,
+<code><a href="linalg_det.html">linalg_det</a>()</code>,
+<code><a href="linalg_eigh.html">linalg_eigh</a>()</code>,
+<code><a href="linalg_eigvalsh.html">linalg_eigvalsh</a>()</code>,
+<code><a href="linalg_eigvals.html">linalg_eigvals</a>()</code>,
+<code><a href="linalg_eig.html">linalg_eig</a>()</code>,
+<code><a href="linalg_householder_product.html">linalg_householder_product</a>()</code>,
+<code><a href="linalg_inv_ex.html">linalg_inv_ex</a>()</code>,
+<code><a href="linalg_inv.html">linalg_inv</a>()</code>,
+<code><a href="linalg_lstsq.html">linalg_lstsq</a>()</code>,
+<code><a href="linalg_matrix_norm.html">linalg_matrix_norm</a>()</code>,
+<code><a href="linalg_matrix_power.html">linalg_matrix_power</a>()</code>,
+<code><a href="linalg_matrix_rank.html">linalg_matrix_rank</a>()</code>,
+<code><a href="linalg_multi_dot.html">linalg_multi_dot</a>()</code>,
+<code><a href="linalg_norm.html">linalg_norm</a>()</code>,
+<code><a href="linalg_pinv.html">linalg_pinv</a>()</code>,
+<code><a href="linalg_qr.html">linalg_qr</a>()</code>,
+<code><a href="linalg_slogdet.html">linalg_slogdet</a>()</code>,
+<code><a href="linalg_solve.html">linalg_solve</a>()</code>,
+<code><a href="linalg_svdvals.html">linalg_svdvals</a>()</code>,
+<code><a href="linalg_svd.html">linalg_svd</a>()</code>,
+<code><a href="linalg_tensorinv.html">linalg_tensorinv</a>()</code>,
+<code><a href="linalg_vector_norm.html">linalg_vector_norm</a>()</code></p></div>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>A</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_eye.html'>torch_eye</a></span><span class='op'>(</span><span class='fl'>2</span> <span class='op'>*</span> <span class='fl'>3</span> <span class='op'>*</span> <span class='fl'>4</span><span class='op'>)</span><span class='op'>$</span><span class='fu'>reshape</span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>2</span> <span class='op'>*</span> <span class='fl'>3</span>, <span class='fl'>4</span>, <span class='fl'>2</span>, <span class='fl'>3</span>, <span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>B</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>2</span> <span class='op'>*</span> <span class='fl'>3</span>, <span class='fl'>4</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>X</span> <span class='op'>&lt;-</span> <span class='fu'>linalg_tensorsolve</span><span class='op'>(</span><span class='va'>A</span>, <span class='va'>B</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>X</span><span class='op'>$</span><span class='va'>shape</span></span>
-<span class='r-in'><span class='fu'><a href='torch_allclose.html'>torch_allclose</a></span><span class='op'>(</span><span class='fu'><a href='torch_tensordot.html'>torch_tensordot</a></span><span class='op'>(</span><span class='va'>A</span>, <span class='va'>X</span>, dims<span class='op'>=</span><span class='va'>X</span><span class='op'>$</span><span class='va'>ndim</span><span class='op'>)</span>, <span class='va'>B</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>A</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>6</span>, <span class='fl'>4</span>, <span class='fl'>4</span>, <span class='fl'>3</span>, <span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>B</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>4</span>, <span class='fl'>3</span>, <span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>X</span> <span class='op'>&lt;-</span> <span class='fu'>linalg_tensorsolve</span><span class='op'>(</span><span class='va'>A</span>, <span class='va'>B</span>, dims<span class='op'>=</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>A</span> <span class='op'>&lt;-</span> <span class='va'>A</span><span class='op'>$</span><span class='fu'>permute</span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>4</span>, <span class='fl'>5</span>, <span class='fl'>1</span>, <span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'><a href='torch_allclose.html'>torch_allclose</a></span><span class='op'>(</span><span class='fu'><a href='torch_tensordot.html'>torch_tensordot</a></span><span class='op'>(</span><span class='va'>A</span>, <span class='va'>X</span>, dims<span class='op'>=</span><span class='va'>X</span><span class='op'>$</span><span class='va'>ndim</span><span class='op'>)</span>, <span class='va'>B</span>, atol<span class='op'>=</span><span class='fl'>1e-6</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [1] FALSE</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">A</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_eye.html">torch_eye</a></span><span class="op">(</span><span class="fl">2</span> <span class="op">*</span> <span class="fl">3</span> <span class="op">*</span> <span class="fl">4</span><span class="op">)</span><span class="op">$</span><span class="fu">reshape</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span> <span class="op">*</span> <span class="fl">3</span>, <span class="fl">4</span>, <span class="fl">2</span>, <span class="fl">3</span>, <span class="fl">4</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">B</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">2</span> <span class="op">*</span> <span class="fl">3</span>, <span class="fl">4</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">X</span> <span class="op">&lt;-</span> <span class="fu">linalg_tensorsolve</span><span class="op">(</span><span class="va">A</span>, <span class="va">B</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">X</span><span class="op">$</span><span class="va">shape</span></span>
+<span class="r-in"><span class="fu"><a href="torch_allclose.html">torch_allclose</a></span><span class="op">(</span><span class="fu"><a href="torch_tensordot.html">torch_tensordot</a></span><span class="op">(</span><span class="va">A</span>, <span class="va">X</span>, dims <span class="op">=</span> <span class="va">X</span><span class="op">$</span><span class="va">ndim</span><span class="op">)</span>, <span class="va">B</span><span class="op">)</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">A</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">6</span>, <span class="fl">4</span>, <span class="fl">4</span>, <span class="fl">3</span>, <span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">B</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">4</span>, <span class="fl">3</span>, <span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">X</span> <span class="op">&lt;-</span> <span class="fu">linalg_tensorsolve</span><span class="op">(</span><span class="va">A</span>, <span class="va">B</span>, dims <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">A</span> <span class="op">&lt;-</span> <span class="va">A</span><span class="op">$</span><span class="fu">permute</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">4</span>, <span class="fl">5</span>, <span class="fl">1</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu"><a href="torch_allclose.html">torch_allclose</a></span><span class="op">(</span><span class="fu"><a href="torch_tensordot.html">torch_tensordot</a></span><span class="op">(</span><span class="va">A</span>, <span class="va">X</span>, dims <span class="op">=</span> <span class="va">X</span><span class="op">$</span><span class="va">ndim</span><span class="op">)</span>, <span class="va">B</span>, atol <span class="op">=</span> <span class="fl">1e-6</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] FALSE</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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index 195e7d111..c63f1bf4a 100644
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+Supports input of float, double, cfloat and cdouble dtypes.
+This function does not necessarily treat multidimensonal A as a batch of
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Computes a vector norm.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/linalg.R'><code>R/linalg.R</code></a></small>
-    <div class="hidden name"><code>linalg_vector_norm.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Computes a vector norm.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/linalg.R" class="external-link"><code>R/linalg.R</code></a></small>
+      <div class="d-none name"><code>linalg_vector_norm.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>If <code>A</code> is complex valued, it computes the norm of <code>A$abs()</code>
 Supports input of float, double, cfloat and cdouble dtypes.
 This function does not necessarily treat multidimensonal <code>A</code> as a batch of
 vectors, instead:</p>
     </div>
 
-    <pre class="usage"><span class='fu'>linalg_vector_norm</span><span class='op'>(</span><span class='va'>A</span>, ord <span class='op'>=</span> <span class='fl'>2</span>, dim <span class='op'>=</span> <span class='cn'>NULL</span>, keepdim <span class='op'>=</span> <span class='cn'>FALSE</span>, dtype <span class='op'>=</span> <span class='cn'>NULL</span><span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">linalg_vector_norm</span><span class="op">(</span><span class="va">A</span>, ord <span class="op">=</span> <span class="fl">2</span>, dim <span class="op">=</span> <span class="cn">NULL</span>, keepdim <span class="op">=</span> <span class="cn">FALSE</span>, dtype <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>A</th>
-      <td><p>(Tensor): tensor, flattened by default, but this behavior can be
-controlled using <code>dim</code>.</p></td>
-    </tr>
-    <tr>
-      <th>ord</th>
-      <td><p>(int, float, inf, -inf, 'fro', 'nuc', optional): order of norm. Default: <code>2</code></p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int, Tupleint, optional): dimensions over which to compute
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>A</dt>
+<dd><p>(Tensor): tensor, flattened by default, but this behavior can be
+controlled using <code>dim</code>.</p></dd>
+<dt>ord</dt>
+<dd><p>(int, float, inf, -inf, 'fro', 'nuc', optional): order of norm. Default: <code>2</code></p></dd>
+<dt>dim</dt>
+<dd><p>(int, Tupleint, optional): dimensions over which to compute
 the vector or matrix norm. See above for the behavior when <code>dim=NULL</code>.
-Default: <code>NULL</code></p></td>
-    </tr>
-    <tr>
-      <th>keepdim</th>
-      <td><p>(bool, optional): If set to <code>TRUE</code>, the reduced dimensions are retained
-in the result as dimensions with size one. Default: <code>FALSE</code></p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>dtype (<code>torch_dtype</code>, optional): If specified, the input tensor is cast to
+Default: <code>NULL</code></p></dd>
+<dt>keepdim</dt>
+<dd><p>(bool, optional): If set to <code>TRUE</code>, the reduced dimensions are retained
+in the result as dimensions with size one. Default: <code>FALSE</code></p></dd>
+<dt>dtype</dt>
+<dd><p>dtype (<code>torch_dtype</code>, optional): If specified, the input tensor is cast to
 <code>dtype</code> before performing the operation, and the returned tensor's type
-will be <code>dtype</code>. Default: <code>NULL</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
+will be <code>dtype</code>. Default: <code>NULL</code></p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     
-<ul>
-<li><p>If <code>dim=NULL</code>, <code>A</code> will be flattened before the norm is computed.</p></li>
+<ul><li><p>If <code>dim=NULL</code>, <code>A</code> will be flattened before the norm is computed.</p></li>
 <li><p>If <code>dim</code> is an <code>int</code> or a <code>tuple</code>, the norm will be computed over these dimensions
 and the other dimensions will be treated as batch dimensions.</p></li>
-</ul>
-
-<p>This behavior is for consistency with <code><a href='linalg_norm.html'>linalg_norm()</a></code>.</p>
+</ul><p>This behavior is for consistency with <code><a href="linalg_norm.html">linalg_norm()</a></code>.</p>
 <p><code>ord</code> defines the norm that is computed. The following norms are
-supported:</p><table class='table'>
-<tr><td><code>ord</code></td><td>norm for matrices</td><td>norm for vectors</td></tr>
-<tr><td><code>NULL</code> (default)</td><td>Frobenius norm</td><td><code>2</code>-norm (see below)</td></tr>
-<tr><td><code>"fro"</code></td><td>Frobenius norm</td><td>– not supported –</td></tr>
-<tr><td><code>"nuc"</code></td><td>nuclear norm</td><td>– not supported –</td></tr>
-<tr><td><code>Inf</code></td><td><code>max(sum(abs(x), dim=2))</code></td><td><code>max(abs(x))</code></td></tr>
-<tr><td><code>-Inf</code></td><td><code>min(sum(abs(x), dim=2))</code></td><td><code>min(abs(x))</code></td></tr>
-<tr><td><code>0</code></td><td>– not supported –</td><td><code>sum(x != 0)</code></td></tr>
-<tr><td><code>1</code></td><td><code>max(sum(abs(x), dim=1))</code></td><td>as below</td></tr>
-<tr><td><code>-1</code></td><td><code>min(sum(abs(x), dim=1))</code></td><td>as below</td></tr>
-<tr><td><code>2</code></td><td>largest singular value</td><td>as below</td></tr>
-<tr><td><code>-2</code></td><td>smallest singular value</td><td>as below</td></tr>
-<tr><td>other <code>int</code> or <code>float</code></td><td>– not supported –</td><td><code>sum(abs(x)^{ord})^{(1 / ord)}</code></td></tr>
-</table>
-
-
-    <h2 class="hasAnchor" id="see-also"><a class="anchor" href="#see-also"></a>See also</h2>
-
-    <div class='dont-index'><p>Other linalg: 
-<code><a href='linalg_cholesky_ex.html'>linalg_cholesky_ex</a>()</code>,
-<code><a href='linalg_cholesky.html'>linalg_cholesky</a>()</code>,
-<code><a href='linalg_det.html'>linalg_det</a>()</code>,
-<code><a href='linalg_eigh.html'>linalg_eigh</a>()</code>,
-<code><a href='linalg_eigvalsh.html'>linalg_eigvalsh</a>()</code>,
-<code><a href='linalg_eigvals.html'>linalg_eigvals</a>()</code>,
-<code><a href='linalg_eig.html'>linalg_eig</a>()</code>,
-<code><a href='linalg_householder_product.html'>linalg_householder_product</a>()</code>,
-<code><a href='linalg_inv_ex.html'>linalg_inv_ex</a>()</code>,
-<code><a href='linalg_inv.html'>linalg_inv</a>()</code>,
-<code><a href='linalg_lstsq.html'>linalg_lstsq</a>()</code>,
-<code><a href='linalg_matrix_norm.html'>linalg_matrix_norm</a>()</code>,
-<code><a href='linalg_matrix_power.html'>linalg_matrix_power</a>()</code>,
-<code><a href='linalg_matrix_rank.html'>linalg_matrix_rank</a>()</code>,
-<code><a href='linalg_multi_dot.html'>linalg_multi_dot</a>()</code>,
-<code><a href='linalg_norm.html'>linalg_norm</a>()</code>,
-<code><a href='linalg_pinv.html'>linalg_pinv</a>()</code>,
-<code><a href='linalg_qr.html'>linalg_qr</a>()</code>,
-<code><a href='linalg_slogdet.html'>linalg_slogdet</a>()</code>,
-<code><a href='linalg_solve.html'>linalg_solve</a>()</code>,
-<code><a href='linalg_svdvals.html'>linalg_svdvals</a>()</code>,
-<code><a href='linalg_svd.html'>linalg_svd</a>()</code>,
-<code><a href='linalg_tensorinv.html'>linalg_tensorinv</a>()</code>,
-<code><a href='linalg_tensorsolve.html'>linalg_tensorsolve</a>()</code></p></div>
+supported:</p><table class="table table"><tr><td><code>ord</code></td><td>norm for matrices</td><td>norm for vectors</td></tr><tr><td><code>NULL</code> (default)</td><td>Frobenius norm</td><td><code>2</code>-norm (see below)</td></tr><tr><td><code>"fro"</code></td><td>Frobenius norm</td><td>– not supported –</td></tr><tr><td><code>"nuc"</code></td><td>nuclear norm</td><td>– not supported –</td></tr><tr><td><code>Inf</code></td><td><code>max(sum(abs(x), dim=2))</code></td><td><code>max(abs(x))</code></td></tr><tr><td><code>-Inf</code></td><td><code>min(sum(abs(x), dim=2))</code></td><td><code>min(abs(x))</code></td></tr><tr><td><code>0</code></td><td>– not supported –</td><td><code>sum(x != 0)</code></td></tr><tr><td><code>1</code></td><td><code>max(sum(abs(x), dim=1))</code></td><td>as below</td></tr><tr><td><code>-1</code></td><td><code>min(sum(abs(x), dim=1))</code></td><td>as below</td></tr><tr><td><code>2</code></td><td>largest singular value</td><td>as below</td></tr><tr><td><code>-2</code></td><td>smallest singular value</td><td>as below</td></tr><tr><td>other <code>int</code> or <code>float</code></td><td>– not supported –</td><td><code>sum(abs(x)^{ord})^{(1 / ord)}</code></td></tr></table></div>
+    <div class="section level2">
+    <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
+    <div class="dont-index"><p>Other linalg: 
+<code><a href="linalg_cholesky_ex.html">linalg_cholesky_ex</a>()</code>,
+<code><a href="linalg_cholesky.html">linalg_cholesky</a>()</code>,
+<code><a href="linalg_det.html">linalg_det</a>()</code>,
+<code><a href="linalg_eigh.html">linalg_eigh</a>()</code>,
+<code><a href="linalg_eigvalsh.html">linalg_eigvalsh</a>()</code>,
+<code><a href="linalg_eigvals.html">linalg_eigvals</a>()</code>,
+<code><a href="linalg_eig.html">linalg_eig</a>()</code>,
+<code><a href="linalg_householder_product.html">linalg_householder_product</a>()</code>,
+<code><a href="linalg_inv_ex.html">linalg_inv_ex</a>()</code>,
+<code><a href="linalg_inv.html">linalg_inv</a>()</code>,
+<code><a href="linalg_lstsq.html">linalg_lstsq</a>()</code>,
+<code><a href="linalg_matrix_norm.html">linalg_matrix_norm</a>()</code>,
+<code><a href="linalg_matrix_power.html">linalg_matrix_power</a>()</code>,
+<code><a href="linalg_matrix_rank.html">linalg_matrix_rank</a>()</code>,
+<code><a href="linalg_multi_dot.html">linalg_multi_dot</a>()</code>,
+<code><a href="linalg_norm.html">linalg_norm</a>()</code>,
+<code><a href="linalg_pinv.html">linalg_pinv</a>()</code>,
+<code><a href="linalg_qr.html">linalg_qr</a>()</code>,
+<code><a href="linalg_slogdet.html">linalg_slogdet</a>()</code>,
+<code><a href="linalg_solve.html">linalg_solve</a>()</code>,
+<code><a href="linalg_svdvals.html">linalg_svdvals</a>()</code>,
+<code><a href="linalg_svd.html">linalg_svd</a>()</code>,
+<code><a href="linalg_tensorinv.html">linalg_tensorinv</a>()</code>,
+<code><a href="linalg_tensorsolve.html">linalg_tensorsolve</a>()</code></p></div>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span><span class='op'>(</span><span class='fl'>0</span>, <span class='fl'>8</span>, dtype<span class='op'>=</span><span class='fu'><a href='torch_dtype.html'>torch_float</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>-</span> <span class='fl'>4</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='va'>b</span> <span class='op'>&lt;-</span> <span class='va'>a</span><span class='op'>$</span><span class='fu'>reshape</span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>b</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='fu'>linalg_vector_norm</span><span class='op'>(</span><span class='va'>a</span>, ord <span class='op'>=</span> <span class='fl'>3.5</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>linalg_vector_norm</span><span class='op'>(</span><span class='va'>b</span>, ord <span class='op'>=</span> <span class='fl'>3.5</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> 5.43449</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_arange.html">torch_arange</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">8</span>, dtype <span class="op">=</span> <span class="fu"><a href="torch_dtype.html">torch_float</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">-</span> <span class="fl">4</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="va">b</span> <span class="op">&lt;-</span> <span class="va">a</span><span class="op">$</span><span class="fu">reshape</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">b</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="fu">linalg_vector_norm</span><span class="op">(</span><span class="va">a</span>, ord <span class="op">=</span> <span class="fl">3.5</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">linalg_vector_norm</span><span class="op">(</span><span class="va">b</span>, ord <span class="op">=</span> <span class="fl">3.5</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 5.43449</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer>
-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
 </div>
 
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+<div class="pkgdown-footer-right">
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diff --git a/reference/load_state_dict.html b/reference/load_state_dict.html
index 6b5dd6d38..312cf9a50 100644
--- a/reference/load_state_dict.html
+++ b/reference/load_state_dict.html
@@ -1,244 +1,131 @@
-<!-- Generated by pkgdown: do not edit by hand -->
 <!DOCTYPE html>
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-  <meta charset="utf-8">
-<meta http-equiv="X-UA-Compatible" content="IE=edge">
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-<title>Load a state dict file — load_state_dict • torch</title>
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-<meta property="og:description" content="This function should only be used to load models saved in python.
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="This function should only be used to load models saved in python.
 For it to work correctly you need to use torch.save with the flag:
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-    <div class="hidden name"><code>load_state_dict.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Load a state dict file</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/save.R" class="external-link"><code>R/save.R</code></a></small>
+      <div class="d-none name"><code>load_state_dict.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>This function should only be used to load models saved in python.
 For it to work correctly you need to use <code>torch.save</code> with the flag:
 <code>_use_new_zipfile_serialization=True</code> and also remove all <code>nn.Parameter</code>
 classes from the tensors in the dict.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>load_state_dict</span><span class='op'>(</span><span class='va'>path</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>path</th>
-      <td><p>to the state dict file</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">load_state_dict</span><span class="op">(</span><span class="va">path</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>path</dt>
+<dd><p>to the state dict file</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
     <p>a named list of tensors.</p>
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>The above might change with development of <a href='https://github.com/pytorch/pytorch/issues/37213'>this</a>
+    </div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
+    <p>The above might change with development of <a href="https://github.com/pytorch/pytorch/issues/37213" class="external-link">this</a>
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+    </div>
 
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-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Sets the learning rate of each parameter group to the initial lr
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Sets the learning rate of each parameter group to the initial lr
 times a given function. When last_epoch=-1, sets initial lr as lr.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/optim-lr_scheduler.R'><code>R/optim-lr_scheduler.R</code></a></small>
-    <div class="hidden name"><code>lr_lambda.Rd</code></div>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/optim-lr_scheduler.R" class="external-link"><code>R/optim-lr_scheduler.R</code></a></small>
+      <div class="d-none name"><code>lr_lambda.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Sets the learning rate of each parameter group to the initial lr
 times a given function. When last_epoch=-1, sets initial lr as lr.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>lr_lambda</span><span class='op'>(</span><span class='va'>optimizer</span>, <span class='va'>lr_lambda</span>, last_epoch <span class='op'>=</span> <span class='op'>-</span><span class='fl'>1</span>, verbose <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">lr_lambda</span><span class="op">(</span><span class="va">optimizer</span>, <span class="va">lr_lambda</span>, last_epoch <span class="op">=</span> <span class="op">-</span><span class="fl">1</span>, verbose <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>optimizer</th>
-      <td><p>(Optimizer): Wrapped optimizer.</p></td>
-    </tr>
-    <tr>
-      <th>lr_lambda</th>
-      <td><p>(function or list): A function which computes a multiplicative
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>optimizer</dt>
+<dd><p>(Optimizer): Wrapped optimizer.</p></dd>
+<dt>lr_lambda</dt>
+<dd><p>(function or list): A function which computes a multiplicative
 factor given an integer parameter epoch, or a list of such
-functions, one for each group in optimizer.param_groups.</p></td>
-    </tr>
-    <tr>
-      <th>last_epoch</th>
-      <td><p>(int): The index of last epoch. Default: -1.</p></td>
-    </tr>
-    <tr>
-      <th>verbose</th>
-      <td><p>(bool): If <code>TRUE</code>, prints a message to stdout for
-each update. Default: <code>FALSE</code>.</p></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='co'># Assuming optimizer has two groups.</span></span>
-<span class='r-in'><span class='va'>lambda1</span> <span class='op'>&lt;-</span> <span class='kw'>function</span><span class='op'>(</span><span class='va'>epoch</span><span class='op'>)</span> <span class='va'>epoch</span> <span class='op'><a href='https://rdrr.io/r/base/Arithmetic.html'>%/%</a></span> <span class='fl'>30</span></span>
-<span class='r-in'><span class='va'>lambda2</span> <span class='op'>&lt;-</span> <span class='kw'>function</span><span class='op'>(</span><span class='va'>epoch</span><span class='op'>)</span> <span class='fl'>0.95</span><span class='op'>^</span><span class='va'>epoch</span></span>
-<span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='cn'>FALSE</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>scheduler</span> <span class='op'>&lt;-</span> <span class='fu'>lr_lambda</span><span class='op'>(</span><span class='va'>optimizer</span>, lr_lambda <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span><span class='va'>lambda1</span>, <span class='va'>lambda2</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='kw'>for</span> <span class='op'>(</span><span class='va'>epoch</span> <span class='kw'>in</span> <span class='fl'>1</span><span class='op'>:</span><span class='fl'>100</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'>  <span class='fu'>train</span><span class='op'>(</span><span class='va'>...</span><span class='op'>)</span></span>
-<span class='r-in'>  <span class='fu'>validate</span><span class='op'>(</span><span class='va'>...</span><span class='op'>)</span></span>
-<span class='r-in'>  <span class='va'>scheduler</span><span class='op'>$</span><span class='fu'>step</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+functions, one for each group in optimizer.param_groups.</p></dd>
+<dt>last_epoch</dt>
+<dd><p>(int): The index of last epoch. Default: -1.</p></dd>
+<dt>verbose</dt>
+<dd><p>(bool): If <code>TRUE</code>, prints a message to stdout for
+each update. Default: <code>FALSE</code>.</p></dd>
+</dl></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="co"># Assuming optimizer has two groups.</span></span>
+<span class="r-in"><span class="va">lambda1</span> <span class="op">&lt;-</span> <span class="kw">function</span><span class="op">(</span><span class="va">epoch</span><span class="op">)</span> <span class="va">epoch</span> <span class="op"><a href="https://rdrr.io/r/base/Arithmetic.html" class="external-link">%/%</a></span> <span class="fl">30</span></span>
+<span class="r-in"><span class="va">lambda2</span> <span class="op">&lt;-</span> <span class="kw">function</span><span class="op">(</span><span class="va">epoch</span><span class="op">)</span> <span class="fl">0.95</span><span class="op">^</span><span class="va">epoch</span></span>
+<span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">scheduler</span> <span class="op">&lt;-</span> <span class="fu">lr_lambda</span><span class="op">(</span><span class="va">optimizer</span>, lr_lambda <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">lambda1</span>, <span class="va">lambda2</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="kw">for</span> <span class="op">(</span><span class="va">epoch</span> <span class="kw">in</span> <span class="fl">1</span><span class="op">:</span><span class="fl">100</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in">  <span class="fu">train</span><span class="op">(</span><span class="va">...</span><span class="op">)</span></span>
+<span class="r-in">  <span class="fu">validate</span><span class="op">(</span><span class="va">...</span><span class="op">)</span></span>
+<span class="r-in">  <span class="va">scheduler</span><span class="op">$</span><span class="fu">step</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Multiply the learning rate of each parameter group by the factor given
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Multiply the learning rate of each parameter group by the factor given
 in the specified function. When last_epoch=-1, sets initial lr as lr.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/optim-lr_scheduler.R'><code>R/optim-lr_scheduler.R</code></a></small>
-    <div class="hidden name"><code>lr_multiplicative.Rd</code></div>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/optim-lr_scheduler.R" class="external-link"><code>R/optim-lr_scheduler.R</code></a></small>
+      <div class="d-none name"><code>lr_multiplicative.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Multiply the learning rate of each parameter group by the factor given
 in the specified function. When last_epoch=-1, sets initial lr as lr.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>lr_multiplicative</span><span class='op'>(</span><span class='va'>optimizer</span>, <span class='va'>lr_lambda</span>, last_epoch <span class='op'>=</span> <span class='op'>-</span><span class='fl'>1</span>, verbose <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">lr_multiplicative</span><span class="op">(</span><span class="va">optimizer</span>, <span class="va">lr_lambda</span>, last_epoch <span class="op">=</span> <span class="op">-</span><span class="fl">1</span>, verbose <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>optimizer</th>
-      <td><p>(Optimizer): Wrapped optimizer.</p></td>
-    </tr>
-    <tr>
-      <th>lr_lambda</th>
-      <td><p>(function or list): A function which computes a multiplicative
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>optimizer</dt>
+<dd><p>(Optimizer): Wrapped optimizer.</p></dd>
+<dt>lr_lambda</dt>
+<dd><p>(function or list): A function which computes a multiplicative
 factor given an integer parameter epoch, or a list of such
-functions, one for each group in optimizer.param_groups.</p></td>
-    </tr>
-    <tr>
-      <th>last_epoch</th>
-      <td><p>(int): The index of last epoch. Default: -1.</p></td>
-    </tr>
-    <tr>
-      <th>verbose</th>
-      <td><p>(bool): If <code>TRUE</code>, prints a message to stdout for
-each update. Default: <code>FALSE</code>.</p></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='cn'>FALSE</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>lmbda</span> <span class='op'>&lt;-</span> <span class='kw'>function</span><span class='op'>(</span><span class='va'>epoch</span><span class='op'>)</span> <span class='fl'>0.95</span></span>
-<span class='r-in'><span class='va'>scheduler</span> <span class='op'>&lt;-</span> <span class='fu'>lr_multiplicative</span><span class='op'>(</span><span class='va'>optimizer</span>, lr_lambda<span class='op'>=</span><span class='va'>lmbda</span><span class='op'>)</span></span>
-<span class='r-in'><span class='kw'>for</span> <span class='op'>(</span><span class='va'>epoch</span> <span class='kw'>in</span> <span class='fl'>1</span><span class='op'>:</span><span class='fl'>100</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'>  <span class='fu'>train</span><span class='op'>(</span><span class='va'>...</span><span class='op'>)</span></span>
-<span class='r-in'>  <span class='fu'>validate</span><span class='op'>(</span><span class='va'>...</span><span class='op'>)</span></span>
-<span class='r-in'>  <span class='va'>scheduler</span><span class='op'>$</span><span class='fu'>step</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+functions, one for each group in optimizer.param_groups.</p></dd>
+<dt>last_epoch</dt>
+<dd><p>(int): The index of last epoch. Default: -1.</p></dd>
+<dt>verbose</dt>
+<dd><p>(bool): If <code>TRUE</code>, prints a message to stdout for
+each update. Default: <code>FALSE</code>.</p></dd>
+</dl></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">lmbda</span> <span class="op">&lt;-</span> <span class="kw">function</span><span class="op">(</span><span class="va">epoch</span><span class="op">)</span> <span class="fl">0.95</span></span>
+<span class="r-in"><span class="va">scheduler</span> <span class="op">&lt;-</span> <span class="fu">lr_multiplicative</span><span class="op">(</span><span class="va">optimizer</span>, lr_lambda <span class="op">=</span> <span class="va">lmbda</span><span class="op">)</span></span>
+<span class="r-in"><span class="kw">for</span> <span class="op">(</span><span class="va">epoch</span> <span class="kw">in</span> <span class="fl">1</span><span class="op">:</span><span class="fl">100</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in">  <span class="fu">train</span><span class="op">(</span><span class="va">...</span><span class="op">)</span></span>
+<span class="r-in">  <span class="fu">validate</span><span class="op">(</span><span class="va">...</span><span class="op">)</span></span>
+<span class="r-in">  <span class="va">scheduler</span><span class="op">$</span><span class="fu">step</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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diff --git a/reference/lr_one_cycle.html b/reference/lr_one_cycle.html
index 4df2c82a5..3abddd0fc 100644
--- a/reference/lr_one_cycle.html
+++ b/reference/lr_one_cycle.html
@@ -1,195 +1,90 @@
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-<meta property="og:description" content="Sets the learning rate of each parameter group according to the
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+from that maximum learning rate to some minimum learning rate much lower
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+  </div>
+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Once cycle learning rate</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/optim-lr_scheduler.R'><code>R/optim-lr_scheduler.R</code></a></small>
-    <div class="hidden name"><code>lr_one_cycle.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Once cycle learning rate</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/optim-lr_scheduler.R" class="external-link"><code>R/optim-lr_scheduler.R</code></a></small>
+      <div class="d-none name"><code>lr_one_cycle.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Sets the learning rate of each parameter group according to the
 1cycle learning rate policy. The 1cycle policy anneals the learning
 rate from an initial learning rate to some maximum learning rate and then
@@ -197,183 +92,150 @@ from that maximum learning rate to some minimum learning rate much lower
 than the initial learning rate.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>lr_one_cycle</span><span class='op'>(</span>
-  <span class='va'>optimizer</span>,
-  <span class='va'>max_lr</span>,
-  total_steps <span class='op'>=</span> <span class='cn'>NULL</span>,
-  epochs <span class='op'>=</span> <span class='cn'>NULL</span>,
-  steps_per_epoch <span class='op'>=</span> <span class='cn'>NULL</span>,
-  pct_start <span class='op'>=</span> <span class='fl'>0.3</span>,
-  anneal_strategy <span class='op'>=</span> <span class='st'>"cos"</span>,
-  cycle_momentum <span class='op'>=</span> <span class='cn'>TRUE</span>,
-  base_momentum <span class='op'>=</span> <span class='fl'>0.85</span>,
-  max_momentum <span class='op'>=</span> <span class='fl'>0.95</span>,
-  div_factor <span class='op'>=</span> <span class='fl'>25</span>,
-  final_div_factor <span class='op'>=</span> <span class='fl'>10000</span>,
-  last_epoch <span class='op'>=</span> <span class='op'>-</span><span class='fl'>1</span>,
-  verbose <span class='op'>=</span> <span class='cn'>FALSE</span>
-<span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">lr_one_cycle</span><span class="op">(</span>
+  <span class="va">optimizer</span>,
+  <span class="va">max_lr</span>,
+  total_steps <span class="op">=</span> <span class="cn">NULL</span>,
+  epochs <span class="op">=</span> <span class="cn">NULL</span>,
+  steps_per_epoch <span class="op">=</span> <span class="cn">NULL</span>,
+  pct_start <span class="op">=</span> <span class="fl">0.3</span>,
+  anneal_strategy <span class="op">=</span> <span class="st">"cos"</span>,
+  cycle_momentum <span class="op">=</span> <span class="cn">TRUE</span>,
+  base_momentum <span class="op">=</span> <span class="fl">0.85</span>,
+  max_momentum <span class="op">=</span> <span class="fl">0.95</span>,
+  div_factor <span class="op">=</span> <span class="fl">25</span>,
+  final_div_factor <span class="op">=</span> <span class="fl">10000</span>,
+  last_epoch <span class="op">=</span> <span class="op">-</span><span class="fl">1</span>,
+  verbose <span class="op">=</span> <span class="cn">FALSE</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>optimizer</th>
-      <td><p>(Optimizer): Wrapped optimizer.</p></td>
-    </tr>
-    <tr>
-      <th>max_lr</th>
-      <td><p>(float or list): Upper learning rate boundaries in the cycle
-for each parameter group.</p></td>
-    </tr>
-    <tr>
-      <th>total_steps</th>
-      <td><p>(int): The total number of steps in the cycle. Note that
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>optimizer</dt>
+<dd><p>(Optimizer): Wrapped optimizer.</p></dd>
+<dt>max_lr</dt>
+<dd><p>(float or list): Upper learning rate boundaries in the cycle
+for each parameter group.</p></dd>
+<dt>total_steps</dt>
+<dd><p>(int): The total number of steps in the cycle. Note that
 if a value is not provided here, then it must be inferred by providing
 a value for epochs and steps_per_epoch.
-Default: NULL</p></td>
-    </tr>
-    <tr>
-      <th>epochs</th>
-      <td><p>(int): The number of epochs to train for. This is used along
+Default: NULL</p></dd>
+<dt>epochs</dt>
+<dd><p>(int): The number of epochs to train for. This is used along
 with steps_per_epoch in order to infer the total number of steps in the cycle
 if a value for total_steps is not provided.
-Default: NULL</p></td>
-    </tr>
-    <tr>
-      <th>steps_per_epoch</th>
-      <td><p>(int): The number of steps per epoch to train for. This is
+Default: NULL</p></dd>
+<dt>steps_per_epoch</dt>
+<dd><p>(int): The number of steps per epoch to train for. This is
 used along with epochs in order to infer the total number of steps in the
 cycle if a value for total_steps is not provided.
-Default: NULL</p></td>
-    </tr>
-    <tr>
-      <th>pct_start</th>
-      <td><p>(float): The percentage of the cycle (in number of steps) spent
+Default: NULL</p></dd>
+<dt>pct_start</dt>
+<dd><p>(float): The percentage of the cycle (in number of steps) spent
 increasing the learning rate.
-Default: 0.3</p></td>
-    </tr>
-    <tr>
-      <th>anneal_strategy</th>
-      <td><p>(str): 'cos', 'linear'
+Default: 0.3</p></dd>
+<dt>anneal_strategy</dt>
+<dd><p>(str): 'cos', 'linear'
 Specifies the annealing strategy: "cos" for cosine annealing, "linear" for
 linear annealing.
-Default: 'cos'</p></td>
-    </tr>
-    <tr>
-      <th>cycle_momentum</th>
-      <td><p>(bool): If <code>TRUE</code>, momentum is cycled inversely
+Default: 'cos'</p></dd>
+<dt>cycle_momentum</dt>
+<dd><p>(bool): If <code>TRUE</code>, momentum is cycled inversely
 to learning rate between 'base_momentum' and 'max_momentum'.
-Default: TRUE</p></td>
-    </tr>
-    <tr>
-      <th>base_momentum</th>
-      <td><p>(float or list): Lower momentum boundaries in the cycle
+Default: TRUE</p></dd>
+<dt>base_momentum</dt>
+<dd><p>(float or list): Lower momentum boundaries in the cycle
 for each parameter group. Note that momentum is cycled inversely
 to learning rate; at the peak of a cycle, momentum is
 'base_momentum' and learning rate is 'max_lr'.
-Default: 0.85</p></td>
-    </tr>
-    <tr>
-      <th>max_momentum</th>
-      <td><p>(float or list): Upper momentum boundaries in the cycle
+Default: 0.85</p></dd>
+<dt>max_momentum</dt>
+<dd><p>(float or list): Upper momentum boundaries in the cycle
 for each parameter group. Functionally,
 it defines the cycle amplitude (max_momentum - base_momentum).
 Note that momentum is cycled inversely
 to learning rate; at the start of a cycle, momentum is 'max_momentum'
 and learning rate is 'base_lr'
-Default: 0.95</p></td>
-    </tr>
-    <tr>
-      <th>div_factor</th>
-      <td><p>(float): Determines the initial learning rate via
+Default: 0.95</p></dd>
+<dt>div_factor</dt>
+<dd><p>(float): Determines the initial learning rate via
 initial_lr = max_lr/div_factor
-Default: 25</p></td>
-    </tr>
-    <tr>
-      <th>final_div_factor</th>
-      <td><p>(float): Determines the minimum learning rate via
+Default: 25</p></dd>
+<dt>final_div_factor</dt>
+<dd><p>(float): Determines the minimum learning rate via
 min_lr = initial_lr/final_div_factor
-Default: 1e4</p></td>
-    </tr>
-    <tr>
-      <th>last_epoch</th>
-      <td><p>(int): The index of the last batch. This parameter is used when
-resuming a training job. Since <code><a href='https://rdrr.io/r/stats/step.html'>step()</a></code> should be invoked after each
+Default: 1e4</p></dd>
+<dt>last_epoch</dt>
+<dd><p>(int): The index of the last batch. This parameter is used when
+resuming a training job. Since <code><a href="https://rdrr.io/r/stats/step.html" class="external-link">step()</a></code> should be invoked after each
 batch instead of after each epoch, this number represents the total
 number of <em>batches</em> computed, not the total number of epochs computed.
 When last_epoch=-1, the schedule is started from the beginning.
-Default: -1</p></td>
-    </tr>
-    <tr>
-      <th>verbose</th>
-      <td><p>(bool): If <code>TRUE</code>, prints a message to stdout for
-each update. Default: <code>FALSE</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
+Default: -1</p></dd>
+<dt>verbose</dt>
+<dd><p>(bool): If <code>TRUE</code>, prints a message to stdout for
+each update. Default: <code>FALSE</code>.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>This policy was initially described in the paper
-<a href='https://arxiv.org/abs/1708.07120'>Super-Convergence: Very Fast Training of Neural Networks Using Large Learning Rates</a>.</p>
+<a href="https://arxiv.org/abs/1708.07120" class="external-link">Super-Convergence: Very Fast Training of Neural Networks Using Large Learning Rates</a>.</p>
 <p>The 1cycle learning rate policy changes the learning rate after every batch.
 <code>step</code> should be called after a batch has been used for training.
 This scheduler is not chainable.</p>
 <p>Note also that the total number of steps in the cycle can be determined in one
-of two ways (listed in order of precedence):</p><ul>
-<li><p>A value for total_steps is explicitly provided.</p></li>
+of two ways (listed in order of precedence):</p><ul><li><p>A value for total_steps is explicitly provided.</p></li>
 <li><p>A number of epochs (epochs) and a number of steps per epoch
 (steps_per_epoch) are provided.</p></li>
-</ul>
-
-<p>In this case, the number of total steps is inferred by
+</ul><p>In this case, the number of total steps is inferred by
 total_steps = epochs * steps_per_epoch</p>
 <p>You must either provide a value for total_steps or provide a value for both
 epochs and steps_per_epoch.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='cn'>FALSE</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>data_loader</span> <span class='op'>&lt;-</span> <span class='fu'><a href='dataloader.html'>dataloader</a></span><span class='op'>(</span><span class='va'>...</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>optimizer</span> <span class='op'>&lt;-</span> <span class='fu'><a href='optim_sgd.html'>optim_sgd</a></span><span class='op'>(</span><span class='va'>model</span><span class='op'>$</span><span class='va'>parameters</span>, lr<span class='op'>=</span><span class='fl'>0.1</span>, momentum<span class='op'>=</span><span class='fl'>0.9</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>scheduler</span> <span class='op'>&lt;-</span> <span class='fu'>lr_one_cycle</span><span class='op'>(</span><span class='va'>optimizer</span>, max_lr<span class='op'>=</span><span class='fl'>0.01</span>, steps_per_epoch<span class='op'>=</span><span class='fu'><a href='https://rdrr.io/r/base/length.html'>length</a></span><span class='op'>(</span><span class='va'>data_loader</span><span class='op'>)</span>, </span>
-<span class='r-in'>                          epochs<span class='op'>=</span><span class='fl'>10</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='kw'>for</span> <span class='op'>(</span><span class='va'>i</span> <span class='kw'>in</span> <span class='fl'>1</span><span class='op'>:</span><span class='va'>epochs</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'>  <span class='fu'>coro</span><span class='fu'>::</span><span class='fu'><a href='https://rdrr.io/pkg/coro/man/collect.html'>loop</a></span><span class='op'>(</span><span class='kw'>for</span> <span class='op'>(</span><span class='va'>batch</span> <span class='kw'>in</span> <span class='va'>data_loader</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'>     <span class='fu'>train_batch</span><span class='op'>(</span><span class='va'>...</span><span class='op'>)</span></span>
-<span class='r-in'>     <span class='va'>scheduler</span><span class='op'>$</span><span class='fu'>step</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'>  <span class='op'>}</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">data_loader</span> <span class="op">&lt;-</span> <span class="fu"><a href="dataloader.html">dataloader</a></span><span class="op">(</span><span class="va">...</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">optimizer</span> <span class="op">&lt;-</span> <span class="fu"><a href="optim_sgd.html">optim_sgd</a></span><span class="op">(</span><span class="va">model</span><span class="op">$</span><span class="va">parameters</span>, lr <span class="op">=</span> <span class="fl">0.1</span>, momentum <span class="op">=</span> <span class="fl">0.9</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">scheduler</span> <span class="op">&lt;-</span> <span class="fu">lr_one_cycle</span><span class="op">(</span><span class="va">optimizer</span>,</span>
+<span class="r-in">  max_lr <span class="op">=</span> <span class="fl">0.01</span>, steps_per_epoch <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/length.html" class="external-link">length</a></span><span class="op">(</span><span class="va">data_loader</span><span class="op">)</span>,</span>
+<span class="r-in">  epochs <span class="op">=</span> <span class="fl">10</span></span>
+<span class="r-in"><span class="op">)</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="kw">for</span> <span class="op">(</span><span class="va">i</span> <span class="kw">in</span> <span class="fl">1</span><span class="op">:</span><span class="va">epochs</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in">  <span class="fu">coro</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/pkg/coro/man/collect.html" class="external-link">loop</a></span><span class="op">(</span><span class="kw">for</span> <span class="op">(</span><span class="va">batch</span> <span class="kw">in</span> <span class="va">data_loader</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in">    <span class="fu">train_batch</span><span class="op">(</span><span class="va">...</span><span class="op">)</span></span>
+<span class="r-in">    <span class="va">scheduler</span><span class="op">$</span><span class="fu">step</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in">  <span class="op">}</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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-    <h1>Creates learning rate schedulers</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/optim-lr_scheduler.R'><code>R/optim-lr_scheduler.R</code></a></small>
-    <div class="hidden name"><code>lr_scheduler.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Creates learning rate schedulers</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/optim-lr_scheduler.R" class="external-link"><code>R/optim-lr_scheduler.R</code></a></small>
+      <div class="d-none name"><code>lr_scheduler.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Creates learning rate schedulers</p>
     </div>
 
-    <pre class="usage"><span class='fu'>lr_scheduler</span><span class='op'>(</span>
-  classname <span class='op'>=</span> <span class='cn'>NULL</span>,
-  inherit <span class='op'>=</span> <span class='va'>LRScheduler</span>,
-  <span class='va'>...</span>,
-  parent_env <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/sys.parent.html'>parent.frame</a></span><span class='op'>(</span><span class='op'>)</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>classname</th>
-      <td><p>optional name for the learning rate scheduler</p></td>
-    </tr>
-    <tr>
-      <th>inherit</th>
-      <td><p>an optional learning rate scheduler to inherit from</p></td>
-    </tr>
-    <tr>
-      <th>...</th>
-      <td><p>named list of methods. You must implement the <code>get_lr()</code>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">lr_scheduler</span><span class="op">(</span>
+  classname <span class="op">=</span> <span class="cn">NULL</span>,
+  inherit <span class="op">=</span> <span class="va">LRScheduler</span>,
+  <span class="va">...</span>,
+  parent_env <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sys.parent.html" class="external-link">parent.frame</a></span><span class="op">(</span><span class="op">)</span>
+<span class="op">)</span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>classname</dt>
+<dd><p>optional name for the learning rate scheduler</p></dd>
+<dt>inherit</dt>
+<dd><p>an optional learning rate scheduler to inherit from</p></dd>
+<dt>...</dt>
+<dd><p>named list of methods. You must implement the <code>get_lr()</code>
 method that doesn't take any argument and returns learning rates
-for each <code>param_group</code> in the optimizer.</p></td>
-    </tr>
-    <tr>
-      <th>parent_env</th>
-      <td><p>passed to <code><a href='https://r6.r-lib.org/reference/R6Class.html'>R6::R6Class()</a></code>.</p></td>
-    </tr>
-    </table>
+for each <code>param_group</code> in the optimizer.</p></dd>
+<dt>parent_env</dt>
+<dd><p>passed to <code><a href="https://r6.r-lib.org/reference/R6Class.html" class="external-link">R6::R6Class()</a></code>.</p></dd>
+</dl></div>
 
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Step learning rate decay</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/optim-lr_scheduler.R'><code>R/optim-lr_scheduler.R</code></a></small>
-    <div class="hidden name"><code>lr_step.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Step learning rate decay</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/optim-lr_scheduler.R" class="external-link"><code>R/optim-lr_scheduler.R</code></a></small>
+      <div class="d-none name"><code>lr_step.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Decays the learning rate of each parameter group by gamma every
 step_size epochs. Notice that such decay can happen simultaneously with
 other changes to the learning rate from outside this scheduler. When
 last_epoch=-1, sets initial lr as lr.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>lr_step</span><span class='op'>(</span><span class='va'>optimizer</span>, <span class='va'>step_size</span>, gamma <span class='op'>=</span> <span class='fl'>0.1</span>, last_epoch <span class='op'>=</span> <span class='op'>-</span><span class='fl'>1</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>optimizer</th>
-      <td><p>(Optimizer): Wrapped optimizer.</p></td>
-    </tr>
-    <tr>
-      <th>step_size</th>
-      <td><p>(int): Period of learning rate decay.</p></td>
-    </tr>
-    <tr>
-      <th>gamma</th>
-      <td><p>(float): Multiplicative factor of learning rate decay.
-Default: 0.1.</p></td>
-    </tr>
-    <tr>
-      <th>last_epoch</th>
-      <td><p>(int): The index of last epoch. Default: -1.</p></td>
-    </tr>
-    </table>
-
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">lr_step</span><span class="op">(</span><span class="va">optimizer</span>, <span class="va">step_size</span>, gamma <span class="op">=</span> <span class="fl">0.1</span>, last_epoch <span class="op">=</span> <span class="op">-</span><span class="fl">1</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='cn'>FALSE</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='co'># Assuming optimizer uses lr = 0.05 for all groups</span></span>
-<span class='r-in'><span class='co'># lr = 0.05     if epoch &lt; 30</span></span>
-<span class='r-in'><span class='co'># lr = 0.005    if 30 &lt;= epoch &lt; 60</span></span>
-<span class='r-in'><span class='co'># lr = 0.0005   if 60 &lt;= epoch &lt; 90</span></span>
-<span class='r-in'><span class='co'># ...</span></span>
-<span class='r-in'><span class='va'>scheduler</span> <span class='op'>&lt;-</span> <span class='fu'>lr_step</span><span class='op'>(</span><span class='va'>optimizer</span>, step_size<span class='op'>=</span><span class='fl'>30</span>, gamma<span class='op'>=</span><span class='fl'>0.1</span><span class='op'>)</span></span>
-<span class='r-in'><span class='kw'>for</span> <span class='op'>(</span><span class='va'>epoch</span> <span class='kw'>in</span> <span class='fl'>1</span><span class='op'>:</span><span class='fl'>100</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'>  <span class='fu'>train</span><span class='op'>(</span><span class='va'>...</span><span class='op'>)</span></span>
-<span class='r-in'>  <span class='fu'>validate</span><span class='op'>(</span><span class='va'>...</span><span class='op'>)</span></span>
-<span class='r-in'>  <span class='va'>scheduler</span><span class='op'>$</span><span class='fu'>step</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>optimizer</dt>
+<dd><p>(Optimizer): Wrapped optimizer.</p></dd>
+<dt>step_size</dt>
+<dd><p>(int): Period of learning rate decay.</p></dd>
+<dt>gamma</dt>
+<dd><p>(float): Multiplicative factor of learning rate decay.
+Default: 0.1.</p></dd>
+<dt>last_epoch</dt>
+<dd><p>(int): The index of last epoch. Default: -1.</p></dd>
+</dl></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="co"># Assuming optimizer uses lr = 0.05 for all groups</span></span>
+<span class="r-in"><span class="co"># lr = 0.05     if epoch &lt; 30</span></span>
+<span class="r-in"><span class="co"># lr = 0.005    if 30 &lt;= epoch &lt; 60</span></span>
+<span class="r-in"><span class="co"># lr = 0.0005   if 60 &lt;= epoch &lt; 90</span></span>
+<span class="r-in"><span class="co"># ...</span></span>
+<span class="r-in"><span class="va">scheduler</span> <span class="op">&lt;-</span> <span class="fu">lr_step</span><span class="op">(</span><span class="va">optimizer</span>, step_size <span class="op">=</span> <span class="fl">30</span>, gamma <span class="op">=</span> <span class="fl">0.1</span><span class="op">)</span></span>
+<span class="r-in"><span class="kw">for</span> <span class="op">(</span><span class="va">epoch</span> <span class="kw">in</span> <span class="fl">1</span><span class="op">:</span><span class="fl">100</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in">  <span class="fu">train</span><span class="op">(</span><span class="va">...</span><span class="op">)</span></span>
+<span class="r-in">  <span class="fu">validate</span><span class="op">(</span><span class="va">...</span><span class="op">)</span></span>
+<span class="r-in">  <span class="va">scheduler</span><span class="op">$</span><span class="fu">step</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer>
-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Applies a 1D adaptive average pooling over an input signal composed of several input planes.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
-    <div class="hidden name"><code>nn_adaptive_avg_pool1d.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Applies a 1D adaptive average pooling over an input signal composed of several input planes.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-pooling.R" class="external-link"><code>R/nn-pooling.R</code></a></small>
+      <div class="d-none name"><code>nn_adaptive_avg_pool1d.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>The output size is H, for any input size.
 The number of output features is equal to the number of input planes.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_adaptive_avg_pool1d</span><span class='op'>(</span><span class='va'>output_size</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>output_size</th>
-      <td><p>the target output size H</p></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='co'># target output size of 5</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>=</span> <span class='fu'>nn_adaptive_avg_pool1d</span><span class='op'>(</span><span class='fl'>5</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>64</span>, <span class='fl'>8</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
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-      <h2 data-toc-skip>Contents</h2>
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-</div>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_adaptive_avg_pool1d</span><span class="op">(</span><span class="va">output_size</span><span class="op">)</span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>output_size</dt>
+<dd><p>the target output size H</p></dd>
+</dl></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="co"># target output size of 5</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">nn_adaptive_avg_pool1d</span><span class="op">(</span><span class="fl">5</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">64</span>, <span class="fl">8</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span> <span class="op">&lt;-</span> <span class="fu">m</span><span class="op">(</span><span class="va">input</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
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+      </ul></div>
 
+    
+  </div>
+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Applies a 2D adaptive average pooling over an input signal composed of several input planes.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
-    <div class="hidden name"><code>nn_adaptive_avg_pool2d.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Applies a 2D adaptive average pooling over an input signal composed of several input planes.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-pooling.R" class="external-link"><code>R/nn-pooling.R</code></a></small>
+      <div class="d-none name"><code>nn_adaptive_avg_pool2d.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>The output is of size H x W, for any input size.
 The number of output features is equal to the number of input planes.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_adaptive_avg_pool2d</span><span class='op'>(</span><span class='va'>output_size</span><span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_adaptive_avg_pool2d</span><span class="op">(</span><span class="va">output_size</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>output_size</th>
-      <td><p>the target output size of the image of the form H x W.
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>output_size</dt>
+<dd><p>the target output size of the image of the form H x W.
 Can be a tuple (H, W) or a single H for a square image H x H.
 H and W can be either a <code>int</code>, or <code>NULL</code> which means the size will
-be the same as that of the input.</p></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='co'># target output size of 5x7</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_adaptive_avg_pool2d</span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>5</span>,<span class='fl'>7</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>64</span>, <span class='fl'>8</span>, <span class='fl'>9</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span></span>
-<span class='r-in'><span class='co'># target output size of 7x7 (square)</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_adaptive_avg_pool2d</span><span class='op'>(</span><span class='fl'>7</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>64</span>, <span class='fl'>10</span>, <span class='fl'>9</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+be the same as that of the input.</p></dd>
+</dl></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="co"># target output size of 5x7</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">nn_adaptive_avg_pool2d</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">5</span>, <span class="fl">7</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">64</span>, <span class="fl">8</span>, <span class="fl">9</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span> <span class="op">&lt;-</span> <span class="fu">m</span><span class="op">(</span><span class="va">input</span><span class="op">)</span></span>
+<span class="r-in"><span class="co"># target output size of 7x7 (square)</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">nn_adaptive_avg_pool2d</span><span class="op">(</span><span class="fl">7</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">64</span>, <span class="fl">10</span>, <span class="fl">9</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span> <span class="op">&lt;-</span> <span class="fu">m</span><span class="op">(</span><span class="va">input</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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+      </ul><form class="form-inline my-2 my-lg-0" role="search">
+        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
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+    
+  </div>
+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Applies a 3D adaptive average pooling over an input signal composed of several input planes.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
-    <div class="hidden name"><code>nn_adaptive_avg_pool3d.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Applies a 3D adaptive average pooling over an input signal composed of several input planes.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-pooling.R" class="external-link"><code>R/nn-pooling.R</code></a></small>
+      <div class="d-none name"><code>nn_adaptive_avg_pool3d.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>The output is of size D x H x W, for any input size.
 The number of output features is equal to the number of input planes.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_adaptive_avg_pool3d</span><span class='op'>(</span><span class='va'>output_size</span><span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_adaptive_avg_pool3d</span><span class="op">(</span><span class="va">output_size</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>output_size</th>
-      <td><p>the target output size of the form D x H x W.
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>output_size</dt>
+<dd><p>the target output size of the form D x H x W.
 Can be a tuple (D, H, W) or a single number D for a cube D x D x D.
 D, H and W can be either a <code>int</code>, or <code>None</code> which means the size will
-be the same as that of the input.</p></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='co'># target output size of 5x7x9</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_adaptive_avg_pool3d</span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>5</span>,<span class='fl'>7</span>,<span class='fl'>9</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>64</span>, <span class='fl'>8</span>, <span class='fl'>9</span>, <span class='fl'>10</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span></span>
-<span class='r-in'><span class='co'># target output size of 7x7x7 (cube)</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_adaptive_avg_pool3d</span><span class='op'>(</span><span class='fl'>7</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>64</span>, <span class='fl'>10</span>, <span class='fl'>9</span>, <span class='fl'>8</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+be the same as that of the input.</p></dd>
+</dl></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="co"># target output size of 5x7x9</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">nn_adaptive_avg_pool3d</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">5</span>, <span class="fl">7</span>, <span class="fl">9</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">64</span>, <span class="fl">8</span>, <span class="fl">9</span>, <span class="fl">10</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span> <span class="op">&lt;-</span> <span class="fu">m</span><span class="op">(</span><span class="va">input</span><span class="op">)</span></span>
+<span class="r-in"><span class="co"># target output size of 7x7x7 (cube)</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">nn_adaptive_avg_pool3d</span><span class="op">(</span><span class="fl">7</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">64</span>, <span class="fl">10</span>, <span class="fl">9</span>, <span class="fl">8</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span> <span class="op">&lt;-</span> <span class="fu">m</span><span class="op">(</span><span class="va">input</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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+  </div>
+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>AdaptiveLogSoftmaxWithLoss module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn_adaptive.R'><code>R/nn_adaptive.R</code></a></small>
-    <div class="hidden name"><code>nn_adaptive_log_softmax_with_loss.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>AdaptiveLogSoftmaxWithLoss module</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn_adaptive.R" class="external-link"><code>R/nn_adaptive.R</code></a></small>
+      <div class="d-none name"><code>nn_adaptive_log_softmax_with_loss.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Efficient softmax approximation as described in
-<a href='https://arxiv.org/abs/1609.04309'>Efficient softmax approximation for GPUs by Edouard Grave, Armand Joulin, Moustapha Cissé, David Grangier, and Hervé Jégou</a></p>
+<a href="https://arxiv.org/abs/1609.04309" class="external-link">Efficient softmax approximation for GPUs by Edouard Grave, Armand Joulin, Moustapha Cissé, David Grangier, and Hervé Jégou</a></p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_adaptive_log_softmax_with_loss</span><span class='op'>(</span>
-  <span class='va'>in_features</span>,
-  <span class='va'>n_classes</span>,
-  <span class='va'>cutoffs</span>,
-  div_value <span class='op'>=</span> <span class='fl'>4</span>,
-  head_bias <span class='op'>=</span> <span class='cn'>FALSE</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>in_features</th>
-      <td><p>(int): Number of features in the input tensor</p></td>
-    </tr>
-    <tr>
-      <th>n_classes</th>
-      <td><p>(int): Number of classes in the dataset</p></td>
-    </tr>
-    <tr>
-      <th>cutoffs</th>
-      <td><p>(Sequence): Cutoffs used to assign targets to their buckets</p></td>
-    </tr>
-    <tr>
-      <th>div_value</th>
-      <td><p>(float, optional): value used as an exponent to compute sizes
-of the clusters. Default: 4.0</p></td>
-    </tr>
-    <tr>
-      <th>head_bias</th>
-      <td><p>(bool, optional): If <code>True</code>, adds a bias term to the 'head' of the
-adaptive softmax. Default: <code>False</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_adaptive_log_softmax_with_loss</span><span class="op">(</span>
+  <span class="va">in_features</span>,
+  <span class="va">n_classes</span>,
+  <span class="va">cutoffs</span>,
+  div_value <span class="op">=</span> <span class="fl">4</span>,
+  head_bias <span class="op">=</span> <span class="cn">FALSE</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
-    <p><code>NamedTuple</code> with <code>output</code> and <code>loss</code> fields:</p><ul>
-<li><p><strong>output</strong> is a Tensor of size <code>N</code> containing computed target
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>in_features</dt>
+<dd><p>(int): Number of features in the input tensor</p></dd>
+<dt>n_classes</dt>
+<dd><p>(int): Number of classes in the dataset</p></dd>
+<dt>cutoffs</dt>
+<dd><p>(Sequence): Cutoffs used to assign targets to their buckets</p></dd>
+<dt>div_value</dt>
+<dd><p>(float, optional): value used as an exponent to compute sizes
+of the clusters. Default: 4.0</p></dd>
+<dt>head_bias</dt>
+<dd><p>(bool, optional): If <code>True</code>, adds a bias term to the 'head' of the
+adaptive softmax. Default: <code>False</code></p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p><code>NamedTuple</code> with <code>output</code> and <code>loss</code> fields:</p><ul><li><p><strong>output</strong> is a Tensor of size <code>N</code> containing computed target
 log probabilities for each example</p></li>
 <li><p><strong>loss</strong> is a Scalar representing the computed negative
 log likelihood loss</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
+</ul></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>Adaptive softmax is an approximate strategy for training models with large
 output spaces. It is most effective when the label distribution is highly
 imbalanced, for example in natural language modelling, where the word
@@ -251,8 +132,7 @@ present are evaluated.</p>
 <p>The idea is that the clusters which are accessed frequently
 (like the first one, containing most frequent labels), should also be cheap
 to compute -- that is, contain a small number of assigned labels.
-We highly recommend taking a look at the original paper for more details.</p><ul>
-<li><p><code>cutoffs</code> should be an ordered Sequence of integers sorted
+We highly recommend taking a look at the original paper for more details.</p><ul><li><p><code>cutoffs</code> should be an ordered Sequence of integers sorted
 in the increasing order.
 It controls number of clusters and the partitioning of targets into
 clusters. For example setting <code>cutoffs = c(10, 100, 1000)</code>
@@ -271,59 +151,50 @@ and indices starting from \(1\)).</p></li>
 <li><p><code>head_bias</code> if set to True, adds a bias term to the 'head' of the
 adaptive softmax. See paper for details. Set to False in the official
 implementation.</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
+</ul></div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p>This module returns a <code>NamedTuple</code> with <code>output</code>
 and <code>loss</code> fields. See further documentation for details.</p>
 <p>To compute log-probabilities for all classes, the <code>log_prob</code>
 method can be used.</p>
-    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></a></h2>
     
 
 <p>Labels passed as inputs to this module should be sorted according to
 their frequency. This means that the most frequent label should be
 represented by the index <code>0</code>, and the least frequent
 label should be represented by the index <code>n_classes - 1</code>.</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
     
 
-<ul>
-<li><p>input: \((N, \mbox{in\_features})\)</p></li>
+<ul><li><p>input: \((N, \mbox{in\_features})\)</p></li>
 <li><p>target: \((N)\) where each value satisfies \(0 &lt;= \mbox{target[i]} &lt;= \mbox{n\_classes}\)</p></li>
 <li><p>output1: \((N)\)</p></li>
 <li><p>output2: <code>Scalar</code></p></li>
-</ul>
+</ul></div>
 
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-      <h2 data-toc-skip>Contents</h2>
-    </nav>
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-
-      <footer>
-      <div class="copyright">
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+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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+<div class="pkgdown-footer-right">
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Applies a 1D adaptive max pooling over an input signal composed of several input planes.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
-    <div class="hidden name"><code>nn_adaptive_max_pool1d.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Applies a 1D adaptive max pooling over an input signal composed of several input planes.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-pooling.R" class="external-link"><code>R/nn-pooling.R</code></a></small>
+      <div class="d-none name"><code>nn_adaptive_max_pool1d.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>The output size is H, for any input size.
 The number of output features is equal to the number of input planes.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_adaptive_max_pool1d</span><span class='op'>(</span><span class='va'>output_size</span>, return_indices <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>output_size</th>
-      <td><p>the target output size H</p></td>
-    </tr>
-    <tr>
-      <th>return_indices</th>
-      <td><p>if <code>TRUE</code>, will return the indices along with the outputs.
-Useful to pass to <code><a href='nn_max_unpool1d.html'>nn_max_unpool1d()</a></code>. Default: <code>FALSE</code></p></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='co'># target output size of 5</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_adaptive_max_pool1d</span><span class='op'>(</span><span class='fl'>5</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>64</span>, <span class='fl'>8</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
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-      <h2 data-toc-skip>Contents</h2>
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-</div>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_adaptive_max_pool1d</span><span class="op">(</span><span class="va">output_size</span>, return_indices <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>output_size</dt>
+<dd><p>the target output size H</p></dd>
+<dt>return_indices</dt>
+<dd><p>if <code>TRUE</code>, will return the indices along with the outputs.
+Useful to pass to <code><a href="nn_max_unpool1d.html">nn_max_unpool1d()</a></code>. Default: <code>FALSE</code></p></dd>
+</dl></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="co"># target output size of 5</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">nn_adaptive_max_pool1d</span><span class="op">(</span><span class="fl">5</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">64</span>, <span class="fl">8</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span> <span class="op">&lt;-</span> <span class="fu">m</span><span class="op">(</span><span class="va">input</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Applies a 2D adaptive max pooling over an input signal composed of several input planes.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
-    <div class="hidden name"><code>nn_adaptive_max_pool2d.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Applies a 2D adaptive max pooling over an input signal composed of several input planes.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-pooling.R" class="external-link"><code>R/nn-pooling.R</code></a></small>
+      <div class="d-none name"><code>nn_adaptive_max_pool2d.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>The output is of size H x W, for any input size.
 The number of output features is equal to the number of input planes.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_adaptive_max_pool2d</span><span class='op'>(</span><span class='va'>output_size</span>, return_indices <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_adaptive_max_pool2d</span><span class="op">(</span><span class="va">output_size</span>, return_indices <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>output_size</th>
-      <td><p>the target output size of the image of the form H x W.
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>output_size</dt>
+<dd><p>the target output size of the image of the form H x W.
 Can be a tuple <code>(H, W)</code> or a single H for a square image H x H.
 H and W can be either a <code>int</code>, or <code>None</code> which means the size will
-be the same as that of the input.</p></td>
-    </tr>
-    <tr>
-      <th>return_indices</th>
-      <td><p>if <code>TRUE</code>, will return the indices along with the outputs.
-Useful to pass to <code><a href='nn_max_unpool2d.html'>nn_max_unpool2d()</a></code>. Default: <code>FALSE</code></p></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='co'># target output size of 5x7</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_adaptive_max_pool2d</span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>5</span>,<span class='fl'>7</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>64</span>, <span class='fl'>8</span>, <span class='fl'>9</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span></span>
-<span class='r-in'><span class='co'># target output size of 7x7 (square)</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_adaptive_max_pool2d</span><span class='op'>(</span><span class='fl'>7</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>64</span>, <span class='fl'>10</span>, <span class='fl'>9</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+be the same as that of the input.</p></dd>
+<dt>return_indices</dt>
+<dd><p>if <code>TRUE</code>, will return the indices along with the outputs.
+Useful to pass to <code><a href="nn_max_unpool2d.html">nn_max_unpool2d()</a></code>. Default: <code>FALSE</code></p></dd>
+</dl></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="co"># target output size of 5x7</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">nn_adaptive_max_pool2d</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">5</span>, <span class="fl">7</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">64</span>, <span class="fl">8</span>, <span class="fl">9</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span> <span class="op">&lt;-</span> <span class="fu">m</span><span class="op">(</span><span class="va">input</span><span class="op">)</span></span>
+<span class="r-in"><span class="co"># target output size of 7x7 (square)</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">nn_adaptive_max_pool2d</span><span class="op">(</span><span class="fl">7</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">64</span>, <span class="fl">10</span>, <span class="fl">9</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span> <span class="op">&lt;-</span> <span class="fu">m</span><span class="op">(</span><span class="va">input</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
+    </div>
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Applies a 3D adaptive max pooling over an input signal composed of several input planes.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
-    <div class="hidden name"><code>nn_adaptive_max_pool3d.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Applies a 3D adaptive max pooling over an input signal composed of several input planes.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-pooling.R" class="external-link"><code>R/nn-pooling.R</code></a></small>
+      <div class="d-none name"><code>nn_adaptive_max_pool3d.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>The output is of size D x H x W, for any input size.
 The number of output features is equal to the number of input planes.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_adaptive_max_pool3d</span><span class='op'>(</span><span class='va'>output_size</span>, return_indices <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_adaptive_max_pool3d</span><span class="op">(</span><span class="va">output_size</span>, return_indices <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>output_size</th>
-      <td><p>the target output size of the image of the form D x H x W.
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>output_size</dt>
+<dd><p>the target output size of the image of the form D x H x W.
 Can be a tuple (D, H, W) or a single D for a cube D x D x D.
 D, H and W can be either a <code>int</code>, or <code>None</code> which means the size will
-be the same as that of the input.</p></td>
-    </tr>
-    <tr>
-      <th>return_indices</th>
-      <td><p>if <code>TRUE</code>, will return the indices along with the outputs.
-Useful to pass to <code><a href='nn_max_unpool3d.html'>nn_max_unpool3d()</a></code>. Default: <code>FALSE</code></p></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='co'># target output size of 5x7x9</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_adaptive_max_pool3d</span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>5</span>,<span class='fl'>7</span>,<span class='fl'>9</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>64</span>, <span class='fl'>8</span>, <span class='fl'>9</span>, <span class='fl'>10</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span></span>
-<span class='r-in'><span class='co'># target output size of 7x7x7 (cube)</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_adaptive_max_pool3d</span><span class='op'>(</span><span class='fl'>7</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>64</span>, <span class='fl'>10</span>, <span class='fl'>9</span>, <span class='fl'>8</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+be the same as that of the input.</p></dd>
+<dt>return_indices</dt>
+<dd><p>if <code>TRUE</code>, will return the indices along with the outputs.
+Useful to pass to <code><a href="nn_max_unpool3d.html">nn_max_unpool3d()</a></code>. Default: <code>FALSE</code></p></dd>
+</dl></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="co"># target output size of 5x7x9</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">nn_adaptive_max_pool3d</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">5</span>, <span class="fl">7</span>, <span class="fl">9</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">64</span>, <span class="fl">8</span>, <span class="fl">9</span>, <span class="fl">10</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span> <span class="op">&lt;-</span> <span class="fu">m</span><span class="op">(</span><span class="va">input</span><span class="op">)</span></span>
+<span class="r-in"><span class="co"># target output size of 7x7x7 (cube)</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">nn_adaptive_max_pool3d</span><span class="op">(</span><span class="fl">7</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">64</span>, <span class="fl">10</span>, <span class="fl">9</span>, <span class="fl">8</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span> <span class="op">&lt;-</span> <span class="fu">m</span><span class="op">(</span><span class="va">input</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="In the simplest case, the output value of the layer with input size \((N, C, L)\),
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+      </ul></div>
 
+    
+  </div>
+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Applies a 1D average pooling over an input signal composed of several
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Applies a 1D average pooling over an input signal composed of several
 input planes.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
-    <div class="hidden name"><code>nn_avg_pool1d.Rd</code></div>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-pooling.R" class="external-link"><code>R/nn-pooling.R</code></a></small>
+      <div class="d-none name"><code>nn_avg_pool1d.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>In the simplest case, the output value of the layer with input size \((N, C, L)\),
 output \((N, C, L_{out})\) and <code>kernel_size</code> \(k\)
 can be precisely described as:</p>
-<p>$$
-  \mbox{out}(N_i, C_j, l) = \frac{1}{k} \sum_{m=0}^{k-1}
-\mbox{input}(N_i, C_j, \mbox{stride} \times l + m)
-$$</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_avg_pool1d</span><span class='op'>(</span>
-  <span class='va'>kernel_size</span>,
-  stride <span class='op'>=</span> <span class='cn'>NULL</span>,
-  padding <span class='op'>=</span> <span class='fl'>0</span>,
-  ceil_mode <span class='op'>=</span> <span class='cn'>FALSE</span>,
-  count_include_pad <span class='op'>=</span> <span class='cn'>TRUE</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>the size of the window</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>the stride of the window. Default value is <code>kernel_size</code></p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>implicit zero padding to be added on both sides</p></td>
-    </tr>
-    <tr>
-      <th>ceil_mode</th>
-      <td><p>when TRUE, will use <code>ceil</code> instead of <code>floor</code> to compute the output shape</p></td>
-    </tr>
-    <tr>
-      <th>count_include_pad</th>
-      <td><p>when TRUE, will include the zero-padding in the averaging calculation</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_avg_pool1d</span><span class="op">(</span>
+  <span class="va">kernel_size</span>,
+  stride <span class="op">=</span> <span class="cn">NULL</span>,
+  padding <span class="op">=</span> <span class="fl">0</span>,
+  ceil_mode <span class="op">=</span> <span class="cn">FALSE</span>,
+  count_include_pad <span class="op">=</span> <span class="cn">TRUE</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
-    <p>If <code>padding</code> is non-zero, then the input is implicitly zero-padded on both sides
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>kernel_size</dt>
+<dd><p>the size of the window</p></dd>
+<dt>stride</dt>
+<dd><p>the stride of the window. Default value is <code>kernel_size</code></p></dd>
+<dt>padding</dt>
+<dd><p>implicit zero padding to be added on both sides</p></dd>
+<dt>ceil_mode</dt>
+<dd><p>when TRUE, will use <code>ceil</code> instead of <code>floor</code> to compute the output shape</p></dd>
+<dt>count_include_pad</dt>
+<dd><p>when TRUE, will include the zero-padding in the averaging calculation</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
+    <p>$$
+  \mbox{out}(N_i, C_j, l) = \frac{1}{k} \sum_{m=0}^{k-1}
+\mbox{input}(N_i, C_j, \mbox{stride} \times l + m)
+$$</p>
+<p>If <code>padding</code> is non-zero, then the input is implicitly zero-padded on both sides
 for <code>padding</code> number of points.</p>
 <p>The parameters <code>kernel_size</code>, <code>stride</code>, <code>padding</code> can each be
 an <code>int</code> or a one-element tuple.</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
     
 
-<ul>
-<li><p>Input: \((N, C, L_{in})\)</p></li>
+<ul><li><p>Input: \((N, C, L_{in})\)</p></li>
 <li><p>Output: \((N, C, L_{out})\), where</p></li>
-</ul>
-
-<p>$$
+</ul><p>$$
   L_{out} = \left\lfloor \frac{L_{in} +
       2 \times \mbox{padding} - \mbox{kernel\_size}}{\mbox{stride}} + 1\right\rfloor
 $$</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'>  </span>
-<span class='r-in'><span class='co'># pool with window of size=3, stride=2</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_avg_pool1d</span><span class='op'>(</span><span class='fl'>3</span>, stride<span class='op'>=</span><span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>m</span><span class='op'>(</span><span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>1</span>, <span class='fl'>8</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> (1,.,.) = </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  -0.5559 -0.2468  0.7077</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{1,1,3} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="co"># pool with window of size=3, stride=2</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">nn_avg_pool1d</span><span class="op">(</span><span class="fl">3</span>, stride <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">m</span><span class="op">(</span><span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">1</span>, <span class="fl">8</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> (1,.,.) = </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.4666 -0.0392  0.3291</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{1,1,3} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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-      <div class="copyright">
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+    <footer><div class="pkgdown-footer-left">
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-<meta property="og:description" content="In the simplest case, the output value of the layer with input size \((N, C, H, W)\),
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="In the simplest case, the output value of the layer with input size \((N, C, H, W)\),
 output \((N, C, H_{out}, W_{out})\) and kernel_size \((kH, kW)\)
-can be precisely described as:
-$$
-  out(N_i, C_j, h, w)  = \frac{1}{kH * kW} \sum_{m=0}^{kH-1} \sum_{n=0}^{kW-1}
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+planes. — nn_avg_pool2d"><meta property="og:description" content="In the simplest case, the output value of the layer with input size \((N, C, H, W)\),
+output \((N, C, H_{out}, W_{out})\) and kernel_size \((kH, kW)\)
+can be precisely described as:"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Applies a 2D average pooling over an input signal composed of several input
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Applies a 2D average pooling over an input signal composed of several input
 planes.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
-    <div class="hidden name"><code>nn_avg_pool2d.Rd</code></div>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-pooling.R" class="external-link"><code>R/nn-pooling.R</code></a></small>
+      <div class="d-none name"><code>nn_avg_pool2d.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>In the simplest case, the output value of the layer with input size \((N, C, H, W)\),
 output \((N, C, H_{out}, W_{out})\) and <code>kernel_size</code> \((kH, kW)\)
 can be precisely described as:</p>
-<p>$$
-  out(N_i, C_j, h, w)  = \frac{1}{kH * kW} \sum_{m=0}^{kH-1} \sum_{n=0}^{kW-1}
-input(N_i, C_j, stride[0] \times h + m, stride[1] \times w + n)
-$$</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_avg_pool2d</span><span class='op'>(</span>
-  <span class='va'>kernel_size</span>,
-  stride <span class='op'>=</span> <span class='cn'>NULL</span>,
-  padding <span class='op'>=</span> <span class='fl'>0</span>,
-  ceil_mode <span class='op'>=</span> <span class='cn'>FALSE</span>,
-  count_include_pad <span class='op'>=</span> <span class='cn'>TRUE</span>,
-  divisor_override <span class='op'>=</span> <span class='cn'>NULL</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>the size of the window</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>the stride of the window. Default value is <code>kernel_size</code></p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>implicit zero padding to be added on both sides</p></td>
-    </tr>
-    <tr>
-      <th>ceil_mode</th>
-      <td><p>when TRUE, will use <code>ceil</code> instead of <code>floor</code> to compute the output shape</p></td>
-    </tr>
-    <tr>
-      <th>count_include_pad</th>
-      <td><p>when TRUE, will include the zero-padding in the averaging calculation</p></td>
-    </tr>
-    <tr>
-      <th>divisor_override</th>
-      <td><p>if specified, it will be used as divisor, otherwise <code>kernel_size</code> will be used</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_avg_pool2d</span><span class="op">(</span>
+  <span class="va">kernel_size</span>,
+  stride <span class="op">=</span> <span class="cn">NULL</span>,
+  padding <span class="op">=</span> <span class="fl">0</span>,
+  ceil_mode <span class="op">=</span> <span class="cn">FALSE</span>,
+  count_include_pad <span class="op">=</span> <span class="cn">TRUE</span>,
+  divisor_override <span class="op">=</span> <span class="cn">NULL</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
-    <p>If <code>padding</code> is non-zero, then the input is implicitly zero-padded on both sides
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>kernel_size</dt>
+<dd><p>the size of the window</p></dd>
+<dt>stride</dt>
+<dd><p>the stride of the window. Default value is <code>kernel_size</code></p></dd>
+<dt>padding</dt>
+<dd><p>implicit zero padding to be added on both sides</p></dd>
+<dt>ceil_mode</dt>
+<dd><p>when TRUE, will use <code>ceil</code> instead of <code>floor</code> to compute the output shape</p></dd>
+<dt>count_include_pad</dt>
+<dd><p>when TRUE, will include the zero-padding in the averaging calculation</p></dd>
+<dt>divisor_override</dt>
+<dd><p>if specified, it will be used as divisor, otherwise <code>kernel_size</code> will be used</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
+    <p>$$
+  out(N_i, C_j, h, w)  = \frac{1}{kH * kW} \sum_{m=0}^{kH-1} \sum_{n=0}^{kW-1}
+input(N_i, C_j, stride[0] \times h + m, stride[1] \times w + n)
+$$</p>
+<p>If <code>padding</code> is non-zero, then the input is implicitly zero-padded on both sides
 for <code>padding</code> number of points.</p>
-<p>The parameters <code>kernel_size</code>, <code>stride</code>, <code>padding</code> can either be:</p><ul>
-<li><p>a single <code>int</code> -- in which case the same value is used for the height and width dimension</p></li>
+<p>The parameters <code>kernel_size</code>, <code>stride</code>, <code>padding</code> can either be:</p><ul><li><p>a single <code>int</code> -- in which case the same value is used for the height and width dimension</p></li>
 <li><p>a <code>tuple</code> of two ints -- in which case, the first <code>int</code> is used for the height dimension,
 and the second <code>int</code> for the width dimension</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
+</ul></div>
+    <div class="section level2">
+    <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
     
 
-<ul>
-<li><p>Input: \((N, C, H_{in}, W_{in})\)</p></li>
+<ul><li><p>Input: \((N, C, H_{in}, W_{in})\)</p></li>
 <li><p>Output: \((N, C, H_{out}, W_{out})\), where</p></li>
-</ul>
-
-<p>$$
+</ul><p>$$
   H_{out} = \left\lfloor\frac{H_{in}  + 2 \times \mbox{padding}[0] -
       \mbox{kernel\_size}[0]}{\mbox{stride}[0]} + 1\right\rfloor
 $$
@@ -269,44 +142,38 @@ $$
   W_{out} = \left\lfloor\frac{W_{in}  + 2 \times \mbox{padding}[1] -
       \mbox{kernel\_size}[1]}{\mbox{stride}[1]} + 1\right\rfloor
 $$</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'>  </span>
-<span class='r-in'><span class='co'># pool of square window of size=3, stride=2</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_avg_pool2d</span><span class='op'>(</span><span class='fl'>3</span>, stride<span class='op'>=</span><span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='co'># pool of non-square window</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_avg_pool2d</span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>2</span><span class='op'>)</span>, stride<span class='op'>=</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>1</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>50</span>, <span class='fl'>32</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="co"># pool of square window of size=3, stride=2</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">nn_avg_pool2d</span><span class="op">(</span><span class="fl">3</span>, stride <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="co"># pool of non-square window</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">nn_avg_pool2d</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">2</span><span class="op">)</span>, stride <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">1</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">20</span>, <span class="fl">16</span>, <span class="fl">50</span>, <span class="fl">32</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span> <span class="op">&lt;-</span> <span class="fu">m</span><span class="op">(</span><span class="va">input</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
+    </div>
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="In the simplest case, the output value of the layer with input size \((N, C, D, H, W)\),
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Applies a 3D average pooling over an input signal composed of several input
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Applies a 3D average pooling over an input signal composed of several input
 planes.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
-    <div class="hidden name"><code>nn_avg_pool3d.Rd</code></div>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-pooling.R" class="external-link"><code>R/nn-pooling.R</code></a></small>
+      <div class="d-none name"><code>nn_avg_pool3d.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>In the simplest case, the output value of the layer with input size \((N, C, D, H, W)\),
 output \((N, C, D_{out}, H_{out}, W_{out})\) and <code>kernel_size</code> \((kD, kH, kW)\)
 can be precisely described as:</p>
-<p>$$
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_avg_pool3d</span><span class="op">(</span>
+  <span class="va">kernel_size</span>,
+  stride <span class="op">=</span> <span class="cn">NULL</span>,
+  padding <span class="op">=</span> <span class="fl">0</span>,
+  ceil_mode <span class="op">=</span> <span class="cn">FALSE</span>,
+  count_include_pad <span class="op">=</span> <span class="cn">TRUE</span>,
+  divisor_override <span class="op">=</span> <span class="cn">NULL</span>
+<span class="op">)</span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>kernel_size</dt>
+<dd><p>the size of the window</p></dd>
+<dt>stride</dt>
+<dd><p>the stride of the window. Default value is <code>kernel_size</code></p></dd>
+<dt>padding</dt>
+<dd><p>implicit zero padding to be added on all three sides</p></dd>
+<dt>ceil_mode</dt>
+<dd><p>when TRUE, will use <code>ceil</code> instead of <code>floor</code> to compute the output shape</p></dd>
+<dt>count_include_pad</dt>
+<dd><p>when TRUE, will include the zero-padding in the averaging calculation</p></dd>
+<dt>divisor_override</dt>
+<dd><p>if specified, it will be used as divisor, otherwise <code>kernel_size</code> will be used</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
+    <p>$$
 \begin{array}{ll}
 \mbox{out}(N_i, C_j, d, h, w) = &amp; \sum_{k=0}^{kD-1} \sum_{m=0}^{kH-1} \sum_{n=0}^{kW-1} \\
 &amp; \frac{\mbox{input}(N_i, C_j, \mbox{stride}[0] \times d + k, \mbox{stride}[1] \times h + m, \mbox{stride}[2] \times w + n)}{kD \times kH \times kW}
 \end{array}
 $$</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>nn_avg_pool3d</span><span class='op'>(</span>
-  <span class='va'>kernel_size</span>,
-  stride <span class='op'>=</span> <span class='cn'>NULL</span>,
-  padding <span class='op'>=</span> <span class='fl'>0</span>,
-  ceil_mode <span class='op'>=</span> <span class='cn'>FALSE</span>,
-  count_include_pad <span class='op'>=</span> <span class='cn'>TRUE</span>,
-  divisor_override <span class='op'>=</span> <span class='cn'>NULL</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>the size of the window</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>the stride of the window. Default value is <code>kernel_size</code></p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>implicit zero padding to be added on all three sides</p></td>
-    </tr>
-    <tr>
-      <th>ceil_mode</th>
-      <td><p>when TRUE, will use <code>ceil</code> instead of <code>floor</code> to compute the output shape</p></td>
-    </tr>
-    <tr>
-      <th>count_include_pad</th>
-      <td><p>when TRUE, will include the zero-padding in the averaging calculation</p></td>
-    </tr>
-    <tr>
-      <th>divisor_override</th>
-      <td><p>if specified, it will be used as divisor, otherwise <code>kernel_size</code> will be used</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>If <code>padding</code> is non-zero, then the input is implicitly zero-padded on all three sides
+<p>If <code>padding</code> is non-zero, then the input is implicitly zero-padded on all three sides
 for <code>padding</code> number of points.</p>
-<p>The parameters <code>kernel_size</code>, <code>stride</code> can either be:</p><ul>
-<li><p>a single <code>int</code> -- in which case the same value is used for the depth, height and width dimension</p></li>
+<p>The parameters <code>kernel_size</code>, <code>stride</code> can either be:</p><ul><li><p>a single <code>int</code> -- in which case the same value is used for the depth, height and width dimension</p></li>
 <li><p>a <code>tuple</code> of three ints -- in which case, the first <code>int</code> is used for the depth dimension,
 the second <code>int</code> for the height dimension and the third <code>int</code> for the width dimension</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
+</ul></div>
+    <div class="section level2">
+    <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
     
 
-<ul>
-<li><p>Input: \((N, C, D_{in}, H_{in}, W_{in})\)</p></li>
+<ul><li><p>Input: \((N, C, D_{in}, H_{in}, W_{in})\)</p></li>
 <li><p>Output: \((N, C, D_{out}, H_{out}, W_{out})\), where</p></li>
-</ul>
-
-<p>$$
+</ul><p>$$
   D_{out} = \left\lfloor\frac{D_{in} + 2 \times \mbox{padding}[0] -
       \mbox{kernel\_size}[0]}{\mbox{stride}[0]} + 1\right\rfloor
 $$
@@ -277,44 +148,38 @@ $$
   W_{out} = \left\lfloor\frac{W_{in} + 2 \times \mbox{padding}[2] -
       \mbox{kernel\_size}[2]}{\mbox{stride}[2]} + 1\right\rfloor
 $$</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'>  </span>
-<span class='r-in'><span class='co'># pool of square window of size=3, stride=2</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>=</span> <span class='fu'>nn_avg_pool3d</span><span class='op'>(</span><span class='fl'>3</span>, stride<span class='op'>=</span><span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='co'># pool of non-square window</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>=</span> <span class='fu'>nn_avg_pool3d</span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>2</span>, <span class='fl'>2</span><span class='op'>)</span>, stride<span class='op'>=</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>1</span>, <span class='fl'>2</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>50</span>,<span class='fl'>44</span>, <span class='fl'>31</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span> <span class='op'>=</span> <span class='fu'>m</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="co"># pool of square window of size=3, stride=2</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">nn_avg_pool3d</span><span class="op">(</span><span class="fl">3</span>, stride <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="co"># pool of non-square window</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">nn_avg_pool3d</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">2</span>, <span class="fl">2</span><span class="op">)</span>, stride <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">1</span>, <span class="fl">2</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">20</span>, <span class="fl">16</span>, <span class="fl">50</span>, <span class="fl">44</span>, <span class="fl">31</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span> <span class="op">&lt;-</span> <span class="fu">m</span><span class="op">(</span><span class="va">input</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer>
-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
 </div>
 
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+<div class="pkgdown-footer-right">
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-    <h1>BatchNorm1D module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-batchnorm.R'><code>R/nn-batchnorm.R</code></a></small>
-    <div class="hidden name"><code>nn_batch_norm1d.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>BatchNorm1D module</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-batchnorm.R" class="external-link"><code>R/nn-batchnorm.R</code></a></small>
+      <div class="d-none name"><code>nn_batch_norm1d.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Applies Batch Normalization over a 2D or 3D input (a mini-batch of 1D
 inputs with optional additional channel dimension) as described in the paper
-<a href='https://arxiv.org/abs/1502.03167'>Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift</a></p>
+<a href="https://arxiv.org/abs/1502.03167" class="external-link">Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift</a></p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_batch_norm1d</span><span class='op'>(</span>
-  <span class='va'>num_features</span>,
-  eps <span class='op'>=</span> <span class='fl'>1e-05</span>,
-  momentum <span class='op'>=</span> <span class='fl'>0.1</span>,
-  affine <span class='op'>=</span> <span class='cn'>TRUE</span>,
-  track_running_stats <span class='op'>=</span> <span class='cn'>TRUE</span>
-<span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_batch_norm1d</span><span class="op">(</span>
+  <span class="va">num_features</span>,
+  eps <span class="op">=</span> <span class="fl">1e-05</span>,
+  momentum <span class="op">=</span> <span class="fl">0.1</span>,
+  affine <span class="op">=</span> <span class="cn">TRUE</span>,
+  track_running_stats <span class="op">=</span> <span class="cn">TRUE</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>num_features</th>
-      <td><p>\(C\) from an expected input of size
-\((N, C, L)\) or \(L\) from input of size \((N, L)\)</p></td>
-    </tr>
-    <tr>
-      <th>eps</th>
-      <td><p>a value added to the denominator for numerical stability.
-Default: 1e-5</p></td>
-    </tr>
-    <tr>
-      <th>momentum</th>
-      <td><p>the value used for the running_mean and running_var
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>num_features</dt>
+<dd><p>\(C\) from an expected input of size
+\((N, C, L)\) or \(L\) from input of size \((N, L)\)</p></dd>
+<dt>eps</dt>
+<dd><p>a value added to the denominator for numerical stability.
+Default: 1e-5</p></dd>
+<dt>momentum</dt>
+<dd><p>the value used for the running_mean and running_var
 computation. Can be set to <code>NULL</code> for cumulative moving average
-(i.e. simple average). Default: 0.1</p></td>
-    </tr>
-    <tr>
-      <th>affine</th>
-      <td><p>a boolean value that when set to <code>TRUE</code>, this module has
-learnable affine parameters. Default: <code>TRUE</code></p></td>
-    </tr>
-    <tr>
-      <th>track_running_stats</th>
-      <td><p>a boolean value that when set to <code>TRUE</code>, this
+(i.e. simple average). Default: 0.1</p></dd>
+<dt>affine</dt>
+<dd><p>a boolean value that when set to <code>TRUE</code>, this module has
+learnable affine parameters. Default: <code>TRUE</code></p></dd>
+<dt>track_running_stats</dt>
+<dd><p>a boolean value that when set to <code>TRUE</code>, this
 module tracks the running mean and variance, and when set to <code>FALSE</code>,
 this module does not track such statistics and always uses batch
-statistics in both training and eval modes. Default: <code>TRUE</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
+statistics in both training and eval modes. Default: <code>TRUE</code></p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>$$
 y = \frac{x - \mathrm{E}[x]}{\sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta
 $$</p>
@@ -250,8 +134,9 @@ of 0.1.
 If <code>track_running_stats</code> is set to <code>FALSE</code>, this layer then does not
 keep running estimates, and batch statistics are instead used during
 evaluation time as well.</p>
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     
 
 
@@ -263,52 +148,44 @@ where \(\hat{x}\) is the estimated statistic and \(x_t\) is the
 new observed value.</p>
 <p>Because the Batch Normalization is done over the <code>C</code> dimension, computing statistics
 on <code>(N, L)</code> slices, it's common terminology to call this Temporal Batch Normalization.</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
     
 
-<ul>
-<li><p>Input: \((N, C)\) or \((N, C, L)\)</p></li>
+<ul><li><p>Input: \((N, C)\) or \((N, C, L)\)</p></li>
 <li><p>Output: \((N, C)\) or \((N, C, L)\) (same shape as input)</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='co'># With Learnable Parameters</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_batch_norm1d</span><span class='op'>(</span><span class='fl'>100</span><span class='op'>)</span></span>
-<span class='r-in'><span class='co'># Without Learnable Parameters</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_batch_norm1d</span><span class='op'>(</span><span class='fl'>100</span>, affine <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>20</span>, <span class='fl'>100</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span> </span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+</ul></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="co"># With Learnable Parameters</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">nn_batch_norm1d</span><span class="op">(</span><span class="fl">100</span><span class="op">)</span></span>
+<span class="r-in"><span class="co"># Without Learnable Parameters</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">nn_batch_norm1d</span><span class="op">(</span><span class="fl">100</span>, affine <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">20</span>, <span class="fl">100</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span> <span class="op">&lt;-</span> <span class="fu">m</span><span class="op">(</span><span class="va">input</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer>
-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
 </div>
 
-<div class="pkgdown">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p>
+<div class="pkgdown-footer-right">
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>BatchNorm2D</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-batchnorm.R'><code>R/nn-batchnorm.R</code></a></small>
-    <div class="hidden name"><code>nn_batch_norm2d.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>BatchNorm2D</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-batchnorm.R" class="external-link"><code>R/nn-batchnorm.R</code></a></small>
+      <div class="d-none name"><code>nn_batch_norm2d.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Applies Batch Normalization over a 4D input (a mini-batch of 2D inputs
 additional channel dimension) as described in the paper
-<a href='https://arxiv.org/abs/1502.03167'>Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift</a>.</p>
+<a href="https://arxiv.org/abs/1502.03167" class="external-link">Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift</a>.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_batch_norm2d</span><span class='op'>(</span>
-  <span class='va'>num_features</span>,
-  eps <span class='op'>=</span> <span class='fl'>1e-05</span>,
-  momentum <span class='op'>=</span> <span class='fl'>0.1</span>,
-  affine <span class='op'>=</span> <span class='cn'>TRUE</span>,
-  track_running_stats <span class='op'>=</span> <span class='cn'>TRUE</span>
-<span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_batch_norm2d</span><span class="op">(</span>
+  <span class="va">num_features</span>,
+  eps <span class="op">=</span> <span class="fl">1e-05</span>,
+  momentum <span class="op">=</span> <span class="fl">0.1</span>,
+  affine <span class="op">=</span> <span class="cn">TRUE</span>,
+  track_running_stats <span class="op">=</span> <span class="cn">TRUE</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>num_features</th>
-      <td><p>\(C\) from an expected input of size
-\((N, C, H, W)\)</p></td>
-    </tr>
-    <tr>
-      <th>eps</th>
-      <td><p>a value added to the denominator for numerical stability.
-Default: 1e-5</p></td>
-    </tr>
-    <tr>
-      <th>momentum</th>
-      <td><p>the value used for the running_mean and running_var
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>num_features</dt>
+<dd><p>\(C\) from an expected input of size
+\((N, C, H, W)\)</p></dd>
+<dt>eps</dt>
+<dd><p>a value added to the denominator for numerical stability.
+Default: 1e-5</p></dd>
+<dt>momentum</dt>
+<dd><p>the value used for the running_mean and running_var
 computation. Can be set to <code>None</code> for cumulative moving average
-(i.e. simple average). Default: 0.1</p></td>
-    </tr>
-    <tr>
-      <th>affine</th>
-      <td><p>a boolean value that when set to <code>TRUE</code>, this module has
-learnable affine parameters. Default: <code>TRUE</code></p></td>
-    </tr>
-    <tr>
-      <th>track_running_stats</th>
-      <td><p>a boolean value that when set to <code>TRUE</code>, this
+(i.e. simple average). Default: 0.1</p></dd>
+<dt>affine</dt>
+<dd><p>a boolean value that when set to <code>TRUE</code>, this module has
+learnable affine parameters. Default: <code>TRUE</code></p></dd>
+<dt>track_running_stats</dt>
+<dd><p>a boolean value that when set to <code>TRUE</code>, this
 module tracks the running mean and variance, and when set to <code>FALSE</code>,
 this module does not track such statistics and uses batch statistics instead
 in both training and eval modes if the running mean and variance are <code>None</code>.
-Default: <code>TRUE</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
+Default: <code>TRUE</code></p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>$$
   y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta
 $$</p>
@@ -252,8 +136,9 @@ of 0.1.</p>
 <p>If <code>track_running_stats</code> is set to <code>FALSE</code>, this layer then does not
 keep running estimates, and batch statistics are instead used during
 evaluation time as well.</p>
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p>This <code>momentum</code> argument is different from one used in optimizer
 classes and the conventional notion of momentum. Mathematically, the
 update rule for running statistics here is
@@ -262,52 +147,44 @@ where \(\hat{x}\) is the estimated statistic and \(x_t\) is the
 new observed value.
 Because the Batch Normalization is done over the <code>C</code> dimension, computing statistics
 on <code>(N, H, W)</code> slices, it's common terminology to call this Spatial Batch Normalization.</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
     
 
-<ul>
-<li><p>Input: \((N, C, H, W)\)</p></li>
+<ul><li><p>Input: \((N, C, H, W)\)</p></li>
 <li><p>Output: \((N, C, H, W)\) (same shape as input)</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='co'># With Learnable Parameters</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_batch_norm2d</span><span class='op'>(</span><span class='fl'>100</span><span class='op'>)</span></span>
-<span class='r-in'><span class='co'># Without Learnable Parameters</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_batch_norm2d</span><span class='op'>(</span><span class='fl'>100</span>, affine<span class='op'>=</span><span class='cn'>FALSE</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>20</span>, <span class='fl'>100</span>, <span class='fl'>35</span>, <span class='fl'>45</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+</ul></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="co"># With Learnable Parameters</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">nn_batch_norm2d</span><span class="op">(</span><span class="fl">100</span><span class="op">)</span></span>
+<span class="r-in"><span class="co"># Without Learnable Parameters</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">nn_batch_norm2d</span><span class="op">(</span><span class="fl">100</span>, affine <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">20</span>, <span class="fl">100</span>, <span class="fl">35</span>, <span class="fl">45</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span> <span class="op">&lt;-</span> <span class="fu">m</span><span class="op">(</span><span class="va">input</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer>
-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
 </div>
 
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-  <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p>
+<div class="pkgdown-footer-right">
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+        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
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+    
+  </div>
+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>BatchNorm3D</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-batchnorm.R'><code>R/nn-batchnorm.R</code></a></small>
-    <div class="hidden name"><code>nn_batch_norm3d.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>BatchNorm3D</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-batchnorm.R" class="external-link"><code>R/nn-batchnorm.R</code></a></small>
+      <div class="d-none name"><code>nn_batch_norm3d.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Applies Batch Normalization over a 5D input (a mini-batch of 3D inputs
 with additional channel dimension) as described in the paper
-<a href='https://arxiv.org/abs/1502.03167'>Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift</a>.</p>
+<a href="https://arxiv.org/abs/1502.03167" class="external-link">Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift</a>.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_batch_norm3d</span><span class='op'>(</span>
-  <span class='va'>num_features</span>,
-  eps <span class='op'>=</span> <span class='fl'>1e-05</span>,
-  momentum <span class='op'>=</span> <span class='fl'>0.1</span>,
-  affine <span class='op'>=</span> <span class='cn'>TRUE</span>,
-  track_running_stats <span class='op'>=</span> <span class='cn'>TRUE</span>
-<span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_batch_norm3d</span><span class="op">(</span>
+  <span class="va">num_features</span>,
+  eps <span class="op">=</span> <span class="fl">1e-05</span>,
+  momentum <span class="op">=</span> <span class="fl">0.1</span>,
+  affine <span class="op">=</span> <span class="cn">TRUE</span>,
+  track_running_stats <span class="op">=</span> <span class="cn">TRUE</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>num_features</th>
-      <td><p>\(C\) from an expected input of size
-\((N, C, D, H, W)\)</p></td>
-    </tr>
-    <tr>
-      <th>eps</th>
-      <td><p>a value added to the denominator for numerical stability.
-Default: 1e-5</p></td>
-    </tr>
-    <tr>
-      <th>momentum</th>
-      <td><p>the value used for the running_mean and running_var
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>num_features</dt>
+<dd><p>\(C\) from an expected input of size
+\((N, C, D, H, W)\)</p></dd>
+<dt>eps</dt>
+<dd><p>a value added to the denominator for numerical stability.
+Default: 1e-5</p></dd>
+<dt>momentum</dt>
+<dd><p>the value used for the running_mean and running_var
 computation. Can be set to <code>None</code> for cumulative moving average
-(i.e. simple average). Default: 0.1</p></td>
-    </tr>
-    <tr>
-      <th>affine</th>
-      <td><p>a boolean value that when set to <code>TRUE</code>, this module has
-learnable affine parameters. Default: <code>TRUE</code></p></td>
-    </tr>
-    <tr>
-      <th>track_running_stats</th>
-      <td><p>a boolean value that when set to <code>TRUE</code>, this
+(i.e. simple average). Default: 0.1</p></dd>
+<dt>affine</dt>
+<dd><p>a boolean value that when set to <code>TRUE</code>, this module has
+learnable affine parameters. Default: <code>TRUE</code></p></dd>
+<dt>track_running_stats</dt>
+<dd><p>a boolean value that when set to <code>TRUE</code>, this
 module tracks the running mean and variance, and when set to <code>FALSE</code>,
 this module does not track such statistics and uses batch statistics instead
 in both training and eval modes if the running mean and variance are <code>None</code>.
-Default: <code>TRUE</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
+Default: <code>TRUE</code></p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>$$
   y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta
 $$</p>
@@ -253,8 +137,9 @@ of 0.1.</p>
 <p>If <code>track_running_stats</code> is set to <code>FALSE</code>, this layer then does not
 keep running estimates, and batch statistics are instead used during
 evaluation time as well.</p>
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p>This <code>momentum</code> argument is different from one used in optimizer
 classes and the conventional notion of momentum. Mathematically, the
 update rule for running statistics here is:
@@ -264,52 +149,44 @@ new observed value.</p>
 <p>Because the Batch Normalization is done over the <code>C</code> dimension, computing
 statistics on <code>(N, D, H, W)</code> slices, it's common terminology to call this
 Volumetric Batch Normalization or Spatio-temporal Batch Normalization.</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
     
 
-<ul>
-<li><p>Input: \((N, C, D, H, W)\)</p></li>
+<ul><li><p>Input: \((N, C, D, H, W)\)</p></li>
 <li><p>Output: \((N, C, D, H, W)\) (same shape as input)</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='co'># With Learnable Parameters</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_batch_norm3d</span><span class='op'>(</span><span class='fl'>100</span><span class='op'>)</span></span>
-<span class='r-in'><span class='co'># Without Learnable Parameters</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_batch_norm3d</span><span class='op'>(</span><span class='fl'>100</span>, affine<span class='op'>=</span><span class='cn'>FALSE</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>20</span>, <span class='fl'>100</span>, <span class='fl'>35</span>, <span class='fl'>45</span>, <span class='fl'>55</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+</ul></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="co"># With Learnable Parameters</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">nn_batch_norm3d</span><span class="op">(</span><span class="fl">100</span><span class="op">)</span></span>
+<span class="r-in"><span class="co"># Without Learnable Parameters</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">nn_batch_norm3d</span><span class="op">(</span><span class="fl">100</span>, affine <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">20</span>, <span class="fl">100</span>, <span class="fl">35</span>, <span class="fl">45</span>, <span class="fl">55</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span> <span class="op">&lt;-</span> <span class="fu">m</span><span class="op">(</span><span class="va">input</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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-      <div class="copyright">
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Binary cross entropy loss</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-loss.R'><code>R/nn-loss.R</code></a></small>
-    <div class="hidden name"><code>nn_bce_loss.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Binary cross entropy loss</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-loss.R" class="external-link"><code>R/nn-loss.R</code></a></small>
+      <div class="d-none name"><code>nn_bce_loss.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Creates a criterion that measures the Binary Cross Entropy
 between the target and the output:</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_bce_loss</span><span class='op'>(</span>weight <span class='op'>=</span> <span class='cn'>NULL</span>, reduction <span class='op'>=</span> <span class='st'>"mean"</span><span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_bce_loss</span><span class="op">(</span>weight <span class="op">=</span> <span class="cn">NULL</span>, reduction <span class="op">=</span> <span class="st">"mean"</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>weight</th>
-      <td><p>(Tensor, optional): a manual rescaling weight given to the loss
-of each batch element. If given, has to be a Tensor of size <code>nbatch</code>.</p></td>
-    </tr>
-    <tr>
-      <th>reduction</th>
-      <td><p>(string, optional): Specifies the reduction to apply to the output:
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>weight</dt>
+<dd><p>(Tensor, optional): a manual rescaling weight given to the loss
+of each batch element. If given, has to be a Tensor of size <code>nbatch</code>.</p></dd>
+<dt>reduction</dt>
+<dd><p>(string, optional): Specifies the reduction to apply to the output:
 <code>'none'</code> | <code>'mean'</code> | <code>'sum'</code>. <code>'none'</code>: no reduction will be applied,
 <code>'mean'</code>: the sum of the output will be divided by the number of
 elements in the output, <code>'sum'</code>: the output will be summed. Note: <code>size_average</code>
 and <code>reduce</code> are in the process of being deprecated, and in the meantime,
-specifying either of those two args will override <code>reduction</code>. Default: <code>'mean'</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
+specifying either of those two args will override <code>reduction</code>. Default: <code>'mean'</code></p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>The unreduced (i.e. with <code>reduction</code> set to <code>'none'</code>) loss can be described as:
 $$
   \ell(x, y) = L = \{l_1,\dots,l_N\}^\top, \quad
@@ -244,55 +133,47 @@ and using it for things like linear regression would not be straight-forward.
 Our solution is that BCELoss clamps its log function outputs to be greater than
 or equal to -100. This way, we can always have a finite loss value and a linear
 backward method.</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
     
 
-<ul>
-<li><p>Input: \((N, *)\) where \(*\) means, any number of additional
+<ul><li><p>Input: \((N, *)\) where \(*\) means, any number of additional
 dimensions</p></li>
 <li><p>Target: \((N, *)\), same shape as the input</p></li>
 <li><p>Output: scalar. If <code>reduction</code> is <code>'none'</code>, then \((N, *)\), same
 shape as input.</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>&lt;-</span> <span class='fu'><a href='nn_sigmoid.html'>nn_sigmoid</a></span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>loss</span> <span class='op'>&lt;-</span> <span class='fu'>nn_bce_loss</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>3</span>, requires_grad<span class='op'>=</span><span class='cn'>TRUE</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>target</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_rand.html'>torch_rand</a></span><span class='op'>(</span><span class='fl'>3</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span> <span class='op'>&lt;-</span> <span class='fu'>loss</span><span class='op'>(</span><span class='fu'>m</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span>, <span class='va'>target</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span><span class='op'>$</span><span class='fu'>backward</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+</ul></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu"><a href="nn_sigmoid.html">nn_sigmoid</a></span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">loss</span> <span class="op">&lt;-</span> <span class="fu">nn_bce_loss</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">3</span>, requires_grad <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">target</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_rand.html">torch_rand</a></span><span class="op">(</span><span class="fl">3</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span> <span class="op">&lt;-</span> <span class="fu">loss</span><span class="op">(</span><span class="fu">m</span><span class="op">(</span><span class="va">input</span><span class="op">)</span>, <span class="va">target</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span><span class="op">$</span><span class="fu">backward</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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diff --git a/reference/nn_bce_with_logits_loss.html b/reference/nn_bce_with_logits_loss.html
index ff9a4a5b3..de7962a3c 100644
--- a/reference/nn_bce_with_logits_loss.html
+++ b/reference/nn_bce_with_logits_loss.html
@@ -1,228 +1,117 @@
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-<meta property="og:description" content="This loss combines a Sigmoid layer and the BCELoss in one single
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="This loss combines a Sigmoid layer and the BCELoss in one single
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+we take advantage of the log-sum-exp trick for numerical stability."><title>BCE with logits loss — nn_bce_with_logits_loss • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.1.0/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.1.0/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="BCE with logits loss — nn_bce_with_logits_loss"><meta property="og:description" content="This loss combines a Sigmoid layer and the BCELoss in one single
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+followed by a BCELoss as, by combining the operations into one layer,
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>BCE with logits loss</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-loss.R'><code>R/nn-loss.R</code></a></small>
-    <div class="hidden name"><code>nn_bce_with_logits_loss.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>BCE with logits loss</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-loss.R" class="external-link"><code>R/nn-loss.R</code></a></small>
+      <div class="d-none name"><code>nn_bce_with_logits_loss.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>This loss combines a <code>Sigmoid</code> layer and the <code>BCELoss</code> in one single
 class. This version is more numerically stable than using a plain <code>Sigmoid</code>
 followed by a <code>BCELoss</code> as, by combining the operations into one layer,
 we take advantage of the log-sum-exp trick for numerical stability.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_bce_with_logits_loss</span><span class='op'>(</span>weight <span class='op'>=</span> <span class='cn'>NULL</span>, reduction <span class='op'>=</span> <span class='st'>"mean"</span>, pos_weight <span class='op'>=</span> <span class='cn'>NULL</span><span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_bce_with_logits_loss</span><span class="op">(</span>weight <span class="op">=</span> <span class="cn">NULL</span>, reduction <span class="op">=</span> <span class="st">"mean"</span>, pos_weight <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>weight</th>
-      <td><p>(Tensor, optional): a manual rescaling weight given to the loss
-of each batch element. If given, has to be a Tensor of size <code>nbatch</code>.</p></td>
-    </tr>
-    <tr>
-      <th>reduction</th>
-      <td><p>(string, optional): Specifies the reduction to apply to the output:
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>weight</dt>
+<dd><p>(Tensor, optional): a manual rescaling weight given to the loss
+of each batch element. If given, has to be a Tensor of size <code>nbatch</code>.</p></dd>
+<dt>reduction</dt>
+<dd><p>(string, optional): Specifies the reduction to apply to the output:
 <code>'none'</code> | <code>'mean'</code> | <code>'sum'</code>. <code>'none'</code>: no reduction will be applied,
 <code>'mean'</code>: the sum of the output will be divided by the number of
 elements in the output, <code>'sum'</code>: the output will be summed. Note: <code>size_average</code>
 and <code>reduce</code> are in the process of being deprecated, and in the meantime,
-specifying either of those two args will override <code>reduction</code>. Default: <code>'mean'</code></p></td>
-    </tr>
-    <tr>
-      <th>pos_weight</th>
-      <td><p>(Tensor, optional): a weight of positive examples.
-Must be a vector with length equal to the number of classes.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
+specifying either of those two args will override <code>reduction</code>. Default: <code>'mean'</code></p></dd>
+<dt>pos_weight</dt>
+<dd><p>(Tensor, optional): a weight of positive examples.
+Must be a vector with length equal to the number of classes.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>The unreduced (i.e. with <code>reduction</code> set to <code>'none'</code>) loss can be described as:</p>
 <p>$$
   \ell(x, y) = L = \{l_1,\dots,l_N\}^\top, \quad
@@ -256,62 +145,54 @@ classification,</p>
 For example, if a dataset contains 100 positive and 300 negative examples of a single class,
 then <code>pos_weight</code> for the class should be equal to \(\frac{300}{100}=3\).
 The loss would act as if the dataset contains \(3\times 100=300\) positive examples.</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
     
 
-<ul>
-<li><p>Input: \((N, *)\) where \(*\) means, any number of additional dimensions</p></li>
+<ul><li><p>Input: \((N, *)\) where \(*\) means, any number of additional dimensions</p></li>
 <li><p>Target: \((N, *)\), same shape as the input</p></li>
 <li><p>Output: scalar. If <code>reduction</code> is <code>'none'</code>, then \((N, *)\), same
 shape as input.</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>loss</span> <span class='op'>&lt;-</span> <span class='fu'>nn_bce_with_logits_loss</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>3</span>, requires_grad<span class='op'>=</span><span class='cn'>TRUE</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>target</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span><span class='op'>(</span><span class='fl'>3</span><span class='op'>)</span><span class='op'>$</span><span class='fu'>random_</span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span> <span class='op'>&lt;-</span> <span class='fu'>loss</span><span class='op'>(</span><span class='va'>input</span>, <span class='va'>target</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span><span class='op'>$</span><span class='fu'>backward</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>target</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_ones.html'>torch_ones</a></span><span class='op'>(</span><span class='fl'>10</span>, <span class='fl'>64</span>, dtype<span class='op'>=</span><span class='fu'><a href='torch_dtype.html'>torch_float32</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span>  <span class='co'># 64 classes, batch size = 10</span></span>
-<span class='r-in'><span class='va'>output</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_full.html'>torch_full</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>10</span>, <span class='fl'>64</span><span class='op'>)</span>, <span class='fl'>1.5</span><span class='op'>)</span>  <span class='co'># A prediction (logit)</span></span>
-<span class='r-in'><span class='va'>pos_weight</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_ones.html'>torch_ones</a></span><span class='op'>(</span><span class='fl'>64</span><span class='op'>)</span>  <span class='co'># All weights are equal to 1</span></span>
-<span class='r-in'><span class='va'>criterion</span> <span class='op'>&lt;-</span> <span class='fu'>nn_bce_with_logits_loss</span><span class='op'>(</span>pos_weight<span class='op'>=</span><span class='va'>pos_weight</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>criterion</span><span class='op'>(</span><span class='va'>output</span>, <span class='va'>target</span><span class='op'>)</span>  <span class='co'># -log(sigmoid(1.5))</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> 0.201413</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+</ul></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">loss</span> <span class="op">&lt;-</span> <span class="fu">nn_bce_with_logits_loss</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">3</span>, requires_grad <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">target</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_empty.html">torch_empty</a></span><span class="op">(</span><span class="fl">3</span><span class="op">)</span><span class="op">$</span><span class="fu">random_</span><span class="op">(</span><span class="fl">1</span>, <span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span> <span class="op">&lt;-</span> <span class="fu">loss</span><span class="op">(</span><span class="va">input</span>, <span class="va">target</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span><span class="op">$</span><span class="fu">backward</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">target</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_ones.html">torch_ones</a></span><span class="op">(</span><span class="fl">10</span>, <span class="fl">64</span>, dtype <span class="op">=</span> <span class="fu"><a href="torch_dtype.html">torch_float32</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="co"># 64 classes, batch size = 10</span></span>
+<span class="r-in"><span class="va">output</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_full.html">torch_full</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">10</span>, <span class="fl">64</span><span class="op">)</span>, <span class="fl">1.5</span><span class="op">)</span> <span class="co"># A prediction (logit)</span></span>
+<span class="r-in"><span class="va">pos_weight</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_ones.html">torch_ones</a></span><span class="op">(</span><span class="fl">64</span><span class="op">)</span> <span class="co"># All weights are equal to 1</span></span>
+<span class="r-in"><span class="va">criterion</span> <span class="op">&lt;-</span> <span class="fu">nn_bce_with_logits_loss</span><span class="op">(</span>pos_weight <span class="op">=</span> <span class="va">pos_weight</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">criterion</span><span class="op">(</span><span class="va">output</span>, <span class="va">target</span><span class="op">)</span> <span class="co"># -log(sigmoid(1.5))</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 0.201413</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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 <div class="row">
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-    <div class="page-header">
-    <h1>Bilinear module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-linear.R'><code>R/nn-linear.R</code></a></small>
-    <div class="hidden name"><code>nn_bilinear.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Bilinear module</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-linear.R" class="external-link"><code>R/nn-linear.R</code></a></small>
+      <div class="d-none name"><code>nn_bilinear.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Applies a bilinear transformation to the incoming data
 \(y = x_1^T A x_2 + b\)</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_bilinear</span><span class='op'>(</span><span class='va'>in1_features</span>, <span class='va'>in2_features</span>, <span class='va'>out_features</span>, bias <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>in1_features</th>
-      <td><p>size of each first input sample</p></td>
-    </tr>
-    <tr>
-      <th>in2_features</th>
-      <td><p>size of each second input sample</p></td>
-    </tr>
-    <tr>
-      <th>out_features</th>
-      <td><p>size of each output sample</p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>If set to <code>FALSE</code>, the layer will not learn an additive bias.
-Default: <code>TRUE</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_bilinear</span><span class="op">(</span><span class="va">in1_features</span>, <span class="va">in2_features</span>, <span class="va">out_features</span>, bias <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>in1_features</dt>
+<dd><p>size of each first input sample</p></dd>
+<dt>in2_features</dt>
+<dd><p>size of each second input sample</p></dd>
+<dt>out_features</dt>
+<dd><p>size of each output sample</p></dd>
+<dt>bias</dt>
+<dd><p>If set to <code>FALSE</code>, the layer will not learn an additive bias.
+Default: <code>TRUE</code></p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
     
 
-<ul>
-<li><p>Input1: \((N, *, H_{in1})\) \(H_{in1}=\mbox{in1\_features}\) and
+<ul><li><p>Input1: \((N, *, H_{in1})\) \(H_{in1}=\mbox{in1\_features}\) and
 \(*\) means any number of additional dimensions. All but the last
 dimension of the inputs should be the same.</p></li>
 <li><p>Input2: \((N, *, H_{in2})\) where \(H_{in2}=\mbox{in2\_features}\).</p></li>
 <li><p>Output: \((N, *, H_{out})\) where \(H_{out}=\mbox{out\_features}\)
 and all but the last dimension are the same shape as the input.</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="attributes"><a class="anchor" href="#attributes"></a>Attributes</h2>
-
+</ul></div>
+    <div class="section level2">
+    <h2 id="attributes">Attributes<a class="anchor" aria-label="anchor" href="#attributes"></a></h2>
     
 
-<ul>
-<li><p>weight: the learnable weights of the module of shape
+<ul><li><p>weight: the learnable weights of the module of shape
 \((\mbox{out\_features}, \mbox{in1\_features}, \mbox{in2\_features})\).
 The values are initialized from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\), where
 \(k = \frac{1}{\mbox{in1\_features}}\)</p></li>
@@ -241,45 +123,37 @@ The values are initialized from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\), where
 If <code>bias</code> is <code>TRUE</code>, the values are initialized from
 \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\), where
 \(k = \frac{1}{\mbox{in1\_features}}\)</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_bilinear</span><span class='op'>(</span><span class='fl'>20</span>, <span class='fl'>30</span>, <span class='fl'>50</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>input1</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>128</span>, <span class='fl'>20</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>input2</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>128</span>, <span class='fl'>30</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span> <span class='op'>=</span> <span class='fu'>m</span><span class='op'>(</span><span class='va'>input1</span>, <span class='va'>input2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span><span class='op'>(</span><span class='va'>output</span><span class='op'>$</span><span class='fu'>size</span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> </span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [1] 128  50</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+</ul></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">nn_bilinear</span><span class="op">(</span><span class="fl">20</span>, <span class="fl">30</span>, <span class="fl">50</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">input1</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">128</span>, <span class="fl">20</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">input2</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">128</span>, <span class="fl">30</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span> <span class="op">&lt;-</span> <span class="fu">m</span><span class="op">(</span><span class="va">input1</span>, <span class="va">input2</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">output</span><span class="op">$</span><span class="fu">size</span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 128  50</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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index 9c96e8905..d1e606ef1 100644
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-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Creates a nn_buffer</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn.R'><code>R/nn.R</code></a></small>
-    <div class="hidden name"><code>nn_buffer.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Creates a nn_buffer</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn.R" class="external-link"><code>R/nn.R</code></a></small>
+      <div class="d-none name"><code>nn_buffer.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Indicates that a tensor is a buffer in a nn_module</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_buffer</span><span class='op'>(</span><span class='va'>x</span>, persistent <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_buffer</span><span class="op">(</span><span class="va">x</span>, persistent <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>x</th>
-      <td><p>the tensor that will be converted to nn_buffer</p></td>
-    </tr>
-    <tr>
-      <th>persistent</th>
-      <td><p>whether the buffer should be persistent or not.</p></td>
-    </tr>
-    </table>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>x</dt>
+<dd><p>the tensor that will be converted to nn_buffer</p></dd>
+<dt>persistent</dt>
+<dd><p>whether the buffer should be persistent or not.</p></dd>
+</dl></div>
 
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>CELU module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
-    <div class="hidden name"><code>nn_celu.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>CELU module</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-activation.R" class="external-link"><code>R/nn-activation.R</code></a></small>
+      <div class="d-none name"><code>nn_celu.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Applies the element-wise function:</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_celu</span><span class='op'>(</span>alpha <span class='op'>=</span> <span class='fl'>1</span>, inplace <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>alpha</th>
-      <td><p>the \(\alpha\) value for the CELU formulation. Default: 1.0</p></td>
-    </tr>
-    <tr>
-      <th>inplace</th>
-      <td><p>can optionally do the operation in-place. Default: <code>FALSE</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_celu</span><span class="op">(</span>alpha <span class="op">=</span> <span class="fl">1</span>, inplace <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>alpha</dt>
+<dd><p>the \(\alpha\) value for the CELU formulation. Default: 1.0</p></dd>
+<dt>inplace</dt>
+<dd><p>can optionally do the operation in-place. Default: <code>FALSE</code></p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>$$
   \mbox{CELU}(x) = \max(0,x) + \min(0, \alpha * (\exp(x/\alpha) - 1))
 $$</p>
 <p>More details can be found in the paper
-<a href='https://arxiv.org/abs/1704.07483'>Continuously Differentiable Exponential Linear Units</a>.</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
+<a href="https://arxiv.org/abs/1704.07483" class="external-link">Continuously Differentiable Exponential Linear Units</a>.</p>
+    </div>
+    <div class="section level2">
+    <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
     
 
-<ul>
-<li><p>Input: \((N, *)\) where <code>*</code> means, any number of additional
+<ul><li><p>Input: \((N, *)\) where <code>*</code> means, any number of additional
 dimensions</p></li>
 <li><p>Output: \((N, *)\), same shape as the input</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_celu</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+</ul></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">nn_celu</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span> <span class="op">&lt;-</span> <span class="fu">m</span><span class="op">(</span><span class="va">input</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
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-    <h1>Sparsemax activation</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
-    <div class="hidden name"><code>nn_contrib_sparsemax.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Sparsemax activation</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-activation.R" class="external-link"><code>R/nn-activation.R</code></a></small>
+      <div class="d-none name"><code>nn_contrib_sparsemax.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Sparsemax activation module.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_contrib_sparsemax</span><span class='op'>(</span>dim <span class='op'>=</span> <span class='op'>-</span><span class='fl'>1</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>dim</th>
-      <td><p>The dimension over which to apply the sparsemax function. (-1)</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_contrib_sparsemax</span><span class="op">(</span>dim <span class="op">=</span> <span class="op">-</span><span class="fl">1</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>dim</dt>
+<dd><p>The dimension over which to apply the sparsemax function. (-1)</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>The SparseMax activation is described in
-<a href='https://arxiv.org/abs/1602.02068'>'From Softmax to Sparsemax: A Sparse Model of Attention and Multi-Label Classification'</a>
-The implementation is based on <a href='https://github.com/aced125/sparsemax/tree/master/sparsemax'>aced125/sparsemax</a></p>
+<a href="https://arxiv.org/abs/1602.02068" class="external-link">'From Softmax to Sparsemax: A Sparse Model of Attention and Multi-Label Classification'</a>
+The implementation is based on <a href="https://github.com/aced125/sparsemax/tree/master/sparsemax" class="external-link">aced125/sparsemax</a></p>
+    </div>
 
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 In the simplest case, the output value of the layer with input size
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+\((N, C_{\mbox{in}}, L)\) and output \((N, C_{\mbox{out}}, L_{\mbox{out}})\) can be
+precisely described as:"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Conv1D module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-conv.R'><code>R/nn-conv.R</code></a></small>
-    <div class="hidden name"><code>nn_conv1d.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Conv1D module</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-conv.R" class="external-link"><code>R/nn-conv.R</code></a></small>
+      <div class="d-none name"><code>nn_conv1d.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Applies a 1D convolution over an input signal composed of several input
 planes.
 In the simplest case, the output value of the layer with input size
@@ -197,87 +92,68 @@ In the simplest case, the output value of the layer with input size
 precisely described as:</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_conv1d</span><span class='op'>(</span>
-  <span class='va'>in_channels</span>,
-  <span class='va'>out_channels</span>,
-  <span class='va'>kernel_size</span>,
-  stride <span class='op'>=</span> <span class='fl'>1</span>,
-  padding <span class='op'>=</span> <span class='fl'>0</span>,
-  dilation <span class='op'>=</span> <span class='fl'>1</span>,
-  groups <span class='op'>=</span> <span class='fl'>1</span>,
-  bias <span class='op'>=</span> <span class='cn'>TRUE</span>,
-  padding_mode <span class='op'>=</span> <span class='st'>"zeros"</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>in_channels</th>
-      <td><p>(int): Number of channels in the input image</p></td>
-    </tr>
-    <tr>
-      <th>out_channels</th>
-      <td><p>(int): Number of channels produced by the convolution</p></td>
-    </tr>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>(int or tuple): Size of the convolving kernel</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>(int or tuple, optional): Stride of the convolution. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>(int, tuple or str, optional) – Padding added to both sides of
-the input. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>dilation</th>
-      <td><p>(int or tuple, optional): Spacing between kernel
-elements. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>groups</th>
-      <td><p>(int, optional): Number of blocked connections from input
-channels to output channels. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>(bool, optional): If <code>TRUE</code>, adds a learnable bias to the
-output. Default: <code>TRUE</code></p></td>
-    </tr>
-    <tr>
-      <th>padding_mode</th>
-      <td><p>(string, optional): <code>'zeros'</code>, <code>'reflect'</code>,
-<code>'replicate'</code> or <code>'circular'</code>. Default: <code>'zeros'</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_conv1d</span><span class="op">(</span>
+  <span class="va">in_channels</span>,
+  <span class="va">out_channels</span>,
+  <span class="va">kernel_size</span>,
+  stride <span class="op">=</span> <span class="fl">1</span>,
+  padding <span class="op">=</span> <span class="fl">0</span>,
+  dilation <span class="op">=</span> <span class="fl">1</span>,
+  groups <span class="op">=</span> <span class="fl">1</span>,
+  bias <span class="op">=</span> <span class="cn">TRUE</span>,
+  padding_mode <span class="op">=</span> <span class="st">"zeros"</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>in_channels</dt>
+<dd><p>(int): Number of channels in the input image</p></dd>
+<dt>out_channels</dt>
+<dd><p>(int): Number of channels produced by the convolution</p></dd>
+<dt>kernel_size</dt>
+<dd><p>(int or tuple): Size of the convolving kernel</p></dd>
+<dt>stride</dt>
+<dd><p>(int or tuple, optional): Stride of the convolution. Default: 1</p></dd>
+<dt>padding</dt>
+<dd><p>(int, tuple or str, optional) – Padding added to both sides of
+the input. Default: 0</p></dd>
+<dt>dilation</dt>
+<dd><p>(int or tuple, optional): Spacing between kernel
+elements. Default: 1</p></dd>
+<dt>groups</dt>
+<dd><p>(int, optional): Number of blocked connections from input
+channels to output channels. Default: 1</p></dd>
+<dt>bias</dt>
+<dd><p>(bool, optional): If <code>TRUE</code>, adds a learnable bias to the
+output. Default: <code>TRUE</code></p></dd>
+<dt>padding_mode</dt>
+<dd><p>(string, optional): <code>'zeros'</code>, <code>'reflect'</code>,
+<code>'replicate'</code> or <code>'circular'</code>. Default: <code>'zeros'</code></p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>$$
 \mbox{out}(N_i, C_{\mbox{out}_j}) = \mbox{bias}(C_{\mbox{out}_j}) +
   \sum_{k = 0}^{C_{in} - 1} \mbox{weight}(C_{\mbox{out}_j}, k)
 \star \mbox{input}(N_i, k)
 $$</p>
 <p>where \(\star\) is the valid
-<a href='https://en.wikipedia.org/wiki/Cross-correlation'>cross-correlation</a> operator,
+<a href="https://en.wikipedia.org/wiki/Cross-correlation" class="external-link">cross-correlation</a> operator,
 \(N\) is a batch size, \(C\) denotes a number of channels,
-\(L\) is a length of signal sequence.</p><ul>
-<li><p><code>stride</code> controls the stride for the cross-correlation, a single
+\(L\) is a length of signal sequence.</p><ul><li><p><code>stride</code> controls the stride for the cross-correlation, a single
 number or a one-element tuple.</p></li>
 <li><p><code>padding</code> controls the amount of implicit zero-paddings on both sides
 for <code>padding</code> number of points.</p></li>
 <li><p><code>dilation</code> controls the spacing between the kernel points; also
 known as the à trous algorithm. It is harder to describe, but this
-<a href='https://github.com/vdumoulin/conv_arithmetic/blob/master/README.md'>link</a>
+<a href="https://github.com/vdumoulin/conv_arithmetic/blob/master/README.md" class="external-link">link</a>
 has a nice visualization of what <code>dilation</code> does.</p></li>
 <li><p><code>groups</code> controls the connections between inputs and outputs.
 <code>in_channels</code> and <code>out_channels</code> must both be divisible by
-<code>groups</code>. For example,</p><ul>
-<li><p>At groups=1, all inputs are convolved to all outputs.</p></li>
+<code>groups</code>. For example,</p><ul><li><p>At groups=1, all inputs are convolved to all outputs.</p></li>
 <li><p>At groups=2, the operation becomes equivalent to having two conv
 layers side by side, each seeing half the input channels,
 and producing half the output channels, and both subsequently
@@ -286,10 +162,9 @@ concatenated.</p></li>
 its own set of filters,
 of size \(\left\lfloor\frac{out\_channels}{in\_channels}\right\rfloor\).</p></li>
 </ul></li>
-</ul>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
+</ul></div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     
 
 
@@ -303,25 +178,23 @@ literature as depthwise convolution.
 In other words, for an input of size \((N, C_{in}, L_{in})\),
 a depthwise convolution with a depthwise multiplier <code>K</code>, can be constructed by arguments
 \((C_{\mbox{in}}=C_{in}, C_{\mbox{out}}=C_{in} \times K, ..., \mbox{groups}=C_{in})\).</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
     
 
-<ul>
-<li><p>Input: \((N, C_{in}, L_{in})\)</p></li>
+<ul><li><p>Input: \((N, C_{in}, L_{in})\)</p></li>
 <li><p>Output: \((N, C_{out}, L_{out})\) where</p></li>
-</ul>
-
-<p>$$
+</ul><p>$$
   L_{out} = \left\lfloor\frac{L_{in} + 2 \times \mbox{padding} - \mbox{dilation}
     \times (\mbox{kernel\_size} - 1) - 1}{\mbox{stride}} + 1\right\rfloor
 $$</p>
-    <h2 class="hasAnchor" id="attributes"><a class="anchor" href="#attributes"></a>Attributes</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="attributes">Attributes<a class="anchor" aria-label="anchor" href="#attributes"></a></h2>
     
 
-<ul>
-<li><p>weight (Tensor): the learnable weights of the module of shape
+<ul><li><p>weight (Tensor): the learnable weights of the module of shape
 \((\mbox{out\_channels}, \frac{\mbox{in\_channels}}{\mbox{groups}}, \mbox{kernel\_size})\).
 The values of these weights are sampled from
 \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where
@@ -330,42 +203,34 @@ The values of these weights are sampled from
 (out_channels). If <code>bias</code> is <code>TRUE</code>, then the values of these weights are
 sampled from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where
 \(k = \frac{groups}{C_{\mbox{in}} * \mbox{kernel\_size}}\)</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_conv1d</span><span class='op'>(</span><span class='fl'>16</span>, <span class='fl'>33</span>, <span class='fl'>3</span>, stride<span class='op'>=</span><span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>50</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
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-</div>
+</ul></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">nn_conv1d</span><span class="op">(</span><span class="fl">16</span>, <span class="fl">33</span>, <span class="fl">3</span>, stride <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">20</span>, <span class="fl">16</span>, <span class="fl">50</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span> <span class="op">&lt;-</span> <span class="fu">m</span><span class="op">(</span><span class="va">input</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Conv2D module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-conv.R'><code>R/nn-conv.R</code></a></small>
-    <div class="hidden name"><code>nn_conv2d.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Conv2D module</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-conv.R" class="external-link"><code>R/nn-conv.R</code></a></small>
+      <div class="d-none name"><code>nn_conv2d.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Applies a 2D convolution over an input signal composed of several input
 planes.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_conv2d</span><span class='op'>(</span>
-  <span class='va'>in_channels</span>,
-  <span class='va'>out_channels</span>,
-  <span class='va'>kernel_size</span>,
-  stride <span class='op'>=</span> <span class='fl'>1</span>,
-  padding <span class='op'>=</span> <span class='fl'>0</span>,
-  dilation <span class='op'>=</span> <span class='fl'>1</span>,
-  groups <span class='op'>=</span> <span class='fl'>1</span>,
-  bias <span class='op'>=</span> <span class='cn'>TRUE</span>,
-  padding_mode <span class='op'>=</span> <span class='st'>"zeros"</span>
-<span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_conv2d</span><span class="op">(</span>
+  <span class="va">in_channels</span>,
+  <span class="va">out_channels</span>,
+  <span class="va">kernel_size</span>,
+  stride <span class="op">=</span> <span class="fl">1</span>,
+  padding <span class="op">=</span> <span class="fl">0</span>,
+  dilation <span class="op">=</span> <span class="fl">1</span>,
+  groups <span class="op">=</span> <span class="fl">1</span>,
+  bias <span class="op">=</span> <span class="cn">TRUE</span>,
+  padding_mode <span class="op">=</span> <span class="st">"zeros"</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>in_channels</th>
-      <td><p>(int): Number of channels in the input image</p></td>
-    </tr>
-    <tr>
-      <th>out_channels</th>
-      <td><p>(int): Number of channels produced by the convolution</p></td>
-    </tr>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>(int or tuple): Size of the convolving kernel</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>(int or tuple, optional): Stride of the convolution. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>(int or tuple or string, optional): Zero-padding added to both sides of
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>in_channels</dt>
+<dd><p>(int): Number of channels in the input image</p></dd>
+<dt>out_channels</dt>
+<dd><p>(int): Number of channels produced by the convolution</p></dd>
+<dt>kernel_size</dt>
+<dd><p>(int or tuple): Size of the convolving kernel</p></dd>
+<dt>stride</dt>
+<dd><p>(int or tuple, optional): Stride of the convolution. Default: 1</p></dd>
+<dt>padding</dt>
+<dd><p>(int or tuple or string, optional): Zero-padding added to both sides of
 the input. controls the amount of padding applied to the input. It
 can be either a string <code>'valid'</code>, <code>'same'</code> or a tuple of ints giving the
-amount of implicit padding applied on both sides. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>dilation</th>
-      <td><p>(int or tuple, optional): Spacing between kernel elements. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>groups</th>
-      <td><p>(int, optional): Number of blocked connections from input
-channels to output channels. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>(bool, optional): If <code>TRUE</code>, adds a learnable bias to the
-output. Default: <code>TRUE</code></p></td>
-    </tr>
-    <tr>
-      <th>padding_mode</th>
-      <td><p>(string, optional): <code>'zeros'</code>, <code>'reflect'</code>,
-<code>'replicate'</code> or <code>'circular'</code>. Default: <code>'zeros'</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
+amount of implicit padding applied on both sides. Default: 0</p></dd>
+<dt>dilation</dt>
+<dd><p>(int or tuple, optional): Spacing between kernel elements. Default: 1</p></dd>
+<dt>groups</dt>
+<dd><p>(int, optional): Number of blocked connections from input
+channels to output channels. Default: 1</p></dd>
+<dt>bias</dt>
+<dd><p>(bool, optional): If <code>TRUE</code>, adds a learnable bias to the
+output. Default: <code>TRUE</code></p></dd>
+<dt>padding_mode</dt>
+<dd><p>(string, optional): <code>'zeros'</code>, <code>'reflect'</code>,
+<code>'replicate'</code> or <code>'circular'</code>. Default: <code>'zeros'</code></p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>In the simplest case, the output value of the layer with input size
 \((N, C_{\mbox{in}}, H, W)\) and output \((N, C_{\mbox{out}}, H_{\mbox{out}}, W_{\mbox{out}})\)
 can be precisely described as:</p>
@@ -262,8 +137,7 @@ $$</p>
 <p>where \(\star\) is the valid 2D cross-correlation operator,
 \(N\) is a batch size, \(C\) denotes a number of channels,
 \(H\) is a height of input planes in pixels, and \(W\) is
-width in pixels.</p><ul>
-<li><p><code>stride</code> controls the stride for the cross-correlation, a single
+width in pixels.</p><ul><li><p><code>stride</code> controls the stride for the cross-correlation, a single
 number or a tuple.</p></li>
 <li><p><code>padding</code> controls the amount of implicit zero-paddings on both
 sides for <code>padding</code> number of points for each dimension.</p></li>
@@ -272,8 +146,7 @@ known as the à trous algorithm. It is harder to describe, but this <code>link</
 has a nice visualization of what <code>dilation</code> does.</p></li>
 <li><p><code>groups</code> controls the connections between inputs and outputs.
 <code>in_channels</code> and <code>out_channels</code> must both be divisible by
-<code>groups</code>. For example,</p><ul>
-<li><p>At groups=1, all inputs are convolved to all outputs.</p></li>
+<code>groups</code>. For example,</p><ul><li><p>At groups=1, all inputs are convolved to all outputs.</p></li>
 <li><p>At groups=2, the operation becomes equivalent to having two conv
 layers side by side, each seeing half the input channels,
 and producing half the output channels, and both subsequently
@@ -282,17 +155,13 @@ concatenated.</p></li>
 its own set of filters, of size:
 \(\left\lfloor\frac{out\_channels}{in\_channels}\right\rfloor\).</p></li>
 </ul></li>
-</ul>
-
-<p>The parameters <code>kernel_size</code>, <code>stride</code>, <code>padding</code>, <code>dilation</code> can either be:</p><ul>
-<li><p>a single <code>int</code> -- in which case the same value is used for the height and
+</ul><p>The parameters <code>kernel_size</code>, <code>stride</code>, <code>padding</code>, <code>dilation</code> can either be:</p><ul><li><p>a single <code>int</code> -- in which case the same value is used for the height and
 width dimension</p></li>
 <li><p>a <code>tuple</code> of two ints -- in which case, the first <code>int</code> is used for the height dimension,
 and the second <code>int</code> for the width dimension</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
+</ul></div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     
 
 
@@ -310,12 +179,12 @@ a depthwise convolution with a depthwise multiplier <code>K</code>, can be const
 may select a nondeterministic algorithm to increase performance. If this is
 undesirable, you can try to make the operation deterministic (potentially at
 a performance cost) by setting <code>backends_cudnn_deterministic = TRUE</code>.</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
     
 
-<ul>
-<li><p>Input: \((N, C_{in}, H_{in}, W_{in})\)</p></li>
+<ul><li><p>Input: \((N, C_{in}, H_{in}, W_{in})\)</p></li>
 <li><p>Output: \((N, C_{out}, H_{out}, W_{out})\) where
 $$
   H_{out} = \left\lfloor\frac{H_{in}  + 2 \times \mbox{padding}[0] - \mbox{dilation}[0]
@@ -325,14 +194,12 @@ $$
   W_{out} = \left\lfloor\frac{W_{in}  + 2 \times \mbox{padding}[1] - \mbox{dilation}[1]
     \times (\mbox{kernel\_size}[1] - 1) - 1}{\mbox{stride}[1]} + 1\right\rfloor
 $$</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="attributes"><a class="anchor" href="#attributes"></a>Attributes</h2>
-
+</ul></div>
+    <div class="section level2">
+    <h2 id="attributes">Attributes<a class="anchor" aria-label="anchor" href="#attributes"></a></h2>
     
 
-<ul>
-<li><p>weight (Tensor): the learnable weights of the module of shape
+<ul><li><p>weight (Tensor): the learnable weights of the module of shape
 \((\mbox{out\_channels}, \frac{\mbox{in\_channels}}{\mbox{groups}}\),
 \(\mbox{kernel\_size[0]}, \mbox{kernel\_size[1]})\).
 The values of these weights are sampled from
@@ -343,48 +210,40 @@ The values of these weights are sampled from
 then the values of these weights are
 sampled from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where
 \(k = \frac{groups}{C_{\mbox{in}} * \prod_{i=0}^{1}\mbox{kernel\_size}[i]}\)</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='co'># With square kernels and equal stride</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_conv2d</span><span class='op'>(</span><span class='fl'>16</span>, <span class='fl'>33</span>, <span class='fl'>3</span>, stride <span class='op'>=</span> <span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='co'># non-square kernels and unequal stride and with padding</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_conv2d</span><span class='op'>(</span><span class='fl'>16</span>, <span class='fl'>33</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>5</span><span class='op'>)</span>, stride<span class='op'>=</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>1</span><span class='op'>)</span>, padding<span class='op'>=</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span>, <span class='fl'>2</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='co'># non-square kernels and unequal stride and with padding and dilation</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_conv2d</span><span class='op'>(</span><span class='fl'>16</span>, <span class='fl'>33</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>5</span><span class='op'>)</span>, stride<span class='op'>=</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>1</span><span class='op'>)</span>, padding<span class='op'>=</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span>, <span class='fl'>2</span><span class='op'>)</span>, dilation<span class='op'>=</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>1</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>50</span>, <span class='fl'>100</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span>  </span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+</ul></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="co"># With square kernels and equal stride</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">nn_conv2d</span><span class="op">(</span><span class="fl">16</span>, <span class="fl">33</span>, <span class="fl">3</span>, stride <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="co"># non-square kernels and unequal stride and with padding</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">nn_conv2d</span><span class="op">(</span><span class="fl">16</span>, <span class="fl">33</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">5</span><span class="op">)</span>, stride <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">1</span><span class="op">)</span>, padding <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span>, <span class="fl">2</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="co"># non-square kernels and unequal stride and with padding and dilation</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">nn_conv2d</span><span class="op">(</span><span class="fl">16</span>, <span class="fl">33</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">5</span><span class="op">)</span>, stride <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">1</span><span class="op">)</span>, padding <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span>, <span class="fl">2</span><span class="op">)</span>, dilation <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">1</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">20</span>, <span class="fl">16</span>, <span class="fl">50</span>, <span class="fl">100</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span> <span class="op">&lt;-</span> <span class="fu">m</span><span class="op">(</span><span class="va">input</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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-      <div class="copyright">
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+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
 </div>
 
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+<div class="pkgdown-footer-right">
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diff --git a/reference/nn_conv3d.html b/reference/nn_conv3d.html
index 635f57de6..3aaf4e7d0 100644
--- a/reference/nn_conv3d.html
+++ b/reference/nn_conv3d.html
@@ -1,261 +1,137 @@
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-<meta property="og:description" content="Applies a 3D convolution over an input signal composed of several input
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Applies a 3D convolution over an input signal composed of several input
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 In the simplest case, the output value of the layer with input size \((N, C_{in}, D, H, W)\)
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+and output \((N, C_{out}, D_{out}, H_{out}, W_{out})\) can be precisely described as:"><title>Conv3D module — nn_conv3d • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.1.0/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.1.0/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Conv3D module — nn_conv3d"><meta property="og:description" content="Applies a 3D convolution over an input signal composed of several input
+planes.
+In the simplest case, the output value of the layer with input size \((N, C_{in}, D, H, W)\)
+and output \((N, C_{out}, D_{out}, H_{out}, W_{out})\) can be precisely described as:"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Conv3D module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-conv.R'><code>R/nn-conv.R</code></a></small>
-    <div class="hidden name"><code>nn_conv3d.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Conv3D module</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-conv.R" class="external-link"><code>R/nn-conv.R</code></a></small>
+      <div class="d-none name"><code>nn_conv3d.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Applies a 3D convolution over an input signal composed of several input
 planes.
 In the simplest case, the output value of the layer with input size \((N, C_{in}, D, H, W)\)
 and output \((N, C_{out}, D_{out}, H_{out}, W_{out})\) can be precisely described as:</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_conv3d</span><span class='op'>(</span>
-  <span class='va'>in_channels</span>,
-  <span class='va'>out_channels</span>,
-  <span class='va'>kernel_size</span>,
-  stride <span class='op'>=</span> <span class='fl'>1</span>,
-  padding <span class='op'>=</span> <span class='fl'>0</span>,
-  dilation <span class='op'>=</span> <span class='fl'>1</span>,
-  groups <span class='op'>=</span> <span class='fl'>1</span>,
-  bias <span class='op'>=</span> <span class='cn'>TRUE</span>,
-  padding_mode <span class='op'>=</span> <span class='st'>"zeros"</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>in_channels</th>
-      <td><p>(int): Number of channels in the input image</p></td>
-    </tr>
-    <tr>
-      <th>out_channels</th>
-      <td><p>(int): Number of channels produced by the convolution</p></td>
-    </tr>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>(int or tuple): Size of the convolving kernel</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>(int or tuple, optional): Stride of the convolution. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>(int, tuple or str, optional): padding added to all six sides of the input. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>dilation</th>
-      <td><p>(int or tuple, optional): Spacing between kernel elements. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>groups</th>
-      <td><p>(int, optional): Number of blocked connections from input channels to output channels. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>(bool, optional): If <code>TRUE</code>, adds a learnable bias to the output. Default: <code>TRUE</code></p></td>
-    </tr>
-    <tr>
-      <th>padding_mode</th>
-      <td><p>(string, optional): <code>'zeros'</code>, <code>'reflect'</code>, <code>'replicate'</code> or <code>'circular'</code>. Default: <code>'zeros'</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_conv3d</span><span class="op">(</span>
+  <span class="va">in_channels</span>,
+  <span class="va">out_channels</span>,
+  <span class="va">kernel_size</span>,
+  stride <span class="op">=</span> <span class="fl">1</span>,
+  padding <span class="op">=</span> <span class="fl">0</span>,
+  dilation <span class="op">=</span> <span class="fl">1</span>,
+  groups <span class="op">=</span> <span class="fl">1</span>,
+  bias <span class="op">=</span> <span class="cn">TRUE</span>,
+  padding_mode <span class="op">=</span> <span class="st">"zeros"</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>in_channels</dt>
+<dd><p>(int): Number of channels in the input image</p></dd>
+<dt>out_channels</dt>
+<dd><p>(int): Number of channels produced by the convolution</p></dd>
+<dt>kernel_size</dt>
+<dd><p>(int or tuple): Size of the convolving kernel</p></dd>
+<dt>stride</dt>
+<dd><p>(int or tuple, optional): Stride of the convolution. Default: 1</p></dd>
+<dt>padding</dt>
+<dd><p>(int, tuple or str, optional): padding added to all six sides of the input. Default: 0</p></dd>
+<dt>dilation</dt>
+<dd><p>(int or tuple, optional): Spacing between kernel elements. Default: 1</p></dd>
+<dt>groups</dt>
+<dd><p>(int, optional): Number of blocked connections from input channels to output channels. Default: 1</p></dd>
+<dt>bias</dt>
+<dd><p>(bool, optional): If <code>TRUE</code>, adds a learnable bias to the output. Default: <code>TRUE</code></p></dd>
+<dt>padding_mode</dt>
+<dd><p>(string, optional): <code>'zeros'</code>, <code>'reflect'</code>, <code>'replicate'</code> or <code>'circular'</code>. Default: <code>'zeros'</code></p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>$$
   out(N_i, C_{out_j}) = bias(C_{out_j}) +
   \sum_{k = 0}^{C_{in} - 1} weight(C_{out_j}, k) \star input(N_i, k)
 $$</p>
-<p>where \(\star\) is the valid 3D <code>cross-correlation</code> operator</p><ul>
-<li><p><code>stride</code> controls the stride for the cross-correlation.</p></li>
+<p>where \(\star\) is the valid 3D <code>cross-correlation</code> operator</p><ul><li><p><code>stride</code> controls the stride for the cross-correlation.</p></li>
 <li><p><code>padding</code> controls the amount of implicit zero-paddings on both
 sides for <code>padding</code> number of points for each dimension.</p></li>
 <li><p><code>dilation</code> controls the spacing between the kernel points; also known as the à trous algorithm.
@@ -271,16 +147,12 @@ concatenated.</p></li>
 <li><p>At groups= <code>in_channels</code>, each input channel is convolved with
 its own set of filters, of size
 \(\left\lfloor\frac{out\_channels}{in\_channels}\right\rfloor\).</p></li>
-</ul>
-
-<p>The parameters <code>kernel_size</code>, <code>stride</code>, <code>padding</code>, <code>dilation</code> can either be:</p><ul>
-<li><p>a single <code>int</code> -- in which case the same value is used for the depth, height and width dimension</p></li>
+</ul><p>The parameters <code>kernel_size</code>, <code>stride</code>, <code>padding</code>, <code>dilation</code> can either be:</p><ul><li><p>a single <code>int</code> -- in which case the same value is used for the depth, height and width dimension</p></li>
 <li><p>a <code>tuple</code> of three ints -- in which case, the first <code>int</code> is used for the depth dimension,
 the second <code>int</code> for the height dimension and the third <code>int</code> for the width dimension</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
+</ul></div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p>Depending of the size of your kernel, several (of the last)
 columns of the input might be lost, because it is a valid <code>cross-correlation</code><em>,
 and not a full <code>cross-correlation</code></em>.
@@ -296,12 +168,12 @@ may select a nondeterministic algorithm to increase performance. If this is
 undesirable, you can try to make the operation deterministic (potentially at
 a performance cost) by setting <code>torch.backends.cudnn.deterministic = TRUE</code>.
 Please see the notes on :doc:<code>/notes/randomness</code> for background.</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
     
 
-<ul>
-<li><p>Input: \((N, C_{in}, D_{in}, H_{in}, W_{in})\)</p></li>
+<ul><li><p>Input: \((N, C_{in}, D_{in}, H_{in}, W_{in})\)</p></li>
 <li><p>Output: \((N, C_{out}, D_{out}, H_{out}, W_{out})\) where
 $$
   D_{out} = \left\lfloor\frac{D_{in} + 2 \times \mbox{padding}[0] - \mbox{dilation}[0]
@@ -315,14 +187,12 @@ $$
   W_{out} = \left\lfloor\frac{W_{in} + 2 \times \mbox{padding}[2] - \mbox{dilation}[2]
     \times (\mbox{kernel\_size}[2] - 1) - 1}{\mbox{stride}[2]} + 1\right\rfloor
  $$</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="attributes"><a class="anchor" href="#attributes"></a>Attributes</h2>
-
+</ul></div>
+    <div class="section level2">
+    <h2 id="attributes">Attributes<a class="anchor" aria-label="anchor" href="#attributes"></a></h2>
     
 
-<ul>
-<li><p>weight (Tensor): the learnable weights of the module of shape
+<ul><li><p>weight (Tensor): the learnable weights of the module of shape
 \((\mbox{out\_channels}, \frac{\mbox{in\_channels}}{\mbox{groups}},\)
 \(\mbox{kernel\_size[0]}, \mbox{kernel\_size[1]}, \mbox{kernel\_size[2]})\).
 The values of these weights are sampled from
@@ -332,45 +202,37 @@ The values of these weights are sampled from
 then the values of these weights are
 sampled from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where
 \(k = \frac{groups}{C_{\mbox{in}} * \prod_{i=0}^{2}\mbox{kernel\_size}[i]}\)</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='co'># With square kernels and equal stride</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_conv3d</span><span class='op'>(</span><span class='fl'>16</span>, <span class='fl'>33</span>, <span class='fl'>3</span>, stride<span class='op'>=</span><span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='co'># non-square kernels and unequal stride and with padding</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_conv3d</span><span class='op'>(</span><span class='fl'>16</span>, <span class='fl'>33</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>5</span>, <span class='fl'>2</span><span class='op'>)</span>, stride<span class='op'>=</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>1</span>, <span class='fl'>1</span><span class='op'>)</span>, padding<span class='op'>=</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span>, <span class='fl'>2</span>, <span class='fl'>0</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>10</span>, <span class='fl'>50</span>, <span class='fl'>100</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+</ul></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="co"># With square kernels and equal stride</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">nn_conv3d</span><span class="op">(</span><span class="fl">16</span>, <span class="fl">33</span>, <span class="fl">3</span>, stride <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="co"># non-square kernels and unequal stride and with padding</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">nn_conv3d</span><span class="op">(</span><span class="fl">16</span>, <span class="fl">33</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">5</span>, <span class="fl">2</span><span class="op">)</span>, stride <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">1</span>, <span class="fl">1</span><span class="op">)</span>, padding <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span>, <span class="fl">2</span>, <span class="fl">0</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">20</span>, <span class="fl">16</span>, <span class="fl">10</span>, <span class="fl">50</span>, <span class="fl">100</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span> <span class="op">&lt;-</span> <span class="fu">m</span><span class="op">(</span><span class="va">input</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>ConvTranspose1D</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-conv.R'><code>R/nn-conv.R</code></a></small>
-    <div class="hidden name"><code>nn_conv_transpose1d.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>ConvTranspose1D</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-conv.R" class="external-link"><code>R/nn-conv.R</code></a></small>
+      <div class="d-none name"><code>nn_conv_transpose1d.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Applies a 1D transposed convolution operator over an input image
 composed of several input planes.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_conv_transpose1d</span><span class='op'>(</span>
-  <span class='va'>in_channels</span>,
-  <span class='va'>out_channels</span>,
-  <span class='va'>kernel_size</span>,
-  stride <span class='op'>=</span> <span class='fl'>1</span>,
-  padding <span class='op'>=</span> <span class='fl'>0</span>,
-  output_padding <span class='op'>=</span> <span class='fl'>0</span>,
-  groups <span class='op'>=</span> <span class='fl'>1</span>,
-  bias <span class='op'>=</span> <span class='cn'>TRUE</span>,
-  dilation <span class='op'>=</span> <span class='fl'>1</span>,
-  padding_mode <span class='op'>=</span> <span class='st'>"zeros"</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>in_channels</th>
-      <td><p>(int): Number of channels in the input image</p></td>
-    </tr>
-    <tr>
-      <th>out_channels</th>
-      <td><p>(int): Number of channels produced by the convolution</p></td>
-    </tr>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>(int or tuple): Size of the convolving kernel</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>(int or tuple, optional): Stride of the convolution. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>(int or tuple, optional): <code>dilation * (kernel_size - 1) - padding</code> zero-padding
-will be added to both sides of the input. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>output_padding</th>
-      <td><p>(int or tuple, optional): Additional size added to one side
-of the output shape. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>groups</th>
-      <td><p>(int, optional): Number of blocked connections from input channels to output channels. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>(bool, optional): If <code>True</code>, adds a learnable bias to the output. Default: <code>TRUE</code></p></td>
-    </tr>
-    <tr>
-      <th>dilation</th>
-      <td><p>(int or tuple, optional): Spacing between kernel elements. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>padding_mode</th>
-      <td><p>(string, optional): <code>'zeros'</code>, <code>'reflect'</code>,
-<code>'replicate'</code> or <code>'circular'</code>. Default: <code>'zeros'</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_conv_transpose1d</span><span class="op">(</span>
+  <span class="va">in_channels</span>,
+  <span class="va">out_channels</span>,
+  <span class="va">kernel_size</span>,
+  stride <span class="op">=</span> <span class="fl">1</span>,
+  padding <span class="op">=</span> <span class="fl">0</span>,
+  output_padding <span class="op">=</span> <span class="fl">0</span>,
+  groups <span class="op">=</span> <span class="fl">1</span>,
+  bias <span class="op">=</span> <span class="cn">TRUE</span>,
+  dilation <span class="op">=</span> <span class="fl">1</span>,
+  padding_mode <span class="op">=</span> <span class="st">"zeros"</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>in_channels</dt>
+<dd><p>(int): Number of channels in the input image</p></dd>
+<dt>out_channels</dt>
+<dd><p>(int): Number of channels produced by the convolution</p></dd>
+<dt>kernel_size</dt>
+<dd><p>(int or tuple): Size of the convolving kernel</p></dd>
+<dt>stride</dt>
+<dd><p>(int or tuple, optional): Stride of the convolution. Default: 1</p></dd>
+<dt>padding</dt>
+<dd><p>(int or tuple, optional): <code>dilation * (kernel_size - 1) - padding</code> zero-padding
+will be added to both sides of the input. Default: 0</p></dd>
+<dt>output_padding</dt>
+<dd><p>(int or tuple, optional): Additional size added to one side
+of the output shape. Default: 0</p></dd>
+<dt>groups</dt>
+<dd><p>(int, optional): Number of blocked connections from input channels to output channels. Default: 1</p></dd>
+<dt>bias</dt>
+<dd><p>(bool, optional): If <code>True</code>, adds a learnable bias to the output. Default: <code>TRUE</code></p></dd>
+<dt>dilation</dt>
+<dd><p>(int or tuple, optional): Spacing between kernel elements. Default: 1</p></dd>
+<dt>padding_mode</dt>
+<dd><p>(string, optional): <code>'zeros'</code>, <code>'reflect'</code>,
+<code>'replicate'</code> or <code>'circular'</code>. Default: <code>'zeros'</code></p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>This module can be seen as the gradient of Conv1d with respect to its input.
 It is also known as a fractionally-strided convolution or
-a deconvolution (although it is not an actual deconvolution operation).</p><ul>
-<li><p><code>stride</code> controls the stride for the cross-correlation.</p></li>
+a deconvolution (although it is not an actual deconvolution operation).</p><ul><li><p><code>stride</code> controls the stride for the cross-correlation.</p></li>
 <li><p><code>padding</code> controls the amount of implicit zero-paddings on both
 sides for <code>dilation * (kernel_size - 1) - padding</code> number of points. See note
 below for details.</p></li>
 <li><p><code>output_padding</code> controls the additional size added to one side
 of the output shape. See note below for details.</p></li>
 <li><p><code>dilation</code> controls the spacing between the kernel points; also known as the
-à trous algorithm. It is harder to describe, but this <a href='https://github.com/vdumoulin/conv_arithmetic'>link</a>
+à trous algorithm. It is harder to describe, but this <a href="https://github.com/vdumoulin/conv_arithmetic" class="external-link">link</a>
 has a nice visualization of what <code>dilation</code> does.</p></li>
 <li><p><code>groups</code> controls the connections between inputs and outputs.
 <code>in_channels</code> and <code>out_channels</code> must both be divisible by
-<code>groups</code>. For example,</p><ul>
-<li><p>At groups=1, all inputs are convolved to all outputs.</p></li>
+<code>groups</code>. For example,</p><ul><li><p>At groups=1, all inputs are convolved to all outputs.</p></li>
 <li><p>At groups=2, the operation becomes equivalent to having two conv
 layers side by side, each seeing half the input channels,
 and producing half the output channels, and both subsequently
@@ -278,10 +149,9 @@ concatenated.</p></li>
 its own set of filters (of size
 \(\left\lfloor\frac{out\_channels}{in\_channels}\right\rfloor\)).</p></li>
 </ul></li>
-</ul>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
+</ul></div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p>Depending of the size of your kernel, several (of the last)
 columns of the input might be lost, because it is a valid <code>cross-correlation</code><em>,
 and not a full <code>cross-correlation</code></em>.
@@ -300,25 +170,23 @@ not actually add zero-padding to output.</p>
 may select a nondeterministic algorithm to increase performance. If this is
 undesirable, you can try to make the operation deterministic (potentially at
 a performance cost) by setting <code>torch.backends.cudnn.deterministic = TRUE</code>.</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
     
 
-<ul>
-<li><p>Input: \((N, C_{in}, L_{in})\)</p></li>
+<ul><li><p>Input: \((N, C_{in}, L_{in})\)</p></li>
 <li><p>Output: \((N, C_{out}, L_{out})\) where
 $$
   L_{out} = (L_{in} - 1) \times \mbox{stride} - 2 \times \mbox{padding} + \mbox{dilation}
 \times (\mbox{kernel\_size} - 1) + \mbox{output\_padding} + 1
 $$</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="attributes"><a class="anchor" href="#attributes"></a>Attributes</h2>
-
+</ul></div>
+    <div class="section level2">
+    <h2 id="attributes">Attributes<a class="anchor" aria-label="anchor" href="#attributes"></a></h2>
     
 
-<ul>
-<li><p>weight (Tensor): the learnable weights of the module of shape
+<ul><li><p>weight (Tensor): the learnable weights of the module of shape
 \((\mbox{in\_channels}, \frac{\mbox{out\_channels}}{\mbox{groups}},\)
 \(\mbox{kernel\_size})\).
 The values of these weights are sampled from
@@ -328,42 +196,34 @@ The values of these weights are sampled from
 If <code>bias</code> is <code>TRUE</code>, then the values of these weights are
 sampled from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where
 \(k = \frac{groups}{C_{\mbox{out}} * \mbox{kernel\_size}}\)</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_conv_transpose1d</span><span class='op'>(</span><span class='fl'>32</span>, <span class='fl'>16</span>, <span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>10</span>, <span class='fl'>32</span>, <span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
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-  </div>
-</div>
+</ul></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">nn_conv_transpose1d</span><span class="op">(</span><span class="fl">32</span>, <span class="fl">16</span>, <span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">10</span>, <span class="fl">32</span>, <span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span> <span class="op">&lt;-</span> <span class="fu">m</span><span class="op">(</span><span class="va">input</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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-      <div class="copyright">
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+  <a class="nav-link" href="../reference/index.html">Reference</a>
 </li>
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+      </ul><form class="form-inline my-2 my-lg-0" role="search">
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+      </ul></div>
 
+    
+  </div>
+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>ConvTranpose2D module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-conv.R'><code>R/nn-conv.R</code></a></small>
-    <div class="hidden name"><code>nn_conv_transpose2d.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>ConvTranpose2D module</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-conv.R" class="external-link"><code>R/nn-conv.R</code></a></small>
+      <div class="d-none name"><code>nn_conv_transpose2d.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Applies a 2D transposed convolution operator over an input image
 composed of several input planes.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_conv_transpose2d</span><span class='op'>(</span>
-  <span class='va'>in_channels</span>,
-  <span class='va'>out_channels</span>,
-  <span class='va'>kernel_size</span>,
-  stride <span class='op'>=</span> <span class='fl'>1</span>,
-  padding <span class='op'>=</span> <span class='fl'>0</span>,
-  output_padding <span class='op'>=</span> <span class='fl'>0</span>,
-  groups <span class='op'>=</span> <span class='fl'>1</span>,
-  bias <span class='op'>=</span> <span class='cn'>TRUE</span>,
-  dilation <span class='op'>=</span> <span class='fl'>1</span>,
-  padding_mode <span class='op'>=</span> <span class='st'>"zeros"</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>in_channels</th>
-      <td><p>(int): Number of channels in the input image</p></td>
-    </tr>
-    <tr>
-      <th>out_channels</th>
-      <td><p>(int): Number of channels produced by the convolution</p></td>
-    </tr>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>(int or tuple): Size of the convolving kernel</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>(int or tuple, optional): Stride of the convolution. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>(int or tuple, optional): <code>dilation * (kernel_size - 1) - padding</code> zero-padding
-will be added to both sides of each dimension in the input. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>output_padding</th>
-      <td><p>(int or tuple, optional): Additional size added to one side
-of each dimension in the output shape. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>groups</th>
-      <td><p>(int, optional): Number of blocked connections from input channels to output channels. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>(bool, optional): If <code>True</code>, adds a learnable bias to the output. Default: <code>True</code></p></td>
-    </tr>
-    <tr>
-      <th>dilation</th>
-      <td><p>(int or tuple, optional): Spacing between kernel elements. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>padding_mode</th>
-      <td><p>(string, optional): <code>'zeros'</code>, <code>'reflect'</code>,
-<code>'replicate'</code> or <code>'circular'</code>. Default: <code>'zeros'</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_conv_transpose2d</span><span class="op">(</span>
+  <span class="va">in_channels</span>,
+  <span class="va">out_channels</span>,
+  <span class="va">kernel_size</span>,
+  stride <span class="op">=</span> <span class="fl">1</span>,
+  padding <span class="op">=</span> <span class="fl">0</span>,
+  output_padding <span class="op">=</span> <span class="fl">0</span>,
+  groups <span class="op">=</span> <span class="fl">1</span>,
+  bias <span class="op">=</span> <span class="cn">TRUE</span>,
+  dilation <span class="op">=</span> <span class="fl">1</span>,
+  padding_mode <span class="op">=</span> <span class="st">"zeros"</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>in_channels</dt>
+<dd><p>(int): Number of channels in the input image</p></dd>
+<dt>out_channels</dt>
+<dd><p>(int): Number of channels produced by the convolution</p></dd>
+<dt>kernel_size</dt>
+<dd><p>(int or tuple): Size of the convolving kernel</p></dd>
+<dt>stride</dt>
+<dd><p>(int or tuple, optional): Stride of the convolution. Default: 1</p></dd>
+<dt>padding</dt>
+<dd><p>(int or tuple, optional): <code>dilation * (kernel_size - 1) - padding</code> zero-padding
+will be added to both sides of each dimension in the input. Default: 0</p></dd>
+<dt>output_padding</dt>
+<dd><p>(int or tuple, optional): Additional size added to one side
+of each dimension in the output shape. Default: 0</p></dd>
+<dt>groups</dt>
+<dd><p>(int, optional): Number of blocked connections from input channels to output channels. Default: 1</p></dd>
+<dt>bias</dt>
+<dd><p>(bool, optional): If <code>True</code>, adds a learnable bias to the output. Default: <code>True</code></p></dd>
+<dt>dilation</dt>
+<dd><p>(int or tuple, optional): Spacing between kernel elements. Default: 1</p></dd>
+<dt>padding_mode</dt>
+<dd><p>(string, optional): <code>'zeros'</code>, <code>'reflect'</code>,
+<code>'replicate'</code> or <code>'circular'</code>. Default: <code>'zeros'</code></p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>This module can be seen as the gradient of Conv2d with respect to its input.
 It is also known as a fractionally-strided convolution or
-a deconvolution (although it is not an actual deconvolution operation).</p><ul>
-<li><p><code>stride</code> controls the stride for the cross-correlation.</p></li>
+a deconvolution (although it is not an actual deconvolution operation).</p><ul><li><p><code>stride</code> controls the stride for the cross-correlation.</p></li>
 <li><p><code>padding</code> controls the amount of implicit zero-paddings on both
 sides for <code>dilation * (kernel_size - 1) - padding</code> number of points. See note
 below for details.</p></li>
@@ -267,8 +139,7 @@ of the output shape. See note below for details.</p></li>
 It is harder to describe, but this <code>link</code>_ has a nice visualization of what <code>dilation</code> does.</p></li>
 <li><p><code>groups</code> controls the connections between inputs and outputs.
 <code>in_channels</code> and <code>out_channels</code> must both be divisible by
-<code>groups</code>. For example,</p><ul>
-<li><p>At groups=1, all inputs are convolved to all outputs.</p></li>
+<code>groups</code>. For example,</p><ul><li><p>At groups=1, all inputs are convolved to all outputs.</p></li>
 <li><p>At groups=2, the operation becomes equivalent to having two conv
 layers side by side, each seeing half the input channels,
 and producing half the output channels, and both subsequently
@@ -277,26 +148,22 @@ concatenated.</p></li>
 its own set of filters (of size
 \(\left\lfloor\frac{out\_channels}{in\_channels}\right\rfloor\)).</p></li>
 </ul></li>
-</ul>
-
-<p>The parameters <code>kernel_size</code>, <code>stride</code>, <code>padding</code>, <code>output_padding</code>
-can either be:</p><ul>
-<li><p>a single <code>int</code> -- in which case the same value is used for the height and width dimensions</p></li>
+</ul><p>The parameters <code>kernel_size</code>, <code>stride</code>, <code>padding</code>, <code>output_padding</code>
+can either be:</p><ul><li><p>a single <code>int</code> -- in which case the same value is used for the height and width dimensions</p></li>
 <li><p>a <code>tuple</code> of two ints -- in which case, the first <code>int</code> is used for the height dimension,
 and the second <code>int</code> for the width dimension</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
+</ul></div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p>Depending of the size of your kernel, several (of the last)
 columns of the input might be lost, because it is a valid <code>cross-correlation</code>_,
 and not a full <code>cross-correlation</code>. It is up to the user to add proper padding.</p>
 <p>The <code>padding</code> argument effectively adds <code>dilation * (kernel_size - 1) - padding</code>
 amount of zero padding to both sizes of the input. This is set so that
-when a <a href='nn_conv2d.html'>nn_conv2d</a> and a nn_conv_transpose2d are initialized with same
+when a <a href="nn_conv2d.html">nn_conv2d</a> and a nn_conv_transpose2d are initialized with same
 parameters, they are inverses of each other in
 regard to the input and output shapes. However, when <code>stride &gt; 1</code>,
-<a href='nn_conv2d.html'>nn_conv2d</a> maps multiple input shapes to the same output
+<a href="nn_conv2d.html">nn_conv2d</a> maps multiple input shapes to the same output
 shape. <code>output_padding</code> is provided to resolve this ambiguity by
 effectively increasing the calculated output shape on one side. Note
 that <code>output_padding</code> is only used to find output shape, but does
@@ -305,12 +172,12 @@ not actually add zero-padding to output.</p>
 may select a nondeterministic algorithm to increase performance. If this is
 undesirable, you can try to make the operation deterministic (potentially at
 a performance cost) by setting <code>torch.backends.cudnn.deterministic = TRUE</code>.</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
     
 
-<ul>
-<li><p>Input: \((N, C_{in}, H_{in}, W_{in})\)</p></li>
+<ul><li><p>Input: \((N, C_{in}, H_{in}, W_{in})\)</p></li>
 <li><p>Output: \((N, C_{out}, H_{out}, W_{out})\) where
 $$
   H_{out} = (H_{in} - 1) \times \mbox{stride}[0] - 2 \times \mbox{padding}[0] + \mbox{dilation}[0]
@@ -320,14 +187,12 @@ $$
   W_{out} = (W_{in} - 1) \times \mbox{stride}[1] - 2 \times \mbox{padding}[1] + \mbox{dilation}[1]
 \times (\mbox{kernel\_size}[1] - 1) + \mbox{output\_padding}[1] + 1
 $$</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="attributes"><a class="anchor" href="#attributes"></a>Attributes</h2>
-
+</ul></div>
+    <div class="section level2">
+    <h2 id="attributes">Attributes<a class="anchor" aria-label="anchor" href="#attributes"></a></h2>
     
 
-<ul>
-<li><p>weight (Tensor): the learnable weights of the module of shape
+<ul><li><p>weight (Tensor): the learnable weights of the module of shape
 \((\mbox{in\_channels}, \frac{\mbox{out\_channels}}{\mbox{groups}},\)
 \(\mbox{kernel\_size[0]}, \mbox{kernel\_size[1]})\).
 The values of these weights are sampled from
@@ -337,54 +202,46 @@ The values of these weights are sampled from
 If <code>bias</code> is <code>True</code>, then the values of these weights are
 sampled from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where
 \(k = \frac{groups}{C_{\mbox{out}} * \prod_{i=0}^{1}\mbox{kernel\_size}[i]}\)</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='co'># With square kernels and equal stride</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_conv_transpose2d</span><span class='op'>(</span><span class='fl'>16</span>, <span class='fl'>33</span>, <span class='fl'>3</span>, stride<span class='op'>=</span><span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='co'># non-square kernels and unequal stride and with padding</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_conv_transpose2d</span><span class='op'>(</span><span class='fl'>16</span>, <span class='fl'>33</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>5</span><span class='op'>)</span>, stride<span class='op'>=</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>1</span><span class='op'>)</span>, padding<span class='op'>=</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span>, <span class='fl'>2</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>50</span>, <span class='fl'>100</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span></span>
-<span class='r-in'><span class='co'># exact output size can be also specified as an argument</span></span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>16</span>, <span class='fl'>12</span>, <span class='fl'>12</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>downsample</span> <span class='op'>&lt;-</span> <span class='fu'><a href='nn_conv2d.html'>nn_conv2d</a></span><span class='op'>(</span><span class='fl'>16</span>, <span class='fl'>16</span>, <span class='fl'>3</span>, stride<span class='op'>=</span><span class='fl'>2</span>, padding<span class='op'>=</span><span class='fl'>1</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>upsample</span> <span class='op'>&lt;-</span> <span class='fu'>nn_conv_transpose2d</span><span class='op'>(</span><span class='fl'>16</span>, <span class='fl'>16</span>, <span class='fl'>3</span>, stride<span class='op'>=</span><span class='fl'>2</span>, padding<span class='op'>=</span><span class='fl'>1</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>h</span> <span class='op'>&lt;-</span> <span class='fu'>downsample</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>h</span><span class='op'>$</span><span class='fu'>size</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span> <span class='op'>&lt;-</span> <span class='fu'>upsample</span><span class='op'>(</span><span class='va'>h</span>, output_size<span class='op'>=</span><span class='va'>input</span><span class='op'>$</span><span class='fu'>size</span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span><span class='op'>$</span><span class='fu'>size</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [1]  1 16 12 12</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+</ul></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="co"># With square kernels and equal stride</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">nn_conv_transpose2d</span><span class="op">(</span><span class="fl">16</span>, <span class="fl">33</span>, <span class="fl">3</span>, stride <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="co"># non-square kernels and unequal stride and with padding</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">nn_conv_transpose2d</span><span class="op">(</span><span class="fl">16</span>, <span class="fl">33</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">5</span><span class="op">)</span>, stride <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">1</span><span class="op">)</span>, padding <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span>, <span class="fl">2</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">20</span>, <span class="fl">16</span>, <span class="fl">50</span>, <span class="fl">100</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span> <span class="op">&lt;-</span> <span class="fu">m</span><span class="op">(</span><span class="va">input</span><span class="op">)</span></span>
+<span class="r-in"><span class="co"># exact output size can be also specified as an argument</span></span>
+<span class="r-in"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">16</span>, <span class="fl">12</span>, <span class="fl">12</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">downsample</span> <span class="op">&lt;-</span> <span class="fu"><a href="nn_conv2d.html">nn_conv2d</a></span><span class="op">(</span><span class="fl">16</span>, <span class="fl">16</span>, <span class="fl">3</span>, stride <span class="op">=</span> <span class="fl">2</span>, padding <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">upsample</span> <span class="op">&lt;-</span> <span class="fu">nn_conv_transpose2d</span><span class="op">(</span><span class="fl">16</span>, <span class="fl">16</span>, <span class="fl">3</span>, stride <span class="op">=</span> <span class="fl">2</span>, padding <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">h</span> <span class="op">&lt;-</span> <span class="fu">downsample</span><span class="op">(</span><span class="va">input</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">h</span><span class="op">$</span><span class="fu">size</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span> <span class="op">&lt;-</span> <span class="fu">upsample</span><span class="op">(</span><span class="va">h</span>, output_size <span class="op">=</span> <span class="va">input</span><span class="op">$</span><span class="fu">size</span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span><span class="op">$</span><span class="fu">size</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1]  1 16 12 12</span>
+</code></pre></div>
+    </div>
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>ConvTranpose3D module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-conv.R'><code>R/nn-conv.R</code></a></small>
-    <div class="hidden name"><code>nn_conv_transpose3d.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>ConvTranpose3D module</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-conv.R" class="external-link"><code>R/nn-conv.R</code></a></small>
+      <div class="d-none name"><code>nn_conv_transpose3d.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Applies a 3D transposed convolution operator over an input image composed of several input
 planes.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_conv_transpose3d</span><span class='op'>(</span>
-  <span class='va'>in_channels</span>,
-  <span class='va'>out_channels</span>,
-  <span class='va'>kernel_size</span>,
-  stride <span class='op'>=</span> <span class='fl'>1</span>,
-  padding <span class='op'>=</span> <span class='fl'>0</span>,
-  output_padding <span class='op'>=</span> <span class='fl'>0</span>,
-  groups <span class='op'>=</span> <span class='fl'>1</span>,
-  bias <span class='op'>=</span> <span class='cn'>TRUE</span>,
-  dilation <span class='op'>=</span> <span class='fl'>1</span>,
-  padding_mode <span class='op'>=</span> <span class='st'>"zeros"</span>
-<span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_conv_transpose3d</span><span class="op">(</span>
+  <span class="va">in_channels</span>,
+  <span class="va">out_channels</span>,
+  <span class="va">kernel_size</span>,
+  stride <span class="op">=</span> <span class="fl">1</span>,
+  padding <span class="op">=</span> <span class="fl">0</span>,
+  output_padding <span class="op">=</span> <span class="fl">0</span>,
+  groups <span class="op">=</span> <span class="fl">1</span>,
+  bias <span class="op">=</span> <span class="cn">TRUE</span>,
+  dilation <span class="op">=</span> <span class="fl">1</span>,
+  padding_mode <span class="op">=</span> <span class="st">"zeros"</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>in_channels</th>
-      <td><p>(int): Number of channels in the input image</p></td>
-    </tr>
-    <tr>
-      <th>out_channels</th>
-      <td><p>(int): Number of channels produced by the convolution</p></td>
-    </tr>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>(int or tuple): Size of the convolving kernel</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>(int or tuple, optional): Stride of the convolution. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>(int or tuple, optional): <code>dilation * (kernel_size - 1) - padding</code> zero-padding
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>in_channels</dt>
+<dd><p>(int): Number of channels in the input image</p></dd>
+<dt>out_channels</dt>
+<dd><p>(int): Number of channels produced by the convolution</p></dd>
+<dt>kernel_size</dt>
+<dd><p>(int or tuple): Size of the convolving kernel</p></dd>
+<dt>stride</dt>
+<dd><p>(int or tuple, optional): Stride of the convolution. Default: 1</p></dd>
+<dt>padding</dt>
+<dd><p>(int or tuple, optional): <code>dilation * (kernel_size - 1) - padding</code> zero-padding
 will be added to both sides of each dimension in the input. Default: 0
 output_padding (int or tuple, optional): Additional size added to one side
-of each dimension in the output shape. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>output_padding</th>
-      <td><p>(int or tuple, optional): Additional size added to one side
-of each dimension in the output shape. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>groups</th>
-      <td><p>(int, optional): Number of blocked connections from input channels to output channels. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>(bool, optional): If <code>True</code>, adds a learnable bias to the output. Default: <code>True</code></p></td>
-    </tr>
-    <tr>
-      <th>dilation</th>
-      <td><p>(int or tuple, optional): Spacing between kernel elements. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>padding_mode</th>
-      <td><p>(string, optional): <code>'zeros'</code>, <code>'reflect'</code>, <code>'replicate'</code> or <code>'circular'</code>. Default: <code>'zeros'</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
+of each dimension in the output shape. Default: 0</p></dd>
+<dt>output_padding</dt>
+<dd><p>(int or tuple, optional): Additional size added to one side
+of each dimension in the output shape. Default: 0</p></dd>
+<dt>groups</dt>
+<dd><p>(int, optional): Number of blocked connections from input channels to output channels. Default: 1</p></dd>
+<dt>bias</dt>
+<dd><p>(bool, optional): If <code>True</code>, adds a learnable bias to the output. Default: <code>True</code></p></dd>
+<dt>dilation</dt>
+<dd><p>(int or tuple, optional): Spacing between kernel elements. Default: 1</p></dd>
+<dt>padding_mode</dt>
+<dd><p>(string, optional): <code>'zeros'</code>, <code>'reflect'</code>, <code>'replicate'</code> or <code>'circular'</code>. Default: <code>'zeros'</code></p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>The transposed convolution operator multiplies each input value element-wise by a learnable kernel,
 and sums over the outputs from all input feature planes.</p>
 <p>This module can be seen as the gradient of Conv3d with respect to its input.
 It is also known as a fractionally-strided convolution or
-a deconvolution (although it is not an actual deconvolution operation).</p><ul>
-<li><p><code>stride</code> controls the stride for the cross-correlation.</p></li>
+a deconvolution (although it is not an actual deconvolution operation).</p><ul><li><p><code>stride</code> controls the stride for the cross-correlation.</p></li>
 <li><p><code>padding</code> controls the amount of implicit zero-paddings on both
 sides for <code>dilation * (kernel_size - 1) - padding</code> number of points. See note
 below for details.</p></li>
@@ -270,8 +142,7 @@ of the output shape. See note below for details.</p></li>
 It is harder to describe, but this <code>link</code>_ has a nice visualization of what <code>dilation</code> does.</p></li>
 <li><p><code>groups</code> controls the connections between inputs and outputs.
 <code>in_channels</code> and <code>out_channels</code> must both be divisible by
-<code>groups</code>. For example,</p><ul>
-<li><p>At groups=1, all inputs are convolved to all outputs.</p></li>
+<code>groups</code>. For example,</p><ul><li><p>At groups=1, all inputs are convolved to all outputs.</p></li>
 <li><p>At groups=2, the operation becomes equivalent to having two conv
 layers side by side, each seeing half the input channels,
 and producing half the output channels, and both subsequently
@@ -280,17 +151,13 @@ concatenated.</p></li>
 its own set of filters (of size
 \(\left\lfloor\frac{out\_channels}{in\_channels}\right\rfloor\)).</p></li>
 </ul></li>
-</ul>
-
-<p>The parameters <code>kernel_size</code>, <code>stride</code>, <code>padding</code>, <code>output_padding</code>
-can either be:</p><ul>
-<li><p>a single <code>int</code> -- in which case the same value is used for the depth, height and width dimensions</p></li>
+</ul><p>The parameters <code>kernel_size</code>, <code>stride</code>, <code>padding</code>, <code>output_padding</code>
+can either be:</p><ul><li><p>a single <code>int</code> -- in which case the same value is used for the depth, height and width dimensions</p></li>
 <li><p>a <code>tuple</code> of three ints -- in which case, the first <code>int</code> is used for the depth dimension,
 the second <code>int</code> for the height dimension and the third <code>int</code> for the width dimension</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
+</ul></div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p>Depending of the size of your kernel, several (of the last)
 columns of the input might be lost, because it is a valid <code>cross-correlation</code><em>,
 and not a full <code>cross-correlation</code></em>.
@@ -309,12 +176,12 @@ not actually add zero-padding to output.</p>
 may select a nondeterministic algorithm to increase performance. If this is
 undesirable, you can try to make the operation deterministic (potentially at
 a performance cost) by setting <code>torch.backends.cudnn.deterministic = TRUE</code>.</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
     
 
-<ul>
-<li><p>Input: \((N, C_{in}, D_{in}, H_{in}, W_{in})\)</p></li>
+<ul><li><p>Input: \((N, C_{in}, D_{in}, H_{in}, W_{in})\)</p></li>
 <li><p>Output: \((N, C_{out}, D_{out}, H_{out}, W_{out})\) where
 $$
   D_{out} = (D_{in} - 1) \times \mbox{stride}[0] - 2 \times \mbox{padding}[0] + \mbox{dilation}[0]
@@ -328,14 +195,12 @@ $$
   W_{out} = (W_{in} - 1) \times \mbox{stride}[2] - 2 \times \mbox{padding}[2] + \mbox{dilation}[2]
 \times (\mbox{kernel\_size}[2] - 1) + \mbox{output\_padding}[2] + 1
 $$</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="attributes"><a class="anchor" href="#attributes"></a>Attributes</h2>
-
+</ul></div>
+    <div class="section level2">
+    <h2 id="attributes">Attributes<a class="anchor" aria-label="anchor" href="#attributes"></a></h2>
     
 
-<ul>
-<li><p>weight (Tensor): the learnable weights of the module of shape
+<ul><li><p>weight (Tensor): the learnable weights of the module of shape
 \((\mbox{in\_channels}, \frac{\mbox{out\_channels}}{\mbox{groups}},\)
 \(\mbox{kernel\_size[0]}, \mbox{kernel\_size[1]}, \mbox{kernel\_size[2]})\).
 The values of these weights are sampled from
@@ -345,46 +210,39 @@ The values of these weights are sampled from
 If <code>bias</code> is <code>True</code>, then the values of these weights are
 sampled from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where
 \(k = \frac{groups}{C_{\mbox{out}} * \prod_{i=0}^{2}\mbox{kernel\_size}[i]}\)</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='cn'>FALSE</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='co'># With square kernels and equal stride</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_conv_transpose3d</span><span class='op'>(</span><span class='fl'>16</span>, <span class='fl'>33</span>, <span class='fl'>3</span>, stride<span class='op'>=</span><span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='co'># non-square kernels and unequal stride and with padding</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_conv_transpose3d</span><span class='op'>(</span><span class='fl'>16</span>, <span class='fl'>33</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>5</span>, <span class='fl'>2</span><span class='op'>)</span>, stride<span class='op'>=</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>1</span>, <span class='fl'>1</span><span class='op'>)</span>, padding<span class='op'>=</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>0</span>, <span class='fl'>4</span>, <span class='fl'>2</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>10</span>, <span class='fl'>50</span>, <span class='fl'>100</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+</ul></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="co"># With square kernels and equal stride</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">nn_conv_transpose3d</span><span class="op">(</span><span class="fl">16</span>, <span class="fl">33</span>, <span class="fl">3</span>, stride <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="co"># non-square kernels and unequal stride and with padding</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">nn_conv_transpose3d</span><span class="op">(</span><span class="fl">16</span>, <span class="fl">33</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">5</span>, <span class="fl">2</span><span class="op">)</span>, stride <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">1</span>, <span class="fl">1</span><span class="op">)</span>, padding <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">4</span>, <span class="fl">2</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">20</span>, <span class="fl">16</span>, <span class="fl">10</span>, <span class="fl">50</span>, <span class="fl">100</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span> <span class="op">&lt;-</span> <span class="fu">m</span><span class="op">(</span><span class="va">input</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Cosine embedding loss</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-loss.R'><code>R/nn-loss.R</code></a></small>
-    <div class="hidden name"><code>nn_cosine_embedding_loss.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Cosine embedding loss</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-loss.R" class="external-link"><code>R/nn-loss.R</code></a></small>
+      <div class="d-none name"><code>nn_cosine_embedding_loss.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Creates a criterion that measures the loss given input tensors
 \(x_1\), \(x_2\) and a <code>Tensor</code> label \(y\) with values 1 or -1.
 This is used for measuring whether two inputs are similar or dissimilar,
@@ -199,30 +95,27 @@ embeddings or semi-supervised learning.
 The loss function for each sample is:</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_cosine_embedding_loss</span><span class='op'>(</span>margin <span class='op'>=</span> <span class='fl'>0</span>, reduction <span class='op'>=</span> <span class='st'>"mean"</span><span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_cosine_embedding_loss</span><span class="op">(</span>margin <span class="op">=</span> <span class="fl">0</span>, reduction <span class="op">=</span> <span class="st">"mean"</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>margin</th>
-      <td><p>(float, optional): Should be a number from \(-1\) to \(1\),
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>margin</dt>
+<dd><p>(float, optional): Should be a number from \(-1\) to \(1\),
 \(0\) to \(0.5\) is suggested. If <code>margin</code> is missing, the
-default value is \(0\).</p></td>
-    </tr>
-    <tr>
-      <th>reduction</th>
-      <td><p>(string, optional): Specifies the reduction to apply to the output:
+default value is \(0\).</p></dd>
+<dt>reduction</dt>
+<dd><p>(string, optional): Specifies the reduction to apply to the output:
 <code>'none'</code> | <code>'mean'</code> | <code>'sum'</code>. <code>'none'</code>: no reduction will be applied,
 <code>'mean'</code>: the sum of the output will be divided by the number of
 elements in the output, <code>'sum'</code>: the output will be summed. Note: <code>size_average</code>
 and <code>reduce</code> are in the process of being deprecated, and in the meantime,
-specifying either of those two args will override <code>reduction</code>. Default: <code>'mean'</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
+specifying either of those two args will override <code>reduction</code>. Default: <code>'mean'</code></p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>$$
   \mbox{loss}(x, y) =
   \begin{array}{ll}
@@ -230,32 +123,25 @@ specifying either of those two args will override <code>reduction</code>. Defaul
 \max(0, \cos(x_1, x_2) - \mbox{margin}), &amp; \mbox{if } y = -1
 \end{array}
 $$</p>
+    </div>
 
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+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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+<div class="pkgdown-footer-right">
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 </li>
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+      </ul><form class="form-inline my-2 my-lg-0" role="search">
+        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
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+    
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>CrossEntropyLoss module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-loss.R'><code>R/nn-loss.R</code></a></small>
-    <div class="hidden name"><code>nn_cross_entropy_loss.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>CrossEntropyLoss module</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-loss.R" class="external-link"><code>R/nn-loss.R</code></a></small>
+      <div class="d-none name"><code>nn_cross_entropy_loss.Rd</code></div>
     </div>
 
-    <div class="ref-description">
-    <p>This criterion combines <code><a href='nn_log_softmax.html'>nn_log_softmax()</a></code> and <code><a href='nn_nll_loss.html'>nn_nll_loss()</a></code> in one single class.
+    <div class="ref-description section level2">
+    <p>This criterion combines <code><a href="nn_log_softmax.html">nn_log_softmax()</a></code> and <code><a href="nn_nll_loss.html">nn_nll_loss()</a></code> in one single class.
 It is useful when training a classification problem with <code>C</code> classes.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_cross_entropy_loss</span><span class='op'>(</span>weight <span class='op'>=</span> <span class='cn'>NULL</span>, ignore_index <span class='op'>=</span> <span class='op'>-</span><span class='fl'>100</span>, reduction <span class='op'>=</span> <span class='st'>"mean"</span><span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_cross_entropy_loss</span><span class="op">(</span>weight <span class="op">=</span> <span class="cn">NULL</span>, ignore_index <span class="op">=</span> <span class="op">-</span><span class="fl">100</span>, reduction <span class="op">=</span> <span class="st">"mean"</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>weight</th>
-      <td><p>(Tensor, optional): a manual rescaling weight given to each class.
-If given, has to be a Tensor of size <code>C</code></p></td>
-    </tr>
-    <tr>
-      <th>ignore_index</th>
-      <td><p>(int, optional): Specifies a target value that is ignored
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>weight</dt>
+<dd><p>(Tensor, optional): a manual rescaling weight given to each class.
+If given, has to be a Tensor of size <code>C</code></p></dd>
+<dt>ignore_index</dt>
+<dd><p>(int, optional): Specifies a target value that is ignored
 and does not contribute to the input gradient. When <code>size_average</code> is
-<code>TRUE</code>, the loss is averaged over non-ignored targets.</p></td>
-    </tr>
-    <tr>
-      <th>reduction</th>
-      <td><p>(string, optional): Specifies the reduction to apply to the output:
+<code>TRUE</code>, the loss is averaged over non-ignored targets.</p></dd>
+<dt>reduction</dt>
+<dd><p>(string, optional): Specifies the reduction to apply to the output:
 <code>'none'</code> | <code>'mean'</code> | <code>'sum'</code>. <code>'none'</code>: no reduction will be applied,
 <code>'mean'</code>: the sum of the output will be divided by the number of
 elements in the output, <code>'sum'</code>: the output will be summed. Note: <code>size_average</code>
 and <code>reduce</code> are in the process of being deprecated, and in the meantime,
-specifying either of those two args will override <code>reduction</code>. Default: <code>'mean'</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
+specifying either of those two args will override <code>reduction</code>. Default: <code>'mean'</code></p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>If provided, the optional argument <code>weight</code> should be a 1D <code>Tensor</code>
 assigning weight to each of the classes.</p>
 <p>This is particularly useful when you have an unbalanced training set.
@@ -245,12 +132,12 @@ Can also be used for higher dimension inputs, such as 2D images, by providing
 an input of size \((minibatch, C, d_1, d_2, ..., d_K)\) with \(K \geq 1\),
 where \(K\) is the number of dimensions, and a target of appropriate shape
 (see below).</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
     
 
-<ul>
-<li><p>Input: \((N, C)\) where <code>C = number of classes</code>, or
+<ul><li><p>Input: \((N, C)\) where <code>C = number of classes</code>, or
 \((N, C, d_1, d_2, ..., d_K)\) with \(K \geq 1\)
 in the case of <code>K</code>-dimensional loss.</p></li>
 <li><p>Target: \((N)\) where each value is \(0 \leq \mbox{targets}[i] \leq C-1\), or
@@ -261,44 +148,36 @@ If <code>reduction</code> is <code>'none'</code>, then the same size as the targ
 \((N)\), or
 \((N, d_1, d_2, ..., d_K)\) with \(K \geq 1\) in the case
 of K-dimensional loss.</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>loss</span> <span class='op'>&lt;-</span> <span class='fu'>nn_cross_entropy_loss</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>5</span>, requires_grad<span class='op'>=</span><span class='cn'>TRUE</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>target</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randint.html'>torch_randint</a></span><span class='op'>(</span>low <span class='op'>=</span> <span class='fl'>1</span>, high <span class='op'>=</span> <span class='fl'>5</span>, size <span class='op'>=</span> <span class='fl'>3</span>, dtype <span class='op'>=</span> <span class='fu'><a href='torch_dtype.html'>torch_long</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span> <span class='op'>&lt;-</span> <span class='fu'>loss</span><span class='op'>(</span><span class='va'>input</span>, <span class='va'>target</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span><span class='op'>$</span><span class='fu'>backward</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+</ul></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">loss</span> <span class="op">&lt;-</span> <span class="fu">nn_cross_entropy_loss</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">5</span>, requires_grad <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">target</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randint.html">torch_randint</a></span><span class="op">(</span>low <span class="op">=</span> <span class="fl">1</span>, high <span class="op">=</span> <span class="fl">5</span>, size <span class="op">=</span> <span class="fl">3</span>, dtype <span class="op">=</span> <span class="fu"><a href="torch_dtype.html">torch_long</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span> <span class="op">&lt;-</span> <span class="fu">loss</span><span class="op">(</span><span class="va">input</span>, <span class="va">target</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span><span class="op">$</span><span class="fu">backward</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer>
-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
 </div>
 
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+with respect to each input node. The alignment of input to target is assumed to be "many-to-one", which
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-        <li>
-  <a href="https://github.com/mlverse/torch/">
-    <span class="fab fa-github fa-lg"></span>
+      </ul><form class="form-inline my-2 my-lg-0" role="search">
+        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
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+    
+  </div>
+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>The Connectionist Temporal Classification loss.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-loss.R'><code>R/nn-loss.R</code></a></small>
-    <div class="hidden name"><code>nn_ctc_loss.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>The Connectionist Temporal Classification loss.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-loss.R" class="external-link"><code>R/nn-loss.R</code></a></small>
+      <div class="d-none name"><code>nn_ctc_loss.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Calculates loss between a continuous (unsegmented) time series and a target sequence. CTCLoss sums over the
 probability of possible alignments of input to target, producing a loss value which is differentiable
 with respect to each input node. The alignment of input to target is assumed to be "many-to-one", which
 limits the length of the target sequence such that it must be \(\leq\) the input length.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_ctc_loss</span><span class='op'>(</span>blank <span class='op'>=</span> <span class='fl'>0</span>, reduction <span class='op'>=</span> <span class='st'>"mean"</span>, zero_infinity <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_ctc_loss</span><span class="op">(</span>blank <span class="op">=</span> <span class="fl">0</span>, reduction <span class="op">=</span> <span class="st">"mean"</span>, zero_infinity <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>blank</th>
-      <td><p>(int, optional): blank label. Default \(0\).</p></td>
-    </tr>
-    <tr>
-      <th>reduction</th>
-      <td><p>(string, optional): Specifies the reduction to apply to the output:
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>blank</dt>
+<dd><p>(int, optional): blank label. Default \(0\).</p></dd>
+<dt>reduction</dt>
+<dd><p>(string, optional): Specifies the reduction to apply to the output:
 <code>'none'</code> | <code>'mean'</code> | <code>'sum'</code>. <code>'none'</code>: no reduction will be applied,
 <code>'mean'</code>: the output losses will be divided by the target lengths and
-then the mean over the batch is taken. Default: <code>'mean'</code></p></td>
-    </tr>
-    <tr>
-      <th>zero_infinity</th>
-      <td><p>(bool, optional):
+then the mean over the batch is taken. Default: <code>'mean'</code></p></dd>
+<dt>zero_infinity</dt>
+<dd><p>(bool, optional):
 Whether to zero infinite losses and the associated gradients.
 Default: <code>FALSE</code>
 Infinite losses mainly occur when the inputs are too short
-to be aligned to the targets.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
+to be aligned to the targets.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p>In order to use CuDNN, the following must be satisfied: <code>targets</code> must be
 in concatenated format, all <code>input_lengths</code> must be <code>T</code>.  \(blank=0\),
 <code>target_lengths</code> \(\leq 256\), the integer arguments must be of
@@ -232,12 +121,12 @@ dtype <code>torch_int32</code>.</p>
 may select a nondeterministic algorithm to increase performance. If this is
 undesirable, you can try to make the operation deterministic (potentially at
 a performance cost) by setting <code>torch.backends.cudnn.deterministic = TRUE</code>.</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
     
 
-<ul>
-<li><p>Log_probs: Tensor of size \((T, N, C)\),
+<ul><li><p>Log_probs: Tensor of size \((T, N, C)\),
 where \(T = \mbox{input length}\),
 \(N = \mbox{batch size}\), and
 \(C = \mbox{number of classes (including blank)}\).
@@ -270,81 +159,77 @@ If the targets are given as a 1d tensor that is the concatenation of individual
 targets, the target_lengths must add up to the total length of the tensor.</p></li>
 <li><p>Output: scalar. If <code>reduction</code> is <code>'none'</code>, then
 \((N)\), where \(N = \mbox{batch size}\).</p></li>
-</ul>
-
-<p>[nnf)log_softmax()]: R:nnf)log_softmax()
+</ul><p>[nnf)log_softmax()]: R:nnf)log_softmax()
 [n,0:s_n]: R:n,0:s_n</p>
-    <h2 class="hasAnchor" id="references"><a class="anchor" href="#references"></a>References</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
     <p>A. Graves et al.: Connectionist Temporal Classification:
 Labelling Unsegmented Sequence Data with Recurrent Neural Networks:
 https://www.cs.toronto.edu/~graves/icml_2006.pdf</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='co'># Target are to be padded</span></span>
-<span class='r-in'><span class='cn'>T</span> <span class='op'>&lt;-</span> <span class='fl'>50</span>      <span class='co'># Input sequence length</span></span>
-<span class='r-in'><span class='va'>C</span> <span class='op'>&lt;-</span> <span class='fl'>20</span>      <span class='co'># Number of classes (including blank)</span></span>
-<span class='r-in'><span class='va'>N</span> <span class='op'>&lt;-</span> <span class='fl'>16</span>      <span class='co'># Batch size</span></span>
-<span class='r-in'><span class='va'>S</span> <span class='op'>&lt;-</span> <span class='fl'>30</span>      <span class='co'># Target sequence length of longest target in batch (padding length)</span></span>
-<span class='r-in'><span class='va'>S_min</span> <span class='op'>&lt;-</span> <span class='fl'>10</span>  <span class='co'># Minimum target length, for demonstration purposes</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='co'># Initialize random batch of input vectors, for *size = (T,N,C)</span></span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='cn'>T</span>, <span class='va'>N</span>, <span class='va'>C</span><span class='op'>)</span><span class='op'>$</span><span class='fu'>log_softmax</span><span class='op'>(</span><span class='fl'>2</span><span class='op'>)</span><span class='op'>$</span><span class='kw'>detach</span><span class='op'>(</span><span class='op'>)</span><span class='op'>$</span><span class='fu'>requires_grad_</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='co'># Initialize random batch of targets (0 = blank, 1:C = classes)</span></span>
-<span class='r-in'><span class='va'>target</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randint.html'>torch_randint</a></span><span class='op'>(</span>low<span class='op'>=</span><span class='fl'>1</span>, high<span class='op'>=</span><span class='va'>C</span>, size<span class='op'>=</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='va'>N</span>, <span class='va'>S</span><span class='op'>)</span>, dtype<span class='op'>=</span><span class='fu'><a href='torch_dtype.html'>torch_long</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>input_lengths</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_full.html'>torch_full</a></span><span class='op'>(</span>size<span class='op'>=</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='va'>N</span><span class='op'>)</span>, fill_value<span class='op'>=</span><span class='cn'>TRUE</span>, dtype<span class='op'>=</span><span class='fu'><a href='torch_dtype.html'>torch_long</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>target_lengths</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randint.html'>torch_randint</a></span><span class='op'>(</span>low<span class='op'>=</span><span class='va'>S_min</span>, high<span class='op'>=</span><span class='va'>S</span>, size<span class='op'>=</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='va'>N</span><span class='op'>)</span>, dtype<span class='op'>=</span><span class='fu'><a href='torch_dtype.html'>torch_long</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>ctc_loss</span> <span class='op'>&lt;-</span> <span class='fu'>nn_ctc_loss</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>loss</span> <span class='op'>&lt;-</span> <span class='fu'>ctc_loss</span><span class='op'>(</span><span class='va'>input</span>, <span class='va'>target</span>, <span class='va'>input_lengths</span>, <span class='va'>target_lengths</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>loss</span><span class='op'>$</span><span class='fu'>backward</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='co'># Target are to be un-padded</span></span>
-<span class='r-in'><span class='cn'>T</span> <span class='op'>&lt;-</span> <span class='fl'>50</span>      <span class='co'># Input sequence length</span></span>
-<span class='r-in'><span class='va'>C</span> <span class='op'>&lt;-</span> <span class='fl'>20</span>      <span class='co'># Number of classes (including blank)</span></span>
-<span class='r-in'><span class='va'>N</span> <span class='op'>&lt;-</span> <span class='fl'>16</span>      <span class='co'># Batch size</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='co'># Initialize random batch of input vectors, for *size = (T,N,C)</span></span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='cn'>T</span>, <span class='va'>N</span>, <span class='va'>C</span><span class='op'>)</span><span class='op'>$</span><span class='fu'>log_softmax</span><span class='op'>(</span><span class='fl'>2</span><span class='op'>)</span><span class='op'>$</span><span class='kw'>detach</span><span class='op'>(</span><span class='op'>)</span><span class='op'>$</span><span class='fu'>requires_grad_</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>input_lengths</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_full.html'>torch_full</a></span><span class='op'>(</span>size<span class='op'>=</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='va'>N</span><span class='op'>)</span>, fill_value<span class='op'>=</span><span class='cn'>TRUE</span>, dtype<span class='op'>=</span><span class='fu'><a href='torch_dtype.html'>torch_long</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='co'># Initialize random batch of targets (0 = blank, 1:C = classes)</span></span>
-<span class='r-in'><span class='va'>target_lengths</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randint.html'>torch_randint</a></span><span class='op'>(</span>low<span class='op'>=</span><span class='fl'>1</span>, high<span class='op'>=</span><span class='cn'>T</span>, size<span class='op'>=</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='va'>N</span><span class='op'>)</span>, dtype<span class='op'>=</span><span class='fu'><a href='torch_dtype.html'>torch_long</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>target</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randint.html'>torch_randint</a></span><span class='op'>(</span>low<span class='op'>=</span><span class='fl'>1</span>, high<span class='op'>=</span><span class='va'>C</span>, size<span class='op'>=</span><span class='fu'><a href='https://rdrr.io/r/base/integer.html'>as.integer</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/sum.html'>sum</a></span><span class='op'>(</span><span class='va'>target_lengths</span><span class='op'>)</span><span class='op'>)</span>, dtype<span class='op'>=</span><span class='fu'><a href='torch_dtype.html'>torch_long</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>ctc_loss</span> <span class='op'>&lt;-</span> <span class='fu'>nn_ctc_loss</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>loss</span> <span class='op'>&lt;-</span> <span class='fu'>ctc_loss</span><span class='op'>(</span><span class='va'>input</span>, <span class='va'>target</span>, <span class='va'>input_lengths</span>, <span class='va'>target_lengths</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>loss</span><span class='op'>$</span><span class='fu'>backward</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="co"># Target are to be padded</span></span>
+<span class="r-in"><span class="cn">T</span> <span class="op">&lt;-</span> <span class="fl">50</span> <span class="co"># Input sequence length</span></span>
+<span class="r-in"><span class="va">C</span> <span class="op">&lt;-</span> <span class="fl">20</span> <span class="co"># Number of classes (including blank)</span></span>
+<span class="r-in"><span class="va">N</span> <span class="op">&lt;-</span> <span class="fl">16</span> <span class="co"># Batch size</span></span>
+<span class="r-in"><span class="va">S</span> <span class="op">&lt;-</span> <span class="fl">30</span> <span class="co"># Target sequence length of longest target in batch (padding length)</span></span>
+<span class="r-in"><span class="va">S_min</span> <span class="op">&lt;-</span> <span class="fl">10</span> <span class="co"># Minimum target length, for demonstration purposes</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="co"># Initialize random batch of input vectors, for *size = (T,N,C)</span></span>
+<span class="r-in"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="cn">T</span>, <span class="va">N</span>, <span class="va">C</span><span class="op">)</span><span class="op">$</span><span class="fu">log_softmax</span><span class="op">(</span><span class="fl">2</span><span class="op">)</span><span class="op">$</span><span class="kw">detach</span><span class="op">(</span><span class="op">)</span><span class="op">$</span><span class="fu">requires_grad_</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="co"># Initialize random batch of targets (0 = blank, 1:C = classes)</span></span>
+<span class="r-in"><span class="va">target</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randint.html">torch_randint</a></span><span class="op">(</span>low <span class="op">=</span> <span class="fl">1</span>, high <span class="op">=</span> <span class="va">C</span>, size <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="va">N</span>, <span class="va">S</span><span class="op">)</span>, dtype <span class="op">=</span> <span class="fu"><a href="torch_dtype.html">torch_long</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">input_lengths</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_full.html">torch_full</a></span><span class="op">(</span>size <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="va">N</span><span class="op">)</span>, fill_value <span class="op">=</span> <span class="cn">TRUE</span>, dtype <span class="op">=</span> <span class="fu"><a href="torch_dtype.html">torch_long</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">target_lengths</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randint.html">torch_randint</a></span><span class="op">(</span>low <span class="op">=</span> <span class="va">S_min</span>, high <span class="op">=</span> <span class="va">S</span>, size <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="va">N</span><span class="op">)</span>, dtype <span class="op">=</span> <span class="fu"><a href="torch_dtype.html">torch_long</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">ctc_loss</span> <span class="op">&lt;-</span> <span class="fu">nn_ctc_loss</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">loss</span> <span class="op">&lt;-</span> <span class="fu">ctc_loss</span><span class="op">(</span><span class="va">input</span>, <span class="va">target</span>, <span class="va">input_lengths</span>, <span class="va">target_lengths</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">loss</span><span class="op">$</span><span class="fu">backward</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="co"># Target are to be un-padded</span></span>
+<span class="r-in"><span class="cn">T</span> <span class="op">&lt;-</span> <span class="fl">50</span> <span class="co"># Input sequence length</span></span>
+<span class="r-in"><span class="va">C</span> <span class="op">&lt;-</span> <span class="fl">20</span> <span class="co"># Number of classes (including blank)</span></span>
+<span class="r-in"><span class="va">N</span> <span class="op">&lt;-</span> <span class="fl">16</span> <span class="co"># Batch size</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="co"># Initialize random batch of input vectors, for *size = (T,N,C)</span></span>
+<span class="r-in"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="cn">T</span>, <span class="va">N</span>, <span class="va">C</span><span class="op">)</span><span class="op">$</span><span class="fu">log_softmax</span><span class="op">(</span><span class="fl">2</span><span class="op">)</span><span class="op">$</span><span class="kw">detach</span><span class="op">(</span><span class="op">)</span><span class="op">$</span><span class="fu">requires_grad_</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">input_lengths</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_full.html">torch_full</a></span><span class="op">(</span>size <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="va">N</span><span class="op">)</span>, fill_value <span class="op">=</span> <span class="cn">TRUE</span>, dtype <span class="op">=</span> <span class="fu"><a href="torch_dtype.html">torch_long</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="co"># Initialize random batch of targets (0 = blank, 1:C = classes)</span></span>
+<span class="r-in"><span class="va">target_lengths</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randint.html">torch_randint</a></span><span class="op">(</span>low <span class="op">=</span> <span class="fl">1</span>, high <span class="op">=</span> <span class="cn">T</span>, size <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="va">N</span><span class="op">)</span>, dtype <span class="op">=</span> <span class="fu"><a href="torch_dtype.html">torch_long</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">target</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randint.html">torch_randint</a></span><span class="op">(</span></span>
+<span class="r-in">  low <span class="op">=</span> <span class="fl">1</span>, high <span class="op">=</span> <span class="va">C</span>, size <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/integer.html" class="external-link">as.integer</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/sum.html" class="external-link">sum</a></span><span class="op">(</span><span class="va">target_lengths</span><span class="op">)</span><span class="op">)</span>,</span>
+<span class="r-in">  dtype <span class="op">=</span> <span class="fu"><a href="torch_dtype.html">torch_long</a></span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">)</span></span>
+<span class="r-in"><span class="va">ctc_loss</span> <span class="op">&lt;-</span> <span class="fu">nn_ctc_loss</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">loss</span> <span class="op">&lt;-</span> <span class="fu">ctc_loss</span><span class="op">(</span><span class="va">input</span>, <span class="va">target</span>, <span class="va">input_lengths</span>, <span class="va">target_lengths</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">loss</span><span class="op">$</span><span class="fu">backward</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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-      <div class="copyright">
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+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
 </div>
 
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diff --git a/reference/nn_dropout.html b/reference/nn_dropout.html
index 8bfee36a2..3ebcef3ff 100644
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@@ -1,266 +1,149 @@
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="During training, randomly zeroes some of the elements of the input
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+call."><title>Dropout module — nn_dropout • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.1.0/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.1.0/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Dropout module — nn_dropout"><meta property="og:description" content="During training, randomly zeroes some of the elements of the input
+tensor with probability p using samples from a Bernoulli
+distribution. Each channel will be zeroed out independently on every forward
+call."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Dropout module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-dropout.R'><code>R/nn-dropout.R</code></a></small>
-    <div class="hidden name"><code>nn_dropout.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Dropout module</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-dropout.R" class="external-link"><code>R/nn-dropout.R</code></a></small>
+      <div class="d-none name"><code>nn_dropout.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>During training, randomly zeroes some of the elements of the input
 tensor with probability <code>p</code> using samples from a Bernoulli
 distribution. Each channel will be zeroed out independently on every forward
 call.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_dropout</span><span class='op'>(</span>p <span class='op'>=</span> <span class='fl'>0.5</span>, inplace <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>p</th>
-      <td><p>probability of an element to be zeroed. Default: 0.5</p></td>
-    </tr>
-    <tr>
-      <th>inplace</th>
-      <td><p>If set to <code>TRUE</code>, will do this operation in-place. Default: <code>FALSE</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_dropout</span><span class="op">(</span>p <span class="op">=</span> <span class="fl">0.5</span>, inplace <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>p</dt>
+<dd><p>probability of an element to be zeroed. Default: 0.5</p></dd>
+<dt>inplace</dt>
+<dd><p>If set to <code>TRUE</code>, will do this operation in-place. Default: <code>FALSE</code>.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>This has proven to be an effective technique for regularization and
 preventing the co-adaptation of neurons as described in the paper
-<a href='https://arxiv.org/abs/1207.0580'>Improving neural networks by preventing co-adaptation of feature detectors</a>.</p>
+<a href="https://arxiv.org/abs/1207.0580" class="external-link">Improving neural networks by preventing co-adaptation of feature detectors</a>.</p>
 <p>Furthermore, the outputs are scaled by a factor of :math:<code>\frac{1}{1-p}</code> during
 training. This means that during evaluation the module simply computes an
 identity function.</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
     
 
-<ul>
-<li><p>Input: \((*)\). Input can be of any shape</p></li>
+<ul><li><p>Input: \((*)\). Input can be of any shape</p></li>
 <li><p>Output: \((*)\). Output is of the same shape as input</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_dropout</span><span class='op'>(</span>p <span class='op'>=</span> <span class='fl'>0.2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>20</span>, <span class='fl'>16</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+</ul></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">nn_dropout</span><span class="op">(</span>p <span class="op">=</span> <span class="fl">0.2</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">20</span>, <span class="fl">16</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span> <span class="op">&lt;-</span> <span class="fu">m</span><span class="op">(</span><span class="va">input</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Dropout2D module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-dropout.R'><code>R/nn-dropout.R</code></a></small>
-    <div class="hidden name"><code>nn_dropout2d.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Dropout2D module</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-dropout.R" class="external-link"><code>R/nn-dropout.R</code></a></small>
+      <div class="d-none name"><code>nn_dropout2d.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Randomly zero out entire channels (a channel is a 2D feature map,
 e.g., the \(j\)-th channel of the \(i\)-th sample in the
 batched input is a 2D tensor \(\mbox{input}[i, j]\)).</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_dropout2d</span><span class='op'>(</span>p <span class='op'>=</span> <span class='fl'>0.5</span>, inplace <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>p</th>
-      <td><p>(float, optional): probability of an element to be zero-ed.</p></td>
-    </tr>
-    <tr>
-      <th>inplace</th>
-      <td><p>(bool, optional): If set to <code>TRUE</code>, will do this operation
-in-place</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_dropout2d</span><span class="op">(</span>p <span class="op">=</span> <span class="fl">0.5</span>, inplace <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>p</dt>
+<dd><p>(float, optional): probability of an element to be zero-ed.</p></dd>
+<dt>inplace</dt>
+<dd><p>(bool, optional): If set to <code>TRUE</code>, will do this operation
+in-place</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>Each channel will be zeroed out independently on every forward call with
 probability <code>p</code> using samples from a Bernoulli distribution.
-Usually the input comes from <a href='nn_conv2d.html'>nn_conv2d</a> modules.</p>
+Usually the input comes from <a href="nn_conv2d.html">nn_conv2d</a> modules.</p>
 <p>As described in the paper
-<a href='https://arxiv.org/abs/1411.4280'>Efficient Object Localization Using Convolutional Networks</a> ,
+<a href="https://arxiv.org/abs/1411.4280" class="external-link">Efficient Object Localization Using Convolutional Networks</a> ,
 if adjacent pixels within feature maps are strongly correlated
 (as is normally the case in early convolution layers) then i.i.d. dropout
 will not regularize the activations and will otherwise just result
 in an effective learning rate decrease.
 In this case, nn_dropout2d will help promote independence between
 feature maps and should be used instead.</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
     
 
-<ul>
-<li><p>Input: \((N, C, H, W)\)</p></li>
+<ul><li><p>Input: \((N, C, H, W)\)</p></li>
 <li><p>Output: \((N, C, H, W)\) (same shape as input)</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_dropout2d</span><span class='op'>(</span>p <span class='op'>=</span> <span class='fl'>0.2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>32</span>, <span class='fl'>32</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+</ul></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">nn_dropout2d</span><span class="op">(</span>p <span class="op">=</span> <span class="fl">0.2</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">20</span>, <span class="fl">16</span>, <span class="fl">32</span>, <span class="fl">32</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span> <span class="op">&lt;-</span> <span class="fu">m</span><span class="op">(</span><span class="va">input</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Dropout3D module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-dropout.R'><code>R/nn-dropout.R</code></a></small>
-    <div class="hidden name"><code>nn_dropout3d.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Dropout3D module</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-dropout.R" class="external-link"><code>R/nn-dropout.R</code></a></small>
+      <div class="d-none name"><code>nn_dropout3d.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Randomly zero out entire channels (a channel is a 3D feature map,
 e.g., the \(j\)-th channel of the \(i\)-th sample in the
 batched input is a 3D tensor \(\mbox{input}[i, j]\)).</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_dropout3d</span><span class='op'>(</span>p <span class='op'>=</span> <span class='fl'>0.5</span>, inplace <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>p</th>
-      <td><p>(float, optional): probability of an element to be zeroed.</p></td>
-    </tr>
-    <tr>
-      <th>inplace</th>
-      <td><p>(bool, optional): If set to <code>TRUE</code>, will do this operation
-in-place</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_dropout3d</span><span class="op">(</span>p <span class="op">=</span> <span class="fl">0.5</span>, inplace <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>p</dt>
+<dd><p>(float, optional): probability of an element to be zeroed.</p></dd>
+<dt>inplace</dt>
+<dd><p>(bool, optional): If set to <code>TRUE</code>, will do this operation
+in-place</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>Each channel will be zeroed out independently on every forward call with
 probability <code>p</code> using samples from a Bernoulli distribution.
-Usually the input comes from <a href='nn_conv2d.html'>nn_conv2d</a> modules.</p>
+Usually the input comes from <a href="nn_conv2d.html">nn_conv2d</a> modules.</p>
 <p>As described in the paper
-<a href='https://arxiv.org/abs/1411.4280'>Efficient Object Localization Using Convolutional Networks</a> ,
+<a href="https://arxiv.org/abs/1411.4280" class="external-link">Efficient Object Localization Using Convolutional Networks</a> ,
 if adjacent pixels within feature maps are strongly correlated
 (as is normally the case in early convolution layers) then i.i.d. dropout
 will not regularize the activations and will otherwise just result
 in an effective learning rate decrease.</p>
 <p>In this case, nn_dropout3d will help promote independence between
 feature maps and should be used instead.</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
     
 
-<ul>
-<li><p>Input: \((N, C, D, H, W)\)</p></li>
+<ul><li><p>Input: \((N, C, D, H, W)\)</p></li>
 <li><p>Output: \((N, C, D, H, W)\) (same shape as input)</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_dropout3d</span><span class='op'>(</span>p <span class='op'>=</span> <span class='fl'>0.2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>4</span>, <span class='fl'>32</span>, <span class='fl'>32</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+</ul></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">nn_dropout3d</span><span class="op">(</span>p <span class="op">=</span> <span class="fl">0.2</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">20</span>, <span class="fl">16</span>, <span class="fl">4</span>, <span class="fl">32</span>, <span class="fl">32</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span> <span class="op">&lt;-</span> <span class="fu">m</span><span class="op">(</span><span class="va">input</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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+</nav><div class="container template-reference-topic">
 <div class="row">
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-    <div class="page-header">
-    <h1>ELU module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
-    <div class="hidden name"><code>nn_elu.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>ELU module</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-activation.R" class="external-link"><code>R/nn-activation.R</code></a></small>
+      <div class="d-none name"><code>nn_elu.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Applies the element-wise function:</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_elu</span><span class='op'>(</span>alpha <span class='op'>=</span> <span class='fl'>1</span>, inplace <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>alpha</th>
-      <td><p>the \(\alpha\) value for the ELU formulation. Default: 1.0</p></td>
-    </tr>
-    <tr>
-      <th>inplace</th>
-      <td><p>can optionally do the operation in-place. Default: <code>FALSE</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_elu</span><span class="op">(</span>alpha <span class="op">=</span> <span class="fl">1</span>, inplace <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>alpha</dt>
+<dd><p>the \(\alpha\) value for the ELU formulation. Default: 1.0</p></dd>
+<dt>inplace</dt>
+<dd><p>can optionally do the operation in-place. Default: <code>FALSE</code></p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>$$
   \mbox{ELU}(x) = \max(0,x) + \min(0, \alpha * (\exp(x) - 1))
 $$</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
     
 
-<ul>
-<li><p>Input: \((N, *)\) where <code>*</code> means, any number of additional
+<ul><li><p>Input: \((N, *)\) where <code>*</code> means, any number of additional
 dimensions</p></li>
 <li><p>Output: \((N, *)\), same shape as the input</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_elu</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span> <span class='op'>&lt;-</span>  <span class='fu'>m</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+</ul></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">nn_elu</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span> <span class="op">&lt;-</span> <span class="fu">m</span><span class="op">(</span><span class="va">input</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer>
-      <div class="copyright">
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+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Embedding module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-sparse.R'><code>R/nn-sparse.R</code></a></small>
-    <div class="hidden name"><code>nn_embedding.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Embedding module</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-sparse.R" class="external-link"><code>R/nn-sparse.R</code></a></small>
+      <div class="d-none name"><code>nn_embedding.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>A simple lookup table that stores embeddings of a fixed dictionary and size.
 This module is often used to store word embeddings and retrieve them using indices.
 The input to the module is a list of indices, and the output is the corresponding
 word embeddings.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_embedding</span><span class='op'>(</span>
-  <span class='va'>num_embeddings</span>,
-  <span class='va'>embedding_dim</span>,
-  padding_idx <span class='op'>=</span> <span class='cn'>NULL</span>,
-  max_norm <span class='op'>=</span> <span class='cn'>NULL</span>,
-  norm_type <span class='op'>=</span> <span class='fl'>2</span>,
-  scale_grad_by_freq <span class='op'>=</span> <span class='cn'>FALSE</span>,
-  sparse <span class='op'>=</span> <span class='cn'>FALSE</span>,
-  .weight <span class='op'>=</span> <span class='cn'>NULL</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>num_embeddings</th>
-      <td><p>(int): size of the dictionary of embeddings</p></td>
-    </tr>
-    <tr>
-      <th>embedding_dim</th>
-      <td><p>(int): the size of each embedding vector</p></td>
-    </tr>
-    <tr>
-      <th>padding_idx</th>
-      <td><p>(int, optional): If given, pads the output with the embedding vector at <code>padding_idx</code>
-(initialized to zeros) whenever it encounters the index.</p></td>
-    </tr>
-    <tr>
-      <th>max_norm</th>
-      <td><p>(float, optional): If given, each embedding vector with norm larger than <code>max_norm</code>
-is renormalized to have norm <code>max_norm</code>.</p></td>
-    </tr>
-    <tr>
-      <th>norm_type</th>
-      <td><p>(float, optional): The p of the p-norm to compute for the <code>max_norm</code> option. Default <code>2</code>.</p></td>
-    </tr>
-    <tr>
-      <th>scale_grad_by_freq</th>
-      <td><p>(boolean, optional): If given, this will scale gradients by the inverse of frequency of
-the words in the mini-batch. Default <code>False</code>.</p></td>
-    </tr>
-    <tr>
-      <th>sparse</th>
-      <td><p>(bool, optional): If <code>True</code>, gradient w.r.t. <code>weight</code> matrix will be a sparse tensor.</p></td>
-    </tr>
-    <tr>
-      <th>.weight</th>
-      <td><p>(Tensor) embeddings weights (in case you want to set it manually)</p>
-<p>See Notes for more details regarding sparse gradients.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_embedding</span><span class="op">(</span>
+  <span class="va">num_embeddings</span>,
+  <span class="va">embedding_dim</span>,
+  padding_idx <span class="op">=</span> <span class="cn">NULL</span>,
+  max_norm <span class="op">=</span> <span class="cn">NULL</span>,
+  norm_type <span class="op">=</span> <span class="fl">2</span>,
+  scale_grad_by_freq <span class="op">=</span> <span class="cn">FALSE</span>,
+  sparse <span class="op">=</span> <span class="cn">FALSE</span>,
+  .weight <span class="op">=</span> <span class="cn">NULL</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>num_embeddings</dt>
+<dd><p>(int): size of the dictionary of embeddings</p></dd>
+<dt>embedding_dim</dt>
+<dd><p>(int): the size of each embedding vector</p></dd>
+<dt>padding_idx</dt>
+<dd><p>(int, optional): If given, pads the output with the embedding vector at <code>padding_idx</code>
+(initialized to zeros) whenever it encounters the index.</p></dd>
+<dt>max_norm</dt>
+<dd><p>(float, optional): If given, each embedding vector with norm larger than <code>max_norm</code>
+is renormalized to have norm <code>max_norm</code>.</p></dd>
+<dt>norm_type</dt>
+<dd><p>(float, optional): The p of the p-norm to compute for the <code>max_norm</code> option. Default <code>2</code>.</p></dd>
+<dt>scale_grad_by_freq</dt>
+<dd><p>(boolean, optional): If given, this will scale gradients by the inverse of frequency of
+the words in the mini-batch. Default <code>False</code>.</p></dd>
+<dt>sparse</dt>
+<dd><p>(bool, optional): If <code>True</code>, gradient w.r.t. <code>weight</code> matrix will be a sparse tensor.</p></dd>
+<dt>.weight</dt>
+<dd><p>(Tensor) embeddings weights (in case you want to set it manually)</p>
+<p>See Notes for more details regarding sparse gradients.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p>Keep in mind that only a limited number of optimizers support
 sparse gradients: currently it's <code>optim.SGD</code> (<code>CUDA</code> and <code>CPU</code>),
 <code>optim.SparseAdam</code> (<code>CUDA</code> and <code>CPU</code>) and <code>optim.Adagrad</code> (<code>CPU</code>)</p>
@@ -258,71 +137,61 @@ vector can be modified afterwards, e.g., using a customized
 initialization method, and thus changing the vector used to pad the
 output. The gradient for this vector from nn_embedding
 is always zero.</p>
-    <h2 class="hasAnchor" id="attributes"><a class="anchor" href="#attributes"></a>Attributes</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="attributes">Attributes<a class="anchor" aria-label="anchor" href="#attributes"></a></h2>
     
 
-<ul>
-<li><p>weight (Tensor): the learnable weights of the module of shape (num_embeddings, embedding_dim)
+<ul><li><p>weight (Tensor): the learnable weights of the module of shape (num_embeddings, embedding_dim)
 initialized from \(\mathcal{N}(0, 1)\)</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
+</ul></div>
+    <div class="section level2">
+    <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
     
 
-<ul>
-<li><p>Input: \((*)\), LongTensor of arbitrary shape containing the indices to extract</p></li>
+<ul><li><p>Input: \((*)\), LongTensor of arbitrary shape containing the indices to extract</p></li>
 <li><p>Output: \((*, H)\), where <code>*</code> is the input shape and \(H=\mbox{embedding\_dim}\)</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='co'># an Embedding module containing 10 tensors of size 3</span></span>
-<span class='r-in'><span class='va'>embedding</span> <span class='op'>&lt;-</span> <span class='fu'>nn_embedding</span><span class='op'>(</span><span class='fl'>10</span>, <span class='fl'>3</span><span class='op'>)</span></span>
-<span class='r-in'><span class='co'># a batch of 2 samples of 4 indices each</span></span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/cbind.html'>rbind</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>,<span class='fl'>2</span>,<span class='fl'>4</span>,<span class='fl'>5</span><span class='op'>)</span>,<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span>,<span class='fl'>3</span>,<span class='fl'>2</span>,<span class='fl'>9</span><span class='op'>)</span><span class='op'>)</span>, dtype <span class='op'>=</span> <span class='fu'><a href='torch_dtype.html'>torch_long</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>embedding</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span></span>
-<span class='r-in'><span class='co'># example with padding_idx</span></span>
-<span class='r-in'><span class='va'>embedding</span> <span class='op'>&lt;-</span> <span class='fu'>nn_embedding</span><span class='op'>(</span><span class='fl'>10</span>, <span class='fl'>3</span>, padding_idx<span class='op'>=</span><span class='fl'>1</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>,<span class='fl'>3</span>,<span class='fl'>1</span>,<span class='fl'>6</span><span class='op'>)</span>, nrow <span class='op'>=</span> <span class='fl'>1</span><span class='op'>)</span>, dtype <span class='op'>=</span> <span class='fu'><a href='torch_dtype.html'>torch_long</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>embedding</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> (1,.,.) = </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   0.0000  0.0000  0.0000</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   0.0767 -1.3438  0.4823</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   0.0000  0.0000  0.0000</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  -0.8698  0.2783 -0.6980</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{1,4,3} ][ grad_fn = &lt;EmbeddingBackward&gt; ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+</ul></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="co"># an Embedding module containing 10 tensors of size 3</span></span>
+<span class="r-in"><span class="va">embedding</span> <span class="op">&lt;-</span> <span class="fu">nn_embedding</span><span class="op">(</span><span class="fl">10</span>, <span class="fl">3</span><span class="op">)</span></span>
+<span class="r-in"><span class="co"># a batch of 2 samples of 4 indices each</span></span>
+<span class="r-in"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/cbind.html" class="external-link">rbind</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">2</span>, <span class="fl">4</span>, <span class="fl">5</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span>, <span class="fl">3</span>, <span class="fl">2</span>, <span class="fl">9</span><span class="op">)</span><span class="op">)</span>, dtype <span class="op">=</span> <span class="fu"><a href="torch_dtype.html">torch_long</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">embedding</span><span class="op">(</span><span class="va">input</span><span class="op">)</span></span>
+<span class="r-in"><span class="co"># example with padding_idx</span></span>
+<span class="r-in"><span class="va">embedding</span> <span class="op">&lt;-</span> <span class="fu">nn_embedding</span><span class="op">(</span><span class="fl">10</span>, <span class="fl">3</span>, padding_idx <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/matrix.html" class="external-link">matrix</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">3</span>, <span class="fl">1</span>, <span class="fl">6</span><span class="op">)</span>, nrow <span class="op">=</span> <span class="fl">1</span><span class="op">)</span>, dtype <span class="op">=</span> <span class="fu"><a href="torch_dtype.html">torch_long</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">embedding</span><span class="op">(</span><span class="va">input</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> (1,.,.) = </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.0000  0.0000  0.0000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.3464  1.2327 -2.9766</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.0000  0.0000  0.0000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.2314  0.4479 -0.6888</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{1,4,3} ][ grad_fn = &lt;EmbeddingBackward0&gt; ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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-      <div class="copyright">
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+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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new file mode 100644
index 000000000..fa12af473
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+++ b/reference/nn_flatten.html
@@ -0,0 +1,171 @@
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+  <a class="nav-link" href="../reference/index.html">Reference</a>
+</li>
+<li class="nav-item">
+  <a class="nav-link" href="../news/index.html">Changelog</a>
+</li>
+      </ul><form class="form-inline my-2 my-lg-0" role="search">
+        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
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+  <a class="external-link nav-link" href="https://github.com/mlverse/torch/" aria-label="github">
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+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Flattens a contiguous range of dims into a tensor.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-flatten.R" class="external-link"><code>R/nn-flatten.R</code></a></small>
+      <div class="d-none name"><code>nn_flatten.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>For use with <a href="nn_sequential.html">nn_sequential</a>.</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_flatten</span><span class="op">(</span>start_dim <span class="op">=</span> <span class="fl">2</span>, end_dim <span class="op">=</span> <span class="op">-</span><span class="fl">1</span><span class="op">)</span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>start_dim</dt>
+<dd><p>first dim to flatten (default = 2).</p></dd>
+<dt>end_dim</dt>
+<dd><p>last dim to flatten (default = -1).</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
+    
+
+<ul><li><p>Input: <code>(*, S_start,..., S_i, ..., S_end, *)</code>,
+where <code>S_i</code> is the size at dimension <code>i</code> and <code>*</code> means any
+number of dimensions including none.</p></li>
+<li><p>Output: <code>(*, S_start*...*S_i*...S_end, *)</code>.</p></li>
+</ul></div>
+    <div class="section level2">
+    <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
+    <div class="dont-index"><p><a href="nn_unflatten.html">nn_unflatten</a></p></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">32</span>, <span class="fl">1</span>, <span class="fl">5</span>, <span class="fl">5</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">nn_flatten</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">m</span><span class="op">(</span><span class="va">input</span><span class="op">)</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Columns 1 to 10-0.0009  0.0037  0.5204 -0.3667 -2.0531  0.2671 -0.5804 -0.2076 -0.6655  1.1677</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -1.6947 -2.3617 -0.0151 -0.9661 -0.5683 -2.4593  1.0112 -0.6164 -1.1062  0.1686</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  3.0387  0.7551 -2.3594  1.6931 -0.4003 -0.9425 -0.1783  1.5772 -0.4279  0.7089</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.7899  1.6087 -0.0747 -1.7557  0.0622  0.3763  0.3831  0.5895  0.3669  0.0486</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -1.3491 -0.1465 -0.0981 -0.1463  0.7424  0.5160 -0.3618 -0.5935 -0.2595 -0.6434</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.7859 -1.3648 -0.0414  0.2196 -0.8855  1.3220  0.0348  0.9984  1.1103 -0.2425</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.8881  1.0576 -0.4742 -0.4879 -1.1058 -0.6111  0.4100 -0.4836 -2.2143 -0.5398</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.5408 -2.1837 -0.2428  1.4708  0.2433  1.0932  0.7066 -0.0637  0.9693 -0.9876</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -1.5370 -0.3757 -0.9456 -0.5917 -1.4087  0.9152 -0.2538 -0.1560 -1.5692  0.3750</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.1467 -0.0512 -0.3315 -0.2021  1.3404  0.7150  0.6986  1.8484  0.1063 -1.9778</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.4032 -0.7702 -1.5078  0.2106 -1.3181  1.4029 -0.2071  0.6988  0.5814 -1.2226</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.2139  0.2826 -0.6051  1.4271 -0.5023  0.1380  0.7487 -0.6512 -1.0667  0.6083</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.1542 -1.1072 -0.0416 -0.7018 -0.2876 -0.5697  1.3263  1.3296 -0.7422  1.0843</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.3823  0.3361  0.9064 -0.6069  1.0543 -1.0274 -1.0400 -1.9305 -0.0312  0.1556</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.1169 -0.3283  0.1506  0.5543  0.8330 -0.6179  0.0503 -2.2701  0.7123  0.7061</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.1040  1.7993 -0.8401 -0.0205 -0.0334 -0.9733 -1.3252 -0.4537 -0.3771 -0.5454</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.6753 -0.3866  0.1658 -0.5853  0.8411 -0.6265  1.0690  0.8731 -0.9840 -0.0668</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -1.3578  1.6471 -1.1438  0.1968 -0.1045 -0.2291  0.1634 -1.6033 -0.4936  0.3136</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.3749  0.8226 -1.4063 -2.0339  0.8500 -0.2165  0.4049  0.3529  0.2333 -1.1750</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.1571 -0.0908  0.3734  0.4290 -0.8236 -0.4525  0.6144  0.3749  0.0308 -0.1780</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.1249 -0.0379  0.8394  0.0880  1.3442 -0.3450 -0.8468 -0.4069  1.3823 -1.0155</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -1.7637  1.3288 -1.7400 -1.2977  2.1511 -0.6051  0.4648  0.7022  1.5900  1.9261</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -1.2073  1.6272  0.8857  1.5837  0.8221 -0.0738 -0.8853  1.6199  0.0518  0.1564</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.6355  0.8778  1.1055  0.6252  0.8287 -1.6925 -0.7142 -0.0719 -0.5059 -0.9019</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.3946  0.4433  0.7126  0.2315  0.0714 -1.3102  0.5767  0.2772 -0.2313 -1.4692</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.3674 -0.8891  0.0545 -1.2286 -0.3329 -0.8300 -0.0932  1.3368  0.1294  1.9112</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.8811  1.1714  0.9356  0.0622 -0.1098  1.6281  2.4690  0.8353 -0.6021  1.1895</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.9665  0.7614 -0.7413 -0.0969  1.6492 -0.8877  1.1068  1.9219  1.6268 -0.3664</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -1.3160  1.0413  0.0369 -1.9015 -0.9639 -1.8736 -0.5516  0.0897 -1.4888  1.9580</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.9566  0.2562  0.9344  0.5393  0.0169  0.8452 -0.8670 -0.6659 -1.0230 -0.2476</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> ... [the output was truncated (use n=-1 to disable)]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{32,25} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
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+    
+  </div>
+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Applies a 2D fractional max pooling over an input signal composed of several input planes.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
-    <div class="hidden name"><code>nn_fractional_max_pool2d.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Applies a 2D fractional max pooling over an input signal composed of several input planes.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-pooling.R" class="external-link"><code>R/nn-pooling.R</code></a></small>
+      <div class="d-none name"><code>nn_fractional_max_pool2d.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Fractional MaxPooling is described in detail in the paper
-<a href='https://arxiv.org/abs/1412.6071'>Fractional MaxPooling</a> by Ben Graham</p>
+<a href="https://arxiv.org/abs/1412.6071" class="external-link">Fractional MaxPooling</a> by Ben Graham</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_fractional_max_pool2d</span><span class='op'>(</span>
-  <span class='va'>kernel_size</span>,
-  output_size <span class='op'>=</span> <span class='cn'>NULL</span>,
-  output_ratio <span class='op'>=</span> <span class='cn'>NULL</span>,
-  return_indices <span class='op'>=</span> <span class='cn'>FALSE</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>the size of the window to take a max over.
-Can be a single number k (for a square kernel of k x k) or a tuple <code>(kh, kw)</code></p></td>
-    </tr>
-    <tr>
-      <th>output_size</th>
-      <td><p>the target output size of the image of the form <code>oH x oW</code>.
-Can be a tuple <code>(oH, oW)</code> or a single number oH for a square image <code>oH x oH</code></p></td>
-    </tr>
-    <tr>
-      <th>output_ratio</th>
-      <td><p>If one wants to have an output size as a ratio of the input size, this option can be given.
-This has to be a number or tuple in the range (0, 1)</p></td>
-    </tr>
-    <tr>
-      <th>return_indices</th>
-      <td><p>if <code>TRUE</code>, will return the indices along with the outputs.
-Useful to pass to <code><a href='nn_max_unpool2d.html'>nn_max_unpool2d()</a></code>. Default: <code>FALSE</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_fractional_max_pool2d</span><span class="op">(</span>
+  <span class="va">kernel_size</span>,
+  output_size <span class="op">=</span> <span class="cn">NULL</span>,
+  output_ratio <span class="op">=</span> <span class="cn">NULL</span>,
+  return_indices <span class="op">=</span> <span class="cn">FALSE</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>kernel_size</dt>
+<dd><p>the size of the window to take a max over.
+Can be a single number k (for a square kernel of k x k) or a tuple <code>(kh, kw)</code></p></dd>
+<dt>output_size</dt>
+<dd><p>the target output size of the image of the form <code>oH x oW</code>.
+Can be a tuple <code>(oH, oW)</code> or a single number oH for a square image <code>oH x oH</code></p></dd>
+<dt>output_ratio</dt>
+<dd><p>If one wants to have an output size as a ratio of the input size, this option can be given.
+This has to be a number or tuple in the range (0, 1)</p></dd>
+<dt>return_indices</dt>
+<dd><p>if <code>TRUE</code>, will return the indices along with the outputs.
+Useful to pass to <code><a href="nn_max_unpool2d.html">nn_max_unpool2d()</a></code>. Default: <code>FALSE</code></p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>The max-pooling operation is applied in \(kH \times kW\) regions by a stochastic
 step size determined by the target output size.
 The number of output features is equal to the number of input planes.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> NULL</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="co"># pool of square window of size=3, and target output size 13x12</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">nn_fractional_max_pool2d</span><span class="op">(</span><span class="fl">3</span>, output_size <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">13</span>, <span class="fl">12</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="co"># pool of square window and target output size being half of input image size</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">nn_fractional_max_pool2d</span><span class="op">(</span><span class="fl">3</span>, output_ratio <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.5</span>, <span class="fl">0.5</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">20</span>, <span class="fl">16</span>, <span class="fl">50</span>, <span class="fl">32</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span> <span class="op">&lt;-</span> <span class="fu">m</span><span class="op">(</span><span class="va">input</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Applies a 3D fractional max pooling over an input signal composed of several input planes.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
-    <div class="hidden name"><code>nn_fractional_max_pool3d.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Applies a 3D fractional max pooling over an input signal composed of several input planes.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-pooling.R" class="external-link"><code>R/nn-pooling.R</code></a></small>
+      <div class="d-none name"><code>nn_fractional_max_pool3d.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Fractional MaxPooling is described in detail in the paper
-<a href='https://arxiv.org/abs/1412.6071'>Fractional MaxPooling</a> by Ben Graham</p>
+<a href="https://arxiv.org/abs/1412.6071" class="external-link">Fractional MaxPooling</a> by Ben Graham</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_fractional_max_pool3d</span><span class='op'>(</span>
-  <span class='va'>kernel_size</span>,
-  output_size <span class='op'>=</span> <span class='cn'>NULL</span>,
-  output_ratio <span class='op'>=</span> <span class='cn'>NULL</span>,
-  return_indices <span class='op'>=</span> <span class='cn'>FALSE</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>the size of the window to take a max over.
-Can be a single number k (for a square kernel of k x k x k) or a tuple <code>(kt x kh x kw)</code></p></td>
-    </tr>
-    <tr>
-      <th>output_size</th>
-      <td><p>the target output size of the image of the form <code>oT x oH x oW</code>.
-Can be a tuple <code>(oT, oH, oW)</code> or a single number oH for a square image <code>oH x oH x oH</code></p></td>
-    </tr>
-    <tr>
-      <th>output_ratio</th>
-      <td><p>If one wants to have an output size as a ratio of the input size, this option can be given.
-This has to be a number or tuple in the range (0, 1)</p></td>
-    </tr>
-    <tr>
-      <th>return_indices</th>
-      <td><p>if <code>TRUE</code>, will return the indices along with the outputs.
-Useful to pass to <code><a href='nn_max_unpool3d.html'>nn_max_unpool3d()</a></code>. Default: <code>FALSE</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_fractional_max_pool3d</span><span class="op">(</span>
+  <span class="va">kernel_size</span>,
+  output_size <span class="op">=</span> <span class="cn">NULL</span>,
+  output_ratio <span class="op">=</span> <span class="cn">NULL</span>,
+  return_indices <span class="op">=</span> <span class="cn">FALSE</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>kernel_size</dt>
+<dd><p>the size of the window to take a max over.
+Can be a single number k (for a square kernel of k x k x k) or a tuple <code>(kt x kh x kw)</code></p></dd>
+<dt>output_size</dt>
+<dd><p>the target output size of the image of the form <code>oT x oH x oW</code>.
+Can be a tuple <code>(oT, oH, oW)</code> or a single number oH for a square image <code>oH x oH x oH</code></p></dd>
+<dt>output_ratio</dt>
+<dd><p>If one wants to have an output size as a ratio of the input size, this option can be given.
+This has to be a number or tuple in the range (0, 1)</p></dd>
+<dt>return_indices</dt>
+<dd><p>if <code>TRUE</code>, will return the indices along with the outputs.
+Useful to pass to <code><a href="nn_max_unpool3d.html">nn_max_unpool3d()</a></code>. Default: <code>FALSE</code></p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>The max-pooling operation is applied in \(kTxkHxkW\) regions by a stochastic
 step size determined by the target output size.
 The number of output features is equal to the number of input planes.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='co'># pool of cubic window of size=3, and target output size 13x12x11</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>=</span> <span class='fu'>nn_fractional_max_pool3d</span><span class='op'>(</span><span class='fl'>3</span>, output_size<span class='op'>=</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>13</span>, <span class='fl'>12</span>, <span class='fl'>11</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='co'># pool of cubic window and target output size being half of input size</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>=</span> <span class='fu'>nn_fractional_max_pool3d</span><span class='op'>(</span><span class='fl'>3</span>, output_ratio<span class='op'>=</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>0.5</span>, <span class='fl'>0.5</span>, <span class='fl'>0.5</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>50</span>, <span class='fl'>32</span>, <span class='fl'>16</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span> <span class='op'>=</span> <span class='fu'>m</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="co"># pool of cubic window of size=3, and target output size 13x12x11</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">nn_fractional_max_pool3d</span><span class="op">(</span><span class="fl">3</span>, output_size <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">13</span>, <span class="fl">12</span>, <span class="fl">11</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="co"># pool of cubic window and target output size being half of input size</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">nn_fractional_max_pool3d</span><span class="op">(</span><span class="fl">3</span>, output_ratio <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.5</span>, <span class="fl">0.5</span>, <span class="fl">0.5</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">20</span>, <span class="fl">16</span>, <span class="fl">50</span>, <span class="fl">32</span>, <span class="fl">16</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span> <span class="op">&lt;-</span> <span class="fu">m</span><span class="op">(</span><span class="va">input</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
+    </div>
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 <div class="row">
-  <div class="col-md-9 contents">
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-    <h1>GELU module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
-    <div class="hidden name"><code>nn_gelu.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>GELU module</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-activation.R" class="external-link"><code>R/nn-activation.R</code></a></small>
+      <div class="d-none name"><code>nn_gelu.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Applies the Gaussian Error Linear Units function:
 $$\mbox{GELU}(x) = x * \Phi(x)$$</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_gelu</span><span class='op'>(</span><span class='op'>)</span></pre>
-
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_gelu</span><span class="op">(</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>where \(\Phi(x)\) is the Cumulative Distribution Function for Gaussian Distribution.</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
     
 
-<ul>
-<li><p>Input: \((N, *)\) where <code>*</code> means, any number of additional
+<ul><li><p>Input: \((N, *)\) where <code>*</code> means, any number of additional
 dimensions</p></li>
 <li><p>Output: \((N, *)\), same shape as the input</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>=</span> <span class='fu'>nn_gelu</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
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-</div>
+</ul></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">nn_gelu</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span> <span class="op">&lt;-</span> <span class="fu">m</span><span class="op">(</span><span class="va">input</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
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 <div class="row">
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-    <h1>GLU module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
-    <div class="hidden name"><code>nn_glu.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>GLU module</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-activation.R" class="external-link"><code>R/nn-activation.R</code></a></small>
+      <div class="d-none name"><code>nn_glu.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Applies the gated linear unit function
 \({GLU}(a, b)= a \otimes \sigma(b)\) where \(a\) is the first half
 of the input matrices and \(b\) is the second half.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_glu</span><span class='op'>(</span>dim <span class='op'>=</span> <span class='op'>-</span><span class='fl'>1</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>dim</th>
-      <td><p>(int): the dimension on which to split the input. Default: -1</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_glu</span><span class="op">(</span>dim <span class="op">=</span> <span class="op">-</span><span class="fl">1</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>dim</dt>
+<dd><p>(int): the dimension on which to split the input. Default: -1</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
     
 
-<ul>
-<li><p>Input: \((\ast_1, N, \ast_2)\) where <code>*</code> means, any number of additional
+<ul><li><p>Input: \((\ast_1, N, \ast_2)\) where <code>*</code> means, any number of additional
 dimensions</p></li>
 <li><p>Output: \((\ast_1, M, \ast_2)\) where \(M=N/2\)</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_glu</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>4</span>, <span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+</ul></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">nn_glu</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">4</span>, <span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span> <span class="op">&lt;-</span> <span class="fu">m</span><span class="op">(</span><span class="va">input</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
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index f46188e3d..f0d143905 100644
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Group normalization</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-normalization.R'><code>R/nn-normalization.R</code></a></small>
-    <div class="hidden name"><code>nn_group_norm.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Group normalization</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-normalization.R" class="external-link"><code>R/nn-normalization.R</code></a></small>
+      <div class="d-none name"><code>nn_group_norm.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Applies Group Normalization over a mini-batch of inputs as described in
-the paper <a href='https://arxiv.org/abs/1803.08494'>Group Normalization</a>.</p>
+the paper <a href="https://arxiv.org/abs/1803.08494" class="external-link">Group Normalization</a>.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_group_norm</span><span class='op'>(</span><span class='va'>num_groups</span>, <span class='va'>num_channels</span>, eps <span class='op'>=</span> <span class='fl'>1e-05</span>, affine <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_group_norm</span><span class="op">(</span><span class="va">num_groups</span>, <span class="va">num_channels</span>, eps <span class="op">=</span> <span class="fl">1e-05</span>, affine <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>num_groups</th>
-      <td><p>(int): number of groups to separate the channels into</p></td>
-    </tr>
-    <tr>
-      <th>num_channels</th>
-      <td><p>(int): number of channels expected in input</p></td>
-    </tr>
-    <tr>
-      <th>eps</th>
-      <td><p>a value added to the denominator for numerical stability. Default: 1e-5</p></td>
-    </tr>
-    <tr>
-      <th>affine</th>
-      <td><p>a boolean value that when set to <code>TRUE</code>, this module
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>num_groups</dt>
+<dd><p>(int): number of groups to separate the channels into</p></dd>
+<dt>num_channels</dt>
+<dd><p>(int): number of channels expected in input</p></dd>
+<dt>eps</dt>
+<dd><p>a value added to the denominator for numerical stability. Default: 1e-5</p></dd>
+<dt>affine</dt>
+<dd><p>a boolean value that when set to <code>TRUE</code>, this module
 has learnable per-channel affine parameters initialized to ones (for weights)
-and zeros (for biases). Default: <code>TRUE</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
+and zeros (for biases). Default: <code>TRUE</code>.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>$$
   y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta
 $$</p>
@@ -228,60 +113,53 @@ per-channel affine transform parameter vectors of size <code>num_channels</code>
 <code>affine</code> is <code>TRUE</code>.
 The standard-deviation is calculated via the biased estimator, equivalent to
 <code>torch_var(input, unbiased=FALSE)</code>.</p>
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p>This layer uses statistics computed from input data in both training and
 evaluation modes.</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
     
 
-<ul>
-<li><p>Input: \((N, C, *)\) where \(C=\mbox{num\_channels}\)</p></li>
+<ul><li><p>Input: \((N, C, *)\) where \(C=\mbox{num\_channels}\)</p></li>
 <li><p>Output: \((N, C, *)\)` (same shape as input)</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>20</span>, <span class='fl'>6</span>, <span class='fl'>10</span>, <span class='fl'>10</span><span class='op'>)</span></span>
-<span class='r-in'><span class='co'># Separate 6 channels into 3 groups</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_group_norm</span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>6</span><span class='op'>)</span></span>
-<span class='r-in'><span class='co'># Separate 6 channels into 6 groups (equivalent with [nn_instance_morm])</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_group_norm</span><span class='op'>(</span><span class='fl'>6</span>, <span class='fl'>6</span><span class='op'>)</span></span>
-<span class='r-in'><span class='co'># Put all 6 channels into a single group (equivalent with [nn_layer_norm])</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_group_norm</span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>6</span><span class='op'>)</span></span>
-<span class='r-in'><span class='co'># Activating the module</span></span>
-<span class='r-in'><span class='va'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+</ul></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">20</span>, <span class="fl">6</span>, <span class="fl">10</span>, <span class="fl">10</span><span class="op">)</span></span>
+<span class="r-in"><span class="co"># Separate 6 channels into 3 groups</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">nn_group_norm</span><span class="op">(</span><span class="fl">3</span>, <span class="fl">6</span><span class="op">)</span></span>
+<span class="r-in"><span class="co"># Separate 6 channels into 6 groups (equivalent with [nn_instance_morm])</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">nn_group_norm</span><span class="op">(</span><span class="fl">6</span>, <span class="fl">6</span><span class="op">)</span></span>
+<span class="r-in"><span class="co"># Put all 6 channels into a single group (equivalent with [nn_layer_norm])</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">nn_group_norm</span><span class="op">(</span><span class="fl">1</span>, <span class="fl">6</span><span class="op">)</span></span>
+<span class="r-in"><span class="co"># Activating the module</span></span>
+<span class="r-in"><span class="va">output</span> <span class="op">&lt;-</span> <span class="fu">m</span><span class="op">(</span><span class="va">input</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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-      <div class="copyright">
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+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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diff --git a/reference/nn_gru.html b/reference/nn_gru.html
index 7d7e06d82..d8fb453a0 100644
--- a/reference/nn_gru.html
+++ b/reference/nn_gru.html
@@ -1,253 +1,130 @@
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Applies a multi-layer gated recurrent unit (GRU) RNN to an input sequence.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-rnn.R'><code>R/nn-rnn.R</code></a></small>
-    <div class="hidden name"><code>nn_gru.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Applies a multi-layer gated recurrent unit (GRU) RNN to an input sequence.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-rnn.R" class="external-link"><code>R/nn-rnn.R</code></a></small>
+      <div class="d-none name"><code>nn_gru.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>For each element in the input sequence, each layer computes the following
 function:</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_gru</span><span class='op'>(</span>
-  <span class='va'>input_size</span>,
-  <span class='va'>hidden_size</span>,
-  num_layers <span class='op'>=</span> <span class='fl'>1</span>,
-  bias <span class='op'>=</span> <span class='cn'>TRUE</span>,
-  batch_first <span class='op'>=</span> <span class='cn'>FALSE</span>,
-  dropout <span class='op'>=</span> <span class='fl'>0</span>,
-  bidirectional <span class='op'>=</span> <span class='cn'>FALSE</span>,
-  <span class='va'>...</span>
-<span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_gru</span><span class="op">(</span>
+  <span class="va">input_size</span>,
+  <span class="va">hidden_size</span>,
+  num_layers <span class="op">=</span> <span class="fl">1</span>,
+  bias <span class="op">=</span> <span class="cn">TRUE</span>,
+  batch_first <span class="op">=</span> <span class="cn">FALSE</span>,
+  dropout <span class="op">=</span> <span class="fl">0</span>,
+  bidirectional <span class="op">=</span> <span class="cn">FALSE</span>,
+  <span class="va">...</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input_size</th>
-      <td><p>The number of expected features in the input <code>x</code></p></td>
-    </tr>
-    <tr>
-      <th>hidden_size</th>
-      <td><p>The number of features in the hidden state <code>h</code></p></td>
-    </tr>
-    <tr>
-      <th>num_layers</th>
-      <td><p>Number of recurrent layers. E.g., setting <code>num_layers=2</code>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input_size</dt>
+<dd><p>The number of expected features in the input <code>x</code></p></dd>
+<dt>hidden_size</dt>
+<dd><p>The number of features in the hidden state <code>h</code></p></dd>
+<dt>num_layers</dt>
+<dd><p>Number of recurrent layers. E.g., setting <code>num_layers=2</code>
 would mean stacking two GRUs together to form a <code>stacked GRU</code>,
 with the second GRU taking in outputs of the first GRU and
-computing the final results. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>If <code>FALSE</code>, then the layer does not use bias weights <code>b_ih</code> and <code>b_hh</code>.
-Default: <code>TRUE</code></p></td>
-    </tr>
-    <tr>
-      <th>batch_first</th>
-      <td><p>If <code>TRUE</code>, then the input and output tensors are provided
-as (batch, seq, feature). Default: <code>FALSE</code></p></td>
-    </tr>
-    <tr>
-      <th>dropout</th>
-      <td><p>If non-zero, introduces a <code>Dropout</code> layer on the outputs of each
+computing the final results. Default: 1</p></dd>
+<dt>bias</dt>
+<dd><p>If <code>FALSE</code>, then the layer does not use bias weights <code>b_ih</code> and <code>b_hh</code>.
+Default: <code>TRUE</code></p></dd>
+<dt>batch_first</dt>
+<dd><p>If <code>TRUE</code>, then the input and output tensors are provided
+as (batch, seq, feature). Default: <code>FALSE</code></p></dd>
+<dt>dropout</dt>
+<dd><p>If non-zero, introduces a <code>Dropout</code> layer on the outputs of each
 GRU layer except the last layer, with dropout probability equal to
-<code>dropout</code>. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>bidirectional</th>
-      <td><p>If <code>TRUE</code>, becomes a bidirectional GRU. Default: <code>FALSE</code></p></td>
-    </tr>
-    <tr>
-      <th>...</th>
-      <td><p>currently unused.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
+<code>dropout</code>. Default: 0</p></dd>
+<dt>bidirectional</dt>
+<dd><p>If <code>TRUE</code>, becomes a bidirectional GRU. Default: <code>FALSE</code></p></dd>
+<dt>...</dt>
+<dd><p>currently unused.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>$$
 \begin{array}{ll}
 r_t = \sigma(W_{ir} x_t + b_{ir} + W_{hr} h_{(t-1)} + b_{hr}) \\
@@ -261,32 +138,31 @@ at time <code>t</code>, \(h_{(t-1)}\) is the hidden state of the previous layer
 at time <code>t-1</code> or the initial hidden state at time <code>0</code>, and \(r_t\),
 \(z_t\), \(n_t\) are the reset, update, and new gates, respectively.
 \(\sigma\) is the sigmoid function.</p>
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p>All the weights and biases are initialized from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\)
 where \(k = \frac{1}{\mbox{hidden\_size}}\)</p>
-    <h2 class="hasAnchor" id="inputs"><a class="anchor" href="#inputs"></a>Inputs</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="inputs">Inputs<a class="anchor" aria-label="anchor" href="#inputs"></a></h2>
     
 
 
-<p>Inputs: input, h_0</p><ul>
-<li><p><strong>input</strong> of shape <code>(seq_len, batch, input_size)</code>: tensor containing the features
+<p>Inputs: input, h_0</p><ul><li><p><strong>input</strong> of shape <code>(seq_len, batch, input_size)</code>: tensor containing the features
 of the input sequence. The input can also be a packed variable length
-sequence. See <code><a href='nn_utils_rnn_pack_padded_sequence.html'>nn_utils_rnn_pack_padded_sequence()</a></code>
+sequence. See <code><a href="nn_utils_rnn_pack_padded_sequence.html">nn_utils_rnn_pack_padded_sequence()</a></code>
 for details.</p></li>
 <li><p><strong>h_0</strong> of shape <code>(num_layers * num_directions, batch, hidden_size)</code>: tensor
 containing the initial hidden state for each element in the batch.
 Defaults to zero if not provided.</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="outputs"><a class="anchor" href="#outputs"></a>Outputs</h2>
-
+</ul></div>
+    <div class="section level2">
+    <h2 id="outputs">Outputs<a class="anchor" aria-label="anchor" href="#outputs"></a></h2>
     
 
 
-<p>Outputs: output, h_n</p><ul>
-<li><p><strong>output</strong> of shape <code>(seq_len, batch, num_directions * hidden_size)</code>: tensor
+<p>Outputs: output, h_n</p><ul><li><p><strong>output</strong> of shape <code>(seq_len, batch, num_directions * hidden_size)</code>: tensor
 containing the output features h_t from the last layer of the GRU,
 for each t. If a <code>PackedSequence</code> has been
 given as the input, the output will also be a packed sequence.
@@ -298,14 +174,12 @@ Similarly, the directions can be separated in the packed case.</p></li>
 containing the hidden state for <code>t = seq_len</code>
 Like <em>output</em>, the layers can be separated using
 <code>h_n$view(num_layers, num_directions, batch, hidden_size)</code>.</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="attributes"><a class="anchor" href="#attributes"></a>Attributes</h2>
-
+</ul></div>
+    <div class="section level2">
+    <h2 id="attributes">Attributes<a class="anchor" aria-label="anchor" href="#attributes"></a></h2>
     
 
-<ul>
-<li><p><code>weight_ih_l[k]</code> : the learnable input-hidden weights of the \(\mbox{k}^{th}\) layer
+<ul><li><p><code>weight_ih_l[k]</code> : the learnable input-hidden weights of the \(\mbox{k}^{th}\) layer
 (W_ir|W_iz|W_in), of shape <code>(3*hidden_size x input_size)</code></p></li>
 <li><p><code>weight_hh_l[k]</code> : the learnable hidden-hidden weights of the \(\mbox{k}^{th}\) layer
 (W_hr|W_hz|W_hn), of shape <code>(3*hidden_size x hidden_size)</code></p></li>
@@ -313,44 +187,36 @@ Like <em>output</em>, the layers can be separated using
 (b_ir|b_iz|b_in), of shape <code>(3*hidden_size)</code></p></li>
 <li><p><code>bias_hh_l[k]</code> : the learnable hidden-hidden bias of the \(\mbox{k}^{th}\) layer
 (b_hr|b_hz|b_hn), of shape <code>(3*hidden_size)</code></p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>rnn</span> <span class='op'>&lt;-</span> <span class='fu'>nn_gru</span><span class='op'>(</span><span class='fl'>10</span>, <span class='fl'>20</span>, <span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>5</span>, <span class='fl'>3</span>, <span class='fl'>10</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>h0</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>3</span>, <span class='fl'>20</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span> <span class='op'>&lt;-</span> <span class='fu'>rnn</span><span class='op'>(</span><span class='va'>input</span>, <span class='va'>h0</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+</ul></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">rnn</span> <span class="op">&lt;-</span> <span class="fu">nn_gru</span><span class="op">(</span><span class="fl">10</span>, <span class="fl">20</span>, <span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">5</span>, <span class="fl">3</span>, <span class="fl">10</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">h0</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">3</span>, <span class="fl">20</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span> <span class="op">&lt;-</span> <span class="fu">rnn</span><span class="op">(</span><span class="va">input</span>, <span class="va">h0</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer>
-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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index 0fbe4e4d3..04046f725 100644
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Hardshwink module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
-    <div class="hidden name"><code>nn_hardshrink.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Hardshwink module</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-activation.R" class="external-link"><code>R/nn-activation.R</code></a></small>
+      <div class="d-none name"><code>nn_hardshrink.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Applies the hard shrinkage function element-wise:</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_hardshrink</span><span class='op'>(</span>lambd <span class='op'>=</span> <span class='fl'>0.5</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>lambd</th>
-      <td><p>the \(\lambda\) value for the Hardshrink formulation. Default: 0.5</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_hardshrink</span><span class="op">(</span>lambd <span class="op">=</span> <span class="fl">0.5</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>lambd</dt>
+<dd><p>the \(\lambda\) value for the Hardshrink formulation. Default: 0.5</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>$$
   \mbox{HardShrink}(x) =
   \left\{ \begin{array}{ll}
@@ -211,50 +101,42 @@ x, &amp; \mbox{ if } x &lt; -\lambda \\
 \end{array}
 \right.
 $$</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
     
 
-<ul>
-<li><p>Input: \((N, *)\) where <code>*</code> means, any number of additional
+<ul><li><p>Input: \((N, *)\) where <code>*</code> means, any number of additional
 dimensions</p></li>
 <li><p>Output: \((N, *)\), same shape as the input</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_hardshrink</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+</ul></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">nn_hardshrink</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span> <span class="op">&lt;-</span> <span class="fu">m</span><span class="op">(</span><span class="va">input</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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index 51acd14de..1c141bc57 100644
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@@ -1,199 +1,92 @@
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Hardsigmoid module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
-    <div class="hidden name"><code>nn_hardsigmoid.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Hardsigmoid module</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-activation.R" class="external-link"><code>R/nn-activation.R</code></a></small>
+      <div class="d-none name"><code>nn_hardsigmoid.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Applies the element-wise function:</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_hardsigmoid</span><span class='op'>(</span><span class='op'>)</span></pre>
-
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_hardsigmoid</span><span class="op">(</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>$$
 \mbox{Hardsigmoid}(x) = \left\{ \begin{array}{ll}
   0 &amp; \mbox{if~} x \le -3, \\
@@ -202,50 +95,42 @@
 \end{array}
 \right.
 $$</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
     
 
-<ul>
-<li><p>Input: \((N, *)\) where <code>*</code> means, any number of additional
+<ul><li><p>Input: \((N, *)\) where <code>*</code> means, any number of additional
 dimensions</p></li>
 <li><p>Output: \((N, *)\), same shape as the input</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_hardsigmoid</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+</ul></div>
+
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+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">nn_hardsigmoid</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span> <span class="op">&lt;-</span> <span class="fu">m</span><span class="op">(</span><span class="va">input</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
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@@ -1,201 +1,95 @@
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Hardswish module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
-    <div class="hidden name"><code>nn_hardswish.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Hardswish module</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-activation.R" class="external-link"><code>R/nn-activation.R</code></a></small>
+      <div class="d-none name"><code>nn_hardswish.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Applies the hardswish function, element-wise, as described in the paper:
-<a href='https://arxiv.org/abs/1905.02244'>Searching for MobileNetV3</a></p>
+<a href="https://arxiv.org/abs/1905.02244" class="external-link">Searching for MobileNetV3</a></p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_hardswish</span><span class='op'>(</span><span class='op'>)</span></pre>
-
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_hardswish</span><span class="op">(</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>$$ \mbox{Hardswish}(x) = \left\{
   \begin{array}{ll}
   0 &amp; \mbox{if } x \le -3, \\
@@ -203,52 +97,45 @@ Searching for MobileNetV3" />
   x \cdot (x + 3)/6 &amp; \mbox{otherwise}
   \end{array}
   \right. $$</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
     
 
-<ul>
-<li><p>Input: \((N, *)\) where <code>*</code> means, any number of additional
+<ul><li><p>Input: \((N, *)\) where <code>*</code> means, any number of additional
 dimensions</p></li>
 <li><p>Output: \((N, *)\), same shape as the input</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='cn'>FALSE</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_hardswish</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+</ul></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">nn_hardswish</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span> <span class="op">&lt;-</span> <span class="fu">m</span><span class="op">(</span><span class="va">input</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
+    </div>
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Hardtanh module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
-    <div class="hidden name"><code>nn_hardtanh.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Hardtanh module</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-activation.R" class="external-link"><code>R/nn-activation.R</code></a></small>
+      <div class="d-none name"><code>nn_hardtanh.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Applies the HardTanh function element-wise
 HardTanh is defined as:</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_hardtanh</span><span class='op'>(</span>min_val <span class='op'>=</span> <span class='op'>-</span><span class='fl'>1</span>, max_val <span class='op'>=</span> <span class='fl'>1</span>, inplace <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>min_val</th>
-      <td><p>minimum value of the linear region range. Default: -1</p></td>
-    </tr>
-    <tr>
-      <th>max_val</th>
-      <td><p>maximum value of the linear region range. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>inplace</th>
-      <td><p>can optionally do the operation in-place. Default: <code>FALSE</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_hardtanh</span><span class="op">(</span>min_val <span class="op">=</span> <span class="op">-</span><span class="fl">1</span>, max_val <span class="op">=</span> <span class="fl">1</span>, inplace <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>min_val</dt>
+<dd><p>minimum value of the linear region range. Default: -1</p></dd>
+<dt>max_val</dt>
+<dd><p>maximum value of the linear region range. Default: 1</p></dd>
+<dt>inplace</dt>
+<dd><p>can optionally do the operation in-place. Default: <code>FALSE</code></p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>$$
 \mbox{HardTanh}(x) = \left\{ \begin{array}{ll}
   1 &amp; \mbox{ if } x &gt; 1 \\
@@ -222,50 +109,42 @@ HardTanh is defined as:</p>
 $$</p>
 <p>The range of the linear region :math:<code>[-1, 1]</code> can be adjusted using
 <code>min_val</code> and <code>max_val</code>.</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
     
 
-<ul>
-<li><p>Input: \((N, *)\) where <code>*</code> means, any number of additional
+<ul><li><p>Input: \((N, *)\) where <code>*</code> means, any number of additional
 dimensions</p></li>
 <li><p>Output: \((N, *)\), same shape as the input</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_hardtanh</span><span class='op'>(</span><span class='op'>-</span><span class='fl'>2</span>, <span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+</ul></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">nn_hardtanh</span><span class="op">(</span><span class="op">-</span><span class="fl">2</span>, <span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span> <span class="op">&lt;-</span> <span class="fu">m</span><span class="op">(</span><span class="va">input</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Hinge embedding loss</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-loss.R'><code>R/nn-loss.R</code></a></small>
-    <div class="hidden name"><code>nn_hinge_embedding_loss.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Hinge embedding loss</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-loss.R" class="external-link"><code>R/nn-loss.R</code></a></small>
+      <div class="d-none name"><code>nn_hinge_embedding_loss.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Measures the loss given an input tensor \(x\) and a labels tensor \(y\)
 (containing 1 or -1).</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_hinge_embedding_loss</span><span class='op'>(</span>margin <span class='op'>=</span> <span class='fl'>1</span>, reduction <span class='op'>=</span> <span class='st'>"mean"</span><span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_hinge_embedding_loss</span><span class="op">(</span>margin <span class="op">=</span> <span class="fl">1</span>, reduction <span class="op">=</span> <span class="st">"mean"</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>margin</th>
-      <td><p>(float, optional): Has a default value of <code>1</code>.</p></td>
-    </tr>
-    <tr>
-      <th>reduction</th>
-      <td><p>(string, optional): Specifies the reduction to apply to the output:
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>margin</dt>
+<dd><p>(float, optional): Has a default value of <code>1</code>.</p></dd>
+<dt>reduction</dt>
+<dd><p>(string, optional): Specifies the reduction to apply to the output:
 <code>'none'</code> | <code>'mean'</code> | <code>'sum'</code>. <code>'none'</code>: no reduction will be applied,
 <code>'mean'</code>: the sum of the output will be divided by the number of
 elements in the output, <code>'sum'</code>: the output will be summed. Note: <code>size_average</code>
 and <code>reduce</code> are in the process of being deprecated, and in the meantime,
-specifying either of those two args will override <code>reduction</code>. Default: <code>'mean'</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
+specifying either of those two args will override <code>reduction</code>. Default: <code>'mean'</code></p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>This is usually used for measuring whether two inputs are similar or
 dissimilar, e.g. using the L1 pairwise distance as \(x\), and is typically
 used for learning nonlinear embeddings or semi-supervised learning.
@@ -231,43 +120,34 @@ $$</p>
 \end{array}
 $$</p>
 <p>where \(L = \{l_1,\dots,l_N\}^\top\).</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
     
 
-<ul>
-<li><p>Input: \((*)\) where \(*\) means, any number of dimensions. The sum operation
+<ul><li><p>Input: \((*)\) where \(*\) means, any number of dimensions. The sum operation
 operates over all the elements.</p></li>
 <li><p>Target: \((*)\), same shape as the input</p></li>
 <li><p>Output: scalar. If <code>reduction</code> is <code>'none'</code>, then same shape as the input</p></li>
-</ul>
+</ul></div>
 
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
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-      <h2 data-toc-skip>Contents</h2>
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-</div>
 
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+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
 </div>
 
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diff --git a/reference/nn_identity.html b/reference/nn_identity.html
index 99e4d6eec..22f943fd2 100644
--- a/reference/nn_identity.html
+++ b/reference/nn_identity.html
@@ -1,241 +1,124 @@
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-    <h1>Identity module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-linear.R'><code>R/nn-linear.R</code></a></small>
-    <div class="hidden name"><code>nn_identity.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Identity module</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-linear.R" class="external-link"><code>R/nn-linear.R</code></a></small>
+      <div class="d-none name"><code>nn_identity.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>A placeholder identity operator that is argument-insensitive.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_identity</span><span class='op'>(</span><span class='va'>...</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>...</th>
-      <td><p>any arguments (unused)</p></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_identity</span><span class='op'>(</span><span class='fl'>54</span>, unused_argument1 <span class='op'>=</span> <span class='fl'>0.1</span>, unused_argument2 <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>128</span>, <span class='fl'>20</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span><span class='op'>(</span><span class='va'>output</span><span class='op'>$</span><span class='fu'>size</span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [1] 128  20</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_identity</span><span class="op">(</span><span class="va">...</span><span class="op">)</span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>...</dt>
+<dd><p>any arguments (unused)</p></dd>
+</dl></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">nn_identity</span><span class="op">(</span><span class="fl">54</span>, unused_argument1 <span class="op">=</span> <span class="fl">0.1</span>, unused_argument2 <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">128</span>, <span class="fl">20</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span> <span class="op">&lt;-</span> <span class="fu">m</span><span class="op">(</span><span class="va">input</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">output</span><span class="op">$</span><span class="fu">size</span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 128  20</span>
+</code></pre></div>
+    </div>
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diff --git a/reference/nn_init_calculate_gain.html b/reference/nn_init_calculate_gain.html
index 08b27cef6..677602a0e 100644
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-    <h1>Calculate gain</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-init.R'><code>R/nn-init.R</code></a></small>
-    <div class="hidden name"><code>nn_init_calculate_gain.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Calculate gain</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-init.R" class="external-link"><code>R/nn-init.R</code></a></small>
+      <div class="d-none name"><code>nn_init_calculate_gain.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Return the recommended gain value for the given nonlinearity function.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_init_calculate_gain</span><span class='op'>(</span><span class='va'>nonlinearity</span>, param <span class='op'>=</span> <span class='cn'>NULL</span><span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_init_calculate_gain</span><span class="op">(</span><span class="va">nonlinearity</span>, param <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>nonlinearity</th>
-      <td><p>the non-linear function</p></td>
-    </tr>
-    <tr>
-      <th>param</th>
-      <td><p>optional parameter for the non-linear function</p></td>
-    </tr>
-    </table>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>nonlinearity</dt>
+<dd><p>the non-linear function</p></dd>
+<dt>param</dt>
+<dd><p>optional parameter for the non-linear function</p></dd>
+</dl></div>
 
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-    <h1>Constant initialization</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-init.R'><code>R/nn-init.R</code></a></small>
-    <div class="hidden name"><code>nn_init_constant_.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Constant initialization</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-init.R" class="external-link"><code>R/nn-init.R</code></a></small>
+      <div class="d-none name"><code>nn_init_constant_.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Fills the input Tensor with the value <code>val</code>.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_init_constant_</span><span class='op'>(</span><span class='va'>tensor</span>, <span class='va'>val</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>tensor</th>
-      <td><p>an n-dimensional <code>Tensor</code></p></td>
-    </tr>
-    <tr>
-      <th>val</th>
-      <td><p>the value to fill the tensor with</p></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>w</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>5</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>nn_init_constant_</span><span class='op'>(</span><span class='va'>w</span>, <span class='fl'>0.3</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.3000  0.3000  0.3000  0.3000  0.3000</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.3000  0.3000  0.3000  0.3000  0.3000</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.3000  0.3000  0.3000  0.3000  0.3000</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{3,5} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_init_constant_</span><span class="op">(</span><span class="va">tensor</span>, <span class="va">val</span><span class="op">)</span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>tensor</dt>
+<dd><p>an n-dimensional <code>Tensor</code></p></dd>
+<dt>val</dt>
+<dd><p>the value to fill the tensor with</p></dd>
+</dl></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">w</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_empty.html">torch_empty</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">5</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">nn_init_constant_</span><span class="op">(</span><span class="va">w</span>, <span class="fl">0.3</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.3000  0.3000  0.3000  0.3000  0.3000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.3000  0.3000  0.3000  0.3000  0.3000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.3000  0.3000  0.3000  0.3000  0.3000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3,5} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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diff --git a/reference/nn_init_dirac_.html b/reference/nn_init_dirac_.html
index d68e386b8..e0c5c20a4 100644
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Dirac initialization</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-init.R'><code>R/nn-init.R</code></a></small>
-    <div class="hidden name"><code>nn_init_dirac_.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Dirac initialization</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-init.R" class="external-link"><code>R/nn-init.R</code></a></small>
+      <div class="d-none name"><code>nn_init_dirac_.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Fills the 3, 4, 5-dimensional input <code>Tensor</code> with the Dirac
 delta function. Preserves the identity of the inputs in <code>Convolutional</code>
 layers, where as many input channels are preserved as possible. In case
 of groups&gt;1, each group of channels preserves identity.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_init_dirac_</span><span class='op'>(</span><span class='va'>tensor</span>, groups <span class='op'>=</span> <span class='fl'>1</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>tensor</th>
-      <td><p>a 3, 4, 5-dimensional <code>torch.Tensor</code></p></td>
-    </tr>
-    <tr>
-      <th>groups</th>
-      <td><p>(optional) number of groups in the conv layer (default: 1)</p></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='cn'>FALSE</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>w</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>16</span>, <span class='fl'>5</span>, <span class='fl'>5</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>nn_init_dirac_</span><span class='op'>(</span><span class='va'>w</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_init_dirac_</span><span class="op">(</span><span class="va">tensor</span>, groups <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>tensor</dt>
+<dd><p>a 3, 4, 5-dimensional <code>torch.Tensor</code></p></dd>
+<dt>groups</dt>
+<dd><p>(optional) number of groups in the conv layer (default: 1)</p></dd>
+</dl></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">w</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_empty.html">torch_empty</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">16</span>, <span class="fl">5</span>, <span class="fl">5</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">nn_init_dirac_</span><span class="op">(</span><span class="va">w</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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diff --git a/reference/nn_init_eye_.html b/reference/nn_init_eye_.html
index 40c7eaf57..078443e60 100644
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Eye initialization</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-init.R'><code>R/nn-init.R</code></a></small>
-    <div class="hidden name"><code>nn_init_eye_.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Eye initialization</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-init.R" class="external-link"><code>R/nn-init.R</code></a></small>
+      <div class="d-none name"><code>nn_init_eye_.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Fills the 2-dimensional input <code>Tensor</code> with the identity matrix.
 Preserves the identity of the inputs in <code>Linear</code> layers, where as
 many inputs are preserved as possible.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_init_eye_</span><span class='op'>(</span><span class='va'>tensor</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>tensor</th>
-      <td><p>a 2-dimensional torch tensor.</p></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>w</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>5</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>nn_init_eye_</span><span class='op'>(</span><span class='va'>w</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1  0  0  0  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0  1  0  0  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0  0  1  0  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{3,5} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_init_eye_</span><span class="op">(</span><span class="va">tensor</span><span class="op">)</span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>tensor</dt>
+<dd><p>a 2-dimensional torch tensor.</p></dd>
+</dl></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">w</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_empty.html">torch_empty</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">5</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">nn_init_eye_</span><span class="op">(</span><span class="va">w</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1  0  0  0  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0  1  0  0  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0  0  1  0  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3,5} ]</span>
+</code></pre></div>
+    </div>
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diff --git a/reference/nn_init_kaiming_normal_.html b/reference/nn_init_kaiming_normal_.html
index 472b5be66..9039993b3 100644
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Kaiming normal initialization</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-init.R'><code>R/nn-init.R</code></a></small>
-    <div class="hidden name"><code>nn_init_kaiming_normal_.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Kaiming normal initialization</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-init.R" class="external-link"><code>R/nn-init.R</code></a></small>
+      <div class="d-none name"><code>nn_init_kaiming_normal_.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Fills the input <code>Tensor</code> with values according to the method
 described in <code>Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification</code> - He, K. et al. (2015), using a
 normal distribution.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_init_kaiming_normal_</span><span class='op'>(</span>
-  <span class='va'>tensor</span>,
-  a <span class='op'>=</span> <span class='fl'>0</span>,
-  mode <span class='op'>=</span> <span class='st'>"fan_in"</span>,
-  nonlinearity <span class='op'>=</span> <span class='st'>"leaky_relu"</span>
-<span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_init_kaiming_normal_</span><span class="op">(</span>
+  <span class="va">tensor</span>,
+  a <span class="op">=</span> <span class="fl">0</span>,
+  mode <span class="op">=</span> <span class="st">"fan_in"</span>,
+  nonlinearity <span class="op">=</span> <span class="st">"leaky_relu"</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>tensor</th>
-      <td><p>an n-dimensional <code>torch.Tensor</code></p></td>
-    </tr>
-    <tr>
-      <th>a</th>
-      <td><p>the negative slope of the rectifier used after this layer (only used
-with <code>'leaky_relu'</code>)</p></td>
-    </tr>
-    <tr>
-      <th>mode</th>
-      <td><p>either 'fan_in' (default) or 'fan_out'. Choosing 'fan_in' preserves
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>tensor</dt>
+<dd><p>an n-dimensional <code>torch.Tensor</code></p></dd>
+<dt>a</dt>
+<dd><p>the negative slope of the rectifier used after this layer (only used
+with <code>'leaky_relu'</code>)</p></dd>
+<dt>mode</dt>
+<dd><p>either 'fan_in' (default) or 'fan_out'. Choosing 'fan_in' preserves
 the magnitude of the variance of the weights in the forward pass. Choosing
-'fan_out' preserves the magnitudes in the backwards pass.</p></td>
-    </tr>
-    <tr>
-      <th>nonlinearity</th>
-      <td><p>the non-linear function. recommended to use only with 'relu'
-or 'leaky_relu' (default).</p></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>w</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>5</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>nn_init_kaiming_normal_</span><span class='op'>(</span><span class='va'>w</span>, mode <span class='op'>=</span> <span class='st'>"fan_in"</span>, nonlinearity <span class='op'>=</span> <span class='st'>"leaky_relu"</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.6895  0.4607  0.4321 -0.1901  0.4749</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.1143 -0.3050  0.0197 -0.6261  0.2653</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.0018 -0.5399  0.4308 -0.1641 -0.4567</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{3,5} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+'fan_out' preserves the magnitudes in the backwards pass.</p></dd>
+<dt>nonlinearity</dt>
+<dd><p>the non-linear function. recommended to use only with 'relu'
+or 'leaky_relu' (default).</p></dd>
+</dl></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">w</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_empty.html">torch_empty</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">5</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">nn_init_kaiming_normal_</span><span class="op">(</span><span class="va">w</span>, mode <span class="op">=</span> <span class="st">"fan_in"</span>, nonlinearity <span class="op">=</span> <span class="st">"leaky_relu"</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.0119  0.6924 -0.9304  1.5996 -1.0900</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -1.4419  0.8208 -0.2098 -0.2067  0.6960</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.8102  0.5133  0.9255 -0.6329 -0.7233</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3,5} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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-      <div class="copyright">
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+    <footer><div class="pkgdown-footer-left">
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index 08afe250c..581f08ae3 100644
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Kaiming uniform initialization</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-init.R'><code>R/nn-init.R</code></a></small>
-    <div class="hidden name"><code>nn_init_kaiming_uniform_.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Kaiming uniform initialization</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-init.R" class="external-link"><code>R/nn-init.R</code></a></small>
+      <div class="d-none name"><code>nn_init_kaiming_uniform_.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Fills the input <code>Tensor</code> with values according to the method
 described in <code>Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification</code> - He, K. et al. (2015), using a
 uniform distribution.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_init_kaiming_uniform_</span><span class='op'>(</span>
-  <span class='va'>tensor</span>,
-  a <span class='op'>=</span> <span class='fl'>0</span>,
-  mode <span class='op'>=</span> <span class='st'>"fan_in"</span>,
-  nonlinearity <span class='op'>=</span> <span class='st'>"leaky_relu"</span>
-<span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_init_kaiming_uniform_</span><span class="op">(</span>
+  <span class="va">tensor</span>,
+  a <span class="op">=</span> <span class="fl">0</span>,
+  mode <span class="op">=</span> <span class="st">"fan_in"</span>,
+  nonlinearity <span class="op">=</span> <span class="st">"leaky_relu"</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>tensor</th>
-      <td><p>an n-dimensional <code>torch.Tensor</code></p></td>
-    </tr>
-    <tr>
-      <th>a</th>
-      <td><p>the negative slope of the rectifier used after this layer (only used
-with <code>'leaky_relu'</code>)</p></td>
-    </tr>
-    <tr>
-      <th>mode</th>
-      <td><p>either 'fan_in' (default) or 'fan_out'. Choosing 'fan_in' preserves
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>tensor</dt>
+<dd><p>an n-dimensional <code>torch.Tensor</code></p></dd>
+<dt>a</dt>
+<dd><p>the negative slope of the rectifier used after this layer (only used
+with <code>'leaky_relu'</code>)</p></dd>
+<dt>mode</dt>
+<dd><p>either 'fan_in' (default) or 'fan_out'. Choosing 'fan_in' preserves
 the magnitude of the variance of the weights in the forward pass. Choosing
-'fan_out' preserves the magnitudes in the backwards pass.</p></td>
-    </tr>
-    <tr>
-      <th>nonlinearity</th>
-      <td><p>the non-linear function. recommended to use only with 'relu'
-or 'leaky_relu' (default).</p></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>w</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>5</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>nn_init_kaiming_uniform_</span><span class='op'>(</span><span class='va'>w</span>, mode <span class='op'>=</span> <span class='st'>"fan_in"</span>, nonlinearity <span class='op'>=</span> <span class='st'>"leaky_relu"</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.8571 -0.1492  0.4588  0.3201 -0.4766</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.4704  0.7825 -0.1705 -0.3313  0.4815</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.5182 -0.7111 -0.5438 -0.3267  0.3343</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{3,5} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+'fan_out' preserves the magnitudes in the backwards pass.</p></dd>
+<dt>nonlinearity</dt>
+<dd><p>the non-linear function. recommended to use only with 'relu'
+or 'leaky_relu' (default).</p></dd>
+</dl></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">w</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_empty.html">torch_empty</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">5</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">nn_init_kaiming_uniform_</span><span class="op">(</span><span class="va">w</span>, mode <span class="op">=</span> <span class="st">"fan_in"</span>, nonlinearity <span class="op">=</span> <span class="st">"leaky_relu"</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -1.0127  0.0117  0.1522 -0.2595  0.4061</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.6062  0.1464 -0.8295  0.0521 -0.7205</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.7301 -0.4684 -0.6242 -0.4436  0.1059</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3,5} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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-      <div class="copyright">
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+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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diff --git a/reference/nn_init_normal_.html b/reference/nn_init_normal_.html
index 7da4a7901..4fae3529f 100644
--- a/reference/nn_init_normal_.html
+++ b/reference/nn_init_normal_.html
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-    <h1>Normal initialization</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-init.R'><code>R/nn-init.R</code></a></small>
-    <div class="hidden name"><code>nn_init_normal_.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Normal initialization</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-init.R" class="external-link"><code>R/nn-init.R</code></a></small>
+      <div class="d-none name"><code>nn_init_normal_.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Fills the input Tensor with values drawn from the normal distribution</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_init_normal_</span><span class='op'>(</span><span class='va'>tensor</span>, mean <span class='op'>=</span> <span class='fl'>0</span>, std <span class='op'>=</span> <span class='fl'>1</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>tensor</th>
-      <td><p>an n-dimensional Tensor</p></td>
-    </tr>
-    <tr>
-      <th>mean</th>
-      <td><p>the mean of the normal distribution</p></td>
-    </tr>
-    <tr>
-      <th>std</th>
-      <td><p>the standard deviation of the normal distribution</p></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>w</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>5</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>nn_init_normal_</span><span class='op'>(</span><span class='va'>w</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.9060  0.9877 -0.5091  0.3334  0.7528</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.4211  0.2504 -1.8502  1.1405 -0.0957</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.7028 -2.0466  0.0873  0.6092 -0.4497</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{3,5} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_init_normal_</span><span class="op">(</span><span class="va">tensor</span>, mean <span class="op">=</span> <span class="fl">0</span>, std <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>tensor</dt>
+<dd><p>an n-dimensional Tensor</p></dd>
+<dt>mean</dt>
+<dd><p>the mean of the normal distribution</p></dd>
+<dt>std</dt>
+<dd><p>the standard deviation of the normal distribution</p></dd>
+</dl></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">w</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_empty.html">torch_empty</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">5</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">nn_init_normal_</span><span class="op">(</span><span class="va">w</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.1488  0.9012 -0.5716 -0.1876 -0.3635</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.5749 -0.3356 -0.2284 -1.4978 -0.2064</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.6891 -0.6885 -0.0284 -0.2636  0.5177</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3,5} ]</span>
+</code></pre></div>
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diff --git a/reference/nn_init_ones_.html b/reference/nn_init_ones_.html
index 0504a2079..cb94d230e 100644
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-    <h1>Ones initialization</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-init.R'><code>R/nn-init.R</code></a></small>
-    <div class="hidden name"><code>nn_init_ones_.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Ones initialization</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-init.R" class="external-link"><code>R/nn-init.R</code></a></small>
+      <div class="d-none name"><code>nn_init_ones_.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Fills the input Tensor with the scalar value <code>1</code></p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_init_ones_</span><span class='op'>(</span><span class='va'>tensor</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>tensor</th>
-      <td><p>an n-dimensional <code>Tensor</code></p></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>w</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>5</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>nn_init_ones_</span><span class='op'>(</span><span class='va'>w</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1  1  1  1  1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1  1  1  1  1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1  1  1  1  1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{3,5} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_init_ones_</span><span class="op">(</span><span class="va">tensor</span><span class="op">)</span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>tensor</dt>
+<dd><p>an n-dimensional <code>Tensor</code></p></dd>
+</dl></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">w</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_empty.html">torch_empty</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">5</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">nn_init_ones_</span><span class="op">(</span><span class="va">w</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1  1  1  1  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1  1  1  1  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1  1  1  1  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3,5} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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diff --git a/reference/nn_init_orthogonal_.html b/reference/nn_init_orthogonal_.html
index 0ee26eef9..f8f4966df 100644
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Fills the input Tensor with a (semi) orthogonal matrix, as
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Orthogonal initialization</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-init.R'><code>R/nn-init.R</code></a></small>
-    <div class="hidden name"><code>nn_init_orthogonal_.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Orthogonal initialization</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-init.R" class="external-link"><code>R/nn-init.R</code></a></small>
+      <div class="d-none name"><code>nn_init_orthogonal_.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Fills the input <code>Tensor</code> with a (semi) orthogonal matrix, as
 described in <code>Exact solutions to the nonlinear dynamics of learning in deep linear neural networks</code> - Saxe, A. et al. (2013). The input tensor must have
 at least 2 dimensions, and for tensors with more than 2 dimensions the
 trailing dimensions are flattened.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_init_orthogonal_</span><span class='op'>(</span><span class='va'>tensor</span>, gain <span class='op'>=</span> <span class='fl'>1</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>tensor</th>
-      <td><p>an n-dimensional <code>Tensor</code></p></td>
-    </tr>
-    <tr>
-      <th>gain</th>
-      <td><p>optional scaling factor</p></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>w</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span><span class='op'>(</span><span class='fl'>3</span>,<span class='fl'>5</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>nn_init_orthogonal_</span><span class='op'>(</span><span class='va'>w</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.3695 -0.7616 -0.0206 -0.5319 -0.0007</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.2854 -0.4272 -0.2480  0.8196  0.0526</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.0272 -0.1496  0.9612  0.1956  0.1210</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{3,5} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_init_orthogonal_</span><span class="op">(</span><span class="va">tensor</span>, gain <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>tensor</dt>
+<dd><p>an n-dimensional <code>Tensor</code></p></dd>
+<dt>gain</dt>
+<dd><p>optional scaling factor</p></dd>
+</dl></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">w</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_empty.html">torch_empty</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">5</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">nn_init_orthogonal_</span><span class="op">(</span><span class="va">w</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.1985 -0.1051 -0.7007  0.1139 -0.6675</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.5548  0.4203 -0.2981  0.6254  0.1885</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.1771  0.5799 -0.4552 -0.5535  0.3447</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3,5} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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diff --git a/reference/nn_init_sparse_.html b/reference/nn_init_sparse_.html
index e235256c0..549bb69cb 100644
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Sparse initialization</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-init.R'><code>R/nn-init.R</code></a></small>
-    <div class="hidden name"><code>nn_init_sparse_.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Sparse initialization</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-init.R" class="external-link"><code>R/nn-init.R</code></a></small>
+      <div class="d-none name"><code>nn_init_sparse_.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Fills the 2D input <code>Tensor</code> as a sparse matrix, where the
 non-zero elements will be drawn from the normal distribution
 as described in <code>Deep learning via Hessian-free optimization</code> - Martens, J. (2010).</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_init_sparse_</span><span class='op'>(</span><span class='va'>tensor</span>, <span class='va'>sparsity</span>, std <span class='op'>=</span> <span class='fl'>0.01</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>tensor</th>
-      <td><p>an n-dimensional <code>Tensor</code></p></td>
-    </tr>
-    <tr>
-      <th>sparsity</th>
-      <td><p>The fraction of elements in each column to be set to zero</p></td>
-    </tr>
-    <tr>
-      <th>std</th>
-      <td><p>the standard deviation of the normal distribution used to generate
-the non-zero values</p></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='cn'>FALSE</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>w</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>5</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>nn_init_sparse_</span><span class='op'>(</span><span class='va'>w</span>, sparsity <span class='op'>=</span> <span class='fl'>0.1</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_init_sparse_</span><span class="op">(</span><span class="va">tensor</span>, <span class="va">sparsity</span>, std <span class="op">=</span> <span class="fl">0.01</span><span class="op">)</span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>tensor</dt>
+<dd><p>an n-dimensional <code>Tensor</code></p></dd>
+<dt>sparsity</dt>
+<dd><p>The fraction of elements in each column to be set to zero</p></dd>
+<dt>std</dt>
+<dd><p>the standard deviation of the normal distribution used to generate
+the non-zero values</p></dd>
+</dl></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">w</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_empty.html">torch_empty</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">5</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">nn_init_sparse_</span><span class="op">(</span><span class="va">w</span>, sparsity <span class="op">=</span> <span class="fl">0.1</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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diff --git a/reference/nn_init_trunc_normal_.html b/reference/nn_init_trunc_normal_.html
index 35ae648c9..a9bce86e7 100644
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-    <h1>Truncated normal initialization</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-init.R'><code>R/nn-init.R</code></a></small>
-    <div class="hidden name"><code>nn_init_trunc_normal_.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Truncated normal initialization</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-init.R" class="external-link"><code>R/nn-init.R</code></a></small>
+      <div class="d-none name"><code>nn_init_trunc_normal_.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Fills the input Tensor with values drawn from a truncated
 normal distribution.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_init_trunc_normal_</span><span class='op'>(</span><span class='va'>tensor</span>, mean <span class='op'>=</span> <span class='fl'>0</span>, std <span class='op'>=</span> <span class='fl'>1</span>, a <span class='op'>=</span> <span class='op'>-</span><span class='fl'>2</span>, b <span class='op'>=</span> <span class='fl'>2</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>tensor</th>
-      <td><p>an n-dimensional Tensor</p></td>
-    </tr>
-    <tr>
-      <th>mean</th>
-      <td><p>the mean of the normal distribution</p></td>
-    </tr>
-    <tr>
-      <th>std</th>
-      <td><p>the standard deviation of the normal distribution</p></td>
-    </tr>
-    <tr>
-      <th>a</th>
-      <td><p>the minimum cutoff value</p></td>
-    </tr>
-    <tr>
-      <th>b</th>
-      <td><p>the maximum cutoff value</p></td>
-    </tr>
-    </table>
-
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_init_trunc_normal_</span><span class="op">(</span><span class="va">tensor</span>, mean <span class="op">=</span> <span class="fl">0</span>, std <span class="op">=</span> <span class="fl">1</span>, a <span class="op">=</span> <span class="op">-</span><span class="fl">2</span>, b <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>w</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>5</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>nn_init_trunc_normal_</span><span class='op'>(</span><span class='va'>w</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.3490 -0.3199  0.9045 -1.4732  0.6701</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.6281  0.2493  1.5146 -0.9175  0.9158</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.3318 -0.4601  0.6081  1.4417 -0.8560</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{3,5} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>tensor</dt>
+<dd><p>an n-dimensional Tensor</p></dd>
+<dt>mean</dt>
+<dd><p>the mean of the normal distribution</p></dd>
+<dt>std</dt>
+<dd><p>the standard deviation of the normal distribution</p></dd>
+<dt>a</dt>
+<dd><p>the minimum cutoff value</p></dd>
+<dt>b</dt>
+<dd><p>the maximum cutoff value</p></dd>
+</dl></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">w</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_empty.html">torch_empty</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">5</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">nn_init_trunc_normal_</span><span class="op">(</span><span class="va">w</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.3541  0.0827  0.5821  1.2080 -1.4691</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0323  0.3596 -1.3777  0.2376  1.0621</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.6410 -0.5628  0.3718 -0.3494 -0.9663</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3,5} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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diff --git a/reference/nn_init_uniform_.html b/reference/nn_init_uniform_.html
index 248f3e29d..f1aca753f 100644
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-    <h1>Uniform initialization</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-init.R'><code>R/nn-init.R</code></a></small>
-    <div class="hidden name"><code>nn_init_uniform_.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Uniform initialization</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-init.R" class="external-link"><code>R/nn-init.R</code></a></small>
+      <div class="d-none name"><code>nn_init_uniform_.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Fills the input Tensor with values drawn from the uniform distribution</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_init_uniform_</span><span class='op'>(</span><span class='va'>tensor</span>, a <span class='op'>=</span> <span class='fl'>0</span>, b <span class='op'>=</span> <span class='fl'>1</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>tensor</th>
-      <td><p>an n-dimensional Tensor</p></td>
-    </tr>
-    <tr>
-      <th>a</th>
-      <td><p>the lower bound of the uniform distribution</p></td>
-    </tr>
-    <tr>
-      <th>b</th>
-      <td><p>the upper bound of the uniform distribution</p></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>w</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>5</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>nn_init_uniform_</span><span class='op'>(</span><span class='va'>w</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.6100  0.4132  0.8617  0.7889  0.4149</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.0658  0.7837  0.3378  0.6437  0.0555</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.5377  0.4231  0.0359  0.2787  0.3752</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{3,5} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_init_uniform_</span><span class="op">(</span><span class="va">tensor</span>, a <span class="op">=</span> <span class="fl">0</span>, b <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>tensor</dt>
+<dd><p>an n-dimensional Tensor</p></dd>
+<dt>a</dt>
+<dd><p>the lower bound of the uniform distribution</p></dd>
+<dt>b</dt>
+<dd><p>the upper bound of the uniform distribution</p></dd>
+</dl></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">w</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_empty.html">torch_empty</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">5</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">nn_init_uniform_</span><span class="op">(</span><span class="va">w</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.6015  0.1084  0.7769  0.5726  0.9784</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.3043  0.3190  0.9327  0.7583  0.7635</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.9027  0.4158  0.7853  0.1498  0.0483</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3,5} ]</span>
+</code></pre></div>
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diff --git a/reference/nn_init_xavier_normal_.html b/reference/nn_init_xavier_normal_.html
index 78cec7975..aeebc785f 100644
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-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Xavier normal initialization</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-init.R'><code>R/nn-init.R</code></a></small>
-    <div class="hidden name"><code>nn_init_xavier_normal_.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Xavier normal initialization</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-init.R" class="external-link"><code>R/nn-init.R</code></a></small>
+      <div class="d-none name"><code>nn_init_xavier_normal_.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Fills the input <code>Tensor</code> with values according to the method
 described in <code>Understanding the difficulty of training deep feedforward neural networks</code> - Glorot, X. &amp; Bengio, Y. (2010), using a normal
 distribution.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_init_xavier_normal_</span><span class='op'>(</span><span class='va'>tensor</span>, gain <span class='op'>=</span> <span class='fl'>1</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>tensor</th>
-      <td><p>an n-dimensional <code>Tensor</code></p></td>
-    </tr>
-    <tr>
-      <th>gain</th>
-      <td><p>an optional scaling factor</p></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>w</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>5</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>nn_init_xavier_normal_</span><span class='op'>(</span><span class='va'>w</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.0989 -0.1761 -1.2245 -0.5875  0.1136</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.5803 -0.7237 -0.5109 -0.2005 -0.9200</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.1265 -0.1250  0.3422  0.0301 -0.3376</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{3,5} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_init_xavier_normal_</span><span class="op">(</span><span class="va">tensor</span>, gain <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>tensor</dt>
+<dd><p>an n-dimensional <code>Tensor</code></p></dd>
+<dt>gain</dt>
+<dd><p>an optional scaling factor</p></dd>
+</dl></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">w</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_empty.html">torch_empty</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">5</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">nn_init_xavier_normal_</span><span class="op">(</span><span class="va">w</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -1.0378  1.2928 -0.5099 -0.1658  0.1370</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.9178  0.4987 -0.3369  0.2306 -0.1576</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.2634 -0.3560  0.4081  0.5059 -0.5513</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3,5} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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diff --git a/reference/nn_init_xavier_uniform_.html b/reference/nn_init_xavier_uniform_.html
index 165bc8026..d9a39b489 100644
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-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Xavier uniform initialization</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-init.R'><code>R/nn-init.R</code></a></small>
-    <div class="hidden name"><code>nn_init_xavier_uniform_.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Xavier uniform initialization</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-init.R" class="external-link"><code>R/nn-init.R</code></a></small>
+      <div class="d-none name"><code>nn_init_xavier_uniform_.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Fills the input <code>Tensor</code> with values according to the method
 described in <code>Understanding the difficulty of training deep feedforward neural networks</code> - Glorot, X. &amp; Bengio, Y. (2010), using a uniform
 distribution.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_init_xavier_uniform_</span><span class='op'>(</span><span class='va'>tensor</span>, gain <span class='op'>=</span> <span class='fl'>1</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>tensor</th>
-      <td><p>an n-dimensional <code>Tensor</code></p></td>
-    </tr>
-    <tr>
-      <th>gain</th>
-      <td><p>an optional scaling factor</p></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>w</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>5</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>nn_init_xavier_uniform_</span><span class='op'>(</span><span class='va'>w</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.2398  0.4477  0.6793  0.0742 -0.2015</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.1586  0.0591  0.5739  0.8485 -0.2851</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.5342 -0.6632 -0.8265 -0.6771 -0.4256</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{3,5} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_init_xavier_uniform_</span><span class="op">(</span><span class="va">tensor</span>, gain <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>tensor</dt>
+<dd><p>an n-dimensional <code>Tensor</code></p></dd>
+<dt>gain</dt>
+<dd><p>an optional scaling factor</p></dd>
+</dl></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">w</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_empty.html">torch_empty</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">5</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">nn_init_xavier_uniform_</span><span class="op">(</span><span class="va">w</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.3992 -0.5062 -0.1031 -0.4119 -0.3039</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.5257 -0.5671 -0.0562 -0.4832 -0.4938</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0203 -0.4390  0.5743 -0.2024 -0.8208</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3,5} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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index 9f1e3cfdf..4ad9c2877 100644
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-  <div class="col-md-9 contents">
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-    <h1>Zeros initialization</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-init.R'><code>R/nn-init.R</code></a></small>
-    <div class="hidden name"><code>nn_init_zeros_.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Zeros initialization</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-init.R" class="external-link"><code>R/nn-init.R</code></a></small>
+      <div class="d-none name"><code>nn_init_zeros_.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Fills the input Tensor with the scalar value <code>0</code></p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_init_zeros_</span><span class='op'>(</span><span class='va'>tensor</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>tensor</th>
-      <td><p>an n-dimensional tensor</p></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>w</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>5</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>nn_init_zeros_</span><span class='op'>(</span><span class='va'>w</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0  0  0  0  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0  0  0  0  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0  0  0  0  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{3,5} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_init_zeros_</span><span class="op">(</span><span class="va">tensor</span><span class="op">)</span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>tensor</dt>
+<dd><p>an n-dimensional tensor</p></dd>
+</dl></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">w</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_empty.html">torch_empty</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">5</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">nn_init_zeros_</span><span class="op">(</span><span class="va">w</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0  0  0  0  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0  0  0  0  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0  0  0  0  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3,5} ]</span>
+</code></pre></div>
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diff --git a/reference/nn_kl_div_loss.html b/reference/nn_kl_div_loss.html
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-  <div class="col-md-9 contents">
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-    <h1>Kullback-Leibler divergence loss</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-loss.R'><code>R/nn-loss.R</code></a></small>
-    <div class="hidden name"><code>nn_kl_div_loss.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Kullback-Leibler divergence loss</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-loss.R" class="external-link"><code>R/nn-loss.R</code></a></small>
+      <div class="d-none name"><code>nn_kl_div_loss.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>The Kullback-Leibler divergence loss measure
-<a href='https://en.wikipedia.org/wiki/Kullback-Leibler_divergence'>Kullback-Leibler divergence</a>
+<a href="https://en.wikipedia.org/wiki/Kullback-Leibler_divergence" class="external-link">Kullback-Leibler divergence</a>
 is a useful distance measure for continuous distributions and is often
 useful when performing direct regression over the space of (discretely sampled)
 continuous output distributions.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_kl_div_loss</span><span class='op'>(</span>reduction <span class='op'>=</span> <span class='st'>"mean"</span><span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_kl_div_loss</span><span class="op">(</span>reduction <span class="op">=</span> <span class="st">"mean"</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>reduction</th>
-      <td><p>(string, optional): Specifies the reduction to apply to the output:
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>reduction</dt>
+<dd><p>(string, optional): Specifies the reduction to apply to the output:
 <code>'none'</code> | <code>'batchmean'</code> | <code>'sum'</code> | <code>'mean'</code>.
 <code>'none'</code>: no reduction will be applied.
 <code>'batchmean'</code>: the sum of the output will be divided by batchsize.
 <code>'sum'</code>: the output will be summed.
 <code>'mean'</code>: the output will be divided by the number of elements in the output.
-Default: <code>'mean'</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
-    <p>As with <code><a href='nn_nll_loss.html'>nn_nll_loss()</a></code>, the <code>input</code> given is expected to contain
+Default: <code>'mean'</code></p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
+    <p>As with <code><a href="nn_nll_loss.html">nn_nll_loss()</a></code>, the <code>input</code> given is expected to contain
 <em>log-probabilities</em> and is not restricted to a 2D Tensor.</p>
 <p>The targets are interpreted as <em>probabilities</em> by default, but could be considered
 as <em>log-probabilities</em> with <code>log_target</code> set to <code>TRUE</code>.</p>
@@ -241,51 +135,43 @@ over observations <strong>as well as</strong> over dimensions. <code>'batchmean'
 correct KL divergence where losses are averaged over batch dimension only.
 <code>'mean'</code> mode's behavior will be changed to the same as <code>'batchmean'</code> in the next
 major release.</p>
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p><code>reduction</code> = <code>'mean'</code> doesn't return the true kl divergence value,
 please use <code>reduction</code> = <code>'batchmean'</code> which aligns with KL math
 definition.
 In the next major release, <code>'mean'</code> will be changed to be the same as
 <code>'batchmean'</code>.</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
     
 
-<ul>
-<li><p>Input: \((N, *)\) where \(*\) means, any number of additional
+<ul><li><p>Input: \((N, *)\) where \(*\) means, any number of additional
 dimensions</p></li>
 <li><p>Target: \((N, *)\), same shape as the input</p></li>
 <li><p>Output: scalar by default. If <code>reduction</code> is <code>'none'</code>, then \((N, *)\),
 the same shape as the input</p></li>
-</ul>
+</ul></div>
 
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
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+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>L1 loss</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-loss.R'><code>R/nn-loss.R</code></a></small>
-    <div class="hidden name"><code>nn_l1_loss.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>L1 loss</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-loss.R" class="external-link"><code>R/nn-loss.R</code></a></small>
+      <div class="d-none name"><code>nn_l1_loss.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Creates a criterion that measures the mean absolute error (MAE) between each
 element in the input \(x\) and target \(y\).</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_l1_loss</span><span class='op'>(</span>reduction <span class='op'>=</span> <span class='st'>"mean"</span><span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_l1_loss</span><span class="op">(</span>reduction <span class="op">=</span> <span class="st">"mean"</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>reduction</th>
-      <td><p>(string, optional): Specifies the reduction to apply to the output:
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>reduction</dt>
+<dd><p>(string, optional): Specifies the reduction to apply to the output:
 <code>'none'</code> | <code>'mean'</code> | <code>'sum'</code>. <code>'none'</code>: no reduction will be applied,
 <code>'mean'</code>: the sum of the output will be divided by the number of
 elements in the output, <code>'sum'</code>: the output will be summed. Note: <code>size_average</code>
 and <code>reduce</code> are in the process of being deprecated, and in the meantime,
-specifying either of those two args will override <code>reduction</code>. Default: <code>'mean'</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
+specifying either of those two args will override <code>reduction</code>. Default: <code>'mean'</code></p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>The unreduced (i.e. with <code>reduction</code> set to <code>'none'</code>) loss can be described
 as:</p>
 <p>$$
@@ -228,54 +119,46 @@ $$</p>
 of \(n\) elements each.</p>
 <p>The sum operation still operates over all the elements, and divides by \(n\).
 The division by \(n\) can be avoided if one sets <code>reduction = 'sum'</code>.</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
     
 
-<ul>
-<li><p>Input: \((N, *)\) where \(*\) means, any number of additional
+<ul><li><p>Input: \((N, *)\) where \(*\) means, any number of additional
 dimensions</p></li>
 <li><p>Target: \((N, *)\), same shape as the input</p></li>
 <li><p>Output: scalar. If <code>reduction</code> is <code>'none'</code>, then
 \((N, *)\), same shape as the input</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>loss</span> <span class='op'>&lt;-</span> <span class='fu'>nn_l1_loss</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>5</span>, requires_grad<span class='op'>=</span><span class='cn'>TRUE</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>target</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>5</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span> <span class='op'>&lt;-</span> <span class='fu'>loss</span><span class='op'>(</span><span class='va'>input</span>, <span class='va'>target</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span><span class='op'>$</span><span class='fu'>backward</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+</ul></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">loss</span> <span class="op">&lt;-</span> <span class="fu">nn_l1_loss</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">5</span>, requires_grad <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">target</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">5</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span> <span class="op">&lt;-</span> <span class="fu">loss</span><span class="op">(</span><span class="va">input</span>, <span class="va">target</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span><span class="op">$</span><span class="fu">backward</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <footer><div class="pkgdown-footer-left">
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diff --git a/reference/nn_layer_norm.html b/reference/nn_layer_norm.html
index dc2872f8d..3e0ecbfef 100644
--- a/reference/nn_layer_norm.html
+++ b/reference/nn_layer_norm.html
@@ -1,223 +1,110 @@
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Layer normalization</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-normalization.R'><code>R/nn-normalization.R</code></a></small>
-    <div class="hidden name"><code>nn_layer_norm.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Layer normalization</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-normalization.R" class="external-link"><code>R/nn-normalization.R</code></a></small>
+      <div class="d-none name"><code>nn_layer_norm.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Applies Layer Normalization over a mini-batch of inputs as described in
-the paper <a href='https://arxiv.org/abs/1607.06450'>Layer Normalization</a></p>
+the paper <a href="https://arxiv.org/abs/1607.06450" class="external-link">Layer Normalization</a></p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_layer_norm</span><span class='op'>(</span><span class='va'>normalized_shape</span>, eps <span class='op'>=</span> <span class='fl'>1e-05</span>, elementwise_affine <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_layer_norm</span><span class="op">(</span><span class="va">normalized_shape</span>, eps <span class="op">=</span> <span class="fl">1e-05</span>, elementwise_affine <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>normalized_shape</th>
-      <td><p>(int or list): input shape from an expected input
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>normalized_shape</dt>
+<dd><p>(int or list): input shape from an expected input
 of size
 \([* \times \mbox{normalized\_shape}[0] \times \mbox{normalized\_shape}[1] \times \ldots \times \mbox{normalized\_shape}[-1]]\)
 If a single integer is used, it is treated as a singleton list, and this module will
-normalize over the last dimension which is expected to be of that specific size.</p></td>
-    </tr>
-    <tr>
-      <th>eps</th>
-      <td><p>a value added to the denominator for numerical stability. Default: 1e-5</p></td>
-    </tr>
-    <tr>
-      <th>elementwise_affine</th>
-      <td><p>a boolean value that when set to <code>TRUE</code>, this module
+normalize over the last dimension which is expected to be of that specific size.</p></dd>
+<dt>eps</dt>
+<dd><p>a value added to the denominator for numerical stability. Default: 1e-5</p></dd>
+<dt>elementwise_affine</dt>
+<dd><p>a boolean value that when set to <code>TRUE</code>, this module
 has learnable per-element affine parameters initialized to ones (for weights)
-and zeros (for biases). Default: <code>TRUE</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
+and zeros (for biases). Default: <code>TRUE</code>.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>$$
   y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta
 $$</p>
@@ -228,66 +115,59 @@ certain number dimensions which have to be of the shape specified by
 <code>normalized_shape</code> if <code>elementwise_affine</code> is <code>TRUE</code>.</p>
 <p>The standard-deviation is calculated via the biased estimator, equivalent to
 <code>torch_var(input, unbiased=FALSE)</code>.</p>
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p>Unlike Batch Normalization and Instance Normalization, which applies
 scalar scale and bias for each entire channel/plane with the
 <code>affine</code> option, Layer Normalization applies per-element scale and
 bias with <code>elementwise_affine</code>.</p>
 <p>This layer uses statistics computed from input data in both training and
 evaluation modes.</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
     
 
-<ul>
-<li><p>Input: \((N, *)\)</p></li>
+<ul><li><p>Input: \((N, *)\)</p></li>
 <li><p>Output: \((N, *)\) (same shape as input)</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'>  </span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>20</span>, <span class='fl'>5</span>, <span class='fl'>10</span>, <span class='fl'>10</span><span class='op'>)</span></span>
-<span class='r-in'><span class='co'># With Learnable Parameters</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_layer_norm</span><span class='op'>(</span><span class='va'>input</span><span class='op'>$</span><span class='fu'>size</span><span class='op'>(</span><span class='op'>)</span><span class='op'>[</span><span class='op'>-</span><span class='fl'>1</span><span class='op'>]</span><span class='op'>)</span></span>
-<span class='r-in'><span class='co'># Without Learnable Parameters</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_layer_norm</span><span class='op'>(</span><span class='va'>input</span><span class='op'>$</span><span class='fu'>size</span><span class='op'>(</span><span class='op'>)</span><span class='op'>[</span><span class='op'>-</span><span class='fl'>1</span><span class='op'>]</span>, elementwise_affine<span class='op'>=</span><span class='cn'>FALSE</span><span class='op'>)</span></span>
-<span class='r-in'><span class='co'># Normalize over last two dimensions</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_layer_norm</span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>10</span>, <span class='fl'>10</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='co'># Normalize over last dimension of size 10</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_layer_norm</span><span class='op'>(</span><span class='fl'>10</span><span class='op'>)</span></span>
-<span class='r-in'><span class='co'># Activating the module</span></span>
-<span class='r-in'><span class='va'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+</ul></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">20</span>, <span class="fl">5</span>, <span class="fl">10</span>, <span class="fl">10</span><span class="op">)</span></span>
+<span class="r-in"><span class="co"># With Learnable Parameters</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">nn_layer_norm</span><span class="op">(</span><span class="va">input</span><span class="op">$</span><span class="fu">size</span><span class="op">(</span><span class="op">)</span><span class="op">[</span><span class="op">-</span><span class="fl">1</span><span class="op">]</span><span class="op">)</span></span>
+<span class="r-in"><span class="co"># Without Learnable Parameters</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">nn_layer_norm</span><span class="op">(</span><span class="va">input</span><span class="op">$</span><span class="fu">size</span><span class="op">(</span><span class="op">)</span><span class="op">[</span><span class="op">-</span><span class="fl">1</span><span class="op">]</span>, elementwise_affine <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span>
+<span class="r-in"><span class="co"># Normalize over last two dimensions</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">nn_layer_norm</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">10</span>, <span class="fl">10</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="co"># Normalize over last dimension of size 10</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">nn_layer_norm</span><span class="op">(</span><span class="fl">10</span><span class="op">)</span></span>
+<span class="r-in"><span class="co"># Activating the module</span></span>
+<span class="r-in"><span class="va">output</span> <span class="op">&lt;-</span> <span class="fu">m</span><span class="op">(</span><span class="va">input</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
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+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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index 948e200d4..f7aa73d9a 100644
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>LeakyReLU module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
-    <div class="hidden name"><code>nn_leaky_relu.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>LeakyReLU module</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-activation.R" class="external-link"><code>R/nn-activation.R</code></a></small>
+      <div class="d-none name"><code>nn_leaky_relu.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Applies the element-wise function:</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_leaky_relu</span><span class='op'>(</span>negative_slope <span class='op'>=</span> <span class='fl'>0.01</span>, inplace <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>negative_slope</th>
-      <td><p>Controls the angle of the negative slope. Default: 1e-2</p></td>
-    </tr>
-    <tr>
-      <th>inplace</th>
-      <td><p>can optionally do the operation in-place. Default: <code>FALSE</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_leaky_relu</span><span class="op">(</span>negative_slope <span class="op">=</span> <span class="fl">0.01</span>, inplace <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>negative_slope</dt>
+<dd><p>Controls the angle of the negative slope. Default: 1e-2</p></dd>
+<dt>inplace</dt>
+<dd><p>can optionally do the operation in-place. Default: <code>FALSE</code></p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>$$
   \mbox{LeakyReLU}(x) = \max(0, x) + \mbox{negative\_slope} * \min(0, x)
 $$
@@ -218,50 +106,42 @@ x, &amp; \mbox{ if } x \geq 0 \\
 \end{array}
 \right.
 $$</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
     
 
-<ul>
-<li><p>Input: \((N, *)\) where <code>*</code> means, any number of additional
+<ul><li><p>Input: \((N, *)\) where <code>*</code> means, any number of additional
 dimensions</p></li>
 <li><p>Output: \((N, *)\), same shape as the input</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_leaky_relu</span><span class='op'>(</span><span class='fl'>0.1</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+</ul></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">nn_leaky_relu</span><span class="op">(</span><span class="fl">0.1</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span> <span class="op">&lt;-</span> <span class="fu">m</span><span class="op">(</span><span class="va">input</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Linear module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-linear.R'><code>R/nn-linear.R</code></a></small>
-    <div class="hidden name"><code>nn_linear.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Linear module</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-linear.R" class="external-link"><code>R/nn-linear.R</code></a></small>
+      <div class="d-none name"><code>nn_linear.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Applies a linear transformation to the incoming data: <code>y = xA^T + b</code></p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_linear</span><span class='op'>(</span><span class='va'>in_features</span>, <span class='va'>out_features</span>, bias <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>in_features</th>
-      <td><p>size of each input sample</p></td>
-    </tr>
-    <tr>
-      <th>out_features</th>
-      <td><p>size of each output sample</p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>If set to <code>FALSE</code>, the layer will not learn an additive bias.
-Default: <code>TRUE</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_linear</span><span class="op">(</span><span class="va">in_features</span>, <span class="va">out_features</span>, bias <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>in_features</dt>
+<dd><p>size of each input sample</p></dd>
+<dt>out_features</dt>
+<dd><p>size of each output sample</p></dd>
+<dt>bias</dt>
+<dd><p>If set to <code>FALSE</code>, the layer will not learn an additive bias.
+Default: <code>TRUE</code></p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
     
 
-<ul>
-<li><p>Input: <code>(N, *, H_in)</code> where <code>*</code> means any number of
+<ul><li><p>Input: <code>(N, *, H_in)</code> where <code>*</code> means any number of
 additional dimensions and <code>H_in = in_features</code>.</p></li>
 <li><p>Output: <code>(N, *, H_out)</code> where all but the last dimension
 are the same shape as the input and :math:<code>H_out = out_features</code>.</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="attributes"><a class="anchor" href="#attributes"></a>Attributes</h2>
-
+</ul></div>
+    <div class="section level2">
+    <h2 id="attributes">Attributes<a class="anchor" aria-label="anchor" href="#attributes"></a></h2>
     
 
-<ul>
-<li><p>weight: the learnable weights of the module of shape
+<ul><li><p>weight: the learnable weights of the module of shape
 <code>(out_features, in_features)</code>. The values are
 initialized from \(U(-\sqrt{k}, \sqrt{k})\)s, where
 \(k = \frac{1}{\mbox{in\_features}}\)</p></li>
@@ -233,44 +116,36 @@ initialized from \(U(-\sqrt{k}, \sqrt{k})\)s, where
 If <code>bias</code> is <code>TRUE</code>, the values are initialized from
 \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where
 \(k = \frac{1}{\mbox{in\_features}}\)</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_linear</span><span class='op'>(</span><span class='fl'>20</span>, <span class='fl'>30</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>128</span>, <span class='fl'>20</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span><span class='op'>(</span><span class='va'>output</span><span class='op'>$</span><span class='fu'>size</span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [1] 128  30</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+</ul></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">nn_linear</span><span class="op">(</span><span class="fl">20</span>, <span class="fl">30</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">128</span>, <span class="fl">20</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span> <span class="op">&lt;-</span> <span class="fu">m</span><span class="op">(</span><span class="va">input</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">output</span><span class="op">$</span><span class="fu">size</span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 128  30</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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index 650245c1a..96b1d41a9 100644
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Applies the element-wise function:
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+ $$"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>LogSigmoid module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
-    <div class="hidden name"><code>nn_log_sigmoid.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>LogSigmoid module</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-activation.R" class="external-link"><code>R/nn-activation.R</code></a></small>
+      <div class="d-none name"><code>nn_log_sigmoid.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Applies the element-wise function:
 $$
   \mbox{LogSigmoid}(x) = \log\left(\frac{ 1 }{ 1 + \exp(-x)}\right)
  $$</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_log_sigmoid</span><span class='op'>(</span><span class='op'>)</span></pre>
-
-
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_log_sigmoid</span><span class="op">(</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
     
 
-<ul>
-<li><p>Input: \((N, *)\) where <code>*</code> means, any number of additional
+<ul><li><p>Input: \((N, *)\) where <code>*</code> means, any number of additional
 dimensions</p></li>
 <li><p>Output: \((N, *)\), same shape as the input</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_log_sigmoid</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+</ul></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">nn_log_sigmoid</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span> <span class="op">&lt;-</span> <span class="fu">m</span><span class="op">(</span><span class="va">input</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
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+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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diff --git a/reference/nn_log_softmax.html b/reference/nn_log_softmax.html
index 4ee29a333..bb35e6918 100644
--- a/reference/nn_log_softmax.html
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@@ -1,260 +1,144 @@
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>LogSoftmax module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
-    <div class="hidden name"><code>nn_log_softmax.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>LogSoftmax module</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-activation.R" class="external-link"><code>R/nn-activation.R</code></a></small>
+      <div class="d-none name"><code>nn_log_softmax.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Applies the \(\log(\mbox{Softmax}(x))\) function to an n-dimensional
 input Tensor. The LogSoftmax formulation can be simplified as:</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_log_softmax</span><span class='op'>(</span><span class='va'>dim</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>dim</th>
-      <td><p>(int): A dimension along which LogSoftmax will be computed.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_log_softmax</span><span class="op">(</span><span class="va">dim</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>dim</dt>
+<dd><p>(int): A dimension along which LogSoftmax will be computed.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
     <p>a Tensor of the same dimension and shape as the input with
 values in the range [-inf, 0)</p>
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>$$
   \mbox{LogSoftmax}(x_{i}) = \log\left(\frac{\exp(x_i) }{ \sum_j \exp(x_j)} \right)
 $$</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
     
 
-<ul>
-<li><p>Input: \((*)\) where <code>*</code> means, any number of additional
+<ul><li><p>Input: \((*)\) where <code>*</code> means, any number of additional
 dimensions</p></li>
 <li><p>Output: \((*)\), same shape as the input</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_log_softmax</span><span class='op'>(</span><span class='fl'>1</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>3</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+</ul></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">nn_log_softmax</span><span class="op">(</span><span class="fl">1</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">3</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span> <span class="op">&lt;-</span> <span class="fu">m</span><span class="op">(</span><span class="va">input</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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diff --git a/reference/nn_lp_pool1d.html b/reference/nn_lp_pool1d.html
index 64fa65e54..5f515d807 100644
--- a/reference/nn_lp_pool1d.html
+++ b/reference/nn_lp_pool1d.html
@@ -1,286 +1,155 @@
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-<title>Applies a 1D power-average pooling over an input signal composed of several input
-planes. — nn_lp_pool1d • torch</title>
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Applies a 1D power-average pooling over an input signal composed of several input
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Applies a 1D power-average pooling over an input signal composed of several input
 planes.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
-    <div class="hidden name"><code>nn_lp_pool1d.Rd</code></div>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-pooling.R" class="external-link"><code>R/nn-pooling.R</code></a></small>
+      <div class="d-none name"><code>nn_lp_pool1d.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>On each window, the function computed is:</p>
-<p>$$
-  f(X) = \sqrt[p]{\sum_{x \in X} x^{p}}
-$$</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_lp_pool1d</span><span class='op'>(</span><span class='va'>norm_type</span>, <span class='va'>kernel_size</span>, stride <span class='op'>=</span> <span class='cn'>NULL</span>, ceil_mode <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>norm_type</th>
-      <td><p>if inf than one gets max pooling if 0 you get sum pooling (
-proportional to the avg pooling)</p></td>
-    </tr>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>a single int, the size of the window</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>a single int, the stride of the window. Default value is <code>kernel_size</code></p></td>
-    </tr>
-    <tr>
-      <th>ceil_mode</th>
-      <td><p>when TRUE, will use <code>ceil</code> instead of <code>floor</code> to compute the output shape</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_lp_pool1d</span><span class="op">(</span><span class="va">norm_type</span>, <span class="va">kernel_size</span>, stride <span class="op">=</span> <span class="cn">NULL</span>, ceil_mode <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    
-<ul>
-<li><p>At p = \(\infty\), one gets Max Pooling</p></li>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>norm_type</dt>
+<dd><p>if inf than one gets max pooling if 0 you get sum pooling (
+proportional to the avg pooling)</p></dd>
+<dt>kernel_size</dt>
+<dd><p>a single int, the size of the window</p></dd>
+<dt>stride</dt>
+<dd><p>a single int, the stride of the window. Default value is <code>kernel_size</code></p></dd>
+<dt>ceil_mode</dt>
+<dd><p>when TRUE, will use <code>ceil</code> instead of <code>floor</code> to compute the output shape</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
+    <p>$$
+  f(X) = \sqrt[p]{\sum_{x \in X} x^{p}}
+$$</p><ul><li><p>At p = \(\infty\), one gets Max Pooling</p></li>
 <li><p>At p = 1, one gets Sum Pooling (which is proportional to Average Pooling)</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
+</ul></div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p>If the sum to the power of <code>p</code> is zero, the gradient of this function is
 not defined. This implementation will set the gradient to zero in this case.</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
     
 
-<ul>
-<li><p>Input: \((N, C, L_{in})\)</p></li>
+<ul><li><p>Input: \((N, C, L_{in})\)</p></li>
 <li><p>Output: \((N, C, L_{out})\), where</p></li>
-</ul>
-
-<p>$$
+</ul><p>$$
   L_{out} = \left\lfloor\frac{L_{in} - \mbox{kernel\_size}}{\mbox{stride}} + 1\right\rfloor
 $$</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='co'># power-2 pool of window of length 3, with stride 2.</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_lp_pool1d</span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>3</span>, stride<span class='op'>=</span><span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>50</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="co"># power-2 pool of window of length 3, with stride 2.</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">nn_lp_pool1d</span><span class="op">(</span><span class="fl">2</span>, <span class="fl">3</span>, stride <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">20</span>, <span class="fl">16</span>, <span class="fl">50</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span> <span class="op">&lt;-</span> <span class="fu">m</span><span class="op">(</span><span class="va">input</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
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+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Applies a 2D power-average pooling over an input signal composed of several input
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Applies a 2D power-average pooling over an input signal composed of several input
 planes.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
-    <div class="hidden name"><code>nn_lp_pool2d.Rd</code></div>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-pooling.R" class="external-link"><code>R/nn-pooling.R</code></a></small>
+      <div class="d-none name"><code>nn_lp_pool2d.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>On each window, the function computed is:</p>
-<p>$$
-  f(X) = \sqrt[p]{\sum_{x \in X} x^{p}}
-$$</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_lp_pool2d</span><span class='op'>(</span><span class='va'>norm_type</span>, <span class='va'>kernel_size</span>, stride <span class='op'>=</span> <span class='cn'>NULL</span>, ceil_mode <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>norm_type</th>
-      <td><p>if inf than one gets max pooling if 0 you get sum pooling (
-proportional to the avg pooling)</p></td>
-    </tr>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>the size of the window</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>the stride of the window. Default value is <code>kernel_size</code></p></td>
-    </tr>
-    <tr>
-      <th>ceil_mode</th>
-      <td><p>when TRUE, will use <code>ceil</code> instead of <code>floor</code> to compute the output shape</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_lp_pool2d</span><span class="op">(</span><span class="va">norm_type</span>, <span class="va">kernel_size</span>, stride <span class="op">=</span> <span class="cn">NULL</span>, ceil_mode <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    
-<ul>
-<li><p>At p = \(\infty\), one gets Max Pooling</p></li>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>norm_type</dt>
+<dd><p>if inf than one gets max pooling if 0 you get sum pooling (
+proportional to the avg pooling)</p></dd>
+<dt>kernel_size</dt>
+<dd><p>the size of the window</p></dd>
+<dt>stride</dt>
+<dd><p>the stride of the window. Default value is <code>kernel_size</code></p></dd>
+<dt>ceil_mode</dt>
+<dd><p>when TRUE, will use <code>ceil</code> instead of <code>floor</code> to compute the output shape</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
+    <p>$$
+  f(X) = \sqrt[p]{\sum_{x \in X} x^{p}}
+$$</p><ul><li><p>At p = \(\infty\), one gets Max Pooling</p></li>
 <li><p>At p = 1, one gets Sum Pooling (which is proportional to average pooling)</p></li>
-</ul>
-
-<p>The parameters <code>kernel_size</code>, <code>stride</code> can either be:</p><ul>
-<li><p>a single <code>int</code> -- in which case the same value is used for the height and width dimension</p></li>
+</ul><p>The parameters <code>kernel_size</code>, <code>stride</code> can either be:</p><ul><li><p>a single <code>int</code> -- in which case the same value is used for the height and width dimension</p></li>
 <li><p>a <code>tuple</code> of two ints -- in which case, the first <code>int</code> is used for the height dimension,
 and the second <code>int</code> for the width dimension</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
+</ul></div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p>If the sum to the power of <code>p</code> is zero, the gradient of this function is
 not defined. This implementation will set the gradient to zero in this case.</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
     
 
-<ul>
-<li><p>Input: \((N, C, H_{in}, W_{in})\)</p></li>
+<ul><li><p>Input: \((N, C, H_{in}, W_{in})\)</p></li>
 <li><p>Output: \((N, C, H_{out}, W_{out})\), where</p></li>
-</ul>
-
-<p>$$
+</ul><p>$$
   H_{out} = \left\lfloor\frac{H_{in} - \mbox{kernel\_size}[0]}{\mbox{stride}[0]} + 1\right\rfloor
 $$
 $$
   W_{out} = \left\lfloor\frac{W_{in} - \mbox{kernel\_size}[1]}{\mbox{stride}[1]} + 1\right\rfloor
 $$</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'>  </span>
-<span class='r-in'><span class='co'># power-2 pool of square window of size=3, stride=2</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_lp_pool2d</span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>3</span>, stride<span class='op'>=</span><span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='co'># pool of non-square window of power 1.2</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_lp_pool2d</span><span class='op'>(</span><span class='fl'>1.2</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>2</span><span class='op'>)</span>, stride<span class='op'>=</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>1</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>50</span>, <span class='fl'>32</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="co"># power-2 pool of square window of size=3, stride=2</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">nn_lp_pool2d</span><span class="op">(</span><span class="fl">2</span>, <span class="fl">3</span>, stride <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="co"># pool of non-square window of power 1.2</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">nn_lp_pool2d</span><span class="op">(</span><span class="fl">1.2</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">2</span><span class="op">)</span>, stride <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">1</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">20</span>, <span class="fl">16</span>, <span class="fl">50</span>, <span class="fl">32</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span> <span class="op">&lt;-</span> <span class="fu">m</span><span class="op">(</span><span class="va">input</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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+  </div>
+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Applies a multi-layer long short-term memory (LSTM) RNN to an input
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Applies a multi-layer long short-term memory (LSTM) RNN to an input
 sequence.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-rnn.R'><code>R/nn-rnn.R</code></a></small>
-    <div class="hidden name"><code>nn_lstm.Rd</code></div>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-rnn.R" class="external-link"><code>R/nn-rnn.R</code></a></small>
+      <div class="d-none name"><code>nn_lstm.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>For each element in the input sequence, each layer computes the following
 function:</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_lstm</span><span class='op'>(</span>
-  <span class='va'>input_size</span>,
-  <span class='va'>hidden_size</span>,
-  num_layers <span class='op'>=</span> <span class='fl'>1</span>,
-  bias <span class='op'>=</span> <span class='cn'>TRUE</span>,
-  batch_first <span class='op'>=</span> <span class='cn'>FALSE</span>,
-  dropout <span class='op'>=</span> <span class='fl'>0</span>,
-  bidirectional <span class='op'>=</span> <span class='cn'>FALSE</span>,
-  <span class='va'>...</span>
-<span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_lstm</span><span class="op">(</span>
+  <span class="va">input_size</span>,
+  <span class="va">hidden_size</span>,
+  num_layers <span class="op">=</span> <span class="fl">1</span>,
+  bias <span class="op">=</span> <span class="cn">TRUE</span>,
+  batch_first <span class="op">=</span> <span class="cn">FALSE</span>,
+  dropout <span class="op">=</span> <span class="fl">0</span>,
+  bidirectional <span class="op">=</span> <span class="cn">FALSE</span>,
+  <span class="va">...</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input_size</th>
-      <td><p>The number of expected features in the input <code>x</code></p></td>
-    </tr>
-    <tr>
-      <th>hidden_size</th>
-      <td><p>The number of features in the hidden state <code>h</code></p></td>
-    </tr>
-    <tr>
-      <th>num_layers</th>
-      <td><p>Number of recurrent layers. E.g., setting <code>num_layers=2</code>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input_size</dt>
+<dd><p>The number of expected features in the input <code>x</code></p></dd>
+<dt>hidden_size</dt>
+<dd><p>The number of features in the hidden state <code>h</code></p></dd>
+<dt>num_layers</dt>
+<dd><p>Number of recurrent layers. E.g., setting <code>num_layers=2</code>
 would mean stacking two LSTMs together to form a <code>stacked LSTM</code>,
 with the second LSTM taking in outputs of the first LSTM and
-computing the final results. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>If <code>FALSE</code>, then the layer does not use bias weights <code>b_ih</code> and <code>b_hh</code>.
-Default: <code>TRUE</code></p></td>
-    </tr>
-    <tr>
-      <th>batch_first</th>
-      <td><p>If <code>TRUE</code>, then the input and output tensors are provided
-as (batch, seq, feature). Default: <code>FALSE</code></p></td>
-    </tr>
-    <tr>
-      <th>dropout</th>
-      <td><p>If non-zero, introduces a <code>Dropout</code> layer on the outputs of each
+computing the final results. Default: 1</p></dd>
+<dt>bias</dt>
+<dd><p>If <code>FALSE</code>, then the layer does not use bias weights <code>b_ih</code> and <code>b_hh</code>.
+Default: <code>TRUE</code></p></dd>
+<dt>batch_first</dt>
+<dd><p>If <code>TRUE</code>, then the input and output tensors are provided
+as (batch, seq, feature). Default: <code>FALSE</code></p></dd>
+<dt>dropout</dt>
+<dd><p>If non-zero, introduces a <code>Dropout</code> layer on the outputs of each
 LSTM layer except the last layer, with dropout probability equal to
-<code>dropout</code>. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>bidirectional</th>
-      <td><p>If <code>TRUE</code>, becomes a bidirectional LSTM. Default: <code>FALSE</code></p></td>
-    </tr>
-    <tr>
-      <th>...</th>
-      <td><p>currently unused.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
+<code>dropout</code>. Default: 0</p></dd>
+<dt>bidirectional</dt>
+<dd><p>If <code>TRUE</code>, becomes a bidirectional LSTM. Default: <code>FALSE</code></p></dd>
+<dt>...</dt>
+<dd><p>currently unused.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>$$
 \begin{array}{ll} \\
 i_t = \sigma(W_{ii} x_t + b_{ii} + W_{hi} h_{(t-1)} + b_{hi}) \\
@@ -267,35 +144,34 @@ is the hidden state of the previous layer at time <code>t-1</code> or the initia
 state at time <code>0</code>, and \(i_t\), \(f_t\), \(g_t\),
 \(o_t\) are the input, forget, cell, and output gates, respectively.
 \(\sigma\) is the sigmoid function.</p>
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p>All the weights and biases are initialized from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\)
 where \(k = \frac{1}{\mbox{hidden\_size}}\)</p>
-    <h2 class="hasAnchor" id="inputs"><a class="anchor" href="#inputs"></a>Inputs</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="inputs">Inputs<a class="anchor" aria-label="anchor" href="#inputs"></a></h2>
     
 
 
-<p>Inputs: input, (h_0, c_0)</p><ul>
-<li><p><strong>input</strong> of shape <code>(seq_len, batch, input_size)</code>: tensor containing the features
+<p>Inputs: input, (h_0, c_0)</p><ul><li><p><strong>input</strong> of shape <code>(seq_len, batch, input_size)</code>: tensor containing the features
 of the input sequence.
 The input can also be a packed variable length sequence.
-See <code><a href='nn_utils_rnn_pack_padded_sequence.html'>nn_utils_rnn_pack_padded_sequence()</a></code> or
-<code><a href='nn_utils_rnn_pack_sequence.html'>nn_utils_rnn_pack_sequence()</a></code> for details.</p></li>
+See <code><a href="nn_utils_rnn_pack_padded_sequence.html">nn_utils_rnn_pack_padded_sequence()</a></code> or
+<code><a href="nn_utils_rnn_pack_sequence.html">nn_utils_rnn_pack_sequence()</a></code> for details.</p></li>
 <li><p><strong>h_0</strong> of shape <code>(num_layers * num_directions, batch, hidden_size)</code>: tensor
 containing the initial hidden state for each element in the batch.</p></li>
 <li><p><strong>c_0</strong> of shape <code>(num_layers * num_directions, batch, hidden_size)</code>: tensor
 containing the initial cell state for each element in the batch.</p></li>
-</ul>
-
-<p>If <code>(h_0, c_0)</code> is not provided, both <strong>h_0</strong> and <strong>c_0</strong> default to zero.</p>
-    <h2 class="hasAnchor" id="outputs"><a class="anchor" href="#outputs"></a>Outputs</h2>
-
+</ul><p>If <code>(h_0, c_0)</code> is not provided, both <strong>h_0</strong> and <strong>c_0</strong> default to zero.</p>
+    </div>
+    <div class="section level2">
+    <h2 id="outputs">Outputs<a class="anchor" aria-label="anchor" href="#outputs"></a></h2>
     
 
 
-<p>Outputs: output, (h_n, c_n)</p><ul>
-<li><p><strong>output</strong> of shape <code>(seq_len, batch, num_directions * hidden_size)</code>: tensor
+<p>Outputs: output, (h_n, c_n)</p><ul><li><p><strong>output</strong> of shape <code>(seq_len, batch, num_directions * hidden_size)</code>: tensor
 containing the output features <code>(h_t)</code> from the last layer of the LSTM,
 for each t. If a <code>torch_nn.utils.rnn.PackedSequence</code> has been
 given as the input, the output will also be a packed sequence.
@@ -309,14 +185,12 @@ Like <em>output</em>, the layers can be separated using
 <code>h_n$view(c(num_layers, num_directions, batch, hidden_size))</code> and similarly for <em>c_n</em>.</p></li>
 <li><p><strong>c_n</strong> (num_layers * num_directions, batch, hidden_size): tensor
 containing the cell state for <code>t = seq_len</code></p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="attributes"><a class="anchor" href="#attributes"></a>Attributes</h2>
-
+</ul></div>
+    <div class="section level2">
+    <h2 id="attributes">Attributes<a class="anchor" aria-label="anchor" href="#attributes"></a></h2>
     
 
-<ul>
-<li><p><code>weight_ih_l[k]</code> : the learnable input-hidden weights of the \(\mbox{k}^{th}\) layer
+<ul><li><p><code>weight_ih_l[k]</code> : the learnable input-hidden weights of the \(\mbox{k}^{th}\) layer
 <code>(W_ii|W_if|W_ig|W_io)</code>, of shape <code>(4*hidden_size x input_size)</code></p></li>
 <li><p><code>weight_hh_l[k]</code> : the learnable hidden-hidden weights of the \(\mbox{k}^{th}\) layer
 <code>(W_hi|W_hf|W_hg|W_ho)</code>, of shape <code>(4*hidden_size x hidden_size)</code></p></li>
@@ -324,44 +198,36 @@ containing the cell state for <code>t = seq_len</code></p></li>
 <code>(b_ii|b_if|b_ig|b_io)</code>, of shape <code>(4*hidden_size)</code></p></li>
 <li><p><code>bias_hh_l[k]</code> : the learnable hidden-hidden bias of the \(\mbox{k}^{th}\) layer
 <code>(b_hi|b_hf|b_hg|b_ho)</code>, of shape <code>(4*hidden_size)</code></p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>rnn</span> <span class='op'>&lt;-</span> <span class='fu'>nn_lstm</span><span class='op'>(</span><span class='fl'>10</span>, <span class='fl'>20</span>, <span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>5</span>, <span class='fl'>3</span>, <span class='fl'>10</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>h0</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>3</span>, <span class='fl'>20</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>c0</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>3</span>, <span class='fl'>20</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span> <span class='op'>&lt;-</span> <span class='fu'>rnn</span><span class='op'>(</span><span class='va'>input</span>, <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span><span class='va'>h0</span>, <span class='va'>c0</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+</ul></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">rnn</span> <span class="op">&lt;-</span> <span class="fu">nn_lstm</span><span class="op">(</span><span class="fl">10</span>, <span class="fl">20</span>, <span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">5</span>, <span class="fl">3</span>, <span class="fl">10</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">h0</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">3</span>, <span class="fl">20</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">c0</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">3</span>, <span class="fl">20</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span> <span class="op">&lt;-</span> <span class="fu">rnn</span><span class="op">(</span><span class="va">input</span>, <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">h0</span>, <span class="va">c0</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer>
-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
 </div>
 
-<div class="pkgdown">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p>
+<div class="pkgdown-footer-right">
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index ac1e7f42b..958b0237c 100644
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@@ -1,195 +1,90 @@
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+and a label 1D mini-batch tensor \(y\) (containing 1 or -1).
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+    
+  </div>
+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Margin ranking loss</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-loss.R'><code>R/nn-loss.R</code></a></small>
-    <div class="hidden name"><code>nn_margin_ranking_loss.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Margin ranking loss</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-loss.R" class="external-link"><code>R/nn-loss.R</code></a></small>
+      <div class="d-none name"><code>nn_margin_ranking_loss.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Creates a criterion that measures the loss given
 inputs \(x1\), \(x2\), two 1D mini-batch <code>Tensors</code>,
 and a label 1D mini-batch tensor \(y\) (containing 1 or -1).
@@ -197,80 +92,69 @@ If \(y = 1\) then it assumed the first input should be ranked higher
 (have a larger value) than the second input, and vice-versa for \(y = -1\).</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_margin_ranking_loss</span><span class='op'>(</span>margin <span class='op'>=</span> <span class='fl'>0</span>, reduction <span class='op'>=</span> <span class='st'>"mean"</span><span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_margin_ranking_loss</span><span class="op">(</span>margin <span class="op">=</span> <span class="fl">0</span>, reduction <span class="op">=</span> <span class="st">"mean"</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>margin</th>
-      <td><p>(float, optional): Has a default value of \(0\).</p></td>
-    </tr>
-    <tr>
-      <th>reduction</th>
-      <td><p>(string, optional): Specifies the reduction to apply to the output:
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>margin</dt>
+<dd><p>(float, optional): Has a default value of \(0\).</p></dd>
+<dt>reduction</dt>
+<dd><p>(string, optional): Specifies the reduction to apply to the output:
 <code>'none'</code> | <code>'mean'</code> | <code>'sum'</code>. <code>'none'</code>: no reduction will be applied,
 <code>'mean'</code>: the sum of the output will be divided by the number of
 elements in the output, <code>'sum'</code>: the output will be summed. Note: <code>size_average</code>
 and <code>reduce</code> are in the process of being deprecated, and in the meantime,
-specifying either of those two args will override <code>reduction</code>. Default: <code>'mean'</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
+specifying either of those two args will override <code>reduction</code>. Default: <code>'mean'</code></p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>The loss function for each pair of samples in the mini-batch is:</p>
 <p>$$
   \mbox{loss}(x1, x2, y) = \max(0, -y * (x1 - x2) + \mbox{margin})
 $$</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
     
 
-<ul>
-<li><p>Input1: \((N)\) where <code>N</code> is the batch size.</p></li>
+<ul><li><p>Input1: \((N)\) where <code>N</code> is the batch size.</p></li>
 <li><p>Input2: \((N)\), same shape as the Input1.</p></li>
 <li><p>Target: \((N)\), same shape as the inputs.</p></li>
 <li><p>Output: scalar. If <code>reduction</code> is <code>'none'</code>, then \((N)\).</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>loss</span> <span class='op'>&lt;-</span> <span class='fu'>nn_margin_ranking_loss</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>input1</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>3</span>, requires_grad<span class='op'>=</span><span class='cn'>TRUE</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>input2</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>3</span>, requires_grad<span class='op'>=</span><span class='cn'>TRUE</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>target</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>3</span><span class='op'>)</span><span class='op'>$</span><span class='fu'>sign</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span> <span class='op'>&lt;-</span> <span class='fu'>loss</span><span class='op'>(</span><span class='va'>input1</span>, <span class='va'>input2</span>, <span class='va'>target</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span><span class='op'>$</span><span class='fu'>backward</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+</ul></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">loss</span> <span class="op">&lt;-</span> <span class="fu">nn_margin_ranking_loss</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">input1</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">3</span>, requires_grad <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">input2</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">3</span>, requires_grad <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">target</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">3</span><span class="op">)</span><span class="op">$</span><span class="fu">sign</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span> <span class="op">&lt;-</span> <span class="fu">loss</span><span class="op">(</span><span class="va">input1</span>, <span class="va">input2</span>, <span class="va">target</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span><span class="op">$</span><span class="fu">backward</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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-      <div class="copyright">
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+    <footer><div class="pkgdown-footer-left">
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index d6a472d93..2d9b42606 100644
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>MaxPool1D module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
-    <div class="hidden name"><code>nn_max_pool1d.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>MaxPool1D module</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-pooling.R" class="external-link"><code>R/nn-pooling.R</code></a></small>
+      <div class="d-none name"><code>nn_max_pool1d.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Applies a 1D max pooling over an input signal composed of several input
 planes.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_max_pool1d</span><span class='op'>(</span>
-  <span class='va'>kernel_size</span>,
-  stride <span class='op'>=</span> <span class='cn'>NULL</span>,
-  padding <span class='op'>=</span> <span class='fl'>0</span>,
-  dilation <span class='op'>=</span> <span class='fl'>1</span>,
-  return_indices <span class='op'>=</span> <span class='cn'>FALSE</span>,
-  ceil_mode <span class='op'>=</span> <span class='cn'>FALSE</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>the size of the window to take a max over</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>the stride of the window. Default value is <code>kernel_size</code></p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>implicit zero padding to be added on both sides</p></td>
-    </tr>
-    <tr>
-      <th>dilation</th>
-      <td><p>a parameter that controls the stride of elements in the window</p></td>
-    </tr>
-    <tr>
-      <th>return_indices</th>
-      <td><p>if <code>TRUE</code>, will return the max indices along with the outputs.
-Useful for  <code><a href='nn_max_unpool1d.html'>nn_max_unpool1d()</a></code> later.</p></td>
-    </tr>
-    <tr>
-      <th>ceil_mode</th>
-      <td><p>when <code>TRUE</code>, will use <code>ceil</code> instead of <code>floor</code> to compute the output shape</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_max_pool1d</span><span class="op">(</span>
+  <span class="va">kernel_size</span>,
+  stride <span class="op">=</span> <span class="cn">NULL</span>,
+  padding <span class="op">=</span> <span class="fl">0</span>,
+  dilation <span class="op">=</span> <span class="fl">1</span>,
+  return_indices <span class="op">=</span> <span class="cn">FALSE</span>,
+  ceil_mode <span class="op">=</span> <span class="cn">FALSE</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>kernel_size</dt>
+<dd><p>the size of the window to take a max over</p></dd>
+<dt>stride</dt>
+<dd><p>the stride of the window. Default value is <code>kernel_size</code></p></dd>
+<dt>padding</dt>
+<dd><p>implicit zero padding to be added on both sides</p></dd>
+<dt>dilation</dt>
+<dd><p>a parameter that controls the stride of elements in the window</p></dd>
+<dt>return_indices</dt>
+<dd><p>if <code>TRUE</code>, will return the max indices along with the outputs.
+Useful for  <code><a href="nn_max_unpool1d.html">nn_max_unpool1d()</a></code> later.</p></dd>
+<dt>ceil_mode</dt>
+<dd><p>when <code>TRUE</code>, will use <code>ceil</code> instead of <code>floor</code> to compute the output shape</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>In the simplest case, the output value of the layer with input size \((N, C, L)\)
 and output \((N, C, L_{out})\) can be precisely described as:</p>
 <p>$$
@@ -240,56 +121,48 @@ input(N_i, C_j, stride \times k + m)
 $$</p>
 <p>If <code>padding</code> is non-zero, then the input is implicitly zero-padded on both sides
 for <code>padding</code> number of points. <code>dilation</code> controls the spacing between the kernel points.
-It is harder to describe, but this <a href='https://github.com/vdumoulin/conv_arithmetic/blob/master/README.md'>link</a>
+It is harder to describe, but this <a href="https://github.com/vdumoulin/conv_arithmetic/blob/master/README.md" class="external-link">link</a>
 has a nice visualization of what <code>dilation</code> does.</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
     
 
-<ul>
-<li><p>Input: \((N, C, L_{in})\)</p></li>
+<ul><li><p>Input: \((N, C, L_{in})\)</p></li>
 <li><p>Output: \((N, C, L_{out})\), where</p></li>
-</ul>
-
-<p>$$
+</ul><p>$$
   L_{out} = \left\lfloor \frac{L_{in} + 2 \times \mbox{padding} - \mbox{dilation}
     \times (\mbox{kernel\_size} - 1) - 1}{\mbox{stride}} + 1\right\rfloor
 $$</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='co'># pool of size=3, stride=2</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_max_pool1d</span><span class='op'>(</span><span class='fl'>3</span>, stride<span class='op'>=</span><span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>50</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="co"># pool of size=3, stride=2</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">nn_max_pool1d</span><span class="op">(</span><span class="fl">3</span>, stride <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">20</span>, <span class="fl">16</span>, <span class="fl">50</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span> <span class="op">&lt;-</span> <span class="fu">m</span><span class="op">(</span><span class="va">input</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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diff --git a/reference/nn_max_pool2d.html b/reference/nn_max_pool2d.html
index cc350669c..0d51ced0c 100644
--- a/reference/nn_max_pool2d.html
+++ b/reference/nn_max_pool2d.html
@@ -1,237 +1,118 @@
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-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>MaxPool2D module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
-    <div class="hidden name"><code>nn_max_pool2d.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>MaxPool2D module</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-pooling.R" class="external-link"><code>R/nn-pooling.R</code></a></small>
+      <div class="d-none name"><code>nn_max_pool2d.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Applies a 2D max pooling over an input signal composed of several input
 planes.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_max_pool2d</span><span class='op'>(</span>
-  <span class='va'>kernel_size</span>,
-  stride <span class='op'>=</span> <span class='cn'>NULL</span>,
-  padding <span class='op'>=</span> <span class='fl'>0</span>,
-  dilation <span class='op'>=</span> <span class='fl'>1</span>,
-  return_indices <span class='op'>=</span> <span class='cn'>FALSE</span>,
-  ceil_mode <span class='op'>=</span> <span class='cn'>FALSE</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>the size of the window to take a max over</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>the stride of the window. Default value is <code>kernel_size</code></p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>implicit zero padding to be added on both sides</p></td>
-    </tr>
-    <tr>
-      <th>dilation</th>
-      <td><p>a parameter that controls the stride of elements in the window</p></td>
-    </tr>
-    <tr>
-      <th>return_indices</th>
-      <td><p>if <code>TRUE</code>, will return the max indices along with the outputs.
-Useful for <code><a href='nn_max_unpool2d.html'>nn_max_unpool2d()</a></code> later.</p></td>
-    </tr>
-    <tr>
-      <th>ceil_mode</th>
-      <td><p>when <code>TRUE</code>, will use <code>ceil</code> instead of <code>floor</code> to compute the output shape</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_max_pool2d</span><span class="op">(</span>
+  <span class="va">kernel_size</span>,
+  stride <span class="op">=</span> <span class="cn">NULL</span>,
+  padding <span class="op">=</span> <span class="fl">0</span>,
+  dilation <span class="op">=</span> <span class="fl">1</span>,
+  return_indices <span class="op">=</span> <span class="cn">FALSE</span>,
+  ceil_mode <span class="op">=</span> <span class="cn">FALSE</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>kernel_size</dt>
+<dd><p>the size of the window to take a max over</p></dd>
+<dt>stride</dt>
+<dd><p>the stride of the window. Default value is <code>kernel_size</code></p></dd>
+<dt>padding</dt>
+<dd><p>implicit zero padding to be added on both sides</p></dd>
+<dt>dilation</dt>
+<dd><p>a parameter that controls the stride of elements in the window</p></dd>
+<dt>return_indices</dt>
+<dd><p>if <code>TRUE</code>, will return the max indices along with the outputs.
+Useful for <code><a href="nn_max_unpool2d.html">nn_max_unpool2d()</a></code> later.</p></dd>
+<dt>ceil_mode</dt>
+<dd><p>when <code>TRUE</code>, will use <code>ceil</code> instead of <code>floor</code> to compute the output shape</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>In the simplest case, the output value of the layer with input size \((N, C, H, W)\),
 output \((N, C, H_{out}, W_{out})\) and <code>kernel_size</code> \((kH, kW)\)
 can be precisely described as:</p>
@@ -245,22 +126,17 @@ $$</p>
 <p>If <code>padding</code> is non-zero, then the input is implicitly zero-padded on both sides
 for <code>padding</code> number of points. <code>dilation</code> controls the spacing between the kernel points.
 It is harder to describe, but this <code>link</code> has a nice visualization of what <code>dilation</code> does.</p>
-<p>The parameters <code>kernel_size</code>, <code>stride</code>, <code>padding</code>, <code>dilation</code> can either be:</p><ul>
-<li><p>a single <code>int</code> -- in which case the same value is used for the height and width dimension</p></li>
+<p>The parameters <code>kernel_size</code>, <code>stride</code>, <code>padding</code>, <code>dilation</code> can either be:</p><ul><li><p>a single <code>int</code> -- in which case the same value is used for the height and width dimension</p></li>
 <li><p>a <code>tuple</code> of two ints -- in which case, the first <code>int</code> is used for the height dimension,
 and the second <code>int</code> for the width dimension</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
+</ul></div>
+    <div class="section level2">
+    <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
     
 
-<ul>
-<li><p>Input: \((N, C, H_{in}, W_{in})\)</p></li>
+<ul><li><p>Input: \((N, C, H_{in}, W_{in})\)</p></li>
 <li><p>Output: \((N, C, H_{out}, W_{out})\), where</p></li>
-</ul>
-
-<p>$$
+</ul><p>$$
   H_{out} = \left\lfloor\frac{H_{in} + 2 * \mbox{padding[0]} - \mbox{dilation[0]}
     \times (\mbox{kernel\_size[0]} - 1) - 1}{\mbox{stride[0]}} + 1\right\rfloor
 $$</p>
@@ -268,43 +144,37 @@ $$</p>
   W_{out} = \left\lfloor\frac{W_{in} + 2 * \mbox{padding[1]} - \mbox{dilation[1]}
     \times (\mbox{kernel\_size[1]} - 1) - 1}{\mbox{stride[1]}} + 1\right\rfloor
 $$</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='co'># pool of square window of size=3, stride=2</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_max_pool2d</span><span class='op'>(</span><span class='fl'>3</span>, stride<span class='op'>=</span><span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='co'># pool of non-square window</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_max_pool2d</span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>2</span><span class='op'>)</span>, stride<span class='op'>=</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>1</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>50</span>, <span class='fl'>32</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="co"># pool of square window of size=3, stride=2</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">nn_max_pool2d</span><span class="op">(</span><span class="fl">3</span>, stride <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="co"># pool of non-square window</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">nn_max_pool2d</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">2</span><span class="op">)</span>, stride <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">1</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">20</span>, <span class="fl">16</span>, <span class="fl">50</span>, <span class="fl">32</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span> <span class="op">&lt;-</span> <span class="fu">m</span><span class="op">(</span><span class="va">input</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="In the simplest case, the output value of the layer with input size \((N, C, D, H, W)\),
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Applies a 3D max pooling over an input signal composed of several input
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Applies a 3D max pooling over an input signal composed of several input
 planes.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
-    <div class="hidden name"><code>nn_max_pool3d.Rd</code></div>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-pooling.R" class="external-link"><code>R/nn-pooling.R</code></a></small>
+      <div class="d-none name"><code>nn_max_pool3d.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>In the simplest case, the output value of the layer with input size \((N, C, D, H, W)\),
 output \((N, C, D_{out}, H_{out}, W_{out})\) and <code>kernel_size</code> \((kD, kH, kW)\)
 can be precisely described as:</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_max_pool3d</span><span class='op'>(</span>
-  <span class='va'>kernel_size</span>,
-  stride <span class='op'>=</span> <span class='cn'>NULL</span>,
-  padding <span class='op'>=</span> <span class='fl'>0</span>,
-  dilation <span class='op'>=</span> <span class='fl'>1</span>,
-  return_indices <span class='op'>=</span> <span class='cn'>FALSE</span>,
-  ceil_mode <span class='op'>=</span> <span class='cn'>FALSE</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>the size of the window to take a max over</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>the stride of the window. Default value is <code>kernel_size</code></p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>implicit zero padding to be added on all three sides</p></td>
-    </tr>
-    <tr>
-      <th>dilation</th>
-      <td><p>a parameter that controls the stride of elements in the window</p></td>
-    </tr>
-    <tr>
-      <th>return_indices</th>
-      <td><p>if <code>TRUE</code>, will return the max indices along with the outputs.
-Useful for <code>torch_nn.MaxUnpool3d</code> later</p></td>
-    </tr>
-    <tr>
-      <th>ceil_mode</th>
-      <td><p>when TRUE, will use <code>ceil</code> instead of <code>floor</code> to compute the output shape</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_max_pool3d</span><span class="op">(</span>
+  <span class="va">kernel_size</span>,
+  stride <span class="op">=</span> <span class="cn">NULL</span>,
+  padding <span class="op">=</span> <span class="fl">0</span>,
+  dilation <span class="op">=</span> <span class="fl">1</span>,
+  return_indices <span class="op">=</span> <span class="cn">FALSE</span>,
+  ceil_mode <span class="op">=</span> <span class="cn">FALSE</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>kernel_size</dt>
+<dd><p>the size of the window to take a max over</p></dd>
+<dt>stride</dt>
+<dd><p>the stride of the window. Default value is <code>kernel_size</code></p></dd>
+<dt>padding</dt>
+<dd><p>implicit zero padding to be added on all three sides</p></dd>
+<dt>dilation</dt>
+<dd><p>a parameter that controls the stride of elements in the window</p></dd>
+<dt>return_indices</dt>
+<dd><p>if <code>TRUE</code>, will return the max indices along with the outputs.
+Useful for <code>torch_nn.MaxUnpool3d</code> later</p></dd>
+<dt>ceil_mode</dt>
+<dd><p>when TRUE, will use <code>ceil</code> instead of <code>floor</code> to compute the output shape</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>$$
 \begin{array}{ll}
 \mbox{out}(N_i, C_j, d, h, w) = &amp; \max_{k=0, \ldots, kD-1} \max_{m=0, \ldots, kH-1} \max_{n=0, \ldots, kW-1} \\
@@ -246,26 +128,21 @@ $$</p>
 <p>If <code>padding</code> is non-zero, then the input is implicitly zero-padded on both sides
 for <code>padding</code> number of points. <code>dilation</code> controls the spacing between the kernel points.
 It is harder to describe, but this <code>link</code>_ has a nice visualization of what <code>dilation</code> does.
-The parameters <code>kernel_size</code>, <code>stride</code>, <code>padding</code>, <code>dilation</code> can either be:</p><ul>
-<li><p>a single <code>int</code> -- in which case the same value is used for the depth, height and width dimension</p></li>
+The parameters <code>kernel_size</code>, <code>stride</code>, <code>padding</code>, <code>dilation</code> can either be:</p><ul><li><p>a single <code>int</code> -- in which case the same value is used for the depth, height and width dimension</p></li>
 <li><p>a <code>tuple</code> of three ints -- in which case, the first <code>int</code> is used for the depth dimension,
 the second <code>int</code> for the height dimension and the third <code>int</code> for the width dimension</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
+</ul></div>
+    <div class="section level2">
+    <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
     
 
-<ul>
-<li><p>Input: \((N, C, D_{in}, H_{in}, W_{in})\)</p></li>
+<ul><li><p>Input: \((N, C, D_{in}, H_{in}, W_{in})\)</p></li>
 <li><p>Output: \((N, C, D_{out}, H_{out}, W_{out})\), where
 $$
   D_{out} = \left\lfloor\frac{D_{in} + 2 \times \mbox{padding}[0] - \mbox{dilation}[0] \times
     (\mbox{kernel\_size}[0] - 1) - 1}{\mbox{stride}[0]} + 1\right\rfloor
 $$</p></li>
-</ul>
-
-<p>$$
+</ul><p>$$
   H_{out} = \left\lfloor\frac{H_{in} + 2 \times \mbox{padding}[1] - \mbox{dilation}[1] \times
     (\mbox{kernel\_size}[1] - 1) - 1}{\mbox{stride}[1]} + 1\right\rfloor
 $$</p>
@@ -273,43 +150,37 @@ $$</p>
   W_{out} = \left\lfloor\frac{W_{in} + 2 \times \mbox{padding}[2] - \mbox{dilation}[2] \times
     (\mbox{kernel\_size}[2] - 1) - 1}{\mbox{stride}[2]} + 1\right\rfloor
 $$</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='co'># pool of square window of size=3, stride=2</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_max_pool3d</span><span class='op'>(</span><span class='fl'>3</span>, stride<span class='op'>=</span><span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='co'># pool of non-square window</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_max_pool3d</span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>2</span>, <span class='fl'>2</span><span class='op'>)</span>, stride<span class='op'>=</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>1</span>, <span class='fl'>2</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>50</span>,<span class='fl'>44</span>, <span class='fl'>31</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="co"># pool of square window of size=3, stride=2</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">nn_max_pool3d</span><span class="op">(</span><span class="fl">3</span>, stride <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="co"># pool of non-square window</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">nn_max_pool3d</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">2</span>, <span class="fl">2</span><span class="op">)</span>, stride <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">1</span>, <span class="fl">2</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">20</span>, <span class="fl">16</span>, <span class="fl">50</span>, <span class="fl">44</span>, <span class="fl">31</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span> <span class="op">&lt;-</span> <span class="fu">m</span><span class="op">(</span><span class="va">input</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer>
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index be1927641..706ecabd8 100644
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Computes a partial inverse of <code>MaxPool1d</code>.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
-    <div class="hidden name"><code>nn_max_unpool1d.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Computes a partial inverse of <code>MaxPool1d</code>.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-pooling.R" class="external-link"><code>R/nn-pooling.R</code></a></small>
+      <div class="d-none name"><code>nn_max_unpool1d.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p><code>MaxPool1d</code> is not fully invertible, since the non-maximal values are lost.
 <code>MaxUnpool1d</code> takes in as input the output of <code>MaxPool1d</code>
 including the indices of the maximal values and computes a partial inverse
 in which all non-maximal values are set to zero.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_max_unpool1d</span><span class='op'>(</span><span class='va'>kernel_size</span>, stride <span class='op'>=</span> <span class='cn'>NULL</span>, padding <span class='op'>=</span> <span class='fl'>0</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>(int or tuple): Size of the max pooling window.</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>(int or tuple): Stride of the max pooling window.
-It is set to <code>kernel_size</code> by default.</p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>(int or tuple): Padding that was added to the input</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_max_unpool1d</span><span class="op">(</span><span class="va">kernel_size</span>, stride <span class="op">=</span> <span class="cn">NULL</span>, padding <span class="op">=</span> <span class="fl">0</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>kernel_size</dt>
+<dd><p>(int or tuple): Size of the max pooling window.</p></dd>
+<dt>stride</dt>
+<dd><p>(int or tuple): Stride of the max pooling window.
+It is set to <code>kernel_size</code> by default.</p></dd>
+<dt>padding</dt>
+<dd><p>(int or tuple): Padding that was added to the input</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p><code>MaxPool1d</code> can map several input sizes to the same output
 sizes. Hence, the inversion process can get ambiguous.
 To accommodate this, you can provide the needed output size
 as an additional argument <code>output_size</code> in the forward call.
 See the Inputs and Example below.</p>
-    <h2 class="hasAnchor" id="inputs"><a class="anchor" href="#inputs"></a>Inputs</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="inputs">Inputs<a class="anchor" aria-label="anchor" href="#inputs"></a></h2>
     
 
-<ul>
-<li><p><code>input</code>: the input Tensor to invert</p></li>
-<li><p><code>indices</code>: the indices given out by <code><a href='nn_max_pool1d.html'>nn_max_pool1d()</a></code></p></li>
+<ul><li><p><code>input</code>: the input Tensor to invert</p></li>
+<li><p><code>indices</code>: the indices given out by <code><a href="nn_max_pool1d.html">nn_max_pool1d()</a></code></p></li>
 <li><p><code>output_size</code> (optional): the targeted output size</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
+</ul></div>
+    <div class="section level2">
+    <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
     
 
-<ul>
-<li><p>Input: \((N, C, H_{in})\)</p></li>
+<ul><li><p>Input: \((N, C, H_{in})\)</p></li>
 <li><p>Output: \((N, C, H_{out})\), where
 $$
   H_{out} = (H_{in} - 1) \times \mbox{stride}[0] - 2 \times \mbox{padding}[0] + \mbox{kernel\_size}[0]
 $$
 or as given by <code>output_size</code> in the call operator</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>pool</span> <span class='op'>&lt;-</span> <span class='fu'><a href='nn_max_pool1d.html'>nn_max_pool1d</a></span><span class='op'>(</span><span class='fl'>2</span>, stride<span class='op'>=</span><span class='fl'>2</span>, return_indices<span class='op'>=</span><span class='cn'>TRUE</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>unpool</span> <span class='op'>&lt;-</span> <span class='fu'>nn_max_unpool1d</span><span class='op'>(</span><span class='fl'>2</span>, stride<span class='op'>=</span><span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/array.html'>array</a></span><span class='op'>(</span><span class='fl'>1</span><span class='op'>:</span><span class='fl'>8</span><span class='op'>/</span><span class='fl'>1</span>, dim <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>,<span class='fl'>1</span>,<span class='fl'>8</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>out</span> <span class='op'>&lt;-</span> <span class='fu'>pool</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>unpool</span><span class='op'>(</span><span class='va'>out</span><span class='op'>[[</span><span class='fl'>1</span><span class='op'>]</span><span class='op'>]</span>, <span class='va'>out</span><span class='op'>[[</span><span class='fl'>2</span><span class='op'>]</span><span class='op'>]</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='co'># Example showcasing the use of output_size</span></span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/array.html'>array</a></span><span class='op'>(</span><span class='fl'>1</span><span class='op'>:</span><span class='fl'>8</span><span class='op'>/</span><span class='fl'>1</span>, dim <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>,<span class='fl'>1</span>,<span class='fl'>8</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>out</span> <span class='op'>&lt;-</span> <span class='fu'>pool</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>unpool</span><span class='op'>(</span><span class='va'>out</span><span class='op'>[[</span><span class='fl'>1</span><span class='op'>]</span><span class='op'>]</span>, <span class='va'>out</span><span class='op'>[[</span><span class='fl'>2</span><span class='op'>]</span><span class='op'>]</span>, output_size<span class='op'>=</span><span class='va'>input</span><span class='op'>$</span><span class='fu'>size</span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>unpool</span><span class='op'>(</span><span class='va'>out</span><span class='op'>[[</span><span class='fl'>1</span><span class='op'>]</span><span class='op'>]</span>, <span class='va'>out</span><span class='op'>[[</span><span class='fl'>2</span><span class='op'>]</span><span class='op'>]</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> (1,1,.,.) = </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   2</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   4</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   6</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   8</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{1,1,8,1} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+</ul></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">pool</span> <span class="op">&lt;-</span> <span class="fu"><a href="nn_max_pool1d.html">nn_max_pool1d</a></span><span class="op">(</span><span class="fl">2</span>, stride <span class="op">=</span> <span class="fl">2</span>, return_indices <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">unpool</span> <span class="op">&lt;-</span> <span class="fu">nn_max_unpool1d</span><span class="op">(</span><span class="fl">2</span>, stride <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/array.html" class="external-link">array</a></span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="fl">8</span> <span class="op">/</span> <span class="fl">1</span>, dim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">1</span>, <span class="fl">8</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">out</span> <span class="op">&lt;-</span> <span class="fu">pool</span><span class="op">(</span><span class="va">input</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">unpool</span><span class="op">(</span><span class="va">out</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span>, <span class="va">out</span><span class="op">[[</span><span class="fl">2</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="co"># Example showcasing the use of output_size</span></span>
+<span class="r-in"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/array.html" class="external-link">array</a></span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="fl">8</span> <span class="op">/</span> <span class="fl">1</span>, dim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">1</span>, <span class="fl">8</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">out</span> <span class="op">&lt;-</span> <span class="fu">pool</span><span class="op">(</span><span class="va">input</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">unpool</span><span class="op">(</span><span class="va">out</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span>, <span class="va">out</span><span class="op">[[</span><span class="fl">2</span><span class="op">]</span><span class="op">]</span>, output_size <span class="op">=</span> <span class="va">input</span><span class="op">$</span><span class="fu">size</span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">unpool</span><span class="op">(</span><span class="va">out</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span>, <span class="va">out</span><span class="op">[[</span><span class="fl">2</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> (1,1,.,.) = </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   2</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   4</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   6</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   8</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{1,1,8,1} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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-<meta property="og:title" content="Computes a partial inverse of MaxPool2d. — nn_max_unpool2d" />
-<meta property="og:description" content="MaxPool2d is not fully invertible, since the non-maximal values are lost.
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="MaxPool2d is not fully invertible, since the non-maximal values are lost.
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+in which all non-maximal values are set to zero."><title>Computes a partial inverse of MaxPool2d. — nn_max_unpool2d • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.1.0/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.1.0/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Computes a partial inverse of MaxPool2d. — nn_max_unpool2d"><meta property="og:description" content="MaxPool2d is not fully invertible, since the non-maximal values are lost.
+MaxUnpool2d takes in as input the output of MaxPool2d
+including the indices of the maximal values and computes a partial inverse
+in which all non-maximal values are set to zero."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Computes a partial inverse of <code>MaxPool2d</code>.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
-    <div class="hidden name"><code>nn_max_unpool2d.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Computes a partial inverse of <code>MaxPool2d</code>.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-pooling.R" class="external-link"><code>R/nn-pooling.R</code></a></small>
+      <div class="d-none name"><code>nn_max_unpool2d.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p><code>MaxPool2d</code> is not fully invertible, since the non-maximal values are lost.
 <code>MaxUnpool2d</code> takes in as input the output of <code>MaxPool2d</code>
 including the indices of the maximal values and computes a partial inverse
 in which all non-maximal values are set to zero.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_max_unpool2d</span><span class='op'>(</span><span class='va'>kernel_size</span>, stride <span class='op'>=</span> <span class='cn'>NULL</span>, padding <span class='op'>=</span> <span class='fl'>0</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>(int or tuple): Size of the max pooling window.</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>(int or tuple): Stride of the max pooling window.
-It is set to <code>kernel_size</code> by default.</p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>(int or tuple): Padding that was added to the input</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_max_unpool2d</span><span class="op">(</span><span class="va">kernel_size</span>, stride <span class="op">=</span> <span class="cn">NULL</span>, padding <span class="op">=</span> <span class="fl">0</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>kernel_size</dt>
+<dd><p>(int or tuple): Size of the max pooling window.</p></dd>
+<dt>stride</dt>
+<dd><p>(int or tuple): Stride of the max pooling window.
+It is set to <code>kernel_size</code> by default.</p></dd>
+<dt>padding</dt>
+<dd><p>(int or tuple): Padding that was added to the input</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p><code>MaxPool2d</code> can map several input sizes to the same output
 sizes. Hence, the inversion process can get ambiguous.
 To accommodate this, you can provide the needed output size
 as an additional argument <code>output_size</code> in the forward call.
 See the Inputs and Example below.</p>
-    <h2 class="hasAnchor" id="inputs"><a class="anchor" href="#inputs"></a>Inputs</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="inputs">Inputs<a class="anchor" aria-label="anchor" href="#inputs"></a></h2>
     
 
-<ul>
-<li><p><code>input</code>: the input Tensor to invert</p></li>
-<li><p><code>indices</code>: the indices given out by <code><a href='nn_max_pool2d.html'>nn_max_pool2d()</a></code></p></li>
+<ul><li><p><code>input</code>: the input Tensor to invert</p></li>
+<li><p><code>indices</code>: the indices given out by <code><a href="nn_max_pool2d.html">nn_max_pool2d()</a></code></p></li>
 <li><p><code>output_size</code> (optional): the targeted output size</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
+</ul></div>
+    <div class="section level2">
+    <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
     
 
-<ul>
-<li><p>Input: \((N, C, H_{in}, W_{in})\)</p></li>
+<ul><li><p>Input: \((N, C, H_{in}, W_{in})\)</p></li>
 <li><p>Output: \((N, C, H_{out}, W_{out})\), where
 $$
   H_{out} = (H_{in} - 1) \times \mbox{stride[0]} - 2 \times \mbox{padding[0]} + \mbox{kernel\_size[0]}
@@ -246,56 +133,48 @@ $$
   W_{out} = (W_{in} - 1) \times \mbox{stride[1]} - 2 \times \mbox{padding[1]} + \mbox{kernel\_size[1]}
 $$
 or as given by <code>output_size</code> in the call operator</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>pool</span> <span class='op'>&lt;-</span> <span class='fu'><a href='nn_max_pool2d.html'>nn_max_pool2d</a></span><span class='op'>(</span><span class='fl'>2</span>, stride<span class='op'>=</span><span class='fl'>2</span>, return_indices<span class='op'>=</span><span class='cn'>TRUE</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>unpool</span> <span class='op'>&lt;-</span> <span class='fu'>nn_max_unpool2d</span><span class='op'>(</span><span class='fl'>2</span>, stride<span class='op'>=</span><span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>1</span>,<span class='fl'>1</span>,<span class='fl'>4</span>,<span class='fl'>4</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>out</span> <span class='op'>&lt;-</span> <span class='fu'>pool</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>unpool</span><span class='op'>(</span><span class='va'>out</span><span class='op'>[[</span><span class='fl'>1</span><span class='op'>]</span><span class='op'>]</span>, <span class='va'>out</span><span class='op'>[[</span><span class='fl'>2</span><span class='op'>]</span><span class='op'>]</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='co'># specify a different output size than input size</span></span>
-<span class='r-in'><span class='fu'>unpool</span><span class='op'>(</span><span class='va'>out</span><span class='op'>[[</span><span class='fl'>1</span><span class='op'>]</span><span class='op'>]</span>, <span class='va'>out</span><span class='op'>[[</span><span class='fl'>2</span><span class='op'>]</span><span class='op'>]</span>, output_size<span class='op'>=</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>1</span>, <span class='fl'>5</span>, <span class='fl'>5</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> (1,1,.,.) = </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   0.0000  0.0000  0.0000  0.0000  0.6747</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   0.0000  1.5900  0.0000  0.0000  2.3131</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   0.0000  0.0000  0.0000  0.0000  0.0000</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   1.1270  0.0000  0.0000  0.0000  0.0000</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   0.0000  0.0000  0.0000  0.0000  0.0000</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{1,1,5,5} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+</ul></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">pool</span> <span class="op">&lt;-</span> <span class="fu"><a href="nn_max_pool2d.html">nn_max_pool2d</a></span><span class="op">(</span><span class="fl">2</span>, stride <span class="op">=</span> <span class="fl">2</span>, return_indices <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">unpool</span> <span class="op">&lt;-</span> <span class="fu">nn_max_unpool2d</span><span class="op">(</span><span class="fl">2</span>, stride <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">1</span>, <span class="fl">4</span>, <span class="fl">4</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">out</span> <span class="op">&lt;-</span> <span class="fu">pool</span><span class="op">(</span><span class="va">input</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">unpool</span><span class="op">(</span><span class="va">out</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span>, <span class="va">out</span><span class="op">[[</span><span class="fl">2</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="co"># specify a different output size than input size</span></span>
+<span class="r-in"><span class="fu">unpool</span><span class="op">(</span><span class="va">out</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span>, <span class="va">out</span><span class="op">[[</span><span class="fl">2</span><span class="op">]</span><span class="op">]</span>, output_size <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">1</span>, <span class="fl">5</span>, <span class="fl">5</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> (1,1,.,.) = </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.0000  1.4613  1.4102  0.0000  0.0000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.0000  0.0000  0.0000  0.9929  0.0000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.0000  0.0000  0.0000  0.0000  0.0000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   1.3693  0.0000  0.0000  0.0000  0.0000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.0000  0.0000  0.0000  0.0000  0.0000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{1,1,5,5} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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 <div class="row">
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-    <div class="page-header">
-    <h1>Computes a partial inverse of <code>MaxPool3d</code>.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
-    <div class="hidden name"><code>nn_max_unpool3d.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Computes a partial inverse of <code>MaxPool3d</code>.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-pooling.R" class="external-link"><code>R/nn-pooling.R</code></a></small>
+      <div class="d-none name"><code>nn_max_unpool3d.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p><code>MaxPool3d</code> is not fully invertible, since the non-maximal values are lost.
 <code>MaxUnpool3d</code> takes in as input the output of <code>MaxPool3d</code>
 including the indices of the maximal values and computes a partial inverse
 in which all non-maximal values are set to zero.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_max_unpool3d</span><span class='op'>(</span><span class='va'>kernel_size</span>, stride <span class='op'>=</span> <span class='cn'>NULL</span>, padding <span class='op'>=</span> <span class='fl'>0</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>(int or tuple): Size of the max pooling window.</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>(int or tuple): Stride of the max pooling window.
-It is set to <code>kernel_size</code> by default.</p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>(int or tuple): Padding that was added to the input</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_max_unpool3d</span><span class="op">(</span><span class="va">kernel_size</span>, stride <span class="op">=</span> <span class="cn">NULL</span>, padding <span class="op">=</span> <span class="fl">0</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>kernel_size</dt>
+<dd><p>(int or tuple): Size of the max pooling window.</p></dd>
+<dt>stride</dt>
+<dd><p>(int or tuple): Stride of the max pooling window.
+It is set to <code>kernel_size</code> by default.</p></dd>
+<dt>padding</dt>
+<dd><p>(int or tuple): Padding that was added to the input</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p><code>MaxPool3d</code> can map several input sizes to the same output
 sizes. Hence, the inversion process can get ambiguous.
 To accommodate this, you can provide the needed output size
 as an additional argument <code>output_size</code> in the forward call.
 See the Inputs section below.</p>
-    <h2 class="hasAnchor" id="inputs"><a class="anchor" href="#inputs"></a>Inputs</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="inputs">Inputs<a class="anchor" aria-label="anchor" href="#inputs"></a></h2>
     
 
-<ul>
-<li><p><code>input</code>: the input Tensor to invert</p></li>
-<li><p><code>indices</code>: the indices given out by <code><a href='nn_max_pool3d.html'>nn_max_pool3d()</a></code></p></li>
+<ul><li><p><code>input</code>: the input Tensor to invert</p></li>
+<li><p><code>indices</code>: the indices given out by <code><a href="nn_max_pool3d.html">nn_max_pool3d()</a></code></p></li>
 <li><p><code>output_size</code> (optional): the targeted output size</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
+</ul></div>
+    <div class="section level2">
+    <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
     
 
-<ul>
-<li><p>Input: \((N, C, D_{in}, H_{in}, W_{in})\)</p></li>
+<ul><li><p>Input: \((N, C, D_{in}, H_{in}, W_{in})\)</p></li>
 <li><p>Output: \((N, C, D_{out}, H_{out}, W_{out})\), where</p></li>
-</ul>
-
-<p>$$
+</ul><p>$$
   D_{out} = (D_{in} - 1) \times \mbox{stride[0]} - 2 \times \mbox{padding[0]} + \mbox{kernel\_size[0]}
 $$
 $$
@@ -251,45 +136,39 @@ $$
   W_{out} = (W_{in} - 1) \times \mbox{stride[2]} - 2 \times \mbox{padding[2]} + \mbox{kernel\_size[2]}
 $$</p>
 <p>or as given by <code>output_size</code> in the call operator</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'>  </span>
-<span class='r-in'><span class='co'># pool of square window of size=3, stride=2</span></span>
-<span class='r-in'><span class='va'>pool</span> <span class='op'>&lt;-</span> <span class='fu'><a href='nn_max_pool3d.html'>nn_max_pool3d</a></span><span class='op'>(</span><span class='fl'>3</span>, stride<span class='op'>=</span><span class='fl'>2</span>, return_indices<span class='op'>=</span><span class='cn'>TRUE</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>unpool</span> <span class='op'>&lt;-</span> <span class='fu'>nn_max_unpool3d</span><span class='op'>(</span><span class='fl'>3</span>, stride<span class='op'>=</span><span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>out</span> <span class='op'>&lt;-</span> <span class='fu'>pool</span><span class='op'>(</span><span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>51</span>, <span class='fl'>33</span>, <span class='fl'>15</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>unpooled_output</span> <span class='op'>&lt;-</span> <span class='fu'>unpool</span><span class='op'>(</span><span class='va'>out</span><span class='op'>[[</span><span class='fl'>1</span><span class='op'>]</span><span class='op'>]</span>, <span class='va'>out</span><span class='op'>[[</span><span class='fl'>2</span><span class='op'>]</span><span class='op'>]</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>unpooled_output</span><span class='op'>$</span><span class='fu'>size</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [1] 20 16 51 33 15</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="co"># pool of square window of size=3, stride=2</span></span>
+<span class="r-in"><span class="va">pool</span> <span class="op">&lt;-</span> <span class="fu"><a href="nn_max_pool3d.html">nn_max_pool3d</a></span><span class="op">(</span><span class="fl">3</span>, stride <span class="op">=</span> <span class="fl">2</span>, return_indices <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">unpool</span> <span class="op">&lt;-</span> <span class="fu">nn_max_unpool3d</span><span class="op">(</span><span class="fl">3</span>, stride <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">out</span> <span class="op">&lt;-</span> <span class="fu">pool</span><span class="op">(</span><span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">20</span>, <span class="fl">16</span>, <span class="fl">51</span>, <span class="fl">33</span>, <span class="fl">15</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">unpooled_output</span> <span class="op">&lt;-</span> <span class="fu">unpool</span><span class="op">(</span><span class="va">out</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span>, <span class="va">out</span><span class="op">[[</span><span class="fl">2</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">unpooled_output</span><span class="op">$</span><span class="fu">size</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 20 16 51 33 15</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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index 785f5a2c8..b2d3ffb67 100644
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Base class for all neural network modules.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn.R'><code>R/nn.R</code></a></small>
-    <div class="hidden name"><code>nn_module.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Base class for all neural network modules.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn.R" class="external-link"><code>R/nn.R</code></a></small>
+      <div class="d-none name"><code>nn_module.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Your models should also subclass this class.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_module</span><span class='op'>(</span>
-  classname <span class='op'>=</span> <span class='cn'>NULL</span>,
-  inherit <span class='op'>=</span> <span class='va'>nn_Module</span>,
-  <span class='va'>...</span>,
-  private <span class='op'>=</span> <span class='cn'>NULL</span>,
-  active <span class='op'>=</span> <span class='cn'>NULL</span>,
-  parent_env <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/sys.parent.html'>parent.frame</a></span><span class='op'>(</span><span class='op'>)</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>classname</th>
-      <td><p>an optional name for the module</p></td>
-    </tr>
-    <tr>
-      <th>inherit</th>
-      <td><p>an optional module to inherit from</p></td>
-    </tr>
-    <tr>
-      <th>...</th>
-      <td><p>methods implementation</p></td>
-    </tr>
-    <tr>
-      <th>private</th>
-      <td><p>passed to <code><a href='https://r6.r-lib.org/reference/R6Class.html'>R6::R6Class()</a></code>.</p></td>
-    </tr>
-    <tr>
-      <th>active</th>
-      <td><p>passed to <code><a href='https://r6.r-lib.org/reference/R6Class.html'>R6::R6Class()</a></code>.</p></td>
-    </tr>
-    <tr>
-      <th>parent_env</th>
-      <td><p>passed to <code><a href='https://r6.r-lib.org/reference/R6Class.html'>R6::R6Class()</a></code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_module</span><span class="op">(</span>
+  classname <span class="op">=</span> <span class="cn">NULL</span>,
+  inherit <span class="op">=</span> <span class="va">nn_Module</span>,
+  <span class="va">...</span>,
+  private <span class="op">=</span> <span class="cn">NULL</span>,
+  active <span class="op">=</span> <span class="cn">NULL</span>,
+  parent_env <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sys.parent.html" class="external-link">parent.frame</a></span><span class="op">(</span><span class="op">)</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>classname</dt>
+<dd><p>an optional name for the module</p></dd>
+<dt>inherit</dt>
+<dd><p>an optional module to inherit from</p></dd>
+<dt>...</dt>
+<dd><p>methods implementation</p></dd>
+<dt>private</dt>
+<dd><p>passed to <code><a href="https://r6.r-lib.org/reference/R6Class.html" class="external-link">R6::R6Class()</a></code>.</p></dd>
+<dt>active</dt>
+<dd><p>passed to <code><a href="https://r6.r-lib.org/reference/R6Class.html" class="external-link">R6::R6Class()</a></code>.</p></dd>
+<dt>parent_env</dt>
+<dd><p>passed to <code><a href="https://r6.r-lib.org/reference/R6Class.html" class="external-link">R6::R6Class()</a></code>.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>Modules can also contain other Modules, allowing to nest them in a tree
 structure. You can assign the submodules as regular attributes.</p>
 <p>You are expected to implement the <code>initialize</code> and the <code>forward</code> to create a
 new <code>nn_module</code>.</p>
-    <h2 class="hasAnchor" id="initialize"><a class="anchor" href="#initialize"></a>Initialize</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="initialize">Initialize<a class="anchor" aria-label="anchor" href="#initialize"></a></h2>
     
 
 
 <p>The initialize function will be called whenever a new instance of the <code>nn_module</code>
 is created. We use the initialize functions to define submodules and parameters
-of the module. For example:</p><pre><span class='va'>initialize</span> <span class='op'>=</span> <span class='kw'>function</span><span class='op'>(</span><span class='va'>input_size</span>, <span class='va'>output_size</span><span class='op'>)</span> <span class='op'>{</span>
-   <span class='va'>self</span><span class='op'>$</span><span class='va'>conv1</span> <span class='op'>&lt;-</span> <span class='fu'><a href='nn_conv2d.html'>nn_conv2d</a></span><span class='op'>(</span><span class='va'>input_size</span>, <span class='va'>output_size</span>, <span class='fl'>5</span><span class='op'>)</span>
-   <span class='va'>self</span><span class='op'>$</span><span class='va'>conv2</span> <span class='op'>&lt;-</span> <span class='fu'><a href='nn_conv2d.html'>nn_conv2d</a></span><span class='op'>(</span><span class='va'>output_size</span>, <span class='va'>output_size</span>, <span class='fl'>5</span><span class='op'>)</span>
-<span class='op'>}</span>
-</pre>
+of the module. For example:</p><div class="sourceCode"><pre><code><span class="va">initialize</span> <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="va">input_size</span>, <span class="va">output_size</span><span class="op">)</span> <span class="op">{</span>
+   <span class="va">self</span><span class="op">$</span><span class="va">conv1</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/nn_conv2d.html">nn_conv2d</a></span><span class="op">(</span><span class="va">input_size</span>, <span class="va">output_size</span>, <span class="fl">5</span><span class="op">)</span>
+   <span class="va">self</span><span class="op">$</span><span class="va">conv2</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/nn_conv2d.html">nn_conv2d</a></span><span class="op">(</span><span class="va">output_size</span>, <span class="va">output_size</span>, <span class="fl">5</span><span class="op">)</span>
+<span class="op">}</span></code></pre></div>
 
 <p>The initialize function can have any number of parameters. All objects
 assigned to <code>self$</code> will be available for other methods that you implement.
-Tensors wrapped with <code><a href='nn_parameter.html'>nn_parameter()</a></code> or <code><a href='nn_buffer.html'>nn_buffer()</a></code> and submodules are
+Tensors wrapped with <code><a href="nn_parameter.html">nn_parameter()</a></code> or <code><a href="nn_buffer.html">nn_buffer()</a></code> and submodules are
 automatically tracked when assigned to <code>self$</code>.</p>
 <p>The initialize function is optional if the module you are defining doesn't
 have weights, submodules or buffers.</p>
-    <h2 class="hasAnchor" id="forward"><a class="anchor" href="#forward"></a>Forward</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="forward">Forward<a class="anchor" aria-label="anchor" href="#forward"></a></h2>
     
 
 
 <p>The forward method is called whenever an instance of <code>nn_module</code> is called.
 This is usually used to implement the computation that the module does with
 the weights ad submodules defined in the <code>initialize</code> function.</p>
-<p>For example:</p><pre><span class='va'>forward</span> <span class='op'>=</span> <span class='kw'>function</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span> <span class='op'>{</span>
-   <span class='va'>input</span> <span class='op'>&lt;-</span> <span class='va'>self</span><span class='op'>$</span><span class='fu'>conv1</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span>
-   <span class='va'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='nnf_relu.html'>nnf_relu</a></span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span>
-   <span class='va'>input</span> <span class='op'>&lt;-</span> <span class='va'>self</span><span class='op'>$</span><span class='fu'>conv2</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span>
-   <span class='va'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='nnf_relu.html'>nnf_relu</a></span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span>
-   <span class='va'>input</span>
- <span class='op'>}</span>
-</pre>
+<p>For example:</p><div class="sourceCode"><pre><code><span class="va">forward</span> <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="va">input</span><span class="op">)</span> <span class="op">{</span>
+   <span class="va">input</span> <span class="op">&lt;-</span> <span class="va">self</span><span class="op">$</span><span class="fu">conv1</span><span class="op">(</span><span class="va">input</span><span class="op">)</span>
+   <span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/nnf_relu.html">nnf_relu</a></span><span class="op">(</span><span class="va">input</span><span class="op">)</span>
+   <span class="va">input</span> <span class="op">&lt;-</span> <span class="va">self</span><span class="op">$</span><span class="fu">conv2</span><span class="op">(</span><span class="va">input</span><span class="op">)</span>
+   <span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/nnf_relu.html">nnf_relu</a></span><span class="op">(</span><span class="va">input</span><span class="op">)</span>
+   <span class="va">input</span>
+ <span class="op">}</span></code></pre></div>
 
 <p>The <code>forward</code> function can use the <code>self$training</code> attribute to make different
 computations depending wether the model is training or not, for example if you
 were implementing the dropout module.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>model</span> <span class='op'>&lt;-</span> <span class='fu'>nn_module</span><span class='op'>(</span></span>
-<span class='r-in'> initialize <span class='op'>=</span> <span class='kw'>function</span><span class='op'>(</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'>   <span class='va'>self</span><span class='op'>$</span><span class='va'>conv1</span> <span class='op'>&lt;-</span> <span class='fu'><a href='nn_conv2d.html'>nn_conv2d</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>20</span>, <span class='fl'>5</span><span class='op'>)</span></span>
-<span class='r-in'>   <span class='va'>self</span><span class='op'>$</span><span class='va'>conv2</span> <span class='op'>&lt;-</span> <span class='fu'><a href='nn_conv2d.html'>nn_conv2d</a></span><span class='op'>(</span><span class='fl'>20</span>, <span class='fl'>20</span>, <span class='fl'>5</span><span class='op'>)</span></span>
-<span class='r-in'> <span class='op'>}</span>,</span>
-<span class='r-in'> forward <span class='op'>=</span> <span class='kw'>function</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'>   <span class='va'>input</span> <span class='op'>&lt;-</span> <span class='va'>self</span><span class='op'>$</span><span class='fu'>conv1</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span></span>
-<span class='r-in'>   <span class='va'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='nnf_relu.html'>nnf_relu</a></span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span></span>
-<span class='r-in'>   <span class='va'>input</span> <span class='op'>&lt;-</span> <span class='va'>self</span><span class='op'>$</span><span class='fu'>conv2</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span></span>
-<span class='r-in'>   <span class='va'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='nnf_relu.html'>nnf_relu</a></span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span></span>
-<span class='r-in'>   <span class='va'>input</span></span>
-<span class='r-in'> <span class='op'>}</span></span>
-<span class='r-in'><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">model</span> <span class="op">&lt;-</span> <span class="fu">nn_module</span><span class="op">(</span></span>
+<span class="r-in">  initialize <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in">    <span class="va">self</span><span class="op">$</span><span class="va">conv1</span> <span class="op">&lt;-</span> <span class="fu"><a href="nn_conv2d.html">nn_conv2d</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">20</span>, <span class="fl">5</span><span class="op">)</span></span>
+<span class="r-in">    <span class="va">self</span><span class="op">$</span><span class="va">conv2</span> <span class="op">&lt;-</span> <span class="fu"><a href="nn_conv2d.html">nn_conv2d</a></span><span class="op">(</span><span class="fl">20</span>, <span class="fl">20</span>, <span class="fl">5</span><span class="op">)</span></span>
+<span class="r-in">  <span class="op">}</span>,</span>
+<span class="r-in">  forward <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="va">input</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in">    <span class="va">input</span> <span class="op">&lt;-</span> <span class="va">self</span><span class="op">$</span><span class="fu">conv1</span><span class="op">(</span><span class="va">input</span><span class="op">)</span></span>
+<span class="r-in">    <span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="nnf_relu.html">nnf_relu</a></span><span class="op">(</span><span class="va">input</span><span class="op">)</span></span>
+<span class="r-in">    <span class="va">input</span> <span class="op">&lt;-</span> <span class="va">self</span><span class="op">$</span><span class="fu">conv2</span><span class="op">(</span><span class="va">input</span><span class="op">)</span></span>
+<span class="r-in">    <span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="nnf_relu.html">nnf_relu</a></span><span class="op">(</span><span class="va">input</span><span class="op">)</span></span>
+<span class="r-in">    <span class="va">input</span></span>
+<span class="r-in">  <span class="op">}</span></span>
+<span class="r-in"><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer>
-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
 </div>
 
-<div class="pkgdown">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p>
+<div class="pkgdown-footer-right">
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.2.</p>
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diff --git a/reference/nn_module_list.html b/reference/nn_module_list.html
index a65667cc6..ca40e4155 100644
--- a/reference/nn_module_list.html
+++ b/reference/nn_module_list.html
@@ -1,251 +1,137 @@
-<!-- Generated by pkgdown: do not edit by hand -->
 <!DOCTYPE html>
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-<meta http-equiv="X-UA-Compatible" content="IE=edge">
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-<title>Holds submodules in a list. — nn_module_list • torch</title>
-
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-<!-- jquery -->
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="nn_module_list can be indexed like a regular R list, but
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Holds submodules in a list.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn.R'><code>R/nn.R</code></a></small>
-    <div class="hidden name"><code>nn_module_list.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Holds submodules in a list.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn.R" class="external-link"><code>R/nn.R</code></a></small>
+      <div class="d-none name"><code>nn_module_list.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>nn_module_list can be indexed like a regular R list, but
 modules it contains are properly registered, and will be visible by all
 <code>nn_module</code> methods.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_module_list</span><span class='op'>(</span>modules <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>modules</th>
-      <td><p>a list of modules to add</p></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>my_module</span> <span class='op'>&lt;-</span> <span class='fu'><a href='nn_module.html'>nn_module</a></span><span class='op'>(</span></span>
-<span class='r-in'> initialize <span class='op'>=</span> <span class='kw'>function</span><span class='op'>(</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'>   <span class='va'>self</span><span class='op'>$</span><span class='va'>linears</span> <span class='op'>&lt;-</span> <span class='fu'>nn_module_list</span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/lapply.html'>lapply</a></span><span class='op'>(</span><span class='fl'>1</span><span class='op'>:</span><span class='fl'>10</span>, <span class='kw'>function</span><span class='op'>(</span><span class='va'>x</span><span class='op'>)</span> <span class='fu'><a href='nn_linear.html'>nn_linear</a></span><span class='op'>(</span><span class='fl'>10</span>, <span class='fl'>10</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'> <span class='op'>}</span>,</span>
-<span class='r-in'> forward <span class='op'>=</span> <span class='kw'>function</span><span class='op'>(</span><span class='va'>x</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'>  <span class='kw'>for</span> <span class='op'>(</span><span class='va'>i</span> <span class='kw'>in</span> <span class='fl'>1</span><span class='op'>:</span><span class='fu'><a href='https://rdrr.io/r/base/length.html'>length</a></span><span class='op'>(</span><span class='va'>self</span><span class='op'>$</span><span class='va'>linears</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'>    <span class='va'>x</span> <span class='op'>&lt;-</span> <span class='va'>self</span><span class='op'>$</span><span class='va'>linears</span><span class='op'>[[</span><span class='va'>i</span><span class='op'>]</span><span class='op'>]</span><span class='op'>(</span><span class='va'>x</span><span class='op'>)</span></span>
-<span class='r-in'>  <span class='va'>x</span></span>
-<span class='r-in'> <span class='op'>}</span></span>
-<span class='r-in'><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_module_list</span><span class="op">(</span>modules <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>modules</dt>
+<dd><p>a list of modules to add</p></dd>
+</dl></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">my_module</span> <span class="op">&lt;-</span> <span class="fu"><a href="nn_module.html">nn_module</a></span><span class="op">(</span></span>
+<span class="r-in">  initialize <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in">    <span class="va">self</span><span class="op">$</span><span class="va">linears</span> <span class="op">&lt;-</span> <span class="fu">nn_module_list</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="fl">10</span>, <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="fu"><a href="nn_linear.html">nn_linear</a></span><span class="op">(</span><span class="fl">10</span>, <span class="fl">10</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in">  <span class="op">}</span>,</span>
+<span class="r-in">  forward <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in">    <span class="kw">for</span> <span class="op">(</span><span class="va">i</span> <span class="kw">in</span> <span class="fl">1</span><span class="op">:</span><span class="fu"><a href="https://rdrr.io/r/base/length.html" class="external-link">length</a></span><span class="op">(</span><span class="va">self</span><span class="op">$</span><span class="va">linears</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in">      <span class="va">x</span> <span class="op">&lt;-</span> <span class="va">self</span><span class="op">$</span><span class="va">linears</span><span class="op">[[</span><span class="va">i</span><span class="op">]</span><span class="op">]</span><span class="op">(</span><span class="va">x</span><span class="op">)</span></span>
+<span class="r-in">    <span class="op">}</span></span>
+<span class="r-in">    <span class="va">x</span></span>
+<span class="r-in">  <span class="op">}</span></span>
+<span class="r-in"><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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diff --git a/reference/nn_mse_loss.html b/reference/nn_mse_loss.html
index 7b218b6a3..ee99c3754 100644
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>MSE loss</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-loss.R'><code>R/nn-loss.R</code></a></small>
-    <div class="hidden name"><code>nn_mse_loss.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>MSE loss</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-loss.R" class="external-link"><code>R/nn-loss.R</code></a></small>
+      <div class="d-none name"><code>nn_mse_loss.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Creates a criterion that measures the mean squared error (squared L2 norm) between
 each element in the input \(x\) and target \(y\).
 The unreduced (i.e. with <code>reduction</code> set to <code>'none'</code>) loss can be described
 as:</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_mse_loss</span><span class='op'>(</span>reduction <span class='op'>=</span> <span class='st'>"mean"</span><span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_mse_loss</span><span class="op">(</span>reduction <span class="op">=</span> <span class="st">"mean"</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>reduction</th>
-      <td><p>(string, optional): Specifies the reduction to apply to the output:
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>reduction</dt>
+<dd><p>(string, optional): Specifies the reduction to apply to the output:
 <code>'none'</code> | <code>'mean'</code> | <code>'sum'</code>. <code>'none'</code>: no reduction will be applied,
 <code>'mean'</code>: the sum of the output will be divided by the number of
 elements in the output, <code>'sum'</code>: the output will be summed. Note: <code>size_average</code>
 and <code>reduce</code> are in the process of being deprecated, and in the meantime,
-specifying either of those two args will override <code>reduction</code>. Default: <code>'mean'</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
+specifying either of those two args will override <code>reduction</code>. Default: <code>'mean'</code></p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>$$
   \ell(x, y) = L = \{l_1,\dots,l_N\}^\top, \quad
 l_n = \left( x_n - y_n \right)^2,
@@ -230,52 +123,44 @@ $$</p>
 of \(n\) elements each.</p>
 <p>The mean operation still operates over all the elements, and divides by \(n\).
 The division by \(n\) can be avoided if one sets <code>reduction = 'sum'</code>.</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
     
 
-<ul>
-<li><p>Input: \((N, *)\) where \(*\) means, any number of additional
+<ul><li><p>Input: \((N, *)\) where \(*\) means, any number of additional
 dimensions</p></li>
 <li><p>Target: \((N, *)\), same shape as the input</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>loss</span> <span class='op'>&lt;-</span> <span class='fu'>nn_mse_loss</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>5</span>, requires_grad<span class='op'>=</span><span class='cn'>TRUE</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>target</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>5</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span> <span class='op'>&lt;-</span> <span class='fu'>loss</span><span class='op'>(</span><span class='va'>input</span>, <span class='va'>target</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span><span class='op'>$</span><span class='fu'>backward</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+</ul></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">loss</span> <span class="op">&lt;-</span> <span class="fu">nn_mse_loss</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">5</span>, requires_grad <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">target</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">5</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span> <span class="op">&lt;-</span> <span class="fu">loss</span><span class="op">(</span><span class="va">input</span>, <span class="va">target</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span><span class="op">$</span><span class="fu">backward</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer>
-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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diff --git a/reference/nn_multi_margin_loss.html b/reference/nn_multi_margin_loss.html
index 88ed21dab..ea14bf3dd 100644
--- a/reference/nn_multi_margin_loss.html
+++ b/reference/nn_multi_margin_loss.html
@@ -1,233 +1,120 @@
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-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Multi margin loss</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-loss.R'><code>R/nn-loss.R</code></a></small>
-    <div class="hidden name"><code>nn_multi_margin_loss.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Multi margin loss</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-loss.R" class="external-link"><code>R/nn-loss.R</code></a></small>
+      <div class="d-none name"><code>nn_multi_margin_loss.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Creates a criterion that optimizes a multi-class classification hinge
 loss (margin-based loss) between input \(x\) (a 2D mini-batch <code>Tensor</code>) and
 output \(y\) (which is a 1D tensor of target class indices,
 \(0 \leq y \leq \mbox{x.size}(1)-1\)):</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_multi_margin_loss</span><span class='op'>(</span>p <span class='op'>=</span> <span class='fl'>1</span>, margin <span class='op'>=</span> <span class='fl'>1</span>, weight <span class='op'>=</span> <span class='cn'>NULL</span>, reduction <span class='op'>=</span> <span class='st'>"mean"</span><span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_multi_margin_loss</span><span class="op">(</span>p <span class="op">=</span> <span class="fl">1</span>, margin <span class="op">=</span> <span class="fl">1</span>, weight <span class="op">=</span> <span class="cn">NULL</span>, reduction <span class="op">=</span> <span class="st">"mean"</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>p</th>
-      <td><p>(int, optional): Has a default value of \(1\). \(1\) and \(2\)
-are the only supported values.</p></td>
-    </tr>
-    <tr>
-      <th>margin</th>
-      <td><p>(float, optional): Has a default value of \(1\).</p></td>
-    </tr>
-    <tr>
-      <th>weight</th>
-      <td><p>(Tensor, optional): a manual rescaling weight given to each
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>p</dt>
+<dd><p>(int, optional): Has a default value of \(1\). \(1\) and \(2\)
+are the only supported values.</p></dd>
+<dt>margin</dt>
+<dd><p>(float, optional): Has a default value of \(1\).</p></dd>
+<dt>weight</dt>
+<dd><p>(Tensor, optional): a manual rescaling weight given to each
 class. If given, it has to be a Tensor of size <code>C</code>. Otherwise, it is
-treated as if having all ones.</p></td>
-    </tr>
-    <tr>
-      <th>reduction</th>
-      <td><p>(string, optional): Specifies the reduction to apply to the output:
+treated as if having all ones.</p></dd>
+<dt>reduction</dt>
+<dd><p>(string, optional): Specifies the reduction to apply to the output:
 <code>'none'</code> | <code>'mean'</code> | <code>'sum'</code>. <code>'none'</code>: no reduction will be applied,
 <code>'mean'</code>: the sum of the output will be divided by the number of
 elements in the output, <code>'sum'</code>: the output will be summed. Note: <code>size_average</code>
 and <code>reduce</code> are in the process of being deprecated, and in the meantime,
-specifying either of those two args will override <code>reduction</code>. Default: <code>'mean'</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
+specifying either of those two args will override <code>reduction</code>. Default: <code>'mean'</code></p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>For each mini-batch sample, the loss in terms of the 1D input \(x\) and scalar
 output \(y\) is:
 $$
@@ -241,32 +128,25 @@ The loss function then becomes:</p>
 <p>$$
   \mbox{loss}(x, y) = \frac{\sum_i \max(0, w[y] * (\mbox{margin} - x[y] + x[i]))^p)}{\mbox{x.size}(0)}
 $$</p>
+    </div>
 
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-      <h2 data-toc-skip>Contents</h2>
-    </nav>
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+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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diff --git a/reference/nn_multihead_attention.html b/reference/nn_multihead_attention.html
index a97f3bf3b..196959a28 100644
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>MultiHead attention</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
-    <div class="hidden name"><code>nn_multihead_attention.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>MultiHead attention</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-activation.R" class="external-link"><code>R/nn-activation.R</code></a></small>
+      <div class="d-none name"><code>nn_multihead_attention.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Allows the model to jointly attend to information
 from different representation subspaces.
 See reference: Attention Is All You Need</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_multihead_attention</span><span class='op'>(</span>
-  <span class='va'>embed_dim</span>,
-  <span class='va'>num_heads</span>,
-  dropout <span class='op'>=</span> <span class='fl'>0</span>,
-  bias <span class='op'>=</span> <span class='cn'>TRUE</span>,
-  add_bias_kv <span class='op'>=</span> <span class='cn'>FALSE</span>,
-  add_zero_attn <span class='op'>=</span> <span class='cn'>FALSE</span>,
-  kdim <span class='op'>=</span> <span class='cn'>NULL</span>,
-  vdim <span class='op'>=</span> <span class='cn'>NULL</span>
-<span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_multihead_attention</span><span class="op">(</span>
+  <span class="va">embed_dim</span>,
+  <span class="va">num_heads</span>,
+  dropout <span class="op">=</span> <span class="fl">0</span>,
+  bias <span class="op">=</span> <span class="cn">TRUE</span>,
+  add_bias_kv <span class="op">=</span> <span class="cn">FALSE</span>,
+  add_zero_attn <span class="op">=</span> <span class="cn">FALSE</span>,
+  kdim <span class="op">=</span> <span class="cn">NULL</span>,
+  vdim <span class="op">=</span> <span class="cn">NULL</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>embed_dim</th>
-      <td><p>total dimension of the model.</p></td>
-    </tr>
-    <tr>
-      <th>num_heads</th>
-      <td><p>parallel attention heads.</p></td>
-    </tr>
-    <tr>
-      <th>dropout</th>
-      <td><p>a Dropout layer on attn_output_weights. Default: 0.0.</p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>add bias as module parameter. Default: True.</p></td>
-    </tr>
-    <tr>
-      <th>add_bias_kv</th>
-      <td><p>add bias to the key and value sequences at dim=0.</p></td>
-    </tr>
-    <tr>
-      <th>add_zero_attn</th>
-      <td><p>add a new batch of zeros to the key and
-value sequences at dim=1.</p></td>
-    </tr>
-    <tr>
-      <th>kdim</th>
-      <td><p>total number of features in key. Default: <code>NULL</code></p></td>
-    </tr>
-    <tr>
-      <th>vdim</th>
-      <td><p>total number of features in value. Default: <code>NULL</code>.
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>embed_dim</dt>
+<dd><p>total dimension of the model.</p></dd>
+<dt>num_heads</dt>
+<dd><p>parallel attention heads.</p></dd>
+<dt>dropout</dt>
+<dd><p>a Dropout layer on attn_output_weights. Default: 0.0.</p></dd>
+<dt>bias</dt>
+<dd><p>add bias as module parameter. Default: True.</p></dd>
+<dt>add_bias_kv</dt>
+<dd><p>add bias to the key and value sequences at dim=0.</p></dd>
+<dt>add_zero_attn</dt>
+<dd><p>add a new batch of zeros to the key and
+value sequences at dim=1.</p></dd>
+<dt>kdim</dt>
+<dd><p>total number of features in key. Default: <code>NULL</code></p></dd>
+<dt>vdim</dt>
+<dd><p>total number of features in value. Default: <code>NULL</code>.
 Note: if kdim and vdim are <code>NULL</code>, they will be set to embed_dim such that
-query, key, and value have the same number of features.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
+query, key, and value have the same number of features.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>$$
   \mbox{MultiHead}(Q, K, V) = \mbox{Concat}(head_1,\dots,head_h)W^O
 \mbox{where} head_i = \mbox{Attention}(QW_i^Q, KW_i^K, VW_i^V)
 $$</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
     
 
 
-<p>Inputs:</p><ul>
-<li><p>query: \((L, N, E)\) where L is the target sequence length, N is the batch size, E is
+<p>Inputs:</p><ul><li><p>query: \((L, N, E)\) where L is the target sequence length, N is the batch size, E is
 the embedding dimension.</p></li>
 <li><p>key: \((S, N, E)\), where S is the source sequence length, N is the batch size, E is
 the embedding dimension.</p></li>
@@ -273,13 +151,9 @@ positions. If a ByteTensor is provided, the non-zero positions are not allowed t
 while the zero positions will be unchanged. If a BoolTensor is provided, positions with <code>True</code>
 is not allowed to attend while <code>False</code> values will be unchanged. If a FloatTensor
 is provided, it will be added to the attention weight.</p></li>
-</ul>
-
-<p>Outputs:</p><ul>
-<li><p>attn_output: \((L, N, E)\) where L is the target sequence length, N is
+</ul><p>Outputs:</p><ul><li><p>attn_output: \((L, N, E)\) where L is the target sequence length, N is
 the batch size, E is the embedding dimension.</p></li>
-<li><p>attn_output_weights:</p><ul>
-<li><p>if <code>avg_weights</code> is <code>TRUE</code> (the default), the output attention
+<li><p>attn_output_weights:</p><ul><li><p>if <code>avg_weights</code> is <code>TRUE</code> (the default), the output attention
 weights are averaged over the attention heads, giving a tensor of shape
 \((N, L, S)\) where N is the batch size, L is the target sequence
 length, S is the source sequence length.</p></li>
@@ -287,45 +161,38 @@ length, S is the source sequence length.</p></li>
 as-is, with shape \((N, H, L, S)\), where H is the number of attention
 heads.</p></li>
 </ul></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='cn'>FALSE</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>multihead_attn</span> <span class='op'>=</span> <span class='fu'>nn_multihead_attention</span><span class='op'>(</span><span class='va'>embed_dim</span>, <span class='va'>num_heads</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>out</span> <span class='op'>&lt;-</span> <span class='fu'>multihead_attn</span><span class='op'>(</span><span class='va'>query</span>, <span class='va'>key</span>, <span class='va'>value</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>attn_output</span> <span class='op'>&lt;-</span> <span class='va'>out</span><span class='op'>[[</span><span class='fl'>1</span><span class='op'>]</span><span class='op'>]</span></span>
-<span class='r-in'><span class='va'>attn_output_weights</span> <span class='op'>&lt;-</span> <span class='va'>out</span><span class='op'>[[</span><span class='fl'>2</span><span class='op'>]</span><span class='op'>]</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+</ul></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">multihead_attn</span> <span class="op">&lt;-</span> <span class="fu">nn_multihead_attention</span><span class="op">(</span><span class="va">embed_dim</span>, <span class="va">num_heads</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">out</span> <span class="op">&lt;-</span> <span class="fu">multihead_attn</span><span class="op">(</span><span class="va">query</span>, <span class="va">key</span>, <span class="va">value</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">attn_output</span> <span class="op">&lt;-</span> <span class="va">out</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span></span>
+<span class="r-in"><span class="va">attn_output_weights</span> <span class="op">&lt;-</span> <span class="va">out</span><span class="op">[[</span><span class="fl">2</span><span class="op">]</span><span class="op">]</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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index b0df131a0..203b9235e 100644
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+and output \(y\) (which is a 2D Tensor of target class indices).
+For each sample in the mini-batch:"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Multilabel margin loss</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-loss.R'><code>R/nn-loss.R</code></a></small>
-    <div class="hidden name"><code>nn_multilabel_margin_loss.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Multilabel margin loss</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-loss.R" class="external-link"><code>R/nn-loss.R</code></a></small>
+      <div class="d-none name"><code>nn_multilabel_margin_loss.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Creates a criterion that optimizes a multi-class multi-classification
 hinge loss (margin-based loss) between input \(x\) (a 2D mini-batch <code>Tensor</code>)
 and output \(y\) (which is a 2D <code>Tensor</code> of target class indices).
 For each sample in the mini-batch:</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_multilabel_margin_loss</span><span class='op'>(</span>reduction <span class='op'>=</span> <span class='st'>"mean"</span><span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_multilabel_margin_loss</span><span class="op">(</span>reduction <span class="op">=</span> <span class="st">"mean"</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>reduction</th>
-      <td><p>(string, optional): Specifies the reduction to apply to the output:
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>reduction</dt>
+<dd><p>(string, optional): Specifies the reduction to apply to the output:
 <code>'none'</code> | <code>'mean'</code> | <code>'sum'</code>. <code>'none'</code>: no reduction will be applied,
 <code>'mean'</code>: the sum of the output will be divided by the number of
 elements in the output, <code>'sum'</code>: the output will be summed. Note: <code>size_average</code>
 and <code>reduce</code> are in the process of being deprecated, and in the meantime,
-specifying either of those two args will override <code>reduction</code>. Default: <code>'mean'</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
+specifying either of those two args will override <code>reduction</code>. Default: <code>'mean'</code></p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>$$
   \mbox{loss}(x, y) = \sum_{ij}\frac{\max(0, 1 - (x[y[j]] - x[i]))}{\mbox{x.size}(0)}
 $$</p>
@@ -224,56 +117,48 @@ and \(i \neq y[j]\) for all \(i\) and \(j\).
 <p>The criterion only considers a contiguous block of non-negative targets that
 starts at the front.
 This allows for different samples to have variable amounts of target classes.</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
     
 
-<ul>
-<li><p>Input: \((C)\) or \((N, C)\) where <code>N</code> is the batch size and <code>C</code>
+<ul><li><p>Input: \((C)\) or \((N, C)\) where <code>N</code> is the batch size and <code>C</code>
 is the number of classes.</p></li>
 <li><p>Target: \((C)\) or \((N, C)\), label targets padded by -1 ensuring same shape as the input.</p></li>
 <li><p>Output: scalar. If <code>reduction</code> is <code>'none'</code>, then \((N)\).</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>loss</span> <span class='op'>&lt;-</span> <span class='fu'>nn_multilabel_margin_loss</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>x</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>0.1</span>, <span class='fl'>0.2</span>, <span class='fl'>0.4</span>, <span class='fl'>0.8</span><span class='op'>)</span><span class='op'>)</span><span class='op'>$</span><span class='fu'>view</span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>,<span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='co'># for target y, only consider labels 4 and 1, not after label -1</span></span>
-<span class='r-in'><span class='va'>y</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span>, <span class='fl'>1</span>, <span class='op'>-</span><span class='fl'>1</span>, <span class='fl'>2</span><span class='op'>)</span>, dtype <span class='op'>=</span> <span class='fu'><a href='torch_dtype.html'>torch_long</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span><span class='op'>$</span><span class='fu'>view</span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>,<span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>loss</span><span class='op'>(</span><span class='va'>x</span>, <span class='va'>y</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> 0.85</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{} ]</span>
-</pre>
-  </div>
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-    <nav id="toc" data-toggle="toc" class="sticky-top">
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+</ul></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">loss</span> <span class="op">&lt;-</span> <span class="fu">nn_multilabel_margin_loss</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.1</span>, <span class="fl">0.2</span>, <span class="fl">0.4</span>, <span class="fl">0.8</span><span class="op">)</span><span class="op">)</span><span class="op">$</span><span class="fu">view</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">4</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="co"># for target y, only consider labels 4 and 1, not after label -1</span></span>
+<span class="r-in"><span class="va">y</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span>, <span class="fl">1</span>, <span class="op">-</span><span class="fl">1</span>, <span class="fl">2</span><span class="op">)</span>, dtype <span class="op">=</span> <span class="fu"><a href="torch_dtype.html">torch_long</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span><span class="op">$</span><span class="fu">view</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">4</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">loss</span><span class="op">(</span><span class="va">x</span>, <span class="va">y</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 0.85</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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-    <h1>Multi label soft margin loss</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-loss.R'><code>R/nn-loss.R</code></a></small>
-    <div class="hidden name"><code>nn_multilabel_soft_margin_loss.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Multi label soft margin loss</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-loss.R" class="external-link"><code>R/nn-loss.R</code></a></small>
+      <div class="d-none name"><code>nn_multilabel_soft_margin_loss.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Creates a criterion that optimizes a multi-label one-versus-all
 loss based on max-entropy, between input \(x\) and target \(y\) of size
 \((N, C)\).</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_multilabel_soft_margin_loss</span><span class='op'>(</span>weight <span class='op'>=</span> <span class='cn'>NULL</span>, reduction <span class='op'>=</span> <span class='st'>"mean"</span><span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_multilabel_soft_margin_loss</span><span class="op">(</span>weight <span class="op">=</span> <span class="cn">NULL</span>, reduction <span class="op">=</span> <span class="st">"mean"</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>weight</th>
-      <td><p>(Tensor, optional): a manual rescaling weight given to each
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>weight</dt>
+<dd><p>(Tensor, optional): a manual rescaling weight given to each
 class. If given, it has to be a Tensor of size <code>C</code>. Otherwise, it is
-treated as if having all ones.</p></td>
-    </tr>
-    <tr>
-      <th>reduction</th>
-      <td><p>(string, optional): Specifies the reduction to apply to the output:
+treated as if having all ones.</p></dd>
+<dt>reduction</dt>
+<dd><p>(string, optional): Specifies the reduction to apply to the output:
 <code>'none'</code> | <code>'mean'</code> | <code>'sum'</code>. <code>'none'</code>: no reduction will be applied,
 <code>'mean'</code>: the sum of the output will be divided by the number of
 elements in the output, <code>'sum'</code>: the output will be summed. Note: <code>size_average</code>
 and <code>reduce</code> are in the process of being deprecated, and in the meantime,
-specifying either of those two args will override <code>reduction</code>. Default: <code>'mean'</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
+specifying either of those two args will override <code>reduction</code>. Default: <code>'mean'</code></p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>For each sample in the minibatch:</p>
 <p>$$
   loss(x, y) = - \frac{1}{C} * \sum_i y[i] * \log((1 + \exp(-x[i]))^{-1})
@@ -224,42 +114,33 @@ specifying either of those two args will override <code>reduction</code>. Defaul
 $$</p>
 <p>where \(i \in \left\{0, \; \cdots , \; \mbox{x.nElement}() - 1\right\}\),
 \(y[i] \in \left\{0, \; 1\right\}\).</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
     
 
-<ul>
-<li><p>Input: \((N, C)\) where <code>N</code> is the batch size and <code>C</code> is the number of classes.</p></li>
+<ul><li><p>Input: \((N, C)\) where <code>N</code> is the batch size and <code>C</code> is the number of classes.</p></li>
 <li><p>Target: \((N, C)\), label targets padded by -1 ensuring same shape as the input.</p></li>
 <li><p>Output: scalar. If <code>reduction</code> is <code>'none'</code>, then \((N)\).</p></li>
-</ul>
+</ul></div>
 
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
 
-
-      <footer>
-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
 </div>
 
-<div class="pkgdown">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p>
+<div class="pkgdown-footer-right">
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.2.</p>
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index 928721f5c..b437c2451 100644
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Nll loss</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-loss.R'><code>R/nn-loss.R</code></a></small>
-    <div class="hidden name"><code>nn_nll_loss.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Nll loss</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-loss.R" class="external-link"><code>R/nn-loss.R</code></a></small>
+      <div class="d-none name"><code>nn_nll_loss.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>The negative log likelihood loss. It is useful to train a classification
 problem with <code>C</code> classes.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_nll_loss</span><span class='op'>(</span>weight <span class='op'>=</span> <span class='cn'>NULL</span>, ignore_index <span class='op'>=</span> <span class='op'>-</span><span class='fl'>100</span>, reduction <span class='op'>=</span> <span class='st'>"mean"</span><span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_nll_loss</span><span class="op">(</span>weight <span class="op">=</span> <span class="cn">NULL</span>, ignore_index <span class="op">=</span> <span class="op">-</span><span class="fl">100</span>, reduction <span class="op">=</span> <span class="st">"mean"</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>weight</th>
-      <td><p>(Tensor, optional): a manual rescaling weight given to each
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>weight</dt>
+<dd><p>(Tensor, optional): a manual rescaling weight given to each
 class. If given, it has to be a Tensor of size <code>C</code>. Otherwise, it is
-treated as if having all ones.</p></td>
-    </tr>
-    <tr>
-      <th>ignore_index</th>
-      <td><p>(int, optional): Specifies a target value that is ignored
-and does not contribute to the input gradient.</p></td>
-    </tr>
-    <tr>
-      <th>reduction</th>
-      <td><p>(string, optional): Specifies the reduction to apply to the output:
+treated as if having all ones.</p></dd>
+<dt>ignore_index</dt>
+<dd><p>(int, optional): Specifies a target value that is ignored
+and does not contribute to the input gradient.</p></dd>
+<dt>reduction</dt>
+<dd><p>(string, optional): Specifies the reduction to apply to the output:
 <code>'none'</code> | <code>'mean'</code> | <code>'sum'</code>. <code>'none'</code>: no reduction will
 be applied, <code>'mean'</code>: the weighted mean of the output is taken,
 <code>'sum'</code>: the output will be summed. Note: <code>size_average</code>
 and <code>reduce</code> are in the process of being deprecated, and in
 the meantime, specifying either of those two args will override
-<code>reduction</code>. Default: <code>'mean'</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
+<code>reduction</code>. Default: <code>'mean'</code></p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>If provided, the optional argument <code>weight</code> should be a 1D Tensor assigning
 weight to each of the classes. This is particularly useful when you have an
 unbalanced training set.</p>
@@ -256,75 +143,67 @@ $$</p>
 an input of size \((minibatch, C, d_1, d_2, ..., d_K)\) with \(K \geq 1\),
 where \(K\) is the number of dimensions, and a target of appropriate shape
 (see below). In the case of images, it computes NLL loss per-pixel.</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
     
 
-<ul>
-<li><p>Input: \((N, C)\) where <code>C = number of classes</code>, or
+<ul><li><p>Input: \((N, C)\) where <code>C = number of classes</code>, or
 \((N, C, d_1, d_2, ..., d_K)\) with \(K \geq 1\)
 in the case of <code>K</code>-dimensional loss.</p></li>
 <li><p>Target: \((N)\) where each value is \(0 \leq \mbox{targets}[i] \leq C-1\), or
 \((N, d_1, d_2, ..., d_K)\) with \(K \geq 1\) in the case of
 K-dimensional loss.</p></li>
 <li><p>Output: scalar.</p></li>
-</ul>
-
-<p>If <code>reduction</code> is <code>'none'</code>, then the same size as the target: \((N)\), or
+</ul><p>If <code>reduction</code> is <code>'none'</code>, then the same size as the target: \((N)\), or
 \((N, d_1, d_2, ..., d_K)\) with \(K \geq 1\) in the case
 of K-dimensional loss.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>&lt;-</span> <span class='fu'><a href='nn_log_softmax.html'>nn_log_softmax</a></span><span class='op'>(</span>dim<span class='op'>=</span><span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>loss</span> <span class='op'>&lt;-</span> <span class='fu'>nn_nll_loss</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'><span class='co'># input is of size N x C = 3 x 5</span></span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>5</span>, requires_grad<span class='op'>=</span><span class='cn'>TRUE</span><span class='op'>)</span></span>
-<span class='r-in'><span class='co'># each element in target has to have 0 &lt;= value &lt; C</span></span>
-<span class='r-in'><span class='va'>target</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>1</span>, <span class='fl'>5</span><span class='op'>)</span>, dtype <span class='op'>=</span> <span class='fu'><a href='torch_dtype.html'>torch_long</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span> <span class='op'>&lt;-</span> <span class='fu'>loss</span><span class='op'>(</span><span class='fu'>m</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span>, <span class='va'>target</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span><span class='op'>$</span><span class='fu'>backward</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='co'># 2D loss example (used, for example, with image inputs)</span></span>
-<span class='r-in'><span class='va'>N</span> <span class='op'>&lt;-</span> <span class='fl'>5</span></span>
-<span class='r-in'><span class='va'>C</span> <span class='op'>&lt;-</span> <span class='fl'>4</span></span>
-<span class='r-in'><span class='va'>loss</span> <span class='op'>&lt;-</span> <span class='fu'>nn_nll_loss</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'><span class='co'># input is of size N x C x height x width</span></span>
-<span class='r-in'><span class='va'>data</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='va'>N</span>, <span class='fl'>16</span>, <span class='fl'>10</span>, <span class='fl'>10</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>conv</span> <span class='op'>&lt;-</span> <span class='fu'><a href='nn_conv2d.html'>nn_conv2d</a></span><span class='op'>(</span><span class='fl'>16</span>, <span class='va'>C</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>&lt;-</span> <span class='fu'><a href='nn_log_softmax.html'>nn_log_softmax</a></span><span class='op'>(</span>dim<span class='op'>=</span><span class='fl'>1</span><span class='op'>)</span></span>
-<span class='r-in'><span class='co'># each element in target has to have 0 &lt;= value &lt; C</span></span>
-<span class='r-in'><span class='va'>target</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span><span class='op'>(</span><span class='va'>N</span>, <span class='fl'>8</span>, <span class='fl'>8</span>, dtype<span class='op'>=</span><span class='fu'><a href='torch_dtype.html'>torch_long</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span><span class='op'>$</span><span class='fu'>random_</span><span class='op'>(</span><span class='fl'>1</span>, <span class='va'>C</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span> <span class='op'>&lt;-</span> <span class='fu'>loss</span><span class='op'>(</span><span class='fu'>m</span><span class='op'>(</span><span class='fu'>conv</span><span class='op'>(</span><span class='va'>data</span><span class='op'>)</span><span class='op'>)</span>, <span class='va'>target</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span><span class='op'>$</span><span class='fu'>backward</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu"><a href="nn_log_softmax.html">nn_log_softmax</a></span><span class="op">(</span>dim <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">loss</span> <span class="op">&lt;-</span> <span class="fu">nn_nll_loss</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="co"># input is of size N x C = 3 x 5</span></span>
+<span class="r-in"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">5</span>, requires_grad <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
+<span class="r-in"><span class="co"># each element in target has to have 0 &lt;= value &lt; C</span></span>
+<span class="r-in"><span class="va">target</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">1</span>, <span class="fl">5</span><span class="op">)</span>, dtype <span class="op">=</span> <span class="fu"><a href="torch_dtype.html">torch_long</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span> <span class="op">&lt;-</span> <span class="fu">loss</span><span class="op">(</span><span class="fu">m</span><span class="op">(</span><span class="va">input</span><span class="op">)</span>, <span class="va">target</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span><span class="op">$</span><span class="fu">backward</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="co"># 2D loss example (used, for example, with image inputs)</span></span>
+<span class="r-in"><span class="va">N</span> <span class="op">&lt;-</span> <span class="fl">5</span></span>
+<span class="r-in"><span class="va">C</span> <span class="op">&lt;-</span> <span class="fl">4</span></span>
+<span class="r-in"><span class="va">loss</span> <span class="op">&lt;-</span> <span class="fu">nn_nll_loss</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="co"># input is of size N x C x height x width</span></span>
+<span class="r-in"><span class="va">data</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="va">N</span>, <span class="fl">16</span>, <span class="fl">10</span>, <span class="fl">10</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">conv</span> <span class="op">&lt;-</span> <span class="fu"><a href="nn_conv2d.html">nn_conv2d</a></span><span class="op">(</span><span class="fl">16</span>, <span class="va">C</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu"><a href="nn_log_softmax.html">nn_log_softmax</a></span><span class="op">(</span>dim <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span>
+<span class="r-in"><span class="co"># each element in target has to have 0 &lt;= value &lt; C</span></span>
+<span class="r-in"><span class="va">target</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_empty.html">torch_empty</a></span><span class="op">(</span><span class="va">N</span>, <span class="fl">8</span>, <span class="fl">8</span>, dtype <span class="op">=</span> <span class="fu"><a href="torch_dtype.html">torch_long</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span><span class="op">$</span><span class="fu">random_</span><span class="op">(</span><span class="fl">1</span>, <span class="va">C</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span> <span class="op">&lt;-</span> <span class="fu">loss</span><span class="op">(</span><span class="fu">m</span><span class="op">(</span><span class="fu">conv</span><span class="op">(</span><span class="va">data</span><span class="op">)</span><span class="op">)</span>, <span class="va">target</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span><span class="op">$</span><span class="fu">backward</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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-      <div class="copyright">
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+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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+<div class="pkgdown-footer-right">
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index b9095b3f8..a1cda07b2 100644
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+++ b/reference/nn_pairwise_distance.html
@@ -1,267 +1,146 @@
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Pairwise distance</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-distance.R'><code>R/nn-distance.R</code></a></small>
-    <div class="hidden name"><code>nn_pairwise_distance.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Pairwise distance</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-distance.R" class="external-link"><code>R/nn-distance.R</code></a></small>
+      <div class="d-none name"><code>nn_pairwise_distance.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Computes the batchwise pairwise distance between vectors \(v_1\), \(v_2\)
 using the p-norm:</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_pairwise_distance</span><span class='op'>(</span>p <span class='op'>=</span> <span class='fl'>2</span>, eps <span class='op'>=</span> <span class='fl'>1e-06</span>, keepdim <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>p</th>
-      <td><p>(real): the norm degree. Default: 2</p></td>
-    </tr>
-    <tr>
-      <th>eps</th>
-      <td><p>(float, optional): Small value to avoid division by zero.
-Default: 1e-6</p></td>
-    </tr>
-    <tr>
-      <th>keepdim</th>
-      <td><p>(bool, optional): Determines whether or not to keep the vector dimension.
-Default: FALSE</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_pairwise_distance</span><span class="op">(</span>p <span class="op">=</span> <span class="fl">2</span>, eps <span class="op">=</span> <span class="fl">1e-06</span>, keepdim <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>p</dt>
+<dd><p>(real): the norm degree. Default: 2</p></dd>
+<dt>eps</dt>
+<dd><p>(float, optional): Small value to avoid division by zero.
+Default: 1e-6</p></dd>
+<dt>keepdim</dt>
+<dd><p>(bool, optional): Determines whether or not to keep the vector dimension.
+Default: FALSE</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>$$
  \Vert x \Vert _p = \left( \sum_{i=1}^n  \vert x_i \vert ^ p \right) ^ {1/p}.
 $$</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
     
 
-<ul>
-<li><p>Input1: \((N, D)\) where <code>D = vector dimension</code></p></li>
+<ul><li><p>Input1: \((N, D)\) where <code>D = vector dimension</code></p></li>
 <li><p>Input2: \((N, D)\), same shape as the Input1</p></li>
 <li><p>Output: \((N)\). If <code>keepdim</code> is <code>TRUE</code>, then \((N, 1)\).</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>pdist</span> <span class='op'>&lt;-</span> <span class='fu'>nn_pairwise_distance</span><span class='op'>(</span>p<span class='op'>=</span><span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>input1</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>100</span>, <span class='fl'>128</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>input2</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>100</span>, <span class='fl'>128</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span> <span class='op'>&lt;-</span> <span class='fu'>pdist</span><span class='op'>(</span><span class='va'>input1</span>, <span class='va'>input2</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+</ul></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">pdist</span> <span class="op">&lt;-</span> <span class="fu">nn_pairwise_distance</span><span class="op">(</span>p <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">input1</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">100</span>, <span class="fl">128</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">input2</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">100</span>, <span class="fl">128</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span> <span class="op">&lt;-</span> <span class="fu">pdist</span><span class="op">(</span><span class="va">input1</span>, <span class="va">input2</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
+    </div>
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-    <pre class="usage"><span class='fu'>nn_parameter</span><span class='op'>(</span><span class='va'>x</span>, requires_grad <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span></pre>
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+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_parameter</span><span class="op">(</span><span class="va">x</span>, requires_grad <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></code></pre></div>
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-      <th>requires_grad</th>
-      <td><p>whether this parameter should  have
-<code>requires_grad = TRUE</code></p></td>
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-    </table>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>x</dt>
+<dd><p>the tensor that you want to indicate as parameter</p></dd>
+<dt>requires_grad</dt>
+<dd><p>whether this parameter should  have
+<code>requires_grad = TRUE</code></p></dd>
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+    
+  </div>
+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Poisson NLL loss</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-loss.R'><code>R/nn-loss.R</code></a></small>
-    <div class="hidden name"><code>nn_poisson_nll_loss.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Poisson NLL loss</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-loss.R" class="external-link"><code>R/nn-loss.R</code></a></small>
+      <div class="d-none name"><code>nn_poisson_nll_loss.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Negative log likelihood loss with Poisson distribution of target.
 The loss can be described as:</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_poisson_nll_loss</span><span class='op'>(</span>
-  log_input <span class='op'>=</span> <span class='cn'>TRUE</span>,
-  full <span class='op'>=</span> <span class='cn'>FALSE</span>,
-  eps <span class='op'>=</span> <span class='fl'>1e-08</span>,
-  reduction <span class='op'>=</span> <span class='st'>"mean"</span>
-<span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_poisson_nll_loss</span><span class="op">(</span>
+  log_input <span class="op">=</span> <span class="cn">TRUE</span>,
+  full <span class="op">=</span> <span class="cn">FALSE</span>,
+  eps <span class="op">=</span> <span class="fl">1e-08</span>,
+  reduction <span class="op">=</span> <span class="st">"mean"</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>log_input</th>
-      <td><p>(bool, optional): if <code>TRUE</code> the loss is computed as
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>log_input</dt>
+<dd><p>(bool, optional): if <code>TRUE</code> the loss is computed as
 \(\exp(\mbox{input}) - \mbox{target}*\mbox{input}\), if <code>FALSE</code> the loss is
-\(\mbox{input} - \mbox{target}*\log(\mbox{input}+\mbox{eps})\).</p></td>
-    </tr>
-    <tr>
-      <th>full</th>
-      <td><p>(bool, optional): whether to compute full loss, i. e. to add the
+\(\mbox{input} - \mbox{target}*\log(\mbox{input}+\mbox{eps})\).</p></dd>
+<dt>full</dt>
+<dd><p>(bool, optional): whether to compute full loss, i. e. to add the
 Stirling approximation term
-\(\mbox{target}*\log(\mbox{target}) - \mbox{target} + 0.5 * \log(2\pi\mbox{target})\).</p></td>
-    </tr>
-    <tr>
-      <th>eps</th>
-      <td><p>(float, optional): Small value to avoid evaluation of \(\log(0)\) when
-<code>log_input = FALSE</code>. Default: 1e-8</p></td>
-    </tr>
-    <tr>
-      <th>reduction</th>
-      <td><p>(string, optional): Specifies the reduction to apply to the output:
+\(\mbox{target}*\log(\mbox{target}) - \mbox{target} + 0.5 * \log(2\pi\mbox{target})\).</p></dd>
+<dt>eps</dt>
+<dd><p>(float, optional): Small value to avoid evaluation of \(\log(0)\) when
+<code>log_input = FALSE</code>. Default: 1e-8</p></dd>
+<dt>reduction</dt>
+<dd><p>(string, optional): Specifies the reduction to apply to the output:
 <code>'none'</code> | <code>'mean'</code> | <code>'sum'</code>. <code>'none'</code>: no reduction will be applied,
 <code>'mean'</code>: the sum of the output will be divided by the number of
 elements in the output, <code>'sum'</code>: the output will be summed. Note: <code>size_average</code>
 and <code>reduce</code> are in the process of being deprecated, and in the meantime,
-specifying either of those two args will override <code>reduction</code>. Default: <code>'mean'</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
+specifying either of those two args will override <code>reduction</code>. Default: <code>'mean'</code></p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>$$
 \mbox{target} \sim \mathrm{Poisson}(\mbox{input})
 \mbox{loss}(\mbox{input}, \mbox{target}) = \mbox{input} - \mbox{target} * \log(\mbox{input})
@@ -239,54 +124,46 @@ $$</p>
 <p>The last term can be omitted or approximated with Stirling formula. The
 approximation is used for target values more than 1. For targets less or
 equal to 1 zeros are added to the loss.</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
     
 
-<ul>
-<li><p>Input: \((N, *)\) where \(*\) means, any number of additional
+<ul><li><p>Input: \((N, *)\) where \(*\) means, any number of additional
 dimensions</p></li>
 <li><p>Target: \((N, *)\), same shape as the input</p></li>
 <li><p>Output: scalar by default. If <code>reduction</code> is <code>'none'</code>, then \((N, *)\),
 the same shape as the input</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>loss</span> <span class='op'>&lt;-</span> <span class='fu'>nn_poisson_nll_loss</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>log_input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>5</span>, <span class='fl'>2</span>, requires_grad<span class='op'>=</span><span class='cn'>TRUE</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>target</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>5</span>, <span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span> <span class='op'>&lt;-</span> <span class='fu'>loss</span><span class='op'>(</span><span class='va'>log_input</span>, <span class='va'>target</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span><span class='op'>$</span><span class='fu'>backward</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+</ul></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">loss</span> <span class="op">&lt;-</span> <span class="fu">nn_poisson_nll_loss</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">log_input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">5</span>, <span class="fl">2</span>, requires_grad <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">target</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">5</span>, <span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span> <span class="op">&lt;-</span> <span class="fu">loss</span><span class="op">(</span><span class="va">log_input</span>, <span class="va">target</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span><span class="op">$</span><span class="fu">backward</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer>
-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
 </div>
 
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+<div class="pkgdown-footer-right">
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diff --git a/reference/nn_prelu.html b/reference/nn_prelu.html
index 7e32a12e7..3e225d2cf 100644
--- a/reference/nn_prelu.html
+++ b/reference/nn_prelu.html
@@ -1,48 +1,5 @@
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-  
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-<meta property="og:title" content="PReLU module — nn_prelu" />
-<meta property="og:description" content="Applies the element-wise function:
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Applies the element-wise function:
 $$
   \mbox{PReLU}(x) = \max(0,x) + a * \min(0,x)
 $$
@@ -54,150 +11,96 @@ x, &amp;amp; \mbox{ if } x \geq 0 \\
 ax, &amp;amp; \mbox{ otherwise }
 \end{array}
 \right.
-$$" />
-
-
-
-
-<!-- mathjax -->
-<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script>
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-
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+$$"><title>PReLU module — nn_prelu • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.1.0/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.1.0/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="PReLU module — nn_prelu"><meta property="og:description" content="Applies the element-wise function:
+$$
+  \mbox{PReLU}(x) = \max(0,x) + a * \min(0,x)
+$$
+or
+$$
+  \mbox{PReLU}(x) =
+  \left\{ \begin{array}{ll}
+x, &amp;amp; \mbox{ if } x \geq 0 \\
+ax, &amp;amp; \mbox{ otherwise }
+\end{array}
+\right.
+$$"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>PReLU module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
-    <div class="hidden name"><code>nn_prelu.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>PReLU module</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-activation.R" class="external-link"><code>R/nn-activation.R</code></a></small>
+      <div class="d-none name"><code>nn_prelu.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Applies the element-wise function:
 $$
   \mbox{PReLU}(x) = \max(0,x) + a * \min(0,x)
@@ -213,85 +116,73 @@ ax, &amp; \mbox{ otherwise }
 $$</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_prelu</span><span class='op'>(</span>num_parameters <span class='op'>=</span> <span class='fl'>1</span>, init <span class='op'>=</span> <span class='fl'>0.25</span><span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_prelu</span><span class="op">(</span>num_parameters <span class="op">=</span> <span class="fl">1</span>, init <span class="op">=</span> <span class="fl">0.25</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>num_parameters</th>
-      <td><p>(int): number of \(a\) to learn.
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>num_parameters</dt>
+<dd><p>(int): number of \(a\) to learn.
 Although it takes an int as input, there is only two values are legitimate:
-1, or the number of channels at input. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>init</th>
-      <td><p>(float): the initial value of \(a\). Default: 0.25</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
+1, or the number of channels at input. Default: 1</p></dd>
+<dt>init</dt>
+<dd><p>(float): the initial value of \(a\). Default: 0.25</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>Here \(a\) is a learnable parameter. When called without arguments, <code>nn.prelu()</code> uses a single
 parameter \(a\) across all input channels. If called with <code>nn_prelu(nChannels)</code>,
 a separate \(a\) is used for each input channel.</p>
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p>weight decay should not be used when learning \(a\) for good performance.</p>
 <p>Channel dim is the 2nd dim of input. When input has dims &lt; 2, then there is
 no channel dim and the number of channels = 1.</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
     
 
-<ul>
-<li><p>Input: \((N, *)\) where <code>*</code> means, any number of additional
+<ul><li><p>Input: \((N, *)\) where <code>*</code> means, any number of additional
 dimensions</p></li>
 <li><p>Output: \((N, *)\), same shape as the input</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="attributes"><a class="anchor" href="#attributes"></a>Attributes</h2>
-
+</ul></div>
+    <div class="section level2">
+    <h2 id="attributes">Attributes<a class="anchor" aria-label="anchor" href="#attributes"></a></h2>
     
 
-<ul>
-<li><p>weight (Tensor): the learnable weights of shape (<code>num_parameters</code>).</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_prelu</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+<ul><li><p>weight (Tensor): the learnable weights of shape (<code>num_parameters</code>).</p></li>
+</ul></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">nn_prelu</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span> <span class="op">&lt;-</span> <span class="fu">m</span><span class="op">(</span><span class="va">input</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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index 32acbe8d5..a42e5cc96 100644
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+$$\mbox{ReLU}(x) = (x)^+ = \max(0, x)$$"><title>ReLU module — nn_relu • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.1.0/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.1.0/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="ReLU module — nn_relu"><meta property="og:description" content="Applies the rectified linear unit function element-wise
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>ReLU module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
-    <div class="hidden name"><code>nn_relu.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>ReLU module</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-activation.R" class="external-link"><code>R/nn-activation.R</code></a></small>
+      <div class="d-none name"><code>nn_relu.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Applies the rectified linear unit function element-wise
 $$\mbox{ReLU}(x) = (x)^+ = \max(0, x)$$</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_relu</span><span class='op'>(</span>inplace <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>inplace</th>
-      <td><p>can optionally do the operation in-place. Default: <code>FALSE</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_relu</span><span class="op">(</span>inplace <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>inplace</dt>
+<dd><p>can optionally do the operation in-place. Default: <code>FALSE</code></p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
     
 
-<ul>
-<li><p>Input: \((N, *)\) where <code>*</code> means, any number of additional
+<ul><li><p>Input: \((N, *)\) where <code>*</code> means, any number of additional
 dimensions</p></li>
 <li><p>Output: \((N, *)\), same shape as the input</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_relu</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>m</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  2.0717</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.3631</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{2} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+</ul></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">nn_relu</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">m</span><span class="op">(</span><span class="va">input</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.5247</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2} ]</span>
+</code></pre></div>
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-    <h1>ReLu6 module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
-    <div class="hidden name"><code>nn_relu6.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>ReLu6 module</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-activation.R" class="external-link"><code>R/nn-activation.R</code></a></small>
+      <div class="d-none name"><code>nn_relu6.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Applies the element-wise function:</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_relu6</span><span class='op'>(</span>inplace <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>inplace</th>
-      <td><p>can optionally do the operation in-place. Default: <code>FALSE</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_relu6</span><span class="op">(</span>inplace <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>inplace</dt>
+<dd><p>can optionally do the operation in-place. Default: <code>FALSE</code></p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>$$
   \mbox{ReLU6}(x) = \min(\max(0,x), 6)
 $$</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
     
 
-<ul>
-<li><p>Input: \((N, *)\) where <code>*</code> means, any number of additional
+<ul><li><p>Input: \((N, *)\) where <code>*</code> means, any number of additional
 dimensions</p></li>
 <li><p>Output: \((N, *)\), same shape as the input</p></li>
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-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_relu6</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
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+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">nn_relu6</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span> <span class="op">&lt;-</span> <span class="fu">m</span><span class="op">(</span><span class="va">input</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
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+  <a class="nav-link" href="../news/index.html">Changelog</a>
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-        <li>
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-    <span class="fab fa-github fa-lg"></span>
+      </ul><form class="form-inline my-2 my-lg-0" role="search">
+        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
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+      </ul></div>
 
+    
+  </div>
+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>RNN module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-rnn.R'><code>R/nn-rnn.R</code></a></small>
-    <div class="hidden name"><code>nn_rnn.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>RNN module</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-rnn.R" class="external-link"><code>R/nn-rnn.R</code></a></small>
+      <div class="d-none name"><code>nn_rnn.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Applies a multi-layer Elman RNN with \(\tanh\) or \(\mbox{ReLU}\) non-linearity
 to an input sequence.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_rnn</span><span class='op'>(</span>
-  <span class='va'>input_size</span>,
-  <span class='va'>hidden_size</span>,
-  num_layers <span class='op'>=</span> <span class='fl'>1</span>,
-  nonlinearity <span class='op'>=</span> <span class='cn'>NULL</span>,
-  bias <span class='op'>=</span> <span class='cn'>TRUE</span>,
-  batch_first <span class='op'>=</span> <span class='cn'>FALSE</span>,
-  dropout <span class='op'>=</span> <span class='fl'>0</span>,
-  bidirectional <span class='op'>=</span> <span class='cn'>FALSE</span>,
-  <span class='va'>...</span>
-<span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_rnn</span><span class="op">(</span>
+  <span class="va">input_size</span>,
+  <span class="va">hidden_size</span>,
+  num_layers <span class="op">=</span> <span class="fl">1</span>,
+  nonlinearity <span class="op">=</span> <span class="cn">NULL</span>,
+  bias <span class="op">=</span> <span class="cn">TRUE</span>,
+  batch_first <span class="op">=</span> <span class="cn">FALSE</span>,
+  dropout <span class="op">=</span> <span class="fl">0</span>,
+  bidirectional <span class="op">=</span> <span class="cn">FALSE</span>,
+  <span class="va">...</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input_size</th>
-      <td><p>The number of expected features in the input <code>x</code></p></td>
-    </tr>
-    <tr>
-      <th>hidden_size</th>
-      <td><p>The number of features in the hidden state <code>h</code></p></td>
-    </tr>
-    <tr>
-      <th>num_layers</th>
-      <td><p>Number of recurrent layers. E.g., setting <code>num_layers=2</code>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input_size</dt>
+<dd><p>The number of expected features in the input <code>x</code></p></dd>
+<dt>hidden_size</dt>
+<dd><p>The number of features in the hidden state <code>h</code></p></dd>
+<dt>num_layers</dt>
+<dd><p>Number of recurrent layers. E.g., setting <code>num_layers=2</code>
 would mean stacking two RNNs together to form a <code>stacked RNN</code>,
 with the second RNN taking in outputs of the first RNN and
-computing the final results. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>nonlinearity</th>
-      <td><p>The non-linearity to use. Can be either <code>'tanh'</code> or
-<code>'relu'</code>. Default: <code>'tanh'</code></p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>If <code>FALSE</code>, then the layer does not use bias weights <code>b_ih</code> and
-<code>b_hh</code>. Default: <code>TRUE</code></p></td>
-    </tr>
-    <tr>
-      <th>batch_first</th>
-      <td><p>If <code>TRUE</code>, then the input and output tensors are provided
-as <code>(batch, seq, feature)</code>. Default: <code>FALSE</code></p></td>
-    </tr>
-    <tr>
-      <th>dropout</th>
-      <td><p>If non-zero, introduces a <code>Dropout</code> layer on the outputs of each
+computing the final results. Default: 1</p></dd>
+<dt>nonlinearity</dt>
+<dd><p>The non-linearity to use. Can be either <code>'tanh'</code> or
+<code>'relu'</code>. Default: <code>'tanh'</code></p></dd>
+<dt>bias</dt>
+<dd><p>If <code>FALSE</code>, then the layer does not use bias weights <code>b_ih</code> and
+<code>b_hh</code>. Default: <code>TRUE</code></p></dd>
+<dt>batch_first</dt>
+<dd><p>If <code>TRUE</code>, then the input and output tensors are provided
+as <code>(batch, seq, feature)</code>. Default: <code>FALSE</code></p></dd>
+<dt>dropout</dt>
+<dd><p>If non-zero, introduces a <code>Dropout</code> layer on the outputs of each
 RNN layer except the last layer, with dropout probability equal to
-<code>dropout</code>. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>bidirectional</th>
-      <td><p>If <code>TRUE</code>, becomes a bidirectional RNN. Default: <code>FALSE</code></p></td>
-    </tr>
-    <tr>
-      <th>...</th>
-      <td><p>other arguments that can be passed to the super class.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
+<code>dropout</code>. Default: 0</p></dd>
+<dt>bidirectional</dt>
+<dd><p>If <code>TRUE</code>, becomes a bidirectional RNN. Default: <code>FALSE</code></p></dd>
+<dt>...</dt>
+<dd><p>other arguments that can be passed to the super class.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>For each element in the input sequence, each layer computes the following
 function:</p>
 <p>$$
@@ -264,26 +139,24 @@ the input at time <code>t</code>, and \(h_{(t-1)}\) is the hidden state of the
 previous layer at time <code>t-1</code> or the initial hidden state at time <code>0</code>.
 If <code>nonlinearity</code> is <code>'relu'</code>, then \(\mbox{ReLU}\) is used instead of
 \(\tanh\).</p>
-    <h2 class="hasAnchor" id="inputs"><a class="anchor" href="#inputs"></a>Inputs</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="inputs">Inputs<a class="anchor" aria-label="anchor" href="#inputs"></a></h2>
     
 
-<ul>
-<li><p><strong>input</strong> of shape <code>(seq_len, batch, input_size)</code>: tensor containing the features
+<ul><li><p><strong>input</strong> of shape <code>(seq_len, batch, input_size)</code>: tensor containing the features
 of the input sequence. The input can also be a packed variable length
 sequence.</p></li>
 <li><p><strong>h_0</strong> of shape <code>(num_layers * num_directions, batch, hidden_size)</code>: tensor
 containing the initial hidden state for each element in the batch.
 Defaults to zero if not provided. If the RNN is bidirectional,
 num_directions should be 2, else it should be 1.</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="outputs"><a class="anchor" href="#outputs"></a>Outputs</h2>
-
+</ul></div>
+    <div class="section level2">
+    <h2 id="outputs">Outputs<a class="anchor" aria-label="anchor" href="#outputs"></a></h2>
     
 
-<ul>
-<li><p><strong>output</strong> of shape <code>(seq_len, batch, num_directions * hidden_size)</code>: tensor
+<ul><li><p><strong>output</strong> of shape <code>(seq_len, batch, num_directions * hidden_size)</code>: tensor
 containing the output features (<code>h_t</code>) from the last layer of the RNN,
 for each <code>t</code>.  If a :class:<code>nn_packed_sequence</code> has
 been given as the input, the output will also be a packed sequence.
@@ -295,14 +168,12 @@ Similarly, the directions can be separated in the packed case.</p></li>
 containing the hidden state for <code>t = seq_len</code>.
 Like <em>output</em>, the layers can be separated using
 <code>h_n$view(num_layers, num_directions, batch, hidden_size)</code>.</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
+</ul></div>
+    <div class="section level2">
+    <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
     
 
-<ul>
-<li><p>Input1: \((L, N, H_{in})\) tensor containing input features where
+<ul><li><p>Input1: \((L, N, H_{in})\) tensor containing input features where
 \(H_{in}=\mbox{input\_size}\) and <code>L</code> represents a sequence length.</p></li>
 <li><p>Input2: \((S, N, H_{out})\) tensor
 containing the initial hidden state for each element in the batch.
@@ -312,14 +183,12 @@ If the RNN is bidirectional, num_directions should be 2, else it should be 1.</p
 <li><p>Output1: \((L, N, H_{all})\) where \(H_{all}=\mbox{num\_directions} * \mbox{hidden\_size}\)</p></li>
 <li><p>Output2: \((S, N, H_{out})\) tensor containing the next hidden state
 for each element in the batch</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="attributes"><a class="anchor" href="#attributes"></a>Attributes</h2>
-
+</ul></div>
+    <div class="section level2">
+    <h2 id="attributes">Attributes<a class="anchor" aria-label="anchor" href="#attributes"></a></h2>
     
 
-<ul>
-<li><p><code>weight_ih_l[k]</code>: the learnable input-hidden weights of the k-th layer,
+<ul><li><p><code>weight_ih_l[k]</code>: the learnable input-hidden weights of the k-th layer,
 of shape <code>(hidden_size, input_size)</code> for <code>k = 0</code>. Otherwise, the shape is
 <code>(hidden_size, num_directions * hidden_size)</code></p></li>
 <li><p><code>weight_hh_l[k]</code>: the learnable hidden-hidden weights of the k-th layer,
@@ -328,114 +197,107 @@ of shape <code>(hidden_size, hidden_size)</code></p></li>
 of shape <code>(hidden_size)</code></p></li>
 <li><p><code>bias_hh_l[k]</code>: the learnable hidden-hidden bias of the k-th layer,
 of shape <code>(hidden_size)</code></p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
+</ul></div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     
 
 
 <p>All the weights and biases are initialized from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\)
 where \(k = \frac{1}{\mbox{hidden\_size}}\)</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>rnn</span> <span class='op'>&lt;-</span> <span class='fu'>nn_rnn</span><span class='op'>(</span><span class='fl'>10</span>, <span class='fl'>20</span>, <span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>5</span>, <span class='fl'>3</span>, <span class='fl'>10</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>h0</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>3</span>, <span class='fl'>20</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>rnn</span><span class='op'>(</span><span class='va'>input</span>, <span class='va'>h0</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [[1]]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> (1,.,.) = </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  Columns 1 to 9  0.6864  0.3225  0.9595  0.3187 -0.0172  0.2937  0.2644  0.7252 -0.3381</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   0.8515 -0.0848  0.9146 -0.5873 -0.1403  0.7394 -0.1556  0.5255  0.2440</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   0.7645  0.7322  0.7848  0.8814  0.0069  0.6739 -0.8260  0.3791 -0.3123</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> Columns 10 to 18  0.5684 -0.4195 -0.3615 -0.4342  0.5440 -0.6104  0.4424 -0.1427  0.7604</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   0.1216 -0.0909  0.6162 -0.0445 -0.5587  0.3056 -0.3045  0.5446  0.0367</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   0.0989  0.4797  0.8440 -0.2861  0.4818 -0.1453 -0.4120  0.5488  0.5716</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> Columns 19 to 20 -0.4180 -0.3747</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  -0.9024 -0.0594</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   0.0636  0.5893</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> (2,.,.) = </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  Columns 1 to 9  0.5101  0.2731  0.8212 -0.4245 -0.0073  0.2519  0.2130 -0.0860  0.5300</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   0.2136 -0.2918  0.8505 -0.4605 -0.2982  0.6545  0.3091  0.3356 -0.0023</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   0.2573 -0.5528  0.7602 -0.4732 -0.6445  0.1955  0.1427  0.8477 -0.0698</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> Columns 10 to 18  0.1372 -0.2443  0.4683  0.3616 -0.5365  0.0331  0.3115 -0.5107  0.3837</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   0.4006 -0.2590  0.6083  0.1301 -0.0848  0.5897  0.2039  0.1195  0.3776</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   0.3026 -0.4777 -0.0107 -0.3634 -0.4272 -0.0689 -0.1663 -0.7650  0.5264</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> Columns 19 to 20 -0.7450 -0.3099</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  -0.3988 -0.0912</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   0.0459 -0.4274</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> (3,.,.) = </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  Columns 1 to 9  0.3852  0.0985  0.8217 -0.7053 -0.2911  0.4056  0.5919  0.2508  0.3037</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   0.3141 -0.3281  0.7366 -0.6595 -0.3326  0.3187  0.2301  0.4415 -0.0370</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   0.2510  0.2455  0.8623 -0.1934 -0.5162  0.5338 -0.5210  0.3965 -0.3814</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> ... [the output was truncated (use n=-1 to disable)]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{5,3,20} ][ grad_fn = &lt;StackBackward&gt; ]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [[2]]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> (1,.,.) = </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  Columns 1 to 9 -0.7356 -0.8249  0.6582 -0.1027  0.6052 -0.8309  0.5580  0.4659  0.2937</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  -0.1447 -0.7757  0.4063 -0.1988 -0.0253 -0.5706  0.6635  0.0697  0.1841</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  -0.8659 -0.3141  0.0356 -0.3850  0.0096 -0.8585  0.6718  0.1740  0.1586</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> Columns 10 to 18  0.1055  0.6946 -0.7562  0.2249 -0.5665  0.7337  0.1138 -0.2352 -0.1125</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   0.2360  0.6662 -0.6989  0.4278 -0.5136  0.8209  0.0913  0.2466  0.1502</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  -0.7604  0.3955 -0.1137 -0.4311 -0.6155 -0.3598 -0.5083  0.1460 -0.5586</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> Columns 19 to 20  0.3212 -0.6244</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   0.0027 -0.3941</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  -0.0433 -0.2940</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> (2,.,.) = </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  Columns 1 to 9  0.5944 -0.3494  0.8576 -0.6179 -0.4639  0.8061  0.3812  0.3414  0.1511</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   0.6701 -0.4660  0.7686 -0.5458 -0.4757  0.7448  0.2340  0.5333 -0.1945</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   0.4858 -0.2456  0.8743 -0.3067 -0.0028  0.3794 -0.2010  0.1622 -0.0311</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> Columns 10 to 18  0.0759 -0.0939  0.6756 -0.0554 -0.2123  0.5339  0.2261  0.5811  0.2019</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   0.2798 -0.2253  0.6207 -0.2439 -0.0650  0.2131  0.2159  0.2008  0.3749</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   0.0850 -0.2012  0.6334  0.5174 -0.7442  0.4708  0.0094  0.0066  0.1538</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> Columns 19 to 20 -0.4424 -0.0952</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  -0.3185  0.0138</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  -0.6070 -0.2223</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{2,3,20} ][ grad_fn = &lt;StackBackward&gt; ]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">rnn</span> <span class="op">&lt;-</span> <span class="fu">nn_rnn</span><span class="op">(</span><span class="fl">10</span>, <span class="fl">20</span>, <span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">5</span>, <span class="fl">3</span>, <span class="fl">10</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">h0</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">3</span>, <span class="fl">20</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">rnn</span><span class="op">(</span><span class="va">input</span>, <span class="va">h0</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [[1]]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> (1,.,.) = </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  Columns 1 to 9  0.3345 -0.8092  0.8616 -0.3814 -0.6915  0.4852  0.7867 -0.5962  0.9033</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.2259  0.3547  0.1961  0.2019 -0.2665  0.6190  0.4105  0.9266 -0.1081</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.6620 -0.2637  0.4504  0.3002 -0.1003 -0.9604 -0.4974  0.3616  0.2804</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Columns 10 to 18 -0.2662  0.4342  0.0859  0.6060 -0.7298  0.4275 -0.5170 -0.6527  0.0447</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.1489 -0.1532 -0.3676  0.7770  0.3155 -0.4149  0.4342 -0.2582 -0.4781</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.5659 -0.4697 -0.7085 -0.8518  0.8653 -0.0720  0.1830 -0.4675 -0.8121</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Columns 19 to 20  0.8197 -0.8390</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.7136  0.8726</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.6550  0.9490</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> (2,.,.) = </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  Columns 1 to 9  0.1877 -0.0359  0.3635  0.1780  0.2971 -0.9101  0.4571 -0.4257  0.6578</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.0443 -0.2512  0.7624 -0.0894 -0.7454 -0.1553  0.0990  0.1471  0.1659</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.1011 -0.2845  0.2408  0.2113 -0.3587  0.3907  0.6455 -0.4355 -0.0128</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Columns 10 to 18 -0.2258  0.1592  0.4052 -0.3029  0.6346 -0.3514 -0.3189 -0.3640 -0.4152</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.3894  0.3147 -0.3049  0.2987  0.7429 -0.3471  0.1917  0.2159  0.0276</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.1765  0.4714 -0.3878 -0.1281 -0.3013 -0.0446  0.2997  0.2569  0.1633</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Columns 19 to 20  0.1087 -0.4652</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.0069  0.2470</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.8382 -0.4049</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> (3,.,.) = </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  Columns 1 to 9  0.3968 -0.1327  0.4892  0.3467 -0.0534 -0.4809  0.6823 -0.4104  0.2749</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.1936  0.4276  0.5926 -0.2124  0.0078 -0.5926  0.6900 -0.1461  0.2141</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.0320  0.1210  0.3006  0.0531 -0.0199 -0.3555  0.2782 -0.2561  0.6810</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> ... [the output was truncated (use n=-1 to disable)]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{5,3,20} ][ grad_fn = &lt;StackBackward0&gt; ]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [[2]]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> (1,.,.) = </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  Columns 1 to 9  0.3201 -0.0094 -0.5913 -0.2042  0.2034 -0.2921 -0.2716  0.1504  0.0855</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.0986  0.3801 -0.7451 -0.0820  0.5822 -0.5968 -0.1580 -0.0045  0.2787</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.4021  0.3402 -0.5956  0.0071 -0.0387 -0.4006  0.1451 -0.3226  0.3752</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Columns 10 to 18 -0.1617 -0.0465  0.0560 -0.5249 -0.2707  0.7919 -0.4247  0.2552  0.1553</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.3183  0.0216 -0.2405 -0.4657 -0.6597  0.7781 -0.7501 -0.0955  0.5029</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.1261 -0.3039 -0.4118 -0.5502 -0.2039  0.4801 -0.3519  0.0634  0.0357</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Columns 19 to 20  0.4430 -0.2941</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.5167 -0.5764</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.5123 -0.5078</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> (2,.,.) = </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  Columns 1 to 9  0.2787 -0.1695  0.3868  0.0903 -0.3336 -0.6068  0.4856 -0.2817  0.2359</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.2348 -0.1015  0.6116 -0.0421 -0.1798 -0.1931  0.4836 -0.4568  0.5772</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.3405  0.1997  0.5214  0.1526 -0.0931 -0.1824  0.6392 -0.1871  0.3944</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Columns 10 to 18 -0.3941 -0.1537 -0.2288 -0.2495  0.2253  0.0120 -0.2736  0.1356 -0.3282</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.2815  0.0964 -0.5073  0.0956 -0.1618 -0.0885 -0.1228 -0.0379 -0.2024</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.5154 -0.1136 -0.4561 -0.2810 -0.0525 -0.0471 -0.0752  0.0108 -0.1525</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Columns 19 to 20  0.0616  0.0758</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.0229 -0.0540</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.0506 -0.0383</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2,3,20} ][ grad_fn = &lt;StackBackward0&gt; ]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
 </div>
 
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>RReLU module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
-    <div class="hidden name"><code>nn_rrelu.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>RReLU module</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-activation.R" class="external-link"><code>R/nn-activation.R</code></a></small>
+      <div class="d-none name"><code>nn_rrelu.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Applies the randomized leaky rectified liner unit function, element-wise,
 as described in the paper:</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_rrelu</span><span class='op'>(</span>lower <span class='op'>=</span> <span class='fl'>1</span><span class='op'>/</span><span class='fl'>8</span>, upper <span class='op'>=</span> <span class='fl'>1</span><span class='op'>/</span><span class='fl'>3</span>, inplace <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>lower</th>
-      <td><p>lower bound of the uniform distribution. Default: \(\frac{1}{8}\)</p></td>
-    </tr>
-    <tr>
-      <th>upper</th>
-      <td><p>upper bound of the uniform distribution. Default: \(\frac{1}{3}\)</p></td>
-    </tr>
-    <tr>
-      <th>inplace</th>
-      <td><p>can optionally do the operation in-place. Default: <code>FALSE</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_rrelu</span><span class="op">(</span>lower <span class="op">=</span> <span class="fl">1</span><span class="op">/</span><span class="fl">8</span>, upper <span class="op">=</span> <span class="fl">1</span><span class="op">/</span><span class="fl">3</span>, inplace <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>lower</dt>
+<dd><p>lower bound of the uniform distribution. Default: \(\frac{1}{8}\)</p></dd>
+<dt>upper</dt>
+<dd><p>upper bound of the uniform distribution. Default: \(\frac{1}{3}\)</p></dd>
+<dt>inplace</dt>
+<dd><p>can optionally do the operation in-place. Default: <code>FALSE</code></p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p><code>Empirical Evaluation of Rectified Activations in Convolutional Network</code>.</p>
 <p>The function is defined as:</p>
 <p>$$
@@ -225,54 +112,46 @@ $$</p>
 <p>where \(a\) is randomly sampled from uniform distribution
 \(\mathcal{U}(\mbox{lower}, \mbox{upper})\).
 See: https://arxiv.org/pdf/1505.00853.pdf</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
     
 
-<ul>
-<li><p>Input: \((N, *)\) where <code>*</code> means, any number of additional
+<ul><li><p>Input: \((N, *)\) where <code>*</code> means, any number of additional
 dimensions</p></li>
 <li><p>Output: \((N, *)\), same shape as the input</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_rrelu</span><span class='op'>(</span><span class='fl'>0.1</span>, <span class='fl'>0.3</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>m</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.3333</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.6055</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{2} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+</ul></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">nn_rrelu</span><span class="op">(</span><span class="fl">0.1</span>, <span class="fl">0.3</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">m</span><span class="op">(</span><span class="va">input</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.1557</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.7992</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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-      <div class="copyright">
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+    <footer><div class="pkgdown-footer-left">
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>SELU module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
-    <div class="hidden name"><code>nn_selu.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>SELU module</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-activation.R" class="external-link"><code>R/nn-activation.R</code></a></small>
+      <div class="d-none name"><code>nn_selu.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Applied element-wise, as:</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_selu</span><span class='op'>(</span>inplace <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>inplace</th>
-      <td><p>(bool, optional): can optionally do the operation in-place. Default: <code>FALSE</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_selu</span><span class="op">(</span>inplace <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>inplace</dt>
+<dd><p>(bool, optional): can optionally do the operation in-place. Default: <code>FALSE</code></p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>$$
   \mbox{SELU}(x) = \mbox{scale} * (\max(0,x) + \min(0, \alpha * (\exp(x) - 1)))
 $$</p>
 <p>with \(\alpha = 1.6732632423543772848170429916717\) and
 \(\mbox{scale} = 1.0507009873554804934193349852946\).</p>
 <p>More details can be found in the paper
-<a href='https://arxiv.org/abs/1706.02515'>Self-Normalizing Neural Networks</a>.</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
+<a href="https://arxiv.org/abs/1706.02515" class="external-link">Self-Normalizing Neural Networks</a>.</p>
+    </div>
+    <div class="section level2">
+    <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
     
 
-<ul>
-<li><p>Input: \((N, *)\) where <code>*</code> means, any number of additional
+<ul><li><p>Input: \((N, *)\) where <code>*</code> means, any number of additional
 dimensions</p></li>
 <li><p>Output: \((N, *)\), same shape as the input</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_selu</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+</ul></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">nn_selu</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span> <span class="op">&lt;-</span> <span class="fu">m</span><span class="op">(</span><span class="va">input</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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 <div class="row">
-  <div class="col-md-9 contents">
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-    <h1>A sequential container</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn.R'><code>R/nn.R</code></a></small>
-    <div class="hidden name"><code>nn_sequential.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>A sequential container</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn.R" class="external-link"><code>R/nn.R</code></a></small>
+      <div class="d-none name"><code>nn_sequential.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>A sequential container.
 Modules will be added to it in the order they are passed in the constructor.
 See examples.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_sequential</span><span class='op'>(</span><span class='va'>...</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>...</th>
-      <td><p>sequence of modules to be added</p></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>model</span> <span class='op'>&lt;-</span> <span class='fu'>nn_sequential</span><span class='op'>(</span></span>
-<span class='r-in'>  <span class='fu'><a href='nn_conv2d.html'>nn_conv2d</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>20</span>, <span class='fl'>5</span><span class='op'>)</span>,</span>
-<span class='r-in'>  <span class='fu'><a href='nn_relu.html'>nn_relu</a></span><span class='op'>(</span><span class='op'>)</span>,</span>
-<span class='r-in'>  <span class='fu'><a href='nn_conv2d.html'>nn_conv2d</a></span><span class='op'>(</span><span class='fl'>20</span>, <span class='fl'>64</span>, <span class='fl'>5</span><span class='op'>)</span>,</span>
-<span class='r-in'>  <span class='fu'><a href='nn_relu.html'>nn_relu</a></span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>32</span>, <span class='fl'>1</span>, <span class='fl'>28</span>, <span class='fl'>28</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span> <span class='op'>&lt;-</span> <span class='fu'>model</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_sequential</span><span class="op">(</span><span class="va">...</span><span class="op">)</span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>...</dt>
+<dd><p>sequence of modules to be added</p></dd>
+</dl></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">model</span> <span class="op">&lt;-</span> <span class="fu">nn_sequential</span><span class="op">(</span></span>
+<span class="r-in">  <span class="fu"><a href="nn_conv2d.html">nn_conv2d</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">20</span>, <span class="fl">5</span><span class="op">)</span>,</span>
+<span class="r-in">  <span class="fu"><a href="nn_relu.html">nn_relu</a></span><span class="op">(</span><span class="op">)</span>,</span>
+<span class="r-in">  <span class="fu"><a href="nn_conv2d.html">nn_conv2d</a></span><span class="op">(</span><span class="fl">20</span>, <span class="fl">64</span>, <span class="fl">5</span><span class="op">)</span>,</span>
+<span class="r-in">  <span class="fu"><a href="nn_relu.html">nn_relu</a></span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">)</span></span>
+<span class="r-in"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">32</span>, <span class="fl">1</span>, <span class="fl">28</span>, <span class="fl">28</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span> <span class="op">&lt;-</span> <span class="fu">model</span><span class="op">(</span><span class="va">input</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
+    </div>
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 <div class="row">
-  <div class="col-md-9 contents">
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-    <h1>Sigmoid module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
-    <div class="hidden name"><code>nn_sigmoid.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Sigmoid module</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-activation.R" class="external-link"><code>R/nn-activation.R</code></a></small>
+      <div class="d-none name"><code>nn_sigmoid.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Applies the element-wise function:</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_sigmoid</span><span class='op'>(</span><span class='op'>)</span></pre>
-
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_sigmoid</span><span class="op">(</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>$$
   \mbox{Sigmoid}(x) = \sigma(x) = \frac{1}{1 + \exp(-x)}
 $$</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
     
 
-<ul>
-<li><p>Input: \((N, *)\) where <code>*</code> means, any number of additional
+<ul><li><p>Input: \((N, *)\) where <code>*</code> means, any number of additional
 dimensions</p></li>
 <li><p>Output: \((N, *)\), same shape as the input</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_sigmoid</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
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-  </div>
-</div>
+</ul></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">nn_sigmoid</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span> <span class="op">&lt;-</span> <span class="fu">m</span><span class="op">(</span><span class="va">input</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
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diff --git a/reference/nn_smooth_l1_loss.html b/reference/nn_smooth_l1_loss.html
index 7af010d54..27def97e5 100644
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-<meta property="og:description" content="Creates a criterion that uses a squared term if the absolute
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Creates a criterion that uses a squared term if the absolute
 element-wise error falls below 1 and an L1 term otherwise.
 It is less sensitive to outliers than the MSELoss and in some cases
 prevents exploding gradients (e.g. see Fast R-CNN paper by Ross Girshick).
-Also known as the Huber loss:" />
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+Also known as the Huber loss:"><title>Smooth L1 loss — nn_smooth_l1_loss • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.1.0/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.1.0/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Smooth L1 loss — nn_smooth_l1_loss"><meta property="og:description" content="Creates a criterion that uses a squared term if the absolute
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+It is less sensitive to outliers than the MSELoss and in some cases
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Smooth L1 loss</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-loss.R'><code>R/nn-loss.R</code></a></small>
-    <div class="hidden name"><code>nn_smooth_l1_loss.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Smooth L1 loss</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-loss.R" class="external-link"><code>R/nn-loss.R</code></a></small>
+      <div class="d-none name"><code>nn_smooth_l1_loss.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Creates a criterion that uses a squared term if the absolute
 element-wise error falls below 1 and an L1 term otherwise.
 It is less sensitive to outliers than the <code>MSELoss</code> and in some cases
@@ -197,24 +92,23 @@ prevents exploding gradients (e.g. see <code>Fast R-CNN</code> paper by Ross Gir
 Also known as the Huber loss:</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_smooth_l1_loss</span><span class='op'>(</span>reduction <span class='op'>=</span> <span class='st'>"mean"</span><span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_smooth_l1_loss</span><span class="op">(</span>reduction <span class="op">=</span> <span class="st">"mean"</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>reduction</th>
-      <td><p>(string, optional): Specifies the reduction to apply to the output:
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>reduction</dt>
+<dd><p>(string, optional): Specifies the reduction to apply to the output:
 <code>'none'</code> | <code>'mean'</code> | <code>'sum'</code>. <code>'none'</code>: no reduction will be applied,
 <code>'mean'</code>: the sum of the output will be divided by the number of
 elements in the output, <code>'sum'</code>: the output will be summed. Note: <code>size_average</code>
 and <code>reduce</code> are in the process of being deprecated, and in the meantime,
-specifying either of those two args will override <code>reduction</code>. Default: <code>'mean'</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
+specifying either of those two args will override <code>reduction</code>. Default: <code>'mean'</code></p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>$$
   \mbox{loss}(x, y) = \frac{1}{n} \sum_{i} z_{i}
 $$</p>
@@ -229,44 +123,35 @@ $$</p>
 <p>\(x\) and \(y\) arbitrary shapes with a total of \(n\) elements each
 the sum operation still operates over all the elements, and divides by \(n\).
 The division by \(n\) can be avoided if sets <code>reduction = 'sum'</code>.</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
     
 
-<ul>
-<li><p>Input: \((N, *)\) where \(*\) means, any number of additional
+<ul><li><p>Input: \((N, *)\) where \(*\) means, any number of additional
 dimensions</p></li>
 <li><p>Target: \((N, *)\), same shape as the input</p></li>
 <li><p>Output: scalar. If <code>reduction</code> is <code>'none'</code>, then
 \((N, *)\), same shape as the input</p></li>
-</ul>
+</ul></div>
 
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
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+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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diff --git a/reference/nn_soft_margin_loss.html b/reference/nn_soft_margin_loss.html
index f25b95be7..ee8178654 100644
--- a/reference/nn_soft_margin_loss.html
+++ b/reference/nn_soft_margin_loss.html
@@ -1,256 +1,139 @@
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 logistic loss between input tensor \(x\) and target tensor \(y\)
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-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Soft margin loss</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-loss.R'><code>R/nn-loss.R</code></a></small>
-    <div class="hidden name"><code>nn_soft_margin_loss.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Soft margin loss</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-loss.R" class="external-link"><code>R/nn-loss.R</code></a></small>
+      <div class="d-none name"><code>nn_soft_margin_loss.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Creates a criterion that optimizes a two-class classification
 logistic loss between input tensor \(x\) and target tensor \(y\)
 (containing 1 or -1).</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_soft_margin_loss</span><span class='op'>(</span>reduction <span class='op'>=</span> <span class='st'>"mean"</span><span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_soft_margin_loss</span><span class="op">(</span>reduction <span class="op">=</span> <span class="st">"mean"</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>reduction</th>
-      <td><p>(string, optional): Specifies the reduction to apply to the output:
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>reduction</dt>
+<dd><p>(string, optional): Specifies the reduction to apply to the output:
 <code>'none'</code> | <code>'mean'</code> | <code>'sum'</code>. <code>'none'</code>: no reduction will be applied,
 <code>'mean'</code>: the sum of the output will be divided by the number of
 elements in the output, <code>'sum'</code>: the output will be summed. Note: <code>size_average</code>
 and <code>reduce</code> are in the process of being deprecated, and in the meantime,
-specifying either of those two args will override <code>reduction</code>. Default: <code>'mean'</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
+specifying either of those two args will override <code>reduction</code>. Default: <code>'mean'</code></p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>$$
   \mbox{loss}(x, y) = \sum_i \frac{\log(1 + \exp(-y[i]*x[i]))}{\mbox{x.nelement}()}
 $$</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
     
 
-<ul>
-<li><p>Input: \((*)\) where \(*\) means, any number of additional
+<ul><li><p>Input: \((*)\) where \(*\) means, any number of additional
 dimensions</p></li>
 <li><p>Target: \((*)\), same shape as the input</p></li>
 <li><p>Output: scalar. If <code>reduction</code> is <code>'none'</code>, then same shape as the input</p></li>
-</ul>
+</ul></div>
 
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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diff --git a/reference/nn_softmax.html b/reference/nn_softmax.html
index 7dbd8b3b3..ce6ede7be 100644
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+rescaling them so that the elements of the n-dimensional output Tensor
+lie in the range [0,1] and sum to 1.
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Softmax module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
-    <div class="hidden name"><code>nn_softmax.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Softmax module</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-activation.R" class="external-link"><code>R/nn-activation.R</code></a></small>
+      <div class="d-none name"><code>nn_softmax.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Applies the Softmax function to an n-dimensional input Tensor
 rescaling them so that the elements of the n-dimensional output Tensor
 lie in the range <code>[0,1]</code> and sum to 1.
 Softmax is defined as:</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_softmax</span><span class='op'>(</span><span class='va'>dim</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>dim</th>
-      <td><p>(int): A dimension along which Softmax will be computed (so every slice
-along dim will sum to 1).</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_softmax</span><span class="op">(</span><span class="va">dim</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>dim</dt>
+<dd><p>(int): A dimension along which Softmax will be computed (so every slice
+along dim will sum to 1).</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
     <p>:
 a Tensor of the same dimension and shape as the input with
 values in the range <code>[0, 1]</code></p>
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>$$
   \mbox{Softmax}(x_{i}) = \frac{\exp(x_i)}{\sum_j \exp(x_j)}
 $$</p>
 <p>When the input Tensor is a sparse tensor then the unspecifed
 values are treated as <code>-Inf</code>.</p>
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p>This module doesn't work directly with NLLLoss,
 which expects the Log to be computed between the Softmax and itself.
 Use <code>LogSoftmax</code> instead (it's faster and has better numerical properties).</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
     
 
-<ul>
-<li><p>Input: \((*)\) where <code>*</code> means, any number of additional
+<ul><li><p>Input: \((*)\) where <code>*</code> means, any number of additional
 dimensions</p></li>
 <li><p>Output: \((*)\), same shape as the input</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_softmax</span><span class='op'>(</span><span class='fl'>1</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>3</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+</ul></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">nn_softmax</span><span class="op">(</span><span class="fl">1</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">3</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span> <span class="op">&lt;-</span> <span class="fu">m</span><span class="op">(</span><span class="va">input</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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index ca795b4e0..428b9bb3c 100644
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+apply Softmax to each location \((Channels, h_i, w_j)\)"><title>Softmax2d module — nn_softmax2d • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.1.0/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.1.0/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Softmax2d module — nn_softmax2d"><meta property="og:description" content="Applies SoftMax over features to each spatial location.
+When given an image of Channels x Height x Width, it will
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-    <h1>Softmax2d module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
-    <div class="hidden name"><code>nn_softmax2d.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Softmax2d module</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-activation.R" class="external-link"><code>R/nn-activation.R</code></a></small>
+      <div class="d-none name"><code>nn_softmax2d.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Applies SoftMax over features to each spatial location.
 When given an image of <code>Channels x Height x Width</code>, it will
 apply <code>Softmax</code> to each location \((Channels, h_i, w_j)\)</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_softmax2d</span><span class='op'>(</span><span class='op'>)</span></pre>
-
-
-    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_softmax2d</span><span class="op">(</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
     <p>a Tensor of the same dimension and shape as the input with
 values in the range <code>[0, 1]</code></p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
     
 
-<ul>
-<li><p>Input: \((N, C, H, W)\)</p></li>
+<ul><li><p>Input: \((N, C, H, W)\)</p></li>
 <li><p>Output: \((N, C, H, W)\) (same shape as input)</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_softmax2d</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>3</span>, <span class='fl'>12</span>, <span class='fl'>13</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
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-</div>
+</ul></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">nn_softmax2d</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">3</span>, <span class="fl">12</span>, <span class="fl">13</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span> <span class="op">&lt;-</span> <span class="fu">m</span><span class="op">(</span><span class="va">input</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
+    </div>
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diff --git a/reference/nn_softmin.html b/reference/nn_softmin.html
index 27675040b..a8880fe73 100644
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-<meta property="og:description" content="Applies the Softmin function to an n-dimensional input Tensor
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Applies the Softmin function to an n-dimensional input Tensor
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-    <h1>Softmin</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
-    <div class="hidden name"><code>nn_softmin.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Softmin</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-activation.R" class="external-link"><code>R/nn-activation.R</code></a></small>
+      <div class="d-none name"><code>nn_softmin.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Applies the Softmin function to an n-dimensional input Tensor
 rescaling them so that the elements of the n-dimensional output Tensor
 lie in the range <code>[0, 1]</code> and sum to 1.
 Softmin is defined as:</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_softmin</span><span class='op'>(</span><span class='va'>dim</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>dim</th>
-      <td><p>(int): A dimension along which Softmin will be computed (so every slice
-along dim will sum to 1).</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_softmin</span><span class="op">(</span><span class="va">dim</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>dim</dt>
+<dd><p>(int): A dimension along which Softmin will be computed (so every slice
+along dim will sum to 1).</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
     <p>a Tensor of the same dimension and shape as the input, with
 values in the range <code>[0, 1]</code>.</p>
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>$$
   \mbox{Softmin}(x_{i}) = \frac{\exp(-x_i)}{\sum_j \exp(-x_j)}
 $$</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
     
 
-<ul>
-<li><p>Input: \((*)\) where <code>*</code> means, any number of additional
+<ul><li><p>Input: \((*)\) where <code>*</code> means, any number of additional
 dimensions</p></li>
 <li><p>Output: \((*)\), same shape as the input</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_softmin</span><span class='op'>(</span>dim <span class='op'>=</span> <span class='fl'>1</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+</ul></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">nn_softmin</span><span class="op">(</span>dim <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span> <span class="op">&lt;-</span> <span class="fu">m</span><span class="op">(</span><span class="va">input</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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index 4f4b3f41e..29004dfb2 100644
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-<meta property="og:title" content="Softplus module — nn_softplus" />
-<meta property="og:description" content="Applies the element-wise function:
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Applies the element-wise function:
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+$$
+  \mbox{Softplus}(x) = \frac{1}{\beta} * \log(1 + \exp(\beta * x))
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-    <div class="page-header">
-    <h1>Softplus module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
-    <div class="hidden name"><code>nn_softplus.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Softplus module</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-activation.R" class="external-link"><code>R/nn-activation.R</code></a></small>
+      <div class="d-none name"><code>nn_softplus.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Applies the element-wise function:
 $$
   \mbox{Softplus}(x) = \frac{1}{\beta} * \log(1 + \exp(\beta * x))
 $$</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_softplus</span><span class='op'>(</span>beta <span class='op'>=</span> <span class='fl'>1</span>, threshold <span class='op'>=</span> <span class='fl'>20</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>beta</th>
-      <td><p>the \(\beta\) value for the Softplus formulation. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>threshold</th>
-      <td><p>values above this revert to a linear function. Default: 20</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_softplus</span><span class="op">(</span>beta <span class="op">=</span> <span class="fl">1</span>, threshold <span class="op">=</span> <span class="fl">20</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>beta</dt>
+<dd><p>the \(\beta\) value for the Softplus formulation. Default: 1</p></dd>
+<dt>threshold</dt>
+<dd><p>values above this revert to a linear function. Default: 20</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>SoftPlus is a smooth approximation to the ReLU function and can be used
 to constrain the output of a machine to always be positive.
 For numerical stability the implementation reverts to the linear function
 when \(input \times \beta &gt; threshold\).</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
     
 
-<ul>
-<li><p>Input: \((N, *)\) where <code>*</code> means, any number of additional
+<ul><li><p>Input: \((N, *)\) where <code>*</code> means, any number of additional
 dimensions</p></li>
 <li><p>Output: \((N, *)\), same shape as the input</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_softplus</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
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-      <h2 data-toc-skip>Contents</h2>
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+</ul></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">nn_softplus</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span> <span class="op">&lt;-</span> <span class="fu">m</span><span class="op">(</span><span class="va">input</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
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diff --git a/reference/nn_softshrink.html b/reference/nn_softshrink.html
index 0da28c051..3ee6d5716 100644
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Softshrink module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
-    <div class="hidden name"><code>nn_softshrink.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Softshrink module</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-activation.R" class="external-link"><code>R/nn-activation.R</code></a></small>
+      <div class="d-none name"><code>nn_softshrink.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Applies the soft shrinkage function elementwise:</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_softshrink</span><span class='op'>(</span>lambd <span class='op'>=</span> <span class='fl'>0.5</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>lambd</th>
-      <td><p>the \(\lambda\) (must be no less than zero) value for the Softshrink formulation. Default: 0.5</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_softshrink</span><span class="op">(</span>lambd <span class="op">=</span> <span class="fl">0.5</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>lambd</dt>
+<dd><p>the \(\lambda\) (must be no less than zero) value for the Softshrink formulation. Default: 0.5</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>$$
   \mbox{SoftShrinkage}(x) =
   \left\{ \begin{array}{ll}
@@ -211,50 +101,42 @@ x + \lambda, &amp; \mbox{ if } x &lt; -\lambda \\
 \end{array}
 \right.
 $$</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
     
 
-<ul>
-<li><p>Input: \((N, *)\) where <code>*</code> means, any number of additional
+<ul><li><p>Input: \((N, *)\) where <code>*</code> means, any number of additional
 dimensions</p></li>
 <li><p>Output: \((N, *)\), same shape as the input</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_softshrink</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+</ul></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">nn_softshrink</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span> <span class="op">&lt;-</span> <span class="fu">m</span><span class="op">(</span><span class="va">input</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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diff --git a/reference/nn_softsign.html b/reference/nn_softsign.html
index 7cbfc655e..964d2599f 100644
--- a/reference/nn_softsign.html
+++ b/reference/nn_softsign.html
@@ -1,247 +1,134 @@
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-<meta property="og:title" content="Softsign module — nn_softsign" />
-<meta property="og:description" content="Applies the element-wise function:
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Applies the element-wise function:
 $$
   \mbox{SoftSign}(x) = \frac{x}{ 1 + |x|}
-$$" />
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-  <div class="col-md-9 contents">
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-    <h1>Softsign module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
-    <div class="hidden name"><code>nn_softsign.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Softsign module</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-activation.R" class="external-link"><code>R/nn-activation.R</code></a></small>
+      <div class="d-none name"><code>nn_softsign.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Applies the element-wise function:
 $$
   \mbox{SoftSign}(x) = \frac{x}{ 1 + |x|}
 $$</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_softsign</span><span class='op'>(</span><span class='op'>)</span></pre>
-
-
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_softsign</span><span class="op">(</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
     
 
-<ul>
-<li><p>Input: \((N, *)\) where <code>*</code> means, any number of additional
+<ul><li><p>Input: \((N, *)\) where <code>*</code> means, any number of additional
 dimensions</p></li>
 <li><p>Output: \((N, *)\), same shape as the input</p></li>
-</ul>
-
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-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_softsign</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
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+</ul></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">nn_softsign</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span> <span class="op">&lt;-</span> <span class="fu">m</span><span class="op">(</span><span class="va">input</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Tanh module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
-    <div class="hidden name"><code>nn_tanh.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Tanh module</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-activation.R" class="external-link"><code>R/nn-activation.R</code></a></small>
+      <div class="d-none name"><code>nn_tanh.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Applies the element-wise function:</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_tanh</span><span class='op'>(</span><span class='op'>)</span></pre>
-
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_tanh</span><span class="op">(</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>$$
   \mbox{Tanh}(x) = \tanh(x) = \frac{\exp(x) - \exp(-x)} {\exp(x) + \exp(-x)}
 $$</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
     
 
-<ul>
-<li><p>Input: \((N, *)\) where <code>*</code> means, any number of additional
+<ul><li><p>Input: \((N, *)\) where <code>*</code> means, any number of additional
 dimensions</p></li>
 <li><p>Output: \((N, *)\), same shape as the input</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_tanh</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
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-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
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+</ul></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">nn_tanh</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span> <span class="op">&lt;-</span> <span class="fu">m</span><span class="op">(</span><span class="va">input</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
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 <div class="row">
-  <div class="col-md-9 contents">
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-    <h1>Tanhshrink module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
-    <div class="hidden name"><code>nn_tanhshrink.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Tanhshrink module</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-activation.R" class="external-link"><code>R/nn-activation.R</code></a></small>
+      <div class="d-none name"><code>nn_tanhshrink.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Applies the element-wise function:</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_tanhshrink</span><span class='op'>(</span><span class='op'>)</span></pre>
-
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_tanhshrink</span><span class="op">(</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>$$
   \mbox{Tanhshrink}(x) = x - \tanh(x)
 $$</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
     
 
-<ul>
-<li><p>Input: \((N, *)\) where <code>*</code> means, any number of additional
+<ul><li><p>Input: \((N, *)\) where <code>*</code> means, any number of additional
 dimensions</p></li>
 <li><p>Output: \((N, *)\), same shape as the input</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_tanhshrink</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
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-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
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+</ul></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">nn_tanhshrink</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span> <span class="op">&lt;-</span> <span class="fu">m</span><span class="op">(</span><span class="va">input</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Threshoold module</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
-    <div class="hidden name"><code>nn_threshold.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Threshoold module</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-activation.R" class="external-link"><code>R/nn-activation.R</code></a></small>
+      <div class="d-none name"><code>nn_threshold.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Thresholds each element of the input Tensor.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_threshold</span><span class='op'>(</span><span class='va'>threshold</span>, <span class='va'>value</span>, inplace <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>threshold</th>
-      <td><p>The value to threshold at</p></td>
-    </tr>
-    <tr>
-      <th>value</th>
-      <td><p>The value to replace with</p></td>
-    </tr>
-    <tr>
-      <th>inplace</th>
-      <td><p>can optionally do the operation in-place. Default: <code>FALSE</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_threshold</span><span class="op">(</span><span class="va">threshold</span>, <span class="va">value</span>, inplace <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>threshold</dt>
+<dd><p>The value to threshold at</p></dd>
+<dt>value</dt>
+<dd><p>The value to replace with</p></dd>
+<dt>inplace</dt>
+<dd><p>can optionally do the operation in-place. Default: <code>FALSE</code></p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>Threshold is defined as:
 $$
-  y = 
+  y =
   \left\{ \begin{array}{ll}
   x, &amp;\mbox{ if } x &gt; \mbox{threshold} \\
   \mbox{value}, &amp;\mbox{ otherwise }
   \end{array}
   \right.
 $$</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
     
 
-<ul>
-<li><p>Input: \((N, *)\) where <code>*</code> means, any number of additional
+<ul><li><p>Input: \((N, *)\) where <code>*</code> means, any number of additional
 dimensions</p></li>
 <li><p>Output: \((N, *)\), same shape as the input</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_threshold</span><span class='op'>(</span><span class='fl'>0.1</span>, <span class='fl'>20</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+</ul></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">nn_threshold</span><span class="op">(</span><span class="fl">0.1</span>, <span class="fl">20</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span> <span class="op">&lt;-</span> <span class="fu">m</span><span class="op">(</span><span class="va">input</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
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diff --git a/reference/nn_triplet_margin_loss.html b/reference/nn_triplet_margin_loss.html
index e62771986..ebd05e344 100644
--- a/reference/nn_triplet_margin_loss.html
+++ b/reference/nn_triplet_margin_loss.html
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-<meta property="og:title" content="Triplet margin loss — nn_triplet_margin_loss" />
-<meta property="og:description" content="Creates a criterion that measures the triplet loss given an input
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Creates a criterion that measures the triplet loss given an input
 tensors \(x1\), \(x2\), \(x3\) and a margin with a value greater than \(0\).
 This is used for measuring a relative similarity between samples. A triplet
 is composed by a, p and n (i.e., anchor, positive examples and negative examples respectively). The shapes of all input tensors should be
-\((N, D)\)." />
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+\((N, D)\)."><title>Triplet margin loss — nn_triplet_margin_loss • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.1.0/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.1.0/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Triplet margin loss — nn_triplet_margin_loss"><meta property="og:description" content="Creates a criterion that measures the triplet loss given an input
+tensors \(x1\), \(x2\), \(x3\) and a margin with a value greater than \(0\).
+This is used for measuring a relative similarity between samples. A triplet
+is composed by a, p and n (i.e., anchor, positive examples and negative examples respectively). The shapes of all input tensors should be
+\((N, D)\)."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Triplet margin loss</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-loss.R'><code>R/nn-loss.R</code></a></small>
-    <div class="hidden name"><code>nn_triplet_margin_loss.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Triplet margin loss</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-loss.R" class="external-link"><code>R/nn-loss.R</code></a></small>
+      <div class="d-none name"><code>nn_triplet_margin_loss.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Creates a criterion that measures the triplet loss given an input
 tensors \(x1\), \(x2\), \(x3\) and a margin with a value greater than \(0\).
 This is used for measuring a relative similarity between samples. A triplet
@@ -197,50 +92,41 @@ is composed by <code>a</code>, <code>p</code> and <code>n</code> (i.e., <code>an
 \((N, D)\).</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_triplet_margin_loss</span><span class='op'>(</span>
-  margin <span class='op'>=</span> <span class='fl'>1</span>,
-  p <span class='op'>=</span> <span class='fl'>2</span>,
-  eps <span class='op'>=</span> <span class='fl'>1e-06</span>,
-  swap <span class='op'>=</span> <span class='cn'>FALSE</span>,
-  reduction <span class='op'>=</span> <span class='st'>"mean"</span>
-<span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_triplet_margin_loss</span><span class="op">(</span>
+  margin <span class="op">=</span> <span class="fl">1</span>,
+  p <span class="op">=</span> <span class="fl">2</span>,
+  eps <span class="op">=</span> <span class="fl">1e-06</span>,
+  swap <span class="op">=</span> <span class="cn">FALSE</span>,
+  reduction <span class="op">=</span> <span class="st">"mean"</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>margin</th>
-      <td><p>(float, optional): Default: \(1\).</p></td>
-    </tr>
-    <tr>
-      <th>p</th>
-      <td><p>(int, optional): The norm degree for pairwise distance. Default: \(2\).</p></td>
-    </tr>
-    <tr>
-      <th>eps</th>
-      <td><p>constant to avoid NaN's</p></td>
-    </tr>
-    <tr>
-      <th>swap</th>
-      <td><p>(bool, optional): The distance swap is described in detail in the paper
-<a href='http://www.bmva.org/bmvc/2016/papers/paper119/index.html'>Learning shallow convolutional feature descriptors with triplet losses</a> by
-V. Balntas, E. Riba et al. Default: <code>FALSE</code>.</p></td>
-    </tr>
-    <tr>
-      <th>reduction</th>
-      <td><p>(string, optional): Specifies the reduction to apply to the output:
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>margin</dt>
+<dd><p>(float, optional): Default: \(1\).</p></dd>
+<dt>p</dt>
+<dd><p>(int, optional): The norm degree for pairwise distance. Default: \(2\).</p></dd>
+<dt>eps</dt>
+<dd><p>constant to avoid NaN's</p></dd>
+<dt>swap</dt>
+<dd><p>(bool, optional): The distance swap is described in detail in the paper
+<a href="http://www.bmva.org/bmvc/2016/papers/paper119/index.html" class="external-link">Learning shallow convolutional feature descriptors with triplet losses</a> by
+V. Balntas, E. Riba et al. Default: <code>FALSE</code>.</p></dd>
+<dt>reduction</dt>
+<dd><p>(string, optional): Specifies the reduction to apply to the output:
 <code>'none'</code> | <code>'mean'</code> | <code>'sum'</code>. <code>'none'</code>: no reduction will be applied,
 <code>'mean'</code>: the sum of the output will be divided by the number of
 elements in the output, <code>'sum'</code>: the output will be summed. Note: <code>size_average</code>
 and <code>reduce</code> are in the process of being deprecated, and in the meantime,
-specifying either of those two args will override <code>reduction</code>. Default: <code>'mean'</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
+specifying either of those two args will override <code>reduction</code>. Default: <code>'mean'</code></p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>The distance swap is described in detail in the paper
-<a href='http://www.bmva.org/bmvc/2016/papers/paper119/index.html'>Learning shallow convolutional feature descriptors with triplet losses</a> by
+<a href="http://www.bmva.org/bmvc/2016/papers/paper119/index.html" class="external-link">Learning shallow convolutional feature descriptors with triplet losses</a> by
 V. Balntas, E. Riba et al.</p>
 <p>The loss function for each sample in the mini-batch is:</p>
 <p>$$
@@ -250,55 +136,47 @@ $$</p>
 <p>$$
   d(x_i, y_i) = | {\bf x}_i - {\bf y}_i |_p
 $$</p>
-<p>See also <code><a href='nn_triplet_margin_with_distance_loss.html'>nn_triplet_margin_with_distance_loss()</a></code>, which computes the
+<p>See also <code><a href="nn_triplet_margin_with_distance_loss.html">nn_triplet_margin_with_distance_loss()</a></code>, which computes the
 triplet margin loss for input tensors using a custom distance function.</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
     
 
-<ul>
-<li><p>Input: \((N, D)\) where \(D\) is the vector dimension.</p></li>
+<ul><li><p>Input: \((N, D)\) where \(D\) is the vector dimension.</p></li>
 <li><p>Output: A Tensor of shape \((N)\) if <code>reduction</code> is <code>'none'</code>, or a scalar
 otherwise.</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>triplet_loss</span> <span class='op'>&lt;-</span> <span class='fu'>nn_triplet_margin_loss</span><span class='op'>(</span>margin <span class='op'>=</span> <span class='fl'>1</span>, p <span class='op'>=</span> <span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>anchor</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>100</span>, <span class='fl'>128</span>, requires_grad<span class='op'>=</span><span class='cn'>TRUE</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>positive</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>100</span>, <span class='fl'>128</span>, requires_grad<span class='op'>=</span><span class='cn'>TRUE</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>negative</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>100</span>, <span class='fl'>128</span>, requires_grad<span class='op'>=</span><span class='cn'>TRUE</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span> <span class='op'>&lt;-</span> <span class='fu'>triplet_loss</span><span class='op'>(</span><span class='va'>anchor</span>, <span class='va'>positive</span>, <span class='va'>negative</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span><span class='op'>$</span><span class='fu'>backward</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+</ul></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">triplet_loss</span> <span class="op">&lt;-</span> <span class="fu">nn_triplet_margin_loss</span><span class="op">(</span>margin <span class="op">=</span> <span class="fl">1</span>, p <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">anchor</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">100</span>, <span class="fl">128</span>, requires_grad <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">positive</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">100</span>, <span class="fl">128</span>, requires_grad <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">negative</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">100</span>, <span class="fl">128</span>, requires_grad <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span> <span class="op">&lt;-</span> <span class="fu">triplet_loss</span><span class="op">(</span><span class="va">anchor</span>, <span class="va">positive</span>, <span class="va">negative</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span><span class="op">$</span><span class="fu">backward</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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diff --git a/reference/nn_triplet_margin_with_distance_loss.html b/reference/nn_triplet_margin_with_distance_loss.html
index 4f6723ccc..6982f530e 100644
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@@ -1,196 +1,92 @@
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content='Creates a criterion that measures the triplet loss given input
 tensors \(a\), \(p\), and \(n\) (representing anchor,
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-real-valued function (&quot;distance function&quot;) used to compute the relationship
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+real-valued function ("distance function") used to compute the relationship
+between the anchor and positive example ("positive distance") and the
+anchor and negative example ("negative distance").'><title>Triplet margin with distance loss — nn_triplet_margin_with_distance_loss • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.1.0/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.1.0/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Triplet margin with distance loss — nn_triplet_margin_with_distance_loss"><meta property="og:description" content='Creates a criterion that measures the triplet loss given input
+tensors \(a\), \(p\), and \(n\) (representing anchor,
+positive, and negative examples, respectively), and a nonnegative,
+real-valued function ("distance function") used to compute the relationship
+between the anchor and positive example ("positive distance") and the
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Triplet margin with distance loss</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-loss.R'><code>R/nn-loss.R</code></a></small>
-    <div class="hidden name"><code>nn_triplet_margin_with_distance_loss.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Triplet margin with distance loss</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-loss.R" class="external-link"><code>R/nn-loss.R</code></a></small>
+      <div class="d-none name"><code>nn_triplet_margin_with_distance_loss.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Creates a criterion that measures the triplet loss given input
 tensors \(a\), \(p\), and \(n\) (representing anchor,
 positive, and negative examples, respectively), and a nonnegative,
@@ -199,48 +95,41 @@ between the anchor and positive example ("positive distance") and the
 anchor and negative example ("negative distance").</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_triplet_margin_with_distance_loss</span><span class='op'>(</span>
-  distance_function <span class='op'>=</span> <span class='cn'>NULL</span>,
-  margin <span class='op'>=</span> <span class='fl'>1</span>,
-  swap <span class='op'>=</span> <span class='cn'>FALSE</span>,
-  reduction <span class='op'>=</span> <span class='st'>"mean"</span>
-<span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_triplet_margin_with_distance_loss</span><span class="op">(</span>
+  distance_function <span class="op">=</span> <span class="cn">NULL</span>,
+  margin <span class="op">=</span> <span class="fl">1</span>,
+  swap <span class="op">=</span> <span class="cn">FALSE</span>,
+  reduction <span class="op">=</span> <span class="st">"mean"</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>distance_function</th>
-      <td><p>(callable, optional): A nonnegative, real-valued function that
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>distance_function</dt>
+<dd><p>(callable, optional): A nonnegative, real-valued function that
 quantifies the closeness of two tensors. If not specified,
-<code><a href='nn_pairwise_distance.html'>nn_pairwise_distance()</a></code> will be used.  Default: <code>None</code></p></td>
-    </tr>
-    <tr>
-      <th>margin</th>
-      <td><p>(float, optional): A non-negative margin representing the minimum difference
+<code><a href="nn_pairwise_distance.html">nn_pairwise_distance()</a></code> will be used.  Default: <code>None</code></p></dd>
+<dt>margin</dt>
+<dd><p>(float, optional): A non-negative margin representing the minimum difference
 between the positive and negative distances required for the loss to be 0. Larger
 margins penalize cases where the negative examples are not distant enough from the
-anchors, relative to the positives. Default: \(1\).</p></td>
-    </tr>
-    <tr>
-      <th>swap</th>
-      <td><p>(bool, optional): Whether to use the distance swap described in the paper
-<a href='http://www.bmva.org/bmvc/2016/papers/paper119/index.html'>Learning shallow convolutional feature descriptors with triplet losses</a> by
+anchors, relative to the positives. Default: \(1\).</p></dd>
+<dt>swap</dt>
+<dd><p>(bool, optional): Whether to use the distance swap described in the paper
+<a href="http://www.bmva.org/bmvc/2016/papers/paper119/index.html" class="external-link">Learning shallow convolutional feature descriptors with triplet losses</a> by
 V. Balntas, E. Riba et al. If TRUE, and if the positive example is closer to the
 negative example than the anchor is, swaps the positive example and the anchor in
-the loss computation. Default: <code>FALSE</code>.</p></td>
-    </tr>
-    <tr>
-      <th>reduction</th>
-      <td><p>(string, optional): Specifies the (optional) reduction to apply to the output:
+the loss computation. Default: <code>FALSE</code>.</p></dd>
+<dt>reduction</dt>
+<dd><p>(string, optional): Specifies the (optional) reduction to apply to the output:
 <code>'none'</code> | <code>'mean'</code> | <code>'sum'</code>. <code>'none'</code>: no reduction will be applied,
 <code>'mean'</code>: the sum of the output will be divided by the number of
-elements in the output, <code>'sum'</code>: the output will be summed. Default: <code>'mean'</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
+elements in the output, <code>'sum'</code>: the output will be summed. Default: <code>'mean'</code></p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>The unreduced loss (i.e., with <code>reduction</code> set to <code>'none'</code>)
 can be described as:</p>
 <p>$$
@@ -262,83 +151,75 @@ If <code>reduction</code> is not <code>'none'</code>
             \mbox{sum}(L),  &amp;  \mbox{if reduction} = \mbox{`sum'.}
 \end{array}
 $$</p>
-<p>See also <code><a href='nn_triplet_margin_loss.html'>nn_triplet_margin_loss()</a></code>, which computes the triplet
+<p>See also <code><a href="nn_triplet_margin_loss.html">nn_triplet_margin_loss()</a></code>, which computes the triplet
 loss for input tensors using the \(l_p\) distance as the distance function.</p>
-    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
     
 
-<ul>
-<li><p>Input: \((N, *)\) where \(*\) represents any number of additional dimensions
+<ul><li><p>Input: \((N, *)\) where \(*\) represents any number of additional dimensions
 as supported by the distance function.</p></li>
 <li><p>Output: A Tensor of shape \((N)\) if <code>reduction</code> is <code>'none'</code>, or a scalar
 otherwise.</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='co'># Initialize embeddings</span></span>
-<span class='r-in'><span class='va'>embedding</span> <span class='op'>&lt;-</span> <span class='fu'><a href='nn_embedding.html'>nn_embedding</a></span><span class='op'>(</span><span class='fl'>1000</span>, <span class='fl'>128</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>anchor_ids</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randint.html'>torch_randint</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>1000</span>, <span class='fl'>1</span>, dtype <span class='op'>=</span> <span class='fu'><a href='torch_dtype.html'>torch_long</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>positive_ids</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randint.html'>torch_randint</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>1000</span>, <span class='fl'>1</span>, dtype <span class='op'>=</span> <span class='fu'><a href='torch_dtype.html'>torch_long</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>negative_ids</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randint.html'>torch_randint</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>1000</span>, <span class='fl'>1</span>, dtype <span class='op'>=</span> <span class='fu'><a href='torch_dtype.html'>torch_long</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>anchor</span> <span class='op'>&lt;-</span> <span class='fu'>embedding</span><span class='op'>(</span><span class='va'>anchor_ids</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>positive</span> <span class='op'>&lt;-</span> <span class='fu'>embedding</span><span class='op'>(</span><span class='va'>positive_ids</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>negative</span> <span class='op'>&lt;-</span> <span class='fu'>embedding</span><span class='op'>(</span><span class='va'>negative_ids</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='co'># Built-in Distance Function</span></span>
-<span class='r-in'><span class='va'>triplet_loss</span> <span class='op'>&lt;-</span> <span class='fu'>nn_triplet_margin_with_distance_loss</span><span class='op'>(</span></span>
-<span class='r-in'>  distance_function<span class='op'>=</span><span class='fu'><a href='nn_pairwise_distance.html'>nn_pairwise_distance</a></span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span> <span class='op'>&lt;-</span> <span class='fu'>triplet_loss</span><span class='op'>(</span><span class='va'>anchor</span>, <span class='va'>positive</span>, <span class='va'>negative</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='co'># Custom Distance Function</span></span>
-<span class='r-in'><span class='va'>l_infinity</span> <span class='op'>&lt;-</span> <span class='kw'>function</span><span class='op'>(</span><span class='va'>x1</span>, <span class='va'>x2</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'>  <span class='fu'><a href='torch_max.html'>torch_max</a></span><span class='op'>(</span><span class='fu'><a href='torch_abs.html'>torch_abs</a></span><span class='op'>(</span><span class='va'>x1</span> <span class='op'>-</span> <span class='va'>x2</span><span class='op'>)</span>, dim <span class='op'>=</span> <span class='fl'>1</span><span class='op'>)</span><span class='op'>[[</span><span class='fl'>1</span><span class='op'>]</span><span class='op'>]</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>triplet_loss</span> <span class='op'>&lt;-</span> <span class='fu'>nn_triplet_margin_with_distance_loss</span><span class='op'>(</span></span>
-<span class='r-in'>  distance_function<span class='op'>=</span><span class='va'>l_infinity</span>, margin<span class='op'>=</span><span class='fl'>1.5</span></span>
-<span class='r-in'><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span> <span class='op'>&lt;-</span> <span class='fu'>triplet_loss</span><span class='op'>(</span><span class='va'>anchor</span>, <span class='va'>positive</span>, <span class='va'>negative</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='co'># Custom Distance Function (Lambda)</span></span>
-<span class='r-in'><span class='va'>triplet_loss</span> <span class='op'>&lt;-</span> <span class='fu'>nn_triplet_margin_with_distance_loss</span><span class='op'>(</span></span>
-<span class='r-in'>  distance_function <span class='op'>=</span> <span class='kw'>function</span><span class='op'>(</span><span class='va'>x</span>, <span class='va'>y</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'>    <span class='fl'>1</span> <span class='op'>-</span> <span class='fu'><a href='nnf_cosine_similarity.html'>nnf_cosine_similarity</a></span><span class='op'>(</span><span class='va'>x</span>, <span class='va'>y</span><span class='op'>)</span></span>
-<span class='r-in'>  <span class='op'>}</span></span>
-<span class='r-in'><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>output</span> <span class='op'>&lt;-</span> <span class='fu'>triplet_loss</span><span class='op'>(</span><span class='va'>anchor</span>, <span class='va'>positive</span>, <span class='va'>negative</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+</ul></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="co"># Initialize embeddings</span></span>
+<span class="r-in"><span class="va">embedding</span> <span class="op">&lt;-</span> <span class="fu"><a href="nn_embedding.html">nn_embedding</a></span><span class="op">(</span><span class="fl">1000</span>, <span class="fl">128</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">anchor_ids</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randint.html">torch_randint</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">1000</span>, <span class="fl">1</span>, dtype <span class="op">=</span> <span class="fu"><a href="torch_dtype.html">torch_long</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">positive_ids</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randint.html">torch_randint</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">1000</span>, <span class="fl">1</span>, dtype <span class="op">=</span> <span class="fu"><a href="torch_dtype.html">torch_long</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">negative_ids</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randint.html">torch_randint</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">1000</span>, <span class="fl">1</span>, dtype <span class="op">=</span> <span class="fu"><a href="torch_dtype.html">torch_long</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">anchor</span> <span class="op">&lt;-</span> <span class="fu">embedding</span><span class="op">(</span><span class="va">anchor_ids</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">positive</span> <span class="op">&lt;-</span> <span class="fu">embedding</span><span class="op">(</span><span class="va">positive_ids</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">negative</span> <span class="op">&lt;-</span> <span class="fu">embedding</span><span class="op">(</span><span class="va">negative_ids</span><span class="op">)</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="co"># Built-in Distance Function</span></span>
+<span class="r-in"><span class="va">triplet_loss</span> <span class="op">&lt;-</span> <span class="fu">nn_triplet_margin_with_distance_loss</span><span class="op">(</span></span>
+<span class="r-in">  distance_function <span class="op">=</span> <span class="fu"><a href="nn_pairwise_distance.html">nn_pairwise_distance</a></span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span> <span class="op">&lt;-</span> <span class="fu">triplet_loss</span><span class="op">(</span><span class="va">anchor</span>, <span class="va">positive</span>, <span class="va">negative</span><span class="op">)</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="co"># Custom Distance Function</span></span>
+<span class="r-in"><span class="va">l_infinity</span> <span class="op">&lt;-</span> <span class="kw">function</span><span class="op">(</span><span class="va">x1</span>, <span class="va">x2</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in">  <span class="fu"><a href="torch_max.html">torch_max</a></span><span class="op">(</span><span class="fu"><a href="torch_abs.html">torch_abs</a></span><span class="op">(</span><span class="va">x1</span> <span class="op">-</span> <span class="va">x2</span><span class="op">)</span>, dim <span class="op">=</span> <span class="fl">1</span><span class="op">)</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">triplet_loss</span> <span class="op">&lt;-</span> <span class="fu">nn_triplet_margin_with_distance_loss</span><span class="op">(</span></span>
+<span class="r-in">  distance_function <span class="op">=</span> <span class="va">l_infinity</span>, margin <span class="op">=</span> <span class="fl">1.5</span></span>
+<span class="r-in"><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span> <span class="op">&lt;-</span> <span class="fu">triplet_loss</span><span class="op">(</span><span class="va">anchor</span>, <span class="va">positive</span>, <span class="va">negative</span><span class="op">)</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="co"># Custom Distance Function (Lambda)</span></span>
+<span class="r-in"><span class="va">triplet_loss</span> <span class="op">&lt;-</span> <span class="fu">nn_triplet_margin_with_distance_loss</span><span class="op">(</span></span>
+<span class="r-in">  distance_function <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="va">x</span>, <span class="va">y</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in">    <span class="fl">1</span> <span class="op">-</span> <span class="fu"><a href="nnf_cosine_similarity.html">nnf_cosine_similarity</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">y</span><span class="op">)</span></span>
+<span class="r-in">  <span class="op">}</span></span>
+<span class="r-in"><span class="op">)</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">output</span> <span class="op">&lt;-</span> <span class="fu">triplet_loss</span><span class="op">(</span><span class="va">anchor</span>, <span class="va">positive</span>, <span class="va">negative</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
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diff --git a/reference/nn_unflatten.html b/reference/nn_unflatten.html
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+<!DOCTYPE html>
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+For use with [nn_sequential."><title>Unflattens a tensor dim expanding it to a desired shape.
+For use with [nn_sequential. — nn_unflatten • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.1.0/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.1.0/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Unflattens a tensor dim expanding it to a desired shape.
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+      <img src="" class="logo" alt=""><h1>Unflattens a tensor dim expanding it to a desired shape.
+For use with [<a href="nn_sequential.html">nn_sequential</a>.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-flatten.R" class="external-link"><code>R/nn-flatten.R</code></a></small>
+      <div class="d-none name"><code>nn_unflatten.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>Unflattens a tensor dim expanding it to a desired shape.
+For use with [<a href="nn_sequential.html">nn_sequential</a>.</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_unflatten</span><span class="op">(</span><span class="va">dim</span>, <span class="va">unflattened_size</span><span class="op">)</span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>dim</dt>
+<dd><p>Dimension to be unflattened</p></dd>
+<dt>unflattened_size</dt>
+<dd><p>New shape of the unflattened dimension</p></dd>
+</dl></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">50</span><span class="op">)</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu"><a href="nn_sequential.html">nn_sequential</a></span><span class="op">(</span></span>
+<span class="r-in">  <span class="fu"><a href="nn_linear.html">nn_linear</a></span><span class="op">(</span><span class="fl">50</span>, <span class="fl">50</span><span class="op">)</span>,</span>
+<span class="r-in">  <span class="fu">nn_unflatten</span><span class="op">(</span><span class="fl">2</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">5</span>, <span class="fl">5</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span> <span class="op">&lt;-</span> <span class="fu">m</span><span class="op">(</span><span class="va">input</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span><span class="op">$</span><span class="fu">size</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 2 2 5 5</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
+
+
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diff --git a/reference/nn_upsample.html b/reference/nn_upsample.html
new file mode 100644
index 000000000..2f40c640b
--- /dev/null
+++ b/reference/nn_upsample.html
@@ -0,0 +1,163 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Upsamples a given multi-channel 1D (temporal), 2D (spatial) or 3D (volumetric) data.
+The input data is assumed to be of the form minibatch x channels x optional depth x
+optional height] x width. Hence, for spatial inputs, we expect a 4D Tensor and for
+volumetric inputs, we expect a 5D Tensor."><title>Upsample module — nn_upsample • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.1.0/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.1.0/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Upsample module — nn_upsample"><meta property="og:description" content="Upsamples a given multi-channel 1D (temporal), 2D (spatial) or 3D (volumetric) data.
+The input data is assumed to be of the form minibatch x channels x optional depth x
+optional height] x width. Hence, for spatial inputs, we expect a 4D Tensor and for
+volumetric inputs, we expect a 5D Tensor."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Upsample module</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-upsampling.R" class="external-link"><code>R/nn-upsampling.R</code></a></small>
+      <div class="d-none name"><code>nn_upsample.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>Upsamples a given multi-channel 1D (temporal), 2D (spatial) or 3D (volumetric) data.
+The input data is assumed to be of the form minibatch x channels x optional depth x
+optional height] x width. Hence, for spatial inputs, we expect a 4D Tensor and for
+volumetric inputs, we expect a 5D Tensor.</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_upsample</span><span class="op">(</span>
+  size <span class="op">=</span> <span class="cn">NULL</span>,
+  scale_factor <span class="op">=</span> <span class="cn">NULL</span>,
+  mode <span class="op">=</span> <span class="st">"nearest"</span>,
+  align_corners <span class="op">=</span> <span class="cn">NULL</span>
+<span class="op">)</span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>size</dt>
+<dd><p>(int or <code>Tuple[int]</code> or <code>Tuple[int, int]</code> or <code>Tuple[int, int, int]</code>, optional):
+output spatial sizes</p></dd>
+<dt>scale_factor</dt>
+<dd><p>(float or <code>Tuple[float]</code> or <code>Tuple[float, float]</code> or <code>Tuple[float, float, float]</code>, optional):
+multiplier for spatial size. Has to match input size if it is a tuple.</p></dd>
+<dt>mode</dt>
+<dd><p>(str, optional): the upsampling algorithm: one of <code>'nearest'</code>,
+<code>'linear'</code>, <code>'bilinear'</code>, <code>'bicubic'</code> and <code>'trilinear'</code>.
+Default: <code>'nearest'</code></p></dd>
+<dt>align_corners</dt>
+<dd><p>(bool, optional): if <code>TRUE</code>, the corner pixels of the input
+and output tensors are aligned, and thus preserving the values at
+those pixels. This only has effect when <code>mode</code> is
+<code>'linear'</code>, <code>'bilinear'</code>, or <code>'trilinear'</code>. Default: <code>FALSE</code></p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
+    <p>The algorithms available for upsampling are nearest neighbor and linear, bilinear,
+bicubic and trilinear for 3D, 4D and 5D input Tensor, respectively.</p>
+<p>One can either give a scale_factor or the target output size to calculate the
+output size. (You cannot give both, as it is ambiguous)</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_arange.html">torch_arange</a></span><span class="op">(</span>start <span class="op">=</span> <span class="fl">1</span>, end <span class="op">=</span> <span class="fl">4</span>, dtype <span class="op">=</span> <span class="fu"><a href="torch_dtype.html">torch_float</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span><span class="op">$</span><span class="fu">view</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">1</span>, <span class="fl">2</span>, <span class="fl">2</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">nn_upsample</span><span class="op">(</span>scale_factor <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span><span class="op">)</span>, mode <span class="op">=</span> <span class="st">"nearest"</span><span class="op">)</span><span class="op">(</span><span class="va">input</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">nn_upsample</span><span class="op">(</span>scale_factor <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">2</span><span class="op">)</span>, mode <span class="op">=</span> <span class="st">"nearest"</span><span class="op">)</span><span class="op">(</span><span class="va">input</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> (1,1,.,.) = </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   1  1  2  2</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   1  1  2  2</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   3  3  4  4</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   3  3  4  4</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{1,1,4,4} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
+</div>
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+<div class="pkgdown-footer-right">
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+
diff --git a/reference/nn_utils_clip_grad_norm_.html b/reference/nn_utils_clip_grad_norm_.html
index fbb7c2ba5..d4e0e25a8 100644
--- a/reference/nn_utils_clip_grad_norm_.html
+++ b/reference/nn_utils_clip_grad_norm_.html
@@ -1,246 +1,126 @@
-<!-- Generated by pkgdown: do not edit by hand -->
 <!DOCTYPE html>
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-    <h1>Clips gradient norm of an iterable of parameters.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-utils-clip-grad.R'><code>R/nn-utils-clip-grad.R</code></a></small>
-    <div class="hidden name"><code>nn_utils_clip_grad_norm_.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Clips gradient norm of an iterable of parameters.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-utils-clip-grad.R" class="external-link"><code>R/nn-utils-clip-grad.R</code></a></small>
+      <div class="d-none name"><code>nn_utils_clip_grad_norm_.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>The norm is computed over all gradients together, as if they were
 concatenated into a single vector. Gradients are modified in-place.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_utils_clip_grad_norm_</span><span class='op'>(</span><span class='va'>parameters</span>, <span class='va'>max_norm</span>, norm_type <span class='op'>=</span> <span class='fl'>2</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>parameters</th>
-      <td><p>(IterableTensor or Tensor): an iterable of Tensors or a
-single Tensor that will have gradients normalized</p></td>
-    </tr>
-    <tr>
-      <th>max_norm</th>
-      <td><p>(float or int): max norm of the gradients</p></td>
-    </tr>
-    <tr>
-      <th>norm_type</th>
-      <td><p>(float or int): type of the used p-norm. Can be <code>Inf</code> for
-infinity norm.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_utils_clip_grad_norm_</span><span class="op">(</span><span class="va">parameters</span>, <span class="va">max_norm</span>, norm_type <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>parameters</dt>
+<dd><p>(IterableTensor or Tensor): an iterable of Tensors or a
+single Tensor that will have gradients normalized</p></dd>
+<dt>max_norm</dt>
+<dd><p>(float or int): max norm of the gradients</p></dd>
+<dt>norm_type</dt>
+<dd><p>(float or int): type of the used p-norm. Can be <code>Inf</code> for
+infinity norm.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
     <p>Total norm of the parameters (viewed as a single vector).</p>
+    </div>
 
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index 0729b0679..1fa0a6c80 100644
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-  <div class="col-md-9 contents">
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-    <h1>Clips gradient of an iterable of parameters at specified value.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-utils-clip-grad.R'><code>R/nn-utils-clip-grad.R</code></a></small>
-    <div class="hidden name"><code>nn_utils_clip_grad_value_.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Clips gradient of an iterable of parameters at specified value.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-utils-clip-grad.R" class="external-link"><code>R/nn-utils-clip-grad.R</code></a></small>
+      <div class="d-none name"><code>nn_utils_clip_grad_value_.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Gradients are modified in-place.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_utils_clip_grad_value_</span><span class='op'>(</span><span class='va'>parameters</span>, <span class='va'>clip_value</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>parameters</th>
-      <td><p>(Iterable(Tensor) or Tensor): an iterable of Tensors or a
-single Tensor that will have gradients normalized</p></td>
-    </tr>
-    <tr>
-      <th>clip_value</th>
-      <td><p>(float or int): maximum allowed value of the gradients.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_utils_clip_grad_value_</span><span class="op">(</span><span class="va">parameters</span>, <span class="va">clip_value</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>parameters</dt>
+<dd><p>(Iterable(Tensor) or Tensor): an iterable of Tensors or a
+single Tensor that will have gradients normalized</p></dd>
+<dt>clip_value</dt>
+<dd><p>(float or int): maximum allowed value of the gradients.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>The gradients are clipped in the range
 \(\left[\mbox{-clip\_value}, \mbox{clip\_value}\right]\)</p>
+    </div>
 
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diff --git a/reference/nn_utils_rnn_pack_padded_sequence.html b/reference/nn_utils_rnn_pack_padded_sequence.html
index 0e5f0d864..e167182b0 100644
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Packs a Tensor containing padded sequences of variable length.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-utils-rnn.R'><code>R/nn-utils-rnn.R</code></a></small>
-    <div class="hidden name"><code>nn_utils_rnn_pack_padded_sequence.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Packs a Tensor containing padded sequences of variable length.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-utils-rnn.R" class="external-link"><code>R/nn-utils-rnn.R</code></a></small>
+      <div class="d-none name"><code>nn_utils_rnn_pack_padded_sequence.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p><code>input</code> can be of size <code>T x B x *</code> where <code>T</code> is the length of the
 longest sequence (equal to <code>lengths[1]</code>), <code>B</code> is the batch size, and
 <code>*</code> is any number of dimensions (including 0). If <code>batch_first</code> is
 <code>TRUE</code>, <code>B x T x *</code> <code>input</code> is expected.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_utils_rnn_pack_padded_sequence</span><span class='op'>(</span>
-  <span class='va'>input</span>,
-  <span class='va'>lengths</span>,
-  batch_first <span class='op'>=</span> <span class='cn'>FALSE</span>,
-  enforce_sorted <span class='op'>=</span> <span class='cn'>TRUE</span>
-<span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_utils_rnn_pack_padded_sequence</span><span class="op">(</span>
+  <span class="va">input</span>,
+  <span class="va">lengths</span>,
+  batch_first <span class="op">=</span> <span class="cn">FALSE</span>,
+  enforce_sorted <span class="op">=</span> <span class="cn">TRUE</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor): padded batch of variable length sequences.</p></td>
-    </tr>
-    <tr>
-      <th>lengths</th>
-      <td><p>(Tensor): list of sequences lengths of each batch element.</p></td>
-    </tr>
-    <tr>
-      <th>batch_first</th>
-      <td><p>(bool, optional): if <code>TRUE</code>, the input is expected in <code>B x T x *</code>
-format.</p></td>
-    </tr>
-    <tr>
-      <th>enforce_sorted</th>
-      <td><p>(bool, optional): if <code>TRUE</code>, the input is expected to
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>(Tensor): padded batch of variable length sequences.</p></dd>
+<dt>lengths</dt>
+<dd><p>(Tensor): list of sequences lengths of each batch element.</p></dd>
+<dt>batch_first</dt>
+<dd><p>(bool, optional): if <code>TRUE</code>, the input is expected in <code>B x T x *</code>
+format.</p></dd>
+<dt>enforce_sorted</dt>
+<dd><p>(bool, optional): if <code>TRUE</code>, the input is expected to
 contain sequences sorted by length in a decreasing order. If
-<code>FALSE</code>, the input will get sorted unconditionally. Default: <code>TRUE</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
-
+<code>FALSE</code>, the input will get sorted unconditionally. Default: <code>TRUE</code>.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
     <p>a <code>PackedSequence</code> object</p>
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>For unsorted sequences, use <code>enforce_sorted = FALSE</code>. If <code>enforce_sorted</code> is
 <code>TRUE</code>, the sequences should be sorted by length in a decreasing order, i.e.
 <code>input[,1]</code> should be the longest sequence, and <code>input[,B]</code> the shortest
 one. <code>enforce_sorted = TRUE</code> is only necessary for ONNX export.</p>
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p>This function accepts any input that has at least two dimensions. You
 can apply it to pack the labels, and use the output of the RNN with
 them to compute the loss directly. A Tensor can be retrieved from
 a <code>PackedSequence</code> object by accessing its <code>.data</code> attribute.</p>
+    </div>
 
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Packs a list of variable length Tensors</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-utils-rnn.R'><code>R/nn-utils-rnn.R</code></a></small>
-    <div class="hidden name"><code>nn_utils_rnn_pack_sequence.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Packs a list of variable length Tensors</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-utils-rnn.R" class="external-link"><code>R/nn-utils-rnn.R</code></a></small>
+      <div class="d-none name"><code>nn_utils_rnn_pack_sequence.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p><code>sequences</code> should be a list of Tensors of size <code>L x *</code>, where <code>L</code> is
 the length of a sequence and <code>*</code> is any number of trailing dimensions,
 including zero.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_utils_rnn_pack_sequence</span><span class='op'>(</span><span class='va'>sequences</span>, enforce_sorted <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_utils_rnn_pack_sequence</span><span class="op">(</span><span class="va">sequences</span>, enforce_sorted <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>sequences</th>
-      <td><p><code>(list[Tensor])</code>: A list of sequences of decreasing length.</p></td>
-    </tr>
-    <tr>
-      <th>enforce_sorted</th>
-      <td><p>(bool, optional): if <code>TRUE</code>, checks that the input
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>sequences</dt>
+<dd><p><code>(list[Tensor])</code>: A list of sequences of decreasing length.</p></dd>
+<dt>enforce_sorted</dt>
+<dd><p>(bool, optional): if <code>TRUE</code>, checks that the input
 contains sequences sorted by length in a decreasing order. If
-<code>FALSE</code>, this condition is not checked. Default: <code>TRUE</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
-
+<code>FALSE</code>, this condition is not checked. Default: <code>TRUE</code>.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
     <p>a <code>PackedSequence</code> object</p>
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>For unsorted sequences, use <code>enforce_sorted = FALSE</code>. If <code>enforce_sorted</code>
 is <code>TRUE</code>, the sequences should be sorted in the order of decreasing length.
 <code>enforce_sorted = TRUE</code> is only necessary for ONNX export.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>x</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>,<span class='fl'>2</span>,<span class='fl'>3</span><span class='op'>)</span>, dtype <span class='op'>=</span> <span class='fu'><a href='torch_dtype.html'>torch_long</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>y</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span>, <span class='fl'>5</span><span class='op'>)</span>, dtype <span class='op'>=</span> <span class='fu'><a href='torch_dtype.html'>torch_long</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>z</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>6</span><span class='op'>)</span>, dtype <span class='op'>=</span> <span class='fu'><a href='torch_dtype.html'>torch_long</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>p</span> <span class='op'>&lt;-</span> <span class='fu'>nn_utils_rnn_pack_sequence</span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span><span class='va'>x</span>, <span class='va'>y</span>, <span class='va'>z</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">2</span>, <span class="fl">3</span><span class="op">)</span>, dtype <span class="op">=</span> <span class="fu"><a href="torch_dtype.html">torch_long</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">y</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span>, <span class="fl">5</span><span class="op">)</span>, dtype <span class="op">=</span> <span class="fu"><a href="torch_dtype.html">torch_long</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">z</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">6</span><span class="op">)</span>, dtype <span class="op">=</span> <span class="fu"><a href="torch_dtype.html">torch_long</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">p</span> <span class="op">&lt;-</span> <span class="fu">nn_utils_rnn_pack_sequence</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">y</span>, <span class="va">z</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Pads a packed batch of variable length sequences.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-utils-rnn.R'><code>R/nn-utils-rnn.R</code></a></small>
-    <div class="hidden name"><code>nn_utils_rnn_pad_packed_sequence.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Pads a packed batch of variable length sequences.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-utils-rnn.R" class="external-link"><code>R/nn-utils-rnn.R</code></a></small>
+      <div class="d-none name"><code>nn_utils_rnn_pad_packed_sequence.Rd</code></div>
     </div>
 
-    <div class="ref-description">
-    <p>It is an inverse operation to <code><a href='nn_utils_rnn_pack_padded_sequence.html'>nn_utils_rnn_pack_padded_sequence()</a></code>.</p>
+    <div class="ref-description section level2">
+    <p>It is an inverse operation to <code><a href="nn_utils_rnn_pack_padded_sequence.html">nn_utils_rnn_pack_padded_sequence()</a></code>.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_utils_rnn_pad_packed_sequence</span><span class='op'>(</span>
-  <span class='va'>sequence</span>,
-  batch_first <span class='op'>=</span> <span class='cn'>FALSE</span>,
-  padding_value <span class='op'>=</span> <span class='fl'>0</span>,
-  total_length <span class='op'>=</span> <span class='cn'>NULL</span>
-<span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_utils_rnn_pad_packed_sequence</span><span class="op">(</span>
+  <span class="va">sequence</span>,
+  batch_first <span class="op">=</span> <span class="cn">FALSE</span>,
+  padding_value <span class="op">=</span> <span class="fl">0</span>,
+  total_length <span class="op">=</span> <span class="cn">NULL</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>sequence</th>
-      <td><p>(PackedSequence): batch to pad</p></td>
-    </tr>
-    <tr>
-      <th>batch_first</th>
-      <td><p>(bool, optional): if <code>True</code>, the output will be in ``B x T x *`
-format.</p></td>
-    </tr>
-    <tr>
-      <th>padding_value</th>
-      <td><p>(float, optional): values for padded elements.</p></td>
-    </tr>
-    <tr>
-      <th>total_length</th>
-      <td><p>(int, optional): if not <code>NULL</code>, the output will be padded to
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>sequence</dt>
+<dd><p>(PackedSequence): batch to pad</p></dd>
+<dt>batch_first</dt>
+<dd><p>(bool, optional): if <code>True</code>, the output will be in ``B x T x *`
+format.</p></dd>
+<dt>padding_value</dt>
+<dd><p>(float, optional): values for padded elements.</p></dd>
+<dt>total_length</dt>
+<dd><p>(int, optional): if not <code>NULL</code>, the output will be padded to
 have length <code>total_length</code>. This method will throw <code>ValueError</code>
 if <code>total_length</code> is less than the max sequence length in
-<code>sequence</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
-
+<code>sequence</code>.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
     <p>Tuple of Tensor containing the padded sequence, and a Tensor
 containing the list of lengths of each sequence in the batch.
 Batch elements will be re-ordered as they were ordered originally when
-the batch was passed to <code><a href='nn_utils_rnn_pack_padded_sequence.html'>nn_utils_rnn_pack_padded_sequence()</a></code> or
-<code><a href='nn_utils_rnn_pack_sequence.html'>nn_utils_rnn_pack_sequence()</a></code>.</p>
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
+the batch was passed to <code><a href="nn_utils_rnn_pack_padded_sequence.html">nn_utils_rnn_pack_padded_sequence()</a></code> or
+<code><a href="nn_utils_rnn_pack_sequence.html">nn_utils_rnn_pack_sequence()</a></code>.</p>
+    </div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>The returned Tensor's data will be of size <code>T x B x *</code>, where <code>T</code> is the length
 of the longest sequence and <code>B</code> is the batch size. If <code>batch_first</code> is <code>TRUE</code>,
 the data will be transposed into <code>B x T x *</code> format.</p>
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p><code>total_length</code> is useful to implement the
 <code>pack sequence -&gt; recurrent network -&gt; unpack sequence</code> pattern in a
 <code>nn_module</code> wrapped in <code>~torch.nn.DataParallel</code>.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>seq</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/cbind.html'>rbind</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>,<span class='fl'>2</span>,<span class='fl'>0</span><span class='op'>)</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3</span>,<span class='fl'>0</span>,<span class='fl'>0</span><span class='op'>)</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span>,<span class='fl'>5</span>,<span class='fl'>6</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span>        </span>
-<span class='r-in'><span class='va'>lens</span> <span class='op'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>2</span>,<span class='fl'>1</span>,<span class='fl'>3</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>packed</span> <span class='op'>&lt;-</span> <span class='fu'><a href='nn_utils_rnn_pack_padded_sequence.html'>nn_utils_rnn_pack_padded_sequence</a></span><span class='op'>(</span><span class='va'>seq</span>, <span class='va'>lens</span>, batch_first <span class='op'>=</span> <span class='cn'>TRUE</span>,</span>
-<span class='r-in'>                                            enforce_sorted <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>packed</span></span>
-<span class='r-in'><span class='fu'>nn_utils_rnn_pad_packed_sequence</span><span class='op'>(</span><span class='va'>packed</span>, batch_first<span class='op'>=</span><span class='cn'>TRUE</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [[1]]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1  2  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  3  0  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  4  5  6</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{3,3} ]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [[2]]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  2</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  3</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPULongType{3} ]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">seq</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/cbind.html" class="external-link">rbind</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">2</span>, <span class="fl">0</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">0</span>, <span class="fl">0</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span>, <span class="fl">5</span>, <span class="fl">6</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">lens</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">1</span>, <span class="fl">3</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">packed</span> <span class="op">&lt;-</span> <span class="fu"><a href="nn_utils_rnn_pack_padded_sequence.html">nn_utils_rnn_pack_padded_sequence</a></span><span class="op">(</span><span class="va">seq</span>, <span class="va">lens</span>,</span>
+<span class="r-in">  batch_first <span class="op">=</span> <span class="cn">TRUE</span>,</span>
+<span class="r-in">  enforce_sorted <span class="op">=</span> <span class="cn">FALSE</span></span>
+<span class="r-in"><span class="op">)</span></span>
+<span class="r-in"><span class="va">packed</span></span>
+<span class="r-in"><span class="fu">nn_utils_rnn_pad_packed_sequence</span><span class="op">(</span><span class="va">packed</span>, batch_first <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [[1]]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1  2  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  3  0  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  4  5  6</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3,3} ]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [[2]]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  3</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPULongType{3} ]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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diff --git a/reference/nn_utils_rnn_pad_sequence.html b/reference/nn_utils_rnn_pad_sequence.html
index ae369c39a..8d1d3af52 100644
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Pad a list of variable length Tensors with <code>padding_value</code></h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-utils-rnn.R'><code>R/nn-utils-rnn.R</code></a></small>
-    <div class="hidden name"><code>nn_utils_rnn_pad_sequence.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Pad a list of variable length Tensors with <code>padding_value</code></h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-utils-rnn.R" class="external-link"><code>R/nn-utils-rnn.R</code></a></small>
+      <div class="d-none name"><code>nn_utils_rnn_pad_sequence.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p><code>pad_sequence</code> stacks a list of Tensors along a new dimension,
 and pads them to equal length. For example, if the input is list of
 sequences with size <code>L x *</code> and if batch_first is False, and <code>T x B x *</code>
 otherwise.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nn_utils_rnn_pad_sequence</span><span class='op'>(</span><span class='va'>sequences</span>, batch_first <span class='op'>=</span> <span class='cn'>FALSE</span>, padding_value <span class='op'>=</span> <span class='fl'>0</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>sequences</th>
-      <td><p><code>(list[Tensor])</code>: list of variable length sequences.</p></td>
-    </tr>
-    <tr>
-      <th>batch_first</th>
-      <td><p>(bool, optional): output will be in <code>B x T x *</code> if <code>TRUE</code>,
-or in <code>T x B x *</code> otherwise</p></td>
-    </tr>
-    <tr>
-      <th>padding_value</th>
-      <td><p>(float, optional): value for padded elements. Default: 0.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nn_utils_rnn_pad_sequence</span><span class="op">(</span><span class="va">sequences</span>, batch_first <span class="op">=</span> <span class="cn">FALSE</span>, padding_value <span class="op">=</span> <span class="fl">0</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>sequences</dt>
+<dd><p><code>(list[Tensor])</code>: list of variable length sequences.</p></dd>
+<dt>batch_first</dt>
+<dd><p>(bool, optional): output will be in <code>B x T x *</code> if <code>TRUE</code>,
+or in <code>T x B x *</code> otherwise</p></dd>
+<dt>padding_value</dt>
+<dd><p>(float, optional): value for padded elements. Default: 0.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
     <p>Tensor of size <code>T x B x *</code> if <code>batch_first</code> is <code>FALSE</code>.
 Tensor of size <code>B x T x *</code> otherwise</p>
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p><code>B</code> is batch size. It is equal to the number of elements in <code>sequences</code>.
 <code>T</code> is length of the longest sequence.
 <code>L</code> is length of the sequence.
 <code>*</code> is any number of trailing dimensions, including none.</p>
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p>This function returns a Tensor of size <code>T x B x *</code> or <code>B x T x *</code>
 where <code>T</code> is the length of the longest sequence. This function assumes
 trailing dimensions and type of all the Tensors in sequences are same.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_ones.html'>torch_ones</a></span><span class='op'>(</span><span class='fl'>25</span>, <span class='fl'>300</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>b</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_ones.html'>torch_ones</a></span><span class='op'>(</span><span class='fl'>22</span>, <span class='fl'>300</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>c</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_ones.html'>torch_ones</a></span><span class='op'>(</span><span class='fl'>15</span>, <span class='fl'>300</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>nn_utils_rnn_pad_sequence</span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span><span class='va'>a</span>, <span class='va'>b</span>, <span class='va'>c</span><span class='op'>)</span><span class='op'>)</span><span class='op'>$</span><span class='fu'>size</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [1]  25   3 300</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_ones.html">torch_ones</a></span><span class="op">(</span><span class="fl">25</span>, <span class="fl">300</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">b</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_ones.html">torch_ones</a></span><span class="op">(</span><span class="fl">22</span>, <span class="fl">300</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">c</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_ones.html">torch_ones</a></span><span class="op">(</span><span class="fl">15</span>, <span class="fl">300</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">nn_utils_rnn_pad_sequence</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">a</span>, <span class="va">b</span>, <span class="va">c</span><span class="op">)</span><span class="op">)</span><span class="op">$</span><span class="fu">size</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1]  25   3 300</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer>
-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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+<div class="pkgdown-footer-right">
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+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Adaptive_avg_pool1d</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-pooling.R" class="external-link"><code>R/nnf-pooling.R</code></a></small>
+      <div class="d-none name"><code>nnf_adaptive_avg_pool1d.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Applies a 1D adaptive average pooling over an input signal composed of
 several input planes.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_adaptive_avg_pool1d</span><span class='op'>(</span><span class='va'>input</span>, <span class='va'>output_size</span><span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_adaptive_avg_pool1d</span><span class="op">(</span><span class="va">input</span>, <span class="va">output_size</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>input tensor of shape (minibatch , in_channels , iW)</p></td>
-    </tr>
-    <tr>
-      <th>output_size</th>
-      <td><p>the target output size (single integer)</p></td>
-    </tr>
-    </table>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>input tensor of shape (minibatch , in_channels , iW)</p></dd>
+<dt>output_size</dt>
+<dd><p>the target output size (single integer)</p></dd>
+</dl></div>
 
-
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+    <footer><div class="pkgdown-footer-left">
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index 66d90324e..9ee2db8af 100644
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+++ b/reference/nnf_adaptive_avg_pool2d.html
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 <div class="row">
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-    <h1>Adaptive_avg_pool2d</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-pooling.R'><code>R/nnf-pooling.R</code></a></small>
-    <div class="hidden name"><code>nnf_adaptive_avg_pool2d.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Adaptive_avg_pool2d</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-pooling.R" class="external-link"><code>R/nnf-pooling.R</code></a></small>
+      <div class="d-none name"><code>nnf_adaptive_avg_pool2d.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Applies a 2D adaptive average pooling over an input signal composed of
 several input planes.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_adaptive_avg_pool2d</span><span class='op'>(</span><span class='va'>input</span>, <span class='va'>output_size</span><span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_adaptive_avg_pool2d</span><span class="op">(</span><span class="va">input</span>, <span class="va">output_size</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>input tensor (minibatch, in_channels , iH , iW)</p></td>
-    </tr>
-    <tr>
-      <th>output_size</th>
-      <td><p>the target output size (single integer or double-integer tuple)</p></td>
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-    </table>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>input tensor (minibatch, in_channels , iH , iW)</p></dd>
+<dt>output_size</dt>
+<dd><p>the target output size (single integer or double-integer tuple)</p></dd>
+</dl></div>
 
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diff --git a/reference/nnf_adaptive_avg_pool3d.html b/reference/nnf_adaptive_avg_pool3d.html
index 31747e1c1..7d792d47b 100644
--- a/reference/nnf_adaptive_avg_pool3d.html
+++ b/reference/nnf_adaptive_avg_pool3d.html
@@ -1,237 +1,118 @@
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-    <h1>Adaptive_avg_pool3d</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-pooling.R'><code>R/nnf-pooling.R</code></a></small>
-    <div class="hidden name"><code>nnf_adaptive_avg_pool3d.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Adaptive_avg_pool3d</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-pooling.R" class="external-link"><code>R/nnf-pooling.R</code></a></small>
+      <div class="d-none name"><code>nnf_adaptive_avg_pool3d.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Applies a 3D adaptive average pooling over an input signal composed of
 several input planes.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_adaptive_avg_pool3d</span><span class='op'>(</span><span class='va'>input</span>, <span class='va'>output_size</span><span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_adaptive_avg_pool3d</span><span class="op">(</span><span class="va">input</span>, <span class="va">output_size</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>input tensor (minibatch, in_channels , iT * iH , iW)</p></td>
-    </tr>
-    <tr>
-      <th>output_size</th>
-      <td><p>the target output size (single integer or triple-integer tuple)</p></td>
-    </tr>
-    </table>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>input tensor (minibatch, in_channels , iT * iH , iW)</p></dd>
+<dt>output_size</dt>
+<dd><p>the target output size (single integer or triple-integer tuple)</p></dd>
+</dl></div>
 
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diff --git a/reference/nnf_adaptive_max_pool1d.html b/reference/nnf_adaptive_max_pool1d.html
index 1924cc6a5..e9c4e0caf 100644
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-    <h1>Adaptive_max_pool1d</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-pooling.R'><code>R/nnf-pooling.R</code></a></small>
-    <div class="hidden name"><code>nnf_adaptive_max_pool1d.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Adaptive_max_pool1d</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-pooling.R" class="external-link"><code>R/nnf-pooling.R</code></a></small>
+      <div class="d-none name"><code>nnf_adaptive_max_pool1d.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Applies a 1D adaptive max pooling over an input signal composed of
 several input planes.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_adaptive_max_pool1d</span><span class='op'>(</span><span class='va'>input</span>, <span class='va'>output_size</span>, return_indices <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>input tensor of shape (minibatch , in_channels , iW)</p></td>
-    </tr>
-    <tr>
-      <th>output_size</th>
-      <td><p>the target output size (single integer)</p></td>
-    </tr>
-    <tr>
-      <th>return_indices</th>
-      <td><p>whether to return pooling indices. Default: <code>FALSE</code></p></td>
-    </tr>
-    </table>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_adaptive_max_pool1d</span><span class="op">(</span><span class="va">input</span>, <span class="va">output_size</span>, return_indices <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>input tensor of shape (minibatch , in_channels , iW)</p></dd>
+<dt>output_size</dt>
+<dd><p>the target output size (single integer)</p></dd>
+<dt>return_indices</dt>
+<dd><p>whether to return pooling indices. Default: <code>FALSE</code></p></dd>
+</dl></div>
 
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index a5e7c5eba..a21c7c747 100644
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-    <h1>Adaptive_max_pool2d</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-pooling.R'><code>R/nnf-pooling.R</code></a></small>
-    <div class="hidden name"><code>nnf_adaptive_max_pool2d.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Adaptive_max_pool2d</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-pooling.R" class="external-link"><code>R/nnf-pooling.R</code></a></small>
+      <div class="d-none name"><code>nnf_adaptive_max_pool2d.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Applies a 2D adaptive max pooling over an input signal composed of
 several input planes.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_adaptive_max_pool2d</span><span class='op'>(</span><span class='va'>input</span>, <span class='va'>output_size</span>, return_indices <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>input tensor (minibatch, in_channels , iH , iW)</p></td>
-    </tr>
-    <tr>
-      <th>output_size</th>
-      <td><p>the target output size (single integer or double-integer tuple)</p></td>
-    </tr>
-    <tr>
-      <th>return_indices</th>
-      <td><p>whether to return pooling indices. Default: <code>FALSE</code></p></td>
-    </tr>
-    </table>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_adaptive_max_pool2d</span><span class="op">(</span><span class="va">input</span>, <span class="va">output_size</span>, return_indices <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>input tensor (minibatch, in_channels , iH , iW)</p></dd>
+<dt>output_size</dt>
+<dd><p>the target output size (single integer or double-integer tuple)</p></dd>
+<dt>return_indices</dt>
+<dd><p>whether to return pooling indices. Default: <code>FALSE</code></p></dd>
+</dl></div>
 
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+  <main id="main" class="col-md-9"><div class="page-header">
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+    <div class="ref-description section level2">
     <p>Applies a 3D adaptive max pooling over an input signal composed of
 several input planes.</p>
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-    <pre class="usage"><span class='fu'>nnf_adaptive_max_pool3d</span><span class='op'>(</span><span class='va'>input</span>, <span class='va'>output_size</span>, return_indices <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span></pre>
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-    <tr>
-      <th>input</th>
-      <td><p>input tensor (minibatch, in_channels , iT * iH , iW)</p></td>
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-    <tr>
-      <th>output_size</th>
-      <td><p>the target output size (single integer or triple-integer tuple)</p></td>
-    </tr>
-    <tr>
-      <th>return_indices</th>
-      <td><p>whether to return pooling indices. Default:<code>FALSE</code></p></td>
-    </tr>
-    </table>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_adaptive_max_pool3d</span><span class="op">(</span><span class="va">input</span>, <span class="va">output_size</span>, return_indices <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>input tensor (minibatch, in_channels , iT * iH , iW)</p></dd>
+<dt>output_size</dt>
+<dd><p>the target output size (single integer or triple-integer tuple)</p></dd>
+<dt>return_indices</dt>
+<dd><p>whether to return pooling indices. Default:<code>FALSE</code></p></dd>
+</dl></div>
 
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+</nav><div class="container template-reference-topic">
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-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Affine_grid</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-vision.R'><code>R/nnf-vision.R</code></a></small>
-    <div class="hidden name"><code>nnf_affine_grid.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Affine_grid</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-vision.R" class="external-link"><code>R/nnf-vision.R</code></a></small>
+      <div class="d-none name"><code>nnf_affine_grid.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Generates a 2D or 3D flow field (sampling grid), given a batch of
 affine matrices <code>theta</code>.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_affine_grid</span><span class='op'>(</span><span class='va'>theta</span>, <span class='va'>size</span>, align_corners <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>theta</th>
-      <td><p>(Tensor) input batch of affine matrices with shape
-(\(N \times 2 \times 3\)) for 2D or  (\(N \times 3 \times 4\)) for 3D</p></td>
-    </tr>
-    <tr>
-      <th>size</th>
-      <td><p>(torch.Size) the target output image size. (\(N \times C \times H \times W\)
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_affine_grid</span><span class="op">(</span><span class="va">theta</span>, <span class="va">size</span>, align_corners <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>theta</dt>
+<dd><p>(Tensor) input batch of affine matrices with shape
+(\(N \times 2 \times 3\)) for 2D or  (\(N \times 3 \times 4\)) for 3D</p></dd>
+<dt>size</dt>
+<dd><p>(torch.Size) the target output image size. (\(N \times C \times H \times W\)
 for 2D or \(N \times C \times D \times H \times W\) for 3D)
-Example: torch.Size((32, 3, 24, 24))</p></td>
-    </tr>
-    <tr>
-      <th>align_corners</th>
-      <td><p>(bool, optional) if <code>True</code>, consider <code>-1</code> and <code>1</code>
+Example: torch.Size((32, 3, 24, 24))</p></dd>
+<dt>align_corners</dt>
+<dd><p>(bool, optional) if <code>True</code>, consider <code>-1</code> and <code>1</code>
 to refer to the centers of the corner pixels rather than the image corners.
-Refer to <code><a href='nnf_grid_sample.html'>nnf_grid_sample()</a></code> for a more complete description. A grid generated by
-<code>nnf_affine_grid()</code> should be passed to <code><a href='nnf_grid_sample.html'>nnf_grid_sample()</a></code>  with the same setting for
-this option. Default: <code>False</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
+Refer to <code><a href="nnf_grid_sample.html">nnf_grid_sample()</a></code> for a more complete description. A grid generated by
+<code>nnf_affine_grid()</code> should be passed to <code><a href="nnf_grid_sample.html">nnf_grid_sample()</a></code>  with the same setting for
+this option. Default: <code>False</code></p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     
 
 
-<p>This function is often used in conjunction with <code><a href='nnf_grid_sample.html'>nnf_grid_sample()</a></code>
+<p>This function is often used in conjunction with <code><a href="nnf_grid_sample.html">nnf_grid_sample()</a></code>
 to build <code>Spatial Transformer Networks</code>_ .</p>
+    </div>
 
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-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Applies alpha dropout to the input.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_alpha_dropout</span><span class='op'>(</span><span class='va'>input</span>, p <span class='op'>=</span> <span class='fl'>0.5</span>, training <span class='op'>=</span> <span class='cn'>FALSE</span>, inplace <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span></pre>
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-    <table class="ref-arguments">
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-    <tr>
-      <th>input</th>
-      <td><p>the input tensor</p></td>
-    </tr>
-    <tr>
-      <th>p</th>
-      <td><p>probability of an element to be zeroed. Default: 0.5</p></td>
-    </tr>
-    <tr>
-      <th>training</th>
-      <td><p>apply dropout if is <code>TRUE</code>. Default: <code>TRUE</code></p></td>
-    </tr>
-    <tr>
-      <th>inplace</th>
-      <td><p>If set to <code>TRUE</code>, will do this operation in-place.
-Default: <code>FALSE</code></p></td>
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+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_alpha_dropout</span><span class="op">(</span><span class="va">input</span>, p <span class="op">=</span> <span class="fl">0.5</span>, training <span class="op">=</span> <span class="cn">FALSE</span>, inplace <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
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+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>the input tensor</p></dd>
+<dt>p</dt>
+<dd><p>probability of an element to be zeroed. Default: 0.5</p></dd>
+<dt>training</dt>
+<dd><p>apply dropout if is <code>TRUE</code>. Default: <code>TRUE</code></p></dd>
+<dt>inplace</dt>
+<dd><p>If set to <code>TRUE</code>, will do this operation in-place.
+Default: <code>FALSE</code></p></dd>
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Avg_pool1d</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-pooling.R'><code>R/nnf-pooling.R</code></a></small>
-    <div class="hidden name"><code>nnf_avg_pool1d.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Avg_pool1d</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-pooling.R" class="external-link"><code>R/nnf-pooling.R</code></a></small>
+      <div class="d-none name"><code>nnf_avg_pool1d.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Applies a 1D average pooling over an input signal composed of several
 input planes.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_avg_pool1d</span><span class='op'>(</span>
-  <span class='va'>input</span>,
-  <span class='va'>kernel_size</span>,
-  stride <span class='op'>=</span> <span class='cn'>NULL</span>,
-  padding <span class='op'>=</span> <span class='fl'>0</span>,
-  ceil_mode <span class='op'>=</span> <span class='cn'>FALSE</span>,
-  count_include_pad <span class='op'>=</span> <span class='cn'>TRUE</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>input tensor of shape (minibatch , in_channels , iW)</p></td>
-    </tr>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>the size of the window. Can be a single number or a
-tuple <code>(kW,)</code>.</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>the stride of the window. Can be a single number or a tuple
-<code>(sW,)</code>. Default: <code>kernel_size</code></p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>implicit zero paddings on both sides of the input. Can be a
-single number or a tuple <code>(padW,)</code>. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>ceil_mode</th>
-      <td><p>when True, will use <code>ceil</code> instead of <code>floor</code> to compute the
-output shape. Default: <code>FALSE</code></p></td>
-    </tr>
-    <tr>
-      <th>count_include_pad</th>
-      <td><p>when True, will include the zero-padding in the
-averaging calculation. Default: <code>TRUE</code></p></td>
-    </tr>
-    </table>
-
-
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_avg_pool1d</span><span class="op">(</span>
+  <span class="va">input</span>,
+  <span class="va">kernel_size</span>,
+  stride <span class="op">=</span> <span class="cn">NULL</span>,
+  padding <span class="op">=</span> <span class="fl">0</span>,
+  ceil_mode <span class="op">=</span> <span class="cn">FALSE</span>,
+  count_include_pad <span class="op">=</span> <span class="cn">TRUE</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
-      <footer>
-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>input tensor of shape (minibatch , in_channels , iW)</p></dd>
+<dt>kernel_size</dt>
+<dd><p>the size of the window. Can be a single number or a
+tuple <code>(kW,)</code>.</p></dd>
+<dt>stride</dt>
+<dd><p>the stride of the window. Can be a single number or a tuple
+<code>(sW,)</code>. Default: <code>kernel_size</code></p></dd>
+<dt>padding</dt>
+<dd><p>implicit zero paddings on both sides of the input. Can be a
+single number or a tuple <code>(padW,)</code>. Default: 0</p></dd>
+<dt>ceil_mode</dt>
+<dd><p>when True, will use <code>ceil</code> instead of <code>floor</code> to compute the
+output shape. Default: <code>FALSE</code></p></dd>
+<dt>count_include_pad</dt>
+<dd><p>when True, will include the zero-padding in the
+averaging calculation. Default: <code>TRUE</code></p></dd>
+</dl></div>
+
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
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+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Avg_pool2d</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-pooling.R'><code>R/nnf-pooling.R</code></a></small>
-    <div class="hidden name"><code>nnf_avg_pool2d.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Avg_pool2d</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-pooling.R" class="external-link"><code>R/nnf-pooling.R</code></a></small>
+      <div class="d-none name"><code>nnf_avg_pool2d.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Applies 2D average-pooling operation in \(kH * kW\) regions by step size
 \(sH * sW\) steps. The number of output features is equal to the number of
 input planes.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_avg_pool2d</span><span class='op'>(</span>
-  <span class='va'>input</span>,
-  <span class='va'>kernel_size</span>,
-  stride <span class='op'>=</span> <span class='cn'>NULL</span>,
-  padding <span class='op'>=</span> <span class='fl'>0</span>,
-  ceil_mode <span class='op'>=</span> <span class='cn'>FALSE</span>,
-  count_include_pad <span class='op'>=</span> <span class='cn'>TRUE</span>,
-  divisor_override <span class='op'>=</span> <span class='cn'>NULL</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>input tensor (minibatch, in_channels , iH , iW)</p></td>
-    </tr>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>size of the pooling region. Can be a single number or a
-tuple <code>(kH, kW)</code></p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>stride of the pooling operation. Can be a single number or a
-tuple <code>(sH, sW)</code>. Default: <code>kernel_size</code></p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>implicit zero paddings on both sides of the input. Can be a
-single number or a tuple <code>(padH, padW)</code>. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>ceil_mode</th>
-      <td><p>when True, will use <code>ceil</code> instead of <code>floor</code> in the formula
-to compute the output shape. Default: <code>FALSE</code></p></td>
-    </tr>
-    <tr>
-      <th>count_include_pad</th>
-      <td><p>when True, will include the zero-padding in the
-averaging calculation. Default: <code>TRUE</code></p></td>
-    </tr>
-    <tr>
-      <th>divisor_override</th>
-      <td><p>if specified, it will be used as divisor, otherwise
-size of the pooling region will be used. Default: <code>NULL</code></p></td>
-    </tr>
-    </table>
-
-
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
-
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_avg_pool2d</span><span class="op">(</span>
+  <span class="va">input</span>,
+  <span class="va">kernel_size</span>,
+  stride <span class="op">=</span> <span class="cn">NULL</span>,
+  padding <span class="op">=</span> <span class="fl">0</span>,
+  ceil_mode <span class="op">=</span> <span class="cn">FALSE</span>,
+  count_include_pad <span class="op">=</span> <span class="cn">TRUE</span>,
+  divisor_override <span class="op">=</span> <span class="cn">NULL</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
-      <footer>
-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>input tensor (minibatch, in_channels , iH , iW)</p></dd>
+<dt>kernel_size</dt>
+<dd><p>size of the pooling region. Can be a single number or a
+tuple <code>(kH, kW)</code></p></dd>
+<dt>stride</dt>
+<dd><p>stride of the pooling operation. Can be a single number or a
+tuple <code>(sH, sW)</code>. Default: <code>kernel_size</code></p></dd>
+<dt>padding</dt>
+<dd><p>implicit zero paddings on both sides of the input. Can be a
+single number or a tuple <code>(padH, padW)</code>. Default: 0</p></dd>
+<dt>ceil_mode</dt>
+<dd><p>when True, will use <code>ceil</code> instead of <code>floor</code> in the formula
+to compute the output shape. Default: <code>FALSE</code></p></dd>
+<dt>count_include_pad</dt>
+<dd><p>when True, will include the zero-padding in the
+averaging calculation. Default: <code>TRUE</code></p></dd>
+<dt>divisor_override</dt>
+<dd><p>if specified, it will be used as divisor, otherwise
+size of the pooling region will be used. Default: <code>NULL</code></p></dd>
+</dl></div>
+
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
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+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
 </div>
 
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index e28e61a74..a12784aef 100644
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+  </div>
+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Avg_pool3d</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-pooling.R'><code>R/nnf-pooling.R</code></a></small>
-    <div class="hidden name"><code>nnf_avg_pool3d.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Avg_pool3d</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-pooling.R" class="external-link"><code>R/nnf-pooling.R</code></a></small>
+      <div class="d-none name"><code>nnf_avg_pool3d.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Applies 3D average-pooling operation in \(kT * kH * kW\) regions by step
 size \(sT * sH * sW\) steps. The number of output features is equal to
 \(\lfloor \frac{ \mbox{input planes} }{sT} \rfloor\).</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_avg_pool3d</span><span class='op'>(</span>
-  <span class='va'>input</span>,
-  <span class='va'>kernel_size</span>,
-  stride <span class='op'>=</span> <span class='cn'>NULL</span>,
-  padding <span class='op'>=</span> <span class='fl'>0</span>,
-  ceil_mode <span class='op'>=</span> <span class='cn'>FALSE</span>,
-  count_include_pad <span class='op'>=</span> <span class='cn'>TRUE</span>,
-  divisor_override <span class='op'>=</span> <span class='cn'>NULL</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>input tensor (minibatch, in_channels , iT * iH , iW)</p></td>
-    </tr>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>size of the pooling region. Can be a single number or a
-tuple <code>(kT, kH, kW)</code></p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>stride of the pooling operation. Can be a single number or a
-tuple <code>(sT, sH, sW)</code>. Default: <code>kernel_size</code></p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>implicit zero paddings on both sides of the input. Can be a
-single number or a tuple <code>(padT, padH, padW)</code>, Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>ceil_mode</th>
-      <td><p>when True, will use <code>ceil</code> instead of <code>floor</code> in the formula
-to compute the output shape</p></td>
-    </tr>
-    <tr>
-      <th>count_include_pad</th>
-      <td><p>when True, will include the zero-padding in the
-averaging calculation</p></td>
-    </tr>
-    <tr>
-      <th>divisor_override</th>
-      <td><p>NA if specified, it will be used as divisor, otherwise
-size of the pooling region will be used. Default: <code>NULL</code></p></td>
-    </tr>
-    </table>
-
-
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
-
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_avg_pool3d</span><span class="op">(</span>
+  <span class="va">input</span>,
+  <span class="va">kernel_size</span>,
+  stride <span class="op">=</span> <span class="cn">NULL</span>,
+  padding <span class="op">=</span> <span class="fl">0</span>,
+  ceil_mode <span class="op">=</span> <span class="cn">FALSE</span>,
+  count_include_pad <span class="op">=</span> <span class="cn">TRUE</span>,
+  divisor_override <span class="op">=</span> <span class="cn">NULL</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
-      <footer>
-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>input tensor (minibatch, in_channels , iT * iH , iW)</p></dd>
+<dt>kernel_size</dt>
+<dd><p>size of the pooling region. Can be a single number or a
+tuple <code>(kT, kH, kW)</code></p></dd>
+<dt>stride</dt>
+<dd><p>stride of the pooling operation. Can be a single number or a
+tuple <code>(sT, sH, sW)</code>. Default: <code>kernel_size</code></p></dd>
+<dt>padding</dt>
+<dd><p>implicit zero paddings on both sides of the input. Can be a
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+<dd><p>when True, will use <code>ceil</code> instead of <code>floor</code> in the formula
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+<dt>count_include_pad</dt>
+<dd><p>when True, will include the zero-padding in the
+averaging calculation</p></dd>
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Batch_norm</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-batchnorm.R'><code>R/nnf-batchnorm.R</code></a></small>
-    <div class="hidden name"><code>nnf_batch_norm.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Batch_norm</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-batchnorm.R" class="external-link"><code>R/nnf-batchnorm.R</code></a></small>
+      <div class="d-none name"><code>nnf_batch_norm.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Applies Batch Normalization for each channel across a batch of data.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_batch_norm</span><span class='op'>(</span>
-  <span class='va'>input</span>,
-  <span class='va'>running_mean</span>,
-  <span class='va'>running_var</span>,
-  weight <span class='op'>=</span> <span class='cn'>NULL</span>,
-  bias <span class='op'>=</span> <span class='cn'>NULL</span>,
-  training <span class='op'>=</span> <span class='cn'>FALSE</span>,
-  momentum <span class='op'>=</span> <span class='fl'>0.1</span>,
-  eps <span class='op'>=</span> <span class='fl'>1e-05</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>input tensor</p></td>
-    </tr>
-    <tr>
-      <th>running_mean</th>
-      <td><p>the running_mean tensor</p></td>
-    </tr>
-    <tr>
-      <th>running_var</th>
-      <td><p>the running_var tensor</p></td>
-    </tr>
-    <tr>
-      <th>weight</th>
-      <td><p>the weight tensor</p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>the bias tensor</p></td>
-    </tr>
-    <tr>
-      <th>training</th>
-      <td><p>bool wether it's training. Default: FALSE</p></td>
-    </tr>
-    <tr>
-      <th>momentum</th>
-      <td><p>the value used for the <code>running_mean</code> and <code>running_var</code> computation.
-Can be set to None for cumulative moving average (i.e. simple average). Default: 0.1</p></td>
-    </tr>
-    <tr>
-      <th>eps</th>
-      <td><p>a value added to the denominator for numerical stability. Default: 1e-5</p></td>
-    </tr>
-    </table>
-
-
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
-
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_batch_norm</span><span class="op">(</span>
+  <span class="va">input</span>,
+  <span class="va">running_mean</span>,
+  <span class="va">running_var</span>,
+  weight <span class="op">=</span> <span class="cn">NULL</span>,
+  bias <span class="op">=</span> <span class="cn">NULL</span>,
+  training <span class="op">=</span> <span class="cn">FALSE</span>,
+  momentum <span class="op">=</span> <span class="fl">0.1</span>,
+  eps <span class="op">=</span> <span class="fl">1e-05</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
-      <footer>
-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>input tensor</p></dd>
+<dt>running_mean</dt>
+<dd><p>the running_mean tensor</p></dd>
+<dt>running_var</dt>
+<dd><p>the running_var tensor</p></dd>
+<dt>weight</dt>
+<dd><p>the weight tensor</p></dd>
+<dt>bias</dt>
+<dd><p>the bias tensor</p></dd>
+<dt>training</dt>
+<dd><p>bool wether it's training. Default: FALSE</p></dd>
+<dt>momentum</dt>
+<dd><p>the value used for the <code>running_mean</code> and <code>running_var</code> computation.
+Can be set to None for cumulative moving average (i.e. simple average). Default: 0.1</p></dd>
+<dt>eps</dt>
+<dd><p>a value added to the denominator for numerical stability. Default: 1e-5</p></dd>
+</dl></div>
+
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diff --git a/reference/nnf_bilinear.html b/reference/nnf_bilinear.html
index 00ff7fdab..69d15c3c8 100644
--- a/reference/nnf_bilinear.html
+++ b/reference/nnf_bilinear.html
@@ -1,252 +1,129 @@
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-    <h1>Bilinear</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-linear.R'><code>R/nnf-linear.R</code></a></small>
-    <div class="hidden name"><code>nnf_bilinear.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Bilinear</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-linear.R" class="external-link"><code>R/nnf-linear.R</code></a></small>
+      <div class="d-none name"><code>nnf_bilinear.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Applies a bilinear transformation to the incoming data:
 \(y = x_1 A x_2 + b\)</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_bilinear</span><span class='op'>(</span><span class='va'>input1</span>, <span class='va'>input2</span>, <span class='va'>weight</span>, bias <span class='op'>=</span> <span class='cn'>NULL</span><span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_bilinear</span><span class="op">(</span><span class="va">input1</span>, <span class="va">input2</span>, <span class="va">weight</span>, bias <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input1</th>
-      <td><p>\((N, *, H_{in1})\) where \(H_{in1}=\mbox{in1\_features}\)
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input1</dt>
+<dd><p>\((N, *, H_{in1})\) where \(H_{in1}=\mbox{in1\_features}\)
 and \(*\) means any number of additional dimensions.
-All but the last dimension of the inputs should be the same.</p></td>
-    </tr>
-    <tr>
-      <th>input2</th>
-      <td><p>\((N, *, H_{in2})\) where \(H_{in2}=\mbox{in2\_features}\)</p></td>
-    </tr>
-    <tr>
-      <th>weight</th>
-      <td><p>\((\mbox{out\_features}, \mbox{in1\_features},
-\mbox{in2\_features})\)</p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>\((\mbox{out\_features})\)</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
-
-    <p>output \((N, *, H_{out})\) where \(H_{out}=\mbox{out\_features}\)
-and all but the last dimension are the same shape as the input.</p>
+All but the last dimension of the inputs should be the same.</p></dd>
+<dt>input2</dt>
+<dd><p>\((N, *, H_{in2})\) where \(H_{in2}=\mbox{in2\_features}\)</p></dd>
+<dt>weight</dt>
+<dd><p>\((\mbox{out\_features}, \mbox{in1\_features},
+\mbox{in2\_features})\)</p></dd>
+<dt>bias</dt>
+<dd><p>\((\mbox{out\_features})\)</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>output \((N, *, H_{out})\) where \(H_{out}=\mbox{out\_features}\)and all but the last dimension are the same shape as the input.</p>
+    </div>
 
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diff --git a/reference/nnf_binary_cross_entropy.html b/reference/nnf_binary_cross_entropy.html
index ad1c7feb2..84e181c0b 100644
--- a/reference/nnf_binary_cross_entropy.html
+++ b/reference/nnf_binary_cross_entropy.html
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-    <h1>Binary_cross_entropy</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-loss.R'><code>R/nnf-loss.R</code></a></small>
-    <div class="hidden name"><code>nnf_binary_cross_entropy.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Binary_cross_entropy</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-loss.R" class="external-link"><code>R/nnf-loss.R</code></a></small>
+      <div class="d-none name"><code>nnf_binary_cross_entropy.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Function that measures the Binary Cross Entropy
 between the target and the output.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_binary_cross_entropy</span><span class='op'>(</span>
-  <span class='va'>input</span>,
-  <span class='va'>target</span>,
-  weight <span class='op'>=</span> <span class='cn'>NULL</span>,
-  reduction <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='st'>"mean"</span>, <span class='st'>"sum"</span>, <span class='st'>"none"</span><span class='op'>)</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>tensor (N,*) where ** means, any number of additional dimensions</p></td>
-    </tr>
-    <tr>
-      <th>target</th>
-      <td><p>tensor (N,*) , same shape as the input</p></td>
-    </tr>
-    <tr>
-      <th>weight</th>
-      <td><p>(tensor) weight for each value.</p></td>
-    </tr>
-    <tr>
-      <th>reduction</th>
-      <td><p>(string, optional) – Specifies the reduction to apply to the
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_binary_cross_entropy</span><span class="op">(</span>
+  <span class="va">input</span>,
+  <span class="va">target</span>,
+  weight <span class="op">=</span> <span class="cn">NULL</span>,
+  reduction <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"mean"</span>, <span class="st">"sum"</span>, <span class="st">"none"</span><span class="op">)</span>
+<span class="op">)</span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>tensor (N,*) where ** means, any number of additional dimensions</p></dd>
+<dt>target</dt>
+<dd><p>tensor (N,*) , same shape as the input</p></dd>
+<dt>weight</dt>
+<dd><p>(tensor) weight for each value.</p></dd>
+<dt>reduction</dt>
+<dd><p>(string, optional) – Specifies the reduction to apply to the
 output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
 the sum of the output will be divided by the number of elements in the output,
-'sum': the output will be summed. Default: 'mean'</p></td>
-    </tr>
-    </table>
+'sum': the output will be summed. Default: 'mean'</p></dd>
+</dl></div>
 
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diff --git a/reference/nnf_binary_cross_entropy_with_logits.html b/reference/nnf_binary_cross_entropy_with_logits.html
index fafae1697..df0c175d3 100644
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+++ b/reference/nnf_binary_cross_entropy_with_logits.html
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Binary_cross_entropy_with_logits</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-loss.R'><code>R/nnf-loss.R</code></a></small>
-    <div class="hidden name"><code>nnf_binary_cross_entropy_with_logits.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Binary_cross_entropy_with_logits</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-loss.R" class="external-link"><code>R/nnf-loss.R</code></a></small>
+      <div class="d-none name"><code>nnf_binary_cross_entropy_with_logits.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Function that measures Binary Cross Entropy between target and output
 logits.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_binary_cross_entropy_with_logits</span><span class='op'>(</span>
-  <span class='va'>input</span>,
-  <span class='va'>target</span>,
-  weight <span class='op'>=</span> <span class='cn'>NULL</span>,
-  reduction <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='st'>"mean"</span>, <span class='st'>"sum"</span>, <span class='st'>"none"</span><span class='op'>)</span>,
-  pos_weight <span class='op'>=</span> <span class='cn'>NULL</span>
-<span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_binary_cross_entropy_with_logits</span><span class="op">(</span>
+  <span class="va">input</span>,
+  <span class="va">target</span>,
+  weight <span class="op">=</span> <span class="cn">NULL</span>,
+  reduction <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"mean"</span>, <span class="st">"sum"</span>, <span class="st">"none"</span><span class="op">)</span>,
+  pos_weight <span class="op">=</span> <span class="cn">NULL</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>Tensor of arbitrary shape</p></td>
-    </tr>
-    <tr>
-      <th>target</th>
-      <td><p>Tensor of the same shape as input</p></td>
-    </tr>
-    <tr>
-      <th>weight</th>
-      <td><p>(Tensor, optional) a manual rescaling weight if provided it's
-repeated to match input tensor shape.</p></td>
-    </tr>
-    <tr>
-      <th>reduction</th>
-      <td><p>(string, optional) – Specifies the reduction to apply to the
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>Tensor of arbitrary shape</p></dd>
+<dt>target</dt>
+<dd><p>Tensor of the same shape as input</p></dd>
+<dt>weight</dt>
+<dd><p>(Tensor, optional) a manual rescaling weight if provided it's
+repeated to match input tensor shape.</p></dd>
+<dt>reduction</dt>
+<dd><p>(string, optional) – Specifies the reduction to apply to the
 output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
 the sum of the output will be divided by the number of elements in the output,
-'sum': the output will be summed. Default: 'mean'</p></td>
-    </tr>
-    <tr>
-      <th>pos_weight</th>
-      <td><p>(Tensor, optional) a weight of positive examples.
-Must be a vector with length equal to the number of classes.</p></td>
-    </tr>
-    </table>
+'sum': the output will be summed. Default: 'mean'</p></dd>
+<dt>pos_weight</dt>
+<dd><p>(Tensor, optional) a weight of positive examples.
+Must be a vector with length equal to the number of classes.</p></dd>
+</dl></div>
 
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index 65ce5fd1e..11f0ae5ac 100644
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-  <div class="col-md-9 contents">
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-    <h1>Celu</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-activation.R'><code>R/nnf-activation.R</code></a></small>
-    <div class="hidden name"><code>nnf_celu.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Celu</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-activation.R" class="external-link"><code>R/nnf-activation.R</code></a></small>
+      <div class="d-none name"><code>nnf_celu.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Applies element-wise, \(CELU(x) = max(0,x) + min(0, \alpha * (exp(x \alpha) - 1))\).</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_celu</span><span class='op'>(</span><span class='va'>input</span>, alpha <span class='op'>=</span> <span class='fl'>1</span>, inplace <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span>
-
-<span class='fu'>nnf_celu_</span><span class='op'>(</span><span class='va'>input</span>, alpha <span class='op'>=</span> <span class='fl'>1</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(N,*) tensor, where * means, any number of additional
-dimensions</p></td>
-    </tr>
-    <tr>
-      <th>alpha</th>
-      <td><p>the alpha value for the CELU formulation. Default: 1.0</p></td>
-    </tr>
-    <tr>
-      <th>inplace</th>
-      <td><p>can optionally do the operation in-place. Default: FALSE</p></td>
-    </tr>
-    </table>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_celu</span><span class="op">(</span><span class="va">input</span>, alpha <span class="op">=</span> <span class="fl">1</span>, inplace <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>
 
+<span class="fu">nnf_celu_</span><span class="op">(</span><span class="va">input</span>, alpha <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></code></pre></div>
+    </div>
 
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-</div>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>(N,*) tensor, where * means, any number of additional
+dimensions</p></dd>
+<dt>alpha</dt>
+<dd><p>the alpha value for the CELU formulation. Default: 1.0</p></dd>
+<dt>inplace</dt>
+<dd><p>can optionally do the operation in-place. Default: FALSE</p></dd>
+</dl></div>
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diff --git a/reference/nnf_contrib_sparsemax.html b/reference/nnf_contrib_sparsemax.html
index 6adffd9b9..72648dead 100644
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-    <h1>Sparsemax</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-activation.R'><code>R/nnf-activation.R</code></a></small>
-    <div class="hidden name"><code>nnf_contrib_sparsemax.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Sparsemax</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-activation.R" class="external-link"><code>R/nnf-activation.R</code></a></small>
+      <div class="d-none name"><code>nnf_contrib_sparsemax.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Applies the SparseMax activation.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_contrib_sparsemax</span><span class='op'>(</span><span class='va'>input</span>, dim <span class='op'>=</span> <span class='op'>-</span><span class='fl'>1</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>the input tensor</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>The dimension over which to apply the sparsemax function. (-1)</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_contrib_sparsemax</span><span class="op">(</span><span class="va">input</span>, dim <span class="op">=</span> <span class="op">-</span><span class="fl">1</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>the input tensor</p></dd>
+<dt>dim</dt>
+<dd><p>The dimension over which to apply the sparsemax function. (-1)</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>The SparseMax activation is described in
-<a href='https://arxiv.org/abs/1602.02068'>'From Softmax to Sparsemax: A Sparse Model of Attention and Multi-Label Classification'</a>
-The implementation is based on <a href='https://github.com/aced125/sparsemax/tree/master/sparsemax'>aced125/sparsemax</a></p>
+<a href="https://arxiv.org/abs/1602.02068" class="external-link">'From Softmax to Sparsemax: A Sparse Model of Attention and Multi-Label Classification'</a>
+The implementation is based on <a href="https://github.com/aced125/sparsemax/tree/master/sparsemax" class="external-link">aced125/sparsemax</a></p>
+    </div>
 
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Conv1d</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-conv.R'><code>R/nnf-conv.R</code></a></small>
-    <div class="hidden name"><code>nnf_conv1d.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Conv1d</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-conv.R" class="external-link"><code>R/nnf-conv.R</code></a></small>
+      <div class="d-none name"><code>nnf_conv1d.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Applies a 1D convolution over an input signal composed of several input
 planes.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_conv1d</span><span class='op'>(</span>
-  <span class='va'>input</span>,
-  <span class='va'>weight</span>,
-  bias <span class='op'>=</span> <span class='cn'>NULL</span>,
-  stride <span class='op'>=</span> <span class='fl'>1</span>,
-  padding <span class='op'>=</span> <span class='fl'>0</span>,
-  dilation <span class='op'>=</span> <span class='fl'>1</span>,
-  groups <span class='op'>=</span> <span class='fl'>1</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>input tensor of shape (minibatch, in_channels , iW)</p></td>
-    </tr>
-    <tr>
-      <th>weight</th>
-      <td><p>filters of shape (out_channels, in_channels/groups , kW)</p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>optional bias of shape (out_channels). Default: <code>NULL</code></p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>the stride of the convolving kernel. Can be a single number or
-a one-element tuple <code>(sW,)</code>. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>implicit paddings on both sides of the input. Can be a
-single number or a one-element tuple <code>(padW,)</code>. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>dilation</th>
-      <td><p>the spacing between kernel elements. Can be a single number or
-a one-element tuple <code>(dW,)</code>. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>groups</th>
-      <td><p>split input into groups, <code>in_channels</code> should be divisible by
-the number of groups. Default: 1</p></td>
-    </tr>
-    </table>
-
-
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
-
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_conv1d</span><span class="op">(</span>
+  <span class="va">input</span>,
+  <span class="va">weight</span>,
+  bias <span class="op">=</span> <span class="cn">NULL</span>,
+  stride <span class="op">=</span> <span class="fl">1</span>,
+  padding <span class="op">=</span> <span class="fl">0</span>,
+  dilation <span class="op">=</span> <span class="fl">1</span>,
+  groups <span class="op">=</span> <span class="fl">1</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
-      <footer>
-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>input tensor of shape (minibatch, in_channels , iW)</p></dd>
+<dt>weight</dt>
+<dd><p>filters of shape (out_channels, in_channels/groups , kW)</p></dd>
+<dt>bias</dt>
+<dd><p>optional bias of shape (out_channels). Default: <code>NULL</code></p></dd>
+<dt>stride</dt>
+<dd><p>the stride of the convolving kernel. Can be a single number or
+a one-element tuple <code>(sW,)</code>. Default: 1</p></dd>
+<dt>padding</dt>
+<dd><p>implicit paddings on both sides of the input. Can be a
+single number or a one-element tuple <code>(padW,)</code>. Default: 0</p></dd>
+<dt>dilation</dt>
+<dd><p>the spacing between kernel elements. Can be a single number or
+a one-element tuple <code>(dW,)</code>. Default: 1</p></dd>
+<dt>groups</dt>
+<dd><p>split input into groups, <code>in_channels</code> should be divisible by
+the number of groups. Default: 1</p></dd>
+</dl></div>
+
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
 </div>
 
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Conv2d</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-conv.R'><code>R/nnf-conv.R</code></a></small>
-    <div class="hidden name"><code>nnf_conv2d.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Conv2d</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-conv.R" class="external-link"><code>R/nnf-conv.R</code></a></small>
+      <div class="d-none name"><code>nnf_conv2d.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Applies a 2D convolution over an input image composed of several input
 planes.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_conv2d</span><span class='op'>(</span>
-  <span class='va'>input</span>,
-  <span class='va'>weight</span>,
-  bias <span class='op'>=</span> <span class='cn'>NULL</span>,
-  stride <span class='op'>=</span> <span class='fl'>1</span>,
-  padding <span class='op'>=</span> <span class='fl'>0</span>,
-  dilation <span class='op'>=</span> <span class='fl'>1</span>,
-  groups <span class='op'>=</span> <span class='fl'>1</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>input tensor of shape (minibatch, in_channels, iH , iW)</p></td>
-    </tr>
-    <tr>
-      <th>weight</th>
-      <td><p>filters of shape (out_channels , in_channels/groups, kH , kW)</p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>optional bias tensor of shape (out_channels). Default: <code>NULL</code></p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>the stride of the convolving kernel. Can be a single number or a
-tuple <code>(sH, sW)</code>. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>implicit paddings on both sides of the input. Can be a
-single number or a tuple <code>(padH, padW)</code>. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>dilation</th>
-      <td><p>the spacing between kernel elements. Can be a single number or
-a tuple <code>(dH, dW)</code>. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>groups</th>
-      <td><p>split input into groups, <code>in_channels</code> should be divisible by the
-number of groups. Default: 1</p></td>
-    </tr>
-    </table>
-
-
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
-
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_conv2d</span><span class="op">(</span>
+  <span class="va">input</span>,
+  <span class="va">weight</span>,
+  bias <span class="op">=</span> <span class="cn">NULL</span>,
+  stride <span class="op">=</span> <span class="fl">1</span>,
+  padding <span class="op">=</span> <span class="fl">0</span>,
+  dilation <span class="op">=</span> <span class="fl">1</span>,
+  groups <span class="op">=</span> <span class="fl">1</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
-      <footer>
-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>input tensor of shape (minibatch, in_channels, iH , iW)</p></dd>
+<dt>weight</dt>
+<dd><p>filters of shape (out_channels , in_channels/groups, kH , kW)</p></dd>
+<dt>bias</dt>
+<dd><p>optional bias tensor of shape (out_channels). Default: <code>NULL</code></p></dd>
+<dt>stride</dt>
+<dd><p>the stride of the convolving kernel. Can be a single number or a
+tuple <code>(sH, sW)</code>. Default: 1</p></dd>
+<dt>padding</dt>
+<dd><p>implicit paddings on both sides of the input. Can be a
+single number or a tuple <code>(padH, padW)</code>. Default: 0</p></dd>
+<dt>dilation</dt>
+<dd><p>the spacing between kernel elements. Can be a single number or
+a tuple <code>(dH, dW)</code>. Default: 1</p></dd>
+<dt>groups</dt>
+<dd><p>split input into groups, <code>in_channels</code> should be divisible by the
+number of groups. Default: 1</p></dd>
+</dl></div>
+
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
 </div>
 
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Conv3d</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-conv.R'><code>R/nnf-conv.R</code></a></small>
-    <div class="hidden name"><code>nnf_conv3d.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Conv3d</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-conv.R" class="external-link"><code>R/nnf-conv.R</code></a></small>
+      <div class="d-none name"><code>nnf_conv3d.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Applies a 3D convolution over an input image composed of several input
 planes.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_conv3d</span><span class='op'>(</span>
-  <span class='va'>input</span>,
-  <span class='va'>weight</span>,
-  bias <span class='op'>=</span> <span class='cn'>NULL</span>,
-  stride <span class='op'>=</span> <span class='fl'>1</span>,
-  padding <span class='op'>=</span> <span class='fl'>0</span>,
-  dilation <span class='op'>=</span> <span class='fl'>1</span>,
-  groups <span class='op'>=</span> <span class='fl'>1</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>input tensor of shape (minibatch, in_channels , iT , iH , iW)</p></td>
-    </tr>
-    <tr>
-      <th>weight</th>
-      <td><p>filters of shape (out_channels , in_channels/groups, kT , kH , kW)</p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>optional bias tensor of shape (out_channels). Default: <code>NULL</code></p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>the stride of the convolving kernel. Can be a single number or a
-tuple <code>(sT, sH, sW)</code>. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>implicit paddings on both sides of the input. Can be a
-single number or a tuple <code>(padT, padH, padW)</code>. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>dilation</th>
-      <td><p>the spacing between kernel elements. Can be a single number or
-a tuple <code>(dT, dH, dW)</code>. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>groups</th>
-      <td><p>split input into groups, <code>in_channels</code> should be divisible by
-the number of groups. Default: 1</p></td>
-    </tr>
-    </table>
-
-
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
-
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_conv3d</span><span class="op">(</span>
+  <span class="va">input</span>,
+  <span class="va">weight</span>,
+  bias <span class="op">=</span> <span class="cn">NULL</span>,
+  stride <span class="op">=</span> <span class="fl">1</span>,
+  padding <span class="op">=</span> <span class="fl">0</span>,
+  dilation <span class="op">=</span> <span class="fl">1</span>,
+  groups <span class="op">=</span> <span class="fl">1</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
-      <footer>
-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>input tensor of shape (minibatch, in_channels , iT , iH , iW)</p></dd>
+<dt>weight</dt>
+<dd><p>filters of shape (out_channels , in_channels/groups, kT , kH , kW)</p></dd>
+<dt>bias</dt>
+<dd><p>optional bias tensor of shape (out_channels). Default: <code>NULL</code></p></dd>
+<dt>stride</dt>
+<dd><p>the stride of the convolving kernel. Can be a single number or a
+tuple <code>(sT, sH, sW)</code>. Default: 1</p></dd>
+<dt>padding</dt>
+<dd><p>implicit paddings on both sides of the input. Can be a
+single number or a tuple <code>(padT, padH, padW)</code>. Default: 0</p></dd>
+<dt>dilation</dt>
+<dd><p>the spacing between kernel elements. Can be a single number or
+a tuple <code>(dT, dH, dW)</code>. Default: 1</p></dd>
+<dt>groups</dt>
+<dd><p>split input into groups, <code>in_channels</code> should be divisible by
+the number of groups. Default: 1</p></dd>
+</dl></div>
+
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
+
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+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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index 8a550a40c..f0ad2ad23 100644
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-conv.R'><code>R/nnf-conv.R</code></a></small>
-    <div class="hidden name"><code>nnf_conv_tbc.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Conv_tbc</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-conv.R" class="external-link"><code>R/nnf-conv.R</code></a></small>
+      <div class="d-none name"><code>nnf_conv_tbc.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Applies a 1-dimensional sequence convolution over an input sequence.
 Input and output dimensions are (Time, Batch, Channels) - hence TBC.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_conv_tbc</span><span class='op'>(</span><span class='va'>input</span>, <span class='va'>weight</span>, <span class='va'>bias</span>, pad <span class='op'>=</span> <span class='fl'>0</span><span class='op'>)</span></pre>
-
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-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>input tensor of shape \((\mbox{sequence length} \times 
-batch \times \mbox{in\_channels})\)</p></td>
-    </tr>
-    <tr>
-      <th>weight</th>
-      <td><p>filter of shape (\(\mbox{kernel width} \times \mbox{in\_channels} 
-\times \mbox{out\_channels}\))</p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>bias of shape (\(\mbox{out\_channels}\))</p></td>
-    </tr>
-    <tr>
-      <th>pad</th>
-      <td><p>number of timesteps to pad. Default: 0</p></td>
-    </tr>
-    </table>
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+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_conv_tbc</span><span class="op">(</span><span class="va">input</span>, <span class="va">weight</span>, <span class="va">bias</span>, pad <span class="op">=</span> <span class="fl">0</span><span class="op">)</span></code></pre></div>
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+    <div class="section level2">
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+    <dl><dt>input</dt>
+<dd><p>input tensor of shape \((\mbox{sequence length} \times
+batch \times \mbox{in\_channels})\)</p></dd>
+<dt>weight</dt>
+<dd><p>filter of shape (\(\mbox{kernel width} \times \mbox{in\_channels}
+\times \mbox{out\_channels}\))</p></dd>
+<dt>bias</dt>
+<dd><p>bias of shape (\(\mbox{out\_channels}\))</p></dd>
+<dt>pad</dt>
+<dd><p>number of timesteps to pad. Default: 0</p></dd>
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diff --git a/reference/nnf_conv_transpose1d.html b/reference/nnf_conv_transpose1d.html
index 1c52c9496..40c67ec49 100644
--- a/reference/nnf_conv_transpose1d.html
+++ b/reference/nnf_conv_transpose1d.html
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Conv_transpose1d</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-conv.R'><code>R/nnf-conv.R</code></a></small>
-    <div class="hidden name"><code>nnf_conv_transpose1d.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Conv_transpose1d</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-conv.R" class="external-link"><code>R/nnf-conv.R</code></a></small>
+      <div class="d-none name"><code>nnf_conv_transpose1d.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Applies a 1D transposed convolution operator over an input signal
 composed of several input planes, sometimes also called "deconvolution".</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_conv_transpose1d</span><span class='op'>(</span>
-  <span class='va'>input</span>,
-  <span class='va'>weight</span>,
-  bias <span class='op'>=</span> <span class='cn'>NULL</span>,
-  stride <span class='op'>=</span> <span class='fl'>1</span>,
-  padding <span class='op'>=</span> <span class='fl'>0</span>,
-  output_padding <span class='op'>=</span> <span class='fl'>0</span>,
-  groups <span class='op'>=</span> <span class='fl'>1</span>,
-  dilation <span class='op'>=</span> <span class='fl'>1</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>input tensor of shape (minibatch, in_channels , iW)</p></td>
-    </tr>
-    <tr>
-      <th>weight</th>
-      <td><p>filters of shape (out_channels, in_channels/groups , kW)</p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>optional bias of shape (out_channels). Default: <code>NULL</code></p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>the stride of the convolving kernel. Can be a single number or
-a one-element tuple <code>(sW,)</code>. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>implicit paddings on both sides of the input. Can be a
-single number or a one-element tuple <code>(padW,)</code>. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>output_padding</th>
-      <td><p>padding applied to the output</p></td>
-    </tr>
-    <tr>
-      <th>groups</th>
-      <td><p>split input into groups, <code>in_channels</code> should be divisible by
-the number of groups. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>dilation</th>
-      <td><p>the spacing between kernel elements. Can be a single number or
-a one-element tuple <code>(dW,)</code>. Default: 1</p></td>
-    </tr>
-    </table>
-
-
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
-
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_conv_transpose1d</span><span class="op">(</span>
+  <span class="va">input</span>,
+  <span class="va">weight</span>,
+  bias <span class="op">=</span> <span class="cn">NULL</span>,
+  stride <span class="op">=</span> <span class="fl">1</span>,
+  padding <span class="op">=</span> <span class="fl">0</span>,
+  output_padding <span class="op">=</span> <span class="fl">0</span>,
+  groups <span class="op">=</span> <span class="fl">1</span>,
+  dilation <span class="op">=</span> <span class="fl">1</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
-      <footer>
-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>input tensor of shape (minibatch, in_channels , iW)</p></dd>
+<dt>weight</dt>
+<dd><p>filters of shape (out_channels, in_channels/groups , kW)</p></dd>
+<dt>bias</dt>
+<dd><p>optional bias of shape (out_channels). Default: <code>NULL</code></p></dd>
+<dt>stride</dt>
+<dd><p>the stride of the convolving kernel. Can be a single number or
+a one-element tuple <code>(sW,)</code>. Default: 1</p></dd>
+<dt>padding</dt>
+<dd><p>implicit paddings on both sides of the input. Can be a
+single number or a one-element tuple <code>(padW,)</code>. Default: 0</p></dd>
+<dt>output_padding</dt>
+<dd><p>padding applied to the output</p></dd>
+<dt>groups</dt>
+<dd><p>split input into groups, <code>in_channels</code> should be divisible by
+the number of groups. Default: 1</p></dd>
+<dt>dilation</dt>
+<dd><p>the spacing between kernel elements. Can be a single number or
+a one-element tuple <code>(dW,)</code>. Default: 1</p></dd>
+</dl></div>
+
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
 </div>
 
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+<div class="pkgdown-footer-right">
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index f44384e4d..48ea1475f 100644
--- a/reference/nnf_conv_transpose2d.html
+++ b/reference/nnf_conv_transpose2d.html
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Conv_transpose2d</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-conv.R'><code>R/nnf-conv.R</code></a></small>
-    <div class="hidden name"><code>nnf_conv_transpose2d.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Conv_transpose2d</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-conv.R" class="external-link"><code>R/nnf-conv.R</code></a></small>
+      <div class="d-none name"><code>nnf_conv_transpose2d.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Applies a 2D transposed convolution operator over an input image
 composed of several input planes, sometimes also called "deconvolution".</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_conv_transpose2d</span><span class='op'>(</span>
-  <span class='va'>input</span>,
-  <span class='va'>weight</span>,
-  bias <span class='op'>=</span> <span class='cn'>NULL</span>,
-  stride <span class='op'>=</span> <span class='fl'>1</span>,
-  padding <span class='op'>=</span> <span class='fl'>0</span>,
-  output_padding <span class='op'>=</span> <span class='fl'>0</span>,
-  groups <span class='op'>=</span> <span class='fl'>1</span>,
-  dilation <span class='op'>=</span> <span class='fl'>1</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>input tensor of shape (minibatch, in_channels, iH , iW)</p></td>
-    </tr>
-    <tr>
-      <th>weight</th>
-      <td><p>filters of shape (out_channels , in_channels/groups, kH , kW)</p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>optional bias tensor of shape (out_channels). Default: <code>NULL</code></p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>the stride of the convolving kernel. Can be a single number or a
-tuple <code>(sH, sW)</code>. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>implicit paddings on both sides of the input. Can be a
-single number or a tuple <code>(padH, padW)</code>. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>output_padding</th>
-      <td><p>padding applied to the output</p></td>
-    </tr>
-    <tr>
-      <th>groups</th>
-      <td><p>split input into groups, <code>in_channels</code> should be divisible by the
-number of groups. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>dilation</th>
-      <td><p>the spacing between kernel elements. Can be a single number or
-a tuple <code>(dH, dW)</code>. Default: 1</p></td>
-    </tr>
-    </table>
-
-
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
-
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_conv_transpose2d</span><span class="op">(</span>
+  <span class="va">input</span>,
+  <span class="va">weight</span>,
+  bias <span class="op">=</span> <span class="cn">NULL</span>,
+  stride <span class="op">=</span> <span class="fl">1</span>,
+  padding <span class="op">=</span> <span class="fl">0</span>,
+  output_padding <span class="op">=</span> <span class="fl">0</span>,
+  groups <span class="op">=</span> <span class="fl">1</span>,
+  dilation <span class="op">=</span> <span class="fl">1</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
-      <footer>
-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>input tensor of shape (minibatch, in_channels, iH , iW)</p></dd>
+<dt>weight</dt>
+<dd><p>filters of shape (out_channels , in_channels/groups, kH , kW)</p></dd>
+<dt>bias</dt>
+<dd><p>optional bias tensor of shape (out_channels). Default: <code>NULL</code></p></dd>
+<dt>stride</dt>
+<dd><p>the stride of the convolving kernel. Can be a single number or a
+tuple <code>(sH, sW)</code>. Default: 1</p></dd>
+<dt>padding</dt>
+<dd><p>implicit paddings on both sides of the input. Can be a
+single number or a tuple <code>(padH, padW)</code>. Default: 0</p></dd>
+<dt>output_padding</dt>
+<dd><p>padding applied to the output</p></dd>
+<dt>groups</dt>
+<dd><p>split input into groups, <code>in_channels</code> should be divisible by the
+number of groups. Default: 1</p></dd>
+<dt>dilation</dt>
+<dd><p>the spacing between kernel elements. Can be a single number or
+a tuple <code>(dH, dW)</code>. Default: 1</p></dd>
+</dl></div>
+
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
 </div>
 
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+<div class="pkgdown-footer-right">
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diff --git a/reference/nnf_conv_transpose3d.html b/reference/nnf_conv_transpose3d.html
index 31087ed26..c066a7563 100644
--- a/reference/nnf_conv_transpose3d.html
+++ b/reference/nnf_conv_transpose3d.html
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Conv_transpose3d</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-conv.R'><code>R/nnf-conv.R</code></a></small>
-    <div class="hidden name"><code>nnf_conv_transpose3d.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Conv_transpose3d</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-conv.R" class="external-link"><code>R/nnf-conv.R</code></a></small>
+      <div class="d-none name"><code>nnf_conv_transpose3d.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Applies a 3D transposed convolution operator over an input image
 composed of several input planes, sometimes also called "deconvolution"</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_conv_transpose3d</span><span class='op'>(</span>
-  <span class='va'>input</span>,
-  <span class='va'>weight</span>,
-  bias <span class='op'>=</span> <span class='cn'>NULL</span>,
-  stride <span class='op'>=</span> <span class='fl'>1</span>,
-  padding <span class='op'>=</span> <span class='fl'>0</span>,
-  output_padding <span class='op'>=</span> <span class='fl'>0</span>,
-  groups <span class='op'>=</span> <span class='fl'>1</span>,
-  dilation <span class='op'>=</span> <span class='fl'>1</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>input tensor of shape (minibatch, in_channels , iT , iH , iW)</p></td>
-    </tr>
-    <tr>
-      <th>weight</th>
-      <td><p>filters of shape (out_channels , in_channels/groups, kT , kH , kW)</p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>optional bias tensor of shape (out_channels). Default: <code>NULL</code></p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>the stride of the convolving kernel. Can be a single number or a
-tuple <code>(sT, sH, sW)</code>. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>implicit paddings on both sides of the input. Can be a
-single number or a tuple <code>(padT, padH, padW)</code>. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>output_padding</th>
-      <td><p>padding applied to the output</p></td>
-    </tr>
-    <tr>
-      <th>groups</th>
-      <td><p>split input into groups, <code>in_channels</code> should be divisible by
-the number of groups. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>dilation</th>
-      <td><p>the spacing between kernel elements. Can be a single number or
-a tuple <code>(dT, dH, dW)</code>. Default: 1</p></td>
-    </tr>
-    </table>
-
-
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
-
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_conv_transpose3d</span><span class="op">(</span>
+  <span class="va">input</span>,
+  <span class="va">weight</span>,
+  bias <span class="op">=</span> <span class="cn">NULL</span>,
+  stride <span class="op">=</span> <span class="fl">1</span>,
+  padding <span class="op">=</span> <span class="fl">0</span>,
+  output_padding <span class="op">=</span> <span class="fl">0</span>,
+  groups <span class="op">=</span> <span class="fl">1</span>,
+  dilation <span class="op">=</span> <span class="fl">1</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
-      <footer>
-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>input tensor of shape (minibatch, in_channels , iT , iH , iW)</p></dd>
+<dt>weight</dt>
+<dd><p>filters of shape (out_channels , in_channels/groups, kT , kH , kW)</p></dd>
+<dt>bias</dt>
+<dd><p>optional bias tensor of shape (out_channels). Default: <code>NULL</code></p></dd>
+<dt>stride</dt>
+<dd><p>the stride of the convolving kernel. Can be a single number or a
+tuple <code>(sT, sH, sW)</code>. Default: 1</p></dd>
+<dt>padding</dt>
+<dd><p>implicit paddings on both sides of the input. Can be a
+single number or a tuple <code>(padT, padH, padW)</code>. Default: 0</p></dd>
+<dt>output_padding</dt>
+<dd><p>padding applied to the output</p></dd>
+<dt>groups</dt>
+<dd><p>split input into groups, <code>in_channels</code> should be divisible by
+the number of groups. Default: 1</p></dd>
+<dt>dilation</dt>
+<dd><p>the spacing between kernel elements. Can be a single number or
+a tuple <code>(dT, dH, dW)</code>. Default: 1</p></dd>
+</dl></div>
+
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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+<div class="pkgdown-footer-right">
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diff --git a/reference/nnf_cosine_embedding_loss.html b/reference/nnf_cosine_embedding_loss.html
index 016939fae..3b1b1a734 100644
--- a/reference/nnf_cosine_embedding_loss.html
+++ b/reference/nnf_cosine_embedding_loss.html
@@ -1,263 +1,140 @@
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-<meta property="og:description" content="Creates a criterion that measures the loss given input tensors x_1, x_2 and a
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Creates a criterion that measures the loss given input tensors x_1, x_2 and a
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Cosine_embedding_loss</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-loss.R'><code>R/nnf-loss.R</code></a></small>
-    <div class="hidden name"><code>nnf_cosine_embedding_loss.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Cosine_embedding_loss</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-loss.R" class="external-link"><code>R/nnf-loss.R</code></a></small>
+      <div class="d-none name"><code>nnf_cosine_embedding_loss.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Creates a criterion that measures the loss given input tensors x_1, x_2 and a
 Tensor label y with values 1 or -1. This is used for measuring whether two inputs
 are similar or dissimilar, using the cosine distance, and is typically used
 for learning nonlinear embeddings or semi-supervised learning.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_cosine_embedding_loss</span><span class='op'>(</span>
-  <span class='va'>input1</span>,
-  <span class='va'>input2</span>,
-  <span class='va'>target</span>,
-  margin <span class='op'>=</span> <span class='fl'>0</span>,
-  reduction <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='st'>"mean"</span>, <span class='st'>"sum"</span>, <span class='st'>"none"</span><span class='op'>)</span>
-<span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_cosine_embedding_loss</span><span class="op">(</span>
+  <span class="va">input1</span>,
+  <span class="va">input2</span>,
+  <span class="va">target</span>,
+  margin <span class="op">=</span> <span class="fl">0</span>,
+  reduction <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"mean"</span>, <span class="st">"sum"</span>, <span class="st">"none"</span><span class="op">)</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input1</th>
-      <td><p>the input x_1 tensor</p></td>
-    </tr>
-    <tr>
-      <th>input2</th>
-      <td><p>the input x_2 tensor</p></td>
-    </tr>
-    <tr>
-      <th>target</th>
-      <td><p>the target tensor</p></td>
-    </tr>
-    <tr>
-      <th>margin</th>
-      <td><p>Should be a number from -1 to 1 , 0 to 0.5 is suggested. If margin
-is missing, the default value is 0.</p></td>
-    </tr>
-    <tr>
-      <th>reduction</th>
-      <td><p>(string, optional) – Specifies the reduction to apply to the
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input1</dt>
+<dd><p>the input x_1 tensor</p></dd>
+<dt>input2</dt>
+<dd><p>the input x_2 tensor</p></dd>
+<dt>target</dt>
+<dd><p>the target tensor</p></dd>
+<dt>margin</dt>
+<dd><p>Should be a number from -1 to 1 , 0 to 0.5 is suggested. If margin
+is missing, the default value is 0.</p></dd>
+<dt>reduction</dt>
+<dd><p>(string, optional) – Specifies the reduction to apply to the
 output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
 the sum of the output will be divided by the number of elements in the output,
-'sum': the output will be summed. Default: 'mean'</p></td>
-    </tr>
-    </table>
+'sum': the output will be summed. Default: 'mean'</p></dd>
+</dl></div>
 
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
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-  <div class="col-md-9 contents">
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-    <h1>Cosine_similarity</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-distance.R'><code>R/nnf-distance.R</code></a></small>
-    <div class="hidden name"><code>nnf_cosine_similarity.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Cosine_similarity</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-distance.R" class="external-link"><code>R/nnf-distance.R</code></a></small>
+      <div class="d-none name"><code>nnf_cosine_similarity.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Returns cosine similarity between x1 and x2, computed along dim.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_cosine_similarity</span><span class='op'>(</span><span class='va'>x1</span>, <span class='va'>x2</span>, dim <span class='op'>=</span> <span class='fl'>1</span>, eps <span class='op'>=</span> <span class='fl'>1e-08</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>x1</th>
-      <td><p>(Tensor) First input.</p></td>
-    </tr>
-    <tr>
-      <th>x2</th>
-      <td><p>(Tensor) Second input (of size matching x1).</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int, optional) Dimension of vectors. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>eps</th>
-      <td><p>(float, optional) Small value to avoid division by zero.
-Default: 1e-8</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_cosine_similarity</span><span class="op">(</span><span class="va">x1</span>, <span class="va">x2</span>, dim <span class="op">=</span> <span class="fl">2</span>, eps <span class="op">=</span> <span class="fl">1e-08</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>x1</dt>
+<dd><p>(Tensor) First input.</p></dd>
+<dt>x2</dt>
+<dd><p>(Tensor) Second input (of size matching x1).</p></dd>
+<dt>dim</dt>
+<dd><p>(int, optional) Dimension of vectors. Default: 2</p></dd>
+<dt>eps</dt>
+<dd><p>(float, optional) Small value to avoid division by zero.
+Default: 1e-8</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>$$
     \mbox{similarity} = \frac{x_1 \cdot x_2}{\max(\Vert x_1 \Vert _2 \cdot \Vert x_2 \Vert _2, \epsilon)}
 $$</p>
+    </div>
 
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Cross_entropy</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-loss.R'><code>R/nnf-loss.R</code></a></small>
-    <div class="hidden name"><code>nnf_cross_entropy.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Cross_entropy</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-loss.R" class="external-link"><code>R/nnf-loss.R</code></a></small>
+      <div class="d-none name"><code>nnf_cross_entropy.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>This criterion combines <code>log_softmax</code> and <code>nll_loss</code> in a single
 function.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_cross_entropy</span><span class='op'>(</span>
-  <span class='va'>input</span>,
-  <span class='va'>target</span>,
-  weight <span class='op'>=</span> <span class='cn'>NULL</span>,
-  ignore_index <span class='op'>=</span> <span class='op'>-</span><span class='fl'>100</span>,
-  reduction <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='st'>"mean"</span>, <span class='st'>"sum"</span>, <span class='st'>"none"</span><span class='op'>)</span>
-<span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_cross_entropy</span><span class="op">(</span>
+  <span class="va">input</span>,
+  <span class="va">target</span>,
+  weight <span class="op">=</span> <span class="cn">NULL</span>,
+  ignore_index <span class="op">=</span> <span class="op">-</span><span class="fl">100</span>,
+  reduction <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"mean"</span>, <span class="st">"sum"</span>, <span class="st">"none"</span><span class="op">)</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) \((N, C)\) where <code>C = number of classes</code> or \((N, C, H, W)\)
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>(Tensor) \((N, C)\) where <code>C = number of classes</code> or \((N, C, H, W)\)
 in case of 2D Loss, or \((N, C, d_1, d_2, ..., d_K)\) where \(K \geq 1\)
-in the case of K-dimensional loss.</p></td>
-    </tr>
-    <tr>
-      <th>target</th>
-      <td><p>(Tensor) \((N)\) where each value is \(0 \leq \mbox{targets}[i] \leq C-1\),
-or \((N, d_1, d_2, ..., d_K)\) where \(K \geq 1\) for K-dimensional loss.</p></td>
-    </tr>
-    <tr>
-      <th>weight</th>
-      <td><p>(Tensor, optional) a manual rescaling weight given to each class. If
-given, has to be a Tensor of size <code>C</code></p></td>
-    </tr>
-    <tr>
-      <th>ignore_index</th>
-      <td><p>(int, optional) Specifies a target value that is ignored
-and does not contribute to the input gradient.</p></td>
-    </tr>
-    <tr>
-      <th>reduction</th>
-      <td><p>(string, optional) – Specifies the reduction to apply to the
+in the case of K-dimensional loss.</p></dd>
+<dt>target</dt>
+<dd><p>(Tensor) \((N)\) where each value is \(0 \leq \mbox{targets}[i] \leq C-1\),
+or \((N, d_1, d_2, ..., d_K)\) where \(K \geq 1\) for K-dimensional loss.</p></dd>
+<dt>weight</dt>
+<dd><p>(Tensor, optional) a manual rescaling weight given to each class. If
+given, has to be a Tensor of size <code>C</code></p></dd>
+<dt>ignore_index</dt>
+<dd><p>(int, optional) Specifies a target value that is ignored
+and does not contribute to the input gradient.</p></dd>
+<dt>reduction</dt>
+<dd><p>(string, optional) – Specifies the reduction to apply to the
 output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
 the sum of the output will be divided by the number of elements in the output,
-'sum': the output will be summed. Default: 'mean'</p></td>
-    </tr>
-    </table>
+'sum': the output will be summed. Default: 'mean'</p></dd>
+</dl></div>
 
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+    
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Ctc_loss</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-loss.R'><code>R/nnf-loss.R</code></a></small>
-    <div class="hidden name"><code>nnf_ctc_loss.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Ctc_loss</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-loss.R" class="external-link"><code>R/nnf-loss.R</code></a></small>
+      <div class="d-none name"><code>nnf_ctc_loss.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>The Connectionist Temporal Classification loss.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_ctc_loss</span><span class='op'>(</span>
-  <span class='va'>log_probs</span>,
-  <span class='va'>targets</span>,
-  <span class='va'>input_lengths</span>,
-  <span class='va'>target_lengths</span>,
-  blank <span class='op'>=</span> <span class='fl'>0</span>,
-  reduction <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='st'>"mean"</span>, <span class='st'>"sum"</span>, <span class='st'>"none"</span><span class='op'>)</span>,
-  zero_infinity <span class='op'>=</span> <span class='cn'>FALSE</span>
-<span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_ctc_loss</span><span class="op">(</span>
+  <span class="va">log_probs</span>,
+  <span class="va">targets</span>,
+  <span class="va">input_lengths</span>,
+  <span class="va">target_lengths</span>,
+  blank <span class="op">=</span> <span class="fl">0</span>,
+  reduction <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"mean"</span>, <span class="st">"sum"</span>, <span class="st">"none"</span><span class="op">)</span>,
+  zero_infinity <span class="op">=</span> <span class="cn">FALSE</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>log_probs</th>
-      <td><p>\((T, N, C)\) where C = number of characters in alphabet including blank,
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>log_probs</dt>
+<dd><p>\((T, N, C)\) where C = number of characters in alphabet including blank,
 T = input length, and N = batch size. The logarithmized probabilities of
-the outputs (e.g. obtained with <a href='nnf_log_softmax.html'>nnf_log_softmax</a>).</p></td>
-    </tr>
-    <tr>
-      <th>targets</th>
-      <td><p>\((N, S)\) or <code>(sum(target_lengths))</code>. Targets cannot be blank.
-In the second form, the targets are assumed to be concatenated.</p></td>
-    </tr>
-    <tr>
-      <th>input_lengths</th>
-      <td><p>\((N)\). Lengths of the inputs (must each be \(\leq T\))</p></td>
-    </tr>
-    <tr>
-      <th>target_lengths</th>
-      <td><p>\((N)\). Lengths of the targets</p></td>
-    </tr>
-    <tr>
-      <th>blank</th>
-      <td><p>(int, optional) Blank label. Default \(0\).</p></td>
-    </tr>
-    <tr>
-      <th>reduction</th>
-      <td><p>(string, optional) – Specifies the reduction to apply to the
+the outputs (e.g. obtained with <a href="nnf_log_softmax.html">nnf_log_softmax</a>).</p></dd>
+<dt>targets</dt>
+<dd><p>\((N, S)\) or <code>(sum(target_lengths))</code>. Targets cannot be blank.
+In the second form, the targets are assumed to be concatenated.</p></dd>
+<dt>input_lengths</dt>
+<dd><p>\((N)\). Lengths of the inputs (must each be \(\leq T\))</p></dd>
+<dt>target_lengths</dt>
+<dd><p>\((N)\). Lengths of the targets</p></dd>
+<dt>blank</dt>
+<dd><p>(int, optional) Blank label. Default \(0\).</p></dd>
+<dt>reduction</dt>
+<dd><p>(string, optional) – Specifies the reduction to apply to the
 output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
 the sum of the output will be divided by the number of elements in the output,
-'sum': the output will be summed. Default: 'mean'</p></td>
-    </tr>
-    <tr>
-      <th>zero_infinity</th>
-      <td><p>(bool, optional) Whether to zero infinite losses and the
+'sum': the output will be summed. Default: 'mean'</p></dd>
+<dt>zero_infinity</dt>
+<dd><p>(bool, optional) Whether to zero infinite losses and the
 associated gradients. Default: <code>FALSE</code> Infinite losses mainly occur when the
-inputs are too short to be aligned to the targets.</p></td>
-    </tr>
-    </table>
+inputs are too short to be aligned to the targets.</p></dd>
+</dl></div>
 
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diff --git a/reference/nnf_dropout.html b/reference/nnf_dropout.html
index 2764084c4..28c6074d0 100644
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-dropout.R'><code>R/nnf-dropout.R</code></a></small>
-    <div class="hidden name"><code>nnf_dropout.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Dropout</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-dropout.R" class="external-link"><code>R/nnf-dropout.R</code></a></small>
+      <div class="d-none name"><code>nnf_dropout.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>During training, randomly zeroes some of the elements of the input
 tensor with probability <code>p</code> using samples from a Bernoulli
 distribution.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_dropout</span><span class='op'>(</span><span class='va'>input</span>, p <span class='op'>=</span> <span class='fl'>0.5</span>, training <span class='op'>=</span> <span class='cn'>TRUE</span>, inplace <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span></pre>
-
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-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>the input tensor</p></td>
-    </tr>
-    <tr>
-      <th>p</th>
-      <td><p>probability of an element to be zeroed. Default: 0.5</p></td>
-    </tr>
-    <tr>
-      <th>training</th>
-      <td><p>apply dropout if is <code>TRUE</code>. Default: <code>TRUE</code></p></td>
-    </tr>
-    <tr>
-      <th>inplace</th>
-      <td><p>If set to <code>TRUE</code>, will do this operation in-place.
-Default: <code>FALSE</code></p></td>
-    </tr>
-    </table>
-
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+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_dropout</span><span class="op">(</span><span class="va">input</span>, p <span class="op">=</span> <span class="fl">0.5</span>, training <span class="op">=</span> <span class="cn">TRUE</span>, inplace <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
+    </div>
 
-      <footer>
-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>the input tensor</p></dd>
+<dt>p</dt>
+<dd><p>probability of an element to be zeroed. Default: 0.5</p></dd>
+<dt>training</dt>
+<dd><p>apply dropout if is <code>TRUE</code>. Default: <code>TRUE</code></p></dd>
+<dt>inplace</dt>
+<dd><p>If set to <code>TRUE</code>, will do this operation in-place.
+Default: <code>FALSE</code></p></dd>
+</dl></div>
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index 0b2a647e0..5c8a9b385 100644
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+probability p using samples from a Bernoulli distribution."><title>Dropout2d — nnf_dropout2d • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.1.0/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.1.0/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Dropout2d — nnf_dropout2d"><meta property="og:description" content="Randomly zero out entire channels (a channel is a 2D feature map,
+e.g., the \(j\)-th channel of the \(i\)-th sample in the
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+Each channel will be zeroed out independently on every forward call with
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Dropout2d</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-dropout.R'><code>R/nnf-dropout.R</code></a></small>
-    <div class="hidden name"><code>nnf_dropout2d.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Dropout2d</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-dropout.R" class="external-link"><code>R/nnf-dropout.R</code></a></small>
+      <div class="d-none name"><code>nnf_dropout2d.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Randomly zero out entire channels (a channel is a 2D feature map,
 e.g., the \(j\)-th channel of the \(i\)-th sample in the
 batched input is a 2D tensor \(input[i, j]\)) of the input tensor).
@@ -197,56 +92,41 @@ Each channel will be zeroed out independently on every forward call with
 probability <code>p</code> using samples from a Bernoulli distribution.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_dropout2d</span><span class='op'>(</span><span class='va'>input</span>, p <span class='op'>=</span> <span class='fl'>0.5</span>, training <span class='op'>=</span> <span class='cn'>TRUE</span>, inplace <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>the input tensor</p></td>
-    </tr>
-    <tr>
-      <th>p</th>
-      <td><p>probability of a channel to be zeroed. Default: 0.5</p></td>
-    </tr>
-    <tr>
-      <th>training</th>
-      <td><p>apply dropout if is <code>TRUE</code>. Default: <code>TRUE</code>.</p></td>
-    </tr>
-    <tr>
-      <th>inplace</th>
-      <td><p>If set to <code>TRUE</code>, will do this operation in-place.
-Default: <code>FALSE</code></p></td>
-    </tr>
-    </table>
-
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-      <h2 data-toc-skip>Contents</h2>
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-</div>
-
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_dropout2d</span><span class="op">(</span><span class="va">input</span>, p <span class="op">=</span> <span class="fl">0.5</span>, training <span class="op">=</span> <span class="cn">TRUE</span>, inplace <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
+    </div>
 
-      <footer>
-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>the input tensor</p></dd>
+<dt>p</dt>
+<dd><p>probability of a channel to be zeroed. Default: 0.5</p></dd>
+<dt>training</dt>
+<dd><p>apply dropout if is <code>TRUE</code>. Default: <code>TRUE</code>.</p></dd>
+<dt>inplace</dt>
+<dd><p>If set to <code>TRUE</code>, will do this operation in-place.
+Default: <code>FALSE</code></p></dd>
+</dl></div>
+
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index a3bb3b74e..6f966c8fd 100644
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+Each channel will be zeroed out independently on every forward call with
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-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Dropout3d</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-dropout.R'><code>R/nnf-dropout.R</code></a></small>
-    <div class="hidden name"><code>nnf_dropout3d.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Dropout3d</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-dropout.R" class="external-link"><code>R/nnf-dropout.R</code></a></small>
+      <div class="d-none name"><code>nnf_dropout3d.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Randomly zero out entire channels (a channel is a 3D feature map,
 e.g., the \(j\)-th channel of the \(i\)-th sample in the
 batched input is a 3D tensor \(input[i, j]\)) of the input tensor).
@@ -197,56 +92,41 @@ Each channel will be zeroed out independently on every forward call with
 probability <code>p</code> using samples from a Bernoulli distribution.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_dropout3d</span><span class='op'>(</span><span class='va'>input</span>, p <span class='op'>=</span> <span class='fl'>0.5</span>, training <span class='op'>=</span> <span class='cn'>TRUE</span>, inplace <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span></pre>
-
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-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>the input tensor</p></td>
-    </tr>
-    <tr>
-      <th>p</th>
-      <td><p>probability of a channel to be zeroed. Default: 0.5</p></td>
-    </tr>
-    <tr>
-      <th>training</th>
-      <td><p>apply dropout if is <code>TRUE</code>. Default: <code>TRUE</code>.</p></td>
-    </tr>
-    <tr>
-      <th>inplace</th>
-      <td><p>If set to <code>TRUE</code>, will do this operation in-place.
-Default: <code>FALSE</code></p></td>
-    </tr>
-    </table>
-
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+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_dropout3d</span><span class="op">(</span><span class="va">input</span>, p <span class="op">=</span> <span class="fl">0.5</span>, training <span class="op">=</span> <span class="cn">TRUE</span>, inplace <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
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+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>the input tensor</p></dd>
+<dt>p</dt>
+<dd><p>probability of a channel to be zeroed. Default: 0.5</p></dd>
+<dt>training</dt>
+<dd><p>apply dropout if is <code>TRUE</code>. Default: <code>TRUE</code>.</p></dd>
+<dt>inplace</dt>
+<dd><p>If set to <code>TRUE</code>, will do this operation in-place.
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-activation.R'><code>R/nnf-activation.R</code></a></small>
-    <div class="hidden name"><code>nnf_elu.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Elu</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-activation.R" class="external-link"><code>R/nnf-activation.R</code></a></small>
+      <div class="d-none name"><code>nnf_elu.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Applies element-wise,
 $$ELU(x) = max(0,x) + min(0, \alpha * (exp(x) - 1))$$.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_elu</span><span class='op'>(</span><span class='va'>input</span>, alpha <span class='op'>=</span> <span class='fl'>1</span>, inplace <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span>
-
-<span class='fu'>nnf_elu_</span><span class='op'>(</span><span class='va'>input</span>, alpha <span class='op'>=</span> <span class='fl'>1</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(N,*) tensor, where * means, any number of additional
-dimensions</p></td>
-    </tr>
-    <tr>
-      <th>alpha</th>
-      <td><p>the alpha value for the ELU formulation. Default: 1.0</p></td>
-    </tr>
-    <tr>
-      <th>inplace</th>
-      <td><p>can optionally do the operation in-place. Default: FALSE</p></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>x</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>y</span> <span class='op'>&lt;-</span> <span class='fu'>nnf_elu</span><span class='op'>(</span><span class='va'>x</span>, alpha <span class='op'>=</span> <span class='fl'>1</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>nnf_elu_</span><span class='op'>(</span><span class='va'>x</span>, alpha <span class='op'>=</span> <span class='fl'>1</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'><a href='torch_equal.html'>torch_equal</a></span><span class='op'>(</span><span class='va'>x</span>, <span class='va'>y</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [1] TRUE</span>
-</pre>
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-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
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-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_elu</span><span class="op">(</span><span class="va">input</span>, alpha <span class="op">=</span> <span class="fl">1</span>, inplace <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>
 
+<span class="fu">nnf_elu_</span><span class="op">(</span><span class="va">input</span>, alpha <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></code></pre></div>
+    </div>
 
-      <footer>
-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>(N,*) tensor, where * means, any number of additional
+dimensions</p></dd>
+<dt>alpha</dt>
+<dd><p>the alpha value for the ELU formulation. Default: 1.0</p></dd>
+<dt>inplace</dt>
+<dd><p>can optionally do the operation in-place. Default: FALSE</p></dd>
+</dl></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">y</span> <span class="op">&lt;-</span> <span class="fu">nnf_elu</span><span class="op">(</span><span class="va">x</span>, alpha <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">nnf_elu_</span><span class="op">(</span><span class="va">x</span>, alpha <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu"><a href="torch_equal.html">torch_equal</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">y</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] TRUE</span>
+</code></pre></div>
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+    
+  </div>
+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Embedding</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-embedding.R'><code>R/nnf-embedding.R</code></a></small>
-    <div class="hidden name"><code>nnf_embedding.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Embedding</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-embedding.R" class="external-link"><code>R/nnf-embedding.R</code></a></small>
+      <div class="d-none name"><code>nnf_embedding.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>A simple lookup table that looks up embeddings in a fixed dictionary and size.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_embedding</span><span class='op'>(</span>
-  <span class='va'>input</span>,
-  <span class='va'>weight</span>,
-  padding_idx <span class='op'>=</span> <span class='cn'>NULL</span>,
-  max_norm <span class='op'>=</span> <span class='cn'>NULL</span>,
-  norm_type <span class='op'>=</span> <span class='fl'>2</span>,
-  scale_grad_by_freq <span class='op'>=</span> <span class='cn'>FALSE</span>,
-  sparse <span class='op'>=</span> <span class='cn'>FALSE</span>
-<span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_embedding</span><span class="op">(</span>
+  <span class="va">input</span>,
+  <span class="va">weight</span>,
+  padding_idx <span class="op">=</span> <span class="cn">NULL</span>,
+  max_norm <span class="op">=</span> <span class="cn">NULL</span>,
+  norm_type <span class="op">=</span> <span class="fl">2</span>,
+  scale_grad_by_freq <span class="op">=</span> <span class="cn">FALSE</span>,
+  sparse <span class="op">=</span> <span class="cn">FALSE</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(LongTensor) Tensor containing indices into the embedding matrix</p></td>
-    </tr>
-    <tr>
-      <th>weight</th>
-      <td><p>(Tensor) The embedding matrix with number of rows equal to the
-maximum possible index + 1, and number of columns equal to the embedding size</p></td>
-    </tr>
-    <tr>
-      <th>padding_idx</th>
-      <td><p>(int, optional) If given, pads the output with the embedding
-vector at <code>padding_idx</code> (initialized to zeros) whenever it encounters the index.</p></td>
-    </tr>
-    <tr>
-      <th>max_norm</th>
-      <td><p>(float, optional) If given, each embedding vector with norm larger
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>(LongTensor) Tensor containing indices into the embedding matrix</p></dd>
+<dt>weight</dt>
+<dd><p>(Tensor) The embedding matrix with number of rows equal to the
+maximum possible index + 1, and number of columns equal to the embedding size</p></dd>
+<dt>padding_idx</dt>
+<dd><p>(int, optional) If given, pads the output with the embedding
+vector at <code>padding_idx</code> (initialized to zeros) whenever it encounters the index.</p></dd>
+<dt>max_norm</dt>
+<dd><p>(float, optional) If given, each embedding vector with norm larger
 than <code>max_norm</code> is renormalized to have norm <code>max_norm</code>. Note: this will modify
-<code>weight</code> in-place.</p></td>
-    </tr>
-    <tr>
-      <th>norm_type</th>
-      <td><p>(float, optional) The p of the p-norm to compute for the <code>max_norm</code>
-option. Default <code>2</code>.</p></td>
-    </tr>
-    <tr>
-      <th>scale_grad_by_freq</th>
-      <td><p>(boolean, optional) If given, this will scale gradients
-by the inverse of frequency of the words in the mini-batch. Default <code>FALSE</code>.</p></td>
-    </tr>
-    <tr>
-      <th>sparse</th>
-      <td><p>(bool, optional) If <code>TRUE</code>, gradient w.r.t. <code>weight</code> will be a
+<code>weight</code> in-place.</p></dd>
+<dt>norm_type</dt>
+<dd><p>(float, optional) The p of the p-norm to compute for the <code>max_norm</code>
+option. Default <code>2</code>.</p></dd>
+<dt>scale_grad_by_freq</dt>
+<dd><p>(boolean, optional) If given, this will scale gradients
+by the inverse of frequency of the words in the mini-batch. Default <code>FALSE</code>.</p></dd>
+<dt>sparse</dt>
+<dd><p>(bool, optional) If <code>TRUE</code>, gradient w.r.t. <code>weight</code> will be a
 sparse tensor. See Notes under <code>nn_embedding</code> for more details regarding
-sparse gradients.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
+sparse gradients.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>This module is often used to retrieve word embeddings using indices.
 The input to the module is a list of indices, and the embedding matrix,
 and the output is the corresponding word embeddings.</p>
+    </div>
 
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
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+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer>
-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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+      </ul><form class="form-inline my-2 my-lg-0" role="search">
+        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
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+    
+  </div>
+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Embedding_bag</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-embedding.R'><code>R/nnf-embedding.R</code></a></small>
-    <div class="hidden name"><code>nnf_embedding_bag.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Embedding_bag</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-embedding.R" class="external-link"><code>R/nnf-embedding.R</code></a></small>
+      <div class="d-none name"><code>nnf_embedding_bag.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Computes sums, means or maxes of <code>bags</code> of embeddings, without instantiating the
 intermediate embeddings.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_embedding_bag</span><span class='op'>(</span>
-  <span class='va'>input</span>,
-  <span class='va'>weight</span>,
-  offsets <span class='op'>=</span> <span class='cn'>NULL</span>,
-  max_norm <span class='op'>=</span> <span class='cn'>NULL</span>,
-  norm_type <span class='op'>=</span> <span class='fl'>2</span>,
-  scale_grad_by_freq <span class='op'>=</span> <span class='cn'>FALSE</span>,
-  mode <span class='op'>=</span> <span class='st'>"mean"</span>,
-  sparse <span class='op'>=</span> <span class='cn'>FALSE</span>,
-  per_sample_weights <span class='op'>=</span> <span class='cn'>NULL</span>,
-  include_last_offset <span class='op'>=</span> <span class='cn'>FALSE</span>
-<span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_embedding_bag</span><span class="op">(</span>
+  <span class="va">input</span>,
+  <span class="va">weight</span>,
+  offsets <span class="op">=</span> <span class="cn">NULL</span>,
+  max_norm <span class="op">=</span> <span class="cn">NULL</span>,
+  norm_type <span class="op">=</span> <span class="fl">2</span>,
+  scale_grad_by_freq <span class="op">=</span> <span class="cn">FALSE</span>,
+  mode <span class="op">=</span> <span class="st">"mean"</span>,
+  sparse <span class="op">=</span> <span class="cn">FALSE</span>,
+  per_sample_weights <span class="op">=</span> <span class="cn">NULL</span>,
+  include_last_offset <span class="op">=</span> <span class="cn">FALSE</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(LongTensor) Tensor containing bags of indices into the embedding matrix</p></td>
-    </tr>
-    <tr>
-      <th>weight</th>
-      <td><p>(Tensor) The embedding matrix with number of rows equal to the
-maximum possible index + 1, and number of columns equal to the embedding size</p></td>
-    </tr>
-    <tr>
-      <th>offsets</th>
-      <td><p>(LongTensor, optional) Only used when <code>input</code> is 1D. <code>offsets</code>
-determines the starting index position of each bag (sequence) in <code>input</code>.</p></td>
-    </tr>
-    <tr>
-      <th>max_norm</th>
-      <td><p>(float, optional) If given, each embedding vector with norm
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>(LongTensor) Tensor containing bags of indices into the embedding matrix</p></dd>
+<dt>weight</dt>
+<dd><p>(Tensor) The embedding matrix with number of rows equal to the
+maximum possible index + 1, and number of columns equal to the embedding size</p></dd>
+<dt>offsets</dt>
+<dd><p>(LongTensor, optional) Only used when <code>input</code> is 1D. <code>offsets</code>
+determines the starting index position of each bag (sequence) in <code>input</code>.</p></dd>
+<dt>max_norm</dt>
+<dd><p>(float, optional) If given, each embedding vector with norm
 larger than <code>max_norm</code> is renormalized to have norm <code>max_norm</code>.
-Note: this will modify <code>weight</code> in-place.</p></td>
-    </tr>
-    <tr>
-      <th>norm_type</th>
-      <td><p>(float, optional) The <code>p</code> in the <code>p</code>-norm to compute for the
-<code>max_norm</code> option. Default <code>2</code>.</p></td>
-    </tr>
-    <tr>
-      <th>scale_grad_by_freq</th>
-      <td><p>(boolean, optional) if given, this will scale gradients
-by the inverse of frequency of the words in the mini-batch. Default <code>FALSE</code>.                                            Note: this option is not supported when <code>mode="max"</code>.</p></td>
-    </tr>
-    <tr>
-      <th>mode</th>
-      <td><p>(string, optional) <code>"sum"</code>, <code>"mean"</code> or <code>"max"</code>. Specifies
-the way to reduce the bag. Default: 'mean'</p></td>
-    </tr>
-    <tr>
-      <th>sparse</th>
-      <td><p>(bool, optional) if <code>TRUE</code>, gradient w.r.t. <code>weight</code> will be a
+Note: this will modify <code>weight</code> in-place.</p></dd>
+<dt>norm_type</dt>
+<dd><p>(float, optional) The <code>p</code> in the <code>p</code>-norm to compute for the
+<code>max_norm</code> option. Default <code>2</code>.</p></dd>
+<dt>scale_grad_by_freq</dt>
+<dd><p>(boolean, optional) if given, this will scale gradients
+by the inverse of frequency of the words in the mini-batch. Default <code>FALSE</code>.                                            Note: this option is not supported when <code>mode="max"</code>.</p></dd>
+<dt>mode</dt>
+<dd><p>(string, optional) <code>"sum"</code>, <code>"mean"</code> or <code>"max"</code>. Specifies
+the way to reduce the bag. Default: 'mean'</p></dd>
+<dt>sparse</dt>
+<dd><p>(bool, optional) if <code>TRUE</code>, gradient w.r.t. <code>weight</code> will be a
 sparse tensor. See Notes under <code>nn_embedding</code> for more details regarding
-sparse gradients. Note: this option is not supported when <code>mode="max"</code>.</p></td>
-    </tr>
-    <tr>
-      <th>per_sample_weights</th>
-      <td><p>(Tensor, optional) a tensor of float / double weights,
+sparse gradients. Note: this option is not supported when <code>mode="max"</code>.</p></dd>
+<dt>per_sample_weights</dt>
+<dd><p>(Tensor, optional) a tensor of float / double weights,
 or NULL to indicate all weights should be taken to be 1. If specified,
 <code>per_sample_weights</code> must have exactly the same shape as input and is treated
-as having the same <code>offsets</code>, if those are not <code>NULL</code>.</p></td>
-    </tr>
-    <tr>
-      <th>include_last_offset</th>
-      <td><p>(bool, optional) if <code>TRUE</code>, the size of offsets is
-equal to the number of bags + 1.</p></td>
-    </tr>
-    </table>
+as having the same <code>offsets</code>, if those are not <code>NULL</code>.</p></dd>
+<dt>include_last_offset</dt>
+<dd><p>(bool, optional) if <code>TRUE</code>, the size of offsets is
+equal to the number of bags + 1.</p></dd>
+</dl></div>
 
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+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer>
-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-fold.R'><code>R/nnf-fold.R</code></a></small>
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+    <div class="ref-description section level2">
     <p>Combines an array of sliding local blocks into a large containing
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-    <pre class="usage"><span class='fu'>nnf_fold</span><span class='op'>(</span>
-  <span class='va'>input</span>,
-  <span class='va'>output_size</span>,
-  <span class='va'>kernel_size</span>,
-  dilation <span class='op'>=</span> <span class='fl'>1</span>,
-  padding <span class='op'>=</span> <span class='fl'>0</span>,
-  stride <span class='op'>=</span> <span class='fl'>1</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>the input tensor</p></td>
-    </tr>
-    <tr>
-      <th>output_size</th>
-      <td><p>the shape of the spatial dimensions of the output (i.e.,
-<code>output$sizes()[-c(1,2)]</code>)</p></td>
-    </tr>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>the size of the sliding blocks</p></td>
-    </tr>
-    <tr>
-      <th>dilation</th>
-      <td><p>a parameter that controls the stride of elements within the
-neighborhood. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>implicit zero padding to be added on both sides of input.
-Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>the stride of the sliding blocks in the input spatial dimensions.
-Default: 1</p></td>
-    </tr>
-    </table>
-
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+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_fold</span><span class="op">(</span>
+  <span class="va">input</span>,
+  <span class="va">output_size</span>,
+  <span class="va">kernel_size</span>,
+  dilation <span class="op">=</span> <span class="fl">1</span>,
+  padding <span class="op">=</span> <span class="fl">0</span>,
+  stride <span class="op">=</span> <span class="fl">1</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>the input tensor</p></dd>
+<dt>output_size</dt>
+<dd><p>the shape of the spatial dimensions of the output (i.e.,
+<code>output$sizes()[-c(1,2)]</code>)</p></dd>
+<dt>kernel_size</dt>
+<dd><p>the size of the sliding blocks</p></dd>
+<dt>dilation</dt>
+<dd><p>a parameter that controls the stride of elements within the
+neighborhood. Default: 1</p></dd>
+<dt>padding</dt>
+<dd><p>implicit zero padding to be added on both sides of input.
+Default: 0</p></dd>
+<dt>stride</dt>
+<dd><p>the stride of the sliding blocks in the input spatial dimensions.
+Default: 1</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></a></h2>
     
 
 
 <p>Currently, only 4-D output tensors (batched image-like tensors) are
 supported.</p>
+    </div>
 
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index 91137457b..e287371a1 100644
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+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Fractional_max_pool2d</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-pooling.R" class="external-link"><code>R/nnf-pooling.R</code></a></small>
+      <div class="d-none name"><code>nnf_fractional_max_pool2d.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Applies 2D fractional max pooling over an input signal composed of several input planes.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_fractional_max_pool2d</span><span class='op'>(</span>
-  <span class='va'>input</span>,
-  <span class='va'>kernel_size</span>,
-  output_size <span class='op'>=</span> <span class='cn'>NULL</span>,
-  output_ratio <span class='op'>=</span> <span class='cn'>NULL</span>,
-  return_indices <span class='op'>=</span> <span class='cn'>FALSE</span>,
-  random_samples <span class='op'>=</span> <span class='cn'>NULL</span>
-<span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_fractional_max_pool2d</span><span class="op">(</span>
+  <span class="va">input</span>,
+  <span class="va">kernel_size</span>,
+  output_size <span class="op">=</span> <span class="cn">NULL</span>,
+  output_ratio <span class="op">=</span> <span class="cn">NULL</span>,
+  return_indices <span class="op">=</span> <span class="cn">FALSE</span>,
+  random_samples <span class="op">=</span> <span class="cn">NULL</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>the input tensor</p></td>
-    </tr>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>the size of the window to take a max over. Can be a
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>the input tensor</p></dd>
+<dt>kernel_size</dt>
+<dd><p>the size of the window to take a max over. Can be a
 single number \(k\) (for a square kernel of \(k * k\)) or
-a tuple <code>(kH, kW)</code></p></td>
-    </tr>
-    <tr>
-      <th>output_size</th>
-      <td><p>the target output size of the image of the form \(oH * oW\).
-Can be a tuple <code>(oH, oW)</code> or a single number \(oH\) for a square image \(oH * oH\)</p></td>
-    </tr>
-    <tr>
-      <th>output_ratio</th>
-      <td><p>If one wants to have an output size as a ratio of the input size,
-this option can be given. This has to be a number or tuple in the range (0, 1)</p></td>
-    </tr>
-    <tr>
-      <th>return_indices</th>
-      <td><p>if <code>True</code>, will return the indices along with the outputs.</p></td>
-    </tr>
-    <tr>
-      <th>random_samples</th>
-      <td><p>optional random samples.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
+a tuple <code>(kH, kW)</code></p></dd>
+<dt>output_size</dt>
+<dd><p>the target output size of the image of the form \(oH * oW\).
+Can be a tuple <code>(oH, oW)</code> or a single number \(oH\) for a square image \(oH * oH\)</p></dd>
+<dt>output_ratio</dt>
+<dd><p>If one wants to have an output size as a ratio of the input size,
+this option can be given. This has to be a number or tuple in the range (0, 1)</p></dd>
+<dt>return_indices</dt>
+<dd><p>if <code>True</code>, will return the indices along with the outputs.</p></dd>
+<dt>random_samples</dt>
+<dd><p>optional random samples.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>Fractional MaxPooling is described in detail in the paper <code>Fractional MaxPooling</code>_ by Ben Graham</p>
 <p>The max-pooling operation is applied in \(kH * kW\) regions by a stochastic
 step size determined by the target output size.
 The number of output features is equal to the number of input planes.</p>
+    </div>
 
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
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+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
 </div>
 
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+<div class="pkgdown-footer-right">
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Fractional_max_pool3d</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-pooling.R'><code>R/nnf-pooling.R</code></a></small>
-    <div class="hidden name"><code>nnf_fractional_max_pool3d.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Fractional_max_pool3d</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-pooling.R" class="external-link"><code>R/nnf-pooling.R</code></a></small>
+      <div class="d-none name"><code>nnf_fractional_max_pool3d.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Applies 3D fractional max pooling over an input signal composed of several input planes.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_fractional_max_pool3d</span><span class='op'>(</span>
-  <span class='va'>input</span>,
-  <span class='va'>kernel_size</span>,
-  output_size <span class='op'>=</span> <span class='cn'>NULL</span>,
-  output_ratio <span class='op'>=</span> <span class='cn'>NULL</span>,
-  return_indices <span class='op'>=</span> <span class='cn'>FALSE</span>,
-  random_samples <span class='op'>=</span> <span class='cn'>NULL</span>
-<span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_fractional_max_pool3d</span><span class="op">(</span>
+  <span class="va">input</span>,
+  <span class="va">kernel_size</span>,
+  output_size <span class="op">=</span> <span class="cn">NULL</span>,
+  output_ratio <span class="op">=</span> <span class="cn">NULL</span>,
+  return_indices <span class="op">=</span> <span class="cn">FALSE</span>,
+  random_samples <span class="op">=</span> <span class="cn">NULL</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>the input tensor</p></td>
-    </tr>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>the size of the window to take a max over. Can be a single number \(k\)
-(for a square kernel of \(k * k * k\)) or a tuple <code>(kT, kH, kW)</code></p></td>
-    </tr>
-    <tr>
-      <th>output_size</th>
-      <td><p>the target output size of the form \(oT * oH * oW\).
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>the input tensor</p></dd>
+<dt>kernel_size</dt>
+<dd><p>the size of the window to take a max over. Can be a single number \(k\)
+(for a square kernel of \(k * k * k\)) or a tuple <code>(kT, kH, kW)</code></p></dd>
+<dt>output_size</dt>
+<dd><p>the target output size of the form \(oT * oH * oW\).
 Can be a tuple <code>(oT, oH, oW)</code> or a single number \(oH\) for a cubic output
-\(oH * oH * oH\)</p></td>
-    </tr>
-    <tr>
-      <th>output_ratio</th>
-      <td><p>If one wants to have an output size as a ratio of the
+\(oH * oH * oH\)</p></dd>
+<dt>output_ratio</dt>
+<dd><p>If one wants to have an output size as a ratio of the
 input size, this option can be given. This has to be a number or tuple in the
-range (0, 1)</p></td>
-    </tr>
-    <tr>
-      <th>return_indices</th>
-      <td><p>if <code>True</code>, will return the indices along with the outputs.</p></td>
-    </tr>
-    <tr>
-      <th>random_samples</th>
-      <td><p>undocumented argument.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
+range (0, 1)</p></dd>
+<dt>return_indices</dt>
+<dd><p>if <code>True</code>, will return the indices along with the outputs.</p></dd>
+<dt>random_samples</dt>
+<dd><p>undocumented argument.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>Fractional MaxPooling is described in detail in the paper <code>Fractional MaxPooling</code>_ by Ben Graham</p>
 <p>The max-pooling operation is applied in \(kT * kH * kW\) regions by a stochastic
 step size determined by the target output size.
 The number of output features is equal to the number of input planes.</p>
+    </div>
 
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+    </nav></aside></div>
 
 
-      <footer>
-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
 </div>
 
-<div class="pkgdown">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p>
+<div class="pkgdown-footer-right">
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.2.</p>
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index c3c2801ec..966701cd3 100644
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Gelu</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-activation.R'><code>R/nnf-activation.R</code></a></small>
-    <div class="hidden name"><code>nnf_gelu.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Gelu</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-activation.R" class="external-link"><code>R/nnf-activation.R</code></a></small>
+      <div class="d-none name"><code>nnf_gelu.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Gelu</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_gelu</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(N,*) tensor, where * means, any number of additional
-dimensions</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="gelu-input-gt-tensor-"><a class="anchor" href="#gelu-input-gt-tensor-"></a>gelu(input) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_gelu</span><span class="op">(</span><span class="va">input</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>(N,*) tensor, where * means, any number of additional
+dimensions</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="gelu-input-gt-tensor-">gelu(input) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#gelu-input-gt-tensor-"></a></h2>
     
 
 
@@ -210,33 +100,26 @@ dimensions</p></td>
 \(GELU(x) = x * \Phi(x)\)</p>
 <p>where \(\Phi(x)\) is the Cumulative Distribution Function for
 Gaussian Distribution.</p>
-<p>See <a href='https://arxiv.org/abs/1606.08415'>Gaussian Error Linear Units (GELUs)</a>.</p>
+<p>See <a href="https://arxiv.org/abs/1606.08415" class="external-link">Gaussian Error Linear Units (GELUs)</a>.</p>
+    </div>
 
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-      <div class="copyright">
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+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-activation.R'><code>R/nnf-activation.R</code></a></small>
-    <div class="hidden name"><code>nnf_glu.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Glu</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-activation.R" class="external-link"><code>R/nnf-activation.R</code></a></small>
+      <div class="d-none name"><code>nnf_glu.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>The gated linear unit. Computes:</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_glu</span><span class='op'>(</span><span class='va'>input</span>, dim <span class='op'>=</span> <span class='op'>-</span><span class='fl'>1</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) input tensor</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) dimension on which to split the input. Default: -1</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_glu</span><span class="op">(</span><span class="va">input</span>, dim <span class="op">=</span> <span class="op">-</span><span class="fl">1</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>(Tensor) input tensor</p></dd>
+<dt>dim</dt>
+<dd><p>(int) dimension on which to split the input. Default: -1</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>$$GLU(a, b) = a \otimes \sigma(b)$$</p>
 <p>where <code>input</code> is split in half along <code>dim</code> to form <code>a</code> and <code>b</code>, \(\sigma\)
 is the sigmoid function and \(\otimes\) is the element-wise product
 between matrices.</p>
-<p>See <a href='https://arxiv.org/abs/1612.08083'>Language Modeling with Gated Convolutional Networks</a>.</p>
+<p>See <a href="https://arxiv.org/abs/1612.08083" class="external-link">Language Modeling with Gated Convolutional Networks</a>.</p>
+    </div>
 
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Grid_sample</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-vision.R'><code>R/nnf-vision.R</code></a></small>
-    <div class="hidden name"><code>nnf_grid_sample.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Grid_sample</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-vision.R" class="external-link"><code>R/nnf-vision.R</code></a></small>
+      <div class="d-none name"><code>nnf_grid_sample.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Given an <code>input</code> and a flow-field <code>grid</code>, computes the
 <code>output</code> using <code>input</code> values and pixel locations from <code>grid</code>.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_grid_sample</span><span class='op'>(</span>
-  <span class='va'>input</span>,
-  <span class='va'>grid</span>,
-  mode <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='st'>"bilinear"</span>, <span class='st'>"nearest"</span><span class='op'>)</span>,
-  padding_mode <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='st'>"zeros"</span>, <span class='st'>"border"</span>, <span class='st'>"reflection"</span><span class='op'>)</span>,
-  align_corners <span class='op'>=</span> <span class='cn'>FALSE</span>
-<span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_grid_sample</span><span class="op">(</span>
+  <span class="va">input</span>,
+  <span class="va">grid</span>,
+  mode <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"bilinear"</span>, <span class="st">"nearest"</span><span class="op">)</span>,
+  padding_mode <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"zeros"</span>, <span class="st">"border"</span>, <span class="st">"reflection"</span><span class="op">)</span>,
+  align_corners <span class="op">=</span> <span class="cn">FALSE</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) input of shape \((N, C, H_{\mbox{in}}, W_{\mbox{in}})\) (4-D case)                    or \((N, C, D_{\mbox{in}}, H_{\mbox{in}}, W_{\mbox{in}})\) (5-D case)</p></td>
-    </tr>
-    <tr>
-      <th>grid</th>
-      <td><p>(Tensor) flow-field of shape \((N, H_{\mbox{out}}, W_{\mbox{out}}, 2)\) (4-D case)                   or \((N, D_{\mbox{out}}, H_{\mbox{out}}, W_{\mbox{out}}, 3)\) (5-D case)</p></td>
-    </tr>
-    <tr>
-      <th>mode</th>
-      <td><p>(str) interpolation mode to calculate output values <code>'bilinear'</code> | <code>'nearest'</code>.
-Default: <code>'bilinear'</code></p></td>
-    </tr>
-    <tr>
-      <th>padding_mode</th>
-      <td><p>(str) padding mode for outside grid values <code>'zeros'</code> | <code>'border'</code>
-| <code>'reflection'</code>. Default: <code>'zeros'</code></p></td>
-    </tr>
-    <tr>
-      <th>align_corners</th>
-      <td><p>(bool, optional) Geometrically, we consider the pixels of the
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>(Tensor) input of shape \((N, C, H_{\mbox{in}}, W_{\mbox{in}})\) (4-D case)                    or \((N, C, D_{\mbox{in}}, H_{\mbox{in}}, W_{\mbox{in}})\) (5-D case)</p></dd>
+<dt>grid</dt>
+<dd><p>(Tensor) flow-field of shape \((N, H_{\mbox{out}}, W_{\mbox{out}}, 2)\) (4-D case)                   or \((N, D_{\mbox{out}}, H_{\mbox{out}}, W_{\mbox{out}}, 3)\) (5-D case)</p></dd>
+<dt>mode</dt>
+<dd><p>(str) interpolation mode to calculate output values <code>'bilinear'</code> | <code>'nearest'</code>.
+Default: <code>'bilinear'</code></p></dd>
+<dt>padding_mode</dt>
+<dd><p>(str) padding mode for outside grid values <code>'zeros'</code> | <code>'border'</code>
+| <code>'reflection'</code>. Default: <code>'zeros'</code></p></dd>
+<dt>align_corners</dt>
+<dd><p>(bool, optional) Geometrically, we consider the pixels of the
 input  as squares rather than points. If set to <code>True</code>, the extrema (<code>-1</code> and
 <code>1</code>) are considered as referring to the center points of the input's corner pixels.
 If set to <code>False</code>, they are instead considered as referring to the corner
 points of the input's corner pixels, making the sampling more resolution
-agnostic. This option parallels the <code>align_corners</code> option in  <code><a href='nnf_interpolate.html'>nnf_interpolate()</a></code>, and
+agnostic. This option parallels the <code>align_corners</code> option in  <code><a href="nnf_interpolate.html">nnf_interpolate()</a></code>, and
 so whichever option is used here should also be used there to resize the input
-image before grid sampling. Default: <code>False</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
+image before grid sampling. Default: <code>False</code></p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>Currently, only spatial (4-D) and volumetric (5-D) <code>input</code> are
 supported.</p>
 <p>In the spatial (4-D) case, for <code>input</code> with shape
@@ -254,8 +137,7 @@ the range of <code>[-1, 1]</code>. For example, values <code>x = -1, y = -1</cod
 left-top pixel of <code>input</code>, and values  <code>x = 1, y = 1</code> is the
 right-bottom pixel of <code>input</code>.</p>
 <p>If <code>grid</code> has values outside the range of <code>[-1, 1]</code>, the corresponding
-outputs are handled as defined by <code>padding_mode</code>. Options are</p><ul>
-<li><p><code>padding_mode="zeros"</code>: use <code>0</code> for out-of-bound grid locations,</p></li>
+outputs are handled as defined by <code>padding_mode</code>. Options are</p><ul><li><p><code>padding_mode="zeros"</code>: use <code>0</code> for out-of-bound grid locations,</p></li>
 <li><p><code>padding_mode="border"</code>: use border values for out-of-bound grid locations,</p></li>
 <li><p><code>padding_mode="reflection"</code>: use values at locations reflected by
 the border for out-of-bound grid locations. For location far away
@@ -263,41 +145,33 @@ from the border, it will keep being reflected until becoming in bound,
 e.g., (normalized) pixel location <code>x = -3.5</code> reflects by border <code>-1</code>
 and becomes <code>x' = 1.5</code>, then reflects by border <code>1</code> and becomes
 <code>x'' = -0.5</code>.</p></li>
-</ul>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
+</ul></div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     
 
 
-<p>This function is often used in conjunction with <code><a href='nnf_affine_grid.html'>nnf_affine_grid()</a></code>
+<p>This function is often used in conjunction with <code><a href="nnf_affine_grid.html">nnf_affine_grid()</a></code>
 to build <code>Spatial Transformer Networks</code>_ .</p>
+    </div>
 
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
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-</div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer>
-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
 </div>
 
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-  <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p>
+<div class="pkgdown-footer-right">
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.2.</p>
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diff --git a/reference/nnf_group_norm.html b/reference/nnf_group_norm.html
index f679547e7..3b65156c8 100644
--- a/reference/nnf_group_norm.html
+++ b/reference/nnf_group_norm.html
@@ -1,247 +1,121 @@
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+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Group_norm</h1>
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     </div>
 
-    <div class="ref-description">
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     <p>Applies Group Normalization for last certain number of dimensions.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_group_norm</span><span class='op'>(</span><span class='va'>input</span>, <span class='va'>num_groups</span>, weight <span class='op'>=</span> <span class='cn'>NULL</span>, bias <span class='op'>=</span> <span class='cn'>NULL</span>, eps <span class='op'>=</span> <span class='fl'>1e-05</span><span class='op'>)</span></pre>
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-      <th>input</th>
-      <td><p>the input tensor</p></td>
-    </tr>
-    <tr>
-      <th>num_groups</th>
-      <td><p>number of groups to separate the channels into</p></td>
-    </tr>
-    <tr>
-      <th>weight</th>
-      <td><p>the weight tensor</p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>the bias tensor</p></td>
-    </tr>
-    <tr>
-      <th>eps</th>
-      <td><p>a value added to the denominator for numerical stability. Default: 1e-5</p></td>
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+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_group_norm</span><span class="op">(</span><span class="va">input</span>, <span class="va">num_groups</span>, weight <span class="op">=</span> <span class="cn">NULL</span>, bias <span class="op">=</span> <span class="cn">NULL</span>, eps <span class="op">=</span> <span class="fl">1e-05</span><span class="op">)</span></code></pre></div>
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+    <dl><dt>input</dt>
+<dd><p>the input tensor</p></dd>
+<dt>num_groups</dt>
+<dd><p>number of groups to separate the channels into</p></dd>
+<dt>weight</dt>
+<dd><p>the weight tensor</p></dd>
+<dt>bias</dt>
+<dd><p>the bias tensor</p></dd>
+<dt>eps</dt>
+<dd><p>a value added to the denominator for numerical stability. Default: 1e-5</p></dd>
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index 59cba1a63..aa07c67aa 100644
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-    <h1>Gumbel_softmax</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-activation.R'><code>R/nnf-activation.R</code></a></small>
-    <div class="hidden name"><code>nnf_gumbel_softmax.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Gumbel_softmax</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-activation.R" class="external-link"><code>R/nnf-activation.R</code></a></small>
+      <div class="d-none name"><code>nnf_gumbel_softmax.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Samples from the Gumbel-Softmax distribution and
 optionally discretizes.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_gumbel_softmax</span><span class='op'>(</span><span class='va'>logits</span>, tau <span class='op'>=</span> <span class='fl'>1</span>, hard <span class='op'>=</span> <span class='cn'>FALSE</span>, dim <span class='op'>=</span> <span class='op'>-</span><span class='fl'>1</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>logits</th>
-      <td><p><code>[..., num_features]</code> unnormalized log probabilities</p></td>
-    </tr>
-    <tr>
-      <th>tau</th>
-      <td><p>non-negative scalar temperature</p></td>
-    </tr>
-    <tr>
-      <th>hard</th>
-      <td><p>if <code>True</code>, the returned samples will be discretized as one-hot vectors,        but will be differentiated as if it is the soft sample in autograd</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) A dimension along which softmax will be computed. Default: -1.</p></td>
-    </tr>
-    </table>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_gumbel_softmax</span><span class="op">(</span><span class="va">logits</span>, tau <span class="op">=</span> <span class="fl">1</span>, hard <span class="op">=</span> <span class="cn">FALSE</span>, dim <span class="op">=</span> <span class="op">-</span><span class="fl">1</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>logits</dt>
+<dd><p><code>[..., num_features]</code> unnormalized log probabilities</p></dd>
+<dt>tau</dt>
+<dd><p>non-negative scalar temperature</p></dd>
+<dt>hard</dt>
+<dd><p>if <code>True</code>, the returned samples will be discretized as one-hot vectors,        but will be differentiated as if it is the soft sample in autograd</p></dd>
+<dt>dim</dt>
+<dd><p>(int) A dimension along which softmax will be computed. Default: -1.</p></dd>
+</dl></div>
 
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diff --git a/reference/nnf_hardshrink.html b/reference/nnf_hardshrink.html
index b10f85610..77c065de7 100644
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-    <h1>Hardshrink</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-activation.R'><code>R/nnf-activation.R</code></a></small>
-    <div class="hidden name"><code>nnf_hardshrink.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Hardshrink</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-activation.R" class="external-link"><code>R/nnf-activation.R</code></a></small>
+      <div class="d-none name"><code>nnf_hardshrink.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Applies the hard shrinkage function element-wise</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_hardshrink</span><span class='op'>(</span><span class='va'>input</span>, lambd <span class='op'>=</span> <span class='fl'>0.5</span><span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_hardshrink</span><span class="op">(</span><span class="va">input</span>, lambd <span class="op">=</span> <span class="fl">0.5</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(N,*) tensor, where * means, any number of additional
-dimensions</p></td>
-    </tr>
-    <tr>
-      <th>lambd</th>
-      <td><p>the lambda value for the Hardshrink formulation. Default: 0.5</p></td>
-    </tr>
-    </table>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>(N,*) tensor, where * means, any number of additional
+dimensions</p></dd>
+<dt>lambd</dt>
+<dd><p>the lambda value for the Hardshrink formulation. Default: 0.5</p></dd>
+</dl></div>
 
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index 1b0674063..eaaaacff2 100644
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+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Hardsigmoid</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-activation.R" class="external-link"><code>R/nnf-activation.R</code></a></small>
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     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Applies the element-wise function \(\mbox{Hardsigmoid}(x) = \frac{ReLU6(x + 3)}{6}\)</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_hardsigmoid</span><span class='op'>(</span><span class='va'>input</span>, inplace <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_hardsigmoid</span><span class="op">(</span><span class="va">input</span>, inplace <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
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-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
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-    <tr>
-      <th>input</th>
-      <td><p>(N,*) tensor, where * means, any number of additional
-dimensions</p></td>
-    </tr>
-    <tr>
-      <th>inplace</th>
-      <td><p>NA If set to <code>True</code>, will do this operation in-place. Default: <code>False</code></p></td>
-    </tr>
-    </table>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>(N,*) tensor, where * means, any number of additional
+dimensions</p></dd>
+<dt>inplace</dt>
+<dd><p>NA If set to <code>True</code>, will do this operation in-place. Default: <code>False</code></p></dd>
+</dl></div>
 
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-    <h1>Hardswish</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-activation.R'><code>R/nnf-activation.R</code></a></small>
-    <div class="hidden name"><code>nnf_hardswish.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Hardswish</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-activation.R" class="external-link"><code>R/nnf-activation.R</code></a></small>
+      <div class="d-none name"><code>nnf_hardswish.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Applies the hardswish function, element-wise, as described in the paper:
 Searching for MobileNetV3.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_hardswish</span><span class='op'>(</span><span class='va'>input</span>, inplace <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(N,*) tensor, where * means, any number of additional
-dimensions</p></td>
-    </tr>
-    <tr>
-      <th>inplace</th>
-      <td><p>can optionally do the operation in-place. Default: FALSE</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_hardswish</span><span class="op">(</span><span class="va">input</span>, inplace <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>(N,*) tensor, where * means, any number of additional
+dimensions</p></dd>
+<dt>inplace</dt>
+<dd><p>can optionally do the operation in-place. Default: FALSE</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>$$ \mbox{Hardswish}(x) = \left\{
   \begin{array}{ll}
   0 &amp; \mbox{if } x \le -3, \\
@@ -216,32 +105,25 @@ dimensions</p></td>
   x \cdot (x + 3)/6 &amp; \mbox{otherwise}
   \end{array}
   \right. $$</p>
+    </div>
 
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+      <img src="" class="logo" alt=""><h1>Hardtanh</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-activation.R" class="external-link"><code>R/nnf-activation.R</code></a></small>
+      <div class="d-none name"><code>nnf_hardtanh.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Applies the HardTanh function element-wise.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_hardtanh</span><span class='op'>(</span><span class='va'>input</span>, min_val <span class='op'>=</span> <span class='op'>-</span><span class='fl'>1</span>, max_val <span class='op'>=</span> <span class='fl'>1</span>, inplace <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span>
-
-<span class='fu'>nnf_hardtanh_</span><span class='op'>(</span><span class='va'>input</span>, min_val <span class='op'>=</span> <span class='op'>-</span><span class='fl'>1</span>, max_val <span class='op'>=</span> <span class='fl'>1</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(N,*) tensor, where * means, any number of additional
-dimensions</p></td>
-    </tr>
-    <tr>
-      <th>min_val</th>
-      <td><p>minimum value of the linear region range. Default: -1</p></td>
-    </tr>
-    <tr>
-      <th>max_val</th>
-      <td><p>maximum value of the linear region range. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>inplace</th>
-      <td><p>can optionally do the operation in-place. Default: FALSE</p></td>
-    </tr>
-    </table>
-
-
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
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-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_hardtanh</span><span class="op">(</span><span class="va">input</span>, min_val <span class="op">=</span> <span class="op">-</span><span class="fl">1</span>, max_val <span class="op">=</span> <span class="fl">1</span>, inplace <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>
 
+<span class="fu">nnf_hardtanh_</span><span class="op">(</span><span class="va">input</span>, min_val <span class="op">=</span> <span class="op">-</span><span class="fl">1</span>, max_val <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></code></pre></div>
+    </div>
 
-      <footer>
-      <div class="copyright">
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+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>(N,*) tensor, where * means, any number of additional
+dimensions</p></dd>
+<dt>min_val</dt>
+<dd><p>minimum value of the linear region range. Default: -1</p></dd>
+<dt>max_val</dt>
+<dd><p>maximum value of the linear region range. Default: 1</p></dd>
+<dt>inplace</dt>
+<dd><p>can optionally do the operation in-place. Default: FALSE</p></dd>
+</dl></div>
+
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
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-    <h1>Hinge_embedding_loss</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-loss.R'><code>R/nnf-loss.R</code></a></small>
-    <div class="hidden name"><code>nnf_hinge_embedding_loss.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Hinge_embedding_loss</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-loss.R" class="external-link"><code>R/nnf-loss.R</code></a></small>
+      <div class="d-none name"><code>nnf_hinge_embedding_loss.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Measures the loss given an input tensor xx and a labels tensor yy (containing 1 or -1).
 This is usually used for measuring whether two inputs are similar or dissimilar, e.g.
 using the L1 pairwise distance as xx , and is typically used for learning nonlinear
 embeddings or semi-supervised learning.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_hinge_embedding_loss</span><span class='op'>(</span><span class='va'>input</span>, <span class='va'>target</span>, margin <span class='op'>=</span> <span class='fl'>1</span>, reduction <span class='op'>=</span> <span class='st'>"mean"</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>tensor (N,*) where ** means, any number of additional dimensions</p></td>
-    </tr>
-    <tr>
-      <th>target</th>
-      <td><p>tensor (N,*) , same shape as the input</p></td>
-    </tr>
-    <tr>
-      <th>margin</th>
-      <td><p>Has a default value of 1.</p></td>
-    </tr>
-    <tr>
-      <th>reduction</th>
-      <td><p>(string, optional) – Specifies the reduction to apply to the
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_hinge_embedding_loss</span><span class="op">(</span><span class="va">input</span>, <span class="va">target</span>, margin <span class="op">=</span> <span class="fl">1</span>, reduction <span class="op">=</span> <span class="st">"mean"</span><span class="op">)</span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>tensor (N,*) where ** means, any number of additional dimensions</p></dd>
+<dt>target</dt>
+<dd><p>tensor (N,*) , same shape as the input</p></dd>
+<dt>margin</dt>
+<dd><p>Has a default value of 1.</p></dd>
+<dt>reduction</dt>
+<dd><p>(string, optional) – Specifies the reduction to apply to the
 output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
 the sum of the output will be divided by the number of elements in the output,
-'sum': the output will be summed. Default: 'mean'</p></td>
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+'sum': the output will be summed. Default: 'mean'</p></dd>
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Instance_norm</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-instancenorm.R'><code>R/nnf-instancenorm.R</code></a></small>
-    <div class="hidden name"><code>nnf_instance_norm.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Instance_norm</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-instancenorm.R" class="external-link"><code>R/nnf-instancenorm.R</code></a></small>
+      <div class="d-none name"><code>nnf_instance_norm.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Applies Instance Normalization for each channel in each data sample in a
 batch.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_instance_norm</span><span class='op'>(</span>
-  <span class='va'>input</span>,
-  running_mean <span class='op'>=</span> <span class='cn'>NULL</span>,
-  running_var <span class='op'>=</span> <span class='cn'>NULL</span>,
-  weight <span class='op'>=</span> <span class='cn'>NULL</span>,
-  bias <span class='op'>=</span> <span class='cn'>NULL</span>,
-  use_input_stats <span class='op'>=</span> <span class='cn'>TRUE</span>,
-  momentum <span class='op'>=</span> <span class='fl'>0.1</span>,
-  eps <span class='op'>=</span> <span class='fl'>1e-05</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>the input tensor</p></td>
-    </tr>
-    <tr>
-      <th>running_mean</th>
-      <td><p>the running_mean tensor</p></td>
-    </tr>
-    <tr>
-      <th>running_var</th>
-      <td><p>the running var tensor</p></td>
-    </tr>
-    <tr>
-      <th>weight</th>
-      <td><p>the weight tensor</p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>the bias tensor</p></td>
-    </tr>
-    <tr>
-      <th>use_input_stats</th>
-      <td><p>whether to use input stats</p></td>
-    </tr>
-    <tr>
-      <th>momentum</th>
-      <td><p>a double for the momentum</p></td>
-    </tr>
-    <tr>
-      <th>eps</th>
-      <td><p>an eps double for numerical stability</p></td>
-    </tr>
-    </table>
-
-
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
-
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_instance_norm</span><span class="op">(</span>
+  <span class="va">input</span>,
+  running_mean <span class="op">=</span> <span class="cn">NULL</span>,
+  running_var <span class="op">=</span> <span class="cn">NULL</span>,
+  weight <span class="op">=</span> <span class="cn">NULL</span>,
+  bias <span class="op">=</span> <span class="cn">NULL</span>,
+  use_input_stats <span class="op">=</span> <span class="cn">TRUE</span>,
+  momentum <span class="op">=</span> <span class="fl">0.1</span>,
+  eps <span class="op">=</span> <span class="fl">1e-05</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
-      <footer>
-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>the input tensor</p></dd>
+<dt>running_mean</dt>
+<dd><p>the running_mean tensor</p></dd>
+<dt>running_var</dt>
+<dd><p>the running var tensor</p></dd>
+<dt>weight</dt>
+<dd><p>the weight tensor</p></dd>
+<dt>bias</dt>
+<dd><p>the bias tensor</p></dd>
+<dt>use_input_stats</dt>
+<dd><p>whether to use input stats</p></dd>
+<dt>momentum</dt>
+<dd><p>a double for the momentum</p></dd>
+<dt>eps</dt>
+<dd><p>an eps double for numerical stability</p></dd>
+</dl></div>
+
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
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diff --git a/reference/nnf_interpolate.html b/reference/nnf_interpolate.html
index d39d08c8c..0c23ddfcd 100644
--- a/reference/nnf_interpolate.html
+++ b/reference/nnf_interpolate.html
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Interpolate</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-upsampling.R'><code>R/nnf-upsampling.R</code></a></small>
-    <div class="hidden name"><code>nnf_interpolate.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Interpolate</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-upsampling.R" class="external-link"><code>R/nnf-upsampling.R</code></a></small>
+      <div class="d-none name"><code>nnf_interpolate.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Down/up samples the input to either the given <code>size</code> or the given
 <code>scale_factor</code></p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_interpolate</span><span class='op'>(</span>
-  <span class='va'>input</span>,
-  size <span class='op'>=</span> <span class='cn'>NULL</span>,
-  scale_factor <span class='op'>=</span> <span class='cn'>NULL</span>,
-  mode <span class='op'>=</span> <span class='st'>"nearest"</span>,
-  align_corners <span class='op'>=</span> <span class='cn'>FALSE</span>,
-  recompute_scale_factor <span class='op'>=</span> <span class='cn'>NULL</span>
-<span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_interpolate</span><span class="op">(</span>
+  <span class="va">input</span>,
+  size <span class="op">=</span> <span class="cn">NULL</span>,
+  scale_factor <span class="op">=</span> <span class="cn">NULL</span>,
+  mode <span class="op">=</span> <span class="st">"nearest"</span>,
+  align_corners <span class="op">=</span> <span class="cn">FALSE</span>,
+  recompute_scale_factor <span class="op">=</span> <span class="cn">NULL</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor</p></td>
-    </tr>
-    <tr>
-      <th>size</th>
-      <td><p>(int or <code>Tuple[int]</code> or <code>Tuple[int, int]</code> or <code>Tuple[int, int, int]</code>)
-output spatial size.</p></td>
-    </tr>
-    <tr>
-      <th>scale_factor</th>
-      <td><p>(float or <code>Tuple[float]</code>) multiplier for spatial size.
-Has to match input size if it is a tuple.</p></td>
-    </tr>
-    <tr>
-      <th>mode</th>
-      <td><p>(str) algorithm used for upsampling: 'nearest' | 'linear' | 'bilinear'
-| 'bicubic' | 'trilinear' | 'area' Default: 'nearest'</p></td>
-    </tr>
-    <tr>
-      <th>align_corners</th>
-      <td><p>(bool, optional) Geometrically, we consider the pixels
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>(Tensor) the input tensor</p></dd>
+<dt>size</dt>
+<dd><p>(int or <code>Tuple[int]</code> or <code>Tuple[int, int]</code> or <code>Tuple[int, int, int]</code>)
+output spatial size.</p></dd>
+<dt>scale_factor</dt>
+<dd><p>(float or <code>Tuple[float]</code>) multiplier for spatial size.
+Has to match input size if it is a tuple.</p></dd>
+<dt>mode</dt>
+<dd><p>(str) algorithm used for upsampling: 'nearest' | 'linear' | 'bilinear'
+| 'bicubic' | 'trilinear' | 'area' Default: 'nearest'</p></dd>
+<dt>align_corners</dt>
+<dd><p>(bool, optional) Geometrically, we consider the pixels
 of the input and output as squares rather than points. If set to TRUE,
 the input and output tensors are aligned by the center points of their corner
 pixels, preserving the values at the corner pixels. If set to False, the
@@ -232,11 +117,9 @@ input and output tensors are aligned by the corner points of their corner pixels
 and the interpolation uses edge value padding for out-of-boundary values,
 making this operation <em>independent</em> of input size when <code>scale_factor</code> is kept
 the same. This only has an effect when <code>mode</code>  is <code>'linear'</code>, <code>'bilinear'</code>,
-<code>'bicubic'</code> or <code>'trilinear'</code>.  Default: <code>False</code></p></td>
-    </tr>
-    <tr>
-      <th>recompute_scale_factor</th>
-      <td><p>(bool, optional) recompute the scale_factor
+<code>'bicubic'</code> or <code>'trilinear'</code>.  Default: <code>False</code></p></dd>
+<dt>recompute_scale_factor</dt>
+<dd><p>(bool, optional) recompute the scale_factor
 for use in the interpolation calculation.  When <code>scale_factor</code> is passed
 as a parameter, it is used to compute the <code>output_size</code>.  If <code>recompute_scale_factor</code>
 is ```True`` or not specified, a new <code>scale_factor</code> will be computed based on
@@ -245,12 +128,10 @@ computation will be identical to if the computed `output_size` were passed-in
 explicitly).  Otherwise, the passed-in `scale_factor` will be used in the
 interpolation computation.  Note that when `scale_factor` is floating-point,
 the recomputed scale_factor may differ from the one passed in due to rounding
-and precision issues.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
+and precision issues.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>The algorithm used for interpolation is determined by <code>mode</code>.</p>
 <p>Currently temporal, spatial and volumetric sampling are supported, i.e.
 expected inputs are 3-D, 4-D or 5-D in shape.</p>
@@ -258,32 +139,25 @@ expected inputs are 3-D, 4-D or 5-D in shape.</p>
 <code>mini-batch x channels x [optional depth] x [optional height] x width</code>.</p>
 <p>The modes available for resizing are: <code>nearest</code>, <code>linear</code> (3D-only),
 <code>bilinear</code>, <code>bicubic</code> (4D-only), <code>trilinear</code> (5D-only), <code>area</code></p>
+    </div>
 
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-      <h2 data-toc-skip>Contents</h2>
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+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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+<div class="pkgdown-footer-right">
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diff --git a/reference/nnf_kl_div.html b/reference/nnf_kl_div.html
index 61c1fe7cd..bf57b8880 100644
--- a/reference/nnf_kl_div.html
+++ b/reference/nnf_kl_div.html
@@ -1,242 +1,120 @@
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-
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-
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-    <h1>Kl_div</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-loss.R'><code>R/nnf-loss.R</code></a></small>
-    <div class="hidden name"><code>nnf_kl_div.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Kl_div</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-loss.R" class="external-link"><code>R/nnf-loss.R</code></a></small>
+      <div class="d-none name"><code>nnf_kl_div.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>The Kullback-Leibler divergence Loss.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_kl_div</span><span class='op'>(</span><span class='va'>input</span>, <span class='va'>target</span>, reduction <span class='op'>=</span> <span class='st'>"mean"</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>tensor (N,*) where ** means, any number of additional dimensions</p></td>
-    </tr>
-    <tr>
-      <th>target</th>
-      <td><p>tensor (N,*) , same shape as the input</p></td>
-    </tr>
-    <tr>
-      <th>reduction</th>
-      <td><p>(string, optional) – Specifies the reduction to apply to the
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_kl_div</span><span class="op">(</span><span class="va">input</span>, <span class="va">target</span>, reduction <span class="op">=</span> <span class="st">"mean"</span><span class="op">)</span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>tensor (N,*) where ** means, any number of additional dimensions</p></dd>
+<dt>target</dt>
+<dd><p>tensor (N,*) , same shape as the input</p></dd>
+<dt>reduction</dt>
+<dd><p>(string, optional) – Specifies the reduction to apply to the
 output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
 the sum of the output will be divided by the number of elements in the output,
-'sum': the output will be summed. Default: 'mean'</p></td>
-    </tr>
-    </table>
+'sum': the output will be summed. Default: 'mean'</p></dd>
+</dl></div>
 
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index 0ede26be3..c9f09829a 100644
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-    <h1>L1_loss</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-loss.R'><code>R/nnf-loss.R</code></a></small>
-    <div class="hidden name"><code>nnf_l1_loss.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>L1_loss</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-loss.R" class="external-link"><code>R/nnf-loss.R</code></a></small>
+      <div class="d-none name"><code>nnf_l1_loss.Rd</code></div>
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-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Function that takes the mean element-wise absolute value difference.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_l1_loss</span><span class='op'>(</span><span class='va'>input</span>, <span class='va'>target</span>, reduction <span class='op'>=</span> <span class='st'>"mean"</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>tensor (N,*) where ** means, any number of additional dimensions</p></td>
-    </tr>
-    <tr>
-      <th>target</th>
-      <td><p>tensor (N,*) , same shape as the input</p></td>
-    </tr>
-    <tr>
-      <th>reduction</th>
-      <td><p>(string, optional) – Specifies the reduction to apply to the
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_l1_loss</span><span class="op">(</span><span class="va">input</span>, <span class="va">target</span>, reduction <span class="op">=</span> <span class="st">"mean"</span><span class="op">)</span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>tensor (N,*) where ** means, any number of additional dimensions</p></dd>
+<dt>target</dt>
+<dd><p>tensor (N,*) , same shape as the input</p></dd>
+<dt>reduction</dt>
+<dd><p>(string, optional) – Specifies the reduction to apply to the
 output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
 the sum of the output will be divided by the number of elements in the output,
-'sum': the output will be summed. Default: 'mean'</p></td>
-    </tr>
-    </table>
+'sum': the output will be summed. Default: 'mean'</p></dd>
+</dl></div>
 
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-normalization.R'><code>R/nnf-normalization.R</code></a></small>
-    <div class="hidden name"><code>nnf_layer_norm.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Layer_norm</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-normalization.R" class="external-link"><code>R/nnf-normalization.R</code></a></small>
+      <div class="d-none name"><code>nnf_layer_norm.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Applies Layer Normalization for last certain number of dimensions.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_layer_norm</span><span class='op'>(</span>
-  <span class='va'>input</span>,
-  <span class='va'>normalized_shape</span>,
-  weight <span class='op'>=</span> <span class='cn'>NULL</span>,
-  bias <span class='op'>=</span> <span class='cn'>NULL</span>,
-  eps <span class='op'>=</span> <span class='fl'>1e-05</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>the input tensor</p></td>
-    </tr>
-    <tr>
-      <th>normalized_shape</th>
-      <td><p>input shape from an expected input of size. If a single
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_layer_norm</span><span class="op">(</span>
+  <span class="va">input</span>,
+  <span class="va">normalized_shape</span>,
+  weight <span class="op">=</span> <span class="cn">NULL</span>,
+  bias <span class="op">=</span> <span class="cn">NULL</span>,
+  eps <span class="op">=</span> <span class="fl">1e-05</span>
+<span class="op">)</span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>the input tensor</p></dd>
+<dt>normalized_shape</dt>
+<dd><p>input shape from an expected input of size. If a single
 integer is used, it is treated as a singleton list, and this module will normalize
-over the last dimension which is expected to be of that specific size.</p></td>
-    </tr>
-    <tr>
-      <th>weight</th>
-      <td><p>the weight tensor</p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>the bias tensor</p></td>
-    </tr>
-    <tr>
-      <th>eps</th>
-      <td><p>a value added to the denominator for numerical stability. Default: 1e-5</p></td>
-    </tr>
-    </table>
+over the last dimension which is expected to be of that specific size.</p></dd>
+<dt>weight</dt>
+<dd><p>the weight tensor</p></dd>
+<dt>bias</dt>
+<dd><p>the bias tensor</p></dd>
+<dt>eps</dt>
+<dd><p>a value added to the denominator for numerical stability. Default: 1e-5</p></dd>
+</dl></div>
 
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-activation.R'><code>R/nnf-activation.R</code></a></small>
-    <div class="hidden name"><code>nnf_leaky_relu.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Leaky_relu</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-activation.R" class="external-link"><code>R/nnf-activation.R</code></a></small>
+      <div class="d-none name"><code>nnf_leaky_relu.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Applies element-wise,
 \(LeakyReLU(x) = max(0, x) + negative_slope * min(0, x)\)</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_leaky_relu</span><span class='op'>(</span><span class='va'>input</span>, negative_slope <span class='op'>=</span> <span class='fl'>0.01</span>, inplace <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(N,*) tensor, where * means, any number of additional
-dimensions</p></td>
-    </tr>
-    <tr>
-      <th>negative_slope</th>
-      <td><p>Controls the angle of the negative slope. Default: 1e-2</p></td>
-    </tr>
-    <tr>
-      <th>inplace</th>
-      <td><p>can optionally do the operation in-place. Default: FALSE</p></td>
-    </tr>
-    </table>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_leaky_relu</span><span class="op">(</span><span class="va">input</span>, negative_slope <span class="op">=</span> <span class="fl">0.01</span>, inplace <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>(N,*) tensor, where * means, any number of additional
+dimensions</p></dd>
+<dt>negative_slope</dt>
+<dd><p>Controls the angle of the negative slope. Default: 1e-2</p></dd>
+<dt>inplace</dt>
+<dd><p>can optionally do the operation in-place. Default: FALSE</p></dd>
+</dl></div>
 
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+additional dimensions</p></dd>
+<dt>weight</dt>
+<dd><p>\((out\_features, in\_features)\) the weights tensor.</p></dd>
+<dt>bias</dt>
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-    <h1>Local_response_norm</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-normalization.R'><code>R/nnf-normalization.R</code></a></small>
-    <div class="hidden name"><code>nnf_local_response_norm.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Local_response_norm</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-normalization.R" class="external-link"><code>R/nnf-normalization.R</code></a></small>
+      <div class="d-none name"><code>nnf_local_response_norm.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Applies local response normalization over an input signal composed of
 several input planes, where channels occupy the second dimension.
 Applies normalization across channels.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_local_response_norm</span><span class='op'>(</span><span class='va'>input</span>, <span class='va'>size</span>, alpha <span class='op'>=</span> <span class='fl'>1e-04</span>, beta <span class='op'>=</span> <span class='fl'>0.75</span>, k <span class='op'>=</span> <span class='fl'>1</span><span class='op'>)</span></pre>
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-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>the input tensor</p></td>
-    </tr>
-    <tr>
-      <th>size</th>
-      <td><p>amount of neighbouring channels used for normalization</p></td>
-    </tr>
-    <tr>
-      <th>alpha</th>
-      <td><p>multiplicative factor. Default: 0.0001</p></td>
-    </tr>
-    <tr>
-      <th>beta</th>
-      <td><p>exponent. Default: 0.75</p></td>
-    </tr>
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-      <th>k</th>
-      <td><p>additive factor. Default: 1</p></td>
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+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_local_response_norm</span><span class="op">(</span><span class="va">input</span>, <span class="va">size</span>, alpha <span class="op">=</span> <span class="fl">1e-04</span>, beta <span class="op">=</span> <span class="fl">0.75</span>, k <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></code></pre></div>
+    </div>
 
-      <footer>
-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>the input tensor</p></dd>
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+<dd><p>amount of neighbouring channels used for normalization</p></dd>
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Log_softmax</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-activation.R'><code>R/nnf-activation.R</code></a></small>
-    <div class="hidden name"><code>nnf_log_softmax.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Log_softmax</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-activation.R" class="external-link"><code>R/nnf-activation.R</code></a></small>
+      <div class="d-none name"><code>nnf_log_softmax.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Applies a softmax followed by a logarithm.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_log_softmax</span><span class='op'>(</span><span class='va'>input</span>, dim <span class='op'>=</span> <span class='cn'>NULL</span>, dtype <span class='op'>=</span> <span class='cn'>NULL</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) input</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) A dimension along which log_softmax will be computed.</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_log_softmax</span><span class="op">(</span><span class="va">input</span>, dim <span class="op">=</span> <span class="cn">NULL</span>, dtype <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>(Tensor) input</p></dd>
+<dt>dim</dt>
+<dd><p>(int) A dimension along which log_softmax will be computed.</p></dd>
+<dt>dtype</dt>
+<dd><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.
 If specified, the input tensor is casted to <code>dtype</code> before the operation
 is performed. This is useful for preventing data type overflows.
-Default: <code>NULL</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
+Default: <code>NULL</code>.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>While mathematically equivalent to log(softmax(x)), doing these two
 operations separately is slower, and numerically unstable. This function
 uses an alternative formulation to compute the output and gradient correctly.</p>
+    </div>
 
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diff --git a/reference/nnf_logsigmoid.html b/reference/nnf_logsigmoid.html
index cf5677d33..bff24d45d 100644
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-    <h1>Logsigmoid</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-activation.R'><code>R/nnf-activation.R</code></a></small>
-    <div class="hidden name"><code>nnf_logsigmoid.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Logsigmoid</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-activation.R" class="external-link"><code>R/nnf-activation.R</code></a></small>
+      <div class="d-none name"><code>nnf_logsigmoid.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Applies element-wise \(LogSigmoid(x_i) = log(\frac{1}{1 + exp(-x_i)})\)</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_logsigmoid</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_logsigmoid</span><span class="op">(</span><span class="va">input</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
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-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(N,*) tensor, where * means, any number of additional
-dimensions</p></td>
-    </tr>
-    </table>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>(N,*) tensor, where * means, any number of additional
+dimensions</p></dd>
+</dl></div>
 
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diff --git a/reference/nnf_lp_pool1d.html b/reference/nnf_lp_pool1d.html
index 34c2c01f4..cf42b2e0c 100644
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-  <div class="col-md-9 contents">
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-    <h1>Lp_pool1d</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-pooling.R'><code>R/nnf-pooling.R</code></a></small>
-    <div class="hidden name"><code>nnf_lp_pool1d.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Lp_pool1d</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-pooling.R" class="external-link"><code>R/nnf-pooling.R</code></a></small>
+      <div class="d-none name"><code>nnf_lp_pool1d.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Applies a 1D power-average pooling over an input signal composed of
 several input planes. If the sum of all inputs to the power of <code>p</code> is
 zero, the gradient is set to zero as well.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_lp_pool1d</span><span class='op'>(</span><span class='va'>input</span>, <span class='va'>norm_type</span>, <span class='va'>kernel_size</span>, stride <span class='op'>=</span> <span class='cn'>NULL</span>, ceil_mode <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>the input tensor</p></td>
-    </tr>
-    <tr>
-      <th>norm_type</th>
-      <td><p>if inf than one gets max pooling if 0 you get sum pooling (
-proportional to the avg pooling)</p></td>
-    </tr>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>a single int, the size of the window</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>a single int, the stride of the window. Default value is kernel_size</p></td>
-    </tr>
-    <tr>
-      <th>ceil_mode</th>
-      <td><p>when True, will use ceil instead of floor to compute the output shape</p></td>
-    </tr>
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+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_lp_pool1d</span><span class="op">(</span><span class="va">input</span>, <span class="va">norm_type</span>, <span class="va">kernel_size</span>, stride <span class="op">=</span> <span class="cn">NULL</span>, ceil_mode <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
+    </div>
 
-      <footer>
-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>the input tensor</p></dd>
+<dt>norm_type</dt>
+<dd><p>if inf than one gets max pooling if 0 you get sum pooling (
+proportional to the avg pooling)</p></dd>
+<dt>kernel_size</dt>
+<dd><p>a single int, the size of the window</p></dd>
+<dt>stride</dt>
+<dd><p>a single int, the stride of the window. Default value is kernel_size</p></dd>
+<dt>ceil_mode</dt>
+<dd><p>when True, will use ceil instead of floor to compute the output shape</p></dd>
+</dl></div>
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-    <h1>Lp_pool2d</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-pooling.R'><code>R/nnf-pooling.R</code></a></small>
-    <div class="hidden name"><code>nnf_lp_pool2d.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Lp_pool2d</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-pooling.R" class="external-link"><code>R/nnf-pooling.R</code></a></small>
+      <div class="d-none name"><code>nnf_lp_pool2d.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Applies a 2D power-average pooling over an input signal composed of
 several input planes. If the sum of all inputs to the power of <code>p</code> is
 zero, the gradient is set to zero as well.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_lp_pool2d</span><span class='op'>(</span><span class='va'>input</span>, <span class='va'>norm_type</span>, <span class='va'>kernel_size</span>, stride <span class='op'>=</span> <span class='cn'>NULL</span>, ceil_mode <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>the input tensor</p></td>
-    </tr>
-    <tr>
-      <th>norm_type</th>
-      <td><p>if inf than one gets max pooling if 0 you get sum pooling (
-proportional to the avg pooling)</p></td>
-    </tr>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>a single int, the size of the window</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>a single int, the stride of the window. Default value is kernel_size</p></td>
-    </tr>
-    <tr>
-      <th>ceil_mode</th>
-      <td><p>when True, will use ceil instead of floor to compute the output shape</p></td>
-    </tr>
-    </table>
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+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_lp_pool2d</span><span class="op">(</span><span class="va">input</span>, <span class="va">norm_type</span>, <span class="va">kernel_size</span>, stride <span class="op">=</span> <span class="cn">NULL</span>, ceil_mode <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
+    </div>
 
-      <footer>
-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>the input tensor</p></dd>
+<dt>norm_type</dt>
+<dd><p>if inf than one gets max pooling if 0 you get sum pooling (
+proportional to the avg pooling)</p></dd>
+<dt>kernel_size</dt>
+<dd><p>a single int, the size of the window</p></dd>
+<dt>stride</dt>
+<dd><p>a single int, the stride of the window. Default value is kernel_size</p></dd>
+<dt>ceil_mode</dt>
+<dd><p>when True, will use ceil instead of floor to compute the output shape</p></dd>
+</dl></div>
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diff --git a/reference/nnf_margin_ranking_loss.html b/reference/nnf_margin_ranking_loss.html
index aad597025..73d9cbf27 100644
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-    <h1>Margin_ranking_loss</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-loss.R'><code>R/nnf-loss.R</code></a></small>
-    <div class="hidden name"><code>nnf_margin_ranking_loss.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Margin_ranking_loss</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-loss.R" class="external-link"><code>R/nnf-loss.R</code></a></small>
+      <div class="d-none name"><code>nnf_margin_ranking_loss.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Creates a criterion that measures the loss given inputs x1 , x2 , two 1D
 mini-batch Tensors, and a label 1D mini-batch tensor y (containing 1 or -1).</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_margin_ranking_loss</span><span class='op'>(</span><span class='va'>input1</span>, <span class='va'>input2</span>, <span class='va'>target</span>, margin <span class='op'>=</span> <span class='fl'>0</span>, reduction <span class='op'>=</span> <span class='st'>"mean"</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input1</th>
-      <td><p>the first tensor</p></td>
-    </tr>
-    <tr>
-      <th>input2</th>
-      <td><p>the second input tensor</p></td>
-    </tr>
-    <tr>
-      <th>target</th>
-      <td><p>the target tensor</p></td>
-    </tr>
-    <tr>
-      <th>margin</th>
-      <td><p>Has a default value of 00 .</p></td>
-    </tr>
-    <tr>
-      <th>reduction</th>
-      <td><p>(string, optional) – Specifies the reduction to apply to the
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_margin_ranking_loss</span><span class="op">(</span><span class="va">input1</span>, <span class="va">input2</span>, <span class="va">target</span>, margin <span class="op">=</span> <span class="fl">0</span>, reduction <span class="op">=</span> <span class="st">"mean"</span><span class="op">)</span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input1</dt>
+<dd><p>the first tensor</p></dd>
+<dt>input2</dt>
+<dd><p>the second input tensor</p></dd>
+<dt>target</dt>
+<dd><p>the target tensor</p></dd>
+<dt>margin</dt>
+<dd><p>Has a default value of 00 .</p></dd>
+<dt>reduction</dt>
+<dd><p>(string, optional) – Specifies the reduction to apply to the
 output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
 the sum of the output will be divided by the number of elements in the output,
-'sum': the output will be summed. Default: 'mean'</p></td>
-    </tr>
-    </table>
+'sum': the output will be summed. Default: 'mean'</p></dd>
+</dl></div>
 
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index 4f3c1f18e..78b7cf9d5 100644
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Max_pool1d</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-pooling.R'><code>R/nnf-pooling.R</code></a></small>
-    <div class="hidden name"><code>nnf_max_pool1d.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Max_pool1d</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-pooling.R" class="external-link"><code>R/nnf-pooling.R</code></a></small>
+      <div class="d-none name"><code>nnf_max_pool1d.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Applies a 1D max pooling over an input signal composed of several input
 planes.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_max_pool1d</span><span class='op'>(</span>
-  <span class='va'>input</span>,
-  <span class='va'>kernel_size</span>,
-  stride <span class='op'>=</span> <span class='cn'>NULL</span>,
-  padding <span class='op'>=</span> <span class='fl'>0</span>,
-  dilation <span class='op'>=</span> <span class='fl'>1</span>,
-  ceil_mode <span class='op'>=</span> <span class='cn'>FALSE</span>,
-  return_indices <span class='op'>=</span> <span class='cn'>FALSE</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>input tensor of shape (minibatch , in_channels , iW)</p></td>
-    </tr>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>the size of the window. Can be a single number or a
-tuple <code>(kW,)</code>.</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>the stride of the window. Can be a single number or a tuple
-<code>(sW,)</code>. Default: <code>kernel_size</code></p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>implicit zero paddings on both sides of the input. Can be a
-single number or a tuple <code>(padW,)</code>. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>dilation</th>
-      <td><p>controls the spacing between the kernel points; also known as
-the à trous algorithm.</p></td>
-    </tr>
-    <tr>
-      <th>ceil_mode</th>
-      <td><p>when True, will use <code>ceil</code> instead of <code>floor</code> to compute the
-output shape. Default: <code>FALSE</code></p></td>
-    </tr>
-    <tr>
-      <th>return_indices</th>
-      <td><p>whether to return the indices where the max occurs.</p></td>
-    </tr>
-    </table>
-
-
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
-
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_max_pool1d</span><span class="op">(</span>
+  <span class="va">input</span>,
+  <span class="va">kernel_size</span>,
+  stride <span class="op">=</span> <span class="cn">NULL</span>,
+  padding <span class="op">=</span> <span class="fl">0</span>,
+  dilation <span class="op">=</span> <span class="fl">1</span>,
+  ceil_mode <span class="op">=</span> <span class="cn">FALSE</span>,
+  return_indices <span class="op">=</span> <span class="cn">FALSE</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
-      <footer>
-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>input tensor of shape (minibatch , in_channels , iW)</p></dd>
+<dt>kernel_size</dt>
+<dd><p>the size of the window. Can be a single number or a
+tuple <code>(kW,)</code>.</p></dd>
+<dt>stride</dt>
+<dd><p>the stride of the window. Can be a single number or a tuple
+<code>(sW,)</code>. Default: <code>kernel_size</code></p></dd>
+<dt>padding</dt>
+<dd><p>implicit zero paddings on both sides of the input. Can be a
+single number or a tuple <code>(padW,)</code>. Default: 0</p></dd>
+<dt>dilation</dt>
+<dd><p>controls the spacing between the kernel points; also known as
+the à trous algorithm.</p></dd>
+<dt>ceil_mode</dt>
+<dd><p>when True, will use <code>ceil</code> instead of <code>floor</code> to compute the
+output shape. Default: <code>FALSE</code></p></dd>
+<dt>return_indices</dt>
+<dd><p>whether to return the indices where the max occurs.</p></dd>
+</dl></div>
+
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
 </div>
 
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index 4e25a5c50..185eea72d 100644
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Max_pool2d</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-pooling.R'><code>R/nnf-pooling.R</code></a></small>
-    <div class="hidden name"><code>nnf_max_pool2d.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Max_pool2d</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-pooling.R" class="external-link"><code>R/nnf-pooling.R</code></a></small>
+      <div class="d-none name"><code>nnf_max_pool2d.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Applies a 2D max pooling over an input signal composed of several input
 planes.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_max_pool2d</span><span class='op'>(</span>
-  <span class='va'>input</span>,
-  <span class='va'>kernel_size</span>,
-  stride <span class='op'>=</span> <span class='va'>kernel_size</span>,
-  padding <span class='op'>=</span> <span class='fl'>0</span>,
-  dilation <span class='op'>=</span> <span class='fl'>1</span>,
-  ceil_mode <span class='op'>=</span> <span class='cn'>FALSE</span>,
-  return_indices <span class='op'>=</span> <span class='cn'>FALSE</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>input tensor (minibatch, in_channels , iH , iW)</p></td>
-    </tr>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>size of the pooling region. Can be a single number or a
-tuple <code>(kH, kW)</code></p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>stride of the pooling operation. Can be a single number or a
-tuple <code>(sH, sW)</code>. Default: <code>kernel_size</code></p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>implicit zero paddings on both sides of the input. Can be a
-single number or a tuple <code>(padH, padW)</code>. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>dilation</th>
-      <td><p>controls the spacing between the kernel points; also known as
-the à trous algorithm.</p></td>
-    </tr>
-    <tr>
-      <th>ceil_mode</th>
-      <td><p>when True, will use <code>ceil</code> instead of <code>floor</code> in the formula
-to compute the output shape. Default: <code>FALSE</code></p></td>
-    </tr>
-    <tr>
-      <th>return_indices</th>
-      <td><p>whether to return the indices where the max occurs.</p></td>
-    </tr>
-    </table>
-
-
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
-
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_max_pool2d</span><span class="op">(</span>
+  <span class="va">input</span>,
+  <span class="va">kernel_size</span>,
+  stride <span class="op">=</span> <span class="va">kernel_size</span>,
+  padding <span class="op">=</span> <span class="fl">0</span>,
+  dilation <span class="op">=</span> <span class="fl">1</span>,
+  ceil_mode <span class="op">=</span> <span class="cn">FALSE</span>,
+  return_indices <span class="op">=</span> <span class="cn">FALSE</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
-      <footer>
-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>input tensor (minibatch, in_channels , iH , iW)</p></dd>
+<dt>kernel_size</dt>
+<dd><p>size of the pooling region. Can be a single number or a
+tuple <code>(kH, kW)</code></p></dd>
+<dt>stride</dt>
+<dd><p>stride of the pooling operation. Can be a single number or a
+tuple <code>(sH, sW)</code>. Default: <code>kernel_size</code></p></dd>
+<dt>padding</dt>
+<dd><p>implicit zero paddings on both sides of the input. Can be a
+single number or a tuple <code>(padH, padW)</code>. Default: 0</p></dd>
+<dt>dilation</dt>
+<dd><p>controls the spacing between the kernel points; also known as
+the à trous algorithm.</p></dd>
+<dt>ceil_mode</dt>
+<dd><p>when True, will use <code>ceil</code> instead of <code>floor</code> in the formula
+to compute the output shape. Default: <code>FALSE</code></p></dd>
+<dt>return_indices</dt>
+<dd><p>whether to return the indices where the max occurs.</p></dd>
+</dl></div>
+
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
 </div>
 
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index 00e1ab499..1377465aa 100644
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Max_pool3d</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-pooling.R'><code>R/nnf-pooling.R</code></a></small>
-    <div class="hidden name"><code>nnf_max_pool3d.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Max_pool3d</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-pooling.R" class="external-link"><code>R/nnf-pooling.R</code></a></small>
+      <div class="d-none name"><code>nnf_max_pool3d.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Applies a 3D max pooling over an input signal composed of several input
 planes.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_max_pool3d</span><span class='op'>(</span>
-  <span class='va'>input</span>,
-  <span class='va'>kernel_size</span>,
-  stride <span class='op'>=</span> <span class='cn'>NULL</span>,
-  padding <span class='op'>=</span> <span class='fl'>0</span>,
-  dilation <span class='op'>=</span> <span class='fl'>1</span>,
-  ceil_mode <span class='op'>=</span> <span class='cn'>FALSE</span>,
-  return_indices <span class='op'>=</span> <span class='cn'>FALSE</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>input tensor (minibatch, in_channels , iT * iH , iW)</p></td>
-    </tr>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>size of the pooling region. Can be a single number or a
-tuple <code>(kT, kH, kW)</code></p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>stride of the pooling operation. Can be a single number or a
-tuple <code>(sT, sH, sW)</code>. Default: <code>kernel_size</code></p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>implicit zero paddings on both sides of the input. Can be a
-single number or a tuple <code>(padT, padH, padW)</code>, Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>dilation</th>
-      <td><p>controls the spacing between the kernel points; also known as
-the à trous algorithm.</p></td>
-    </tr>
-    <tr>
-      <th>ceil_mode</th>
-      <td><p>when True, will use <code>ceil</code> instead of <code>floor</code> in the formula
-to compute the output shape</p></td>
-    </tr>
-    <tr>
-      <th>return_indices</th>
-      <td><p>whether to return the indices where the max occurs.</p></td>
-    </tr>
-    </table>
-
-
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
-
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_max_pool3d</span><span class="op">(</span>
+  <span class="va">input</span>,
+  <span class="va">kernel_size</span>,
+  stride <span class="op">=</span> <span class="cn">NULL</span>,
+  padding <span class="op">=</span> <span class="fl">0</span>,
+  dilation <span class="op">=</span> <span class="fl">1</span>,
+  ceil_mode <span class="op">=</span> <span class="cn">FALSE</span>,
+  return_indices <span class="op">=</span> <span class="cn">FALSE</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
-      <footer>
-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>input tensor (minibatch, in_channels , iT * iH , iW)</p></dd>
+<dt>kernel_size</dt>
+<dd><p>size of the pooling region. Can be a single number or a
+tuple <code>(kT, kH, kW)</code></p></dd>
+<dt>stride</dt>
+<dd><p>stride of the pooling operation. Can be a single number or a
+tuple <code>(sT, sH, sW)</code>. Default: <code>kernel_size</code></p></dd>
+<dt>padding</dt>
+<dd><p>implicit zero paddings on both sides of the input. Can be a
+single number or a tuple <code>(padT, padH, padW)</code>, Default: 0</p></dd>
+<dt>dilation</dt>
+<dd><p>controls the spacing between the kernel points; also known as
+the à trous algorithm.</p></dd>
+<dt>ceil_mode</dt>
+<dd><p>when True, will use <code>ceil</code> instead of <code>floor</code> in the formula
+to compute the output shape</p></dd>
+<dt>return_indices</dt>
+<dd><p>whether to return the indices where the max occurs.</p></dd>
+</dl></div>
+
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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diff --git a/reference/nnf_max_unpool1d.html b/reference/nnf_max_unpool1d.html
index d53ce901c..abf98765f 100644
--- a/reference/nnf_max_unpool1d.html
+++ b/reference/nnf_max_unpool1d.html
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 <div class="row">
-  <div class="col-md-9 contents">
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-    <h1>Max_unpool1d</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-pooling.R'><code>R/nnf-pooling.R</code></a></small>
-    <div class="hidden name"><code>nnf_max_unpool1d.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Max_unpool1d</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-pooling.R" class="external-link"><code>R/nnf-pooling.R</code></a></small>
+      <div class="d-none name"><code>nnf_max_unpool1d.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Computes a partial inverse of <code>MaxPool1d</code>.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_max_unpool1d</span><span class='op'>(</span>
-  <span class='va'>input</span>,
-  <span class='va'>indices</span>,
-  <span class='va'>kernel_size</span>,
-  stride <span class='op'>=</span> <span class='cn'>NULL</span>,
-  padding <span class='op'>=</span> <span class='fl'>0</span>,
-  output_size <span class='op'>=</span> <span class='cn'>NULL</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>the input Tensor to invert</p></td>
-    </tr>
-    <tr>
-      <th>indices</th>
-      <td><p>the indices given out by max pool</p></td>
-    </tr>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>Size of the max pooling window.</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>Stride of the max pooling window. It is set to kernel_size by default.</p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>Padding that was added to the input</p></td>
-    </tr>
-    <tr>
-      <th>output_size</th>
-      <td><p>the targeted output size</p></td>
-    </tr>
-    </table>
-
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
-
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_max_unpool1d</span><span class="op">(</span>
+  <span class="va">input</span>,
+  <span class="va">indices</span>,
+  <span class="va">kernel_size</span>,
+  stride <span class="op">=</span> <span class="cn">NULL</span>,
+  padding <span class="op">=</span> <span class="fl">0</span>,
+  output_size <span class="op">=</span> <span class="cn">NULL</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
-      <footer>
-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>the input Tensor to invert</p></dd>
+<dt>indices</dt>
+<dd><p>the indices given out by max pool</p></dd>
+<dt>kernel_size</dt>
+<dd><p>Size of the max pooling window.</p></dd>
+<dt>stride</dt>
+<dd><p>Stride of the max pooling window. It is set to kernel_size by default.</p></dd>
+<dt>padding</dt>
+<dd><p>Padding that was added to the input</p></dd>
+<dt>output_size</dt>
+<dd><p>the targeted output size</p></dd>
+</dl></div>
+
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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index 4452ca3e5..002e254a9 100644
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-    <h1>Max_unpool2d</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-pooling.R'><code>R/nnf-pooling.R</code></a></small>
-    <div class="hidden name"><code>nnf_max_unpool2d.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Max_unpool2d</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-pooling.R" class="external-link"><code>R/nnf-pooling.R</code></a></small>
+      <div class="d-none name"><code>nnf_max_unpool2d.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Computes a partial inverse of <code>MaxPool2d</code>.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_max_unpool2d</span><span class='op'>(</span>
-  <span class='va'>input</span>,
-  <span class='va'>indices</span>,
-  <span class='va'>kernel_size</span>,
-  stride <span class='op'>=</span> <span class='cn'>NULL</span>,
-  padding <span class='op'>=</span> <span class='fl'>0</span>,
-  output_size <span class='op'>=</span> <span class='cn'>NULL</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>the input Tensor to invert</p></td>
-    </tr>
-    <tr>
-      <th>indices</th>
-      <td><p>the indices given out by max pool</p></td>
-    </tr>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>Size of the max pooling window.</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>Stride of the max pooling window. It is set to kernel_size by default.</p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>Padding that was added to the input</p></td>
-    </tr>
-    <tr>
-      <th>output_size</th>
-      <td><p>the targeted output size</p></td>
-    </tr>
-    </table>
-
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
-
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_max_unpool2d</span><span class="op">(</span>
+  <span class="va">input</span>,
+  <span class="va">indices</span>,
+  <span class="va">kernel_size</span>,
+  stride <span class="op">=</span> <span class="cn">NULL</span>,
+  padding <span class="op">=</span> <span class="fl">0</span>,
+  output_size <span class="op">=</span> <span class="cn">NULL</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
-      <footer>
-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>the input Tensor to invert</p></dd>
+<dt>indices</dt>
+<dd><p>the indices given out by max pool</p></dd>
+<dt>kernel_size</dt>
+<dd><p>Size of the max pooling window.</p></dd>
+<dt>stride</dt>
+<dd><p>Stride of the max pooling window. It is set to kernel_size by default.</p></dd>
+<dt>padding</dt>
+<dd><p>Padding that was added to the input</p></dd>
+<dt>output_size</dt>
+<dd><p>the targeted output size</p></dd>
+</dl></div>
+
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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index d06b1a86b..43c4c49a7 100644
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 <div class="row">
-  <div class="col-md-9 contents">
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-    <h1>Max_unpool3d</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-pooling.R'><code>R/nnf-pooling.R</code></a></small>
-    <div class="hidden name"><code>nnf_max_unpool3d.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Max_unpool3d</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-pooling.R" class="external-link"><code>R/nnf-pooling.R</code></a></small>
+      <div class="d-none name"><code>nnf_max_unpool3d.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Computes a partial inverse of <code>MaxPool3d</code>.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_max_unpool3d</span><span class='op'>(</span>
-  <span class='va'>input</span>,
-  <span class='va'>indices</span>,
-  <span class='va'>kernel_size</span>,
-  stride <span class='op'>=</span> <span class='cn'>NULL</span>,
-  padding <span class='op'>=</span> <span class='fl'>0</span>,
-  output_size <span class='op'>=</span> <span class='cn'>NULL</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>the input Tensor to invert</p></td>
-    </tr>
-    <tr>
-      <th>indices</th>
-      <td><p>the indices given out by max pool</p></td>
-    </tr>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>Size of the max pooling window.</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>Stride of the max pooling window. It is set to kernel_size by default.</p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>Padding that was added to the input</p></td>
-    </tr>
-    <tr>
-      <th>output_size</th>
-      <td><p>the targeted output size</p></td>
-    </tr>
-    </table>
-
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
-
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_max_unpool3d</span><span class="op">(</span>
+  <span class="va">input</span>,
+  <span class="va">indices</span>,
+  <span class="va">kernel_size</span>,
+  stride <span class="op">=</span> <span class="cn">NULL</span>,
+  padding <span class="op">=</span> <span class="fl">0</span>,
+  output_size <span class="op">=</span> <span class="cn">NULL</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
-      <footer>
-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>the input Tensor to invert</p></dd>
+<dt>indices</dt>
+<dd><p>the indices given out by max pool</p></dd>
+<dt>kernel_size</dt>
+<dd><p>Size of the max pooling window.</p></dd>
+<dt>stride</dt>
+<dd><p>Stride of the max pooling window. It is set to kernel_size by default.</p></dd>
+<dt>padding</dt>
+<dd><p>Padding that was added to the input</p></dd>
+<dt>output_size</dt>
+<dd><p>the targeted output size</p></dd>
+</dl></div>
+
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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diff --git a/reference/nnf_mse_loss.html b/reference/nnf_mse_loss.html
index 58cd90d48..9a171be6b 100644
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-    <h1>Mse_loss</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-loss.R'><code>R/nnf-loss.R</code></a></small>
-    <div class="hidden name"><code>nnf_mse_loss.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Mse_loss</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-loss.R" class="external-link"><code>R/nnf-loss.R</code></a></small>
+      <div class="d-none name"><code>nnf_mse_loss.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Measures the element-wise mean squared error.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_mse_loss</span><span class='op'>(</span><span class='va'>input</span>, <span class='va'>target</span>, reduction <span class='op'>=</span> <span class='st'>"mean"</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>tensor (N,*) where ** means, any number of additional dimensions</p></td>
-    </tr>
-    <tr>
-      <th>target</th>
-      <td><p>tensor (N,*) , same shape as the input</p></td>
-    </tr>
-    <tr>
-      <th>reduction</th>
-      <td><p>(string, optional) – Specifies the reduction to apply to the
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_mse_loss</span><span class="op">(</span><span class="va">input</span>, <span class="va">target</span>, reduction <span class="op">=</span> <span class="st">"mean"</span><span class="op">)</span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>tensor (N,*) where ** means, any number of additional dimensions</p></dd>
+<dt>target</dt>
+<dd><p>tensor (N,*) , same shape as the input</p></dd>
+<dt>reduction</dt>
+<dd><p>(string, optional) – Specifies the reduction to apply to the
 output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
 the sum of the output will be divided by the number of elements in the output,
-'sum': the output will be summed. Default: 'mean'</p></td>
-    </tr>
-    </table>
+'sum': the output will be summed. Default: 'mean'</p></dd>
+</dl></div>
 
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diff --git a/reference/nnf_multi_head_attention_forward.html b/reference/nnf_multi_head_attention_forward.html
index a97710f4c..e8d61fc56 100644
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Multi head attention forward</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-activation.R'><code>R/nnf-activation.R</code></a></small>
-    <div class="hidden name"><code>nnf_multi_head_attention_forward.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Multi head attention forward</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-activation.R" class="external-link"><code>R/nnf-activation.R</code></a></small>
+      <div class="d-none name"><code>nnf_multi_head_attention_forward.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Allows the model to jointly attend to information from different representation
 subspaces. See reference: Attention Is All You Need</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_multi_head_attention_forward</span><span class='op'>(</span>
-  <span class='va'>query</span>,
-  <span class='va'>key</span>,
-  <span class='va'>value</span>,
-  <span class='va'>embed_dim_to_check</span>,
-  <span class='va'>num_heads</span>,
-  <span class='va'>in_proj_weight</span>,
-  <span class='va'>in_proj_bias</span>,
-  <span class='va'>bias_k</span>,
-  <span class='va'>bias_v</span>,
-  <span class='va'>add_zero_attn</span>,
-  <span class='va'>dropout_p</span>,
-  <span class='va'>out_proj_weight</span>,
-  <span class='va'>out_proj_bias</span>,
-  training <span class='op'>=</span> <span class='cn'>TRUE</span>,
-  key_padding_mask <span class='op'>=</span> <span class='cn'>NULL</span>,
-  need_weights <span class='op'>=</span> <span class='cn'>TRUE</span>,
-  attn_mask <span class='op'>=</span> <span class='cn'>NULL</span>,
-  avg_weights <span class='op'>=</span> <span class='cn'>TRUE</span>,
-  use_separate_proj_weight <span class='op'>=</span> <span class='cn'>FALSE</span>,
-  q_proj_weight <span class='op'>=</span> <span class='cn'>NULL</span>,
-  k_proj_weight <span class='op'>=</span> <span class='cn'>NULL</span>,
-  v_proj_weight <span class='op'>=</span> <span class='cn'>NULL</span>,
-  static_k <span class='op'>=</span> <span class='cn'>NULL</span>,
-  static_v <span class='op'>=</span> <span class='cn'>NULL</span>
-<span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_multi_head_attention_forward</span><span class="op">(</span>
+  <span class="va">query</span>,
+  <span class="va">key</span>,
+  <span class="va">value</span>,
+  <span class="va">embed_dim_to_check</span>,
+  <span class="va">num_heads</span>,
+  <span class="va">in_proj_weight</span>,
+  <span class="va">in_proj_bias</span>,
+  <span class="va">bias_k</span>,
+  <span class="va">bias_v</span>,
+  <span class="va">add_zero_attn</span>,
+  <span class="va">dropout_p</span>,
+  <span class="va">out_proj_weight</span>,
+  <span class="va">out_proj_bias</span>,
+  training <span class="op">=</span> <span class="cn">TRUE</span>,
+  key_padding_mask <span class="op">=</span> <span class="cn">NULL</span>,
+  need_weights <span class="op">=</span> <span class="cn">TRUE</span>,
+  attn_mask <span class="op">=</span> <span class="cn">NULL</span>,
+  avg_weights <span class="op">=</span> <span class="cn">TRUE</span>,
+  use_separate_proj_weight <span class="op">=</span> <span class="cn">FALSE</span>,
+  q_proj_weight <span class="op">=</span> <span class="cn">NULL</span>,
+  k_proj_weight <span class="op">=</span> <span class="cn">NULL</span>,
+  v_proj_weight <span class="op">=</span> <span class="cn">NULL</span>,
+  static_k <span class="op">=</span> <span class="cn">NULL</span>,
+  static_v <span class="op">=</span> <span class="cn">NULL</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>query</th>
-      <td><p>\((L, N, E)\) where L is the target sequence length, N is the batch size, E is
-the embedding dimension.</p></td>
-    </tr>
-    <tr>
-      <th>key</th>
-      <td><p>\((S, N, E)\), where S is the source sequence length, N is the batch size, E is
-the embedding dimension.</p></td>
-    </tr>
-    <tr>
-      <th>value</th>
-      <td><p>\((S, N, E)\) where S is the source sequence length, N is the batch size, E is
-the embedding dimension.</p></td>
-    </tr>
-    <tr>
-      <th>embed_dim_to_check</th>
-      <td><p>total dimension of the model.</p></td>
-    </tr>
-    <tr>
-      <th>num_heads</th>
-      <td><p>parallel attention heads.</p></td>
-    </tr>
-    <tr>
-      <th>in_proj_weight</th>
-      <td><p>input projection weight and bias.</p></td>
-    </tr>
-    <tr>
-      <th>in_proj_bias</th>
-      <td><p>currently undocumented.</p></td>
-    </tr>
-    <tr>
-      <th>bias_k</th>
-      <td><p>bias of the key and value sequences to be added at dim=0.</p></td>
-    </tr>
-    <tr>
-      <th>bias_v</th>
-      <td><p>currently undocumented.</p></td>
-    </tr>
-    <tr>
-      <th>add_zero_attn</th>
-      <td><p>add a new batch of zeros to the key and
-value sequences at dim=1.</p></td>
-    </tr>
-    <tr>
-      <th>dropout_p</th>
-      <td><p>probability of an element to be zeroed.</p></td>
-    </tr>
-    <tr>
-      <th>out_proj_weight</th>
-      <td><p>the output projection weight and bias.</p></td>
-    </tr>
-    <tr>
-      <th>out_proj_bias</th>
-      <td><p>currently undocumented.</p></td>
-    </tr>
-    <tr>
-      <th>training</th>
-      <td><p>apply dropout if is <code>TRUE</code>.</p></td>
-    </tr>
-    <tr>
-      <th>key_padding_mask</th>
-      <td><p>\((N, S)\) where N is the batch size, S is the source sequence length.
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>query</dt>
+<dd><p>\((L, N, E)\) where L is the target sequence length, N is the batch size, E is
+the embedding dimension.</p></dd>
+<dt>key</dt>
+<dd><p>\((S, N, E)\), where S is the source sequence length, N is the batch size, E is
+the embedding dimension.</p></dd>
+<dt>value</dt>
+<dd><p>\((S, N, E)\) where S is the source sequence length, N is the batch size, E is
+the embedding dimension.</p></dd>
+<dt>embed_dim_to_check</dt>
+<dd><p>total dimension of the model.</p></dd>
+<dt>num_heads</dt>
+<dd><p>parallel attention heads.</p></dd>
+<dt>in_proj_weight</dt>
+<dd><p>input projection weight and bias.</p></dd>
+<dt>in_proj_bias</dt>
+<dd><p>currently undocumented.</p></dd>
+<dt>bias_k</dt>
+<dd><p>bias of the key and value sequences to be added at dim=0.</p></dd>
+<dt>bias_v</dt>
+<dd><p>currently undocumented.</p></dd>
+<dt>add_zero_attn</dt>
+<dd><p>add a new batch of zeros to the key and
+value sequences at dim=1.</p></dd>
+<dt>dropout_p</dt>
+<dd><p>probability of an element to be zeroed.</p></dd>
+<dt>out_proj_weight</dt>
+<dd><p>the output projection weight and bias.</p></dd>
+<dt>out_proj_bias</dt>
+<dd><p>currently undocumented.</p></dd>
+<dt>training</dt>
+<dd><p>apply dropout if is <code>TRUE</code>.</p></dd>
+<dt>key_padding_mask</dt>
+<dd><p>\((N, S)\) where N is the batch size, S is the source sequence length.
 If a ByteTensor is provided, the non-zero positions will be ignored while the position
 with the zero positions will be unchanged. If a BoolTensor is provided, the positions with the
-value of <code>True</code> will be ignored while the position with the value of <code>False</code> will be unchanged.</p></td>
-    </tr>
-    <tr>
-      <th>need_weights</th>
-      <td><p>output attn_output_weights.</p></td>
-    </tr>
-    <tr>
-      <th>attn_mask</th>
-      <td><p>2D mask \((L, S)\) where L is the target sequence length, S is the source sequence length.
+value of <code>True</code> will be ignored while the position with the value of <code>False</code> will be unchanged.</p></dd>
+<dt>need_weights</dt>
+<dd><p>output attn_output_weights.</p></dd>
+<dt>attn_mask</dt>
+<dd><p>2D mask \((L, S)\) where L is the target sequence length, S is the source sequence length.
 3D mask \((N*num_heads, L, S)\) where N is the batch size, L is the target sequence length,
 S is the source sequence length. attn_mask ensure that position i is allowed to attend the unmasked
 positions. If a ByteTensor is provided, the non-zero positions are not allowed to attend
 while the zero positions will be unchanged. If a BoolTensor is provided, positions with <code>True</code>
 is not allowed to attend while <code>False</code> values will be unchanged. If a FloatTensor
-is provided, it will be added to the attention weight.</p></td>
-    </tr>
-    <tr>
-      <th>avg_weights</th>
-      <td><p>Logical; whether to average attn_output_weights over the
+is provided, it will be added to the attention weight.</p></dd>
+<dt>avg_weights</dt>
+<dd><p>Logical; whether to average attn_output_weights over the
 attention heads before outputting them. This doesn't change the returned
-value of attn_output; it only affects the returned attention weight matrix.</p></td>
-    </tr>
-    <tr>
-      <th>use_separate_proj_weight</th>
-      <td><p>the function accept the proj. weights for
+value of attn_output; it only affects the returned attention weight matrix.</p></dd>
+<dt>use_separate_proj_weight</dt>
+<dd><p>the function accept the proj. weights for
 query, key, and value in different forms. If false, in_proj_weight will be used,
-which is a combination of q_proj_weight, k_proj_weight, v_proj_weight.</p></td>
-    </tr>
-    <tr>
-      <th>q_proj_weight</th>
-      <td><p>input projection weight and bias.</p></td>
-    </tr>
-    <tr>
-      <th>k_proj_weight</th>
-      <td><p>currently undocumented.</p></td>
-    </tr>
-    <tr>
-      <th>v_proj_weight</th>
-      <td><p>currently undocumented.</p></td>
-    </tr>
-    <tr>
-      <th>static_k</th>
-      <td><p>static key and value used for attention operators.</p></td>
-    </tr>
-    <tr>
-      <th>static_v</th>
-      <td><p>currently undocumented.</p></td>
-    </tr>
-    </table>
-
-
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-    <nav id="toc" data-toggle="toc" class="sticky-top">
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-    </nav>
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+which is a combination of q_proj_weight, k_proj_weight, v_proj_weight.</p></dd>
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+<dd><p>currently undocumented.</p></dd>
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+<dt>static_k</dt>
+<dd><p>static key and value used for attention operators.</p></dd>
+<dt>static_v</dt>
+<dd><p>currently undocumented.</p></dd>
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Multi_margin_loss</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-loss.R'><code>R/nnf-loss.R</code></a></small>
-    <div class="hidden name"><code>nnf_multi_margin_loss.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Multi_margin_loss</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-loss.R" class="external-link"><code>R/nnf-loss.R</code></a></small>
+      <div class="d-none name"><code>nnf_multi_margin_loss.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Creates a criterion that optimizes a multi-class classification hinge loss
 (margin-based loss) between input x (a 2D mini-batch Tensor) and output y
 (which is a 1D tensor of target class indices, <code>0 &lt;= y &lt;= x$size(2) - 1</code> ).</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_multi_margin_loss</span><span class='op'>(</span>
-  <span class='va'>input</span>,
-  <span class='va'>target</span>,
-  p <span class='op'>=</span> <span class='fl'>1</span>,
-  margin <span class='op'>=</span> <span class='fl'>1</span>,
-  weight <span class='op'>=</span> <span class='cn'>NULL</span>,
-  reduction <span class='op'>=</span> <span class='st'>"mean"</span>
-<span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_multi_margin_loss</span><span class="op">(</span>
+  <span class="va">input</span>,
+  <span class="va">target</span>,
+  p <span class="op">=</span> <span class="fl">1</span>,
+  margin <span class="op">=</span> <span class="fl">1</span>,
+  weight <span class="op">=</span> <span class="cn">NULL</span>,
+  reduction <span class="op">=</span> <span class="st">"mean"</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>tensor (N,*) where ** means, any number of additional dimensions</p></td>
-    </tr>
-    <tr>
-      <th>target</th>
-      <td><p>tensor (N,*) , same shape as the input</p></td>
-    </tr>
-    <tr>
-      <th>p</th>
-      <td><p>Has a default value of 1. 1 and 2 are the only supported values.</p></td>
-    </tr>
-    <tr>
-      <th>margin</th>
-      <td><p>Has a default value of 1.</p></td>
-    </tr>
-    <tr>
-      <th>weight</th>
-      <td><p>a manual rescaling weight given to each class. If given, it has to
-be a Tensor of size C. Otherwise, it is treated as if having all ones.</p></td>
-    </tr>
-    <tr>
-      <th>reduction</th>
-      <td><p>(string, optional) – Specifies the reduction to apply to the
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>tensor (N,*) where ** means, any number of additional dimensions</p></dd>
+<dt>target</dt>
+<dd><p>tensor (N,*) , same shape as the input</p></dd>
+<dt>p</dt>
+<dd><p>Has a default value of 1. 1 and 2 are the only supported values.</p></dd>
+<dt>margin</dt>
+<dd><p>Has a default value of 1.</p></dd>
+<dt>weight</dt>
+<dd><p>a manual rescaling weight given to each class. If given, it has to
+be a Tensor of size C. Otherwise, it is treated as if having all ones.</p></dd>
+<dt>reduction</dt>
+<dd><p>(string, optional) – Specifies the reduction to apply to the
 output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
 the sum of the output will be divided by the number of elements in the output,
-'sum': the output will be summed. Default: 'mean'</p></td>
-    </tr>
-    </table>
+'sum': the output will be summed. Default: 'mean'</p></dd>
+</dl></div>
 
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
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-      <div class="copyright">
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diff --git a/reference/nnf_multilabel_margin_loss.html b/reference/nnf_multilabel_margin_loss.html
index a03f8ff67..a1bfc367d 100644
--- a/reference/nnf_multilabel_margin_loss.html
+++ b/reference/nnf_multilabel_margin_loss.html
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-    <h1>Multilabel_margin_loss</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-loss.R'><code>R/nnf-loss.R</code></a></small>
-    <div class="hidden name"><code>nnf_multilabel_margin_loss.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Multilabel_margin_loss</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-loss.R" class="external-link"><code>R/nnf-loss.R</code></a></small>
+      <div class="d-none name"><code>nnf_multilabel_margin_loss.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Creates a criterion that optimizes a multi-class multi-classification hinge loss
 (margin-based loss) between input x (a 2D mini-batch Tensor) and output y (which
 is a 2D Tensor of target class indices).</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_multilabel_margin_loss</span><span class='op'>(</span><span class='va'>input</span>, <span class='va'>target</span>, reduction <span class='op'>=</span> <span class='st'>"mean"</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>tensor (N,*) where ** means, any number of additional dimensions</p></td>
-    </tr>
-    <tr>
-      <th>target</th>
-      <td><p>tensor (N,*) , same shape as the input</p></td>
-    </tr>
-    <tr>
-      <th>reduction</th>
-      <td><p>(string, optional) – Specifies the reduction to apply to the
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_multilabel_margin_loss</span><span class="op">(</span><span class="va">input</span>, <span class="va">target</span>, reduction <span class="op">=</span> <span class="st">"mean"</span><span class="op">)</span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>tensor (N,*) where ** means, any number of additional dimensions</p></dd>
+<dt>target</dt>
+<dd><p>tensor (N,*) , same shape as the input</p></dd>
+<dt>reduction</dt>
+<dd><p>(string, optional) – Specifies the reduction to apply to the
 output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
 the sum of the output will be divided by the number of elements in the output,
-'sum': the output will be summed. Default: 'mean'</p></td>
-    </tr>
-    </table>
+'sum': the output will be summed. Default: 'mean'</p></dd>
+</dl></div>
 
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diff --git a/reference/nnf_multilabel_soft_margin_loss.html b/reference/nnf_multilabel_soft_margin_loss.html
index 564bac73f..2652fdbe7 100644
--- a/reference/nnf_multilabel_soft_margin_loss.html
+++ b/reference/nnf_multilabel_soft_margin_loss.html
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-    <h1>Multilabel_soft_margin_loss</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-loss.R'><code>R/nnf-loss.R</code></a></small>
-    <div class="hidden name"><code>nnf_multilabel_soft_margin_loss.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Multilabel_soft_margin_loss</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-loss.R" class="external-link"><code>R/nnf-loss.R</code></a></small>
+      <div class="d-none name"><code>nnf_multilabel_soft_margin_loss.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Creates a criterion that optimizes a multi-label one-versus-all loss based on
 max-entropy, between input x and target y of size (N, C).</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_multilabel_soft_margin_loss</span><span class='op'>(</span>
-  <span class='va'>input</span>,
-  <span class='va'>target</span>,
-  weight <span class='op'>=</span> <span class='cn'>NULL</span>,
-  reduction <span class='op'>=</span> <span class='st'>"mean"</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>tensor (N,*) where ** means, any number of additional dimensions</p></td>
-    </tr>
-    <tr>
-      <th>target</th>
-      <td><p>tensor (N,*) , same shape as the input</p></td>
-    </tr>
-    <tr>
-      <th>weight</th>
-      <td><p>weight tensor to apply on the loss.</p></td>
-    </tr>
-    <tr>
-      <th>reduction</th>
-      <td><p>(string, optional) – Specifies the reduction to apply to the
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_multilabel_soft_margin_loss</span><span class="op">(</span>
+  <span class="va">input</span>,
+  <span class="va">target</span>,
+  weight <span class="op">=</span> <span class="cn">NULL</span>,
+  reduction <span class="op">=</span> <span class="st">"mean"</span>
+<span class="op">)</span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>tensor (N,*) where ** means, any number of additional dimensions</p></dd>
+<dt>target</dt>
+<dd><p>tensor (N,*) , same shape as the input</p></dd>
+<dt>weight</dt>
+<dd><p>weight tensor to apply on the loss.</p></dd>
+<dt>reduction</dt>
+<dd><p>(string, optional) – Specifies the reduction to apply to the
 output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
 the sum of the output will be divided by the number of elements in the output,
-'sum': the output will be summed. Default: 'mean'</p></td>
-    </tr>
-    </table>
+'sum': the output will be summed. Default: 'mean'</p></dd>
+</dl></div>
 
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Nll_loss</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-loss.R'><code>R/nnf-loss.R</code></a></small>
-    <div class="hidden name"><code>nnf_nll_loss.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Nll_loss</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-loss.R" class="external-link"><code>R/nnf-loss.R</code></a></small>
+      <div class="d-none name"><code>nnf_nll_loss.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>The negative log likelihood loss.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_nll_loss</span><span class='op'>(</span>
-  <span class='va'>input</span>,
-  <span class='va'>target</span>,
-  weight <span class='op'>=</span> <span class='cn'>NULL</span>,
-  ignore_index <span class='op'>=</span> <span class='op'>-</span><span class='fl'>100</span>,
-  reduction <span class='op'>=</span> <span class='st'>"mean"</span>
-<span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_nll_loss</span><span class="op">(</span>
+  <span class="va">input</span>,
+  <span class="va">target</span>,
+  weight <span class="op">=</span> <span class="cn">NULL</span>,
+  ignore_index <span class="op">=</span> <span class="op">-</span><span class="fl">100</span>,
+  reduction <span class="op">=</span> <span class="st">"mean"</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>\((N, C)\) where <code>C = number of classes</code> or \((N, C, H, W)\) in
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>\((N, C)\) where <code>C = number of classes</code> or \((N, C, H, W)\) in
 case of 2D Loss, or \((N, C, d_1, d_2, ..., d_K)\) where \(K \geq 1\) in
-the case of K-dimensional loss.</p></td>
-    </tr>
-    <tr>
-      <th>target</th>
-      <td><p>\((N)\) where each value is \(0 \leq \mbox{targets}[i] \leq C-1\),
-or \((N, d_1, d_2, ..., d_K)\) where \(K \geq 1\) for K-dimensional loss.</p></td>
-    </tr>
-    <tr>
-      <th>weight</th>
-      <td><p>(Tensor, optional) a manual rescaling weight given to each class.
-If given, has to be a Tensor of size <code>C</code></p></td>
-    </tr>
-    <tr>
-      <th>ignore_index</th>
-      <td><p>(int, optional) Specifies a target value that is ignored and
-does not contribute to the input gradient.</p></td>
-    </tr>
-    <tr>
-      <th>reduction</th>
-      <td><p>(string, optional) – Specifies the reduction to apply to the
+the case of K-dimensional loss.</p></dd>
+<dt>target</dt>
+<dd><p>\((N)\) where each value is \(0 \leq \mbox{targets}[i] \leq C-1\),
+or \((N, d_1, d_2, ..., d_K)\) where \(K \geq 1\) for K-dimensional loss.</p></dd>
+<dt>weight</dt>
+<dd><p>(Tensor, optional) a manual rescaling weight given to each class.
+If given, has to be a Tensor of size <code>C</code></p></dd>
+<dt>ignore_index</dt>
+<dd><p>(int, optional) Specifies a target value that is ignored and
+does not contribute to the input gradient.</p></dd>
+<dt>reduction</dt>
+<dd><p>(string, optional) – Specifies the reduction to apply to the
 output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
 the sum of the output will be divided by the number of elements in the output,
-'sum': the output will be summed. Default: 'mean'</p></td>
-    </tr>
-    </table>
+'sum': the output will be summed. Default: 'mean'</p></dd>
+</dl></div>
 
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diff --git a/reference/nnf_normalize.html b/reference/nnf_normalize.html
index 228aad2c3..a3782fede 100644
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-    <h1>Normalize</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-normalization.R'><code>R/nnf-normalization.R</code></a></small>
-    <div class="hidden name"><code>nnf_normalize.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Normalize</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-normalization.R" class="external-link"><code>R/nnf-normalization.R</code></a></small>
+      <div class="d-none name"><code>nnf_normalize.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Performs \(L_p\) normalization of inputs over specified dimension.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_normalize</span><span class='op'>(</span><span class='va'>input</span>, p <span class='op'>=</span> <span class='fl'>2</span>, dim <span class='op'>=</span> <span class='fl'>2</span>, eps <span class='op'>=</span> <span class='fl'>1e-12</span>, out <span class='op'>=</span> <span class='cn'>NULL</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>input tensor of any shape</p></td>
-    </tr>
-    <tr>
-      <th>p</th>
-      <td><p>(float) the exponent value in the norm formulation. Default: 2</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) the dimension to reduce. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>eps</th>
-      <td><p>(float) small value to avoid division by zero. Default: 1e-12</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(Tensor, optional) the output tensor. If <code>out</code> is used, this                            operation won't be differentiable.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_normalize</span><span class="op">(</span><span class="va">input</span>, p <span class="op">=</span> <span class="fl">2</span>, dim <span class="op">=</span> <span class="fl">2</span>, eps <span class="op">=</span> <span class="fl">1e-12</span>, out <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>input tensor of any shape</p></dd>
+<dt>p</dt>
+<dd><p>(float) the exponent value in the norm formulation. Default: 2</p></dd>
+<dt>dim</dt>
+<dd><p>(int) the dimension to reduce. Default: 1</p></dd>
+<dt>eps</dt>
+<dd><p>(float) small value to avoid division by zero. Default: 1e-12</p></dd>
+<dt>out</dt>
+<dd><p>(Tensor, optional) the output tensor. If <code>out</code> is used, this                            operation won't be differentiable.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>For a tensor <code>input</code> of sizes \((n_0, ..., n_{dim}, ..., n_k)\), each
 \(n_{dim}\) -element vector \(v\) along dimension <code>dim</code> is transformed as</p>
 <p>$$
@@ -225,32 +107,25 @@
 $$</p>
 <p>With the default arguments it uses the Euclidean norm over vectors along
 dimension \(1\) for normalization.</p>
+    </div>
 
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index 5a8a1f307..55ea2220d 100644
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>One_hot</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-embedding.R'><code>R/nnf-embedding.R</code></a></small>
-    <div class="hidden name"><code>nnf_one_hot.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>One_hot</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-embedding.R" class="external-link"><code>R/nnf-embedding.R</code></a></small>
+      <div class="d-none name"><code>nnf_one_hot.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Takes LongTensor with index values of shape <code>(*)</code> and returns a tensor
 of shape <code>(*, num_classes)</code> that have zeros everywhere except where the
 index of last dimension matches the corresponding value of the input tensor,
 in which case it will be 1.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_one_hot</span><span class='op'>(</span><span class='va'>tensor</span>, num_classes <span class='op'>=</span> <span class='op'>-</span><span class='fl'>1</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>tensor</th>
-      <td><p>(LongTensor) class values of any shape.</p></td>
-    </tr>
-    <tr>
-      <th>num_classes</th>
-      <td><p>(int) Total number of classes. If set to -1, the number
-of classes will be inferred as one greater than the largest class value in
-the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_one_hot</span><span class="op">(</span><span class="va">tensor</span>, num_classes <span class="op">=</span> <span class="op">-</span><span class="fl">1</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>tensor</dt>
+<dd><p>(LongTensor) class values of any shape.</p></dd>
+<dt>num_classes</dt>
+<dd><p>(int) Total number of classes. If set to -1, the number
+of classes will be inferred as one greater than the largest class value in
+the input tensor.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>One-hot on Wikipedia: https://en.wikipedia.org/wiki/One-hot</p>
+    </div>
 
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index 7a29cba8c..6750f4de6 100644
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-    <h1>Pad</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-padding.R'><code>R/nnf-padding.R</code></a></small>
-    <div class="hidden name"><code>nnf_pad.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Pad</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-padding.R" class="external-link"><code>R/nnf-padding.R</code></a></small>
+      <div class="d-none name"><code>nnf_pad.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Pads tensor.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_pad</span><span class='op'>(</span><span class='va'>input</span>, <span class='va'>pad</span>, mode <span class='op'>=</span> <span class='st'>"constant"</span>, value <span class='op'>=</span> <span class='fl'>0</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) N-dimensional tensor</p></td>
-    </tr>
-    <tr>
-      <th>pad</th>
-      <td><p>(tuple) m-elements tuple, where \(\frac{m}{2} \leq\) input dimensions
-and \(m\) is even.</p></td>
-    </tr>
-    <tr>
-      <th>mode</th>
-      <td><p>'constant', 'reflect', 'replicate' or 'circular'. Default: 'constant'</p></td>
-    </tr>
-    <tr>
-      <th>value</th>
-      <td><p>fill value for 'constant' padding. Default: 0.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="padding-size"><a class="anchor" href="#padding-size"></a>Padding size</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_pad</span><span class="op">(</span><span class="va">input</span>, <span class="va">pad</span>, mode <span class="op">=</span> <span class="st">"constant"</span>, value <span class="op">=</span> <span class="fl">0</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>(Tensor) N-dimensional tensor</p></dd>
+<dt>pad</dt>
+<dd><p>(tuple) m-elements tuple, where \(\frac{m}{2} \leq\) input dimensions
+and \(m\) is even.</p></dd>
+<dt>mode</dt>
+<dd><p>'constant', 'reflect', 'replicate' or 'circular'. Default: 'constant'</p></dd>
+<dt>value</dt>
+<dd><p>fill value for 'constant' padding. Default: 0.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="padding-size">Padding size<a class="anchor" aria-label="anchor" href="#padding-size"></a></h2>
     
 
 
@@ -232,8 +116,9 @@ to pad the last 3 dimensions, use
 \((\mbox{padding\_left}, \mbox{padding\_right},\)
 \(\mbox{padding\_top}, \mbox{padding\_bottom}\)
 \(\mbox{padding\_front}, \mbox{padding\_back})\).</p>
-    <h2 class="hasAnchor" id="padding-mode"><a class="anchor" href="#padding-mode"></a>Padding mode</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="padding-mode">Padding mode<a class="anchor" aria-label="anchor" href="#padding-mode"></a></h2>
     
 
 
@@ -243,32 +128,25 @@ padding modes works. Constant padding is implemented for arbitrary dimensions.
 tensor, or the last 2 dimensions of 4D input tensor, or the last dimension of
 3D input tensor. Reflect padding is only implemented for padding the last 2
 dimensions of 4D input tensor, or the last dimension of 3D input tensor.</p>
+    </div>
 
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     <p>Computes the batchwise pairwise distance between vectors using the p-norm.</p>
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-    <pre class="usage"><span class='fu'>nnf_pairwise_distance</span><span class='op'>(</span><span class='va'>x1</span>, <span class='va'>x2</span>, p <span class='op'>=</span> <span class='fl'>2</span>, eps <span class='op'>=</span> <span class='fl'>1e-06</span>, keepdim <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span></pre>
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-      <th>p</th>
-      <td><p>the norm degree. Default: 2</p></td>
-    </tr>
-    <tr>
-      <th>eps</th>
-      <td><p>(float, optional) Small value to avoid division by zero.
-Default: 1e-8</p></td>
-    </tr>
-    <tr>
-      <th>keepdim</th>
-      <td><p>Determines whether or not to keep the vector dimension. Default: False</p></td>
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+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_pairwise_distance</span><span class="op">(</span><span class="va">x1</span>, <span class="va">x2</span>, p <span class="op">=</span> <span class="fl">2</span>, eps <span class="op">=</span> <span class="fl">1e-06</span>, keepdim <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
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+    <dl><dt>x1</dt>
+<dd><p>(Tensor) First input.</p></dd>
+<dt>x2</dt>
+<dd><p>(Tensor) Second input (of size matching x1).</p></dd>
+<dt>p</dt>
+<dd><p>the norm degree. Default: 2</p></dd>
+<dt>eps</dt>
+<dd><p>(float, optional) Small value to avoid division by zero.
+Default: 1e-8</p></dd>
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+if the rows are contiguous."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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     <p>Computes the p-norm distance between every pair of row vectors in the input.
 This is identical to the upper triangular portion, excluding the diagonal, of
 <code>torch_norm(input[:, None] - input, dim=2, p=p)</code>. This function will be faster
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-    <pre class="usage"><span class='fu'>nnf_pdist</span><span class='op'>(</span><span class='va'>input</span>, p <span class='op'>=</span> <span class='fl'>2</span><span class='op'>)</span></pre>
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-    <tr>
-      <th>input</th>
-      <td><p>input tensor of shape \(N \times M\).</p></td>
-    </tr>
-    <tr>
-      <th>p</th>
-      <td><p>p value for the p-norm distance to calculate between each vector pair
-\(\in [0, \infty]\).</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_pdist</span><span class="op">(</span><span class="va">input</span>, p <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>input tensor of shape \(N \times M\).</p></dd>
+<dt>p</dt>
+<dd><p>p value for the p-norm distance to calculate between each vector pair
+\(\in [0, \infty]\).</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>If input has shape \(N \times M\) then the output will have shape
 \(\frac{1}{2} N (N - 1)\).</p>
+    </div>
 
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diff --git a/reference/nnf_pixel_shuffle.html b/reference/nnf_pixel_shuffle.html
index cdcabdb94..c254ec622 100644
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Pixel_shuffle</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-pixelshuffle.R'><code>R/nnf-pixelshuffle.R</code></a></small>
-    <div class="hidden name"><code>nnf_pixel_shuffle.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Pixel_shuffle</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-pixelshuffle.R" class="external-link"><code>R/nnf-pixelshuffle.R</code></a></small>
+      <div class="d-none name"><code>nnf_pixel_shuffle.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Rearranges elements in a tensor of shape \((*, C \times r^2, H, W)\) to a
 tensor of shape \((*, C, H \times r, W \times r)\).</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_pixel_shuffle</span><span class='op'>(</span><span class='va'>input</span>, <span class='va'>upscale_factor</span><span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_pixel_shuffle</span><span class="op">(</span><span class="va">input</span>, <span class="va">upscale_factor</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Poisson_nll_loss</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-loss.R'><code>R/nnf-loss.R</code></a></small>
-    <div class="hidden name"><code>nnf_poisson_nll_loss.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Poisson_nll_loss</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-loss.R" class="external-link"><code>R/nnf-loss.R</code></a></small>
+      <div class="d-none name"><code>nnf_poisson_nll_loss.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Poisson negative log likelihood loss.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_poisson_nll_loss</span><span class='op'>(</span>
-  <span class='va'>input</span>,
-  <span class='va'>target</span>,
-  log_input <span class='op'>=</span> <span class='cn'>TRUE</span>,
-  full <span class='op'>=</span> <span class='cn'>FALSE</span>,
-  eps <span class='op'>=</span> <span class='fl'>1e-08</span>,
-  reduction <span class='op'>=</span> <span class='st'>"mean"</span>
-<span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_poisson_nll_loss</span><span class="op">(</span>
+  <span class="va">input</span>,
+  <span class="va">target</span>,
+  log_input <span class="op">=</span> <span class="cn">TRUE</span>,
+  full <span class="op">=</span> <span class="cn">FALSE</span>,
+  eps <span class="op">=</span> <span class="fl">1e-08</span>,
+  reduction <span class="op">=</span> <span class="st">"mean"</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>tensor (N,*) where ** means, any number of additional dimensions</p></td>
-    </tr>
-    <tr>
-      <th>target</th>
-      <td><p>tensor (N,*) , same shape as the input</p></td>
-    </tr>
-    <tr>
-      <th>log_input</th>
-      <td><p>if <code>TRUE</code> the loss is computed as \(\exp(\mbox{input}) - \mbox{target} * \mbox{input}\),
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>tensor (N,*) where ** means, any number of additional dimensions</p></dd>
+<dt>target</dt>
+<dd><p>tensor (N,*) , same shape as the input</p></dd>
+<dt>log_input</dt>
+<dd><p>if <code>TRUE</code> the loss is computed as \(\exp(\mbox{input}) - \mbox{target} * \mbox{input}\),
 if <code>FALSE</code> then loss is \(\mbox{input} - \mbox{target} * \log(\mbox{input}+\mbox{eps})\).
-Default: <code>TRUE</code>.</p></td>
-    </tr>
-    <tr>
-      <th>full</th>
-      <td><p>whether to compute full loss, i. e. to add the Stirling approximation
-term. Default: <code>FALSE</code>.</p></td>
-    </tr>
-    <tr>
-      <th>eps</th>
-      <td><p>(float, optional) Small value to avoid evaluation of \(\log(0)\) when
-<code>log_input</code>=<code>FALSE</code>. Default: 1e-8</p></td>
-    </tr>
-    <tr>
-      <th>reduction</th>
-      <td><p>(string, optional) – Specifies the reduction to apply to the
+Default: <code>TRUE</code>.</p></dd>
+<dt>full</dt>
+<dd><p>whether to compute full loss, i. e. to add the Stirling approximation
+term. Default: <code>FALSE</code>.</p></dd>
+<dt>eps</dt>
+<dd><p>(float, optional) Small value to avoid evaluation of \(\log(0)\) when
+<code>log_input</code>=<code>FALSE</code>. Default: 1e-8</p></dd>
+<dt>reduction</dt>
+<dd><p>(string, optional) – Specifies the reduction to apply to the
 output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
 the sum of the output will be divided by the number of elements in the output,
-'sum': the output will be summed. Default: 'mean'</p></td>
-    </tr>
-    </table>
+'sum': the output will be summed. Default: 'mean'</p></dd>
+</dl></div>
 
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index 20272b8bd..eff76269e 100644
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-    <h1>Prelu</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-activation.R'><code>R/nnf-activation.R</code></a></small>
-    <div class="hidden name"><code>nnf_prelu.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Prelu</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-activation.R" class="external-link"><code>R/nnf-activation.R</code></a></small>
+      <div class="d-none name"><code>nnf_prelu.Rd</code></div>
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-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Applies element-wise the function
 \(PReLU(x) = max(0,x) + weight * min(0,x)\)
 where weight is a learnable parameter.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_prelu</span><span class='op'>(</span><span class='va'>input</span>, <span class='va'>weight</span><span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_prelu</span><span class="op">(</span><span class="va">input</span>, <span class="va">weight</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(N,*) tensor, where * means, any number of additional
-dimensions</p></td>
-    </tr>
-    <tr>
-      <th>weight</th>
-      <td><p>(Tensor) the learnable weights</p></td>
-    </tr>
-    </table>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>(N,*) tensor, where * means, any number of additional
+dimensions</p></dd>
+<dt>weight</dt>
+<dd><p>(Tensor) the learnable weights</p></dd>
+</dl></div>
 
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index 20838dd1c..a2b424865 100644
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-activation.R'><code>R/nnf-activation.R</code></a></small>
-    <div class="hidden name"><code>nnf_relu.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Relu</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-activation.R" class="external-link"><code>R/nnf-activation.R</code></a></small>
+      <div class="d-none name"><code>nnf_relu.Rd</code></div>
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-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Applies the rectified linear unit function element-wise.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_relu</span><span class='op'>(</span><span class='va'>input</span>, inplace <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_relu</span><span class="op">(</span><span class="va">input</span>, inplace <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>
 
-<span class='fu'>nnf_relu_</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span></pre>
+<span class="fu">nnf_relu_</span><span class="op">(</span><span class="va">input</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(N,*) tensor, where * means, any number of additional
-dimensions</p></td>
-    </tr>
-    <tr>
-      <th>inplace</th>
-      <td><p>can optionally do the operation in-place. Default: FALSE</p></td>
-    </tr>
-    </table>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>(N,*) tensor, where * means, any number of additional
+dimensions</p></dd>
+<dt>inplace</dt>
+<dd><p>can optionally do the operation in-place. Default: FALSE</p></dd>
+</dl></div>
 
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index ba0301200..5407f441f 100644
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-    <div class="hidden name"><code>nnf_relu6.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
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+    <div class="ref-description section level2">
     <p>Applies the element-wise function \(ReLU6(x) = min(max(0,x), 6)\).</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_relu6</span><span class='op'>(</span><span class='va'>input</span>, inplace <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_relu6</span><span class="op">(</span><span class="va">input</span>, inplace <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
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-      <th>input</th>
-      <td><p>(N,*) tensor, where * means, any number of additional
-dimensions</p></td>
-    </tr>
-    <tr>
-      <th>inplace</th>
-      <td><p>can optionally do the operation in-place. Default: FALSE</p></td>
-    </tr>
-    </table>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>(N,*) tensor, where * means, any number of additional
+dimensions</p></dd>
+<dt>inplace</dt>
+<dd><p>can optionally do the operation in-place. Default: FALSE</p></dd>
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+    <div class="ref-description section level2">
     <p>Randomized leaky ReLU.</p>
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-    <pre class="usage"><span class='fu'>nnf_rrelu</span><span class='op'>(</span><span class='va'>input</span>, lower <span class='op'>=</span> <span class='fl'>1</span><span class='op'>/</span><span class='fl'>8</span>, upper <span class='op'>=</span> <span class='fl'>1</span><span class='op'>/</span><span class='fl'>3</span>, training <span class='op'>=</span> <span class='cn'>FALSE</span>, inplace <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span>
-
-<span class='fu'>nnf_rrelu_</span><span class='op'>(</span><span class='va'>input</span>, lower <span class='op'>=</span> <span class='fl'>1</span><span class='op'>/</span><span class='fl'>8</span>, upper <span class='op'>=</span> <span class='fl'>1</span><span class='op'>/</span><span class='fl'>3</span>, training <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span></pre>
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-dimensions</p></td>
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-      <th>lower</th>
-      <td><p>lower bound of the uniform distribution. Default: 1/8</p></td>
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+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_rrelu</span><span class="op">(</span><span class="va">input</span>, lower <span class="op">=</span> <span class="fl">1</span><span class="op">/</span><span class="fl">8</span>, upper <span class="op">=</span> <span class="fl">1</span><span class="op">/</span><span class="fl">3</span>, training <span class="op">=</span> <span class="cn">FALSE</span>, inplace <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>
 
+<span class="fu">nnf_rrelu_</span><span class="op">(</span><span class="va">input</span>, lower <span class="op">=</span> <span class="fl">1</span><span class="op">/</span><span class="fl">8</span>, upper <span class="op">=</span> <span class="fl">1</span><span class="op">/</span><span class="fl">3</span>, training <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
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+    <dl><dt>input</dt>
+<dd><p>(N,*) tensor, where * means, any number of additional
+dimensions</p></dd>
+<dt>lower</dt>
+<dd><p>lower bound of the uniform distribution. Default: 1/8</p></dd>
+<dt>upper</dt>
+<dd><p>upper bound of the uniform distribution. Default: 1/3</p></dd>
+<dt>training</dt>
+<dd><p>bool wether it's a training pass. DEfault: FALSE</p></dd>
+<dt>inplace</dt>
+<dd><p>can optionally do the operation in-place. Default: FALSE</p></dd>
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     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Applies element-wise,
 $$SELU(x) = scale * (max(0,x) + min(0, \alpha * (exp(x) - 1)))$$,
 with \(\alpha=1.6732632423543772848170429916717\) and
 \(scale=1.0507009873554804934193349852946\).</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_selu</span><span class='op'>(</span><span class='va'>input</span>, inplace <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span>
-
-<span class='fu'>nnf_selu_</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(N,*) tensor, where * means, any number of additional
-dimensions</p></td>
-    </tr>
-    <tr>
-      <th>inplace</th>
-      <td><p>can optionally do the operation in-place. Default: FALSE</p></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>x</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>y</span> <span class='op'>&lt;-</span> <span class='fu'>nnf_selu</span><span class='op'>(</span><span class='va'>x</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>nnf_selu_</span><span class='op'>(</span><span class='va'>x</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'><a href='torch_equal.html'>torch_equal</a></span><span class='op'>(</span><span class='va'>x</span>, <span class='va'>y</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [1] TRUE</span>
-</pre>
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+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_selu</span><span class="op">(</span><span class="va">input</span>, inplace <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>
 
+<span class="fu">nnf_selu_</span><span class="op">(</span><span class="va">input</span><span class="op">)</span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>(N,*) tensor, where * means, any number of additional
+dimensions</p></dd>
+<dt>inplace</dt>
+<dd><p>can optionally do the operation in-place. Default: FALSE</p></dd>
+</dl></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">y</span> <span class="op">&lt;-</span> <span class="fu">nnf_selu</span><span class="op">(</span><span class="va">x</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">nnf_selu_</span><span class="op">(</span><span class="va">x</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu"><a href="torch_equal.html">torch_equal</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">y</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] TRUE</span>
+</code></pre></div>
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-loss.R'><code>R/nnf-loss.R</code></a></small>
-    <div class="hidden name"><code>nnf_smooth_l1_loss.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Smooth_l1_loss</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-loss.R" class="external-link"><code>R/nnf-loss.R</code></a></small>
+      <div class="d-none name"><code>nnf_smooth_l1_loss.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Function that uses a squared term if the absolute
 element-wise error falls below 1 and an L1 term otherwise.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_smooth_l1_loss</span><span class='op'>(</span><span class='va'>input</span>, <span class='va'>target</span>, reduction <span class='op'>=</span> <span class='st'>"mean"</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>tensor (N,*) where ** means, any number of additional dimensions</p></td>
-    </tr>
-    <tr>
-      <th>target</th>
-      <td><p>tensor (N,*) , same shape as the input</p></td>
-    </tr>
-    <tr>
-      <th>reduction</th>
-      <td><p>(string, optional) – Specifies the reduction to apply to the
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_smooth_l1_loss</span><span class="op">(</span><span class="va">input</span>, <span class="va">target</span>, reduction <span class="op">=</span> <span class="st">"mean"</span><span class="op">)</span></code></pre></div>
+    </div>
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+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>tensor (N,*) where ** means, any number of additional dimensions</p></dd>
+<dt>target</dt>
+<dd><p>tensor (N,*) , same shape as the input</p></dd>
+<dt>reduction</dt>
+<dd><p>(string, optional) – Specifies the reduction to apply to the
 output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
 the sum of the output will be divided by the number of elements in the output,
-'sum': the output will be summed. Default: 'mean'</p></td>
-    </tr>
-    </table>
+'sum': the output will be summed. Default: 'mean'</p></dd>
+</dl></div>
 
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diff --git a/reference/nnf_soft_margin_loss.html b/reference/nnf_soft_margin_loss.html
index f1ad5aa28..a0ad658ab 100644
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-    <h1>Soft_margin_loss</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-loss.R'><code>R/nnf-loss.R</code></a></small>
-    <div class="hidden name"><code>nnf_soft_margin_loss.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Soft_margin_loss</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-loss.R" class="external-link"><code>R/nnf-loss.R</code></a></small>
+      <div class="d-none name"><code>nnf_soft_margin_loss.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Creates a criterion that optimizes a two-class classification logistic loss
 between input tensor x and target tensor y (containing 1 or -1).</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_soft_margin_loss</span><span class='op'>(</span><span class='va'>input</span>, <span class='va'>target</span>, reduction <span class='op'>=</span> <span class='st'>"mean"</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>tensor (N,*) where ** means, any number of additional dimensions</p></td>
-    </tr>
-    <tr>
-      <th>target</th>
-      <td><p>tensor (N,*) , same shape as the input</p></td>
-    </tr>
-    <tr>
-      <th>reduction</th>
-      <td><p>(string, optional) – Specifies the reduction to apply to the
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_soft_margin_loss</span><span class="op">(</span><span class="va">input</span>, <span class="va">target</span>, reduction <span class="op">=</span> <span class="st">"mean"</span><span class="op">)</span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>tensor (N,*) where ** means, any number of additional dimensions</p></dd>
+<dt>target</dt>
+<dd><p>tensor (N,*) , same shape as the input</p></dd>
+<dt>reduction</dt>
+<dd><p>(string, optional) – Specifies the reduction to apply to the
 output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
 the sum of the output will be divided by the number of elements in the output,
-'sum': the output will be summed. Default: 'mean'</p></td>
-    </tr>
-    </table>
+'sum': the output will be summed. Default: 'mean'</p></dd>
+</dl></div>
 
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diff --git a/reference/nnf_softmax.html b/reference/nnf_softmax.html
index f01dc65bb..25a9fd9ef 100644
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+++ b/reference/nnf_softmax.html
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-    <h1>Softmax</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-activation.R'><code>R/nnf-activation.R</code></a></small>
-    <div class="hidden name"><code>nnf_softmax.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Softmax</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-activation.R" class="external-link"><code>R/nnf-activation.R</code></a></small>
+      <div class="d-none name"><code>nnf_softmax.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Applies a softmax function.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_softmax</span><span class='op'>(</span><span class='va'>input</span>, <span class='va'>dim</span>, dtype <span class='op'>=</span> <span class='cn'>NULL</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) input</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) A dimension along which softmax will be computed.</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.      If specified, the input tensor is casted to <code>dtype</code> before the operation      is performed. This is useful for preventing data type overflows.
-Default: NULL.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_softmax</span><span class="op">(</span><span class="va">input</span>, <span class="va">dim</span>, dtype <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>(Tensor) input</p></dd>
+<dt>dim</dt>
+<dd><p>(int) A dimension along which softmax will be computed.</p></dd>
+<dt>dtype</dt>
+<dd><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.      If specified, the input tensor is casted to <code>dtype</code> before the operation      is performed. This is useful for preventing data type overflows.
+Default: NULL.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>Softmax is defined as:</p>
 <p>$$Softmax(x_{i}) = exp(x_i)/\sum_j exp(x_j)$$</p>
 <p>It is applied to all slices along dim, and will re-scale them so that the elements
 lie in the range <code>[0, 1]</code> and sum to 1.</p>
+    </div>
 
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-    <h1>Softmin</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-activation.R'><code>R/nnf-activation.R</code></a></small>
-    <div class="hidden name"><code>nnf_softmin.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Softmin</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-activation.R" class="external-link"><code>R/nnf-activation.R</code></a></small>
+      <div class="d-none name"><code>nnf_softmin.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Applies a softmin function.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_softmin</span><span class='op'>(</span><span class='va'>input</span>, <span class='va'>dim</span>, dtype <span class='op'>=</span> <span class='cn'>NULL</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) input</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) A dimension along which softmin will be computed
-(so every slice        along dim will sum to 1).</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.      If specified, the input tensor is casted to <code>dtype</code> before the operation      is performed.
-This is useful for preventing data type overflows. Default: NULL.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_softmin</span><span class="op">(</span><span class="va">input</span>, <span class="va">dim</span>, dtype <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>(Tensor) input</p></dd>
+<dt>dim</dt>
+<dd><p>(int) A dimension along which softmin will be computed
+(so every slice        along dim will sum to 1).</p></dd>
+<dt>dtype</dt>
+<dd><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.      If specified, the input tensor is casted to <code>dtype</code> before the operation      is performed.
+This is useful for preventing data type overflows. Default: NULL.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>Note that</p>
 <p>$$Softmin(x) = Softmax(-x)$$.</p>
-<p>See <a href='nnf_softmax.html'>nnf_softmax</a> definition for mathematical formula.</p>
+<p>See <a href="nnf_softmax.html">nnf_softmax</a> definition for mathematical formula.</p>
+    </div>
 
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index 8d235e454..3b27758c9 100644
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-  <div class="col-md-9 contents">
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-    <h1>Softplus</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-activation.R'><code>R/nnf-activation.R</code></a></small>
-    <div class="hidden name"><code>nnf_softplus.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Softplus</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-activation.R" class="external-link"><code>R/nnf-activation.R</code></a></small>
+      <div class="d-none name"><code>nnf_softplus.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Applies element-wise, the function \(Softplus(x) = 1/\beta * log(1 + exp(\beta * x))\).</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_softplus</span><span class='op'>(</span><span class='va'>input</span>, beta <span class='op'>=</span> <span class='fl'>1</span>, threshold <span class='op'>=</span> <span class='fl'>20</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(N,*) tensor, where * means, any number of additional
-dimensions</p></td>
-    </tr>
-    <tr>
-      <th>beta</th>
-      <td><p>the beta value for the Softplus formulation. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>threshold</th>
-      <td><p>values above this revert to a linear function. Default: 20</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_softplus</span><span class="op">(</span><span class="va">input</span>, beta <span class="op">=</span> <span class="fl">1</span>, threshold <span class="op">=</span> <span class="fl">20</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>(N,*) tensor, where * means, any number of additional
+dimensions</p></dd>
+<dt>beta</dt>
+<dd><p>the beta value for the Softplus formulation. Default: 1</p></dd>
+<dt>threshold</dt>
+<dd><p>values above this revert to a linear function. Default: 20</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>For numerical stability the implementation reverts to the linear function
 when \(input * \beta &gt; threshold\).</p>
+    </div>
 
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index b8afead50..5b9cbe0d2 100644
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Softshrink</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-activation.R'><code>R/nnf-activation.R</code></a></small>
-    <div class="hidden name"><code>nnf_softshrink.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Softshrink</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-activation.R" class="external-link"><code>R/nnf-activation.R</code></a></small>
+      <div class="d-none name"><code>nnf_softshrink.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Applies the soft shrinkage function elementwise</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_softshrink</span><span class='op'>(</span><span class='va'>input</span>, lambd <span class='op'>=</span> <span class='fl'>0.5</span><span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_softshrink</span><span class="op">(</span><span class="va">input</span>, lambd <span class="op">=</span> <span class="fl">0.5</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(N,*) tensor, where * means, any number of additional
-dimensions</p></td>
-    </tr>
-    <tr>
-      <th>lambd</th>
-      <td><p>the lambda (must be no less than zero) value for the Softshrink
-formulation. Default: 0.5</p></td>
-    </tr>
-    </table>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>(N,*) tensor, where * means, any number of additional
+dimensions</p></dd>
+<dt>lambd</dt>
+<dd><p>the lambda (must be no less than zero) value for the Softshrink
+formulation. Default: 0.5</p></dd>
+</dl></div>
 
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diff --git a/reference/nnf_softsign.html b/reference/nnf_softsign.html
index ef305d695..e7472e2df 100644
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-activation.R'><code>R/nnf-activation.R</code></a></small>
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+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Threshold</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-activation.R" class="external-link"><code>R/nnf-activation.R</code></a></small>
+      <div class="d-none name"><code>nnf_threshold.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Thresholds each element of the input Tensor.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_threshold</span><span class='op'>(</span><span class='va'>input</span>, <span class='va'>threshold</span>, <span class='va'>value</span>, inplace <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span>
-
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-      <th>input</th>
-      <td><p>(N,*) tensor, where * means, any number of additional
-dimensions</p></td>
-    </tr>
-    <tr>
-      <th>threshold</th>
-      <td><p>The value to threshold at</p></td>
-    </tr>
-    <tr>
-      <th>value</th>
-      <td><p>The value to replace with</p></td>
-    </tr>
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-      <td><p>can optionally do the operation in-place. Default: FALSE</p></td>
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+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_threshold</span><span class="op">(</span><span class="va">input</span>, <span class="va">threshold</span>, <span class="va">value</span>, inplace <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>
 
+<span class="fu">nnf_threshold_</span><span class="op">(</span><span class="va">input</span>, <span class="va">threshold</span>, <span class="va">value</span><span class="op">)</span></code></pre></div>
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+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>(N,*) tensor, where * means, any number of additional
+dimensions</p></dd>
+<dt>threshold</dt>
+<dd><p>The value to threshold at</p></dd>
+<dt>value</dt>
+<dd><p>The value to replace with</p></dd>
+<dt>inplace</dt>
+<dd><p>can optionally do the operation in-place. Default: FALSE</p></dd>
+</dl></div>
+
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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index 3ad0c91c0..00898d8ea 100644
--- a/reference/nnf_triplet_margin_loss.html
+++ b/reference/nnf_triplet_margin_loss.html
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+anchor, positive examples and negative examples respectively). The shapes of all
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Triplet_margin_loss</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-loss.R'><code>R/nnf-loss.R</code></a></small>
-    <div class="hidden name"><code>nnf_triplet_margin_loss.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Triplet_margin_loss</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-loss.R" class="external-link"><code>R/nnf-loss.R</code></a></small>
+      <div class="d-none name"><code>nnf_triplet_margin_loss.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Creates a criterion that measures the triplet loss given an input tensors x1 ,
 x2 , x3 and a margin with a value greater than 0 . This is used for measuring
 a relative similarity between samples. A triplet is composed by a, p and n (i.e.,
@@ -197,85 +92,62 @@ anchor, positive examples and negative examples respectively). The shapes of all
 input tensors should be (N, D).</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_triplet_margin_loss</span><span class='op'>(</span>
-  <span class='va'>anchor</span>,
-  <span class='va'>positive</span>,
-  <span class='va'>negative</span>,
-  margin <span class='op'>=</span> <span class='fl'>1</span>,
-  p <span class='op'>=</span> <span class='fl'>2</span>,
-  eps <span class='op'>=</span> <span class='fl'>1e-06</span>,
-  swap <span class='op'>=</span> <span class='cn'>FALSE</span>,
-  reduction <span class='op'>=</span> <span class='st'>"mean"</span>
-<span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_triplet_margin_loss</span><span class="op">(</span>
+  <span class="va">anchor</span>,
+  <span class="va">positive</span>,
+  <span class="va">negative</span>,
+  margin <span class="op">=</span> <span class="fl">1</span>,
+  p <span class="op">=</span> <span class="fl">2</span>,
+  eps <span class="op">=</span> <span class="fl">1e-06</span>,
+  swap <span class="op">=</span> <span class="cn">FALSE</span>,
+  reduction <span class="op">=</span> <span class="st">"mean"</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>anchor</th>
-      <td><p>the anchor input tensor</p></td>
-    </tr>
-    <tr>
-      <th>positive</th>
-      <td><p>the positive input tensor</p></td>
-    </tr>
-    <tr>
-      <th>negative</th>
-      <td><p>the negative input tensor</p></td>
-    </tr>
-    <tr>
-      <th>margin</th>
-      <td><p>Default: 1.</p></td>
-    </tr>
-    <tr>
-      <th>p</th>
-      <td><p>The norm degree for pairwise distance. Default: 2.</p></td>
-    </tr>
-    <tr>
-      <th>eps</th>
-      <td><p>(float, optional) Small value to avoid division by zero.</p></td>
-    </tr>
-    <tr>
-      <th>swap</th>
-      <td><p>The distance swap is described in detail in the paper Learning shallow
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>anchor</dt>
+<dd><p>the anchor input tensor</p></dd>
+<dt>positive</dt>
+<dd><p>the positive input tensor</p></dd>
+<dt>negative</dt>
+<dd><p>the negative input tensor</p></dd>
+<dt>margin</dt>
+<dd><p>Default: 1.</p></dd>
+<dt>p</dt>
+<dd><p>The norm degree for pairwise distance. Default: 2.</p></dd>
+<dt>eps</dt>
+<dd><p>(float, optional) Small value to avoid division by zero.</p></dd>
+<dt>swap</dt>
+<dd><p>The distance swap is described in detail in the paper Learning shallow
 convolutional feature descriptors with triplet losses by V. Balntas, E. Riba et al.
-Default: <code>FALSE</code>.</p></td>
-    </tr>
-    <tr>
-      <th>reduction</th>
-      <td><p>(string, optional) – Specifies the reduction to apply to the
+Default: <code>FALSE</code>.</p></dd>
+<dt>reduction</dt>
+<dd><p>(string, optional) – Specifies the reduction to apply to the
 output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
 the sum of the output will be divided by the number of elements in the output,
-'sum': the output will be summed. Default: 'mean'</p></td>
-    </tr>
-    </table>
+'sum': the output will be summed. Default: 'mean'</p></dd>
+</dl></div>
 
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Triplet margin with distance loss</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-loss.R'><code>R/nnf-loss.R</code></a></small>
-    <div class="hidden name"><code>nnf_triplet_margin_with_distance_loss.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Triplet margin with distance loss</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-loss.R" class="external-link"><code>R/nnf-loss.R</code></a></small>
+      <div class="d-none name"><code>nnf_triplet_margin_with_distance_loss.Rd</code></div>
     </div>
 
-    <div class="ref-description">
-    <p>See <code><a href='nn_triplet_margin_with_distance_loss.html'>nn_triplet_margin_with_distance_loss()</a></code></p>
+    <div class="ref-description section level2">
+    <p>See <code><a href="nn_triplet_margin_with_distance_loss.html">nn_triplet_margin_with_distance_loss()</a></code></p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_triplet_margin_with_distance_loss</span><span class='op'>(</span>
-  <span class='va'>anchor</span>,
-  <span class='va'>positive</span>,
-  <span class='va'>negative</span>,
-  distance_function <span class='op'>=</span> <span class='cn'>NULL</span>,
-  margin <span class='op'>=</span> <span class='fl'>1</span>,
-  swap <span class='op'>=</span> <span class='cn'>FALSE</span>,
-  reduction <span class='op'>=</span> <span class='st'>"mean"</span>
-<span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_triplet_margin_with_distance_loss</span><span class="op">(</span>
+  <span class="va">anchor</span>,
+  <span class="va">positive</span>,
+  <span class="va">negative</span>,
+  distance_function <span class="op">=</span> <span class="cn">NULL</span>,
+  margin <span class="op">=</span> <span class="fl">1</span>,
+  swap <span class="op">=</span> <span class="cn">FALSE</span>,
+  reduction <span class="op">=</span> <span class="st">"mean"</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>anchor</th>
-      <td><p>the anchor input tensor</p></td>
-    </tr>
-    <tr>
-      <th>positive</th>
-      <td><p>the positive input tensor</p></td>
-    </tr>
-    <tr>
-      <th>negative</th>
-      <td><p>the negative input tensor</p></td>
-    </tr>
-    <tr>
-      <th>distance_function</th>
-      <td><p>(callable, optional): A nonnegative, real-valued function that
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>anchor</dt>
+<dd><p>the anchor input tensor</p></dd>
+<dt>positive</dt>
+<dd><p>the positive input tensor</p></dd>
+<dt>negative</dt>
+<dd><p>the negative input tensor</p></dd>
+<dt>distance_function</dt>
+<dd><p>(callable, optional): A nonnegative, real-valued function that
 quantifies the closeness of two tensors. If not specified,
-<code><a href='nn_pairwise_distance.html'>nn_pairwise_distance()</a></code> will be used.  Default: <code>None</code></p></td>
-    </tr>
-    <tr>
-      <th>margin</th>
-      <td><p>Default: 1.</p></td>
-    </tr>
-    <tr>
-      <th>swap</th>
-      <td><p>The distance swap is described in detail in the paper Learning shallow
+<code><a href="nn_pairwise_distance.html">nn_pairwise_distance()</a></code> will be used.  Default: <code>None</code></p></dd>
+<dt>margin</dt>
+<dd><p>Default: 1.</p></dd>
+<dt>swap</dt>
+<dd><p>The distance swap is described in detail in the paper Learning shallow
 convolutional feature descriptors with triplet losses by V. Balntas, E. Riba et al.
-Default: <code>FALSE</code>.</p></td>
-    </tr>
-    <tr>
-      <th>reduction</th>
-      <td><p>(string, optional) – Specifies the reduction to apply to the
+Default: <code>FALSE</code>.</p></dd>
+<dt>reduction</dt>
+<dd><p>(string, optional) – Specifies the reduction to apply to the
 output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
 the sum of the output will be divided by the number of elements in the output,
-'sum': the output will be summed. Default: 'mean'</p></td>
-    </tr>
-    </table>
+'sum': the output will be summed. Default: 'mean'</p></dd>
+</dl></div>
 
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diff --git a/reference/nnf_unfold.html b/reference/nnf_unfold.html
index ad69a5fac..a3d5f81dd 100644
--- a/reference/nnf_unfold.html
+++ b/reference/nnf_unfold.html
@@ -1,226 +1,108 @@
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-    <div class="page-header">
-    <h1>Unfold</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-fold.R'><code>R/nnf-fold.R</code></a></small>
-    <div class="hidden name"><code>nnf_unfold.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Unfold</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-fold.R" class="external-link"><code>R/nnf-fold.R</code></a></small>
+      <div class="d-none name"><code>nnf_unfold.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Extracts sliding local blocks from an batched input tensor.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>nnf_unfold</span><span class='op'>(</span><span class='va'>input</span>, <span class='va'>kernel_size</span>, dilation <span class='op'>=</span> <span class='fl'>1</span>, padding <span class='op'>=</span> <span class='fl'>0</span>, stride <span class='op'>=</span> <span class='fl'>1</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>the input tensor</p></td>
-    </tr>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>the size of the sliding blocks</p></td>
-    </tr>
-    <tr>
-      <th>dilation</th>
-      <td><p>a parameter that controls the stride of elements within the
-neighborhood. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>implicit zero padding to be added on both sides of input.
-Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>the stride of the sliding blocks in the input spatial dimensions.
-Default: 1</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nnf_unfold</span><span class="op">(</span><span class="va">input</span>, <span class="va">kernel_size</span>, dilation <span class="op">=</span> <span class="fl">1</span>, padding <span class="op">=</span> <span class="fl">0</span>, stride <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>the input tensor</p></dd>
+<dt>kernel_size</dt>
+<dd><p>the size of the sliding blocks</p></dd>
+<dt>dilation</dt>
+<dd><p>a parameter that controls the stride of elements within the
+neighborhood. Default: 1</p></dd>
+<dt>padding</dt>
+<dd><p>implicit zero padding to be added on both sides of input.
+Default: 0</p></dd>
+<dt>stride</dt>
+<dd><p>the stride of the sliding blocks in the input spatial dimensions.
+Default: 1</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></a></h2>
     
 
 
@@ -232,32 +114,25 @@ supported.</p>
 memory location. As a result, in-place operations (especially ones that
 are vectorized) may result in incorrect behavior. If you need to write
 to the tensor, please clone it first.</p>
+    </div>
 
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diff --git a/reference/optim_adadelta.html b/reference/optim_adadelta.html
index b64288ef7..0b2ec275b 100644
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Adadelta optimizer</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/optim-adadelta.R'><code>R/optim-adadelta.R</code></a></small>
-    <div class="hidden name"><code>optim_adadelta.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Adadelta optimizer</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/optim-adadelta.R" class="external-link"><code>R/optim-adadelta.R</code></a></small>
+      <div class="d-none name"><code>optim_adadelta.Rd</code></div>
     </div>
 
-    <div class="ref-description">
-    <p>It has been proposed in <a href='https://arxiv.org/pdf/1212.5701.pdf'>ADADELTA: An Adaptive Learning Rate Method</a></p>
+    <div class="ref-description section level2">
+    <p>It has been proposed in <a href="https://arxiv.org/pdf/1212.5701.pdf" class="external-link">ADADELTA: An Adaptive Learning Rate Method</a></p>
     </div>
 
-    <pre class="usage"><span class='fu'>optim_adadelta</span><span class='op'>(</span><span class='va'>params</span>, lr <span class='op'>=</span> <span class='fl'>1</span>, rho <span class='op'>=</span> <span class='fl'>0.9</span>, eps <span class='op'>=</span> <span class='fl'>1e-06</span>, weight_decay <span class='op'>=</span> <span class='fl'>0</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>params</th>
-      <td><p>(iterable): list of parameters to optimize or list defining
-parameter groups</p></td>
-    </tr>
-    <tr>
-      <th>lr</th>
-      <td><p>(float, optional): learning rate (default: 1e-3)</p></td>
-    </tr>
-    <tr>
-      <th>rho</th>
-      <td><p>(float, optional): coefficient used for computing a running average
-of squared gradients (default: 0.9)</p></td>
-    </tr>
-    <tr>
-      <th>eps</th>
-      <td><p>(float, optional): term added to the denominator to improve
-numerical stability (default: 1e-6)</p></td>
-    </tr>
-    <tr>
-      <th>weight_decay</th>
-      <td><p>(float, optional): weight decay (L2 penalty) (default: 0)</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">optim_adadelta</span><span class="op">(</span><span class="va">params</span>, lr <span class="op">=</span> <span class="fl">1</span>, rho <span class="op">=</span> <span class="fl">0.9</span>, eps <span class="op">=</span> <span class="fl">1e-06</span>, weight_decay <span class="op">=</span> <span class="fl">0</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>params</dt>
+<dd><p>(iterable): list of parameters to optimize or list defining
+parameter groups</p></dd>
+<dt>lr</dt>
+<dd><p>(float, optional): learning rate (default: 1e-3)</p></dd>
+<dt>rho</dt>
+<dd><p>(float, optional): coefficient used for computing a running average
+of squared gradients (default: 0.9)</p></dd>
+<dt>eps</dt>
+<dd><p>(float, optional): term added to the denominator to improve
+numerical stability (default: 1e-6)</p></dd>
+<dt>weight_decay</dt>
+<dd><p>(float, optional): weight decay (L2 penalty) (default: 0)</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p>According to the original paper, decaying average of the squared gradients
 is computed as follows:
 $$
@@ -237,50 +119,46 @@ $$
  \theta_{t+1} = \theta_{t} + \Delta \theta_{t}
 \end{array}
 $$</p>
-    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></a></h2>
     <p>If you need to move a model to GPU via <code>$cuda()</code>, please do so before
 constructing optimizers for it. Parameters of a model after <code>$cuda()</code>
 will be different objects from those before the call. In general, you
 should make sure that the objects pointed to by model parameters subject
 to optimization remain the same over the whole lifecycle of optimizer
 creation and usage.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='cn'>FALSE</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>optimizer</span> <span class='op'>&lt;-</span> <span class='fu'>optim_adadelta</span><span class='op'>(</span><span class='va'>model</span><span class='op'>$</span><span class='va'>parameters</span>, lr <span class='op'>=</span> <span class='fl'>0.1</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>optimizer</span><span class='op'>$</span><span class='fu'>zero_grad</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>loss_fn</span><span class='op'>(</span><span class='fu'>model</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span>, <span class='va'>target</span><span class='op'>)</span><span class='op'>$</span><span class='fu'>backward</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>optimizer</span><span class='op'>$</span><span class='fu'>step</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">optimizer</span> <span class="op">&lt;-</span> <span class="fu">optim_adadelta</span><span class="op">(</span><span class="va">model</span><span class="op">$</span><span class="va">parameters</span>, lr <span class="op">=</span> <span class="fl">0.1</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">optimizer</span><span class="op">$</span><span class="fu">zero_grad</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">loss_fn</span><span class="op">(</span><span class="fu">model</span><span class="op">(</span><span class="va">input</span><span class="op">)</span>, <span class="va">target</span><span class="op">)</span><span class="op">$</span><span class="fu">backward</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">optimizer</span><span class="op">$</span><span class="fu">step</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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diff --git a/reference/optim_adagrad.html b/reference/optim_adagrad.html
index 3313a7bd5..9e66c3980 100644
--- a/reference/optim_adagrad.html
+++ b/reference/optim_adagrad.html
@@ -1,282 +1,156 @@
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Adagrad optimizer</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/optim-adagrad.R'><code>R/optim-adagrad.R</code></a></small>
-    <div class="hidden name"><code>optim_adagrad.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Adagrad optimizer</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/optim-adagrad.R" class="external-link"><code>R/optim-adagrad.R</code></a></small>
+      <div class="d-none name"><code>optim_adagrad.Rd</code></div>
     </div>
 
-    <div class="ref-description">
-    <p>Proposed in <a href='https://jmlr.org/papers/v12/duchi11a.html'>Adaptive Subgradient Methods for Online Learning and Stochastic Optimization</a></p>
+    <div class="ref-description section level2">
+    <p>Proposed in <a href="https://jmlr.org/papers/v12/duchi11a.html" class="external-link">Adaptive Subgradient Methods for Online Learning and Stochastic Optimization</a></p>
     </div>
 
-    <pre class="usage"><span class='fu'>optim_adagrad</span><span class='op'>(</span>
-  <span class='va'>params</span>,
-  lr <span class='op'>=</span> <span class='fl'>0.01</span>,
-  lr_decay <span class='op'>=</span> <span class='fl'>0</span>,
-  weight_decay <span class='op'>=</span> <span class='fl'>0</span>,
-  initial_accumulator_value <span class='op'>=</span> <span class='fl'>0</span>,
-  eps <span class='op'>=</span> <span class='fl'>1e-10</span>
-<span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">optim_adagrad</span><span class="op">(</span>
+  <span class="va">params</span>,
+  lr <span class="op">=</span> <span class="fl">0.01</span>,
+  lr_decay <span class="op">=</span> <span class="fl">0</span>,
+  weight_decay <span class="op">=</span> <span class="fl">0</span>,
+  initial_accumulator_value <span class="op">=</span> <span class="fl">0</span>,
+  eps <span class="op">=</span> <span class="fl">1e-10</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>params</th>
-      <td><p>(iterable): list of parameters to optimize or list parameter groups</p></td>
-    </tr>
-    <tr>
-      <th>lr</th>
-      <td><p>(float, optional): learning rate (default: 1e-2)</p></td>
-    </tr>
-    <tr>
-      <th>lr_decay</th>
-      <td><p>(float, optional): learning rate decay (default: 0)</p></td>
-    </tr>
-    <tr>
-      <th>weight_decay</th>
-      <td><p>(float, optional): weight decay (L2 penalty) (default: 0)</p></td>
-    </tr>
-    <tr>
-      <th>initial_accumulator_value</th>
-      <td><p>the initial value for the accumulator. (default: 0)</p>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>params</dt>
+<dd><p>(iterable): list of parameters to optimize or list parameter groups</p></dd>
+<dt>lr</dt>
+<dd><p>(float, optional): learning rate (default: 1e-2)</p></dd>
+<dt>lr_decay</dt>
+<dd><p>(float, optional): learning rate decay (default: 0)</p></dd>
+<dt>weight_decay</dt>
+<dd><p>(float, optional): weight decay (L2 penalty) (default: 0)</p></dd>
+<dt>initial_accumulator_value</dt>
+<dd><p>the initial value for the accumulator. (default: 0)</p>
 <p>Adagrad is an especially good optimizer for sparse data.
 It individually modifies learning rate for every single parameter,
 dividing the original learning rate value by sum of the squares of the gradients.
 It causes that the rarely occurring features get greater learning rates.
 The main downside of this method is the fact that learning rate may be
-getting small too fast, so that at some point a model cannot learn anymore.</p></td>
-    </tr>
-    <tr>
-      <th>eps</th>
-      <td><p>(float, optional): term added to the denominator to improve
-numerical stability (default: 1e-10)</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
+getting small too fast, so that at some point a model cannot learn anymore.</p></dd>
+<dt>eps</dt>
+<dd><p>(float, optional): term added to the denominator to improve
+numerical stability (default: 1e-10)</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p>Update rule:
 $$
-\theta_{t+1} = \theta_{t} - \frac{\eta }{\sqrt{G_{t} + \epsilon}} \odot g_{t} 
+\theta_{t+1} = \theta_{t} - \frac{\eta }{\sqrt{G_{t} + \epsilon}} \odot g_{t}
 $$
 The equation above and some remarks quoted
-after <a href='https://ruder.io/optimizing-gradient-descent/index.html#adagrad'><em>An overview of gradient descent optimization algorithms</em></a>
+after <a href="https://ruder.io/optimizing-gradient-descent/index.html#adagrad" class="external-link"><em>An overview of gradient descent optimization algorithms</em></a>
 by Sebastian Ruder.</p>
-    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></a></h2>
     <p>If you need to move a model to GPU via <code>$cuda()</code>, please do so before
 constructing optimizers for it. Parameters of a model after <code>$cuda()</code>
 will be different objects from those before the call. In general, you
 should make sure that the objects pointed to by model parameters subject
 to optimization remain the same over the whole lifecycle of optimizer
 creation and usage.</p>
+    </div>
 
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diff --git a/reference/optim_adam.html b/reference/optim_adam.html
index feef01e09..7062a90a0 100644
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Implements Adam algorithm.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/optim-adam.R'><code>R/optim-adam.R</code></a></small>
-    <div class="hidden name"><code>optim_adam.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Implements Adam algorithm.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/optim-adam.R" class="external-link"><code>R/optim-adam.R</code></a></small>
+      <div class="d-none name"><code>optim_adam.Rd</code></div>
     </div>
 
-    <div class="ref-description">
-    <p>It has been proposed in <a href='https://arxiv.org/abs/1412.6980'>Adam: A Method for Stochastic Optimization</a>.</p>
+    <div class="ref-description section level2">
+    <p>It has been proposed in <a href="https://arxiv.org/abs/1412.6980" class="external-link">Adam: A Method for Stochastic Optimization</a>.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>optim_adam</span><span class='op'>(</span>
-  <span class='va'>params</span>,
-  lr <span class='op'>=</span> <span class='fl'>0.001</span>,
-  betas <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>0.9</span>, <span class='fl'>0.999</span><span class='op'>)</span>,
-  eps <span class='op'>=</span> <span class='fl'>1e-08</span>,
-  weight_decay <span class='op'>=</span> <span class='fl'>0</span>,
-  amsgrad <span class='op'>=</span> <span class='cn'>FALSE</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>params</th>
-      <td><p>(iterable): iterable of parameters to optimize or dicts defining
-parameter groups</p></td>
-    </tr>
-    <tr>
-      <th>lr</th>
-      <td><p>(float, optional): learning rate (default: 1e-3)</p></td>
-    </tr>
-    <tr>
-      <th>betas</th>
-      <td><p>(<code>Tuple[float, float]</code>, optional): coefficients used for computing
-running averages of gradient and its square (default: (0.9, 0.999))</p></td>
-    </tr>
-    <tr>
-      <th>eps</th>
-      <td><p>(float, optional): term added to the denominator to improve
-numerical stability (default: 1e-8)</p></td>
-    </tr>
-    <tr>
-      <th>weight_decay</th>
-      <td><p>(float, optional): weight decay (L2 penalty) (default: 0)</p></td>
-    </tr>
-    <tr>
-      <th>amsgrad</th>
-      <td><p>(boolean, optional): whether to use the AMSGrad variant of this
-algorithm from the paper <a href='https://openreview.net/forum?id=ryQu7f-RZ'>On the Convergence of Adam and Beyond</a>
-(default: FALSE)</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">optim_adam</span><span class="op">(</span>
+  <span class="va">params</span>,
+  lr <span class="op">=</span> <span class="fl">0.001</span>,
+  betas <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.9</span>, <span class="fl">0.999</span><span class="op">)</span>,
+  eps <span class="op">=</span> <span class="fl">1e-08</span>,
+  weight_decay <span class="op">=</span> <span class="fl">0</span>,
+  amsgrad <span class="op">=</span> <span class="cn">FALSE</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>params</dt>
+<dd><p>(iterable): iterable of parameters to optimize or dicts defining
+parameter groups</p></dd>
+<dt>lr</dt>
+<dd><p>(float, optional): learning rate (default: 1e-3)</p></dd>
+<dt>betas</dt>
+<dd><p>(<code>Tuple[float, float]</code>, optional): coefficients used for computing
+running averages of gradient and its square (default: (0.9, 0.999))</p></dd>
+<dt>eps</dt>
+<dd><p>(float, optional): term added to the denominator to improve
+numerical stability (default: 1e-8)</p></dd>
+<dt>weight_decay</dt>
+<dd><p>(float, optional): weight decay (L2 penalty) (default: 0)</p></dd>
+<dt>amsgrad</dt>
+<dd><p>(boolean, optional): whether to use the AMSGrad variant of this
+algorithm from the paper <a href="https://openreview.net/forum?id=ryQu7f-RZ" class="external-link">On the Convergence of Adam and Beyond</a>
+(default: FALSE)</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></a></h2>
     <p>If you need to move a model to GPU via <code>$cuda()</code>, please do so before
 constructing optimizers for it. Parameters of a model after <code>$cuda()</code>
 will be different objects from those before the call. In general, you
 should make sure that the objects pointed to by model parameters subject
 to optimization remain the same over the whole lifecycle of optimizer
 creation and usage.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='cn'>FALSE</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>optimizer</span> <span class='op'>&lt;-</span> <span class='fu'>optim_adam</span><span class='op'>(</span><span class='va'>model</span><span class='op'>$</span><span class='fu'>parameters</span><span class='op'>(</span><span class='op'>)</span>, lr<span class='op'>=</span><span class='fl'>0.1</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>optimizer</span><span class='op'>$</span><span class='fu'>zero_grad</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>loss_fn</span><span class='op'>(</span><span class='fu'>model</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span>, <span class='va'>target</span><span class='op'>)</span><span class='op'>$</span><span class='fu'>backward</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>optimizer</span><span class='op'>$</span><span class='fu'>step</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">optimizer</span> <span class="op">&lt;-</span> <span class="fu">optim_adam</span><span class="op">(</span><span class="va">model</span><span class="op">$</span><span class="fu">parameters</span><span class="op">(</span><span class="op">)</span>, lr <span class="op">=</span> <span class="fl">0.1</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">optimizer</span><span class="op">$</span><span class="fu">zero_grad</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">loss_fn</span><span class="op">(</span><span class="fu">model</span><span class="op">(</span><span class="va">input</span><span class="op">)</span>, <span class="va">target</span><span class="op">)</span><span class="op">$</span><span class="fu">backward</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">optimizer</span><span class="op">$</span><span class="fu">step</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
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+    
+  </div>
+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Averaged Stochastic Gradient Descent optimizer</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/optim-asgd.R'><code>R/optim-asgd.R</code></a></small>
-    <div class="hidden name"><code>optim_asgd.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Averaged Stochastic Gradient Descent optimizer</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/optim-asgd.R" class="external-link"><code>R/optim-asgd.R</code></a></small>
+      <div class="d-none name"><code>optim_asgd.Rd</code></div>
     </div>
 
-    <div class="ref-description">
-    <p>Proposed in <a href='https://dl.acm.org/doi/10.1137/0330046'>Acceleration of stochastic approximation by averaging</a></p>
+    <div class="ref-description section level2">
+    <p>Proposed in <a href="https://dl.acm.org/doi/10.1137/0330046" class="external-link">Acceleration of stochastic approximation by averaging</a></p>
     </div>
 
-    <pre class="usage"><span class='fu'>optim_asgd</span><span class='op'>(</span>
-  <span class='va'>params</span>,
-  lr <span class='op'>=</span> <span class='fl'>0.01</span>,
-  lambda <span class='op'>=</span> <span class='fl'>1e-04</span>,
-  alpha <span class='op'>=</span> <span class='fl'>0.75</span>,
-  t0 <span class='op'>=</span> <span class='fl'>1e+06</span>,
-  weight_decay <span class='op'>=</span> <span class='fl'>0</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>params</th>
-      <td><p>(iterable): iterable of parameters to optimize or lists defining
-parameter groups</p></td>
-    </tr>
-    <tr>
-      <th>lr</th>
-      <td><p>(float): learning rate</p></td>
-    </tr>
-    <tr>
-      <th>lambda</th>
-      <td><p>(float, optional): decay term (default: 1e-4)</p></td>
-    </tr>
-    <tr>
-      <th>alpha</th>
-      <td><p>(float, optional): power for eta update (default: 0.75)</p></td>
-    </tr>
-    <tr>
-      <th>t0</th>
-      <td><p>(float, optional): point at which to start averaging (default: 1e6)</p></td>
-    </tr>
-    <tr>
-      <th>weight_decay</th>
-      <td><p>(float, optional): weight decay (L2 penalty) (default: 0)</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">optim_asgd</span><span class="op">(</span>
+  <span class="va">params</span>,
+  lr <span class="op">=</span> <span class="fl">0.01</span>,
+  lambda <span class="op">=</span> <span class="fl">1e-04</span>,
+  alpha <span class="op">=</span> <span class="fl">0.75</span>,
+  t0 <span class="op">=</span> <span class="fl">1e+06</span>,
+  weight_decay <span class="op">=</span> <span class="fl">0</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>params</dt>
+<dd><p>(iterable): iterable of parameters to optimize or lists defining
+parameter groups</p></dd>
+<dt>lr</dt>
+<dd><p>(float): learning rate</p></dd>
+<dt>lambda</dt>
+<dd><p>(float, optional): decay term (default: 1e-4)</p></dd>
+<dt>alpha</dt>
+<dd><p>(float, optional): power for eta update (default: 0.75)</p></dd>
+<dt>t0</dt>
+<dd><p>(float, optional): point at which to start averaging (default: 1e6)</p></dd>
+<dt>weight_decay</dt>
+<dd><p>(float, optional): weight decay (L2 penalty) (default: 0)</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></a></h2>
     <p>If you need to move a model to GPU via <code>$cuda()</code>, please do so before
 constructing optimizers for it. Parameters of a model after <code>$cuda()</code>
 will be different objects from those before the call. In general, you
 should make sure that the objects pointed to by model parameters subject
 to optimization remain the same over the whole lifecycle of optimizer
 creation and usage.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='cn'>FALSE</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>optimizer</span> <span class='op'>&lt;-</span> <span class='fu'>optim_asgd</span><span class='op'>(</span><span class='va'>model</span><span class='op'>$</span><span class='fu'>parameters</span><span class='op'>(</span><span class='op'>)</span>, lr<span class='op'>=</span><span class='fl'>0.1</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>optimizer</span><span class='op'>$</span><span class='fu'>zero_grad</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>loss_fn</span><span class='op'>(</span><span class='fu'>model</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span>, <span class='va'>target</span><span class='op'>)</span><span class='op'>$</span><span class='fu'>backward</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>optimizer</span><span class='op'>$</span><span class='fu'>step</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">optimizer</span> <span class="op">&lt;-</span> <span class="fu">optim_asgd</span><span class="op">(</span><span class="va">model</span><span class="op">$</span><span class="fu">parameters</span><span class="op">(</span><span class="op">)</span>, lr <span class="op">=</span> <span class="fl">0.1</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">optimizer</span><span class="op">$</span><span class="fu">zero_grad</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">loss_fn</span><span class="op">(</span><span class="fu">model</span><span class="op">(</span><span class="va">input</span><span class="op">)</span>, <span class="va">target</span><span class="op">)</span><span class="op">$</span><span class="fu">backward</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">optimizer</span><span class="op">$</span><span class="fu">step</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>LBFGS optimizer</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/optim-lbfgs.R'><code>R/optim-lbfgs.R</code></a></small>
-    <div class="hidden name"><code>optim_lbfgs.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>LBFGS optimizer</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/optim-lbfgs.R" class="external-link"><code>R/optim-lbfgs.R</code></a></small>
+      <div class="d-none name"><code>optim_lbfgs.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Implements L-BFGS algorithm, heavily inspired by
-<a href='https://www.cs.ubc.ca/~schmidtm/Software/minFunc.html'>minFunc</a></p>
+<a href="https://www.cs.ubc.ca/~schmidtm/Software/minFunc.html" class="external-link">minFunc</a></p>
     </div>
 
-    <pre class="usage"><span class='fu'>optim_lbfgs</span><span class='op'>(</span>
-  <span class='va'>params</span>,
-  lr <span class='op'>=</span> <span class='fl'>1</span>,
-  max_iter <span class='op'>=</span> <span class='fl'>20</span>,
-  max_eval <span class='op'>=</span> <span class='cn'>NULL</span>,
-  tolerance_grad <span class='op'>=</span> <span class='fl'>1e-07</span>,
-  tolerance_change <span class='op'>=</span> <span class='fl'>1e-09</span>,
-  history_size <span class='op'>=</span> <span class='fl'>100</span>,
-  line_search_fn <span class='op'>=</span> <span class='cn'>NULL</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>params</th>
-      <td><p>(iterable): iterable of parameters to optimize or dicts defining
-parameter groups</p></td>
-    </tr>
-    <tr>
-      <th>lr</th>
-      <td><p>(float): learning rate (default: 1)</p></td>
-    </tr>
-    <tr>
-      <th>max_iter</th>
-      <td><p>(int): maximal number of iterations per optimization step
-(default: 20)</p></td>
-    </tr>
-    <tr>
-      <th>max_eval</th>
-      <td><p>(int): maximal number of function evaluations per optimization
-step (default: max_iter * 1.25).</p></td>
-    </tr>
-    <tr>
-      <th>tolerance_grad</th>
-      <td><p>(float): termination tolerance on first order optimality
-(default: 1e-5).</p></td>
-    </tr>
-    <tr>
-      <th>tolerance_change</th>
-      <td><p>(float): termination tolerance on function
-value/parameter changes (default: 1e-9).</p></td>
-    </tr>
-    <tr>
-      <th>history_size</th>
-      <td><p>(int): update history size (default: 100).</p></td>
-    </tr>
-    <tr>
-      <th>line_search_fn</th>
-      <td><p>(str): either 'strong_wolfe' or None (default: None).</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">optim_lbfgs</span><span class="op">(</span>
+  <span class="va">params</span>,
+  lr <span class="op">=</span> <span class="fl">1</span>,
+  max_iter <span class="op">=</span> <span class="fl">20</span>,
+  max_eval <span class="op">=</span> <span class="cn">NULL</span>,
+  tolerance_grad <span class="op">=</span> <span class="fl">1e-07</span>,
+  tolerance_change <span class="op">=</span> <span class="fl">1e-09</span>,
+  history_size <span class="op">=</span> <span class="fl">100</span>,
+  line_search_fn <span class="op">=</span> <span class="cn">NULL</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>params</dt>
+<dd><p>(iterable): iterable of parameters to optimize or dicts defining
+parameter groups</p></dd>
+<dt>lr</dt>
+<dd><p>(float): learning rate (default: 1)</p></dd>
+<dt>max_iter</dt>
+<dd><p>(int): maximal number of iterations per optimization step
+(default: 20)</p></dd>
+<dt>max_eval</dt>
+<dd><p>(int): maximal number of function evaluations per optimization
+step (default: max_iter * 1.25).</p></dd>
+<dt>tolerance_grad</dt>
+<dd><p>(float): termination tolerance on first order optimality
+(default: 1e-5).</p></dd>
+<dt>tolerance_change</dt>
+<dd><p>(float): termination tolerance on function
+value/parameter changes (default: 1e-9).</p></dd>
+<dt>history_size</dt>
+<dd><p>(int): update history size (default: 100).</p></dd>
+<dt>line_search_fn</dt>
+<dd><p>(str): either 'strong_wolfe' or None (default: None).</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p>This is a very memory intensive optimizer (it requires additional
 <code>param_bytes * (history_size + 1)</code> bytes). If it doesn't fit in memory
 try reducing the history size, or use a different algorithm.</p>
-    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></a></h2>
     
 
 
@@ -264,32 +142,25 @@ will be different objects from those before the call. In general, you
 should make sure that the objects pointed to by model parameters subject
 to optimization remain the same over the whole lifecycle of optimizer
 creation and usage.</p>
+    </div>
 
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-    <h1>Dummy value indicating a required value.</h1>
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-    <div class="hidden name"><code>optim_required.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Dummy value indicating a required value.</h1>
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     <p>export</p>
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-    <pre class="usage"><span class='fu'>optim_required</span><span class='op'>(</span><span class='op'>)</span></pre>
+    <div class="section level2">
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diff --git a/reference/optim_rmsprop.html b/reference/optim_rmsprop.html
index 43595bf54..270a53d43 100644
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>RMSprop optimizer</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/optim-rmsprop.R'><code>R/optim-rmsprop.R</code></a></small>
-    <div class="hidden name"><code>optim_rmsprop.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>RMSprop optimizer</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/optim-rmsprop.R" class="external-link"><code>R/optim-rmsprop.R</code></a></small>
+      <div class="d-none name"><code>optim_rmsprop.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Proposed by G. Hinton in his course.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>optim_rmsprop</span><span class='op'>(</span>
-  <span class='va'>params</span>,
-  lr <span class='op'>=</span> <span class='fl'>0.01</span>,
-  alpha <span class='op'>=</span> <span class='fl'>0.99</span>,
-  eps <span class='op'>=</span> <span class='fl'>1e-08</span>,
-  weight_decay <span class='op'>=</span> <span class='fl'>0</span>,
-  momentum <span class='op'>=</span> <span class='fl'>0</span>,
-  centered <span class='op'>=</span> <span class='cn'>FALSE</span>
-<span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">optim_rmsprop</span><span class="op">(</span>
+  <span class="va">params</span>,
+  lr <span class="op">=</span> <span class="fl">0.01</span>,
+  alpha <span class="op">=</span> <span class="fl">0.99</span>,
+  eps <span class="op">=</span> <span class="fl">1e-08</span>,
+  weight_decay <span class="op">=</span> <span class="fl">0</span>,
+  momentum <span class="op">=</span> <span class="fl">0</span>,
+  centered <span class="op">=</span> <span class="cn">FALSE</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>params</th>
-      <td><p>(iterable): iterable of parameters to optimize or list defining parameter groups</p></td>
-    </tr>
-    <tr>
-      <th>lr</th>
-      <td><p>(float, optional): learning rate (default: 1e-2)</p></td>
-    </tr>
-    <tr>
-      <th>alpha</th>
-      <td><p>(float, optional): smoothing constant (default: 0.99)</p></td>
-    </tr>
-    <tr>
-      <th>eps</th>
-      <td><p>(float, optional): term added to the denominator to improve
-numerical stability (default: 1e-8)</p></td>
-    </tr>
-    <tr>
-      <th>weight_decay</th>
-      <td><p>optional weight decay penalty. (default: 0)</p></td>
-    </tr>
-    <tr>
-      <th>momentum</th>
-      <td><p>(float, optional): momentum factor (default: 0)</p></td>
-    </tr>
-    <tr>
-      <th>centered</th>
-      <td><p>(bool, optional) : if <code>TRUE</code>, compute the centered RMSProp,
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>params</dt>
+<dd><p>(iterable): iterable of parameters to optimize or list defining parameter groups</p></dd>
+<dt>lr</dt>
+<dd><p>(float, optional): learning rate (default: 1e-2)</p></dd>
+<dt>alpha</dt>
+<dd><p>(float, optional): smoothing constant (default: 0.99)</p></dd>
+<dt>eps</dt>
+<dd><p>(float, optional): term added to the denominator to improve
+numerical stability (default: 1e-8)</p></dd>
+<dt>weight_decay</dt>
+<dd><p>optional weight decay penalty. (default: 0)</p></dd>
+<dt>momentum</dt>
+<dd><p>(float, optional): momentum factor (default: 0)</p></dd>
+<dt>centered</dt>
+<dd><p>(bool, optional) : if <code>TRUE</code>, compute the centered RMSProp,
 the gradient is normalized by an estimation of its variance
-weight_decay (float, optional): weight decay (L2 penalty) (default: 0)</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
+weight_decay (float, optional): weight decay (L2 penalty) (default: 0)</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p>The centered version first appears in
-<a href='https://arxiv.org/pdf/1308.0850v5.pdf'>Generating Sequences With Recurrent Neural Networks</a>.
+<a href="https://arxiv.org/pdf/1308.0850v5.pdf" class="external-link">Generating Sequences With Recurrent Neural Networks</a>.
 The implementation here takes the square root of the gradient average before
 adding epsilon (note that TensorFlow interchanges these two operations). The effective
 learning rate is thus \(\alpha/(\sqrt{v} + \epsilon)\) where \(\alpha\)
@@ -246,42 +124,36 @@ is the scheduled learning rate and \(v\) is the weighted moving average
 of the squared gradient.</p>
 <p>Update rule:</p>
 <p>$$
-\theta_{t+1} = \theta_{t} - \frac{\eta }{\sqrt{{E[g^2]}_{t} + \epsilon}} * g_{t} 
+\theta_{t+1} = \theta_{t} - \frac{\eta }{\sqrt{{E[g^2]}_{t} + \epsilon}} * g_{t}
 $$</p>
-    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></a></h2>
     <p>If you need to move a model to GPU via <code>$cuda()</code>, please do so before
 constructing optimizers for it. Parameters of a model after <code>$cuda()</code>
 will be different objects from those before the call. In general, you
 should make sure that the objects pointed to by model parameters subject
 to optimization remain the same over the whole lifecycle of optimizer
 creation and usage.</p>
+    </div>
 
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+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Implements the resilient backpropagation algorithm.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/optim-rprop.R'><code>R/optim-rprop.R</code></a></small>
-    <div class="hidden name"><code>optim_rprop.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Implements the resilient backpropagation algorithm.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/optim-rprop.R" class="external-link"><code>R/optim-rprop.R</code></a></small>
+      <div class="d-none name"><code>optim_rprop.Rd</code></div>
     </div>
 
-    <div class="ref-description">
-    <p>Proposed first in <a href='http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.52.4576'>RPROP - A Fast Adaptive Learning Algorithm</a></p>
+    <div class="ref-description section level2">
+    <p>Proposed first in <a href="http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.52.4576" class="external-link">RPROP - A Fast Adaptive Learning Algorithm</a></p>
     </div>
 
-    <pre class="usage"><span class='fu'>optim_rprop</span><span class='op'>(</span><span class='va'>params</span>, lr <span class='op'>=</span> <span class='fl'>0.01</span>, etas <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>0.5</span>, <span class='fl'>1.2</span><span class='op'>)</span>, step_sizes <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1e-06</span>, <span class='fl'>50</span><span class='op'>)</span><span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">optim_rprop</span><span class="op">(</span><span class="va">params</span>, lr <span class="op">=</span> <span class="fl">0.01</span>, etas <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.5</span>, <span class="fl">1.2</span><span class="op">)</span>, step_sizes <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1e-06</span>, <span class="fl">50</span><span class="op">)</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>params</th>
-      <td><p>(iterable): iterable of parameters to optimize or lists defining
-parameter groups</p></td>
-    </tr>
-    <tr>
-      <th>lr</th>
-      <td><p>(float, optional): learning rate (default: 1e-2)</p></td>
-    </tr>
-    <tr>
-      <th>etas</th>
-      <td><p>(Tuple(float, float), optional): pair of (etaminus, etaplis), that
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>params</dt>
+<dd><p>(iterable): iterable of parameters to optimize or lists defining
+parameter groups</p></dd>
+<dt>lr</dt>
+<dd><p>(float, optional): learning rate (default: 1e-2)</p></dd>
+<dt>etas</dt>
+<dd><p>(Tuple(float, float), optional): pair of (etaminus, etaplis), that
 are multiplicative increase and decrease factors
-(default: (0.5, 1.2))</p></td>
-    </tr>
-    <tr>
-      <th>step_sizes</th>
-      <td><p>(vector(float, float), optional): a pair of minimal and
-maximal allowed step sizes (default: (1e-6, 50))</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
-
+(default: (0.5, 1.2))</p></dd>
+<dt>step_sizes</dt>
+<dd><p>(vector(float, float), optional): a pair of minimal and
+maximal allowed step sizes (default: (1e-6, 50))</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></a></h2>
     <p>If you need to move a model to GPU via <code>$cuda()</code>, please do so before
 constructing optimizers for it. Parameters of a model after <code>$cuda()</code>
 will be different objects from those before the call. In general, you
 should make sure that the objects pointed to by model parameters subject
 to optimization remain the same over the whole lifecycle of optimizer
 creation and usage.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='cn'>FALSE</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>optimizer</span> <span class='op'>&lt;-</span> <span class='fu'>optim_rprop</span><span class='op'>(</span><span class='va'>model</span><span class='op'>$</span><span class='fu'>parameters</span><span class='op'>(</span><span class='op'>)</span>, lr<span class='op'>=</span><span class='fl'>0.1</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>optimizer</span><span class='op'>$</span><span class='fu'>zero_grad</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>loss_fn</span><span class='op'>(</span><span class='fu'>model</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span>, <span class='va'>target</span><span class='op'>)</span><span class='op'>$</span><span class='fu'>backward</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>optimizer</span><span class='op'>$</span><span class='fu'>step</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">optimizer</span> <span class="op">&lt;-</span> <span class="fu">optim_rprop</span><span class="op">(</span><span class="va">model</span><span class="op">$</span><span class="fu">parameters</span><span class="op">(</span><span class="op">)</span>, lr <span class="op">=</span> <span class="fl">0.1</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">optimizer</span><span class="op">$</span><span class="fu">zero_grad</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">loss_fn</span><span class="op">(</span><span class="fu">model</span><span class="op">(</span><span class="va">input</span><span class="op">)</span>, <span class="va">target</span><span class="op">)</span><span class="op">$</span><span class="fu">backward</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">optimizer</span><span class="op">$</span><span class="fu">step</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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-<meta property="og:description" content="Implements stochastic gradient descent (optionally with momentum).
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Implements stochastic gradient descent (optionally with momentum).
 Nesterov momentum is based on the formula from
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>SGD optimizer</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/optim-sgd.R'><code>R/optim-sgd.R</code></a></small>
-    <div class="hidden name"><code>optim_sgd.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>SGD optimizer</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/optim-sgd.R" class="external-link"><code>R/optim-sgd.R</code></a></small>
+      <div class="d-none name"><code>optim_sgd.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Implements stochastic gradient descent (optionally with momentum).
 Nesterov momentum is based on the formula from
 On the importance of initialization and momentum in deep learning.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>optim_sgd</span><span class='op'>(</span>
-  <span class='va'>params</span>,
-  lr <span class='op'>=</span> <span class='fu'><a href='optim_required.html'>optim_required</a></span><span class='op'>(</span><span class='op'>)</span>,
-  momentum <span class='op'>=</span> <span class='fl'>0</span>,
-  dampening <span class='op'>=</span> <span class='fl'>0</span>,
-  weight_decay <span class='op'>=</span> <span class='fl'>0</span>,
-  nesterov <span class='op'>=</span> <span class='cn'>FALSE</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>params</th>
-      <td><p>(iterable): iterable of parameters to optimize or dicts defining
-parameter groups</p></td>
-    </tr>
-    <tr>
-      <th>lr</th>
-      <td><p>(float): learning rate</p></td>
-    </tr>
-    <tr>
-      <th>momentum</th>
-      <td><p>(float, optional): momentum factor (default: 0)</p></td>
-    </tr>
-    <tr>
-      <th>dampening</th>
-      <td><p>(float, optional): dampening for momentum (default: 0)</p></td>
-    </tr>
-    <tr>
-      <th>weight_decay</th>
-      <td><p>(float, optional): weight decay (L2 penalty) (default: 0)</p></td>
-    </tr>
-    <tr>
-      <th>nesterov</th>
-      <td><p>(bool, optional): enables Nesterov momentum (default: FALSE)</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">optim_sgd</span><span class="op">(</span>
+  <span class="va">params</span>,
+  lr <span class="op">=</span> <span class="fu"><a href="optim_required.html">optim_required</a></span><span class="op">(</span><span class="op">)</span>,
+  momentum <span class="op">=</span> <span class="fl">0</span>,
+  dampening <span class="op">=</span> <span class="fl">0</span>,
+  weight_decay <span class="op">=</span> <span class="fl">0</span>,
+  nesterov <span class="op">=</span> <span class="cn">FALSE</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>params</dt>
+<dd><p>(iterable): iterable of parameters to optimize or dicts defining
+parameter groups</p></dd>
+<dt>lr</dt>
+<dd><p>(float): learning rate</p></dd>
+<dt>momentum</dt>
+<dd><p>(float, optional): momentum factor (default: 0)</p></dd>
+<dt>dampening</dt>
+<dd><p>(float, optional): dampening for momentum (default: 0)</p></dd>
+<dt>weight_decay</dt>
+<dd><p>(float, optional): weight decay (L2 penalty) (default: 0)</p></dd>
+<dt>nesterov</dt>
+<dd><p>(bool, optional): enables Nesterov momentum (default: FALSE)</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     
 
 
@@ -257,51 +139,47 @@ p_{t+1} &amp; = p_{t} - v_{t+1}.
 \end{array}
 $$
 The Nesterov version is analogously modified.</p>
-    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></a></h2>
     <p>If you need to move a model to GPU via <code>$cuda()</code>, please do so before
 constructing optimizers for it. Parameters of a model after <code>$cuda()</code>
 will be different objects from those before the call. In general, you
 should make sure that the objects pointed to by model parameters subject
 to optimization remain the same over the whole lifecycle of optimizer
 creation and usage.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='cn'>FALSE</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>optimizer</span> <span class='op'>&lt;-</span> <span class='fu'>optim_sgd</span><span class='op'>(</span><span class='va'>model</span><span class='op'>$</span><span class='fu'>parameters</span><span class='op'>(</span><span class='op'>)</span>, lr<span class='op'>=</span><span class='fl'>0.1</span>, momentum<span class='op'>=</span><span class='fl'>0.9</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>optimizer</span><span class='op'>$</span><span class='fu'>zero_grad</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>loss_fn</span><span class='op'>(</span><span class='fu'>model</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span>, <span class='va'>target</span><span class='op'>)</span><span class='op'>$</span><span class='fu'>backward</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>optimizer</span><span class='op'>$</span><span class='fu'>step</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">optimizer</span> <span class="op">&lt;-</span> <span class="fu">optim_sgd</span><span class="op">(</span><span class="va">model</span><span class="op">$</span><span class="fu">parameters</span><span class="op">(</span><span class="op">)</span>, lr <span class="op">=</span> <span class="fl">0.1</span>, momentum <span class="op">=</span> <span class="fl">0.9</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">optimizer</span><span class="op">$</span><span class="fu">zero_grad</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">loss_fn</span><span class="op">(</span><span class="fu">model</span><span class="op">(</span><span class="va">input</span><span class="op">)</span>, <span class="va">target</span><span class="op">)</span><span class="op">$</span><span class="fu">backward</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">optimizer</span><span class="op">$</span><span class="fu">step</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Creates a custom optimizer</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/optim.R'><code>R/optim.R</code></a></small>
-    <div class="hidden name"><code>optimizer.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Creates a custom optimizer</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/optim.R" class="external-link"><code>R/optim.R</code></a></small>
+      <div class="d-none name"><code>optimizer.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>When implementing custom optimizers you will usually need to implement
 the <code>initialize</code> and <code>step</code> methods. See the example section below
 for a full example.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>optimizer</span><span class='op'>(</span>
-  name <span class='op'>=</span> <span class='cn'>NULL</span>,
-  inherit <span class='op'>=</span> <span class='va'>Optimizer</span>,
-  <span class='va'>...</span>,
-  private <span class='op'>=</span> <span class='cn'>NULL</span>,
-  active <span class='op'>=</span> <span class='cn'>NULL</span>,
-  parent_env <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/sys.parent.html'>parent.frame</a></span><span class='op'>(</span><span class='op'>)</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>name</th>
-      <td><p>(optional) name of the optimizer</p></td>
-    </tr>
-    <tr>
-      <th>inherit</th>
-      <td><p>(optional) you can inherit from other optimizers to re-use
-some methods.</p></td>
-    </tr>
-    <tr>
-      <th>...</th>
-      <td><p>Pass any number of fields or methods. You should at least define
-the <code>initialize</code> and <code>step</code> methods. See the examples section.</p></td>
-    </tr>
-    <tr>
-      <th>private</th>
-      <td><p>(optional) a list of private methods for the optimizer.</p></td>
-    </tr>
-    <tr>
-      <th>active</th>
-      <td><p>(optional) a list of active methods for the optimizer.</p></td>
-    </tr>
-    <tr>
-      <th>parent_env</th>
-      <td><p>used to capture the right environment to define the class.
-The default is fine for most situations.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">optimizer</span><span class="op">(</span>
+  name <span class="op">=</span> <span class="cn">NULL</span>,
+  inherit <span class="op">=</span> <span class="va">Optimizer</span>,
+  <span class="va">...</span>,
+  private <span class="op">=</span> <span class="cn">NULL</span>,
+  active <span class="op">=</span> <span class="cn">NULL</span>,
+  parent_env <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sys.parent.html" class="external-link">parent.frame</a></span><span class="op">(</span><span class="op">)</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>name</dt>
+<dd><p>(optional) name of the optimizer</p></dd>
+<dt>inherit</dt>
+<dd><p>(optional) you can inherit from other optimizers to re-use
+some methods.</p></dd>
+<dt>...</dt>
+<dd><p>Pass any number of fields or methods. You should at least define
+the <code>initialize</code> and <code>step</code> methods. See the examples section.</p></dd>
+<dt>private</dt>
+<dd><p>(optional) a list of private methods for the optimizer.</p></dd>
+<dt>active</dt>
+<dd><p>(optional) a list of active methods for the optimizer.</p></dd>
+<dt>parent_env</dt>
+<dd><p>used to capture the right environment to define the class.
+The default is fine for most situations.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></a></h2>
     <p>If you need to move a model to GPU via <code>$cuda()</code>, please do so before
 constructing optimizers for it. Parameters of a model after <code>$cuda()</code>
 will be different objects from those before the call. In general, you
 should make sure that the objects pointed to by model parameters subject
 to optimization remain the same over the whole lifecycle of optimizer
 creation and usage.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='co'># In this example we will create a custom optimizer</span></span>
-<span class='r-in'><span class='co'># that's just a simplified version of the `optim_sgd` function.</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>optim_sgd2</span> <span class='op'>&lt;-</span> <span class='fu'>optimizer</span><span class='op'>(</span></span>
-<span class='r-in'>  initialize <span class='op'>=</span> <span class='kw'>function</span><span class='op'>(</span><span class='va'>params</span>, <span class='va'>learning_rate</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'>    <span class='va'>defaults</span> <span class='op'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span></span>
-<span class='r-in'>      learning_rate <span class='op'>=</span> <span class='va'>learning_rate</span></span>
-<span class='r-in'>    <span class='op'>)</span></span>
-<span class='r-in'>    <span class='va'>super</span><span class='op'>$</span><span class='fu'>initialize</span><span class='op'>(</span><span class='va'>params</span>, <span class='va'>defaults</span><span class='op'>)</span></span>
-<span class='r-in'>  <span class='op'>}</span>,</span>
-<span class='r-in'>  step <span class='op'>=</span> <span class='kw'>function</span><span class='op'>(</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'>    <span class='fu'><a href='with_no_grad.html'>with_no_grad</a></span><span class='op'>(</span><span class='op'>{</span></span>
-<span class='r-in'>      <span class='kw'>for</span> <span class='op'>(</span><span class='va'>g</span> <span class='kw'>in</span> <span class='fu'><a href='https://rdrr.io/r/base/seq.html'>seq_along</a></span><span class='op'>(</span><span class='va'>self</span><span class='op'>$</span><span class='va'>param_groups</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'>        <span class='va'>group</span> <span class='op'>&lt;-</span> <span class='va'>self</span><span class='op'>$</span><span class='va'>param_groups</span><span class='op'>[[</span><span class='va'>g</span><span class='op'>]</span><span class='op'>]</span></span>
-<span class='r-in'>        <span class='kw'>for</span> <span class='op'>(</span><span class='va'>p</span> <span class='kw'>in</span> <span class='fu'><a href='https://rdrr.io/r/base/seq.html'>seq_along</a></span><span class='op'>(</span><span class='va'>group</span><span class='op'>$</span><span class='va'>params</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'>          <span class='va'>param</span> <span class='op'>&lt;-</span> <span class='va'>group</span><span class='op'>$</span><span class='va'>params</span><span class='op'>[[</span><span class='va'>p</span><span class='op'>]</span><span class='op'>]</span></span>
-<span class='r-in'>          </span>
-<span class='r-in'>          <span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/NULL.html'>is.null</a></span><span class='op'>(</span><span class='va'>param</span><span class='op'>$</span><span class='va'>grad</span><span class='op'>)</span> <span class='op'>||</span> <span class='fu'><a href='is_undefined_tensor.html'>is_undefined_tensor</a></span><span class='op'>(</span><span class='va'>param</span><span class='op'>$</span><span class='va'>grad</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'>            <span class='kw'>next</span></span>
-<span class='r-in'>          </span>
-<span class='r-in'>          <span class='va'>param</span><span class='op'>$</span><span class='fu'>add_</span><span class='op'>(</span><span class='va'>param</span><span class='op'>$</span><span class='va'>grad</span>, alpha <span class='op'>=</span> <span class='op'>-</span><span class='va'>group</span><span class='op'>$</span><span class='va'>learning_rate</span><span class='op'>)</span></span>
-<span class='r-in'>        <span class='op'>}</span></span>
-<span class='r-in'>      <span class='op'>}</span></span>
-<span class='r-in'>    <span class='op'>}</span><span class='op'>)</span></span>
-<span class='r-in'>  <span class='op'>}</span></span>
-<span class='r-in'><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>x</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>1</span>, requires_grad <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>opt</span> <span class='op'>&lt;-</span> <span class='fu'>optim_sgd2</span><span class='op'>(</span><span class='va'>x</span>, learning_rate <span class='op'>=</span> <span class='fl'>0.1</span><span class='op'>)</span></span>
-<span class='r-in'><span class='kw'>for</span> <span class='op'>(</span><span class='va'>i</span> <span class='kw'>in</span> <span class='fl'>1</span><span class='op'>:</span><span class='fl'>100</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'>  <span class='va'>opt</span><span class='op'>$</span><span class='fu'>zero_grad</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'>  <span class='va'>y</span> <span class='op'>&lt;-</span> <span class='va'>x</span><span class='op'>^</span><span class='fl'>2</span></span>
-<span class='r-in'>  <span class='va'>y</span><span class='op'>$</span><span class='fu'>backward</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'>  <span class='va'>opt</span><span class='op'>$</span><span class='fu'>step</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-in'><span class='fu'><a href='https://rdrr.io/r/base/all.equal.html'>all.equal</a></span><span class='op'>(</span><span class='va'>x</span><span class='op'>$</span><span class='fu'>item</span><span class='op'>(</span><span class='op'>)</span>, <span class='fl'>0</span>, tolerance <span class='op'>=</span> <span class='fl'>1e-9</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [1] TRUE</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="co"># In this example we will create a custom optimizer</span></span>
+<span class="r-in"><span class="co"># that's just a simplified version of the `optim_sgd` function.</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">optim_sgd2</span> <span class="op">&lt;-</span> <span class="fu">optimizer</span><span class="op">(</span></span>
+<span class="r-in">  initialize <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="va">params</span>, <span class="va">learning_rate</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in">    <span class="va">defaults</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span></span>
+<span class="r-in">      learning_rate <span class="op">=</span> <span class="va">learning_rate</span></span>
+<span class="r-in">    <span class="op">)</span></span>
+<span class="r-in">    <span class="va">super</span><span class="op">$</span><span class="fu">initialize</span><span class="op">(</span><span class="va">params</span>, <span class="va">defaults</span><span class="op">)</span></span>
+<span class="r-in">  <span class="op">}</span>,</span>
+<span class="r-in">  step <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in">    <span class="fu"><a href="with_no_grad.html">with_no_grad</a></span><span class="op">(</span><span class="op">{</span></span>
+<span class="r-in">      <span class="kw">for</span> <span class="op">(</span><span class="va">g</span> <span class="kw">in</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html" class="external-link">seq_along</a></span><span class="op">(</span><span class="va">self</span><span class="op">$</span><span class="va">param_groups</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in">        <span class="va">group</span> <span class="op">&lt;-</span> <span class="va">self</span><span class="op">$</span><span class="va">param_groups</span><span class="op">[[</span><span class="va">g</span><span class="op">]</span><span class="op">]</span></span>
+<span class="r-in">        <span class="kw">for</span> <span class="op">(</span><span class="va">p</span> <span class="kw">in</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html" class="external-link">seq_along</a></span><span class="op">(</span><span class="va">group</span><span class="op">$</span><span class="va">params</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in">          <span class="va">param</span> <span class="op">&lt;-</span> <span class="va">group</span><span class="op">$</span><span class="va">params</span><span class="op">[[</span><span class="va">p</span><span class="op">]</span><span class="op">]</span></span>
+<span class="r-in"></span>
+<span class="r-in">          <span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/NULL.html" class="external-link">is.null</a></span><span class="op">(</span><span class="va">param</span><span class="op">$</span><span class="va">grad</span><span class="op">)</span> <span class="op">||</span> <span class="fu"><a href="is_undefined_tensor.html">is_undefined_tensor</a></span><span class="op">(</span><span class="va">param</span><span class="op">$</span><span class="va">grad</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in">            <span class="kw">next</span></span>
+<span class="r-in">          <span class="op">}</span></span>
+<span class="r-in"></span>
+<span class="r-in">          <span class="va">param</span><span class="op">$</span><span class="fu">add_</span><span class="op">(</span><span class="va">param</span><span class="op">$</span><span class="va">grad</span>, alpha <span class="op">=</span> <span class="op">-</span><span class="va">group</span><span class="op">$</span><span class="va">learning_rate</span><span class="op">)</span></span>
+<span class="r-in">        <span class="op">}</span></span>
+<span class="r-in">      <span class="op">}</span></span>
+<span class="r-in">    <span class="op">}</span><span class="op">)</span></span>
+<span class="r-in">  <span class="op">}</span></span>
+<span class="r-in"><span class="op">)</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">1</span>, requires_grad <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">opt</span> <span class="op">&lt;-</span> <span class="fu">optim_sgd2</span><span class="op">(</span><span class="va">x</span>, learning_rate <span class="op">=</span> <span class="fl">0.1</span><span class="op">)</span></span>
+<span class="r-in"><span class="kw">for</span> <span class="op">(</span><span class="va">i</span> <span class="kw">in</span> <span class="fl">1</span><span class="op">:</span><span class="fl">100</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in">  <span class="va">opt</span><span class="op">$</span><span class="fu">zero_grad</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in">  <span class="va">y</span> <span class="op">&lt;-</span> <span class="va">x</span><span class="op">^</span><span class="fl">2</span></span>
+<span class="r-in">  <span class="va">y</span><span class="op">$</span><span class="fu">backward</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in">  <span class="va">opt</span><span class="op">$</span><span class="fu">step</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/all.equal.html" class="external-link">all.equal</a></span><span class="op">(</span><span class="va">x</span><span class="op">$</span><span class="fu">item</span><span class="op">(</span><span class="op">)</span>, <span class="fl">0</span>, tolerance <span class="op">=</span> <span class="fl">1e-9</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] TRUE</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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+<div class="pkgdown-footer-right">
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-    <h1>Creates a slice</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/indexing.R'><code>R/indexing.R</code></a></small>
-    <div class="hidden name"><code>slc.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Creates a slice</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/indexing.R" class="external-link"><code>R/indexing.R</code></a></small>
+      <div class="d-none name"><code>slc.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Creates a slice object that can be used when indexing torch tensors.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>slc</span><span class='op'>(</span><span class='va'>start</span>, <span class='va'>end</span>, step <span class='op'>=</span> <span class='fl'>1</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>start</th>
-      <td><p>(integer) starting index.</p></td>
-    </tr>
-    <tr>
-      <th>end</th>
-      <td><p>(integer) the last selected index.</p></td>
-    </tr>
-    <tr>
-      <th>step</th>
-      <td><p>(integer) the step between indexes.</p></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>x</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>10</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>x</span><span class='op'>[</span><span class='fu'>slc</span><span class='op'>(</span>start <span class='op'>=</span> <span class='fl'>1</span>, end <span class='op'>=</span> <span class='fl'>5</span>, step <span class='op'>=</span> <span class='fl'>2</span><span class='op'>)</span><span class='op'>]</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.1049</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.8186</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.1099</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{3} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">slc</span><span class="op">(</span><span class="va">start</span>, <span class="va">end</span>, step <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>start</dt>
+<dd><p>(integer) starting index.</p></dd>
+<dt>end</dt>
+<dd><p>(integer) the last selected index.</p></dd>
+<dt>step</dt>
+<dd><p>(integer) the step between indexes.</p></dd>
+</dl></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">10</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">x</span><span class="op">[</span><span class="fu">slc</span><span class="op">(</span>start <span class="op">=</span> <span class="fl">1</span>, end <span class="op">=</span> <span class="fl">5</span>, step <span class="op">=</span> <span class="fl">2</span><span class="op">)</span><span class="op">]</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.8545</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.1855</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.6493</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3} ]</span>
+</code></pre></div>
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-    <h1>Dataset wrapping tensors.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/utils-data.R'><code>R/utils-data.R</code></a></small>
-    <div class="hidden name"><code>tensor_dataset.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Dataset wrapping tensors.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/utils-data.R" class="external-link"><code>R/utils-data.R</code></a></small>
+      <div class="d-none name"><code>tensor_dataset.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Each sample will be retrieved by indexing tensors along the first dimension.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>tensor_dataset</span><span class='op'>(</span><span class='va'>...</span><span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">tensor_dataset</span><span class="op">(</span><span class="va">...</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>...</th>
-      <td><p>tensors that have the same size of the first dimension.</p></td>
-    </tr>
-    </table>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>...</dt>
+<dd><p>tensors that have the same size of the first dimension.</p></dd>
+</dl></div>
 
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diff --git a/reference/threads.html b/reference/threads.html
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-  <div class="col-md-9 contents">
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-    <h1>Number of threads</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/threads.R'><code>R/threads.R</code></a></small>
-    <div class="hidden name"><code>threads.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Number of threads</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/threads.R" class="external-link"><code>R/threads.R</code></a></small>
+      <div class="d-none name"><code>threads.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Get and set the numbers used by torch computations.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_set_num_threads</span><span class='op'>(</span><span class='va'>num_threads</span><span class='op'>)</span>
-
-<span class='fu'>torch_set_num_interop_threads</span><span class='op'>(</span><span class='va'>num_threads</span><span class='op'>)</span>
-
-<span class='fu'>torch_get_num_interop_threads</span><span class='op'>(</span><span class='op'>)</span>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_set_num_threads</span><span class="op">(</span><span class="va">num_threads</span><span class="op">)</span>
 
-<span class='fu'>torch_get_num_threads</span><span class='op'>(</span><span class='op'>)</span></pre>
+<span class="fu">torch_set_num_interop_threads</span><span class="op">(</span><span class="va">num_threads</span><span class="op">)</span>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>num_threads</th>
-      <td><p>number of threads to set.</p></td>
-    </tr>
-    </table>
+<span class="fu">torch_get_num_interop_threads</span><span class="op">(</span><span class="op">)</span>
 
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+<span class="fu">torch_get_num_threads</span><span class="op">(</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <p>For details see the <a href='https://pytorch.org/docs/stable/notes/cpu_threading_torchscript_inference.html?highlight=set_num_threads'>CPU threading article</a>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>num_threads</dt>
+<dd><p>number of threads to set.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
+    <p>For details see the <a href="https://pytorch.org/docs/stable/notes/cpu_threading_torchscript_inference.html?highlight=set_num_threads" class="external-link">CPU threading article</a>
 in the PyTorch documentation.</p>
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p>torch_set_threads do not work on macOS system as it must be 1.</p>
+    </div>
 
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Abs</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_abs.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Abs</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_abs.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Abs</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_abs</span><span class='op'>(</span><span class='va'>self</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="abs-input-gt-tensor-"><a class="anchor" href="#abs-input-gt-tensor-"></a>abs(input) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_abs</span><span class="op">(</span><span class="va">self</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="abs-input-gt-tensor-">abs(input) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#abs-input-gt-tensor-"></a></h2>
     
 
 
@@ -209,43 +99,38 @@
 <p>$$
     \mbox{out}_{i} = |\mbox{input}_{i}|
 $$</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='fu'>torch_abs</span><span class='op'>(</span><span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='op'>-</span><span class='fl'>1</span>, <span class='op'>-</span><span class='fl'>2</span>, <span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  2</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  3</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{3} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="fu">torch_abs</span><span class="op">(</span><span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="op">-</span><span class="fl">1</span>, <span class="op">-</span><span class="fl">2</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  3</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer>
-      <div class="copyright">
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+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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-  <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p>
+<div class="pkgdown-footer-right">
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.2.</p>
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index 2e9b7955c..23e487402 100644
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-    <div class="hidden name"><code>torch_absolute.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Absolute</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
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     <p>Absolute</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_absolute</span><span class='op'>(</span><span class='va'>self</span><span class='op'>)</span></pre>
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-    <h2 class="hasAnchor" id="absolute-input-out-none-gt-tensor"><a class="anchor" href="#absolute-input-out-none-gt-tensor"></a>absolute(input, *, out=None) -&gt; Tensor</h2>
+    <div class="section level2">
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+    </div>
 
+    <div class="section level2">
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+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="absolute-input-out-none-gt-tensor">absolute(input, *, out=None) -&gt; Tensor<a class="anchor" aria-label="anchor" href="#absolute-input-out-none-gt-tensor"></a></h2>
     
 
 
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diff --git a/reference/torch_acos.html b/reference/torch_acos.html
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Acos</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_acos.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Acos</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_acos.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Acos</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_acos</span><span class='op'>(</span><span class='va'>self</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="acos-input-gt-tensor-"><a class="anchor" href="#acos-input-gt-tensor-"></a>acos(input) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_acos</span><span class="op">(</span><span class="va">self</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="acos-input-gt-tensor-">acos(input) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#acos-input-gt-tensor-"></a></h2>
     
 
 
@@ -209,46 +99,41 @@
 <p>$$
     \mbox{out}_{i} = \cos^{-1}(\mbox{input}_{i})
 $$</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='fu'>torch_acos</span><span class='op'>(</span><span class='va'>a</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1.4178</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>     nan</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  2.2039</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>     nan</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{4} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="fu">torch_acos</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.5933</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.7693</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.5475</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.6346</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Acosh</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_acosh.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Acosh</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_acosh.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Acosh</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_acosh</span><span class='op'>(</span><span class='va'>self</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_acosh</span><span class="op">(</span><span class="va">self</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p>The domain of the inverse hyperbolic cosine is <code>[1, inf)</code> and values outside this range
 will be mapped to <code>NaN</code>, except for <code>+ INF</code> for which the output is mapped to <code>+ INF</code>.</p>
 <p>$$
     \mbox{out}_{i} = \cosh^{-1}(\mbox{input}_{i})
 $$</p>
-    <h2 class="hasAnchor" id="acosh-input-out-none-gt-tensor-"><a class="anchor" href="#acosh-input-out-none-gt-tensor-"></a>acosh(input, *, out=None) -&gt; Tensor </h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="acosh-input-out-none-gt-tensor-">acosh(input, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#acosh-input-out-none-gt-tensor-"></a></h2>
     
 
 
 <p>Returns a new tensor with the inverse hyperbolic cosine of the elements of <code>input</code>.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span><span class='op'>$</span><span class='fu'>uniform_</span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='fu'>torch_acosh</span><span class='op'>(</span><span class='va'>a</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1.0305</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.9357</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1.2053</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.8949</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{4} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span><span class="op">)</span><span class="op">)</span><span class="op">$</span><span class="fu">uniform_</span><span class="op">(</span><span class="fl">1</span>, <span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="fu">torch_acosh</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.0354</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.3027</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.2424</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.9036</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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     </div>
 
-    <pre class="usage"><span class='fu'>torch_adaptive_avg_pool1d</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>output_size</span><span class='op'>)</span></pre>
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-      <th>self</th>
-      <td><p>the input tensor</p></td>
-    </tr>
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-      <th>output_size</th>
-      <td><p>the target output size (single integer)</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="adaptive-avg-pool-d-input-output-size-gt-tensor-"><a class="anchor" href="#adaptive-avg-pool-d-input-output-size-gt-tensor-"></a>adaptive_avg_pool1d(input, output_size) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
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+    <dl><dt>self</dt>
+<dd><p>the input tensor</p></dd>
+<dt>output_size</dt>
+<dd><p>the target output size (single integer)</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="adaptive-avg-pool-d-input-output-size-gt-tensor-">adaptive_avg_pool1d(input, output_size) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#adaptive-avg-pool-d-input-output-size-gt-tensor-"></a></h2>
     
 
 
 <p>Applies a 1D adaptive average pooling over an input signal composed of
 several input planes.</p>
-<p>See <code><a href='nn_adaptive_avg_pool1d.html'>nn_adaptive_avg_pool1d()</a></code> for details and output shape.</p>
+<p>See <code><a href="nn_adaptive_avg_pool1d.html">nn_adaptive_avg_pool1d()</a></code> for details and output shape.</p>
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+    
+  </div>
+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Add</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_add.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Add</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_add.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Add</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_add</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>other</span>, alpha <span class='op'>=</span> <span class='fl'>1L</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Tensor/Number) the second input tensor/number.</p></td>
-    </tr>
-    <tr>
-      <th>alpha</th>
-      <td><p>(Number) the scalar multiplier for <code>other</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="add-input-other-out-null-"><a class="anchor" href="#add-input-other-out-null-"></a>add(input, other, out=NULL) </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_add</span><span class="op">(</span><span class="va">self</span>, <span class="va">other</span>, alpha <span class="op">=</span> <span class="fl">1L</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+<dt>other</dt>
+<dd><p>(Tensor/Number) the second input tensor/number.</p></dd>
+<dt>alpha</dt>
+<dd><p>(Number) the scalar multiplier for <code>other</code></p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="add-input-other-out-null-">add(input, other, out=NULL) <a class="anchor" aria-label="anchor" href="#add-input-other-out-null-"></a></h2>
     
 
 
@@ -220,8 +106,9 @@ and returns a new resulting tensor.</p>
 $$
 If <code>input</code> is of type FloatTensor or DoubleTensor, <code>other</code> must be
 a real number, otherwise it should be an integer.</p>
-    <h2 class="hasAnchor" id="add-input-other-alpha-out-null-"><a class="anchor" href="#add-input-other-alpha-out-null-"></a>add(input, other, *, alpha=1, out=NULL) </h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="add-input-other-alpha-out-null-">add(input, other, *, alpha=1, out=NULL) <a class="anchor" aria-label="anchor" href="#add-input-other-alpha-out-null-"></a></h2>
     
 
 
@@ -235,53 +122,48 @@ broadcastable .</p>
 $$
 If <code>other</code> is of type FloatTensor or DoubleTensor, <code>alpha</code> must be
 a real number, otherwise it should be an integer.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='fu'>torch_add</span><span class='op'>(</span><span class='va'>a</span>, <span class='fl'>20</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='va'>b</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span>, <span class='fl'>1</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>b</span></span>
-<span class='r-in'><span class='fu'>torch_add</span><span class='op'>(</span><span class='va'>a</span>, <span class='va'>b</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.9988 -0.0437  0.8633  1.6979</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -3.6223 -2.6672 -1.7603 -0.9257</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.2046  1.1597  2.0667  2.9013</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -3.7232 -2.7681 -1.8612 -1.0266</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{4,4} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="fu">torch_add</span><span class="op">(</span><span class="va">a</span>, <span class="fl">20</span><span class="op">)</span></span>
+<span class="r-in"></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="va">b</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span>, <span class="fl">1</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">b</span></span>
+<span class="r-in"><span class="fu">torch_add</span><span class="op">(</span><span class="va">a</span>, <span class="va">b</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.6325  0.4658 -1.3500  0.4279</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.1713  0.9270 -0.8887  0.8892</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.6622  0.4360 -1.3797  0.3982</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.0610  1.0372 -0.7785  0.9994</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4,4} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Addbmm</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_addbmm.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Addbmm</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_addbmm.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Addbmm</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_addbmm</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>batch1</span>, <span class='va'>batch2</span>, beta <span class='op'>=</span> <span class='fl'>1L</span>, alpha <span class='op'>=</span> <span class='fl'>1L</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) matrix to be added</p></td>
-    </tr>
-    <tr>
-      <th>batch1</th>
-      <td><p>(Tensor) the first batch of matrices to be multiplied</p></td>
-    </tr>
-    <tr>
-      <th>batch2</th>
-      <td><p>(Tensor) the second batch of matrices to be multiplied</p></td>
-    </tr>
-    <tr>
-      <th>beta</th>
-      <td><p>(Number, optional) multiplier for <code>input</code> (\(\beta\))</p></td>
-    </tr>
-    <tr>
-      <th>alpha</th>
-      <td><p>(Number, optional) multiplier for <code>batch1 @ batch2</code> (\(\alpha\))</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="addbmm-input-batch-batch-beta-alpha-out-null-gt-tensor-"><a class="anchor" href="#addbmm-input-batch-batch-beta-alpha-out-null-gt-tensor-"></a>addbmm(input, batch1, batch2, *, beta=1, alpha=1, out=NULL) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_addbmm</span><span class="op">(</span><span class="va">self</span>, <span class="va">batch1</span>, <span class="va">batch2</span>, beta <span class="op">=</span> <span class="fl">1L</span>, alpha <span class="op">=</span> <span class="fl">1L</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) matrix to be added</p></dd>
+<dt>batch1</dt>
+<dd><p>(Tensor) the first batch of matrices to be multiplied</p></dd>
+<dt>batch2</dt>
+<dd><p>(Tensor) the second batch of matrices to be multiplied</p></dd>
+<dt>beta</dt>
+<dd><p>(Number, optional) multiplier for <code>input</code> (\(\beta\))</p></dd>
+<dt>alpha</dt>
+<dd><p>(Number, optional) multiplier for <code>batch1 @ batch2</code> (\(\alpha\))</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="addbmm-input-batch-batch-beta-alpha-out-null-gt-tensor-">addbmm(input, batch1, batch2, *, beta=1, alpha=1, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#addbmm-input-batch-batch-beta-alpha-out-null-gt-tensor-"></a></h2>
     
 
 
@@ -237,46 +119,41 @@ and <code>out</code> will be a \((n \times p)\) tensor.</p>
 $$
 For inputs of type <code>FloatTensor</code> or <code>DoubleTensor</code>, arguments <code>beta</code> and <code>alpha</code>
 must be real numbers, otherwise they should be integers.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>M</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>5</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>batch1</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>10</span>, <span class='fl'>3</span>, <span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>batch2</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>10</span>, <span class='fl'>4</span>, <span class='fl'>5</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_addbmm</span><span class='op'>(</span><span class='va'>M</span>, <span class='va'>batch1</span>, <span class='va'>batch2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  7.1244 -9.0336  0.6945 -3.1988 -1.1722</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  6.4101  4.3066 -7.5768 -2.6872 -3.5341</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -6.3952 -3.0983 -5.6549 -0.1034 -1.5020</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{3,5} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">M</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">5</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">batch1</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">10</span>, <span class="fl">3</span>, <span class="fl">4</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">batch2</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">10</span>, <span class="fl">4</span>, <span class="fl">5</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_addbmm</span><span class="op">(</span><span class="va">M</span>, <span class="va">batch1</span>, <span class="va">batch2</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  12.0062  -8.3688   5.4071  -0.5481   5.2430</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   1.1898   4.0082  -0.8169   2.2780   4.0116</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   5.7964 -10.4914  -1.0635   3.0282  -9.6365</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3,5} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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-      <div class="copyright">
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+    <footer><div class="pkgdown-footer-left">
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+<div class="pkgdown-footer-right">
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index 62666e8d8..0e5f556e3 100644
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Addcdiv</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_addcdiv.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Addcdiv</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_addcdiv.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Addcdiv</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_addcdiv</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>tensor1</span>, <span class='va'>tensor2</span>, value <span class='op'>=</span> <span class='fl'>1L</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the tensor to be added</p></td>
-    </tr>
-    <tr>
-      <th>tensor1</th>
-      <td><p>(Tensor) the numerator tensor</p></td>
-    </tr>
-    <tr>
-      <th>tensor2</th>
-      <td><p>(Tensor) the denominator tensor</p></td>
-    </tr>
-    <tr>
-      <th>value</th>
-      <td><p>(Number, optional) multiplier for \(\mbox{tensor1} / \mbox{tensor2}\)</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="addcdiv-input-tensor-tensor-value-out-null-gt-tensor-"><a class="anchor" href="#addcdiv-input-tensor-tensor-value-out-null-gt-tensor-"></a>addcdiv(input, tensor1, tensor2, *, value=1, out=NULL) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_addcdiv</span><span class="op">(</span><span class="va">self</span>, <span class="va">tensor1</span>, <span class="va">tensor2</span>, value <span class="op">=</span> <span class="fl">1L</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the tensor to be added</p></dd>
+<dt>tensor1</dt>
+<dd><p>(Tensor) the numerator tensor</p></dd>
+<dt>tensor2</dt>
+<dd><p>(Tensor) the denominator tensor</p></dd>
+<dt>value</dt>
+<dd><p>(Number, optional) multiplier for \(\mbox{tensor1} / \mbox{tensor2}\)</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="addcdiv-input-tensor-tensor-value-out-null-gt-tensor-">addcdiv(input, tensor1, tensor2, *, value=1, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#addcdiv-input-tensor-tensor-value-out-null-gt-tensor-"></a></h2>
     
 
 
 <p>Performs the element-wise division of <code>tensor1</code> by <code>tensor2</code>,
 multiply the result by the scalar <code>value</code> and add it to <code>input</code>.</p>
-    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></a></h2>
     
 
 <p>Integer division with addcdiv is deprecated, and in a future release
 addcdiv will perform a true division of <code>tensor1</code> and <code>tensor2</code>.
-The current addcdiv behavior can be replicated using <code><a href='torch_floor_divide.html'>torch_floor_divide()</a></code>
+The current addcdiv behavior can be replicated using <code><a href="torch_floor_divide.html">torch_floor_divide()</a></code>
 for integral inputs
 (<code>input</code> + <code>value</code> * <code>tensor1</code> // <code>tensor2</code>)
-and <code><a href='torch_div.html'>torch_div()</a></code> for float inputs
+and <code><a href="torch_div.html">torch_div()</a></code> for float inputs
 (<code>input</code> + <code>value</code> * <code>tensor1</code> / <code>tensor2</code>).
-The new addcdiv behavior can be implemented with <code><a href='torch_true_divide.html'>torch_true_divide()</a></code>
+The new addcdiv behavior can be implemented with <code><a href="torch_true_divide.html">torch_true_divide()</a></code>
 (<code>input</code> + <code>value</code> * torch.true_divide(<code>tensor1</code>,
 <code>tensor2</code>).</p>
 <p>$$
@@ -240,46 +125,41 @@ $$</p>
 broadcastable .</p>
 <p>For inputs of type <code>FloatTensor</code> or <code>DoubleTensor</code>, <code>value</code> must be
 a real number, otherwise an integer.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>t</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>t1</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>1</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>t2</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_addcdiv</span><span class='op'>(</span><span class='va'>t</span>, <span class='va'>t1</span>, <span class='va'>t2</span>, <span class='fl'>0.1</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.2596 -0.7975 -1.2109</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.3396 -0.9474 -1.3127</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.3317 -0.9325 -1.3026</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{3,3} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">t</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">t1</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">1</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">t2</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_addcdiv</span><span class="op">(</span><span class="va">t</span>, <span class="va">t1</span>, <span class="va">t2</span>, <span class="fl">0.1</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -1.5574   0.1888  -0.2277</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  11.4811  -0.1016  -0.1035</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -3.1919   0.2252  -0.2433</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3,3} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Addcmul</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_addcmul.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Addcmul</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_addcmul.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Addcmul</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_addcmul</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>tensor1</span>, <span class='va'>tensor2</span>, value <span class='op'>=</span> <span class='fl'>1L</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the tensor to be added</p></td>
-    </tr>
-    <tr>
-      <th>tensor1</th>
-      <td><p>(Tensor) the tensor to be multiplied</p></td>
-    </tr>
-    <tr>
-      <th>tensor2</th>
-      <td><p>(Tensor) the tensor to be multiplied</p></td>
-    </tr>
-    <tr>
-      <th>value</th>
-      <td><p>(Number, optional) multiplier for \(tensor1 .* tensor2\)</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="addcmul-input-tensor-tensor-value-out-null-gt-tensor-"><a class="anchor" href="#addcmul-input-tensor-tensor-value-out-null-gt-tensor-"></a>addcmul(input, tensor1, tensor2, *, value=1, out=NULL) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_addcmul</span><span class="op">(</span><span class="va">self</span>, <span class="va">tensor1</span>, <span class="va">tensor2</span>, value <span class="op">=</span> <span class="fl">1L</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the tensor to be added</p></dd>
+<dt>tensor1</dt>
+<dd><p>(Tensor) the tensor to be multiplied</p></dd>
+<dt>tensor2</dt>
+<dd><p>(Tensor) the tensor to be multiplied</p></dd>
+<dt>value</dt>
+<dd><p>(Number, optional) multiplier for \(tensor1 .* tensor2\)</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="addcmul-input-tensor-tensor-value-out-null-gt-tensor-">addcmul(input, tensor1, tensor2, *, value=1, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#addcmul-input-tensor-tensor-value-out-null-gt-tensor-"></a></h2>
     
 
 
@@ -227,46 +111,41 @@ The shapes of <code>tensor</code>, <code>tensor1</code>, and <code>tensor2</code
 broadcastable .</p>
 <p>For inputs of type <code>FloatTensor</code> or <code>DoubleTensor</code>, <code>value</code> must be
 a real number, otherwise an integer.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>t</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>t1</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>1</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>t2</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_addcmul</span><span class='op'>(</span><span class='va'>t</span>, <span class='va'>t1</span>, <span class='va'>t2</span>, <span class='fl'>0.1</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.1057  1.0190 -0.1490</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.3147  0.8652 -0.0029</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.3585  0.8330  0.0277</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{3,3} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">t</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">t1</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">1</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">t2</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_addcmul</span><span class="op">(</span><span class="va">t</span>, <span class="va">t1</span>, <span class="va">t2</span>, <span class="fl">0.1</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.1672  0.4947  2.2721</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.9772  0.4381  2.1526</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.0474  0.4590  2.1968</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3,3} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Addmm</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_addmm.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Addmm</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_addmm.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Addmm</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_addmm</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>mat1</span>, <span class='va'>mat2</span>, beta <span class='op'>=</span> <span class='fl'>1L</span>, alpha <span class='op'>=</span> <span class='fl'>1L</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) matrix to be added</p></td>
-    </tr>
-    <tr>
-      <th>mat1</th>
-      <td><p>(Tensor) the first matrix to be multiplied</p></td>
-    </tr>
-    <tr>
-      <th>mat2</th>
-      <td><p>(Tensor) the second matrix to be multiplied</p></td>
-    </tr>
-    <tr>
-      <th>beta</th>
-      <td><p>(Number, optional) multiplier for <code>input</code> (\(\beta\))</p></td>
-    </tr>
-    <tr>
-      <th>alpha</th>
-      <td><p>(Number, optional) multiplier for \(mat1 @ mat2\) (\(\alpha\))</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="addmm-input-mat-mat-beta-alpha-out-null-gt-tensor-"><a class="anchor" href="#addmm-input-mat-mat-beta-alpha-out-null-gt-tensor-"></a>addmm(input, mat1, mat2, *, beta=1, alpha=1, out=NULL) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_addmm</span><span class="op">(</span><span class="va">self</span>, <span class="va">mat1</span>, <span class="va">mat2</span>, beta <span class="op">=</span> <span class="fl">1L</span>, alpha <span class="op">=</span> <span class="fl">1L</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) matrix to be added</p></dd>
+<dt>mat1</dt>
+<dd><p>(Tensor) the first matrix to be multiplied</p></dd>
+<dt>mat2</dt>
+<dd><p>(Tensor) the second matrix to be multiplied</p></dd>
+<dt>beta</dt>
+<dd><p>(Number, optional) multiplier for <code>input</code> (\(\beta\))</p></dd>
+<dt>alpha</dt>
+<dd><p>(Number, optional) multiplier for \(mat1 @ mat2\) (\(\alpha\))</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="addmm-input-mat-mat-beta-alpha-out-null-gt-tensor-">addmm(input, mat1, mat2, *, beta=1, alpha=1, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#addmm-input-mat-mat-beta-alpha-out-null-gt-tensor-"></a></h2>
     
 
 
@@ -234,45 +116,40 @@ and <code>out</code> will be a \((n \times p)\) tensor.</p>
 $$
 For inputs of type <code>FloatTensor</code> or <code>DoubleTensor</code>, arguments <code>beta</code> and
 <code>alpha</code> must be real numbers, otherwise they should be integers.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>M</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>mat1</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>mat2</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_addmm</span><span class='op'>(</span><span class='va'>M</span>, <span class='va'>mat1</span>, <span class='va'>mat2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.2836  1.8781  0.5575</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -2.7371 -1.5720 -2.5225</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{2,3} ]</span>
-</pre>
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-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">M</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">mat1</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">mat2</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_addmm</span><span class="op">(</span><span class="va">M</span>, <span class="va">mat1</span>, <span class="va">mat2</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -2.4386 -1.5421  1.4678</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2.5538  1.2239  0.7606</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2,3} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Addmv</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_addmv.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Addmv</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_addmv.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Addmv</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_addmv</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>mat</span>, <span class='va'>vec</span>, beta <span class='op'>=</span> <span class='fl'>1L</span>, alpha <span class='op'>=</span> <span class='fl'>1L</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) vector to be added</p></td>
-    </tr>
-    <tr>
-      <th>mat</th>
-      <td><p>(Tensor) matrix to be multiplied</p></td>
-    </tr>
-    <tr>
-      <th>vec</th>
-      <td><p>(Tensor) vector to be multiplied</p></td>
-    </tr>
-    <tr>
-      <th>beta</th>
-      <td><p>(Number, optional) multiplier for <code>input</code> (\(\beta\))</p></td>
-    </tr>
-    <tr>
-      <th>alpha</th>
-      <td><p>(Number, optional) multiplier for \(mat @ vec\) (\(\alpha\))</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="addmv-input-mat-vec-beta-alpha-out-null-gt-tensor-"><a class="anchor" href="#addmv-input-mat-vec-beta-alpha-out-null-gt-tensor-"></a>addmv(input, mat, vec, *, beta=1, alpha=1, out=NULL) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_addmv</span><span class="op">(</span><span class="va">self</span>, <span class="va">mat</span>, <span class="va">vec</span>, beta <span class="op">=</span> <span class="fl">1L</span>, alpha <span class="op">=</span> <span class="fl">1L</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) vector to be added</p></dd>
+<dt>mat</dt>
+<dd><p>(Tensor) matrix to be multiplied</p></dd>
+<dt>vec</dt>
+<dd><p>(Tensor) vector to be multiplied</p></dd>
+<dt>beta</dt>
+<dd><p>(Number, optional) multiplier for <code>input</code> (\(\beta\))</p></dd>
+<dt>alpha</dt>
+<dd><p>(Number, optional) multiplier for \(mat @ vec\) (\(\alpha\))</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="addmv-input-mat-vec-beta-alpha-out-null-gt-tensor-">addmv(input, mat, vec, *, beta=1, alpha=1, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#addmv-input-mat-vec-beta-alpha-out-null-gt-tensor-"></a></h2>
     
 
 
@@ -235,45 +117,40 @@ broadcastable  with a 1-D tensor of size <code>n</code> and
 $$
 For inputs of type <code>FloatTensor</code> or <code>DoubleTensor</code>, arguments <code>beta</code> and
 <code>alpha</code> must be real numbers, otherwise they should be integers</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>M</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>2</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>mat</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>vec</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_addmv</span><span class='op'>(</span><span class='va'>M</span>, <span class='va'>mat</span>, <span class='va'>vec</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -4.0503</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.1071</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{2} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">M</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">mat</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">vec</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_addmv</span><span class="op">(</span><span class="va">M</span>, <span class="va">mat</span>, <span class="va">vec</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -4.2610</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -3.2303</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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index 07a7992aa..85c9159c1 100644
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-    <div class="page-header">
-    <h1>Addr</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_addr.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Addr</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_addr.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Addr</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_addr</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>vec1</span>, <span class='va'>vec2</span>, beta <span class='op'>=</span> <span class='fl'>1L</span>, alpha <span class='op'>=</span> <span class='fl'>1L</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) matrix to be added</p></td>
-    </tr>
-    <tr>
-      <th>vec1</th>
-      <td><p>(Tensor) the first vector of the outer product</p></td>
-    </tr>
-    <tr>
-      <th>vec2</th>
-      <td><p>(Tensor) the second vector of the outer product</p></td>
-    </tr>
-    <tr>
-      <th>beta</th>
-      <td><p>(Number, optional) multiplier for <code>input</code> (\(\beta\))</p></td>
-    </tr>
-    <tr>
-      <th>alpha</th>
-      <td><p>(Number, optional) multiplier for \(\mbox{vec1} \otimes \mbox{vec2}\) (\(\alpha\))</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="addr-input-vec-vec-beta-alpha-out-null-gt-tensor-"><a class="anchor" href="#addr-input-vec-vec-beta-alpha-out-null-gt-tensor-"></a>addr(input, vec1, vec2, *, beta=1, alpha=1, out=NULL) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_addr</span><span class="op">(</span><span class="va">self</span>, <span class="va">vec1</span>, <span class="va">vec2</span>, beta <span class="op">=</span> <span class="fl">1L</span>, alpha <span class="op">=</span> <span class="fl">1L</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) matrix to be added</p></dd>
+<dt>vec1</dt>
+<dd><p>(Tensor) the first vector of the outer product</p></dd>
+<dt>vec2</dt>
+<dd><p>(Tensor) the second vector of the outer product</p></dd>
+<dt>beta</dt>
+<dd><p>(Number, optional) multiplier for <code>input</code> (\(\beta\))</p></dd>
+<dt>alpha</dt>
+<dd><p>(Number, optional) multiplier for \(\mbox{vec1} \otimes \mbox{vec2}\) (\(\alpha\))</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="addr-input-vec-vec-beta-alpha-out-null-gt-tensor-">addr(input, vec1, vec2, *, beta=1, alpha=1, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#addr-input-vec-vec-beta-alpha-out-null-gt-tensor-"></a></h2>
     
 
 
@@ -236,46 +118,41 @@ broadcastable  with a matrix of size
 \((n \times m)\).</p>
 <p>For inputs of type <code>FloatTensor</code> or <code>DoubleTensor</code>, arguments <code>beta</code> and
 <code>alpha</code> must be real numbers, otherwise they should be integers</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>vec1</span> <span class='op'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>3</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>vec2</span> <span class='op'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>M</span> <span class='op'>=</span> <span class='fu'><a href='torch_zeros.html'>torch_zeros</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>2</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_addr</span><span class='op'>(</span><span class='va'>M</span>, <span class='va'>vec1</span>, <span class='va'>vec2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1  2</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  2  4</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  3  6</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{3,2} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">vec1</span> <span class="op">=</span> <span class="fu"><a href="torch_arange.html">torch_arange</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">3</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">vec2</span> <span class="op">=</span> <span class="fu"><a href="torch_arange.html">torch_arange</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">M</span> <span class="op">=</span> <span class="fu"><a href="torch_zeros.html">torch_zeros</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">2</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_addr</span><span class="op">(</span><span class="va">M</span>, <span class="va">vec1</span>, <span class="va">vec2</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1  2</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2  4</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  3  6</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3,2} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer>
-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <footer><div class="pkgdown-footer-left">
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+<div class="pkgdown-footer-right">
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index 9b37cd1cf..433ea4d35 100644
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+  <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-examples">Examples</a>
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+      </ul><form class="form-inline my-2 my-lg-0" role="search">
+        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
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+    
+  </div>
+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Allclose</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_allclose.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Allclose</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_allclose.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Allclose</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_allclose</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>other</span>, rtol <span class='op'>=</span> <span class='fl'>1e-05</span>, atol <span class='op'>=</span> <span class='fl'>0</span>, equal_nan <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) first tensor to compare</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Tensor) second tensor to compare</p></td>
-    </tr>
-    <tr>
-      <th>rtol</th>
-      <td><p>(float, optional) relative tolerance. Default: 1e-05</p></td>
-    </tr>
-    <tr>
-      <th>atol</th>
-      <td><p>(float, optional) absolute tolerance. Default: 1e-08</p></td>
-    </tr>
-    <tr>
-      <th>equal_nan</th>
-      <td><p>(bool, optional) if <code>TRUE</code>, then two <code>NaN</code> s will be compared as equal. Default: <code>FALSE</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="allclose-input-other-rtol-e-atol-e-equal-nan-false-gt-bool-"><a class="anchor" href="#allclose-input-other-rtol-e-atol-e-equal-nan-false-gt-bool-"></a>allclose(input, other, rtol=1e-05, atol=1e-08, equal_nan=False) -&gt; bool </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_allclose</span><span class="op">(</span><span class="va">self</span>, <span class="va">other</span>, rtol <span class="op">=</span> <span class="fl">1e-05</span>, atol <span class="op">=</span> <span class="fl">1e-08</span>, equal_nan <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) first tensor to compare</p></dd>
+<dt>other</dt>
+<dd><p>(Tensor) second tensor to compare</p></dd>
+<dt>rtol</dt>
+<dd><p>(float, optional) relative tolerance. Default: 1e-05</p></dd>
+<dt>atol</dt>
+<dd><p>(float, optional) absolute tolerance. Default: 1e-08</p></dd>
+<dt>equal_nan</dt>
+<dd><p>(bool, optional) if <code>TRUE</code>, then two <code>NaN</code> s will be compared as equal. Default: <code>FALSE</code></p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="allclose-input-other-rtol-e-atol-e-equal-nan-false-gt-bool-">allclose(input, other, rtol=1e-05, atol=1e-08, equal_nan=False) -&gt; bool <a class="anchor" aria-label="anchor" href="#allclose-input-other-rtol-e-atol-e-equal-nan-false-gt-bool-"></a></h2>
     
 
 
@@ -227,42 +109,37 @@
 $$
 elementwise, for all elements of <code>input</code> and <code>other</code>. The behaviour of this function is analogous to
 <code>numpy.allclose &lt;https://docs.scipy.org/doc/numpy/reference/generated/numpy.allclose.html&gt;</code>_</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='fu'>torch_allclose</span><span class='op'>(</span><span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>10000.</span>, <span class='fl'>1e-07</span><span class='op'>)</span><span class='op'>)</span>, <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>10000.1</span>, <span class='fl'>1e-08</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_allclose</span><span class='op'>(</span><span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>10000.</span>, <span class='fl'>1e-08</span><span class='op'>)</span><span class='op'>)</span>, <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>10000.1</span>, <span class='fl'>1e-09</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_allclose</span><span class='op'>(</span><span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1.0</span>, <span class='cn'>NaN</span><span class='op'>)</span><span class='op'>)</span>, <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1.0</span>, <span class='cn'>NaN</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_allclose</span><span class='op'>(</span><span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1.0</span>, <span class='cn'>NaN</span><span class='op'>)</span><span class='op'>)</span>, <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1.0</span>, <span class='cn'>NaN</span><span class='op'>)</span><span class='op'>)</span>, equal_nan<span class='op'>=</span><span class='cn'>TRUE</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [1] TRUE</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="fu">torch_allclose</span><span class="op">(</span><span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">10000.</span>, <span class="fl">1e-07</span><span class="op">)</span><span class="op">)</span>, <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">10000.1</span>, <span class="fl">1e-08</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_allclose</span><span class="op">(</span><span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">10000.</span>, <span class="fl">1e-08</span><span class="op">)</span><span class="op">)</span>, <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">10000.1</span>, <span class="fl">1e-09</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_allclose</span><span class="op">(</span><span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1.0</span>, <span class="cn">NaN</span><span class="op">)</span><span class="op">)</span>, <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1.0</span>, <span class="cn">NaN</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_allclose</span><span class="op">(</span><span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1.0</span>, <span class="cn">NaN</span><span class="op">)</span><span class="op">)</span>, <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1.0</span>, <span class="cn">NaN</span><span class="op">)</span><span class="op">)</span>, equal_nan<span class="op">=</span><span class="cn">TRUE</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] TRUE</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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-      <div class="copyright">
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+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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+<div class="pkgdown-footer-right">
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diff --git a/reference/torch_amax.html b/reference/torch_amax.html
index b41db5450..e4faff769 100644
--- a/reference/torch_amax.html
+++ b/reference/torch_amax.html
@@ -1,271 +1,150 @@
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Amax</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_amax.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Amax</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_amax.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Amax</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_amax</span><span class='op'>(</span><span class='va'>self</span>, dim <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span><span class='op'>)</span>, keepdim <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int or tuple of ints) the dimension or dimensions to reduce.</p></td>
-    </tr>
-    <tr>
-      <th>keepdim</th>
-      <td><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_amax</span><span class="op">(</span><span class="va">self</span>, dim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="op">)</span>, keepdim <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <p>The difference between <code>max</code>/<code>min</code> and <code>amax</code>/<code>amin</code> is:</p><ul>
-<li><p><code>amax</code>/<code>amin</code> supports reducing on multiple dimensions,</p></li>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+<dt>dim</dt>
+<dd><p>(int or tuple of ints) the dimension or dimensions to reduce.</p></dd>
+<dt>keepdim</dt>
+<dd><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
+    <p>The difference between <code>max</code>/<code>min</code> and <code>amax</code>/<code>amin</code> is:</p><ul><li><p><code>amax</code>/<code>amin</code> supports reducing on multiple dimensions,</p></li>
 <li><p><code>amax</code>/<code>amin</code> does not return indices,</p></li>
 <li><p><code>amax</code>/<code>amin</code> evenly distributes gradient between equal values,
 while <code>max(dim)</code>/<code>min(dim)</code> propagates gradient only to a single
 index in the source tensor.</p></li>
-</ul>
-
-<p>If <code>keepdim is </code>TRUE<code>, the output tensors are of the same size as </code>input<code>except in the dimension(s)</code>dim<code>where they are of size 1. Otherwise,</code>dim<code>s are squeezed (see [torch_squeeze()]), resulting in the output tensors having fewer dimension than </code>input`.</p>
-    <h2 class="hasAnchor" id="amax-input-dim-keepdim-false-out-none-gt-tensor-"><a class="anchor" href="#amax-input-dim-keepdim-false-out-none-gt-tensor-"></a>amax(input, dim, keepdim=FALSE, *, out=None) -&gt; Tensor </h2>
-
+</ul><p>If <code>keepdim is </code>TRUE<code>, the output tensors are of the same size as </code>input<code>except in the dimension(s)</code>dim<code>where they are of size 1. Otherwise,</code>dim<code>s are squeezed (see [torch_squeeze()]), resulting in the output tensors having fewer dimension than </code>input`.</p>
+    </div>
+    <div class="section level2">
+    <h2 id="amax-input-dim-keepdim-false-out-none-gt-tensor-">amax(input, dim, keepdim=FALSE, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#amax-input-dim-keepdim-false-out-none-gt-tensor-"></a></h2>
     
 
 
 <p>Returns the maximum value of each slice of the <code>input</code> tensor in the given
 dimension(s) <code>dim</code>.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span>, <span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='fu'>torch_amax</span><span class='op'>(</span><span class='va'>a</span>, <span class='fl'>1</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1.1838</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.0996</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.2704</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.6430</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{4} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span>, <span class="fl">4</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="fu">torch_amax</span><span class="op">(</span><span class="va">a</span>, <span class="fl">1</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.3746</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.4029</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.7899</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.4408</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Amin</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_amin.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Amin</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_amin.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Amin</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_amin</span><span class='op'>(</span><span class='va'>self</span>, dim <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span><span class='op'>)</span>, keepdim <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int or tuple of ints) the dimension or dimensions to reduce.</p></td>
-    </tr>
-    <tr>
-      <th>keepdim</th>
-      <td><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_amin</span><span class="op">(</span><span class="va">self</span>, dim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="op">)</span>, keepdim <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <p>The difference between <code>max</code>/<code>min</code> and <code>amax</code>/<code>amin</code> is:</p><ul>
-<li><p><code>amax</code>/<code>amin</code> supports reducing on multiple dimensions,</p></li>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+<dt>dim</dt>
+<dd><p>(int or tuple of ints) the dimension or dimensions to reduce.</p></dd>
+<dt>keepdim</dt>
+<dd><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
+    <p>The difference between <code>max</code>/<code>min</code> and <code>amax</code>/<code>amin</code> is:</p><ul><li><p><code>amax</code>/<code>amin</code> supports reducing on multiple dimensions,</p></li>
 <li><p><code>amax</code>/<code>amin</code> does not return indices,</p></li>
 <li><p><code>amax</code>/<code>amin</code> evenly distributes gradient between equal values,
 while <code>max(dim)</code>/<code>min(dim)</code> propagates gradient only to a single
 index in the source tensor.</p></li>
-</ul>
-
-<p>If <code>keepdim</code> is <code>TRUE</code>, the output tensors are of the same size as
+</ul><p>If <code>keepdim</code> is <code>TRUE</code>, the output tensors are of the same size as
 <code>input</code> except in the dimension(s) <code>dim</code> where they are of size 1.
-Otherwise, <code>dim</code>s are squeezed (see <code><a href='torch_squeeze.html'>torch_squeeze()</a></code>), resulting in
+Otherwise, <code>dim</code>s are squeezed (see <code><a href="torch_squeeze.html">torch_squeeze()</a></code>), resulting in
 the output tensors having fewer dimensions than <code>input</code>.</p>
-    <h2 class="hasAnchor" id="amin-input-dim-keepdim-false-out-none-gt-tensor-"><a class="anchor" href="#amin-input-dim-keepdim-false-out-none-gt-tensor-"></a>amin(input, dim, keepdim=FALSE, *, out=None) -&gt; Tensor </h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="amin-input-dim-keepdim-false-out-none-gt-tensor-">amin(input, dim, keepdim=FALSE, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#amin-input-dim-keepdim-false-out-none-gt-tensor-"></a></h2>
     
 
 
 <p>Returns the minimum value of each slice of the <code>input</code> tensor in the given
 dimension(s) <code>dim</code>.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span>, <span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='fu'>torch_amin</span><span class='op'>(</span><span class='va'>a</span>, <span class='fl'>1</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -1.4546</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -1.3826</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.7699</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -2.1347</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{4} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span>, <span class="fl">4</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="fu">torch_amin</span><span class="op">(</span><span class="va">a</span>, <span class="fl">1</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.9522</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -1.7592</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.1854</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -2.5131</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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diff --git a/reference/torch_angle.html b/reference/torch_angle.html
index a8c817d4a..4e190f964 100644
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+++ b/reference/torch_angle.html
@@ -1,207 +1,97 @@
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Angle</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_angle.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Angle</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_angle.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Angle</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_angle</span><span class='op'>(</span><span class='va'>self</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="angle-input-gt-tensor-"><a class="anchor" href="#angle-input-gt-tensor-"></a>angle(input) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_angle</span><span class="op">(</span><span class="va">self</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="angle-input-gt-tensor-">angle(input) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#angle-input-gt-tensor-"></a></h2>
     
 
 
@@ -209,40 +99,35 @@
 <p>$$
     \mbox{out}_{i} = angle(\mbox{input}_{i})
 $$</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='cn'>FALSE</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='fu'>torch_angle</span><span class='op'>(</span><span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='op'>-</span><span class='fl'>1</span> <span class='op'>+</span> <span class='fl'>1i</span>, <span class='op'>-</span><span class='fl'>2</span> <span class='op'>+</span> <span class='fl'>2i</span>, <span class='fl'>3</span> <span class='op'>-</span> <span class='fl'>3i</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span><span class='op'>*</span><span class='fl'>180</span><span class='op'>/</span><span class='fl'>3.14159</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="fu">torch_angle</span><span class="op">(</span><span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="op">-</span><span class="fl">1</span> <span class="op">+</span> <span class="fl">1i</span>, <span class="op">-</span><span class="fl">2</span> <span class="op">+</span> <span class="fl">2i</span>, <span class="fl">3</span> <span class="op">-</span> <span class="fl">3i</span><span class="op">)</span><span class="op">)</span><span class="op">)</span><span class="op">*</span><span class="fl">180</span><span class="op">/</span><span class="fl">3.14159</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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diff --git a/reference/torch_arange.html b/reference/torch_arange.html
index 04ab3f8bc..cab99b013 100644
--- a/reference/torch_arange.html
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Arange</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/creation-ops.R'><code>R/creation-ops.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_arange.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Arange</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/creation-ops.R" class="external-link"><code>R/creation-ops.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a></small>
+      <div class="d-none name"><code>torch_arange.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Arange</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_arange</span><span class='op'>(</span>
-  <span class='va'>start</span>,
-  <span class='va'>end</span>,
-  step <span class='op'>=</span> <span class='fl'>1</span>,
-  dtype <span class='op'>=</span> <span class='cn'>NULL</span>,
-  layout <span class='op'>=</span> <span class='fu'><a href='torch_layout.html'>torch_strided</a></span><span class='op'>(</span><span class='op'>)</span>,
-  device <span class='op'>=</span> <span class='cn'>NULL</span>,
-  requires_grad <span class='op'>=</span> <span class='cn'>FALSE</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>start</th>
-      <td><p>(Number) the starting value for the set of points. Default: <code>0</code>.</p></td>
-    </tr>
-    <tr>
-      <th>end</th>
-      <td><p>(Number) the ending value for the set of points</p></td>
-    </tr>
-    <tr>
-      <th>step</th>
-      <td><p>(Number) the gap between each pair of adjacent points. Default: <code>1</code>.</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>). If <code>dtype</code> is not given, infer the data type from the other input        arguments. If any of <code>start</code>, <code>end</code>, or <code>stop</code> are floating-point, the        <code>dtype</code> is inferred to be the default dtype, see        <code>~torch.get_default_dtype</code>. Otherwise, the <code>dtype</code> is inferred to        be <code>torch.int64</code>.</p></td>
-    </tr>
-    <tr>
-      <th>layout</th>
-      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
-    </tr>
-    <tr>
-      <th>requires_grad</th>
-      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="arange-start-end-step-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"><a class="anchor" href="#arange-start-end-step-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"></a>arange(start=0, end, step=1, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_arange</span><span class="op">(</span>
+  <span class="va">start</span>,
+  <span class="va">end</span>,
+  step <span class="op">=</span> <span class="fl">1</span>,
+  dtype <span class="op">=</span> <span class="cn">NULL</span>,
+  layout <span class="op">=</span> <span class="fu"><a href="torch_layout.html">torch_strided</a></span><span class="op">(</span><span class="op">)</span>,
+  device <span class="op">=</span> <span class="cn">NULL</span>,
+  requires_grad <span class="op">=</span> <span class="cn">FALSE</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>start</dt>
+<dd><p>(Number) the starting value for the set of points. Default: <code>0</code>.</p></dd>
+<dt>end</dt>
+<dd><p>(Number) the ending value for the set of points</p></dd>
+<dt>step</dt>
+<dd><p>(Number) the gap between each pair of adjacent points. Default: <code>1</code>.</p></dd>
+<dt>dtype</dt>
+<dd><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>). If <code>dtype</code> is not given, infer the data type from the other input        arguments. If any of <code>start</code>, <code>end</code>, or <code>stop</code> are floating-point, the        <code>dtype</code> is inferred to be the default dtype, see        <code>~torch.get_default_dtype</code>. Otherwise, the <code>dtype</code> is inferred to        be <code>torch.int64</code>.</p></dd>
+<dt>layout</dt>
+<dd><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></dd>
+<dt>device</dt>
+<dd><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></dd>
+<dt>requires_grad</dt>
+<dd><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="arange-start-end-step-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-">arange(start=0, end, step=1, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#arange-start-end-step-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"></a></h2>
     
 
 
@@ -246,46 +124,41 @@ in such cases.</p>
 <p>$$
     \mbox{out}_{{i+1}} = \mbox{out}_{i} + \mbox{step}
 $$</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='fu'>torch_arange</span><span class='op'>(</span>start <span class='op'>=</span> <span class='fl'>0</span>, end <span class='op'>=</span> <span class='fl'>5</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_arange</span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>4</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_arange</span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>2.5</span>, <span class='fl'>0.5</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1.0000</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1.5000</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  2.0000</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  2.5000</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{4} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="fu">torch_arange</span><span class="op">(</span>start <span class="op">=</span> <span class="fl">0</span>, end <span class="op">=</span> <span class="fl">5</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_arange</span><span class="op">(</span><span class="fl">1</span>, <span class="fl">4</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_arange</span><span class="op">(</span><span class="fl">1</span>, <span class="fl">2.5</span>, <span class="fl">0.5</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.0000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.5000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2.0000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2.5000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4} ]</span>
+</code></pre></div>
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+    </nav></aside></div>
 
 
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Arccos</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_arccos.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Arccos</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_arccos.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Arccos</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_arccos</span><span class='op'>(</span><span class='va'>self</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="arccos-input-out-none-gt-tensor-"><a class="anchor" href="#arccos-input-out-none-gt-tensor-"></a>arccos(input, *, out=None) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_arccos</span><span class="op">(</span><span class="va">self</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="arccos-input-out-none-gt-tensor-">arccos(input, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#arccos-input-out-none-gt-tensor-"></a></h2>
     
 
 
-<p>Alias for <code><a href='torch_acos.html'>torch_acos()</a></code>.</p>
+<p>Alias for <code><a href="torch_acos.html">torch_acos()</a></code>.</p>
+    </div>
 
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-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Arccosh</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_arccosh.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Arccosh</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_arccosh.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Arccosh</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_arccosh</span><span class='op'>(</span><span class='va'>self</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="arccosh-input-out-none-gt-tensor-"><a class="anchor" href="#arccosh-input-out-none-gt-tensor-"></a>arccosh(input, *, out=None) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_arccosh</span><span class="op">(</span><span class="va">self</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="arccosh-input-out-none-gt-tensor-">arccosh(input, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#arccosh-input-out-none-gt-tensor-"></a></h2>
     
 
 
-<p>Alias for <code><a href='torch_acosh.html'>torch_acosh()</a></code>.</p>
+<p>Alias for <code><a href="torch_acosh.html">torch_acosh()</a></code>.</p>
+    </div>
 
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diff --git a/reference/torch_arcsin.html b/reference/torch_arcsin.html
index 97d44c2f6..e859f2daf 100644
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-    <h1>Arcsin</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_arcsin.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Arcsin</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_arcsin.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Arcsin</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_arcsin</span><span class='op'>(</span><span class='va'>self</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="arcsin-input-out-none-gt-tensor-"><a class="anchor" href="#arcsin-input-out-none-gt-tensor-"></a>arcsin(input, *, out=None) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_arcsin</span><span class="op">(</span><span class="va">self</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="arcsin-input-out-none-gt-tensor-">arcsin(input, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#arcsin-input-out-none-gt-tensor-"></a></h2>
     
 
 
-<p>Alias for <code><a href='torch_asin.html'>torch_asin()</a></code>.</p>
+<p>Alias for <code><a href="torch_asin.html">torch_asin()</a></code>.</p>
+    </div>
 
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-  <div class="col-md-9 contents">
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-    <h1>Arcsinh</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_arcsinh.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Arcsinh</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_arcsinh.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Arcsinh</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_arcsinh</span><span class='op'>(</span><span class='va'>self</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="arcsinh-input-out-none-gt-tensor-"><a class="anchor" href="#arcsinh-input-out-none-gt-tensor-"></a>arcsinh(input, *, out=None) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_arcsinh</span><span class="op">(</span><span class="va">self</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="arcsinh-input-out-none-gt-tensor-">arcsinh(input, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#arcsinh-input-out-none-gt-tensor-"></a></h2>
     
 
 
-<p>Alias for <code><a href='torch_asinh.html'>torch_asinh()</a></code>.</p>
+<p>Alias for <code><a href="torch_asinh.html">torch_asinh()</a></code>.</p>
+    </div>
 
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-    <h1>Arctan</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_arctan.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Arctan</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_arctan.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Arctan</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_arctan</span><span class='op'>(</span><span class='va'>self</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
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-    </table>
-
-    <h2 class="hasAnchor" id="arctan-input-out-none-gt-tensor-"><a class="anchor" href="#arctan-input-out-none-gt-tensor-"></a>arctan(input, *, out=None) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_arctan</span><span class="op">(</span><span class="va">self</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="arctan-input-out-none-gt-tensor-">arctan(input, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#arctan-input-out-none-gt-tensor-"></a></h2>
     
 
 
-<p>Alias for <code><a href='torch_atan.html'>torch_atan()</a></code>.</p>
+<p>Alias for <code><a href="torch_atan.html">torch_atan()</a></code>.</p>
+    </div>
 
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-  <div class="col-md-9 contents">
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-    <h1>Argmax</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_argmax.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Argmax</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a></small>
+      <div class="d-none name"><code>torch_argmax.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Argmax</p>
     </div>
 
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) the dimension to reduce. If <code>NULL</code>, the argmax of the flattened input is returned.</p></td>
-    </tr>
-    <tr>
-      <th>keepdim</th>
-      <td><p>(bool) whether the output tensor has <code>dim</code> retained or not. Ignored if <code>dim=NULL</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="argmax-input-gt-longtensor-"><a class="anchor" href="#argmax-input-gt-longtensor-"></a>argmax(input) -&gt; LongTensor </h2>
-
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+<dt>dim</dt>
+<dd><p>(int) the dimension to reduce. If <code>NULL</code>, the argmax of the flattened input is returned.</p></dd>
+<dt>keepdim</dt>
+<dd><p>(bool) whether the output tensor has <code>dim</code> retained or not. Ignored if <code>dim=NULL</code>.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="argmax-input-gt-longtensor-">argmax(input) -&gt; LongTensor <a class="anchor" aria-label="anchor" href="#argmax-input-gt-longtensor-"></a></h2>
     
 
 
 <p>Returns the indices of the maximum value of all elements in the <code>input</code> tensor.</p>
 <p>This is the second value returned by <code>torch_max</code>. See its
 documentation for the exact semantics of this method.</p>
-    <h2 class="hasAnchor" id="argmax-input-dim-keepdim-false-gt-longtensor-"><a class="anchor" href="#argmax-input-dim-keepdim-false-gt-longtensor-"></a>argmax(input, dim, keepdim=False) -&gt; LongTensor </h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="argmax-input-dim-keepdim-false-gt-longtensor-">argmax(input, dim, keepdim=False) -&gt; LongTensor <a class="anchor" aria-label="anchor" href="#argmax-input-dim-keepdim-false-gt-longtensor-"></a></h2>
     
 
 
 <p>Returns the indices of the maximum values of a tensor across a dimension.</p>
 <p>This is the second value returned by <code>torch_max</code>. See its
 documentation for the exact semantics of this method.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='cn'>FALSE</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span>, <span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='fu'>torch_argmax</span><span class='op'>(</span><span class='va'>a</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-in'></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span>, <span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='fu'>torch_argmax</span><span class='op'>(</span><span class='va'>a</span>, dim<span class='op'>=</span><span class='fl'>1</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  4</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  3</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPULongType{4} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span>, <span class="fl">4</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="fu">torch_argmax</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-in"></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span>, <span class="fl">4</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="fu">torch_argmax</span><span class="op">(</span><span class="va">a</span>, dim<span class="op">=</span><span class="fl">1</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  4</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  3</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPULongType{4} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Argmin</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_argmin.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Argmin</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a></small>
+      <div class="d-none name"><code>torch_argmin.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Argmin</p>
     </div>
 
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) the dimension to reduce. If <code>NULL</code>, the argmin of the flattened input is returned.</p></td>
-    </tr>
-    <tr>
-      <th>keepdim</th>
-      <td><p>(bool) whether the output tensor has <code>dim</code> retained or not. Ignored if <code>dim=NULL</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="argmin-input-gt-longtensor-"><a class="anchor" href="#argmin-input-gt-longtensor-"></a>argmin(input) -&gt; LongTensor </h2>
-
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+<dt>dim</dt>
+<dd><p>(int) the dimension to reduce. If <code>NULL</code>, the argmin of the flattened input is returned.</p></dd>
+<dt>keepdim</dt>
+<dd><p>(bool) whether the output tensor has <code>dim</code> retained or not. Ignored if <code>dim=NULL</code>.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="argmin-input-gt-longtensor-">argmin(input) -&gt; LongTensor <a class="anchor" aria-label="anchor" href="#argmin-input-gt-longtensor-"></a></h2>
     
 
 
 <p>Returns the indices of the minimum value of all elements in the <code>input</code> tensor.</p>
 <p>This is the second value returned by <code>torch_min</code>. See its
 documentation for the exact semantics of this method.</p>
-    <h2 class="hasAnchor" id="argmin-input-dim-keepdim-false-out-null-gt-longtensor-"><a class="anchor" href="#argmin-input-dim-keepdim-false-out-null-gt-longtensor-"></a>argmin(input, dim, keepdim=False, out=NULL) -&gt; LongTensor </h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="argmin-input-dim-keepdim-false-out-null-gt-longtensor-">argmin(input, dim, keepdim=False, out=NULL) -&gt; LongTensor <a class="anchor" aria-label="anchor" href="#argmin-input-dim-keepdim-false-out-null-gt-longtensor-"></a></h2>
     
 
 
 <p>Returns the indices of the minimum values of a tensor across a dimension.</p>
 <p>This is the second value returned by <code>torch_min</code>. See its
 documentation for the exact semantics of this method.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span>, <span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='fu'>torch_argmin</span><span class='op'>(</span><span class='va'>a</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span>, <span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='fu'>torch_argmin</span><span class='op'>(</span><span class='va'>a</span>, dim<span class='op'>=</span><span class='fl'>1</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  4</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  2</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  4</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPULongType{4} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span>, <span class="fl">4</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="fu">torch_argmin</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span>
+<span class="r-in"></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span>, <span class="fl">4</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="fu">torch_argmin</span><span class="op">(</span><span class="va">a</span>, dim<span class="op">=</span><span class="fl">1</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPULongType{4} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Argsort</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/with-indices.R'><code>R/with-indices.R</code></a></small>
-    <div class="hidden name"><code>torch_argsort.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Argsort</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/with-indices.R" class="external-link"><code>R/with-indices.R</code></a></small>
+      <div class="d-none name"><code>torch_argsort.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Argsort</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_argsort</span><span class='op'>(</span><span class='va'>self</span>, dim <span class='op'>=</span> <span class='op'>-</span><span class='fl'>1L</span>, descending <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int, optional) the dimension to sort along</p></td>
-    </tr>
-    <tr>
-      <th>descending</th>
-      <td><p>(bool, optional) controls the sorting order (ascending or descending)</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="argsort-input-dim-descending-false-gt-longtensor-"><a class="anchor" href="#argsort-input-dim-descending-false-gt-longtensor-"></a>argsort(input, dim=-1, descending=False) -&gt; LongTensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_argsort</span><span class="op">(</span><span class="va">self</span>, dim <span class="op">=</span> <span class="op">-</span><span class="fl">1L</span>, descending <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+<dt>dim</dt>
+<dd><p>(int, optional) the dimension to sort along</p></dd>
+<dt>descending</dt>
+<dd><p>(bool, optional) controls the sorting order (ascending or descending)</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="argsort-input-dim-descending-false-gt-longtensor-">argsort(input, dim=-1, descending=False) -&gt; LongTensor <a class="anchor" aria-label="anchor" href="#argsort-input-dim-descending-false-gt-longtensor-"></a></h2>
     
 
 
@@ -217,46 +103,41 @@
 order by value.</p>
 <p>This is the second value returned by <code>torch_sort</code>.  See its documentation
 for the exact semantics of this method.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span>, <span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='fu'>torch_argsort</span><span class='op'>(</span><span class='va'>a</span>, dim<span class='op'>=</span><span class='fl'>1</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  4  3  1  4</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  3  4  3  1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  2  2  4  3</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1  1  2  2</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPULongType{4,4} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span>, <span class="fl">4</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="fu">torch_argsort</span><span class="op">(</span><span class="va">a</span>, dim<span class="op">=</span><span class="fl">1</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2  3  3  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1  2  4  2</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  4  1  1  4</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  3  4  2  3</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPULongType{4,4} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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-      <div class="copyright">
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index 921c8cb60..57533a476 100644
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>As_strided</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_as_strided.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>As_strided</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_as_strided.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>As_strided</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_as_strided</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>size</span>, <span class='va'>stride</span>, storage_offset <span class='op'>=</span> <span class='cn'>NULL</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>size</th>
-      <td><p>(tuple or ints) the shape of the output tensor</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>(tuple or ints) the stride of the output tensor</p></td>
-    </tr>
-    <tr>
-      <th>storage_offset</th>
-      <td><p>(int, optional) the offset in the underlying storage of the output tensor</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="as-strided-input-size-stride-storage-offset-gt-tensor-"><a class="anchor" href="#as-strided-input-size-stride-storage-offset-gt-tensor-"></a>as_strided(input, size, stride, storage_offset=0) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_as_strided</span><span class="op">(</span><span class="va">self</span>, <span class="va">size</span>, <span class="va">stride</span>, storage_offset <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+<dt>size</dt>
+<dd><p>(tuple or ints) the shape of the output tensor</p></dd>
+<dt>stride</dt>
+<dd><p>(tuple or ints) the stride of the output tensor</p></dd>
+<dt>storage_offset</dt>
+<dd><p>(int, optional) the offset in the underlying storage of the output tensor</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="as-strided-input-size-stride-storage-offset-gt-tensor-">as_strided(input, size, stride, storage_offset=0) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#as-strided-input-size-stride-storage-offset-gt-tensor-"></a></h2>
     
 
 
 <p>Create a view of an existing <code>torch_Tensor</code> <code>input</code> with specified
 <code>size</code>, <code>stride</code> and <code>storage_offset</code>.</p>
-    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></a></h2>
     
 
 <p>More than one element of a created tensor may refer to a single memory
 location. As a result, in-place operations (especially ones that are
 vectorized) may result in incorrect behavior. If you need to write to
-the tensors, please clone them first.</p><pre>Many PyTorch functions, which return a view of a tensor, are internally
+the tensors, please clone them first.</p><div class="sourceCode"><pre><code>Many PyTorch functions, which return a view of a tensor, are internally
 implemented with this function. Those functions, like
 `torch_Tensor.expand`, are easier to read and are therefore more
 advisable to use.
-</pre>
+</code></pre></div>
 
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>x</span></span>
-<span class='r-in'><span class='va'>t</span> <span class='op'>=</span> <span class='fu'>torch_as_strided</span><span class='op'>(</span><span class='va'>x</span>, <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>2</span><span class='op'>)</span>, <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>2</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>t</span></span>
-<span class='r-in'><span class='va'>t</span> <span class='op'>=</span> <span class='fu'>torch_as_strided</span><span class='op'>(</span><span class='va'>x</span>, <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>2</span><span class='op'>)</span>, <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>2</span><span class='op'>)</span>, <span class='fl'>1</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>t</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> 0.01 *</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -2.9272 -3.6327</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  -53.3657 -43.9588</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{2,2} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">x</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">x</span></span>
+<span class="r-in"><span class="va">t</span> <span class="op">=</span> <span class="fu">torch_as_strided</span><span class="op">(</span><span class="va">x</span>, <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">2</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">2</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">t</span></span>
+<span class="r-in"><span class="va">t</span> <span class="op">=</span> <span class="fu">torch_as_strided</span><span class="op">(</span><span class="va">x</span>, <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">2</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">2</span><span class="op">)</span>, <span class="fl">1</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">t</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0357  0.2241</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.1163  0.0760</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2,2} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Asin</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_asin.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Asin</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_asin.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Asin</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_asin</span><span class='op'>(</span><span class='va'>self</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="asin-input-out-null-gt-tensor-"><a class="anchor" href="#asin-input-out-null-gt-tensor-"></a>asin(input, out=NULL) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_asin</span><span class="op">(</span><span class="va">self</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="asin-input-out-null-gt-tensor-">asin(input, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#asin-input-out-null-gt-tensor-"></a></h2>
     
 
 
@@ -209,46 +99,41 @@
 <p>$$
     \mbox{out}_{i} = \sin^{-1}(\mbox{input}_{i})
 $$</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='fu'>torch_asin</span><span class='op'>(</span><span class='va'>a</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.1636</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>     nan</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.1514</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>     nan</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{4} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="fu">torch_asin</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>     nan</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>     nan</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.4348</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>     nan</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer>
-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
 </div>
 
-<div class="pkgdown">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p>
+<div class="pkgdown-footer-right">
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index 252ef9155..6175e36f0 100644
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Asinh</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_asinh.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Asinh</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_asinh.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Asinh</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_asinh</span><span class='op'>(</span><span class='va'>self</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="asinh-input-out-none-gt-tensor-"><a class="anchor" href="#asinh-input-out-none-gt-tensor-"></a>asinh(input, *, out=None) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_asinh</span><span class="op">(</span><span class="va">self</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="asinh-input-out-none-gt-tensor-">asinh(input, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#asinh-input-out-none-gt-tensor-"></a></h2>
     
 
 
@@ -209,46 +99,41 @@
 <p>$$
     \mbox{out}_{i} = \sinh^{-1}(\mbox{input}_{i})
 $$</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='fu'>torch_asinh</span><span class='op'>(</span><span class='va'>a</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.9202</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.0908</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.3526</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.0275</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{4} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="fu">torch_asinh</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.9800</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.7436</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -1.3847</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.8686</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer>
-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
 </div>
 
-<div class="pkgdown">
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+<div class="pkgdown-footer-right">
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diff --git a/reference/torch_atan.html b/reference/torch_atan.html
index d87c80a01..73c00a45e 100644
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@@ -1,207 +1,97 @@
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Atan</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_atan.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Atan</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_atan.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Atan</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_atan</span><span class='op'>(</span><span class='va'>self</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="atan-input-out-null-gt-tensor-"><a class="anchor" href="#atan-input-out-null-gt-tensor-"></a>atan(input, out=NULL) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_atan</span><span class="op">(</span><span class="va">self</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="atan-input-out-null-gt-tensor-">atan(input, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#atan-input-out-null-gt-tensor-"></a></h2>
     
 
 
@@ -209,46 +99,41 @@
 <p>$$
     \mbox{out}_{i} = \tan^{-1}(\mbox{input}_{i})
 $$</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='fu'>torch_atan</span><span class='op'>(</span><span class='va'>a</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.9006</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.3594</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.4036</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1.1058</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{4} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="fu">torch_atan</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.5585</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.3162</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.5697</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.7496</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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+<div class="pkgdown-footer-right">
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diff --git a/reference/torch_atan2.html b/reference/torch_atan2.html
index 8a60acad4..005f6999d 100644
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@@ -1,211 +1,99 @@
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Atan2</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_atan2.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Atan2</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_atan2.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Atan2</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_atan2</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>other</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the first input tensor</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Tensor) the second input tensor</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="atan-input-other-out-null-gt-tensor-"><a class="anchor" href="#atan-input-other-out-null-gt-tensor-"></a>atan2(input, other, out=NULL) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_atan2</span><span class="op">(</span><span class="va">self</span>, <span class="va">other</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the first input tensor</p></dd>
+<dt>other</dt>
+<dd><p>(Tensor) the second input tensor</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="atan-input-other-out-null-gt-tensor-">atan2(input, other, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#atan-input-other-out-null-gt-tensor-"></a></h2>
     
 
 
@@ -217,46 +105,41 @@ parameter, is the x-coordinate, while \(\mbox{input}_{i}\), the first
 parameter, is the y-coordinate.)</p>
 <p>The shapes of <code>input</code> and <code>other</code> must be
 broadcastable .</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='fu'>torch_atan2</span><span class='op'>(</span><span class='va'>a</span>, <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.9539</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -1.4061</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1.7324</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.6967</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{4} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="fu">torch_atan2</span><span class="op">(</span><span class="va">a</span>, <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.6994</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -2.0529</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -2.1063</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.4267</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer>
-      <div class="copyright">
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+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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diff --git a/reference/torch_atanh.html b/reference/torch_atanh.html
index deb2e63c0..f45353cf0 100644
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Atanh</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_atanh.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Atanh</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_atanh.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Atanh</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_atanh</span><span class='op'>(</span><span class='va'>self</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_atanh</span><span class="op">(</span><span class="va">self</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p>The domain of the inverse hyperbolic tangent is <code>(-1, 1)</code> and values outside this range
 will be mapped to <code>NaN</code>, except for the values <code>1</code> and <code>-1</code> for which the output is
 mapped to <code>+/-INF</code> respectively.</p>
 <p>$$
     \mbox{out}_{i} = \tanh^{-1}(\mbox{input}_{i})
 $$</p>
-    <h2 class="hasAnchor" id="atanh-input-out-none-gt-tensor-"><a class="anchor" href="#atanh-input-out-none-gt-tensor-"></a>atanh(input, *, out=None) -&gt; Tensor </h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="atanh-input-out-none-gt-tensor-">atanh(input, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#atanh-input-out-none-gt-tensor-"></a></h2>
     
 
 
 <p>Returns a new tensor with the inverse hyperbolic tangent of the elements of <code>input</code>.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span><span class='op'>$</span><span class='fu'>uniform_</span><span class='op'>(</span><span class='op'>-</span><span class='fl'>1</span>, <span class='fl'>1</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='fu'>torch_atanh</span><span class='op'>(</span><span class='va'>a</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.1150</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.5992</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1.2106</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1.3505</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{4} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span><span class="op">)</span><span class="op">)</span><span class="op">$</span><span class="fu">uniform_</span><span class="op">(</span><span class="op">-</span><span class="fl">1</span>, <span class="fl">1</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="fu">torch_atanh</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.1883</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.3554</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.1854</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.7062</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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diff --git a/reference/torch_atleast_1d.html b/reference/torch_atleast_1d.html
index eb27d89c1..a87d015c1 100644
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Atleast_1d</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/wrapers.R'><code>R/wrapers.R</code></a></small>
-    <div class="hidden name"><code>torch_atleast_1d.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Atleast_1d</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
+      <div class="d-none name"><code>torch_atleast_1d.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Returns a 1-dimensional view of each input tensor with zero dimensions.
 Input tensors with one or more dimensions are returned as-is.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_atleast_1d</span><span class='op'>(</span><span class='va'>self</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor or list of Tensors)</p></td>
-    </tr>
-    </table>
-
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_atleast_1d</span><span class="op">(</span><span class="va">self</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>x</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>2</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>x</span></span>
-<span class='r-in'><span class='fu'>torch_atleast_1d</span><span class='op'>(</span><span class='va'>x</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>x</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fl'>1.</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>x</span></span>
-<span class='r-in'><span class='fu'>torch_atleast_1d</span><span class='op'>(</span><span class='va'>x</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>x</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fl'>0.5</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>y</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fl'>1.</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_atleast_1d</span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span><span class='va'>x</span>,<span class='va'>y</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [[1]]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.5000</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{1} ]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [[2]]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{1} ]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor or list of Tensors)</p></dd>
+</dl></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">x</span></span>
+<span class="r-in"><span class="fu">torch_atleast_1d</span><span class="op">(</span><span class="va">x</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fl">1.</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">x</span></span>
+<span class="r-in"><span class="fu">torch_atleast_1d</span><span class="op">(</span><span class="va">x</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fl">0.5</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">y</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fl">1.</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_atleast_1d</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">x</span>,<span class="va">y</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [[1]]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.5000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{1} ]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [[2]]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{1} ]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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diff --git a/reference/torch_atleast_2d.html b/reference/torch_atleast_2d.html
index 36bdfb521..d9f4eec67 100644
--- a/reference/torch_atleast_2d.html
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+Input tensors with two or more dimensions are returned as-is."><title>Atleast_2d — torch_atleast_2d • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.1.0/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.1.0/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Atleast_2d — torch_atleast_2d"><meta property="og:description" content="Returns a 2-dimensional view of each each input tensor with zero dimensions.
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Atleast_2d</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/wrapers.R'><code>R/wrapers.R</code></a></small>
-    <div class="hidden name"><code>torch_atleast_2d.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Atleast_2d</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
+      <div class="d-none name"><code>torch_atleast_2d.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Returns a 2-dimensional view of each each input tensor with zero dimensions.
 Input tensors with two or more dimensions are returned as-is.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_atleast_2d</span><span class='op'>(</span><span class='va'>self</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor or list of Tensors)</p></td>
-    </tr>
-    </table>
-
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_atleast_2d</span><span class="op">(</span><span class="va">self</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>x</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fl'>1.</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>x</span></span>
-<span class='r-in'><span class='fu'>torch_atleast_2d</span><span class='op'>(</span><span class='va'>x</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>x</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>2</span>,<span class='fl'>2</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>x</span></span>
-<span class='r-in'><span class='fu'>torch_atleast_2d</span><span class='op'>(</span><span class='va'>x</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>x</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fl'>0.5</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>y</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fl'>1.</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_atleast_2d</span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span><span class='va'>x</span>,<span class='va'>y</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [[1]]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.5000</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{1,1} ]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [[2]]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{1,1} ]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor or list of Tensors)</p></dd>
+</dl></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fl">1.</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">x</span></span>
+<span class="r-in"><span class="fu">torch_atleast_2d</span><span class="op">(</span><span class="va">x</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>,<span class="fl">2</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">x</span></span>
+<span class="r-in"><span class="fu">torch_atleast_2d</span><span class="op">(</span><span class="va">x</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fl">0.5</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">y</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fl">1.</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_atleast_2d</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">x</span>,<span class="va">y</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [[1]]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.5000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{1,1} ]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [[2]]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{1,1} ]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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index 45e589e80..b04ca0a83 100644
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Atleast_3d</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/wrapers.R'><code>R/wrapers.R</code></a></small>
-    <div class="hidden name"><code>torch_atleast_3d.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Atleast_3d</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
+      <div class="d-none name"><code>torch_atleast_3d.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Returns a 3-dimensional view of each each input tensor with zero dimensions.
 Input tensors with three or more dimensions are returned as-is.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_atleast_3d</span><span class='op'>(</span><span class='va'>self</span><span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_atleast_3d</span><span class="op">(</span><span class="va">self</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor or list of Tensors)</p></td>
-    </tr>
-    </table>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor or list of Tensors)</p></dd>
+</dl></div>
 
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diff --git a/reference/torch_avg_pool1d.html b/reference/torch_avg_pool1d.html
index 5ee772888..772f4a211 100644
--- a/reference/torch_avg_pool1d.html
+++ b/reference/torch_avg_pool1d.html
@@ -1,266 +1,139 @@
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Avg_pool1d</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_avg_pool1d.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Avg_pool1d</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_avg_pool1d.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Avg_pool1d</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_avg_pool1d</span><span class='op'>(</span>
-  <span class='va'>self</span>,
-  <span class='va'>kernel_size</span>,
-  stride <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span><span class='op'>)</span>,
-  padding <span class='op'>=</span> <span class='fl'>0L</span>,
-  ceil_mode <span class='op'>=</span> <span class='cn'>FALSE</span>,
-  count_include_pad <span class='op'>=</span> <span class='cn'>TRUE</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>input tensor of shape \((\mbox{minibatch} , \mbox{in\_channels} , iW)\)</p></td>
-    </tr>
-    <tr>
-      <th>kernel_size</th>
-      <td><p>the size of the window. Can be a single number or a tuple <code>(kW,)</code></p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>the stride of the window. Can be a single number or a tuple <code>(sW,)</code>. Default: <code>kernel_size</code></p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>implicit zero paddings on both sides of the input. Can be a single number or a tuple <code>(padW,)</code>. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>ceil_mode</th>
-      <td><p>when <code>TRUE</code>, will use <code>ceil</code> instead of <code>floor</code> to compute the output shape. Default: <code>FALSE</code></p></td>
-    </tr>
-    <tr>
-      <th>count_include_pad</th>
-      <td><p>when <code>TRUE</code>, will include the zero-padding in the averaging calculation. Default: <code>TRUE</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="avg-pool-d-input-kernel-size-stride-null-padding-ceil-mode-false-count-include-pad-true-gt-tensor-"><a class="anchor" href="#avg-pool-d-input-kernel-size-stride-null-padding-ceil-mode-false-count-include-pad-true-gt-tensor-"></a>avg_pool1d(input, kernel_size, stride=NULL, padding=0, ceil_mode=FALSE, count_include_pad=TRUE) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_avg_pool1d</span><span class="op">(</span>
+  <span class="va">self</span>,
+  <span class="va">kernel_size</span>,
+  stride <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="op">)</span>,
+  padding <span class="op">=</span> <span class="fl">0L</span>,
+  ceil_mode <span class="op">=</span> <span class="cn">FALSE</span>,
+  count_include_pad <span class="op">=</span> <span class="cn">TRUE</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>input tensor of shape \((\mbox{minibatch} , \mbox{in\_channels} , iW)\)</p></dd>
+<dt>kernel_size</dt>
+<dd><p>the size of the window. Can be a single number or a tuple <code>(kW,)</code></p></dd>
+<dt>stride</dt>
+<dd><p>the stride of the window. Can be a single number or a tuple <code>(sW,)</code>. Default: <code>kernel_size</code></p></dd>
+<dt>padding</dt>
+<dd><p>implicit zero paddings on both sides of the input. Can be a single number or a tuple <code>(padW,)</code>. Default: 0</p></dd>
+<dt>ceil_mode</dt>
+<dd><p>when <code>TRUE</code>, will use <code>ceil</code> instead of <code>floor</code> to compute the output shape. Default: <code>FALSE</code></p></dd>
+<dt>count_include_pad</dt>
+<dd><p>when <code>TRUE</code>, will include the zero-padding in the averaging calculation. Default: <code>TRUE</code></p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="avg-pool-d-input-kernel-size-stride-null-padding-ceil-mode-false-count-include-pad-true-gt-tensor-">avg_pool1d(input, kernel_size, stride=NULL, padding=0, ceil_mode=FALSE, count_include_pad=TRUE) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#avg-pool-d-input-kernel-size-stride-null-padding-ceil-mode-false-count-include-pad-true-gt-tensor-"></a></h2>
     
 
 
 <p>Applies a 1D average pooling over an input signal composed of several
 input planes.</p>
-<p>See <code><a href='nn_avg_pool1d.html'>nn_avg_pool1d()</a></code> for details and output shape.</p>
+<p>See <code><a href="nn_avg_pool1d.html">nn_avg_pool1d()</a></code> for details and output shape.</p>
+    </div>
 
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index 9901a0c5a..9bd7aca93 100644
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Baddbmm</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_baddbmm.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Baddbmm</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_baddbmm.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Baddbmm</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_baddbmm</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>batch1</span>, <span class='va'>batch2</span>, beta <span class='op'>=</span> <span class='fl'>1L</span>, alpha <span class='op'>=</span> <span class='fl'>1L</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the tensor to be added</p></td>
-    </tr>
-    <tr>
-      <th>batch1</th>
-      <td><p>(Tensor) the first batch of matrices to be multiplied</p></td>
-    </tr>
-    <tr>
-      <th>batch2</th>
-      <td><p>(Tensor) the second batch of matrices to be multiplied</p></td>
-    </tr>
-    <tr>
-      <th>beta</th>
-      <td><p>(Number, optional) multiplier for <code>input</code> (\(\beta\))</p></td>
-    </tr>
-    <tr>
-      <th>alpha</th>
-      <td><p>(Number, optional) multiplier for \(\mbox{batch1} \mathbin{@} \mbox{batch2}\) (\(\alpha\))</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="baddbmm-input-batch-batch-beta-alpha-out-null-gt-tensor-"><a class="anchor" href="#baddbmm-input-batch-batch-beta-alpha-out-null-gt-tensor-"></a>baddbmm(input, batch1, batch2, *, beta=1, alpha=1, out=NULL) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_baddbmm</span><span class="op">(</span><span class="va">self</span>, <span class="va">batch1</span>, <span class="va">batch2</span>, beta <span class="op">=</span> <span class="fl">1L</span>, alpha <span class="op">=</span> <span class="fl">1L</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the tensor to be added</p></dd>
+<dt>batch1</dt>
+<dd><p>(Tensor) the first batch of matrices to be multiplied</p></dd>
+<dt>batch2</dt>
+<dd><p>(Tensor) the second batch of matrices to be multiplied</p></dd>
+<dt>beta</dt>
+<dd><p>(Number, optional) multiplier for <code>input</code> (\(\beta\))</p></dd>
+<dt>alpha</dt>
+<dd><p>(Number, optional) multiplier for \(\mbox{batch1} \mathbin{@} \mbox{batch2}\) (\(\alpha\))</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="baddbmm-input-batch-batch-beta-alpha-out-null-gt-tensor-">baddbmm(input, batch1, batch2, *, beta=1, alpha=1, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#baddbmm-input-batch-batch-beta-alpha-out-null-gt-tensor-"></a></h2>
     
 
 
@@ -237,74 +119,69 @@ same as the scaling factors used in <code>torch_addbmm</code>.</p>
 $$
 For inputs of type <code>FloatTensor</code> or <code>DoubleTensor</code>, arguments <code>beta</code> and
 <code>alpha</code> must be real numbers, otherwise they should be integers.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>M</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>10</span>, <span class='fl'>3</span>, <span class='fl'>5</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>batch1</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>10</span>, <span class='fl'>3</span>, <span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>batch2</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>10</span>, <span class='fl'>4</span>, <span class='fl'>5</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_baddbmm</span><span class='op'>(</span><span class='va'>M</span>, <span class='va'>batch1</span>, <span class='va'>batch2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> (1,.,.) = </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   0.2303  1.7239  1.5486  5.0046  1.1075</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   0.6557 -0.9072  1.5080  0.1971  0.2756</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  -1.8457 -0.2563  0.4939 -0.6488  2.4169</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> (2,.,.) = </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   0.2341 -1.9074 -1.4471 -1.4061 -0.4679</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   0.8719  2.1115 -2.1465 -2.8538 -1.4090</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   1.3102  1.1644  0.4341 -0.7445 -0.9976</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> (3,.,.) = </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   0.9724  1.2531 -0.8415 -0.8904  0.6749</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  -0.4525  0.0394  2.2567 -0.2244 -0.6386</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   2.4410 -2.2580  3.8511  2.2041 -1.6416</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> (4,.,.) = </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   2.1128 -0.7692  0.4046  2.6846 -1.2503</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  -0.1032  0.5321 -1.1288  0.9782  0.5619</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  -2.3369  0.5111 -1.0765  0.0863 -0.0667</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> (5,.,.) = </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   4.2007  1.5837 -0.0225  0.9534  2.6164</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  -1.9576 -0.9105 -0.9193 -2.2984 -1.0685</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  -0.1479 -0.0081 -0.1107 -1.9811 -1.3001</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> (6,.,.) = </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  -1.9344  1.8697  1.0306 -0.1607  3.3705</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   1.8013  0.4175  1.8931 -0.8519 -2.5733</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   1.4983 -2.1090  6.1435  1.7658  1.2727</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> ... [the output was truncated (use n=-1 to disable)]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{10,3,5} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">M</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">10</span>, <span class="fl">3</span>, <span class="fl">5</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">batch1</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">10</span>, <span class="fl">3</span>, <span class="fl">4</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">batch2</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">10</span>, <span class="fl">4</span>, <span class="fl">5</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_baddbmm</span><span class="op">(</span><span class="va">M</span>, <span class="va">batch1</span>, <span class="va">batch2</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> (1,.,.) = </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   1.5314  2.1523  0.2472  0.4278 -3.3137</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.8508  1.5365 -1.0732  2.6646 -3.1006</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.1376  2.0982 -0.2566 -0.3295  2.0059</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> (2,.,.) = </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -1.1111 -1.7371  2.3423 -0.7417 -2.2889</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.6591 -1.2342  0.2033 -3.7738  1.1918</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.9252 -1.3643 -0.1002 -1.4271  1.9439</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> (3,.,.) = </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.7869  0.6415 -3.3768 -0.1575  0.1333</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.4960 -2.1156  2.8423 -1.0345  0.3262</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -2.2556 -2.6786 -1.3520 -2.4898 -4.4178</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> (4,.,.) = </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -3.0603 -2.5317  0.4861 -0.5448 -0.5457</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -1.7122 -3.5291 -0.6682  1.4289 -2.2926</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -3.4081 -1.2381 -0.2655 -0.2416 -1.3346</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> (5,.,.) = </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   2.3543 -1.4084 -5.9649 -1.7255  2.7500</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -2.4835  0.1321  5.2300 -2.0340 -0.5638</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -3.0828  0.0535  5.6805 -0.4303 -1.4146</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> (6,.,.) = </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -1.2848 -0.2140  0.2244 -0.9828 -1.6569</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.5352  0.3073  0.0673  2.4922  1.9925</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   2.7745  1.9500  1.8242  3.1233  3.6875</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> ... [the output was truncated (use n=-1 to disable)]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{10,3,5} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Bartlett_window</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/wrapers.R'><code>R/wrapers.R</code></a></small>
-    <div class="hidden name"><code>torch_bartlett_window.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Bartlett_window</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
+      <div class="d-none name"><code>torch_bartlett_window.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Bartlett_window</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_bartlett_window</span><span class='op'>(</span>
-  <span class='va'>window_length</span>,
-  periodic <span class='op'>=</span> <span class='cn'>TRUE</span>,
-  dtype <span class='op'>=</span> <span class='cn'>NULL</span>,
-  layout <span class='op'>=</span> <span class='fu'><a href='torch_layout.html'>torch_strided</a></span><span class='op'>(</span><span class='op'>)</span>,
-  device <span class='op'>=</span> <span class='cn'>NULL</span>,
-  requires_grad <span class='op'>=</span> <span class='cn'>FALSE</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>window_length</th>
-      <td><p>(int) the size of returned window</p></td>
-    </tr>
-    <tr>
-      <th>periodic</th>
-      <td><p>(bool, optional) If TRUE, returns a window to be used as periodic        function. If False, return a symmetric window.</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>). Only floating point types are supported.</p></td>
-    </tr>
-    <tr>
-      <th>layout</th>
-      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned window tensor. Only          <code>torch_strided</code> (dense layout) is supported.</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
-    </tr>
-    <tr>
-      <th>requires_grad</th>
-      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_bartlett_window</span><span class="op">(</span>
+  <span class="va">window_length</span>,
+  periodic <span class="op">=</span> <span class="cn">TRUE</span>,
+  dtype <span class="op">=</span> <span class="cn">NULL</span>,
+  layout <span class="op">=</span> <span class="fu"><a href="torch_layout.html">torch_strided</a></span><span class="op">(</span><span class="op">)</span>,
+  device <span class="op">=</span> <span class="cn">NULL</span>,
+  requires_grad <span class="op">=</span> <span class="cn">FALSE</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>window_length</dt>
+<dd><p>(int) the size of returned window</p></dd>
+<dt>periodic</dt>
+<dd><p>(bool, optional) If TRUE, returns a window to be used as periodic        function. If False, return a symmetric window.</p></dd>
+<dt>dtype</dt>
+<dd><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>). Only floating point types are supported.</p></dd>
+<dt>layout</dt>
+<dd><p>(<code>torch.layout</code>, optional) the desired layout of returned window tensor. Only          <code>torch_strided</code> (dense layout) is supported.</p></dd>
+<dt>device</dt>
+<dd><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></dd>
+<dt>requires_grad</dt>
+<dd><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     
-<pre>If `window_length` \eqn{=1}, the returned window contains a single value 1.
-</pre>
-
-    <h2 class="hasAnchor" id="bartlett-window-window-length-periodic-true-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"><a class="anchor" href="#bartlett-window-window-length-periodic-true-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"></a>bartlett_window(window_length, periodic=TRUE, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor </h2>
+<div class="sourceCode"><pre><code>If `window_length` \eqn{=1}, the returned window contains a single value 1.
+</code></pre></div>
 
+    </div>
+    <div class="section level2">
+    <h2 id="bartlett-window-window-length-periodic-true-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-">bartlett_window(window_length, periodic=TRUE, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#bartlett-window-window-length-periodic-true-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"></a></h2>
     
 
 
@@ -255,32 +136,25 @@ ready to be used as a periodic window with functions like
 above formula is in fact \(\mbox{window\_length} + 1\). Also, we always have
 <code>torch_bartlett_window(L, periodic=TRUE)</code> equal to
 <code>torch_bartlett_window(L + 1, periodic=False)[:-1])</code>.</p>
+    </div>
 
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Bernoulli</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_bernoulli.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Bernoulli</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_bernoulli.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Bernoulli</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_bernoulli</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>p</span>, generator <span class='op'>=</span> <span class='cn'>NULL</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor of probability values for the Bernoulli
-distribution</p></td>
-    </tr>
-    <tr>
-      <th>p</th>
-      <td><p>(Number) a probability value. If <code>p</code> is passed than it's used instead of
-the values in <code>self</code> tensor.</p></td>
-    </tr>
-    <tr>
-      <th>generator</th>
-      <td><p>(<code>torch.Generator</code>, optional) a pseudorandom number generator for sampling</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="bernoulli-input-generator-null-out-null-gt-tensor-"><a class="anchor" href="#bernoulli-input-generator-null-out-null-gt-tensor-"></a>bernoulli(input, *, generator=NULL, out=NULL) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_bernoulli</span><span class="op">(</span><span class="va">self</span>, <span class="va">p</span>, generator <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor of probability values for the Bernoulli
+distribution</p></dd>
+<dt>p</dt>
+<dd><p>(Number) a probability value. If <code>p</code> is passed than it's used instead of
+the values in <code>self</code> tensor.</p></dd>
+<dt>generator</dt>
+<dd><p>(<code>torch.Generator</code>, optional) a pseudorandom number generator for sampling</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="bernoulli-input-generator-null-out-null-gt-tensor-">bernoulli(input, *, generator=NULL, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#bernoulli-input-generator-null-out-null-gt-tensor-"></a></h2>
     
 
 
@@ -230,49 +116,44 @@ The returned <code>out</code> tensor only has values 0 or 1 and is of the same
 shape as <code>input</code>.</p>
 <p><code>out</code> can have integral <code>dtype</code>, but <code>input</code> must have floating
 point <code>dtype</code>.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span><span class='op'>$</span><span class='fu'>uniform_</span><span class='op'>(</span><span class='fl'>0</span>, <span class='fl'>1</span><span class='op'>)</span>  <span class='co'># generate a uniform random matrix with range c(0, 1)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='fu'>torch_bernoulli</span><span class='op'>(</span><span class='va'>a</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_ones.html'>torch_ones</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span> <span class='co'># probability of drawing "1" is 1</span></span>
-<span class='r-in'><span class='fu'>torch_bernoulli</span><span class='op'>(</span><span class='va'>a</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_zeros.html'>torch_zeros</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span> <span class='co'># probability of drawing "1" is 0</span></span>
-<span class='r-in'><span class='fu'>torch_bernoulli</span><span class='op'>(</span><span class='va'>a</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0  0  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0  0  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0  0  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{3,3} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_empty.html">torch_empty</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span><span class="op">$</span><span class="fu">uniform_</span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span><span class="op">)</span>  <span class="co"># generate a uniform random matrix with range c(0, 1)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="fu">torch_bernoulli</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_ones.html">torch_ones</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span> <span class="co"># probability of drawing "1" is 1</span></span>
+<span class="r-in"><span class="fu">torch_bernoulli</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_zeros.html">torch_zeros</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span> <span class="co"># probability of drawing "1" is 0</span></span>
+<span class="r-in"><span class="fu">torch_bernoulli</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0  0  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0  0  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0  0  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3,3} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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+    
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Bincount</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_bincount.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Bincount</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_bincount.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Bincount</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_bincount</span><span class='op'>(</span><span class='va'>self</span>, weights <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span><span class='op'>)</span>, minlength <span class='op'>=</span> <span class='fl'>0L</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) 1-d int tensor</p></td>
-    </tr>
-    <tr>
-      <th>weights</th>
-      <td><p>(Tensor) optional, weight for each value in the input tensor.        Should be of same size as input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>minlength</th>
-      <td><p>(int) optional, minimum number of bins. Should be non-negative.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="bincount-input-weights-null-minlength-gt-tensor-"><a class="anchor" href="#bincount-input-weights-null-minlength-gt-tensor-"></a>bincount(input, weights=NULL, minlength=0) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_bincount</span><span class="op">(</span><span class="va">self</span>, weights <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="op">)</span>, minlength <span class="op">=</span> <span class="fl">0L</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) 1-d int tensor</p></dd>
+<dt>weights</dt>
+<dd><p>(Tensor) optional, weight for each value in the input tensor.        Should be of same size as input tensor.</p></dd>
+<dt>minlength</dt>
+<dd><p>(int) optional, minimum number of bins. Should be non-negative.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="bincount-input-weights-null-minlength-gt-tensor-">bincount(input, weights=NULL, minlength=0) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#bincount-input-weights-null-minlength-gt-tensor-"></a></h2>
     
 
 
@@ -222,52 +108,48 @@ tensor of size 0. If <code>minlength</code> is specified, the number of bins is
 <code>out[n] += weights[i]</code> if <code>weights</code> is specified else
 <code>out[n] += 1</code>.</p>
 <p>.. include:: cuda_deterministic.rst</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>=</span> <span class='fu'><a href='torch_randint.html'>torch_randint</a></span><span class='op'>(</span><span class='fl'>0</span>, <span class='fl'>8</span>, <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span><span class='fl'>5</span><span class='op'>)</span>, dtype<span class='op'>=</span><span class='fu'><a href='torch_dtype.html'>torch_int64</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>weights</span> <span class='op'>=</span> <span class='fu'><a href='torch_linspace.html'>torch_linspace</a></span><span class='op'>(</span><span class='fl'>0</span>, <span class='fl'>1</span>, steps<span class='op'>=</span><span class='fl'>5</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>input</span></span>
-<span class='r-in'><span class='va'>weights</span></span>
-<span class='r-in'><span class='fu'>torch_bincount</span><span class='op'>(</span><span class='va'>input</span>, <span class='va'>weights</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>input</span><span class='op'>$</span><span class='fu'>bincount</span><span class='op'>(</span><span class='va'>weights</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.5000</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.0000</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.7500</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.0000</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1.0000</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.0000</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.2500</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{7} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">input</span> <span class="op">=</span> <span class="fu"><a href="torch_randint.html">torch_randint</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">8</span>, <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="fl">5</span><span class="op">)</span>, dtype<span class="op">=</span><span class="fu"><a href="torch_dtype.html">torch_int64</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">weights</span> <span class="op">=</span> <span class="fu"><a href="torch_linspace.html">torch_linspace</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span>, steps<span class="op">=</span><span class="fl">5</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">input</span></span>
+<span class="r-in"><span class="va">weights</span></span>
+<span class="r-in"><span class="fu">torch_bincount</span><span class="op">(</span><span class="va">input</span>, <span class="va">weights</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">input</span><span class="op">$</span><span class="fu">bincount</span><span class="op">(</span><span class="va">weights</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.7500</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.0000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.5000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.2500</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{8} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
 </div>
 
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+<div class="pkgdown-footer-right">
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diff --git a/reference/torch_bitwise_and.html b/reference/torch_bitwise_and.html
index 96bebc395..ff90480ba 100644
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+++ b/reference/torch_bitwise_and.html
@@ -1,242 +1,123 @@
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-    <h1>Bitwise_and</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_bitwise_and.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Bitwise_and</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_bitwise_and.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Bitwise_and</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_bitwise_and</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>other</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>NA the first input tensor</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>NA the second input tensor</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="bitwise-and-input-other-out-null-gt-tensor-"><a class="anchor" href="#bitwise-and-input-other-out-null-gt-tensor-"></a>bitwise_and(input, other, out=NULL) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_bitwise_and</span><span class="op">(</span><span class="va">self</span>, <span class="va">other</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>NA the first input tensor</p></dd>
+<dt>other</dt>
+<dd><p>NA the second input tensor</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="bitwise-and-input-other-out-null-gt-tensor-">bitwise_and(input, other, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#bitwise-and-input-other-out-null-gt-tensor-"></a></h2>
     
 
 
 <p>Computes the bitwise AND of <code>input</code> and <code>other</code>. The input tensor must be of
 integral or Boolean types. For bool tensors, it computes the logical AND.</p>
+    </div>
 
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-    <h1>Bitwise_not</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_bitwise_not.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Bitwise_not</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_bitwise_not.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Bitwise_not</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_bitwise_not</span><span class='op'>(</span><span class='va'>self</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="bitwise-not-input-out-null-gt-tensor-"><a class="anchor" href="#bitwise-not-input-out-null-gt-tensor-"></a>bitwise_not(input, out=NULL) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_bitwise_not</span><span class="op">(</span><span class="va">self</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="bitwise-not-input-out-null-gt-tensor-">bitwise_not(input, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#bitwise-not-input-out-null-gt-tensor-"></a></h2>
     
 
 
 <p>Computes the bitwise NOT of the given input tensor. The input tensor must be of
 integral or Boolean types. For bool tensors, it computes the logical NOT.</p>
+    </div>
 
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-    <h1>Bitwise_or</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_bitwise_or.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Bitwise_or</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_bitwise_or.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Bitwise_or</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_bitwise_or</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>other</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>NA the first input tensor</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>NA the second input tensor</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="bitwise-or-input-other-out-null-gt-tensor-"><a class="anchor" href="#bitwise-or-input-other-out-null-gt-tensor-"></a>bitwise_or(input, other, out=NULL) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_bitwise_or</span><span class="op">(</span><span class="va">self</span>, <span class="va">other</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>NA the first input tensor</p></dd>
+<dt>other</dt>
+<dd><p>NA the second input tensor</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="bitwise-or-input-other-out-null-gt-tensor-">bitwise_or(input, other, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#bitwise-or-input-other-out-null-gt-tensor-"></a></h2>
     
 
 
 <p>Computes the bitwise OR of <code>input</code> and <code>other</code>. The input tensor must be of
 integral or Boolean types. For bool tensors, it computes the logical OR.</p>
+    </div>
 
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Bitwise_xor</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_bitwise_xor.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Bitwise_xor</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_bitwise_xor.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Bitwise_xor</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_bitwise_xor</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>other</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>NA the first input tensor</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>NA the second input tensor</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="bitwise-xor-input-other-out-null-gt-tensor-"><a class="anchor" href="#bitwise-xor-input-other-out-null-gt-tensor-"></a>bitwise_xor(input, other, out=NULL) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_bitwise_xor</span><span class="op">(</span><span class="va">self</span>, <span class="va">other</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>NA the first input tensor</p></dd>
+<dt>other</dt>
+<dd><p>NA the second input tensor</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="bitwise-xor-input-other-out-null-gt-tensor-">bitwise_xor(input, other, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#bitwise-xor-input-other-out-null-gt-tensor-"></a></h2>
     
 
 
 <p>Computes the bitwise XOR of <code>input</code> and <code>other</code>. The input tensor must be of
 integral or Boolean types. For bool tensors, it computes the logical XOR.</p>
+    </div>
 
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Blackman_window</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/wrapers.R'><code>R/wrapers.R</code></a></small>
-    <div class="hidden name"><code>torch_blackman_window.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Blackman_window</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
+      <div class="d-none name"><code>torch_blackman_window.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Blackman_window</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_blackman_window</span><span class='op'>(</span>
-  <span class='va'>window_length</span>,
-  periodic <span class='op'>=</span> <span class='cn'>TRUE</span>,
-  dtype <span class='op'>=</span> <span class='cn'>NULL</span>,
-  layout <span class='op'>=</span> <span class='fu'><a href='torch_layout.html'>torch_strided</a></span><span class='op'>(</span><span class='op'>)</span>,
-  device <span class='op'>=</span> <span class='cn'>NULL</span>,
-  requires_grad <span class='op'>=</span> <span class='cn'>FALSE</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>window_length</th>
-      <td><p>(int) the size of returned window</p></td>
-    </tr>
-    <tr>
-      <th>periodic</th>
-      <td><p>(bool, optional) If TRUE, returns a window to be used as periodic        function. If False, return a symmetric window.</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>). Only floating point types are supported.</p></td>
-    </tr>
-    <tr>
-      <th>layout</th>
-      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned window tensor. Only          <code>torch_strided</code> (dense layout) is supported.</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
-    </tr>
-    <tr>
-      <th>requires_grad</th>
-      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_blackman_window</span><span class="op">(</span>
+  <span class="va">window_length</span>,
+  periodic <span class="op">=</span> <span class="cn">TRUE</span>,
+  dtype <span class="op">=</span> <span class="cn">NULL</span>,
+  layout <span class="op">=</span> <span class="fu"><a href="torch_layout.html">torch_strided</a></span><span class="op">(</span><span class="op">)</span>,
+  device <span class="op">=</span> <span class="cn">NULL</span>,
+  requires_grad <span class="op">=</span> <span class="cn">FALSE</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>window_length</dt>
+<dd><p>(int) the size of returned window</p></dd>
+<dt>periodic</dt>
+<dd><p>(bool, optional) If TRUE, returns a window to be used as periodic        function. If False, return a symmetric window.</p></dd>
+<dt>dtype</dt>
+<dd><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>). Only floating point types are supported.</p></dd>
+<dt>layout</dt>
+<dd><p>(<code>torch.layout</code>, optional) the desired layout of returned window tensor. Only          <code>torch_strided</code> (dense layout) is supported.</p></dd>
+<dt>device</dt>
+<dd><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></dd>
+<dt>requires_grad</dt>
+<dd><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     
-<pre>If `window_length` \eqn{=1}, the returned window contains a single value 1.
-</pre>
-
-    <h2 class="hasAnchor" id="blackman-window-window-length-periodic-true-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"><a class="anchor" href="#blackman-window-window-length-periodic-true-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"></a>blackman_window(window_length, periodic=TRUE, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor </h2>
+<div class="sourceCode"><pre><code>If `window_length` \eqn{=1}, the returned window contains a single value 1.
+</code></pre></div>
 
+    </div>
+    <div class="section level2">
+    <h2 id="blackman-window-window-length-periodic-true-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-">blackman_window(window_length, periodic=TRUE, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#blackman-window-window-length-periodic-true-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"></a></h2>
     
 
 
@@ -251,32 +132,25 @@ ready to be used as a periodic window with functions like
 above formula is in fact \(\mbox{window\_length} + 1\). Also, we always have
 <code>torch_blackman_window(L, periodic=TRUE)</code> equal to
 <code>torch_blackman_window(L + 1, periodic=False)[:-1])</code>.</p>
+    </div>
 
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+      </ul><form class="form-inline my-2 my-lg-0" role="search">
+        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
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+    
+  </div>
+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Block_diag</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_block_diag.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Block_diag</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_block_diag.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Create a block diagonal matrix from provided tensors.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_block_diag</span><span class='op'>(</span><span class='va'>tensors</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>tensors</th>
-      <td><p>(list of tensors) One or more tensors with 0, 1, or 2
-dimensions.</p></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>A</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/cbind.html'>rbind</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>0</span>, <span class='fl'>1</span><span class='op'>)</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>0</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>B</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/cbind.html'>rbind</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>4</span>, <span class='fl'>5</span><span class='op'>)</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>6</span>, <span class='fl'>7</span>, <span class='fl'>8</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>C</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fl'>7</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>D</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>2</span>, <span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>E</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/cbind.html'>rbind</a></span><span class='op'>(</span><span class='fl'>4</span>, <span class='fl'>5</span>, <span class='fl'>6</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_block_diag</span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span><span class='va'>A</span>, <span class='va'>B</span>, <span class='va'>C</span>, <span class='va'>D</span>, <span class='va'>E</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0  1  0  0  0  0  0  0  0  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1  0  0  0  0  0  0  0  0  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0  0  3  4  5  0  0  0  0  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0  0  6  7  8  0  0  0  0  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0  0  0  0  0  7  0  0  0  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0  0  0  0  0  0  1  2  3  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0  0  0  0  0  0  0  0  0  4</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0  0  0  0  0  0  0  0  0  5</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0  0  0  0  0  0  0  0  0  6</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{9,10} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_block_diag</span><span class="op">(</span><span class="va">tensors</span><span class="op">)</span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>tensors</dt>
+<dd><p>(list of tensors) One or more tensors with 0, 1, or 2
+dimensions.</p></dd>
+</dl></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">A</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/cbind.html" class="external-link">rbind</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">0</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">B</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/cbind.html" class="external-link">rbind</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">4</span>, <span class="fl">5</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">6</span>, <span class="fl">7</span>, <span class="fl">8</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">C</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fl">7</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">D</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">2</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">E</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/cbind.html" class="external-link">rbind</a></span><span class="op">(</span><span class="fl">4</span>, <span class="fl">5</span>, <span class="fl">6</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_block_diag</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">A</span>, <span class="va">B</span>, <span class="va">C</span>, <span class="va">D</span>, <span class="va">E</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0  1  0  0  0  0  0  0  0  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1  0  0  0  0  0  0  0  0  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0  0  3  4  5  0  0  0  0  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0  0  6  7  8  0  0  0  0  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0  0  0  0  0  7  0  0  0  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0  0  0  0  0  0  1  2  3  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0  0  0  0  0  0  0  0  0  4</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0  0  0  0  0  0  0  0  0  5</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0  0  0  0  0  0  0  0  0  6</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{9,10} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Bmm</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_bmm.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Bmm</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_bmm.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Bmm</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_bmm</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>mat2</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the first batch of matrices to be multiplied</p></td>
-    </tr>
-    <tr>
-      <th>mat2</th>
-      <td><p>(Tensor) the second batch of matrices to be multiplied</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_bmm</span><span class="op">(</span><span class="va">self</span>, <span class="va">mat2</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the first batch of matrices to be multiplied</p></dd>
+<dt>mat2</dt>
+<dd><p>(Tensor) the second batch of matrices to be multiplied</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p>This function does not broadcast .
-For broadcasting matrix products, see <code><a href='torch_matmul.html'>torch_matmul</a></code>.</p>
-    <h2 class="hasAnchor" id="bmm-input-mat-out-null-gt-tensor-"><a class="anchor" href="#bmm-input-mat-out-null-gt-tensor-"></a>bmm(input, mat2, out=NULL) -&gt; Tensor </h2>
-
+For broadcasting matrix products, see <code><a href="torch_matmul.html">torch_matmul</a></code>.</p>
+    </div>
+    <div class="section level2">
+    <h2 id="bmm-input-mat-out-null-gt-tensor-">bmm(input, mat2, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#bmm-input-mat-out-null-gt-tensor-"></a></h2>
     
 
 
@@ -223,74 +112,69 @@ the same number of matrices.</p>
 <p>$$
     \mbox{out}_i = \mbox{input}_i \mathbin{@} \mbox{mat2}_i
 $$</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>10</span>, <span class='fl'>3</span>, <span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>mat2</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>10</span>, <span class='fl'>4</span>, <span class='fl'>5</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>res</span> <span class='op'>=</span> <span class='fu'>torch_bmm</span><span class='op'>(</span><span class='va'>input</span>, <span class='va'>mat2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>res</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> (1,.,.) = </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   2.2655  1.9378  2.5198  1.3726 -3.8403</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   2.2778  1.7653  1.5891  3.5884 -5.3448</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   1.0759 -1.3745  0.5789 -0.8449  1.9402</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> (2,.,.) = </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   1.6359 -1.7192  1.0704 -0.4791  2.6113</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   1.5204  2.7068  0.0329 -1.1646 -1.2176</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   0.2371 -0.3059 -1.3119  0.5415 -0.7487</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> (3,.,.) = </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  -1.8744 -1.9695 -2.9007 -2.4763 -1.6933</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   3.3852  2.8630 -2.1903 -2.0737 -2.0241</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   1.6921  0.4611 -2.0360 -5.8404 -2.8988</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> (4,.,.) = </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   2.0901  3.3195  0.5547  3.4882  1.9725</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   0.3501 -0.0963 -0.6596  2.5902  1.8793</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  -0.2411 -0.6783  0.2302 -1.1901 -1.3515</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> (5,.,.) = </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  -1.2202 -0.2029 -0.6504 -0.2147 -2.4243</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  -1.6191 -1.0425 -2.1876  0.3959 -4.2393</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  -2.8500 -2.2471 -0.3194 -2.2912 -3.2552</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> (6,.,.) = </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  -1.5368 -2.2470 -2.0683 -2.7752  0.7073</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  -1.9494 -2.1587 -1.7321 -0.3033 -0.3193</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  -0.5220 -3.3152 -2.3078 -1.1679  0.2863</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> ... [the output was truncated (use n=-1 to disable)]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{10,3,5} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">input</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">10</span>, <span class="fl">3</span>, <span class="fl">4</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">mat2</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">10</span>, <span class="fl">4</span>, <span class="fl">5</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">res</span> <span class="op">=</span> <span class="fu">torch_bmm</span><span class="op">(</span><span class="va">input</span>, <span class="va">mat2</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">res</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> (1,.,.) = </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   1.1496 -1.3264 -0.0155  1.6350 -0.3408</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -3.4424 -2.9396 -1.0153  3.8839  0.2192</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.2311  1.1350 -1.5783 -0.9564 -1.1007</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> (2,.,.) = </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   1.0680 -0.0246 -3.8276 -0.6074  0.8695</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   1.8761 -0.8042  0.2076  2.3332 -0.0249</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -1.2795  2.4611  1.3843 -0.7258  1.1189</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> (3,.,.) = </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   2.0340 -3.7801  0.4020 -2.4461  0.6206</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   4.2814 -5.9884 -1.0261 -4.5182 -1.1445</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   2.2079 -0.4313  0.0599 -2.2269 -2.2408</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> (4,.,.) = </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.0438  1.1780  2.4251 -0.9283  0.1883</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.3184  1.2769 -1.6914  0.1830 -0.3450</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.4319  1.6848  1.9182  1.5409 -1.5786</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> (5,.,.) = </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   1.8508 -1.7860 -1.6277  3.3500 -3.9996</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   1.1662 -0.6075 -0.8746 -0.5356 -1.8053</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.5967 -1.2575  1.4572  2.6120  0.0918</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> (6,.,.) = </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -2.3077 -0.1633 -0.7897  1.3703 -2.5176</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.4733  2.0210 -5.1606 -1.5100 -8.7580</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -1.8399  2.0785 -0.8735 -2.3808  0.0939</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> ... [the output was truncated (use n=-1 to disable)]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{10,3,5} ]</span>
+</code></pre></div>
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 <div class="row">
-  <div class="col-md-9 contents">
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-    <h1>Broadcast_tensors</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_broadcast_tensors.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Broadcast_tensors</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_broadcast_tensors.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Broadcast_tensors</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_broadcast_tensors</span><span class='op'>(</span><span class='va'>tensors</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>tensors</th>
-      <td><p>a list containing any number of tensors of the same type</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="broadcast-tensors-tensors-gt-list-of-tensors-"><a class="anchor" href="#broadcast-tensors-tensors-gt-list-of-tensors-"></a>broadcast_tensors(tensors) -&gt; List of Tensors </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_broadcast_tensors</span><span class="op">(</span><span class="va">tensors</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>tensors</dt>
+<dd><p>a list containing any number of tensors of the same type</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="broadcast-tensors-tensors-gt-list-of-tensors-">broadcast_tensors(tensors) -&gt; List of Tensors <a class="anchor" aria-label="anchor" href="#broadcast-tensors-tensors-gt-list-of-tensors-"></a></h2>
     
 
 
 <p>Broadcasts the given tensors according to broadcasting-semantics.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span><span class='op'>(</span><span class='fl'>0</span>, <span class='fl'>3</span><span class='op'>)</span><span class='op'>$</span><span class='fu'>view</span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>y</span> <span class='op'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span><span class='op'>(</span><span class='fl'>0</span>, <span class='fl'>2</span><span class='op'>)</span><span class='op'>$</span><span class='fu'>view</span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>1</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>out</span> <span class='op'>=</span> <span class='fu'>torch_broadcast_tensors</span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span><span class='va'>x</span>, <span class='va'>y</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>out</span><span class='op'>[[</span><span class='fl'>1</span><span class='op'>]</span><span class='op'>]</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0  1  2  3</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0  1  2  3</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0  1  2  3</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{3,4} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">x</span> <span class="op">=</span> <span class="fu"><a href="torch_arange.html">torch_arange</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">3</span><span class="op">)</span><span class="op">$</span><span class="fu">view</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">4</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">y</span> <span class="op">=</span> <span class="fu"><a href="torch_arange.html">torch_arange</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">2</span><span class="op">)</span><span class="op">$</span><span class="fu">view</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">1</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">out</span> <span class="op">=</span> <span class="fu">torch_broadcast_tensors</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">y</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">out</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0  1  2  3</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0  1  2  3</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0  1  2  3</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3,4} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Bucketize</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_bucketize.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Bucketize</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_bucketize.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Bucketize</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_bucketize</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>boundaries</span>, out_int32 <span class='op'>=</span> <span class='cn'>FALSE</span>, right <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_bucketize</span><span class="op">(</span><span class="va">self</span>, <span class="va">boundaries</span>, out_int32 <span class="op">=</span> <span class="cn">FALSE</span>, right <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor or Scalar) N-D tensor or a Scalar containing the search value(s).</p></td>
-    </tr>
-    <tr>
-      <th>boundaries</th>
-      <td><p>(Tensor) 1-D tensor, must contain a monotonically increasing sequence.</p></td>
-    </tr>
-    <tr>
-      <th>out_int32</th>
-      <td><p>(bool, optional) – indicate the output data type. <code><a href='torch_dtype.html'>torch_int32()</a></code>
-if True, <code><a href='torch_dtype.html'>torch_int64()</a></code> otherwise. Default value is FALSE, i.e. default output
-data type is <code><a href='torch_dtype.html'>torch_int64()</a></code>.</p></td>
-    </tr>
-    <tr>
-      <th>right</th>
-      <td><p>(bool, optional) – if False, return the first suitable location
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor or Scalar) N-D tensor or a Scalar containing the search value(s).</p></dd>
+<dt>boundaries</dt>
+<dd><p>(Tensor) 1-D tensor, must contain a monotonically increasing sequence.</p></dd>
+<dt>out_int32</dt>
+<dd><p>(bool, optional) – indicate the output data type. <code><a href="torch_dtype.html">torch_int32()</a></code>
+if True, <code><a href="torch_dtype.html">torch_int64()</a></code> otherwise. Default value is FALSE, i.e. default output
+data type is <code><a href="torch_dtype.html">torch_int64()</a></code>.</p></dd>
+<dt>right</dt>
+<dd><p>(bool, optional) – if False, return the first suitable location
 that is found. If True, return the last such index. If no suitable index found,
 return 0 for non-numerical value (eg. nan, inf) or the size of boundaries
 (one pass the last index). In other words, if False, gets the lower bound index
 for each value in input from boundaries. If True, gets the upper bound index
-instead. Default value is False.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="bucketize-input-boundaries-out-int-false-right-false-out-none-gt-tensor-"><a class="anchor" href="#bucketize-input-boundaries-out-int-false-right-false-out-none-gt-tensor-"></a>bucketize(input, boundaries, *, out_int32=FALSE, right=FALSE, out=None) -&gt; Tensor </h2>
-
+instead. Default value is False.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="bucketize-input-boundaries-out-int-false-right-false-out-none-gt-tensor-">bucketize(input, boundaries, *, out_int32=FALSE, right=FALSE, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#bucketize-input-boundaries-out-int-false-right-false-out-none-gt-tensor-"></a></h2>
     
 
 
 <p>Returns the indices of the buckets to which each value in the <code>input</code> belongs, where the
 boundaries of the buckets are set by <code>boundaries</code>. Return a new tensor with the same size
 as <code>input</code>. If <code>right</code> is FALSE (default), then the left boundary is closed.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>boundaries</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>3</span>, <span class='fl'>5</span>, <span class='fl'>7</span>, <span class='fl'>9</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>boundaries</span></span>
-<span class='r-in'><span class='va'>v</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/cbind.html'>rbind</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>6</span>, <span class='fl'>9</span><span class='op'>)</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>6</span>, <span class='fl'>9</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>v</span></span>
-<span class='r-in'><span class='fu'>torch_bucketize</span><span class='op'>(</span><span class='va'>v</span>, <span class='va'>boundaries</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_bucketize</span><span class='op'>(</span><span class='va'>v</span>, <span class='va'>boundaries</span>, right<span class='op'>=</span><span class='cn'>TRUE</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  2  3  5</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  2  3  5</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPULongType{2,3} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">boundaries</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">3</span>, <span class="fl">5</span>, <span class="fl">7</span>, <span class="fl">9</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">boundaries</span></span>
+<span class="r-in"><span class="va">v</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/cbind.html" class="external-link">rbind</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">6</span>, <span class="fl">9</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">6</span>, <span class="fl">9</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">v</span></span>
+<span class="r-in"><span class="fu">torch_bucketize</span><span class="op">(</span><span class="va">v</span>, <span class="va">boundaries</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_bucketize</span><span class="op">(</span><span class="va">v</span>, <span class="va">boundaries</span>, right<span class="op">=</span><span class="cn">TRUE</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2  3  5</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2  3  5</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPULongType{2,3} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_can_cast.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Can_cast</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_can_cast.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Can_cast</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_can_cast</span><span class='op'>(</span><span class='va'>from</span>, <span class='va'>to</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>from</th>
-      <td><p>(dtype) The original <code>torch_dtype</code>.</p></td>
-    </tr>
-    <tr>
-      <th>to</th>
-      <td><p>(dtype) The target <code>torch_dtype</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="can-cast-from-to-gt-bool-"><a class="anchor" href="#can-cast-from-to-gt-bool-"></a>can_cast(from, to) -&gt; bool </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_can_cast</span><span class="op">(</span><span class="va">from</span>, <span class="va">to</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>from</dt>
+<dd><p>(dtype) The original <code>torch_dtype</code>.</p></dd>
+<dt>to</dt>
+<dd><p>(dtype) The target <code>torch_dtype</code>.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="can-cast-from-to-gt-bool-">can_cast(from, to) -&gt; bool <a class="anchor" aria-label="anchor" href="#can-cast-from-to-gt-bool-"></a></h2>
     
 
 
 <p>Determines if a type conversion is allowed under PyTorch casting rules
 described in the type promotion documentation .</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='fu'>torch_can_cast</span><span class='op'>(</span><span class='fu'><a href='torch_dtype.html'>torch_double</a></span><span class='op'>(</span><span class='op'>)</span>, <span class='fu'><a href='torch_dtype.html'>torch_float</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_can_cast</span><span class='op'>(</span><span class='fu'><a href='torch_dtype.html'>torch_float</a></span><span class='op'>(</span><span class='op'>)</span>, <span class='fu'><a href='torch_dtype.html'>torch_int</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [1] FALSE</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="fu">torch_can_cast</span><span class="op">(</span><span class="fu"><a href="torch_dtype.html">torch_double</a></span><span class="op">(</span><span class="op">)</span>, <span class="fu"><a href="torch_dtype.html">torch_float</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_can_cast</span><span class="op">(</span><span class="fu"><a href="torch_dtype.html">torch_float</a></span><span class="op">(</span><span class="op">)</span>, <span class="fu"><a href="torch_dtype.html">torch_int</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] FALSE</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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-      <div class="copyright">
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+    <footer><div class="pkgdown-footer-left">
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
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+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Cartesian_prod</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_cartesian_prod.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Do cartesian product of the given sequence of tensors.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_cartesian_prod</span><span class='op'>(</span><span class='va'>tensors</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>tensors</th>
-      <td><p>a list containing any number of 1 dimensional tensors.</p></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>2</span>, <span class='fl'>3</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>b</span> <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span>, <span class='fl'>5</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>tensor_a</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='va'>a</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>tensor_b</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='va'>b</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_cartesian_prod</span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span><span class='va'>tensor_a</span>, <span class='va'>tensor_b</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1  4</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1  5</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  2  4</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  2  5</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  3  4</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  3  5</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{6,2} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_cartesian_prod</span><span class="op">(</span><span class="va">tensors</span><span class="op">)</span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>tensors</dt>
+<dd><p>a list containing any number of 1 dimensional tensors.</p></dd>
+</dl></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">2</span>, <span class="fl">3</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">b</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span>, <span class="fl">5</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">tensor_a</span> <span class="op">=</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">tensor_b</span> <span class="op">=</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="va">b</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_cartesian_prod</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">tensor_a</span>, <span class="va">tensor_b</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1  4</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1  5</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2  4</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2  5</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  3  4</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  3  5</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{6,2} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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-    <div class="page-header">
-    <h1>Cat</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_cat.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Cat</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_cat.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Cat</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_cat</span><span class='op'>(</span><span class='va'>tensors</span>, dim <span class='op'>=</span> <span class='fl'>1L</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>tensors</th>
-      <td><p>(sequence of Tensors) any python sequence of tensors of the same type.        Non-empty tensors provided must have the same shape, except in the        cat dimension.</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int, optional) the dimension over which the tensors are concatenated</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="cat-tensors-dim-out-null-gt-tensor-"><a class="anchor" href="#cat-tensors-dim-out-null-gt-tensor-"></a>cat(tensors, dim=0, out=NULL) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_cat</span><span class="op">(</span><span class="va">tensors</span>, dim <span class="op">=</span> <span class="fl">1L</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>tensors</dt>
+<dd><p>(sequence of Tensors) any python sequence of tensors of the same type.        Non-empty tensors provided must have the same shape, except in the        cat dimension.</p></dd>
+<dt>dim</dt>
+<dd><p>(int, optional) the dimension over which the tensors are concatenated</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="cat-tensors-dim-out-null-gt-tensor-">cat(tensors, dim=0, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#cat-tensors-dim-out-null-gt-tensor-"></a></h2>
     
 
 
 <p>Concatenates the given sequence of <code>seq</code> tensors in the given dimension.
 All tensors must either have the same shape (except in the concatenating
 dimension) or be empty.</p>
-<p><code>torch_cat</code> can be seen as an inverse operation for <code><a href='torch_split.html'>torch_split()</a></code>
-and <code><a href='torch_chunk.html'>torch_chunk</a></code>.</p>
+<p><code>torch_cat</code> can be seen as an inverse operation for <code><a href="torch_split.html">torch_split()</a></code>
+and <code><a href="torch_chunk.html">torch_chunk</a></code>.</p>
 <p><code>torch_cat</code> can be best understood via examples.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>x</span></span>
-<span class='r-in'><span class='fu'>torch_cat</span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span><span class='va'>x</span>, <span class='va'>x</span>, <span class='va'>x</span><span class='op'>)</span>, <span class='fl'>1</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_cat</span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span><span class='va'>x</span>, <span class='va'>x</span>, <span class='va'>x</span><span class='op'>)</span>, <span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -1.4680 -1.7397  0.1790 -1.4680 -1.7397  0.1790 -1.4680 -1.7397  0.1790</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -1.3590  0.3286 -0.9002 -1.3590  0.3286 -0.9002 -1.3590  0.3286 -0.9002</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{2,9} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">x</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">x</span></span>
+<span class="r-in"><span class="fu">torch_cat</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">x</span>, <span class="va">x</span><span class="op">)</span>, <span class="fl">1</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_cat</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">x</span>, <span class="va">x</span><span class="op">)</span>, <span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.8862  0.3090 -0.9575  0.8862  0.3090 -0.9575  0.8862  0.3090 -0.9575</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.2062  0.1593  1.2549 -0.2062  0.1593  1.2549 -0.2062  0.1593  1.2549</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2,9} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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+  <main id="main" class="col-md-9"><div class="page-header">
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+      <div class="d-none name"><code>torch_cdist.Rd</code></div>
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-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Cdist</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_cdist</span><span class='op'>(</span><span class='va'>x1</span>, <span class='va'>x2</span>, p <span class='op'>=</span> <span class='fl'>2L</span>, compute_mode <span class='op'>=</span> <span class='cn'>NULL</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>x1</th>
-      <td><p>(Tensor) input tensor of shape \(B \times P \times M\).</p></td>
-    </tr>
-    <tr>
-      <th>x2</th>
-      <td><p>(Tensor) input tensor of shape \(B \times R \times M\).</p></td>
-    </tr>
-    <tr>
-      <th>p</th>
-      <td><p>NA p value for the p-norm distance to calculate between each vector pair        \(\in [0, \infty]\).</p></td>
-    </tr>
-    <tr>
-      <th>compute_mode</th>
-      <td><p>NA 'use_mm_for_euclid_dist_if_necessary' - will use matrix multiplication approach to calculate        euclidean distance (p = 2) if P &gt; 25 or R &gt; 25        'use_mm_for_euclid_dist' - will always use matrix multiplication approach to calculate        euclidean distance (p = 2)        'donot_use_mm_for_euclid_dist' - will never use matrix multiplication approach to calculate        euclidean distance (p = 2)        Default: use_mm_for_euclid_dist_if_necessary.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="test-"><a class="anchor" href="#test-"></a>TEST </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_cdist</span><span class="op">(</span><span class="va">x1</span>, <span class="va">x2</span>, p <span class="op">=</span> <span class="fl">2L</span>, compute_mode <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>x1</dt>
+<dd><p>(Tensor) input tensor of shape \(B \times P \times M\).</p></dd>
+<dt>x2</dt>
+<dd><p>(Tensor) input tensor of shape \(B \times R \times M\).</p></dd>
+<dt>p</dt>
+<dd><p>NA p value for the p-norm distance to calculate between each vector pair        \(\in [0, \infty]\).</p></dd>
+<dt>compute_mode</dt>
+<dd><p>NA 'use_mm_for_euclid_dist_if_necessary' - will use matrix multiplication approach to calculate        euclidean distance (p = 2) if P &gt; 25 or R &gt; 25        'use_mm_for_euclid_dist' - will always use matrix multiplication approach to calculate        euclidean distance (p = 2)        'donot_use_mm_for_euclid_dist' - will never use matrix multiplication approach to calculate        euclidean distance (p = 2)        Default: use_mm_for_euclid_dist_if_necessary.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="test-">TEST <a class="anchor" aria-label="anchor" href="#test-"></a></h2>
     
 
 
 <p>Computes batched the p-norm distance between each pair of the two collections of row vectors.</p>
+    </div>
 
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-    <div class="page-header">
-    <h1>Ceil</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_ceil.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Ceil</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_ceil.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Ceil</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_ceil</span><span class='op'>(</span><span class='va'>self</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="ceil-input-out-null-gt-tensor-"><a class="anchor" href="#ceil-input-out-null-gt-tensor-"></a>ceil(input, out=NULL) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_ceil</span><span class="op">(</span><span class="va">self</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="ceil-input-out-null-gt-tensor-">ceil(input, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#ceil-input-out-null-gt-tensor-"></a></h2>
     
 
 
@@ -210,46 +100,41 @@ the smallest integer greater than or equal to each element.</p>
 <p>$$
     \mbox{out}_{i} = \left\lceil \mbox{input}_{i} \right\rceil = \left\lfloor \mbox{input}_{i} \right\rfloor + 1
 $$</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='fu'>torch_ceil</span><span class='op'>(</span><span class='va'>a</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{4} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="fu">torch_ceil</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer>
-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
 </div>
 
-<div class="pkgdown">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p>
+<div class="pkgdown-footer-right">
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+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Celu</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_celu.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Celu</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_celu</span><span class='op'>(</span><span class='va'>self</span>, alpha <span class='op'>=</span> <span class='fl'>1</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>the input tensor</p></td>
-    </tr>
-    <tr>
-      <th>alpha</th>
-      <td><p>the alpha value for the CELU formulation. Default: 1.0</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="celu-input-alpha-gt-tensor-"><a class="anchor" href="#celu-input-alpha-gt-tensor-"></a>celu(input, alpha=1.) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_celu</span><span class="op">(</span><span class="va">self</span>, alpha <span class="op">=</span> <span class="fl">1L</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>the input tensor</p></dd>
+<dt>alpha</dt>
+<dd><p>the alpha value for the CELU formulation. Default: 1.0</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="celu-input-alpha-gt-tensor-">celu(input, alpha=1.) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#celu-input-alpha-gt-tensor-"></a></h2>
     
 
 
-<p>See <code><a href='nnf_celu.html'>nnf_celu()</a></code> for more info.</p>
+<p>See <code><a href="nnf_celu.html">nnf_celu()</a></code> for more info.</p>
+    </div>
 
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-  <div class="col-md-9 contents">
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-    <h1>Celu_</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_celu_.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Celu_</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_celu_.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Celu_</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_celu_</span><span class='op'>(</span><span class='va'>self</span>, alpha <span class='op'>=</span> <span class='fl'>1</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>the input tensor</p></td>
-    </tr>
-    <tr>
-      <th>alpha</th>
-      <td><p>the alpha value for the CELU formulation. Default: 1.0</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="celu-input-alpha-gt-tensor-"><a class="anchor" href="#celu-input-alpha-gt-tensor-"></a>celu_(input, alpha=1.) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_celu_</span><span class="op">(</span><span class="va">self</span>, alpha <span class="op">=</span> <span class="fl">1L</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>the input tensor</p></dd>
+<dt>alpha</dt>
+<dd><p>the alpha value for the CELU formulation. Default: 1.0</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="celu-input-alpha-gt-tensor-">celu_(input, alpha=1.) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#celu-input-alpha-gt-tensor-"></a></h2>
     
 
 
-<p>In-place version of <code><a href='torch_celu.html'>torch_celu()</a></code>.</p>
+<p>In-place version of <code><a href="torch_celu.html">torch_celu()</a></code>.</p>
+    </div>
 
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diff --git a/reference/torch_chain_matmul.html b/reference/torch_chain_matmul.html
index d9667d73b..bd02272f4 100644
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Chain_matmul</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_chain_matmul.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Chain_matmul</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_chain_matmul.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Chain_matmul</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_chain_matmul</span><span class='op'>(</span><span class='va'>matrices</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>matrices</th>
-      <td><p>(Tensors...) a sequence of 2 or more 2-D tensors whose product is to be determined.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="test-"><a class="anchor" href="#test-"></a>TEST </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_chain_matmul</span><span class="op">(</span><span class="va">matrices</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>matrices</dt>
+<dd><p>(Tensors...) a sequence of 2 or more 2-D tensors whose product is to be determined.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="test-">TEST <a class="anchor" aria-label="anchor" href="#test-"></a></h2>
     
 
 
@@ -210,47 +100,42 @@ using the matrix chain order algorithm which selects the order in which incurs t
 of arithmetic operations (<code>[CLRS]</code>_). Note that since this is a function to compute the product, \(N\)
 needs to be greater than or equal to 2; if equal to 2 then a trivial matrix-matrix product is returned.
 If \(N\) is 1, then this is a no-op - the original matrix is returned as is.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>b</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span>, <span class='fl'>5</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>c</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>5</span>, <span class='fl'>6</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>d</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>6</span>, <span class='fl'>7</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_chain_matmul</span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span><span class='va'>a</span>, <span class='va'>b</span>, <span class='va'>c</span>, <span class='va'>d</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  -0.0464   7.9599   2.9323  -1.1363   1.6676   1.3814   1.7171</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   1.4181  -1.4026  -0.4507   0.0454  -1.5513  -2.4753   0.6660</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  -4.9378  -6.3514   0.9593  15.1369   8.1517  16.9700  -2.7597</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{3,7} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">4</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">b</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span>, <span class="fl">5</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">c</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">5</span>, <span class="fl">6</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">d</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">6</span>, <span class="fl">7</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_chain_matmul</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">a</span>, <span class="va">b</span>, <span class="va">c</span>, <span class="va">d</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -1.5076  -0.1371  -1.8188   0.3816   2.4127  -4.2103  -0.8165</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   3.0752 -22.0085   5.1744 -14.5629 -20.3681  11.4027  -7.0557</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -1.5996  -2.4373  -1.0525  -1.2670   2.1774  -5.1985   0.4090</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3,7} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Channel_shuffle</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_channel_shuffle.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Channel_shuffle</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_channel_shuffle.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Channel_shuffle</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_channel_shuffle</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>groups</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor</p></td>
-    </tr>
-    <tr>
-      <th>groups</th>
-      <td><p>(int) number of groups to divide channels in and rearrange.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="divide-the-channels-in-a-tensor-of-shape-"><a class="anchor" href="#divide-the-channels-in-a-tensor-of-shape-"></a>Divide the channels in a tensor of shape </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_channel_shuffle</span><span class="op">(</span><span class="va">self</span>, <span class="va">groups</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor</p></dd>
+<dt>groups</dt>
+<dd><p>(int) number of groups to divide channels in and rearrange.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="divide-the-channels-in-a-tensor-of-shape-">Divide the channels in a tensor of shape <a class="anchor" aria-label="anchor" href="#divide-the-channels-in-a-tensor-of-shape-"></a></h2>
     <p>math:<code>(*, C , H, W)</code> :</p>
 <p>Divide the channels in a tensor of shape \((*, C , H, W)\)
 into g groups and rearrange them as \((*, C \frac g, g, H, W)\),
 while keeping the original tensor shape.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>4</span>, <span class='fl'>2</span>, <span class='fl'>2</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span> <span class='op'>&lt;-</span> <span class='fu'>torch_channel_shuffle</span><span class='op'>(</span><span class='va'>input</span>, <span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span><span class='op'>(</span><span class='va'>output</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> (1,1,.,.) = </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  -1.1414  1.5136</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  -0.5447 -0.6636</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> (1,2,.,.) = </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   0.8419  0.4216</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   0.8977 -1.5269</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> (1,3,.,.) = </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   0.6883 -0.0264</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  -0.4067  1.4065</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> (1,4,.,.) = </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   0.0684  0.6940</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  -1.4859  0.4162</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{1,4,2,2} ]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> (1,1,.,.) = </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  -1.1414  1.5136</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  -0.5447 -0.6636</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> (1,2,.,.) = </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   0.6883 -0.0264</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  -0.4067  1.4065</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> (1,3,.,.) = </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   0.8419  0.4216</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   0.8977 -1.5269</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> (1,4,.,.) = </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   0.0684  0.6940</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  -1.4859  0.4162</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{1,4,2,2} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">4</span>, <span class="fl">2</span>, <span class="fl">2</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">input</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span> <span class="op">&lt;-</span> <span class="fu">torch_channel_shuffle</span><span class="op">(</span><span class="va">input</span>, <span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">output</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> (1,1,.,.) = </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.0450 -1.6163</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   1.6970 -2.3007</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> (1,2,.,.) = </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.0475 -1.6849</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.1281  0.1671</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> (1,3,.,.) = </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.4841 -0.6150</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.5135  0.4203</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> (1,4,.,.) = </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.2222 -0.4254</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   2.1632  0.2921</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{1,4,2,2} ]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> (1,1,.,.) = </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.0450 -1.6163</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   1.6970 -2.3007</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> (1,2,.,.) = </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.4841 -0.6150</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.5135  0.4203</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> (1,3,.,.) = </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.0475 -1.6849</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.1281  0.1671</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> (1,4,.,.) = </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.2222 -0.4254</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   2.1632  0.2921</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{1,4,2,2} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Cholesky</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_cholesky.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Cholesky</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_cholesky.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Cholesky</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_cholesky</span><span class='op'>(</span><span class='va'>self</span>, upper <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor \(A\) of size \((*, n, n)\) where <code>*</code> is zero or more
-batch dimensions consisting of symmetric positive-definite matrices.</p></td>
-    </tr>
-    <tr>
-      <th>upper</th>
-      <td><p>(bool, optional) flag that indicates whether to return a
-upper or lower triangular matrix. Default: <code>FALSE</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="cholesky-input-upper-false-out-null-gt-tensor-"><a class="anchor" href="#cholesky-input-upper-false-out-null-gt-tensor-"></a>cholesky(input, upper=False, out=NULL) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_cholesky</span><span class="op">(</span><span class="va">self</span>, upper <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor \(A\) of size \((*, n, n)\) where <code>*</code> is zero or more
+batch dimensions consisting of symmetric positive-definite matrices.</p></dd>
+<dt>upper</dt>
+<dd><p>(bool, optional) flag that indicates whether to return a
+upper or lower triangular matrix. Default: <code>FALSE</code></p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="cholesky-input-upper-false-out-null-gt-tensor-">cholesky(input, upper=False, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#cholesky-input-upper-false-out-null-gt-tensor-"></a></h2>
     
 
 
@@ -228,50 +116,45 @@ matrices, then the returned tensor will be composed of upper-triangular Cholesky
 of each of the individual matrices. Similarly, when <code>upper</code> is <code>FALSE</code>, the returned
 tensor will be composed of lower-triangular Cholesky factors of each of the individual
 matrices.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_mm.html'>torch_mm</a></span><span class='op'>(</span><span class='va'>a</span>, <span class='va'>a</span><span class='op'>$</span><span class='fu'>t</span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='co'># make symmetric positive-definite</span></span>
-<span class='r-in'><span class='va'>l</span> <span class='op'>=</span> <span class='fu'>torch_cholesky</span><span class='op'>(</span><span class='va'>a</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='va'>l</span></span>
-<span class='r-in'><span class='fu'><a href='torch_mm.html'>torch_mm</a></span><span class='op'>(</span><span class='va'>l</span>, <span class='va'>l</span><span class='op'>$</span><span class='fu'>t</span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>2</span>, <span class='fl'>2</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='cn'>FALSE</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_matmul.html'>torch_matmul</a></span><span class='op'>(</span><span class='va'>a</span>, <span class='va'>a</span><span class='op'>$</span><span class='fu'>transpose</span><span class='op'>(</span><span class='op'>-</span><span class='fl'>1</span>, <span class='op'>-</span><span class='fl'>2</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>+</span> <span class='fl'>1e-03</span> <span class='co'># make symmetric positive-definite</span></span>
-<span class='r-in'><span class='va'>l</span> <span class='op'>=</span> <span class='fu'>torch_cholesky</span><span class='op'>(</span><span class='va'>a</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>z</span> <span class='op'>=</span> <span class='fu'><a href='torch_matmul.html'>torch_matmul</a></span><span class='op'>(</span><span class='va'>l</span>, <span class='va'>l</span><span class='op'>$</span><span class='fu'>transpose</span><span class='op'>(</span><span class='op'>-</span><span class='fl'>1</span>, <span class='op'>-</span><span class='fl'>2</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'><a href='torch_max.html'>torch_max</a></span><span class='op'>(</span><span class='fu'><a href='torch_abs.html'>torch_abs</a></span><span class='op'>(</span><span class='va'>z</span> <span class='op'>-</span> <span class='va'>a</span><span class='op'>)</span><span class='op'>)</span> <span class='co'># Max non-zero</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_mm.html">torch_mm</a></span><span class="op">(</span><span class="va">a</span>, <span class="va">a</span><span class="op">$</span><span class="fu">t</span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="co"># make symmetric positive-definite</span></span>
+<span class="r-in"><span class="va">l</span> <span class="op">=</span> <span class="fu">torch_cholesky</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="va">l</span></span>
+<span class="r-in"><span class="fu"><a href="torch_mm.html">torch_mm</a></span><span class="op">(</span><span class="va">l</span>, <span class="va">l</span><span class="op">$</span><span class="fu">t</span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">2</span>, <span class="fl">2</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_matmul.html">torch_matmul</a></span><span class="op">(</span><span class="va">a</span>, <span class="va">a</span><span class="op">$</span><span class="fu">transpose</span><span class="op">(</span><span class="op">-</span><span class="fl">1</span>, <span class="op">-</span><span class="fl">2</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span> <span class="fl">1e-03</span> <span class="co"># make symmetric positive-definite</span></span>
+<span class="r-in"><span class="va">l</span> <span class="op">=</span> <span class="fu">torch_cholesky</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">z</span> <span class="op">=</span> <span class="fu"><a href="torch_matmul.html">torch_matmul</a></span><span class="op">(</span><span class="va">l</span>, <span class="va">l</span><span class="op">$</span><span class="fu">transpose</span><span class="op">(</span><span class="op">-</span><span class="fl">1</span>, <span class="op">-</span><span class="fl">2</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu"><a href="torch_max.html">torch_max</a></span><span class="op">(</span><span class="fu"><a href="torch_abs.html">torch_abs</a></span><span class="op">(</span><span class="va">z</span> <span class="op">-</span> <span class="va">a</span><span class="op">)</span><span class="op">)</span> <span class="co"># Max non-zero</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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diff --git a/reference/torch_cholesky_inverse.html b/reference/torch_cholesky_inverse.html
index ed7bd078b..aa02e2e7e 100644
--- a/reference/torch_cholesky_inverse.html
+++ b/reference/torch_cholesky_inverse.html
@@ -1,211 +1,99 @@
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 <!DOCTYPE html>
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-
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Cholesky_inverse</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_cholesky_inverse.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Cholesky_inverse</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_cholesky_inverse.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Cholesky_inverse</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_cholesky_inverse</span><span class='op'>(</span><span class='va'>self</span>, upper <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input 2-D tensor \(u\), a upper or lower triangular           Cholesky factor</p></td>
-    </tr>
-    <tr>
-      <th>upper</th>
-      <td><p>(bool, optional) whether to return a lower (default) or upper triangular matrix</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="cholesky-inverse-input-upper-false-out-null-gt-tensor-"><a class="anchor" href="#cholesky-inverse-input-upper-false-out-null-gt-tensor-"></a>cholesky_inverse(input, upper=False, out=NULL) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_cholesky_inverse</span><span class="op">(</span><span class="va">self</span>, upper <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input 2-D tensor \(u\), a upper or lower triangular           Cholesky factor</p></dd>
+<dt>upper</dt>
+<dd><p>(bool, optional) whether to return a lower (default) or upper triangular matrix</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="cholesky-inverse-input-upper-false-out-null-gt-tensor-">cholesky_inverse(input, upper=False, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#cholesky-inverse-input-upper-false-out-null-gt-tensor-"></a></h2>
     
 
 
@@ -222,45 +110,40 @@ triangular such that the returned tensor is</p>
 <p>$$
     inv = (u^T u)^{{-1}}
 $$</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='cn'>FALSE</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_mm.html'>torch_mm</a></span><span class='op'>(</span><span class='va'>a</span>, <span class='va'>a</span><span class='op'>$</span><span class='fu'>t</span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>+</span> <span class='fl'>1e-05</span> <span class='op'>*</span> <span class='fu'><a href='torch_eye.html'>torch_eye</a></span><span class='op'>(</span><span class='fl'>3</span><span class='op'>)</span> <span class='co'># make symmetric positive definite</span></span>
-<span class='r-in'><span class='va'>u</span> <span class='op'>=</span> <span class='fu'><a href='torch_cholesky.html'>torch_cholesky</a></span><span class='op'>(</span><span class='va'>a</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='fu'>torch_cholesky_inverse</span><span class='op'>(</span><span class='va'>u</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span><span class='op'>$</span><span class='fu'>inverse</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_mm.html">torch_mm</a></span><span class="op">(</span><span class="va">a</span>, <span class="va">a</span><span class="op">$</span><span class="fu">t</span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span> <span class="fl">1e-05</span> <span class="op">*</span> <span class="fu"><a href="torch_eye.html">torch_eye</a></span><span class="op">(</span><span class="fl">3</span><span class="op">)</span> <span class="co"># make symmetric positive definite</span></span>
+<span class="r-in"><span class="va">u</span> <span class="op">=</span> <span class="fu"><a href="torch_cholesky.html">torch_cholesky</a></span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="fu">torch_cholesky_inverse</span><span class="op">(</span><span class="va">u</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span><span class="op">$</span><span class="fu">inverse</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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-      <div class="copyright">
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+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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+<div class="pkgdown-footer-right">
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diff --git a/reference/torch_cholesky_solve.html b/reference/torch_cholesky_solve.html
index 3892dfade..f64573ac0 100644
--- a/reference/torch_cholesky_solve.html
+++ b/reference/torch_cholesky_solve.html
@@ -1,215 +1,101 @@
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Cholesky_solve</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_cholesky_solve.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Cholesky_solve</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_cholesky_solve.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Cholesky_solve</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_cholesky_solve</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>input2</span>, upper <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) input matrix \(b\) of size \((*, m, k)\),                where \(*\) is zero or more batch dimensions</p></td>
-    </tr>
-    <tr>
-      <th>input2</th>
-      <td><p>(Tensor) input matrix \(u\) of size \((*, m, m)\),                where \(*\) is zero of more batch dimensions composed of                upper or lower triangular Cholesky factor</p></td>
-    </tr>
-    <tr>
-      <th>upper</th>
-      <td><p>(bool, optional) whether to consider the Cholesky factor as a                            lower or upper triangular matrix. Default: <code>FALSE</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="cholesky-solve-input-input-upper-false-out-null-gt-tensor-"><a class="anchor" href="#cholesky-solve-input-input-upper-false-out-null-gt-tensor-"></a>cholesky_solve(input, input2, upper=False, out=NULL) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_cholesky_solve</span><span class="op">(</span><span class="va">self</span>, <span class="va">input2</span>, upper <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) input matrix \(b\) of size \((*, m, k)\),                where \(*\) is zero or more batch dimensions</p></dd>
+<dt>input2</dt>
+<dd><p>(Tensor) input matrix \(u\) of size \((*, m, m)\),                where \(*\) is zero of more batch dimensions composed of                upper or lower triangular Cholesky factor</p></dd>
+<dt>upper</dt>
+<dd><p>(bool, optional) whether to consider the Cholesky factor as a                            lower or upper triangular matrix. Default: <code>FALSE</code>.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="cholesky-solve-input-input-upper-false-out-null-gt-tensor-">cholesky_solve(input, input2, upper=False, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#cholesky-solve-input-input-upper-false-out-null-gt-tensor-"></a></h2>
     
 
 
@@ -228,50 +114,45 @@ $$
 <code>torch_cholesky_solve(b, u)</code> can take in 2D inputs <code>b, u</code> or inputs that are
 batches of 2D matrices. If the inputs are batches, then returns
 batched outputs <code>c</code></p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_mm.html'>torch_mm</a></span><span class='op'>(</span><span class='va'>a</span>, <span class='va'>a</span><span class='op'>$</span><span class='fu'>t</span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='co'># make symmetric positive definite</span></span>
-<span class='r-in'><span class='va'>u</span> <span class='op'>=</span> <span class='fu'><a href='torch_cholesky.html'>torch_cholesky</a></span><span class='op'>(</span><span class='va'>a</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='va'>b</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>2</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>b</span></span>
-<span class='r-in'><span class='fu'>torch_cholesky_solve</span><span class='op'>(</span><span class='va'>b</span>, <span class='va'>u</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'><a href='torch_mm.html'>torch_mm</a></span><span class='op'>(</span><span class='va'>a</span><span class='op'>$</span><span class='fu'>inverse</span><span class='op'>(</span><span class='op'>)</span>, <span class='va'>b</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   2.3262 -24.1792</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  -3.4086  18.5347</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  -2.2180  12.0405</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{3,2} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_mm.html">torch_mm</a></span><span class="op">(</span><span class="va">a</span>, <span class="va">a</span><span class="op">$</span><span class="fu">t</span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="co"># make symmetric positive definite</span></span>
+<span class="r-in"><span class="va">u</span> <span class="op">=</span> <span class="fu"><a href="torch_cholesky.html">torch_cholesky</a></span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="va">b</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">2</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">b</span></span>
+<span class="r-in"><span class="fu">torch_cholesky_solve</span><span class="op">(</span><span class="va">b</span>, <span class="va">u</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu"><a href="torch_mm.html">torch_mm</a></span><span class="op">(</span><span class="va">a</span><span class="op">$</span><span class="fu">inverse</span><span class="op">(</span><span class="op">)</span>, <span class="va">b</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.1763 -0.5688</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.0367 -0.5543</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -1.7456  1.3668</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3,2} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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index 030f7edf8..a5418b630 100644
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-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Chunk</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_chunk.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Chunk</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_chunk.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Chunk</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_chunk</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>chunks</span>, dim <span class='op'>=</span> <span class='fl'>1L</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the tensor to split</p></td>
-    </tr>
-    <tr>
-      <th>chunks</th>
-      <td><p>(int) number of chunks to return</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) dimension along which to split the tensor</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="chunk-input-chunks-dim-gt-list-of-tensors-"><a class="anchor" href="#chunk-input-chunks-dim-gt-list-of-tensors-"></a>chunk(input, chunks, dim=0) -&gt; List of Tensors </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_chunk</span><span class="op">(</span><span class="va">self</span>, <span class="va">chunks</span>, dim <span class="op">=</span> <span class="fl">1L</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the tensor to split</p></dd>
+<dt>chunks</dt>
+<dd><p>(int) number of chunks to return</p></dd>
+<dt>dim</dt>
+<dd><p>(int) dimension along which to split the tensor</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="chunk-input-chunks-dim-gt-list-of-tensors-">chunk(input, chunks, dim=0) -&gt; List of Tensors <a class="anchor" aria-label="anchor" href="#chunk-input-chunks-dim-gt-list-of-tensors-"></a></h2>
     
 
 
@@ -217,32 +103,25 @@
 the input tensor.</p>
 <p>Last chunk will be smaller if the tensor size along the given dimension
 <code>dim</code> is not divisible by <code>chunks</code>.</p>
+    </div>
 
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-      <h2 data-toc-skip>Contents</h2>
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+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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+<div class="pkgdown-footer-right">
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diff --git a/reference/torch_clamp.html b/reference/torch_clamp.html
index 0e4375bbc..72fe3e597 100644
--- a/reference/torch_clamp.html
+++ b/reference/torch_clamp.html
@@ -1,215 +1,101 @@
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Clamp</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_clamp.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Clamp</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_clamp.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Clamp</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_clamp</span><span class='op'>(</span><span class='va'>self</span>, min <span class='op'>=</span> <span class='cn'>NULL</span>, max <span class='op'>=</span> <span class='cn'>NULL</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>min</th>
-      <td><p>(Number) lower-bound of the range to be clamped to</p></td>
-    </tr>
-    <tr>
-      <th>max</th>
-      <td><p>(Number) upper-bound of the range to be clamped to</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="clamp-input-min-max-out-null-gt-tensor-"><a class="anchor" href="#clamp-input-min-max-out-null-gt-tensor-"></a>clamp(input, min, max, out=NULL) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_clamp</span><span class="op">(</span><span class="va">self</span>, min <span class="op">=</span> <span class="cn">NULL</span>, max <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+<dt>min</dt>
+<dd><p>(Number) lower-bound of the range to be clamped to</p></dd>
+<dt>max</dt>
+<dd><p>(Number) upper-bound of the range to be clamped to</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="clamp-input-min-max-out-null-gt-tensor-">clamp(input, min, max, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#clamp-input-min-max-out-null-gt-tensor-"></a></h2>
     
 
 
@@ -225,72 +111,69 @@ a resulting tensor:</p>
 $$
 If <code>input</code> is of type <code>FloatTensor</code> or <code>DoubleTensor</code>, args <code>min</code>
 and <code>max</code> must be real numbers, otherwise they should be integers.</p>
-    <h2 class="hasAnchor" id="clamp-input-min-out-null-gt-tensor-"><a class="anchor" href="#clamp-input-min-out-null-gt-tensor-"></a>clamp(input, *, min, out=NULL) -&gt; Tensor </h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="clamp-input-min-out-null-gt-tensor-">clamp(input, *, min, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#clamp-input-min-out-null-gt-tensor-"></a></h2>
     
 
 
 <p>Clamps all elements in <code>input</code> to be larger or equal <code>min</code>.</p>
 <p>If <code>input</code> is of type <code>FloatTensor</code> or <code>DoubleTensor</code>, <code>value</code>
 should be a real number, otherwise it should be an integer.</p>
-    <h2 class="hasAnchor" id="clamp-input-max-out-null-gt-tensor-"><a class="anchor" href="#clamp-input-max-out-null-gt-tensor-"></a>clamp(input, *, max, out=NULL) -&gt; Tensor </h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="clamp-input-max-out-null-gt-tensor-">clamp(input, *, max, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#clamp-input-max-out-null-gt-tensor-"></a></h2>
     
 
 
 <p>Clamps all elements in <code>input</code> to be smaller or equal <code>max</code>.</p>
 <p>If <code>input</code> is of type <code>FloatTensor</code> or <code>DoubleTensor</code>, <code>value</code>
 should be a real number, otherwise it should be an integer.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='fu'>torch_clamp</span><span class='op'>(</span><span class='va'>a</span>, min<span class='op'>=</span><span class='op'>-</span><span class='fl'>0.5</span>, max<span class='op'>=</span><span class='fl'>0.5</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='fu'>torch_clamp</span><span class='op'>(</span><span class='va'>a</span>, min<span class='op'>=</span><span class='fl'>0.5</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='fu'>torch_clamp</span><span class='op'>(</span><span class='va'>a</span>, max<span class='op'>=</span><span class='fl'>0.5</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.5000</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.1205</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -1.5186</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.5000</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{4} ]</span>
-</pre>
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
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+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="fu">torch_clamp</span><span class="op">(</span><span class="va">a</span>, min<span class="op">=</span><span class="op">-</span><span class="fl">0.5</span>, max<span class="op">=</span><span class="fl">0.5</span><span class="op">)</span></span>
+<span class="r-in"></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="fu">torch_clamp</span><span class="op">(</span><span class="va">a</span>, min<span class="op">=</span><span class="fl">0.5</span><span class="op">)</span></span>
+<span class="r-in"></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="fu">torch_clamp</span><span class="op">(</span><span class="va">a</span>, max<span class="op">=</span><span class="fl">0.5</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.5000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.5000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0811</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.7677</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Clip</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_clip.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Clip</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_clip.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Clip</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_clip</span><span class='op'>(</span><span class='va'>self</span>, min <span class='op'>=</span> <span class='cn'>NULL</span>, max <span class='op'>=</span> <span class='cn'>NULL</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>min</th>
-      <td><p>(Number) lower-bound of the range to be clamped to</p></td>
-    </tr>
-    <tr>
-      <th>max</th>
-      <td><p>(Number) upper-bound of the range to be clamped to</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="clip-input-min-max-out-none-gt-tensor-"><a class="anchor" href="#clip-input-min-max-out-none-gt-tensor-"></a>clip(input, min, max, *, out=None) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_clip</span><span class="op">(</span><span class="va">self</span>, min <span class="op">=</span> <span class="cn">NULL</span>, max <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+<dt>min</dt>
+<dd><p>(Number) lower-bound of the range to be clamped to</p></dd>
+<dt>max</dt>
+<dd><p>(Number) upper-bound of the range to be clamped to</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="clip-input-min-max-out-none-gt-tensor-">clip(input, min, max, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#clip-input-min-max-out-none-gt-tensor-"></a></h2>
     
 
 
-<p>Alias for <code><a href='torch_clamp.html'>torch_clamp()</a></code>.</p>
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 <div class="row">
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-    <h1>Clone</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_clone.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Clone</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_clone.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Clone</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_clone</span><span class='op'>(</span><span class='va'>self</span>, memory_format <span class='op'>=</span> <span class='cn'>NULL</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>memory_format</th>
-      <td><p>a torch memory format. see <code><a href='torch_memory_format.html'>torch_preserve_format()</a></code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_clone</span><span class="op">(</span><span class="va">self</span>, memory_format <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+<dt>memory_format</dt>
+<dd><p>a torch memory format. see <code><a href="torch_memory_format.html">torch_preserve_format()</a></code>.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p>This function is differentiable, so gradients will flow back from the
 result of this operation to <code>input</code>. To create a tensor without an
 autograd relationship to <code>input</code> see <code>Tensor$detach</code>.</p>
-    <h2 class="hasAnchor" id="clone-input-memory-format-torch-preserve-format-gt-tensor-"><a class="anchor" href="#clone-input-memory-format-torch-preserve-format-gt-tensor-"></a>clone(input, *, memory_format=torch.preserve_format) -&gt; Tensor </h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="clone-input-memory-format-torch-preserve-format-gt-tensor-">clone(input, *, memory_format=torch.preserve_format) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#clone-input-memory-format-torch-preserve-format-gt-tensor-"></a></h2>
     
 
 
 <p>Returns a copy of <code>input</code>.</p>
+    </div>
 
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diff --git a/reference/torch_combinations.html b/reference/torch_combinations.html
index f964ca4dd..585ab8887 100644
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Combinations</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_combinations.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Combinations</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_combinations.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Combinations</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_combinations</span><span class='op'>(</span><span class='va'>self</span>, r <span class='op'>=</span> <span class='fl'>2L</span>, with_replacement <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) 1D vector.</p></td>
-    </tr>
-    <tr>
-      <th>r</th>
-      <td><p>(int, optional) number of elements to combine</p></td>
-    </tr>
-    <tr>
-      <th>with_replacement</th>
-      <td><p>(boolean, optional) whether to allow duplication in combination</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="combinations-input-r-with-replacement-false-gt-seq-"><a class="anchor" href="#combinations-input-r-with-replacement-false-gt-seq-"></a>combinations(input, r=2, with_replacement=False) -&gt; seq </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_combinations</span><span class="op">(</span><span class="va">self</span>, r <span class="op">=</span> <span class="fl">2L</span>, with_replacement <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) 1D vector.</p></dd>
+<dt>r</dt>
+<dd><p>(int, optional) number of elements to combine</p></dd>
+<dt>with_replacement</dt>
+<dd><p>(boolean, optional) whether to allow duplication in combination</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="combinations-input-r-with-replacement-false-gt-seq-">combinations(input, r=2, with_replacement=False) -&gt; seq <a class="anchor" aria-label="anchor" href="#combinations-input-r-with-replacement-false-gt-seq-"></a></h2>
     
 
 
 <p>Compute combinations of length \(r\) of the given tensor. The behavior is similar to
 python's <code>itertools.combinations</code> when <code>with_replacement</code> is set to <code>False</code>, and
 <code>itertools.combinations_with_replacement</code> when <code>with_replacement</code> is set to <code>TRUE</code>.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>2</span>, <span class='fl'>3</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>tensor_a</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='va'>a</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_combinations</span><span class='op'>(</span><span class='va'>tensor_a</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_combinations</span><span class='op'>(</span><span class='va'>tensor_a</span>, r<span class='op'>=</span><span class='fl'>3</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_combinations</span><span class='op'>(</span><span class='va'>tensor_a</span>, with_replacement<span class='op'>=</span><span class='cn'>TRUE</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1  1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1  2</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1  3</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  2  2</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  2  3</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  3  3</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{6,2} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">2</span>, <span class="fl">3</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">tensor_a</span> <span class="op">=</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_combinations</span><span class="op">(</span><span class="va">tensor_a</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_combinations</span><span class="op">(</span><span class="va">tensor_a</span>, r<span class="op">=</span><span class="fl">3</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_combinations</span><span class="op">(</span><span class="va">tensor_a</span>, with_replacement<span class="op">=</span><span class="cn">TRUE</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1  2</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1  3</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2  2</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2  3</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  3  3</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{6,2} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Complex</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_complex.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Complex</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_complex.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Complex</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_complex</span><span class='op'>(</span><span class='va'>real</span>, <span class='va'>imag</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>real</th>
-      <td><p>(Tensor) The real part of the complex tensor. Must be float or double.</p></td>
-    </tr>
-    <tr>
-      <th>imag</th>
-      <td><p>(Tensor) The imaginary part of the complex tensor. Must be same dtype
-as <code>real</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="complex-real-imag-out-none-gt-tensor-"><a class="anchor" href="#complex-real-imag-out-none-gt-tensor-"></a>complex(real, imag, *, out=None) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_complex</span><span class="op">(</span><span class="va">real</span>, <span class="va">imag</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>real</dt>
+<dd><p>(Tensor) The real part of the complex tensor. Must be float or double.</p></dd>
+<dt>imag</dt>
+<dd><p>(Tensor) The imaginary part of the complex tensor. Must be same dtype
+as <code>real</code>.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="complex-real-imag-out-none-gt-tensor-">complex(real, imag, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#complex-real-imag-out-none-gt-tensor-"></a></h2>
     
 
 
 <p>Constructs a complex tensor with its real part equal to <code>real</code> and its
 imaginary part equal to <code>imag</code>.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>real</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>2</span><span class='op'>)</span>, dtype<span class='op'>=</span><span class='fu'><a href='torch_dtype.html'>torch_float32</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>imag</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>4</span><span class='op'>)</span>, dtype<span class='op'>=</span><span class='fu'><a href='torch_dtype.html'>torch_float32</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>z</span> <span class='op'>&lt;-</span> <span class='fu'>torch_complex</span><span class='op'>(</span><span class='va'>real</span>, <span class='va'>imag</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>z</span></span>
-<span class='r-in'><span class='va'>z</span><span class='op'>$</span><span class='va'>dtype</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_ComplexFloat</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">real</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">2</span><span class="op">)</span>, dtype<span class="op">=</span><span class="fu"><a href="torch_dtype.html">torch_float32</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">imag</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">4</span><span class="op">)</span>, dtype<span class="op">=</span><span class="fu"><a href="torch_dtype.html">torch_float32</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">z</span> <span class="op">&lt;-</span> <span class="fu">torch_complex</span><span class="op">(</span><span class="va">real</span>, <span class="va">imag</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">z</span></span>
+<span class="r-in"><span class="va">z</span><span class="op">$</span><span class="va">dtype</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_ComplexFloat</span>
+</code></pre></div>
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Conj</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_conj.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Conj</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_conj.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Conj</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_conj</span><span class='op'>(</span><span class='va'>self</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="conj-input-gt-tensor-"><a class="anchor" href="#conj-input-gt-tensor-"></a>conj(input) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_conj</span><span class="op">(</span><span class="va">self</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="conj-input-gt-tensor-">conj(input) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#conj-input-gt-tensor-"></a></h2>
     
 
 
@@ -209,39 +99,34 @@
 <p>$$
     \mbox{out}_{i} = conj(\mbox{input}_{i})
 $$</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='cn'>FALSE</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='fu'>torch_conj</span><span class='op'>(</span><span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='op'>-</span><span class='fl'>1</span> <span class='op'>+</span> <span class='fl'>1i</span>, <span class='op'>-</span><span class='fl'>2</span> <span class='op'>+</span> <span class='fl'>2i</span>, <span class='fl'>3</span> <span class='op'>-</span> <span class='fl'>3i</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="fu">torch_conj</span><span class="op">(</span><span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="op">-</span><span class="fl">1</span> <span class="op">+</span> <span class="fl">1i</span>, <span class="op">-</span><span class="fl">2</span> <span class="op">+</span> <span class="fl">2i</span>, <span class="fl">3</span> <span class="op">-</span> <span class="fl">3i</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Conv1d</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_conv1d.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Conv1d</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_conv1d.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Conv1d</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_conv1d</span><span class='op'>(</span>
-  <span class='va'>input</span>,
-  <span class='va'>weight</span>,
-  bias <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span><span class='op'>)</span>,
-  stride <span class='op'>=</span> <span class='fl'>1L</span>,
-  padding <span class='op'>=</span> <span class='fl'>0L</span>,
-  dilation <span class='op'>=</span> <span class='fl'>1L</span>,
-  groups <span class='op'>=</span> <span class='fl'>1L</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>input tensor of shape \((\mbox{minibatch} , \mbox{in\_channels} , iW)\)</p></td>
-    </tr>
-    <tr>
-      <th>weight</th>
-      <td><p>filters of shape \((\mbox{out\_channels} , \frac{\mbox{in\_channels}}{\mbox{groups}} , kW)\)</p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>optional bias of shape \((\mbox{out\_channels})\). Default: <code>NULL</code></p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>the stride of the convolving kernel. Can be a single number or      a one-element tuple <code>(sW,)</code>. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>implicit paddings on both sides of the input. Can be a      single number or a one-element tuple <code>(padW,)</code>. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>dilation</th>
-      <td><p>the spacing between kernel elements. Can be a single number or      a one-element tuple <code>(dW,)</code>. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>groups</th>
-      <td><p>split input into groups, \(\mbox{in\_channels}\) should be divisible by      the number of groups. Default: 1</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="conv-d-input-weight-bias-null-stride-padding-dilation-groups-gt-tensor-"><a class="anchor" href="#conv-d-input-weight-bias-null-stride-padding-dilation-groups-gt-tensor-"></a>conv1d(input, weight, bias=NULL, stride=1, padding=0, dilation=1, groups=1) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_conv1d</span><span class="op">(</span>
+  <span class="va">input</span>,
+  <span class="va">weight</span>,
+  bias <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="op">)</span>,
+  stride <span class="op">=</span> <span class="fl">1L</span>,
+  padding <span class="op">=</span> <span class="fl">0L</span>,
+  dilation <span class="op">=</span> <span class="fl">1L</span>,
+  groups <span class="op">=</span> <span class="fl">1L</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>input tensor of shape \((\mbox{minibatch} , \mbox{in\_channels} , iW)\)</p></dd>
+<dt>weight</dt>
+<dd><p>filters of shape \((\mbox{out\_channels} , \frac{\mbox{in\_channels}}{\mbox{groups}} , kW)\)</p></dd>
+<dt>bias</dt>
+<dd><p>optional bias of shape \((\mbox{out\_channels})\). Default: <code>NULL</code></p></dd>
+<dt>stride</dt>
+<dd><p>the stride of the convolving kernel. Can be a single number or      a one-element tuple <code>(sW,)</code>. Default: 1</p></dd>
+<dt>padding</dt>
+<dd><p>implicit paddings on both sides of the input. Can be a      single number or a one-element tuple <code>(padW,)</code>. Default: 0</p></dd>
+<dt>dilation</dt>
+<dd><p>the spacing between kernel elements. Can be a single number or      a one-element tuple <code>(dW,)</code>. Default: 1</p></dd>
+<dt>groups</dt>
+<dd><p>split input into groups, \(\mbox{in\_channels}\) should be divisible by      the number of groups. Default: 1</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="conv-d-input-weight-bias-null-stride-padding-dilation-groups-gt-tensor-">conv1d(input, weight, bias=NULL, stride=1, padding=0, dilation=1, groups=1) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#conv-d-input-weight-bias-null-stride-padding-dilation-groups-gt-tensor-"></a></h2>
     
 
 
 <p>Applies a 1D convolution over an input signal composed of several input
 planes.</p>
-<p>See <code><a href='nn_conv1d.html'>nn_conv1d()</a></code> for details and output shape.</p>
+<p>See <code><a href="nn_conv1d.html">nn_conv1d()</a></code> for details and output shape.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>filters</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>33</span>, <span class='fl'>16</span>, <span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>inputs</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>50</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'><a href='nnf_conv1d.html'>nnf_conv1d</a></span><span class='op'>(</span><span class='va'>inputs</span>, <span class='va'>filters</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> (1,.,.) = </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  Columns 1 to 8   3.3746  -0.2532  -1.0603  -6.5254  11.4131  -4.1133   7.5370   7.0185</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>    3.4567  -1.3166   2.6832   1.4494  -7.0862  -1.5150 -12.7763   4.0043</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>    4.0095   0.1812   0.3272   4.7143   3.8728   0.9308  -6.1431   8.0783</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   -4.0516   1.2158   0.5169   3.7920   3.9460   0.1000  -0.0200  -6.2146</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   11.6525  10.9420  -2.6052  -5.6823   0.8469   1.0539  -0.5774   0.6313</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   -5.3559  -2.8526  -4.8970  -0.9602   4.8163  -2.0055  -7.8032   1.6749</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>    8.2102   0.1738 -12.0819   0.6545   9.0426   9.4873   0.7409   8.1268</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>    3.5317  -4.0198   5.1661   0.6331  -5.1333   7.6424  -7.4546  -0.1151</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>    3.5396   1.3904  -6.9904   0.2772   8.8358  -8.7863   2.1195 -13.6604</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>    2.8550  -1.1028  -2.5379   1.9755  -1.8922  -2.2977  -4.8028 -11.4446</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   -1.4432  -3.4228   6.1287  -9.3228   4.0325   3.1510  -1.9499   3.6488</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   12.0905   0.9380   6.1099   3.5246   7.6585 -11.1960  -4.1497   2.2431</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   -9.2757  -8.9934  -4.9230   8.8653   4.3467  13.1400  -5.8178   3.8976</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  -12.4304   7.0766   3.5598  -3.8089  12.3862   1.8477   6.7525   7.0822</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>    0.2847   5.9105   7.3160 -14.6744   4.1314   1.2287   2.1810   0.9493</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   10.2962   3.9844   3.2150  -0.3079   4.1136  -3.7001  -8.3512   1.7375</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   -6.1151  14.0120   4.5010  -4.4643   2.5166  -0.9901  -3.9244  -3.7574</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>    4.8895   0.2396  -6.0321   5.5177   6.5248   6.1109   6.3875   6.5248</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   -6.1309   0.8079   5.1035  -5.4654  -0.6549 -12.9916  -8.8479   0.2457</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>    3.2027   1.4037   0.7203  10.4405   3.3066  -5.6973  -1.7638  -6.5769</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   -3.0996   8.3624  -4.8660  -5.4516   0.4085  -5.7397  -4.8457   7.2576</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   -3.1699  -5.9772   4.1768   2.9515  12.4506  -2.5378   0.3144   5.1413</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   -6.5466  -3.5786   8.0969   6.9951   0.5327  -9.6985   4.5073  -4.4615</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   -5.2188   3.1946   4.5635   4.4593  -8.9051  10.2481  -0.1773  -1.7985</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   -3.5618   1.4772  -4.3829  -1.4887  -0.1706   1.1695   4.2032   4.0437</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>    2.8946  -6.7407   2.7701   2.7323   2.8301  -9.5731   4.8021  -2.0320</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>    2.1886  12.8690  -9.1588  -2.8641  -1.2965   1.1021   0.5039  -3.1554</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   -1.0381   2.4019  -1.3435   2.2652  -1.8233   4.1751  -6.0093   4.2298</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   -0.9240  -2.9119  -3.4393   1.1201   0.3211   7.7959  11.3080   9.8940</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> ... [the output was truncated (use n=-1 to disable)]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{20,33,48} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">filters</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">33</span>, <span class="fl">16</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">inputs</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">20</span>, <span class="fl">16</span>, <span class="fl">50</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu"><a href="nnf_conv1d.html">nnf_conv1d</a></span><span class="op">(</span><span class="va">inputs</span>, <span class="va">filters</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> (1,.,.) = </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  Columns 1 to 8  -0.1542   3.4135  -0.1581  -2.8605   6.4160  -5.6069 -10.3032 -15.2803</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    5.1342  -3.5374  -8.9578   4.5869   2.9852   0.4431  -1.0060   8.9873</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   -1.7416  -9.0486  -7.7904  -3.7909  -3.5183  -5.9111   7.8429  -6.3230</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   -0.3658  15.0050   3.7242   7.0546   2.3607  -8.8656  -7.7292   8.3001</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   -4.1100   3.6249   6.6928  -3.1840   8.1599   0.4516  -2.8514  -8.3353</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    3.2858 -10.2937   5.4372   3.2013  -2.0425   4.1454   2.8838   6.7277</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   -3.4854  -1.6171   4.3029  -4.9802  -2.6822   9.4102 -13.1095  -1.1281</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    8.3718  14.9510   6.1654  19.0315   8.4695   3.6365   7.3711   3.6864</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    2.9024   7.7248   5.4770  -5.8791   1.1387  -7.6156  19.6924 -13.1195</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   14.4638   0.0415  11.7669 -12.8064   6.8673   3.1586   8.9562  -1.1277</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   -5.5094 -12.7778   6.0125  -6.0579   5.1150  -2.0295  -1.1427  -0.6692</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    5.0350   1.7307   9.4901   2.6822   1.6487   0.5533  -4.8333  -6.2269</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    6.8528  -6.1475   0.4119   2.2705  -4.6832  -0.9580  -4.3037   9.4792</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   -1.8328  11.3740   1.9746   5.3325  13.3612  -2.0364   5.8420   6.0938</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   10.3431   0.0416   5.3293   3.3731  -0.0542  11.0566  -4.9786   4.9954</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   -1.5569  -2.0948  -7.7495   3.5368  -5.3445   7.1250   0.9995  -1.8311</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    0.6794   5.7923 -19.8360  11.4253   5.7549  -4.2408   4.7486   0.1175</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    6.4457   1.8916   5.6098  -0.6581  -8.6552   3.2149   5.9491  -4.2275</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    6.3539   0.8091   1.6356   6.8544   4.3692   3.3312   4.6573  -5.9681</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   -5.1080   2.7822  -9.0617 -11.4169   6.9733  -2.7671  -2.9646  -3.8370</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   -3.6828  -7.6977  -3.7632  -2.5626   1.2108  -0.1260  -3.3887   8.4665</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    1.8564  -3.6885  10.4462   8.8728  -3.8608  -3.4630   1.5713  -6.6485</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    7.7897  -9.7382  13.4032  -2.9148  -8.3491   1.2266  -7.9193  14.4212</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   -8.0326   3.4138  -4.4798   2.3124   5.3823 -15.7694  -0.4647  -4.7632</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   -1.9282  10.2627  -2.2343   4.9496   2.1573   1.1019  -6.4203   3.6094</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    5.8772   8.6403   6.4355  14.2855   1.5232   2.6452   1.3816   8.0950</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   12.2064  -9.2259   5.5244  13.7715   1.4958   1.4696   0.7451  -3.1958</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    6.2437  -8.0909   0.9359  -0.9759  -1.2340   4.0282  -8.8889   3.7367</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   -6.4115   3.2042   2.0177 -10.9509  -2.6127  13.2810  -1.2975   3.1901</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> ... [the output was truncated (use n=-1 to disable)]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{20,33,48} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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diff --git a/reference/torch_conv2d.html b/reference/torch_conv2d.html
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Conv2d</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_conv2d.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Conv2d</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_conv2d.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Conv2d</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_conv2d</span><span class='op'>(</span>
-  <span class='va'>input</span>,
-  <span class='va'>weight</span>,
-  bias <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span><span class='op'>)</span>,
-  stride <span class='op'>=</span> <span class='fl'>1L</span>,
-  padding <span class='op'>=</span> <span class='fl'>0L</span>,
-  dilation <span class='op'>=</span> <span class='fl'>1L</span>,
-  groups <span class='op'>=</span> <span class='fl'>1L</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>input tensor of shape \((\mbox{minibatch} , \mbox{in\_channels} , iH , iW)\)</p></td>
-    </tr>
-    <tr>
-      <th>weight</th>
-      <td><p>filters of shape \((\mbox{out\_channels} , \frac{\mbox{in\_channels}}{\mbox{groups}} , kH , kW)\)</p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>optional bias tensor of shape \((\mbox{out\_channels})\). Default: <code>NULL</code></p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>the stride of the convolving kernel. Can be a single number or a      tuple <code>(sH, sW)</code>. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>implicit paddings on both sides of the input. Can be a      single number or a tuple <code>(padH, padW)</code>. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>dilation</th>
-      <td><p>the spacing between kernel elements. Can be a single number or      a tuple <code>(dH, dW)</code>. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>groups</th>
-      <td><p>split input into groups, \(\mbox{in\_channels}\) should be divisible by the      number of groups. Default: 1</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="conv-d-input-weight-bias-null-stride-padding-dilation-groups-gt-tensor-"><a class="anchor" href="#conv-d-input-weight-bias-null-stride-padding-dilation-groups-gt-tensor-"></a>conv2d(input, weight, bias=NULL, stride=1, padding=0, dilation=1, groups=1) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_conv2d</span><span class="op">(</span>
+  <span class="va">input</span>,
+  <span class="va">weight</span>,
+  bias <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="op">)</span>,
+  stride <span class="op">=</span> <span class="fl">1L</span>,
+  padding <span class="op">=</span> <span class="fl">0L</span>,
+  dilation <span class="op">=</span> <span class="fl">1L</span>,
+  groups <span class="op">=</span> <span class="fl">1L</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>input tensor of shape \((\mbox{minibatch} , \mbox{in\_channels} , iH , iW)\)</p></dd>
+<dt>weight</dt>
+<dd><p>filters of shape \((\mbox{out\_channels} , \frac{\mbox{in\_channels}}{\mbox{groups}} , kH , kW)\)</p></dd>
+<dt>bias</dt>
+<dd><p>optional bias tensor of shape \((\mbox{out\_channels})\). Default: <code>NULL</code></p></dd>
+<dt>stride</dt>
+<dd><p>the stride of the convolving kernel. Can be a single number or a      tuple <code>(sH, sW)</code>. Default: 1</p></dd>
+<dt>padding</dt>
+<dd><p>implicit paddings on both sides of the input. Can be a      single number or a tuple <code>(padH, padW)</code>. Default: 0</p></dd>
+<dt>dilation</dt>
+<dd><p>the spacing between kernel elements. Can be a single number or      a tuple <code>(dH, dW)</code>. Default: 1</p></dd>
+<dt>groups</dt>
+<dd><p>split input into groups, \(\mbox{in\_channels}\) should be divisible by the      number of groups. Default: 1</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="conv-d-input-weight-bias-null-stride-padding-dilation-groups-gt-tensor-">conv2d(input, weight, bias=NULL, stride=1, padding=0, dilation=1, groups=1) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#conv-d-input-weight-bias-null-stride-padding-dilation-groups-gt-tensor-"></a></h2>
     
 
 
 <p>Applies a 2D convolution over an input image composed of several input
 planes.</p>
-<p>See <code><a href='nn_conv2d.html'>nn_conv2d()</a></code> for details and output shape.</p>
+<p>See <code><a href="nn_conv2d.html">nn_conv2d()</a></code> for details and output shape.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='co'># With square kernels and equal stride</span></span>
-<span class='r-in'><span class='va'>filters</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>8</span>,<span class='fl'>4</span>,<span class='fl'>3</span>,<span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>inputs</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>,<span class='fl'>4</span>,<span class='fl'>5</span>,<span class='fl'>5</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'><a href='nnf_conv2d.html'>nnf_conv2d</a></span><span class='op'>(</span><span class='va'>inputs</span>, <span class='va'>filters</span>, padding<span class='op'>=</span><span class='fl'>1</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> (1,1,.,.) = </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  -3.5723  0.8277  4.5433  2.9159  1.1588</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   6.3410  3.3620  3.7723 -1.8130  2.9143</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   0.2506 -4.7645  8.8625  4.4912 -3.4789</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  -3.3006 -2.1275 -1.1293 -1.1072 -4.0258</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   0.3424 -0.6190 -2.6842 -2.2828  2.0136</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> (1,2,.,.) = </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  -6.4726e-03  7.7519e+00  3.4312e+00  8.6177e+00  2.9384e+00</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   8.4716e+00 -7.6211e-01 -2.3600e+00  8.5608e-01  3.8443e+00</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  -1.1439e+00  7.0479e+00  5.2532e+00  8.6800e+00  3.9220e+00</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  -2.7147e+00  2.5703e+00  8.2424e+00 -4.8931e+00 -2.4368e+00</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   3.0279e-05 -3.1459e+00  6.6414e+00 -3.3148e-01  1.5765e+00</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> (1,3,.,.) = </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  -0.7278  4.0487  3.1127  6.0714  5.3477</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   5.4650 -8.2736 -14.6049 -9.9517 -3.1917</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  -2.3442  5.3935  1.7110 -1.3521  5.0939</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   2.0864 -1.1464 -0.1592 -3.4892 -8.9277</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   1.6477  4.4974  1.4181  3.6981 -0.5355</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> (1,4,.,.) = </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   3.4518 -1.5328  3.5053  6.2181  3.4021</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  -8.2242  0.6599  2.5007 -5.3494  2.4554</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   5.5912 -6.7169 -4.9636  9.1493  4.4124</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   2.7600 -1.4968 -0.4490 -0.3302  2.8867</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  -1.0579  0.4277  0.0728  4.6599  3.8538</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> (1,5,.,.) = </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  -3.1164  2.9764  0.7545  7.4792 -4.8398</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> ... [the output was truncated (use n=-1 to disable)]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{1,8,5,5} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
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-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="co"># With square kernels and equal stride</span></span>
+<span class="r-in"><span class="va">filters</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">8</span>,<span class="fl">4</span>,<span class="fl">3</span>,<span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">inputs</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>,<span class="fl">4</span>,<span class="fl">5</span>,<span class="fl">5</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu"><a href="nnf_conv2d.html">nnf_conv2d</a></span><span class="op">(</span><span class="va">inputs</span>, <span class="va">filters</span>, padding<span class="op">=</span><span class="fl">1</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> (1,1,.,.) = </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -4.5604  3.6075 -7.7548 -2.0152 -2.6639</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -3.8808 -4.6239 -1.7576  3.6281  1.2977</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -2.3843  7.0486  2.8052 -22.6599  0.6155</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   2.7980 -0.2546  8.9002  8.9416 -9.0826</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.4175 -1.0085 -2.9638  0.4125  5.4529</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> (1,2,.,.) = </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.2811 -0.0460  6.2378  2.9127 -2.7631</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.9616  6.6324 -1.6217 -1.7990 -2.7289</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   4.2375 -6.1837 -6.6807 -21.2378 -1.5258</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -1.0897 -2.9327 -4.3952  6.2345 -0.3323</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.5916  3.3897 -4.8208  6.4621  2.0097</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> (1,3,.,.) = </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    7.5336  -5.0560  -8.2436   1.7727  -7.0711</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    6.8953   7.2543   8.8907  -4.3056  -8.6904</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    0.2697  -0.6551  -1.4192   0.1952   5.8929</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   -6.4188   8.0741  -4.7706   2.8552   8.6150</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   -1.8651   3.0169   1.3711  12.9714  -1.8199</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> (1,4,.,.) = </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -9.2736 -3.3896 -3.9684 -6.7150 -0.5721</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   6.2935 -2.8171 -11.0965 -5.5832  8.9105</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   1.0285 -2.5675  0.5406 -5.0206 -12.5906</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.5946  5.7171  4.3061  5.9100 -3.6071</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   4.6603  4.4622  5.2592 -6.1512 -12.0855</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> (1,5,.,.) = </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   1.9455 -2.0833  6.6330 -8.8840  5.9554</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> ... [the output was truncated (use n=-1 to disable)]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{1,8,5,5} ]</span>
+</code></pre></div>
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Conv3d</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_conv3d.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Conv3d</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_conv3d.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Conv3d</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_conv3d</span><span class='op'>(</span>
-  <span class='va'>input</span>,
-  <span class='va'>weight</span>,
-  bias <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span><span class='op'>)</span>,
-  stride <span class='op'>=</span> <span class='fl'>1L</span>,
-  padding <span class='op'>=</span> <span class='fl'>0L</span>,
-  dilation <span class='op'>=</span> <span class='fl'>1L</span>,
-  groups <span class='op'>=</span> <span class='fl'>1L</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>input tensor of shape \((\mbox{minibatch} , \mbox{in\_channels} , iT , iH , iW)\)</p></td>
-    </tr>
-    <tr>
-      <th>weight</th>
-      <td><p>filters of shape \((\mbox{out\_channels} , \frac{\mbox{in\_channels}}{\mbox{groups}} , kT , kH , kW)\)</p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>optional bias tensor of shape \((\mbox{out\_channels})\). Default: NULL</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>the stride of the convolving kernel. Can be a single number or a      tuple <code>(sT, sH, sW)</code>. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p>implicit paddings on both sides of the input. Can be a      single number or a tuple <code>(padT, padH, padW)</code>. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>dilation</th>
-      <td><p>the spacing between kernel elements. Can be a single number or      a tuple <code>(dT, dH, dW)</code>. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>groups</th>
-      <td><p>split input into groups, \(\mbox{in\_channels}\) should be divisible by      the number of groups. Default: 1</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="conv-d-input-weight-bias-null-stride-padding-dilation-groups-gt-tensor-"><a class="anchor" href="#conv-d-input-weight-bias-null-stride-padding-dilation-groups-gt-tensor-"></a>conv3d(input, weight, bias=NULL, stride=1, padding=0, dilation=1, groups=1) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_conv3d</span><span class="op">(</span>
+  <span class="va">input</span>,
+  <span class="va">weight</span>,
+  bias <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="op">)</span>,
+  stride <span class="op">=</span> <span class="fl">1L</span>,
+  padding <span class="op">=</span> <span class="fl">0L</span>,
+  dilation <span class="op">=</span> <span class="fl">1L</span>,
+  groups <span class="op">=</span> <span class="fl">1L</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>input tensor of shape \((\mbox{minibatch} , \mbox{in\_channels} , iT , iH , iW)\)</p></dd>
+<dt>weight</dt>
+<dd><p>filters of shape \((\mbox{out\_channels} , \frac{\mbox{in\_channels}}{\mbox{groups}} , kT , kH , kW)\)</p></dd>
+<dt>bias</dt>
+<dd><p>optional bias tensor of shape \((\mbox{out\_channels})\). Default: NULL</p></dd>
+<dt>stride</dt>
+<dd><p>the stride of the convolving kernel. Can be a single number or a      tuple <code>(sT, sH, sW)</code>. Default: 1</p></dd>
+<dt>padding</dt>
+<dd><p>implicit paddings on both sides of the input. Can be a      single number or a tuple <code>(padT, padH, padW)</code>. Default: 0</p></dd>
+<dt>dilation</dt>
+<dd><p>the spacing between kernel elements. Can be a single number or      a tuple <code>(dT, dH, dW)</code>. Default: 1</p></dd>
+<dt>groups</dt>
+<dd><p>split input into groups, \(\mbox{in\_channels}\) should be divisible by      the number of groups. Default: 1</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="conv-d-input-weight-bias-null-stride-padding-dilation-groups-gt-tensor-">conv3d(input, weight, bias=NULL, stride=1, padding=0, dilation=1, groups=1) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#conv-d-input-weight-bias-null-stride-padding-dilation-groups-gt-tensor-"></a></h2>
     
 
 
 <p>Applies a 3D convolution over an input image composed of several input
 planes.</p>
-<p>See <code><a href='nn_conv3d.html'>nn_conv3d()</a></code> for details and output shape.</p>
+<p>See <code><a href="nn_conv3d.html">nn_conv3d()</a></code> for details and output shape.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='co'># filters = torch_randn(c(33, 16, 3, 3, 3))</span></span>
-<span class='r-in'><span class='co'># inputs = torch_randn(c(20, 16, 50, 10, 20))</span></span>
-<span class='r-in'><span class='co'># nnf_conv3d(inputs, filters)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> NULL</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="co"># filters = torch_randn(c(33, 16, 3, 3, 3))</span></span>
+<span class="r-in"><span class="co"># inputs = torch_randn(c(20, 16, 50, 10, 20))</span></span>
+<span class="r-in"><span class="co"># nnf_conv3d(inputs, filters)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> NULL</span>
+</code></pre></div>
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
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+  <main id="main" class="col-md-9"><div class="page-header">
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+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_conv_tbc.Rd</code></div>
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-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Conv_tbc</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_conv_tbc</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>weight</span>, <span class='va'>bias</span>, pad <span class='op'>=</span> <span class='fl'>0L</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>NA input tensor of shape \((\mbox{sequence length} \times batch \times \mbox{in\_channels})\)</p></td>
-    </tr>
-    <tr>
-      <th>weight</th>
-      <td><p>NA filter of shape (\(\mbox{kernel width} \times \mbox{in\_channels} \times \mbox{out\_channels}\))</p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>NA bias of shape (\(\mbox{out\_channels}\))</p></td>
-    </tr>
-    <tr>
-      <th>pad</th>
-      <td><p>NA number of timesteps to pad. Default: 0</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="test-"><a class="anchor" href="#test-"></a>TEST </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_conv_tbc</span><span class="op">(</span><span class="va">self</span>, <span class="va">weight</span>, <span class="va">bias</span>, pad <span class="op">=</span> <span class="fl">0L</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>NA input tensor of shape \((\mbox{sequence length} \times batch \times \mbox{in\_channels})\)</p></dd>
+<dt>weight</dt>
+<dd><p>NA filter of shape (\(\mbox{kernel width} \times \mbox{in\_channels} \times \mbox{out\_channels}\))</p></dd>
+<dt>bias</dt>
+<dd><p>NA bias of shape (\(\mbox{out\_channels}\))</p></dd>
+<dt>pad</dt>
+<dd><p>NA number of timesteps to pad. Default: 0</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="test-">TEST <a class="anchor" aria-label="anchor" href="#test-"></a></h2>
     
 
 
 <p>Applies a 1-dimensional sequence convolution over an input sequence.
 Input and output dimensions are (Time, Batch, Channels) - hence TBC.</p>
+    </div>
 
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-    <div class="page-header">
-    <h1>Conv_transpose1d</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_conv_transpose1d.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Conv_transpose1d</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_conv_transpose1d.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Conv_transpose1d</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_conv_transpose1d</span><span class='op'>(</span>
-  <span class='va'>input</span>,
-  <span class='va'>weight</span>,
-  bias <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span><span class='op'>)</span>,
-  stride <span class='op'>=</span> <span class='fl'>1L</span>,
-  padding <span class='op'>=</span> <span class='fl'>0L</span>,
-  output_padding <span class='op'>=</span> <span class='fl'>0L</span>,
-  groups <span class='op'>=</span> <span class='fl'>1L</span>,
-  dilation <span class='op'>=</span> <span class='fl'>1L</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>input tensor of shape \((\mbox{minibatch} , \mbox{in\_channels} , iW)\)</p></td>
-    </tr>
-    <tr>
-      <th>weight</th>
-      <td><p>filters of shape \((\mbox{in\_channels} , \frac{\mbox{out\_channels}}{\mbox{groups}} , kW)\)</p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>optional bias of shape \((\mbox{out\_channels})\). Default: NULL</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>the stride of the convolving kernel. Can be a single number or a      tuple <code>(sW,)</code>. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p><code>dilation * (kernel_size - 1) - padding</code> zero-padding will be added to both      sides of each dimension in the input. Can be a single number or a tuple      <code>(padW,)</code>. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>output_padding</th>
-      <td><p>additional size added to one side of each dimension in the      output shape. Can be a single number or a tuple <code>(out_padW)</code>. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>groups</th>
-      <td><p>split input into groups, \(\mbox{in\_channels}\) should be divisible by the      number of groups. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>dilation</th>
-      <td><p>the spacing between kernel elements. Can be a single number or      a tuple <code>(dW,)</code>. Default: 1</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="conv-transpose-d-input-weight-bias-null-stride-padding-output-padding-groups-dilation-gt-tensor-"><a class="anchor" href="#conv-transpose-d-input-weight-bias-null-stride-padding-output-padding-groups-dilation-gt-tensor-"></a>conv_transpose1d(input, weight, bias=NULL, stride=1, padding=0, output_padding=0, groups=1, dilation=1) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_conv_transpose1d</span><span class="op">(</span>
+  <span class="va">input</span>,
+  <span class="va">weight</span>,
+  bias <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="op">)</span>,
+  stride <span class="op">=</span> <span class="fl">1L</span>,
+  padding <span class="op">=</span> <span class="fl">0L</span>,
+  output_padding <span class="op">=</span> <span class="fl">0L</span>,
+  groups <span class="op">=</span> <span class="fl">1L</span>,
+  dilation <span class="op">=</span> <span class="fl">1L</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>input tensor of shape \((\mbox{minibatch} , \mbox{in\_channels} , iW)\)</p></dd>
+<dt>weight</dt>
+<dd><p>filters of shape \((\mbox{in\_channels} , \frac{\mbox{out\_channels}}{\mbox{groups}} , kW)\)</p></dd>
+<dt>bias</dt>
+<dd><p>optional bias of shape \((\mbox{out\_channels})\). Default: NULL</p></dd>
+<dt>stride</dt>
+<dd><p>the stride of the convolving kernel. Can be a single number or a      tuple <code>(sW,)</code>. Default: 1</p></dd>
+<dt>padding</dt>
+<dd><p><code>dilation * (kernel_size - 1) - padding</code> zero-padding will be added to both      sides of each dimension in the input. Can be a single number or a tuple      <code>(padW,)</code>. Default: 0</p></dd>
+<dt>output_padding</dt>
+<dd><p>additional size added to one side of each dimension in the      output shape. Can be a single number or a tuple <code>(out_padW)</code>. Default: 0</p></dd>
+<dt>groups</dt>
+<dd><p>split input into groups, \(\mbox{in\_channels}\) should be divisible by the      number of groups. Default: 1</p></dd>
+<dt>dilation</dt>
+<dd><p>the spacing between kernel elements. Can be a single number or      a tuple <code>(dW,)</code>. Default: 1</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="conv-transpose-d-input-weight-bias-null-stride-padding-output-padding-groups-dilation-gt-tensor-">conv_transpose1d(input, weight, bias=NULL, stride=1, padding=0, output_padding=0, groups=1, dilation=1) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#conv-transpose-d-input-weight-bias-null-stride-padding-output-padding-groups-dilation-gt-tensor-"></a></h2>
     
 
 
 <p>Applies a 1D transposed convolution operator over an input signal
 composed of several input planes, sometimes also called "deconvolution".</p>
-<p>See <code><a href='nn_conv_transpose1d.html'>nn_conv_transpose1d()</a></code>  for details and output shape.</p>
+<p>See <code><a href="nn_conv_transpose1d.html">nn_conv_transpose1d()</a></code>  for details and output shape.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>inputs</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>50</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>weights</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>16</span>, <span class='fl'>33</span>, <span class='fl'>5</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'><a href='nnf_conv_transpose1d.html'>nnf_conv_transpose1d</a></span><span class='op'>(</span><span class='va'>inputs</span>, <span class='va'>weights</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> (1,.,.) = </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  Columns 1 to 8   0.5428  -1.4827   2.2600  -2.4582   9.3076  -5.5957   3.3819   8.6445</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>    1.6904   6.6309   2.1330 -13.7717   6.5676 -13.1643   8.2698  -7.4368</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   -0.3898   5.6509  -7.1689 -13.2007 -18.2438  -1.3541   0.9436  -0.8365</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  -12.1324   0.0149   4.0324  -3.4427  -5.4998   7.2697  -2.9637  10.0165</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   -2.0082   0.9718   2.9933   8.7925   1.1132   0.5340   3.2511  22.1300</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   -4.4689   5.9727   8.8492   4.4854   4.8938  -0.8070  -7.4855   0.7584</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>    5.2900   7.5839 -13.7783  -9.7290   5.4130   5.4295 -10.2580 -17.6647</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>    3.7072  -3.9714   4.6667   5.6871 -13.2559   4.5061   1.4706  -3.5588</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>    5.7612   6.1874   9.3104   0.5659   5.6682  21.4556  -4.5171  -3.3186</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>    2.2322 -12.1055   6.5679   5.8309  -4.5961  12.5542  14.1560 -14.6685</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>    3.9165  -3.7364   0.7787   5.5085  -8.1523   4.0058  -6.2095  -1.5094</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   -4.2002 -12.6036  -3.1682   3.3901  -3.5375  -2.9805   6.4784  -6.8285</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   -0.9672  -1.3116  -4.5570   2.6595  -1.1091  -3.7821   9.7980   3.0526</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   -6.0223   3.1025  -8.7741  -1.5638  -1.7258  -2.0704   8.2196   6.8509</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   -4.2492  -4.8595   7.6374   7.6418 -12.1324  14.5408  -3.2015  -4.0341</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>    5.3214   1.3502  -2.5592   5.4787 -12.8718   1.2674   7.0179   1.3328</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>    5.1562   0.3929   9.2172  10.7752   2.1644  14.9945   2.9473   7.1872</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   -1.3364   3.4805  -1.5276  10.7889  -1.2891   2.6430   0.5381   3.1557</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>    7.0689   7.1165  13.9607   9.4669   1.2699  -3.4414   4.7536   1.6705</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>    3.8127  -7.4588  -4.3932  10.6924   6.5721  -3.0043  -2.2741 -12.5714</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   -3.3132   2.6595   1.2683  -5.9301  13.3356 -18.3298   7.9474   9.6479</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>    4.4433  -7.1111   6.7120  12.9453  -3.3309  -9.1969  10.2559   6.7810</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>    5.0510  -6.9601  -9.1161   5.1178  -1.9025 -12.6464   0.9989   1.4527</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>    2.9625  -3.4121   5.6125   6.5992  -0.8156 -15.5830  19.7037  -2.4046</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>    1.2153  -0.6656   1.5626   0.6113   1.2127   4.1468  15.9135   4.6278</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>    5.6581   5.4200  -3.0155  -5.8477   3.0419 -16.1614   6.6569  11.0573</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>    1.5731   5.8084   4.7874   4.1126   8.1719   2.8335  17.5286  24.9425</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   -4.2448   1.5183  -4.3200   2.5963  -5.6719  -2.9386   0.2236 -13.2904</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>    4.1144   3.3727  -0.8298  -1.1120   4.2595  12.9041  11.1415  13.7667</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> ... [the output was truncated (use n=-1 to disable)]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{20,33,54} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">inputs</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">20</span>, <span class="fl">16</span>, <span class="fl">50</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">weights</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">16</span>, <span class="fl">33</span>, <span class="fl">5</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu"><a href="nnf_conv_transpose1d.html">nnf_conv_transpose1d</a></span><span class="op">(</span><span class="va">inputs</span>, <span class="va">weights</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> (1,.,.) = </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  Columns 1 to 6  7.2623e+00 -1.7055e+01 -8.1537e+00 -6.3030e+00  2.2526e+01 -9.2313e+00</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   1.2945e+00 -4.6094e+00 -6.6874e-01 -6.2709e+00 -8.0662e+00 -9.8570e+00</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   3.6372e-03  2.7183e-01  1.4382e+01  8.9109e+00 -5.4207e+00  5.8586e+00</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -3.1104e+00  7.4930e+00  9.1830e+00 -6.7250e+00 -9.7572e+00 -2.6488e-01</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   1.2463e+00  7.5699e+00  1.5752e+01 -1.7233e+01 -1.0988e+01  6.5096e+00</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -6.0034e+00 -1.1895e+01  1.5994e+01 -2.5725e-01  2.0101e+01 -2.0946e+00</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   9.4055e-02  9.3410e+00 -8.1753e+00 -4.3880e+00 -1.1199e+01 -1.1815e+00</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   8.0030e-01 -2.1163e+00 -1.0413e+00 -1.0789e-01 -1.4620e+00  3.5262e+00</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   8.5993e-01  8.8738e+00  4.6152e+00  5.8162e-01  8.1022e+00  2.9554e+00</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -5.5466e+00 -9.2410e+00 -2.7887e+00  4.0144e-01  1.3152e+01  8.9877e+00</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   8.7762e+00  5.3933e+00  1.8271e+00  4.8730e+00  5.4257e+00 -9.7746e+00</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   6.0982e-01  2.3967e-02  5.3979e+00 -5.8992e+00  1.8067e+01  1.5937e+00</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   5.2943e+00 -2.6622e+00  6.0488e+00 -1.1476e+01 -2.6782e+01 -2.1627e+01</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -5.6996e+00 -1.5072e+00 -2.8376e+00  1.2578e+01  2.6264e+00 -2.2501e+00</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   2.2144e+00 -8.0781e-02 -1.3545e+00  5.7635e+00 -1.4623e+01 -1.9362e+01</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   7.1008e-02  1.3292e+00 -3.0182e+00  1.1991e+00 -4.4152e+00 -3.6085e+00</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -4.2953e+00  6.8780e-01 -9.2720e+00  5.8672e+00  1.0984e+01  7.7386e+00</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   3.4999e+00  9.5388e-01 -5.5494e+00  5.7161e+00 -1.1978e-01 -3.3832e+00</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -6.9111e-01 -5.1461e+00  1.0320e+01  2.8589e+00 -2.2605e+00  1.1588e+01</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   4.4698e+00  5.3238e+00  4.4958e-01 -9.7556e+00 -9.0811e+00  4.6425e+00</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   2.9048e+00 -1.1800e+01  6.5441e+00 -4.0105e-01 -7.2522e+00  7.0799e+00</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -9.5608e+00  5.6030e+00  2.9562e+00 -3.4275e+00 -1.5356e+00  4.3073e+00</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -2.7037e+00 -1.7313e-02 -1.6407e+00 -7.2330e+00  1.5195e+01  2.5670e+00</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   2.7652e+00 -4.4173e+00 -8.3351e+00  2.8537e+00 -4.7832e+00 -5.4605e+00</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   3.8609e+00  8.5058e+00 -3.4572e-01 -8.6110e+00  1.4051e+01 -7.3177e+00</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -5.0703e+00 -6.0179e+00 -2.7001e+00 -9.1383e+00  1.1483e+01  1.2736e+01</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   3.2076e+00 -5.3511e+00 -6.4300e+00  5.7287e+00 -3.9354e+00 -2.9230e+01</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   1.6252e+00 -1.1506e+01 -1.3173e+00 -1.4891e+01  1.8824e+00  1.1005e+01</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   3.2842e+00 -2.5989e+00  6.7036e+00 -2.3197e+00  1.5627e+00 -1.6391e+00</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> ... [the output was truncated (use n=-1 to disable)]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{20,33,54} ]</span>
+</code></pre></div>
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-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Conv_transpose2d</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_conv_transpose2d.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Conv_transpose2d</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_conv_transpose2d.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Conv_transpose2d</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_conv_transpose2d</span><span class='op'>(</span>
-  <span class='va'>input</span>,
-  <span class='va'>weight</span>,
-  bias <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span><span class='op'>)</span>,
-  stride <span class='op'>=</span> <span class='fl'>1L</span>,
-  padding <span class='op'>=</span> <span class='fl'>0L</span>,
-  output_padding <span class='op'>=</span> <span class='fl'>0L</span>,
-  groups <span class='op'>=</span> <span class='fl'>1L</span>,
-  dilation <span class='op'>=</span> <span class='fl'>1L</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>input tensor of shape \((\mbox{minibatch} , \mbox{in\_channels} , iH , iW)\)</p></td>
-    </tr>
-    <tr>
-      <th>weight</th>
-      <td><p>filters of shape \((\mbox{in\_channels} , \frac{\mbox{out\_channels}}{\mbox{groups}} , kH , kW)\)</p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>optional bias of shape \((\mbox{out\_channels})\). Default: NULL</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>the stride of the convolving kernel. Can be a single number or a      tuple <code>(sH, sW)</code>. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p><code>dilation * (kernel_size - 1) - padding</code> zero-padding will be added to both      sides of each dimension in the input. Can be a single number or a tuple      <code>(padH, padW)</code>. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>output_padding</th>
-      <td><p>additional size added to one side of each dimension in the      output shape. Can be a single number or a tuple <code>(out_padH, out_padW)</code>.      Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>groups</th>
-      <td><p>split input into groups, \(\mbox{in\_channels}\) should be divisible by the      number of groups. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>dilation</th>
-      <td><p>the spacing between kernel elements. Can be a single number or      a tuple <code>(dH, dW)</code>. Default: 1</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="conv-transpose-d-input-weight-bias-null-stride-padding-output-padding-groups-dilation-gt-tensor-"><a class="anchor" href="#conv-transpose-d-input-weight-bias-null-stride-padding-output-padding-groups-dilation-gt-tensor-"></a>conv_transpose2d(input, weight, bias=NULL, stride=1, padding=0, output_padding=0, groups=1, dilation=1) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_conv_transpose2d</span><span class="op">(</span>
+  <span class="va">input</span>,
+  <span class="va">weight</span>,
+  bias <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="op">)</span>,
+  stride <span class="op">=</span> <span class="fl">1L</span>,
+  padding <span class="op">=</span> <span class="fl">0L</span>,
+  output_padding <span class="op">=</span> <span class="fl">0L</span>,
+  groups <span class="op">=</span> <span class="fl">1L</span>,
+  dilation <span class="op">=</span> <span class="fl">1L</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>input tensor of shape \((\mbox{minibatch} , \mbox{in\_channels} , iH , iW)\)</p></dd>
+<dt>weight</dt>
+<dd><p>filters of shape \((\mbox{in\_channels} , \frac{\mbox{out\_channels}}{\mbox{groups}} , kH , kW)\)</p></dd>
+<dt>bias</dt>
+<dd><p>optional bias of shape \((\mbox{out\_channels})\). Default: NULL</p></dd>
+<dt>stride</dt>
+<dd><p>the stride of the convolving kernel. Can be a single number or a      tuple <code>(sH, sW)</code>. Default: 1</p></dd>
+<dt>padding</dt>
+<dd><p><code>dilation * (kernel_size - 1) - padding</code> zero-padding will be added to both      sides of each dimension in the input. Can be a single number or a tuple      <code>(padH, padW)</code>. Default: 0</p></dd>
+<dt>output_padding</dt>
+<dd><p>additional size added to one side of each dimension in the      output shape. Can be a single number or a tuple <code>(out_padH, out_padW)</code>.      Default: 0</p></dd>
+<dt>groups</dt>
+<dd><p>split input into groups, \(\mbox{in\_channels}\) should be divisible by the      number of groups. Default: 1</p></dd>
+<dt>dilation</dt>
+<dd><p>the spacing between kernel elements. Can be a single number or      a tuple <code>(dH, dW)</code>. Default: 1</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="conv-transpose-d-input-weight-bias-null-stride-padding-output-padding-groups-dilation-gt-tensor-">conv_transpose2d(input, weight, bias=NULL, stride=1, padding=0, output_padding=0, groups=1, dilation=1) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#conv-transpose-d-input-weight-bias-null-stride-padding-output-padding-groups-dilation-gt-tensor-"></a></h2>
     
 
 
 <p>Applies a 2D transposed convolution operator over an input image
 composed of several input planes, sometimes also called "deconvolution".</p>
-<p>See <code><a href='nn_conv_transpose2d.html'>nn_conv_transpose2d()</a></code> for details and output shape.</p>
+<p>See <code><a href="nn_conv_transpose2d.html">nn_conv_transpose2d()</a></code> for details and output shape.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='co'># With square kernels and equal stride</span></span>
-<span class='r-in'><span class='va'>inputs</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>4</span>, <span class='fl'>5</span>, <span class='fl'>5</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>weights</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span>, <span class='fl'>8</span>, <span class='fl'>3</span>, <span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'><a href='nnf_conv_transpose2d.html'>nnf_conv_transpose2d</a></span><span class='op'>(</span><span class='va'>inputs</span>, <span class='va'>weights</span>, padding<span class='op'>=</span><span class='fl'>1</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> (1,1,.,.) = </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  -2.9551  3.2806  3.1473  1.9358 -0.7155</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  -2.6919 -11.4539 -6.9268 -7.3098 -3.7022</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   3.0971  3.2243  4.3148  3.7294  2.9245</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  -0.8729  3.2200  0.5826  2.9405 -0.1784</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   0.3649 -3.8582  4.7323  3.2607  0.3702</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> (1,2,.,.) = </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   -0.9922  -0.7000   5.9462   8.5864  -0.8166</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   -3.4293  -0.4303   2.1634  -6.8451 -14.3155</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>    3.6166  11.4646   6.7169   5.4762   7.9259</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   -0.5343  -0.2816   3.8125  -8.8499   1.2210</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   -0.4866   6.0147   2.9839  -1.3956   1.5188</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> (1,3,.,.) = </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   2.9312  8.1574  1.9829 -6.5142 -2.1699</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  -3.4156 -1.9377  6.3300  1.1091  5.9807</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  -1.5365  8.2760  0.2231 -7.0805  1.3043</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   0.8433 -1.8516 -4.4711 -2.2457  2.1211</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  -2.0756 -2.9902  2.2068 -5.9855 -1.8606</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> (1,4,.,.) = </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   -3.7582  -1.6484   2.0811  16.5515  -3.9347</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   -2.8423   1.9938   8.3280  -2.7050  -7.3129</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>    2.8289  -6.6463   5.9209  -6.7933   4.7154</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   -2.3629   2.2774  -0.6954   4.9811   0.9924</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>    1.3509   2.8443   2.2549  -0.0447   7.0440</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> (1,5,.,.) = </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  -6.1277 -1.4621  7.2443  4.9142 -7.5861</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> ... [the output was truncated (use n=-1 to disable)]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{1,8,5,5} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="co"># With square kernels and equal stride</span></span>
+<span class="r-in"><span class="va">inputs</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">4</span>, <span class="fl">5</span>, <span class="fl">5</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">weights</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span>, <span class="fl">8</span>, <span class="fl">3</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu"><a href="nnf_conv_transpose2d.html">nnf_conv_transpose2d</a></span><span class="op">(</span><span class="va">inputs</span>, <span class="va">weights</span>, padding<span class="op">=</span><span class="fl">1</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> (1,1,.,.) = </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.1660 -1.5897  1.0874  0.4090  8.1254</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   1.1556 -4.4604  3.7456  3.4538 -1.9520</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   5.8230  3.2628  2.2321  9.0939 -8.2842</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   2.9295 -3.2340 -0.6061  4.9616  5.0762</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   2.3821 -5.6495  3.3409 -6.2373 -4.7248</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> (1,2,.,.) = </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -1.1295  0.1410  1.3278 -1.9805 -1.5600</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -2.7530  3.6852  0.9425 -4.3010  3.9575</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -4.7844 -0.5516  4.1571  4.7782 -0.4255</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -4.3439  5.6680  1.3968 -2.5844  6.6445</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -1.4198  5.4590  2.0474 -4.4219 -2.4769</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> (1,3,.,.) = </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -1.7355 -2.8450 -3.0814  0.6761 -0.2276</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.0030 -10.6445  0.2677  2.6822 -3.7247</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   3.2591 -8.1509  6.7926  0.1654 -5.7573</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.5255  6.7771  4.9500  3.1723  0.2542</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   1.3332 -4.4439 -2.0032  0.7206  2.7938</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> (1,4,.,.) = </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   -6.3917  -0.5038  -1.3253   0.6057  -0.0418</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   -4.2435   2.4131   4.4904 -11.1527  -4.3966</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   -5.9192   1.7862 -12.0118  -1.0356   6.1951</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    6.7473  10.4891  -5.6258   5.9848  -1.0510</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    3.0320  -0.3424   0.1542  10.4825   3.5601</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> (1,5,.,.) = </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   4.1955 -1.1414 -0.9134  5.7342  2.5826</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> ... [the output was truncated (use n=-1 to disable)]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{1,8,5,5} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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-      <div class="copyright">
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index 5238cb5a3..4c0d14d84 100644
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+++ b/reference/torch_conv_transpose3d.html
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Conv_transpose3d</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_conv_transpose3d.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Conv_transpose3d</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_conv_transpose3d.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Conv_transpose3d</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_conv_transpose3d</span><span class='op'>(</span>
-  <span class='va'>input</span>,
-  <span class='va'>weight</span>,
-  bias <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span><span class='op'>)</span>,
-  stride <span class='op'>=</span> <span class='fl'>1L</span>,
-  padding <span class='op'>=</span> <span class='fl'>0L</span>,
-  output_padding <span class='op'>=</span> <span class='fl'>0L</span>,
-  groups <span class='op'>=</span> <span class='fl'>1L</span>,
-  dilation <span class='op'>=</span> <span class='fl'>1L</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>input tensor of shape \((\mbox{minibatch} , \mbox{in\_channels} , iT , iH , iW)\)</p></td>
-    </tr>
-    <tr>
-      <th>weight</th>
-      <td><p>filters of shape \((\mbox{in\_channels} , \frac{\mbox{out\_channels}}{\mbox{groups}} , kT , kH , kW)\)</p></td>
-    </tr>
-    <tr>
-      <th>bias</th>
-      <td><p>optional bias of shape \((\mbox{out\_channels})\). Default: NULL</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>the stride of the convolving kernel. Can be a single number or a      tuple <code>(sT, sH, sW)</code>. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>padding</th>
-      <td><p><code>dilation * (kernel_size - 1) - padding</code> zero-padding will be added to both      sides of each dimension in the input. Can be a single number or a tuple      <code>(padT, padH, padW)</code>. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>output_padding</th>
-      <td><p>additional size added to one side of each dimension in the      output shape. Can be a single number or a tuple      <code>(out_padT, out_padH, out_padW)</code>. Default: 0</p></td>
-    </tr>
-    <tr>
-      <th>groups</th>
-      <td><p>split input into groups, \(\mbox{in\_channels}\) should be divisible by the      number of groups. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>dilation</th>
-      <td><p>the spacing between kernel elements. Can be a single number or      a tuple <code>(dT, dH, dW)</code>. Default: 1</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="conv-transpose-d-input-weight-bias-null-stride-padding-output-padding-groups-dilation-gt-tensor-"><a class="anchor" href="#conv-transpose-d-input-weight-bias-null-stride-padding-output-padding-groups-dilation-gt-tensor-"></a>conv_transpose3d(input, weight, bias=NULL, stride=1, padding=0, output_padding=0, groups=1, dilation=1) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_conv_transpose3d</span><span class="op">(</span>
+  <span class="va">input</span>,
+  <span class="va">weight</span>,
+  bias <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="op">)</span>,
+  stride <span class="op">=</span> <span class="fl">1L</span>,
+  padding <span class="op">=</span> <span class="fl">0L</span>,
+  output_padding <span class="op">=</span> <span class="fl">0L</span>,
+  groups <span class="op">=</span> <span class="fl">1L</span>,
+  dilation <span class="op">=</span> <span class="fl">1L</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>input tensor of shape \((\mbox{minibatch} , \mbox{in\_channels} , iT , iH , iW)\)</p></dd>
+<dt>weight</dt>
+<dd><p>filters of shape \((\mbox{in\_channels} , \frac{\mbox{out\_channels}}{\mbox{groups}} , kT , kH , kW)\)</p></dd>
+<dt>bias</dt>
+<dd><p>optional bias of shape \((\mbox{out\_channels})\). Default: NULL</p></dd>
+<dt>stride</dt>
+<dd><p>the stride of the convolving kernel. Can be a single number or a      tuple <code>(sT, sH, sW)</code>. Default: 1</p></dd>
+<dt>padding</dt>
+<dd><p><code>dilation * (kernel_size - 1) - padding</code> zero-padding will be added to both      sides of each dimension in the input. Can be a single number or a tuple      <code>(padT, padH, padW)</code>. Default: 0</p></dd>
+<dt>output_padding</dt>
+<dd><p>additional size added to one side of each dimension in the      output shape. Can be a single number or a tuple      <code>(out_padT, out_padH, out_padW)</code>. Default: 0</p></dd>
+<dt>groups</dt>
+<dd><p>split input into groups, \(\mbox{in\_channels}\) should be divisible by the      number of groups. Default: 1</p></dd>
+<dt>dilation</dt>
+<dd><p>the spacing between kernel elements. Can be a single number or      a tuple <code>(dT, dH, dW)</code>. Default: 1</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="conv-transpose-d-input-weight-bias-null-stride-padding-output-padding-groups-dilation-gt-tensor-">conv_transpose3d(input, weight, bias=NULL, stride=1, padding=0, output_padding=0, groups=1, dilation=1) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#conv-transpose-d-input-weight-bias-null-stride-padding-output-padding-groups-dilation-gt-tensor-"></a></h2>
     
 
 
 <p>Applies a 3D transposed convolution operator over an input image
 composed of several input planes, sometimes also called "deconvolution"</p>
-<p>See <code><a href='nn_conv_transpose3d.html'>nn_conv_transpose3d()</a></code> for details and output shape.</p>
+<p>See <code><a href="nn_conv_transpose3d.html">nn_conv_transpose3d()</a></code> for details and output shape.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='cn'>FALSE</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>inputs</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>50</span>, <span class='fl'>10</span>, <span class='fl'>20</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>weights</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>16</span>, <span class='fl'>33</span>, <span class='fl'>3</span>, <span class='fl'>3</span>, <span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'><a href='nnf_conv_transpose3d.html'>nnf_conv_transpose3d</a></span><span class='op'>(</span><span class='va'>inputs</span>, <span class='va'>weights</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">inputs</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">20</span>, <span class="fl">16</span>, <span class="fl">50</span>, <span class="fl">10</span>, <span class="fl">20</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">weights</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">16</span>, <span class="fl">33</span>, <span class="fl">3</span>, <span class="fl">3</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu"><a href="nnf_conv_transpose3d.html">nnf_conv_transpose3d</a></span><span class="op">(</span><span class="va">inputs</span>, <span class="va">weights</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
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diff --git a/reference/torch_cos.html b/reference/torch_cos.html
index 01afadf6c..cde80d63e 100644
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@@ -1,207 +1,97 @@
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+  </div>
+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Cos</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_cos.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Cos</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_cos.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Cos</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_cos</span><span class='op'>(</span><span class='va'>self</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="cos-input-out-null-gt-tensor-"><a class="anchor" href="#cos-input-out-null-gt-tensor-"></a>cos(input, out=NULL) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_cos</span><span class="op">(</span><span class="va">self</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="cos-input-out-null-gt-tensor-">cos(input, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#cos-input-out-null-gt-tensor-"></a></h2>
     
 
 
@@ -209,46 +99,41 @@
 <p>$$
     \mbox{out}_{i} = \cos(\mbox{input}_{i})
 $$</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='fu'>torch_cos</span><span class='op'>(</span><span class='va'>a</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.8070</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.9248</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.9447</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.5951</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{4} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="fu">torch_cos</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.9998</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.5379</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.8324</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.9991</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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diff --git a/reference/torch_cosh.html b/reference/torch_cosh.html
index 44297d056..228be361b 100644
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+    
+  </div>
+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Cosh</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_cosh.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Cosh</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_cosh.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Cosh</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_cosh</span><span class='op'>(</span><span class='va'>self</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="cosh-input-out-null-gt-tensor-"><a class="anchor" href="#cosh-input-out-null-gt-tensor-"></a>cosh(input, out=NULL) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_cosh</span><span class="op">(</span><span class="va">self</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="cosh-input-out-null-gt-tensor-">cosh(input, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#cosh-input-out-null-gt-tensor-"></a></h2>
     
 
 
@@ -210,46 +100,41 @@
 <p>$$
     \mbox{out}_{i} = \cosh(\mbox{input}_{i})
 $$</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='fu'>torch_cosh</span><span class='op'>(</span><span class='va'>a</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1.0025</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1.4719</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  2.4731</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  2.1539</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{4} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="fu">torch_cosh</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.0087</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2.8242</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.1153</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.0097</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Cosine_similarity</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_cosine_similarity.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Cosine_similarity</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_cosine_similarity.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Cosine_similarity</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_cosine_similarity</span><span class='op'>(</span><span class='va'>x1</span>, <span class='va'>x2</span>, dim <span class='op'>=</span> <span class='fl'>2L</span>, eps <span class='op'>=</span> <span class='fl'>0</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>x1</th>
-      <td><p>(Tensor) First input.</p></td>
-    </tr>
-    <tr>
-      <th>x2</th>
-      <td><p>(Tensor) Second input (of size matching x1).</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int, optional) Dimension of vectors. Default: 1</p></td>
-    </tr>
-    <tr>
-      <th>eps</th>
-      <td><p>(float, optional) Small value to avoid division by zero.        Default: 1e-8</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="cosine-similarity-x-x-dim-eps-e-gt-tensor-"><a class="anchor" href="#cosine-similarity-x-x-dim-eps-e-gt-tensor-"></a>cosine_similarity(x1, x2, dim=1, eps=1e-8) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_cosine_similarity</span><span class="op">(</span><span class="va">x1</span>, <span class="va">x2</span>, dim <span class="op">=</span> <span class="fl">2L</span>, eps <span class="op">=</span> <span class="fl">1e-08</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>x1</dt>
+<dd><p>(Tensor) First input.</p></dd>
+<dt>x2</dt>
+<dd><p>(Tensor) Second input (of size matching x1).</p></dd>
+<dt>dim</dt>
+<dd><p>(int, optional) Dimension of vectors. Default: 1</p></dd>
+<dt>eps</dt>
+<dd><p>(float, optional) Small value to avoid division by zero.        Default: 1e-8</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="cosine-similarity-x-x-dim-eps-e-gt-tensor-">cosine_similarity(x1, x2, dim=1, eps=1e-8) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#cosine-similarity-x-x-dim-eps-e-gt-tensor-"></a></h2>
     
 
 
@@ -221,74 +105,69 @@
 <p>$$
     \mbox{similarity} = \frac{x_1 \cdot x_2}{\max(\Vert x_1 \Vert _2 \cdot \Vert x_2 \Vert _2, \epsilon)}
 $$</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>input1</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>100</span>, <span class='fl'>128</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>input2</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>100</span>, <span class='fl'>128</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span> <span class='op'>=</span> <span class='fu'>torch_cosine_similarity</span><span class='op'>(</span><span class='va'>input1</span>, <span class='va'>input2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.0242</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.0679</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.0486</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.0688</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.0051</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.0855</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.0201</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.0189</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.0627</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.1280</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.0992</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.0612</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.0238</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.0120</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.0141</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.1231</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.0950</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.1799</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.0501</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.0035</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.0023</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.0553</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.2438</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.0373</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.0126</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.0391</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.0338</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.0083</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.1829</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.0000</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> ... [the output was truncated (use n=-1 to disable)]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{100} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">input1</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">100</span>, <span class="fl">128</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">input2</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">100</span>, <span class="fl">128</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span> <span class="op">=</span> <span class="fu">torch_cosine_similarity</span><span class="op">(</span><span class="va">input1</span>, <span class="va">input2</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.0444</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.1909</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0705</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0031</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.1083</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.1506</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0003</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0612</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0125</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.1003</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.1032</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.0915</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.0261</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0257</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.0444</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.1241</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.1165</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.0921</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0203</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.0405</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.0685</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0604</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.1574</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.0031</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.0103</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0160</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.0217</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.0528</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.0807</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0173</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> ... [the output was truncated (use n=-1 to disable)]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{100} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Count_nonzero</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_count_nonzero.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Count_nonzero</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_count_nonzero.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Count_nonzero</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_count_nonzero</span><span class='op'>(</span><span class='va'>self</span>, dim <span class='op'>=</span> <span class='cn'>NULL</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int or tuple of ints, optional) Dim or tuple of dims along which
-to count non-zeros.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="count-nonzero-input-dim-none-gt-tensor-"><a class="anchor" href="#count-nonzero-input-dim-none-gt-tensor-"></a>count_nonzero(input, dim=None) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_count_nonzero</span><span class="op">(</span><span class="va">self</span>, dim <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+<dt>dim</dt>
+<dd><p>(int or tuple of ints, optional) Dim or tuple of dims along which
+to count non-zeros.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="count-nonzero-input-dim-none-gt-tensor-">count_nonzero(input, dim=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#count-nonzero-input-dim-none-gt-tensor-"></a></h2>
     
 
 
 <p>Counts the number of non-zero values in the tensor <code>input</code> along the given <code>dim</code>.
 If no dim is specified then all non-zeros in the tensor are counted.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>x</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_zeros.html'>torch_zeros</a></span><span class='op'>(</span><span class='fl'>3</span>,<span class='fl'>3</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>x</span><span class='op'>[</span><span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>3</span>,<span class='fl'>3</span><span class='op'>)</span> <span class='op'>&gt;</span> <span class='fl'>0.5</span><span class='op'>]</span> <span class='op'>=</span> <span class='fl'>1</span></span>
-<span class='r-in'><span class='va'>x</span></span>
-<span class='r-in'><span class='fu'>torch_count_nonzero</span><span class='op'>(</span><span class='va'>x</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_count_nonzero</span><span class='op'>(</span><span class='va'>x</span>, dim<span class='op'>=</span><span class='fl'>1</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPULongType{3} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_zeros.html">torch_zeros</a></span><span class="op">(</span><span class="fl">3</span>,<span class="fl">3</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">x</span><span class="op">[</span><span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">3</span>,<span class="fl">3</span><span class="op">)</span> <span class="op">&gt;</span> <span class="fl">0.5</span><span class="op">]</span> <span class="op">=</span> <span class="fl">1</span></span>
+<span class="r-in"><span class="va">x</span></span>
+<span class="r-in"><span class="fu">torch_count_nonzero</span><span class="op">(</span><span class="va">x</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_count_nonzero</span><span class="op">(</span><span class="va">x</span>, dim<span class="op">=</span><span class="fl">1</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPULongType{3} ]</span>
+</code></pre></div>
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Cross</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_cross.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Cross</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_cross.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Cross</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_cross</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>other</span>, dim <span class='op'>=</span> <span class='cn'>NULL</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Tensor) the second input tensor</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int, optional) the dimension to take the cross-product in.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="cross-input-other-dim-out-null-gt-tensor-"><a class="anchor" href="#cross-input-other-dim-out-null-gt-tensor-"></a>cross(input, other, dim=-1, out=NULL) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_cross</span><span class="op">(</span><span class="va">self</span>, <span class="va">other</span>, dim <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+<dt>other</dt>
+<dd><p>(Tensor) the second input tensor</p></dd>
+<dt>dim</dt>
+<dd><p>(int, optional) the dimension to take the cross-product in.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="cross-input-other-dim-out-null-gt-tensor-">cross(input, other, dim=-1, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#cross-input-other-dim-out-null-gt-tensor-"></a></h2>
     
 
 
@@ -219,49 +105,44 @@ and <code>other</code>.</p>
 <code>dim</code> dimension should be 3.</p>
 <p>If <code>dim</code> is not given, it defaults to the first dimension found with the
 size 3.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span>, <span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='va'>b</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span>, <span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>b</span></span>
-<span class='r-in'><span class='fu'>torch_cross</span><span class='op'>(</span><span class='va'>a</span>, <span class='va'>b</span>, dim<span class='op'>=</span><span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_cross</span><span class='op'>(</span><span class='va'>a</span>, <span class='va'>b</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.4944 -1.0211  0.0861</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -1.1458 -2.5637  0.3503</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.7366 -0.6256 -0.1495</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -2.7773  0.2262 -0.2452</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{4,3} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="va">b</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">b</span></span>
+<span class="r-in"><span class="fu">torch_cross</span><span class="op">(</span><span class="va">a</span>, <span class="va">b</span>, dim<span class="op">=</span><span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_cross</span><span class="op">(</span><span class="va">a</span>, <span class="va">b</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.0845  0.2132  0.4054</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.0907 -2.3545  1.1211</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.2083 -1.6346 -1.6994</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.4848 -0.9600  0.1598</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4,3} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Cummax</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_cummax.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Cummax</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_cummax.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Cummax</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_cummax</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>dim</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) the dimension to do the operation over</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="cummax-input-dim-gt-tensor-longtensor-"><a class="anchor" href="#cummax-input-dim-gt-tensor-longtensor-"></a>cummax(input, dim) -&gt; (Tensor, LongTensor) </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_cummax</span><span class="op">(</span><span class="va">self</span>, <span class="va">dim</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+<dt>dim</dt>
+<dd><p>(int) the dimension to do the operation over</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="cummax-input-dim-gt-tensor-longtensor-">cummax(input, dim) -&gt; (Tensor, LongTensor) <a class="anchor" aria-label="anchor" href="#cummax-input-dim-gt-tensor-longtensor-"></a></h2>
     
 
 
@@ -215,68 +103,63 @@ location of each maximum value found in the dimension <code>dim</code>.</p>
 <p>$$
     y_i = max(x_1, x_2, x_3, \dots, x_i)
 $$</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>10</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='fu'>torch_cummax</span><span class='op'>(</span><span class='va'>a</span>, dim<span class='op'>=</span><span class='fl'>1</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [[1]]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.3695</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.9845</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1.7388</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1.7388</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1.7388</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1.7388</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1.7388</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1.7388</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1.7388</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1.7388</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{10} ]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [[2]]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  2</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  2</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  2</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  2</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  2</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  2</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  2</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  2</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPULongType{10} ]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">10</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="fu">torch_cummax</span><span class="op">(</span><span class="va">a</span>, dim<span class="op">=</span><span class="fl">1</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [[1]]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.6653</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.6653</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.6653</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.6653</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.6653</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.6653</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.6653</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.6653</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.6653</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.6653</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{10} ]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [[2]]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPULongType{10} ]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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+    <footer><div class="pkgdown-footer-left">
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+<div class="pkgdown-footer-right">
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diff --git a/reference/torch_cummin.html b/reference/torch_cummin.html
index 574bd6b79..829703553 100644
--- a/reference/torch_cummin.html
+++ b/reference/torch_cummin.html
@@ -1,211 +1,99 @@
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Cummin</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_cummin.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Cummin</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_cummin.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Cummin</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_cummin</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>dim</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) the dimension to do the operation over</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="cummin-input-dim-gt-tensor-longtensor-"><a class="anchor" href="#cummin-input-dim-gt-tensor-longtensor-"></a>cummin(input, dim) -&gt; (Tensor, LongTensor) </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_cummin</span><span class="op">(</span><span class="va">self</span>, <span class="va">dim</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+<dt>dim</dt>
+<dd><p>(int) the dimension to do the operation over</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="cummin-input-dim-gt-tensor-longtensor-">cummin(input, dim) -&gt; (Tensor, LongTensor) <a class="anchor" aria-label="anchor" href="#cummin-input-dim-gt-tensor-longtensor-"></a></h2>
     
 
 
@@ -215,68 +103,63 @@ location of each maximum value found in the dimension <code>dim</code>.</p>
 <p>$$
     y_i = min(x_1, x_2, x_3, \dots, x_i)
 $$</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>10</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='fu'>torch_cummin</span><span class='op'>(</span><span class='va'>a</span>, dim<span class='op'>=</span><span class='fl'>1</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [[1]]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -1.4771</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -1.4771</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -1.4771</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -1.4771</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -1.4771</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -1.4771</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -1.4771</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -1.4771</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -1.4771</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -1.4771</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{10} ]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [[2]]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPULongType{10} ]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">10</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="fu">torch_cummin</span><span class="op">(</span><span class="va">a</span>, dim<span class="op">=</span><span class="fl">1</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [[1]]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.6368</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.9793</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.9793</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -1.2178</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -1.2178</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -1.2178</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -1.2178</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -1.2178</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -1.2178</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -1.2178</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{10} ]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [[2]]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  3</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  3</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  3</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  3</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  3</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  3</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  3</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPULongType{10} ]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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-      <div class="copyright">
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+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Cumprod</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_cumprod.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Cumprod</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_cumprod.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Cumprod</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_cumprod</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>dim</span>, dtype <span class='op'>=</span> <span class='cn'>NULL</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) the dimension to do the operation over</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        If specified, the input tensor is casted to <code>dtype</code> before the operation        is performed. This is useful for preventing data type overflows. Default: NULL.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="cumprod-input-dim-out-null-dtype-null-gt-tensor-"><a class="anchor" href="#cumprod-input-dim-out-null-dtype-null-gt-tensor-"></a>cumprod(input, dim, out=NULL, dtype=NULL) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_cumprod</span><span class="op">(</span><span class="va">self</span>, <span class="va">dim</span>, dtype <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+<dt>dim</dt>
+<dd><p>(int) the dimension to do the operation over</p></dd>
+<dt>dtype</dt>
+<dd><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        If specified, the input tensor is casted to <code>dtype</code> before the operation        is performed. This is useful for preventing data type overflows. Default: NULL.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="cumprod-input-dim-out-null-dtype-null-gt-tensor-">cumprod(input, dim, out=NULL, dtype=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#cumprod-input-dim-out-null-dtype-null-gt-tensor-"></a></h2>
     
 
 
@@ -220,52 +106,48 @@ a vector of size N, with elements.</p>
 <p>$$
     y_i = x_1 \times x_2\times x_3\times \dots \times x_i
 $$</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>10</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='fu'>torch_cumprod</span><span class='op'>(</span><span class='va'>a</span>, dim<span class='op'>=</span><span class='fl'>1</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.7838</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1.1509</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.0562</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.0222</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.0321</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.0198</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.0172</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.0271</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.0200</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.0090</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{10} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">10</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="fu">torch_cumprod</span><span class="op">(</span><span class="va">a</span>, dim<span class="op">=</span><span class="fl">1</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 0.01 *</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -3.8929</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.3768</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -1.6106</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.4555</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.3654</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.0245</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.0300</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0007</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.0008</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0012</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{10} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Cumsum</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_cumsum.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Cumsum</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_cumsum.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Cumsum</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_cumsum</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>dim</span>, dtype <span class='op'>=</span> <span class='cn'>NULL</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) the dimension to do the operation over</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        If specified, the input tensor is casted to <code>dtype</code> before the operation        is performed. This is useful for preventing data type overflows. Default: NULL.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="cumsum-input-dim-out-null-dtype-null-gt-tensor-"><a class="anchor" href="#cumsum-input-dim-out-null-dtype-null-gt-tensor-"></a>cumsum(input, dim, out=NULL, dtype=NULL) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_cumsum</span><span class="op">(</span><span class="va">self</span>, <span class="va">dim</span>, dtype <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+<dt>dim</dt>
+<dd><p>(int) the dimension to do the operation over</p></dd>
+<dt>dtype</dt>
+<dd><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        If specified, the input tensor is casted to <code>dtype</code> before the operation        is performed. This is useful for preventing data type overflows. Default: NULL.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="cumsum-input-dim-out-null-dtype-null-gt-tensor-">cumsum(input, dim, out=NULL, dtype=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#cumsum-input-dim-out-null-dtype-null-gt-tensor-"></a></h2>
     
 
 
@@ -220,52 +106,47 @@ a vector of size N, with elements.</p>
 <p>$$
     y_i = x_1 + x_2 + x_3 + \dots + x_i
 $$</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>10</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='fu'>torch_cumsum</span><span class='op'>(</span><span class='va'>a</span>, dim<span class='op'>=</span><span class='fl'>1</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.1292</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.9407</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -1.6613</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -1.2153</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -2.0829</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -4.6834</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -4.4764</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -5.0608</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -3.6962</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -2.3656</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{10} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
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+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">10</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="fu">torch_cumsum</span><span class="op">(</span><span class="va">a</span>, dim<span class="op">=</span><span class="fl">1</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.6890</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.2324</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.6022</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0321</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.9204</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0819</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.1066</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0006</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.6855</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2.5382</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{10} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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+  </div>
+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Deg2rad</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_deg2rad.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Deg2rad</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_deg2rad.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Deg2rad</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_deg2rad</span><span class='op'>(</span><span class='va'>self</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="deg-rad-input-out-none-gt-tensor-"><a class="anchor" href="#deg-rad-input-out-none-gt-tensor-"></a>deg2rad(input, *, out=None) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_deg2rad</span><span class="op">(</span><span class="va">self</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="deg-rad-input-out-none-gt-tensor-">deg2rad(input, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#deg-rad-input-out-none-gt-tensor-"></a></h2>
     
 
 
 <p>Returns a new tensor with each of the elements of <code>input</code>
 converted from angles in degrees to radians.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/cbind.html'>rbind</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>180.0</span>, <span class='op'>-</span><span class='fl'>180.0</span><span class='op'>)</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>360.0</span>, <span class='op'>-</span><span class='fl'>360.0</span><span class='op'>)</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>90.0</span>, <span class='op'>-</span><span class='fl'>90.0</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_deg2rad</span><span class='op'>(</span><span class='va'>a</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  3.1416 -3.1416</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  6.2832 -6.2832</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1.5708 -1.5708</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{3,2} ]</span>
-</pre>
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-      <h2 data-toc-skip>Contents</h2>
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+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/cbind.html" class="external-link">rbind</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">180.0</span>, <span class="op">-</span><span class="fl">180.0</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">360.0</span>, <span class="op">-</span><span class="fl">360.0</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">90.0</span>, <span class="op">-</span><span class="fl">90.0</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_deg2rad</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  3.1416 -3.1416</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  6.2832 -6.2832</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.5708 -1.5708</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3,2} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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+  </div>
+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Dequantize</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/wrapers.R'><code>R/wrapers.R</code></a></small>
-    <div class="hidden name"><code>torch_dequantize.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Dequantize</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
+      <div class="d-none name"><code>torch_dequantize.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Dequantize</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_dequantize</span><span class='op'>(</span><span class='va'>tensor</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>tensor</th>
-      <td><p>(Tensor) A quantized Tensor or a list oof quantized tensors</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="dequantize-tensor-gt-tensor-"><a class="anchor" href="#dequantize-tensor-gt-tensor-"></a>dequantize(tensor) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_dequantize</span><span class="op">(</span><span class="va">tensor</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>tensor</dt>
+<dd><p>(Tensor) A quantized Tensor or a list oof quantized tensors</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="dequantize-tensor-gt-tensor-">dequantize(tensor) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#dequantize-tensor-gt-tensor-"></a></h2>
     
 
 
 <p>Returns an fp32 Tensor by dequantizing a quantized Tensor</p>
-    <h2 class="hasAnchor" id="dequantize-tensors-gt-sequence-of-tensors-"><a class="anchor" href="#dequantize-tensors-gt-sequence-of-tensors-"></a>dequantize(tensors) -&gt; sequence of Tensors </h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="dequantize-tensors-gt-sequence-of-tensors-">dequantize(tensors) -&gt; sequence of Tensors <a class="anchor" aria-label="anchor" href="#dequantize-tensors-gt-sequence-of-tensors-"></a></h2>
     
 
 
 <p>Given a list of quantized Tensors, dequantize them and return a list of fp32 Tensors</p>
+    </div>
 
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Det</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_det.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Det</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_det.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Det</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_det</span><span class='op'>(</span><span class='va'>self</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor of size <code>(*, n, n)</code> where <code>*</code> is zero or more                batch dimensions.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_det</span><span class="op">(</span><span class="va">self</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor of size <code>(*, n, n)</code> where <code>*</code> is zero or more                batch dimensions.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     
-<pre>Backward through `det` internally uses SVD results when `input` is
+<div class="sourceCode"><pre><code>Backward through `det` internally uses SVD results when `input` is
 not invertible. In this case, double backward through `det` will be
 unstable in when `input` doesn't have distinct singular values. See
 `~torch.svd` for details.
-</pre>
-
-    <h2 class="hasAnchor" id="det-input-gt-tensor-"><a class="anchor" href="#det-input-gt-tensor-"></a>det(input) -&gt; Tensor </h2>
+</code></pre></div>
 
+    </div>
+    <div class="section level2">
+    <h2 id="det-input-gt-tensor-">det(input) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#det-input-gt-tensor-"></a></h2>
     
 
 
 <p>Calculates determinant of a square matrix or batches of square matrices.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>A</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_det</span><span class='op'>(</span><span class='va'>A</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>A</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>2</span>, <span class='fl'>2</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>A</span></span>
-<span class='r-in'><span class='va'>A</span><span class='op'>$</span><span class='fu'>det</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -2.0279</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1.1793</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1.6157</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{3} ]</span>
-</pre>
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-      <h2 data-toc-skip>Contents</h2>
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+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">A</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_det</span><span class="op">(</span><span class="va">A</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">A</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">2</span>, <span class="fl">2</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">A</span></span>
+<span class="r-in"><span class="va">A</span><span class="op">$</span><span class="fu">det</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2.6027</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.4142</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.3293</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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+    
+  </div>
+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Create a Device object</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/device.R'><code>R/device.R</code></a></small>
-    <div class="hidden name"><code>torch_device.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Create a Device object</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/device.R" class="external-link"><code>R/device.R</code></a></small>
+      <div class="d-none name"><code>torch_device.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>A <code>torch_device</code>  is an object representing the device on which a <code>torch_tensor</code>
 is or will be allocated.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_device</span><span class='op'>(</span><span class='va'>type</span>, index <span class='op'>=</span> <span class='cn'>NULL</span><span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_device</span><span class="op">(</span><span class="va">type</span>, index <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>type</th>
-      <td><p>(character) a device type <code>"cuda"</code> or <code>"cpu"</code></p></td>
-    </tr>
-    <tr>
-      <th>index</th>
-      <td><p>(integer) optional device ordinal for the device type.  If the device ordinal
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>type</dt>
+<dd><p>(character) a device type <code>"cuda"</code> or <code>"cpu"</code></p></dd>
+<dt>index</dt>
+<dd><p>(integer) optional device ordinal for the device type.  If the device ordinal
 is not present, this object will always represent the current device for the device
 type, even after <code>torch_cuda_set_device()</code> is called; e.g., a <code>torch_tensor</code> constructed
 with device <code>'cuda'</code> is equivalent to <code>'cuda:X'</code> where X is the result of
 <code>torch_cuda_current_device()</code>.</p>
-<p>A <code>torch_device</code> can be constructed via a string or via a string and device ordinal</p></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='co'># Via string</span></span>
-<span class='r-in'><span class='fu'>torch_device</span><span class='op'>(</span><span class='st'>"cuda:1"</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_device</span><span class='op'>(</span><span class='st'>"cpu"</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_device</span><span class='op'>(</span><span class='st'>"cuda"</span><span class='op'>)</span> <span class='co'># current cuda device</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='co'># Via string and device ordinal</span></span>
-<span class='r-in'><span class='fu'>torch_device</span><span class='op'>(</span><span class='st'>"cuda"</span>, <span class='fl'>0</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_device</span><span class='op'>(</span><span class='st'>"cpu"</span>, <span class='fl'>0</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_device(type='cpu', index=0)</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+<p>A <code>torch_device</code> can be constructed via a string or via a string and device ordinal</p></dd>
+</dl></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="co"># Via string</span></span>
+<span class="r-in"><span class="fu">torch_device</span><span class="op">(</span><span class="st">"cuda:1"</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_device</span><span class="op">(</span><span class="st">"cpu"</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_device</span><span class="op">(</span><span class="st">"cuda"</span><span class="op">)</span> <span class="co"># current cuda device</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="co"># Via string and device ordinal</span></span>
+<span class="r-in"><span class="fu">torch_device</span><span class="op">(</span><span class="st">"cuda"</span>, <span class="fl">0</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_device</span><span class="op">(</span><span class="st">"cpu"</span>, <span class="fl">0</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_device(type='cpu', index=0)</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Diag</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_diag.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Diag</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_diag.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Diag</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_diag</span><span class='op'>(</span><span class='va'>self</span>, diagonal <span class='op'>=</span> <span class='fl'>0L</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>diagonal</th>
-      <td><p>(int, optional) the diagonal to consider</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="diag-input-diagonal-out-null-gt-tensor-"><a class="anchor" href="#diag-input-diagonal-out-null-gt-tensor-"></a>diag(input, diagonal=0, out=NULL) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_diag</span><span class="op">(</span><span class="va">self</span>, diagonal <span class="op">=</span> <span class="fl">0L</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+<dt>diagonal</dt>
+<dd><p>(int, optional) the diagonal to consider</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="diag-input-diagonal-out-null-gt-tensor-">diag(input, diagonal=0, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#diag-input-diagonal-out-null-gt-tensor-"></a></h2>
     
 
-<ul>
-<li><p>If <code>input</code> is a vector (1-D tensor), then returns a 2-D square tensor
+<ul><li><p>If <code>input</code> is a vector (1-D tensor), then returns a 2-D square tensor
 with the elements of <code>input</code> as the diagonal.</p></li>
 <li><p>If <code>input</code> is a matrix (2-D tensor), then returns a 1-D tensor with
 the diagonal elements of <code>input</code>.</p></li>
-</ul>
-
-<p>The argument <code>diagonal</code> controls which diagonal to consider:</p><ul>
-<li><p>If <code>diagonal</code> = 0, it is the main diagonal.</p></li>
+</ul><p>The argument <code>diagonal</code> controls which diagonal to consider:</p><ul><li><p>If <code>diagonal</code> = 0, it is the main diagonal.</p></li>
 <li><p>If <code>diagonal</code> &gt; 0, it is above the main diagonal.</p></li>
 <li><p>If <code>diagonal</code> &lt; 0, it is below the main diagonal.</p></li>
-</ul>
+</ul></div>
 
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
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index 89c19885d..175d73559 100644
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Diag_embed</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_diag_embed.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Diag_embed</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_diag_embed.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Diag_embed</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_diag_embed</span><span class='op'>(</span><span class='va'>self</span>, offset <span class='op'>=</span> <span class='fl'>0L</span>, dim1 <span class='op'>=</span> <span class='op'>-</span><span class='fl'>2L</span>, dim2 <span class='op'>=</span> <span class='op'>-</span><span class='fl'>1L</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor. Must be at least 1-dimensional.</p></td>
-    </tr>
-    <tr>
-      <th>offset</th>
-      <td><p>(int, optional) which diagonal to consider. Default: 0        (main diagonal).</p></td>
-    </tr>
-    <tr>
-      <th>dim1</th>
-      <td><p>(int, optional) first dimension with respect to which to        take diagonal. Default: -2.</p></td>
-    </tr>
-    <tr>
-      <th>dim2</th>
-      <td><p>(int, optional) second dimension with respect to which to        take diagonal. Default: -1.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="diag-embed-input-offset-dim-dim-gt-tensor-"><a class="anchor" href="#diag-embed-input-offset-dim-dim-gt-tensor-"></a>diag_embed(input, offset=0, dim1=-2, dim2=-1) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_diag_embed</span><span class="op">(</span><span class="va">self</span>, offset <span class="op">=</span> <span class="fl">0L</span>, dim1 <span class="op">=</span> <span class="op">-</span><span class="fl">2L</span>, dim2 <span class="op">=</span> <span class="op">-</span><span class="fl">1L</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor. Must be at least 1-dimensional.</p></dd>
+<dt>offset</dt>
+<dd><p>(int, optional) which diagonal to consider. Default: 0        (main diagonal).</p></dd>
+<dt>dim1</dt>
+<dd><p>(int, optional) first dimension with respect to which to        take diagonal. Default: -2.</p></dd>
+<dt>dim2</dt>
+<dd><p>(int, optional) second dimension with respect to which to        take diagonal. Default: -1.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="diag-embed-input-offset-dim-dim-gt-tensor-">diag_embed(input, offset=0, dim1=-2, dim2=-1) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#diag-embed-input-offset-dim-dim-gt-tensor-"></a></h2>
     
 
 
@@ -221,13 +105,10 @@
 <code>dim1</code> and <code>dim2</code>) are filled by <code>input</code>.
 To facilitate creating batched diagonal matrices, the 2D planes formed by
 the last two dimensions of the returned tensor are chosen by default.</p>
-<p>The argument <code>offset</code> controls which diagonal to consider:</p><ul>
-<li><p>If <code>offset</code> = 0, it is the main diagonal.</p></li>
+<p>The argument <code>offset</code> controls which diagonal to consider:</p><ul><li><p>If <code>offset</code> = 0, it is the main diagonal.</p></li>
 <li><p>If <code>offset</code> &gt; 0, it is above the main diagonal.</p></li>
 <li><p>If <code>offset</code> &lt; 0, it is below the main diagonal.</p></li>
-</ul>
-
-<p>The size of the new matrix will be calculated to make the specified diagonal
+</ul><p>The size of the new matrix will be calculated to make the specified diagonal
 of the size of the last input dimension.
 Note that for <code>offset</code> other than \(0\), the order of <code>dim1</code>
 and <code>dim2</code> matters. Exchanging them is equivalent to changing the
@@ -236,57 +117,52 @@ sign of <code>offset</code>.</p>
 the same arguments yields a matrix identical to input. However,
 <code>torch_diagonal</code> has different default dimensions, so those
 need to be explicitly specified.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_diag_embed</span><span class='op'>(</span><span class='va'>a</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_diag_embed</span><span class='op'>(</span><span class='va'>a</span>, offset<span class='op'>=</span><span class='fl'>1</span>, dim1<span class='op'>=</span><span class='fl'>1</span>, dim2<span class='op'>=</span><span class='fl'>3</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> (1,.,.) = </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   0.0000  0.4335  0.0000  0.0000</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   0.0000 -1.6754  0.0000  0.0000</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> (2,.,.) = </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   0.0000  0.0000 -0.0828  0.0000</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   0.0000  0.0000 -0.2730  0.0000</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> (3,.,.) = </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   0.0000  0.0000  0.0000 -1.2989</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   0.0000  0.0000  0.0000 -2.0003</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> (4,.,.) = </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   0  0  0  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   0  0  0  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{4,2,4} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
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-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_diag_embed</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_diag_embed</span><span class="op">(</span><span class="va">a</span>, offset<span class="op">=</span><span class="fl">1</span>, dim1<span class="op">=</span><span class="fl">1</span>, dim2<span class="op">=</span><span class="fl">3</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> (1,.,.) = </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.0000  1.1072  0.0000  0.0000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.0000  1.4113  0.0000  0.0000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> (2,.,.) = </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.0000  0.0000  1.0521  0.0000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.0000  0.0000  0.6029  0.0000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> (3,.,.) = </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.0000  0.0000  0.0000 -0.7156</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.0000  0.0000  0.0000 -0.2509</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> (4,.,.) = </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0  0  0  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0  0  0  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4,2,4} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Diagflat</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_diagflat.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Diagflat</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_diagflat.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Diagflat</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_diagflat</span><span class='op'>(</span><span class='va'>self</span>, offset <span class='op'>=</span> <span class='fl'>0L</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>offset</th>
-      <td><p>(int, optional) the diagonal to consider. Default: 0 (main        diagonal).</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="diagflat-input-offset-gt-tensor-"><a class="anchor" href="#diagflat-input-offset-gt-tensor-"></a>diagflat(input, offset=0) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_diagflat</span><span class="op">(</span><span class="va">self</span>, offset <span class="op">=</span> <span class="fl">0L</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+<dt>offset</dt>
+<dd><p>(int, optional) the diagonal to consider. Default: 0 (main        diagonal).</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="diagflat-input-offset-gt-tensor-">diagflat(input, offset=0) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#diagflat-input-offset-gt-tensor-"></a></h2>
     
 
-<ul>
-<li><p>If <code>input</code> is a vector (1-D tensor), then returns a 2-D square tensor
+<ul><li><p>If <code>input</code> is a vector (1-D tensor), then returns a 2-D square tensor
 with the elements of <code>input</code> as the diagonal.</p></li>
 <li><p>If <code>input</code> is a tensor with more than one dimension, then returns a
 2-D tensor with diagonal elements equal to a flattened <code>input</code>.</p></li>
-</ul>
-
-<p>The argument <code>offset</code> controls which diagonal to consider:</p><ul>
-<li><p>If <code>offset</code> = 0, it is the main diagonal.</p></li>
+</ul><p>The argument <code>offset</code> controls which diagonal to consider:</p><ul><li><p>If <code>offset</code> = 0, it is the main diagonal.</p></li>
 <li><p>If <code>offset</code> &gt; 0, it is above the main diagonal.</p></li>
 <li><p>If <code>offset</code> &lt; 0, it is below the main diagonal.</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='fu'>torch_diagflat</span><span class='op'>(</span><span class='va'>a</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_diagflat</span><span class='op'>(</span><span class='va'>a</span>, <span class='fl'>1</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>2</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='fu'>torch_diagflat</span><span class='op'>(</span><span class='va'>a</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.0556  0.0000  0.0000  0.0000</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.0000 -0.3686  0.0000  0.0000</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.0000  0.0000  2.1160  0.0000</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.0000  0.0000  0.0000  0.2410</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{4,4} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+</ul></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="fu">torch_diagflat</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_diagflat</span><span class="op">(</span><span class="va">a</span>, <span class="fl">1</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">2</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="fu">torch_diagflat</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.3021  0.0000  0.0000  0.0000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0000  0.4805  0.0000  0.0000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0000  0.0000  1.2035  0.0000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0000  0.0000  0.0000 -0.8634</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4,4} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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+  <a class="external-link nav-link" href="https://github.com/mlverse/torch/" aria-label="github">
+    <span class="fab fa fab fa-github fa-lg"></span>
      
   </a>
 </li>
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-
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-
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+      </ul></div>
 
+    
+  </div>
+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Diagonal</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_diagonal.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Diagonal</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_diagonal.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Diagonal</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_diagonal</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>outdim</span>, dim1 <span class='op'>=</span> <span class='fl'>1L</span>, dim2 <span class='op'>=</span> <span class='fl'>2L</span>, offset <span class='op'>=</span> <span class='fl'>0L</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor. Must be at least 2-dimensional.</p></td>
-    </tr>
-    <tr>
-      <th>outdim</th>
-      <td><p>dimension name if <code>self</code> is a named tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dim1</th>
-      <td><p>(int, optional) first dimension with respect to which to        take diagonal. Default: 0.</p></td>
-    </tr>
-    <tr>
-      <th>dim2</th>
-      <td><p>(int, optional) second dimension with respect to which to        take diagonal. Default: 1.</p></td>
-    </tr>
-    <tr>
-      <th>offset</th>
-      <td><p>(int, optional) which diagonal to consider. Default: 0        (main diagonal).</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="diagonal-input-offset-dim-dim-gt-tensor-"><a class="anchor" href="#diagonal-input-offset-dim-dim-gt-tensor-"></a>diagonal(input, offset=0, dim1=0, dim2=1) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_diagonal</span><span class="op">(</span><span class="va">self</span>, <span class="va">outdim</span>, dim1 <span class="op">=</span> <span class="fl">1L</span>, dim2 <span class="op">=</span> <span class="fl">2L</span>, offset <span class="op">=</span> <span class="fl">0L</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor. Must be at least 2-dimensional.</p></dd>
+<dt>outdim</dt>
+<dd><p>dimension name if <code>self</code> is a named tensor.</p></dd>
+<dt>dim1</dt>
+<dd><p>(int, optional) first dimension with respect to which to        take diagonal. Default: 0.</p></dd>
+<dt>dim2</dt>
+<dd><p>(int, optional) second dimension with respect to which to        take diagonal. Default: 1.</p></dd>
+<dt>offset</dt>
+<dd><p>(int, optional) which diagonal to consider. Default: 0        (main diagonal).</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="diagonal-input-offset-dim-dim-gt-tensor-">diagonal(input, offset=0, dim1=0, dim2=1) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#diagonal-input-offset-dim-dim-gt-tensor-"></a></h2>
     
 
 
 <p>Returns a partial view of <code>input</code> with the its diagonal elements
 with respect to <code>dim1</code> and <code>dim2</code> appended as a dimension
 at the end of the shape.</p>
-<p>The argument <code>offset</code> controls which diagonal to consider:</p><ul>
-<li><p>If <code>offset</code> = 0, it is the main diagonal.</p></li>
+<p>The argument <code>offset</code> controls which diagonal to consider:</p><ul><li><p>If <code>offset</code> = 0, it is the main diagonal.</p></li>
 <li><p>If <code>offset</code> &gt; 0, it is above the main diagonal.</p></li>
 <li><p>If <code>offset</code> &lt; 0, it is below the main diagonal.</p></li>
-</ul>
-
-<p>Applying <code>torch_diag_embed</code> to the output of this function with
+</ul><p>Applying <code>torch_diag_embed</code> to the output of this function with
 the same arguments yields a diagonal matrix with the diagonal entries
 of the input. However, <code>torch_diag_embed</code> has different default
 dimensions, so those need to be explicitly specified.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='fu'>torch_diagonal</span><span class='op'>(</span><span class='va'>a</span>, offset <span class='op'>=</span> <span class='fl'>0</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_diagonal</span><span class='op'>(</span><span class='va'>a</span>, offset <span class='op'>=</span> <span class='fl'>1</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>5</span>, <span class='fl'>4</span>, <span class='fl'>2</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_diagonal</span><span class='op'>(</span><span class='va'>x</span>, offset<span class='op'>=</span><span class='op'>-</span><span class='fl'>1</span>, dim1<span class='op'>=</span><span class='fl'>1</span>, dim2<span class='op'>=</span><span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> (1,.,.) = </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   1.6703</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  -0.7319</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> (2,.,.) = </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  -0.5275</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  -0.6745</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> (3,.,.) = </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   1.8640</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   1.0624</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> (4,.,.) = </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  -0.1643</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   0.0126</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{4,2,1} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="fu">torch_diagonal</span><span class="op">(</span><span class="va">a</span>, offset <span class="op">=</span> <span class="fl">0</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_diagonal</span><span class="op">(</span><span class="va">a</span>, offset <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">x</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">5</span>, <span class="fl">4</span>, <span class="fl">2</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_diagonal</span><span class="op">(</span><span class="va">x</span>, offset<span class="op">=</span><span class="op">-</span><span class="fl">1</span>, dim1<span class="op">=</span><span class="fl">1</span>, dim2<span class="op">=</span><span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> (1,.,.) = </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -1.6822</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.7877</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> (2,.,.) = </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -1.3171</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.4693</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> (3,.,.) = </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   1.2076</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.9640</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> (4,.,.) = </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.8800</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -1.2734</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4,2,1} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Computes the n-th forward difference along the given dimension.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_diff.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Computes the n-th forward difference along the given dimension.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_diff.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>The first-order differences are given by <code>out[i] = input[i + 1] - input[i]</code>.
 Higher-order differences are calculated by using <code>torch_diff()</code> recursively.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_diff</span><span class='op'>(</span><span class='va'>self</span>, n <span class='op'>=</span> <span class='fl'>1L</span>, dim <span class='op'>=</span> <span class='op'>-</span><span class='fl'>1L</span>, prepend <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span><span class='op'>)</span>, append <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_diff</span><span class="op">(</span><span class="va">self</span>, n <span class="op">=</span> <span class="fl">1L</span>, dim <span class="op">=</span> <span class="op">-</span><span class="fl">1L</span>, prepend <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="op">)</span>, append <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>the tensor to compute the differences on</p></td>
-    </tr>
-    <tr>
-      <th>n</th>
-      <td><p>the number of times to recursively compute the difference</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>the dimension to compute the difference along. Default is the last dimension.</p></td>
-    </tr>
-    <tr>
-      <th>prepend</th>
-      <td><p>values to prepend to input along dim before computing the
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>the tensor to compute the differences on</p></dd>
+<dt>n</dt>
+<dd><p>the number of times to recursively compute the difference</p></dd>
+<dt>dim</dt>
+<dd><p>the dimension to compute the difference along. Default is the last dimension.</p></dd>
+<dt>prepend</dt>
+<dd><p>values to prepend to input along dim before computing the
 difference. Their dimensions must be equivalent to that of input, and their
-shapes must match input’s shape except on dim.</p></td>
-    </tr>
-    <tr>
-      <th>append</th>
-      <td><p>values to append to input along dim before computing the
+shapes must match input’s shape except on dim.</p></dd>
+<dt>append</dt>
+<dd><p>values to append to input along dim before computing the
 difference. Their dimensions must be equivalent to that of input, and their
-shapes must match input’s shape except on dim.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
+shapes must match input’s shape except on dim.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p>Only n = 1 is currently supported</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>,<span class='fl'>2</span>,<span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_diff</span><span class='op'>(</span><span class='va'>a</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>b</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span>, <span class='fl'>5</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_diff</span><span class='op'>(</span><span class='va'>a</span>, append <span class='op'>=</span> <span class='va'>b</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>c</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/cbind.html'>rbind</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>,<span class='fl'>2</span>,<span class='fl'>3</span><span class='op'>)</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3</span>,<span class='fl'>4</span>,<span class='fl'>5</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_diff</span><span class='op'>(</span><span class='va'>c</span>, dim <span class='op'>=</span> <span class='fl'>1</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_diff</span><span class='op'>(</span><span class='va'>c</span>, dim <span class='op'>=</span> <span class='fl'>2</span><span class='op'>)</span> </span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1  1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1  1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{2,2} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>,<span class="fl">2</span>,<span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_diff</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">b</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span>, <span class="fl">5</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_diff</span><span class="op">(</span><span class="va">a</span>, append <span class="op">=</span> <span class="va">b</span><span class="op">)</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">c</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/cbind.html" class="external-link">rbind</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>,<span class="fl">2</span>,<span class="fl">3</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>,<span class="fl">4</span>,<span class="fl">5</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_diff</span><span class="op">(</span><span class="va">c</span>, dim <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_diff</span><span class="op">(</span><span class="va">c</span>, dim <span class="op">=</span> <span class="fl">2</span><span class="op">)</span> </span>
+<span class="r-in"></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2,2} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Digamma</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_digamma.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Digamma</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_digamma.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Digamma</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_digamma</span><span class='op'>(</span><span class='va'>self</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the tensor to compute the digamma function on</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="digamma-input-out-null-gt-tensor-"><a class="anchor" href="#digamma-input-out-null-gt-tensor-"></a>digamma(input, out=NULL) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_digamma</span><span class="op">(</span><span class="va">self</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the tensor to compute the digamma function on</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="digamma-input-out-null-gt-tensor-">digamma(input, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#digamma-input-out-null-gt-tensor-"></a></h2>
     
 
 
@@ -209,43 +99,38 @@
 <p>$$
     \psi(x) = \frac{d}{dx} \ln\left(\Gamma\left(x\right)\right) = \frac{\Gamma'(x)}{\Gamma(x)}
 $$</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>0.5</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_digamma</span><span class='op'>(</span><span class='va'>a</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.5772</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -1.9635</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{2} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">0.5</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_digamma</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.5772</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -1.9635</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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-      <div class="copyright">
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+    <footer><div class="pkgdown-footer-left">
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+<div class="pkgdown-footer-right">
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Dist</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_dist.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Dist</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_dist.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Dist</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_dist</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>other</span>, p <span class='op'>=</span> <span class='fl'>2L</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Tensor) the Right-hand-side input tensor</p></td>
-    </tr>
-    <tr>
-      <th>p</th>
-      <td><p>(float, optional) the norm to be computed</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="dist-input-other-p-gt-tensor-"><a class="anchor" href="#dist-input-other-p-gt-tensor-"></a>dist(input, other, p=2) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_dist</span><span class="op">(</span><span class="va">self</span>, <span class="va">other</span>, p <span class="op">=</span> <span class="fl">2L</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+<dt>other</dt>
+<dd><p>(Tensor) the Right-hand-side input tensor</p></dd>
+<dt>p</dt>
+<dd><p>(float, optional) the norm to be computed</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="dist-input-other-p-gt-tensor-">dist(input, other, p=2) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#dist-input-other-p-gt-tensor-"></a></h2>
     
 
 
 <p>Returns the p-norm of (<code>input</code> - <code>other</code>)</p>
 <p>The shapes of <code>input</code> and <code>other</code> must be
 broadcastable .</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>x</span></span>
-<span class='r-in'><span class='va'>y</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>y</span></span>
-<span class='r-in'><span class='fu'>torch_dist</span><span class='op'>(</span><span class='va'>x</span>, <span class='va'>y</span>, <span class='fl'>3.5</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_dist</span><span class='op'>(</span><span class='va'>x</span>, <span class='va'>y</span>, <span class='fl'>3</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_dist</span><span class='op'>(</span><span class='va'>x</span>, <span class='va'>y</span>, <span class='fl'>0</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_dist</span><span class='op'>(</span><span class='va'>x</span>, <span class='va'>y</span>, <span class='fl'>1</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> 4.0643</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">x</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">x</span></span>
+<span class="r-in"><span class="va">y</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">y</span></span>
+<span class="r-in"><span class="fu">torch_dist</span><span class="op">(</span><span class="va">x</span>, <span class="va">y</span>, <span class="fl">3.5</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_dist</span><span class="op">(</span><span class="va">x</span>, <span class="va">y</span>, <span class="fl">3</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_dist</span><span class="op">(</span><span class="va">x</span>, <span class="va">y</span>, <span class="fl">0</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_dist</span><span class="op">(</span><span class="va">x</span>, <span class="va">y</span>, <span class="fl">1</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2.38916</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Div</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_div.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Div</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_div.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Div</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_div</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>other</span>, <span class='va'>rounding_mode</span><span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_div</span><span class="op">(</span><span class="va">self</span>, <span class="va">other</span>, <span class="va">rounding_mode</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Number) the number to be divided to each element of <code>input</code></p></td>
-    </tr>
-    <tr>
-      <th>rounding_mode</th>
-      <td><p>(str, optional) – Type of rounding applied to the result:</p><ul>
-<li><p><code>NULL</code> - default behavior. Performs no rounding and, if both input and
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+<dt>other</dt>
+<dd><p>(Number) the number to be divided to each element of <code>input</code></p></dd>
+<dt>rounding_mode</dt>
+<dd><p>(str, optional) – Type of rounding applied to the result:</p><ul><li><p><code>NULL</code> - default behavior. Performs no rounding and, if both input and
 other are integer types, promotes the inputs to the default scalar type.
 Equivalent to true division in Python (the / operator) and NumPy’s
 <code>np.true_divide</code>.</p></li>
@@ -213,12 +100,10 @@ Equivalent to true division in Python (the / operator) and NumPy’s
 C-style integer division.</p></li>
 <li><p>"floor" - rounds the results of the division down. Equivalent to floor
 division in Python (the // operator) and NumPy’s <code>np.floor_divide</code>.</p></li>
-</ul></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="div-input-other-out-null-gt-tensor-"><a class="anchor" href="#div-input-other-out-null-gt-tensor-"></a>div(input, other, out=NULL) -&gt; Tensor </h2>
-
+</ul></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="div-input-other-out-null-gt-tensor-">div(input, other, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#div-input-other-out-null-gt-tensor-"></a></h2>
     
 
 
@@ -238,13 +123,14 @@ following rules described in the type promotion documentation
 . If <code>out</code> is specified, the result must be
 castable  to the <code>torch_dtype</code> of the
 specified output tensor. Integral division by zero leads to undefined behavior.</p>
-    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></a></h2>
     
 
 <p>Integer division using div is deprecated, and in a future release div will
-perform true division like <code><a href='torch_true_divide.html'>torch_true_divide()</a></code>.
-Use <code><a href='torch_floor_divide.html'>torch_floor_divide()</a></code> to perform integer division,
+perform true division like <code><a href="torch_true_divide.html">torch_true_divide()</a></code>.
+Use <code><a href="torch_floor_divide.html">torch_floor_divide()</a></code> to perform integer division,
 instead.</p>
 <p>$$
     \mbox{out}_i = \frac{\mbox{input}_i}{\mbox{other}}
@@ -255,53 +141,48 @@ described in the type promotion documentation . If
 <code>out</code> is specified, the result must be castable
 to the <code>torch_dtype</code> of the specified output tensor. Integral division
 by zero leads to undefined behavior.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>5</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='fu'>torch_div</span><span class='op'>(</span><span class='va'>a</span>, <span class='fl'>0.5</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span>, <span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='va'>b</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>b</span></span>
-<span class='r-in'><span class='fu'>torch_div</span><span class='op'>(</span><span class='va'>a</span>, <span class='va'>b</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.3690  2.5933 -0.3596  0.9899</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.1896 -0.2787 -0.4618  0.7261</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.5817  0.9983 -0.7150 -1.3386</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.1184  1.7380  0.2600 -0.5345</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{4,4} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">5</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="fu">torch_div</span><span class="op">(</span><span class="va">a</span>, <span class="fl">0.5</span><span class="op">)</span></span>
+<span class="r-in"></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span>, <span class="fl">4</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="va">b</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">b</span></span>
+<span class="r-in"><span class="fu">torch_div</span><span class="op">(</span><span class="va">a</span>, <span class="va">b</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   1.7242   4.5418 -10.1465   0.8887</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.6280  -4.1159  -2.1881   0.5456</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -1.3812  -1.8686  -6.5700  -0.7843</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.8885  -7.5946  35.1556  -0.5610</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4,4} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Divide</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_divide.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Divide</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_divide.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Divide</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_divide</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>other</span>, <span class='va'>rounding_mode</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Number) the number to be divided to each element of <code>input</code></p></td>
-    </tr>
-    <tr>
-      <th>rounding_mode</th>
-      <td><p>(str, optional) – Type of rounding applied to the result:</p><ul>
-<li><p><code>NULL</code> - default behavior. Performs no rounding and, if both input and
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_divide</span><span class="op">(</span><span class="va">self</span>, <span class="va">other</span>, <span class="va">rounding_mode</span><span class="op">)</span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+<dt>other</dt>
+<dd><p>(Number) the number to be divided to each element of <code>input</code></p></dd>
+<dt>rounding_mode</dt>
+<dd><p>(str, optional) – Type of rounding applied to the result:</p><ul><li><p><code>NULL</code> - default behavior. Performs no rounding and, if both input and
 other are integer types, promotes the inputs to the default scalar type.
 Equivalent to true division in Python (the / operator) and NumPy’s
 <code>np.true_divide</code>.</p></li>
@@ -213,42 +100,33 @@ Equivalent to true division in Python (the / operator) and NumPy’s
 C-style integer division.</p></li>
 <li><p>"floor" - rounds the results of the division down. Equivalent to floor
 division in Python (the // operator) and NumPy’s <code>np.floor_divide</code>.</p></li>
-</ul></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="divide-input-other-out-none-gt-tensor-"><a class="anchor" href="#divide-input-other-out-none-gt-tensor-"></a>divide(input, other, *, out=None) -&gt; Tensor </h2>
-
+</ul></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="divide-input-other-out-none-gt-tensor-">divide(input, other, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#divide-input-other-out-none-gt-tensor-"></a></h2>
     
 
 
-<p>Alias for <code><a href='torch_div.html'>torch_div()</a></code>.</p>
+<p>Alias for <code><a href="torch_div.html">torch_div()</a></code>.</p>
+    </div>
 
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Dot</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_dot.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Dot</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_dot.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Dot</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_dot</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>tensor</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>the input tensor</p></td>
-    </tr>
-    <tr>
-      <th>tensor</th>
-      <td><p>the other input tensor</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_dot</span><span class="op">(</span><span class="va">self</span>, <span class="va">tensor</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>the input tensor</p></dd>
+<dt>tensor</dt>
+<dd><p>the other input tensor</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p>This function does not broadcast .</p>
-    <h2 class="hasAnchor" id="dot-input-tensor-gt-tensor-"><a class="anchor" href="#dot-input-tensor-gt-tensor-"></a>dot(input, tensor) -&gt; Tensor </h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="dot-input-tensor-gt-tensor-">dot(input, tensor) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#dot-input-tensor-gt-tensor-"></a></h2>
     
 
 
 <p>Computes the dot product (inner product) of two tensors.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='fu'>torch_dot</span><span class='op'>(</span><span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span>, <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>1</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> 7</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="fu">torch_dot</span><span class="op">(</span><span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span>, <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">1</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 7</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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diff --git a/reference/torch_dstack.html b/reference/torch_dstack.html
index 4b702c116..f6d792cef 100644
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+    
+  </div>
+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Dstack</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_dstack.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Dstack</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_dstack.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Dstack</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_dstack</span><span class='op'>(</span><span class='va'>tensors</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>tensors</th>
-      <td><p>(sequence of Tensors) sequence of tensors to concatenate</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="dstack-tensors-out-none-gt-tensor-"><a class="anchor" href="#dstack-tensors-out-none-gt-tensor-"></a>dstack(tensors, *, out=None) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_dstack</span><span class="op">(</span><span class="va">tensors</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>tensors</dt>
+<dd><p>(sequence of Tensors) sequence of tensors to concatenate</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="dstack-tensors-out-none-gt-tensor-">dstack(tensors, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#dstack-tensors-out-none-gt-tensor-"></a></h2>
     
 
 
 <p>Stack tensors in sequence depthwise (along third axis).</p>
 <p>This is equivalent to concatenation along the third axis after 1-D and 2-D
-tensors have been reshaped by <code><a href='torch_atleast_3d.html'>torch_atleast_3d()</a></code>.</p>
+tensors have been reshaped by <code><a href="torch_atleast_3d.html">torch_atleast_3d()</a></code>.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>2</span>, <span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>b</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span>, <span class='fl'>5</span>, <span class='fl'>6</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_dstack</span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span><span class='va'>a</span>,<span class='va'>b</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/cbind.html'>rbind</a></span><span class='op'>(</span><span class='fl'>1</span>,<span class='fl'>2</span>,<span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>b</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/cbind.html'>rbind</a></span><span class='op'>(</span><span class='fl'>4</span>,<span class='fl'>5</span>,<span class='fl'>6</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_dstack</span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span><span class='va'>a</span>,<span class='va'>b</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> (1,.,.) = </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   1  4</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> (2,.,.) = </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   2  5</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> (3,.,.) = </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   3  6</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{3,1,2} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">2</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">b</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span>, <span class="fl">5</span>, <span class="fl">6</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_dstack</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">a</span>,<span class="va">b</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/cbind.html" class="external-link">rbind</a></span><span class="op">(</span><span class="fl">1</span>,<span class="fl">2</span>,<span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">b</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/cbind.html" class="external-link">rbind</a></span><span class="op">(</span><span class="fl">4</span>,<span class="fl">5</span>,<span class="fl">6</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_dstack</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">a</span>,<span class="va">b</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> (1,.,.) = </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   1  4</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> (2,.,.) = </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   2  5</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> (3,.,.) = </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   3  6</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3,1,2} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Torch data types</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/dtype.R'><code>R/dtype.R</code></a></small>
-    <div class="hidden name"><code>torch_dtype.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Torch data types</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/dtype.R" class="external-link"><code>R/dtype.R</code></a></small>
+      <div class="d-none name"><code>torch_dtype.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Returns the correspondent data type.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_float32</span><span class='op'>(</span><span class='op'>)</span>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_float32</span><span class="op">(</span><span class="op">)</span>
 
-<span class='fu'>torch_float</span><span class='op'>(</span><span class='op'>)</span>
+<span class="fu">torch_float</span><span class="op">(</span><span class="op">)</span>
 
-<span class='fu'>torch_float64</span><span class='op'>(</span><span class='op'>)</span>
+<span class="fu">torch_float64</span><span class="op">(</span><span class="op">)</span>
 
-<span class='fu'>torch_double</span><span class='op'>(</span><span class='op'>)</span>
+<span class="fu">torch_double</span><span class="op">(</span><span class="op">)</span>
 
-<span class='fu'>torch_float16</span><span class='op'>(</span><span class='op'>)</span>
+<span class="fu">torch_float16</span><span class="op">(</span><span class="op">)</span>
 
-<span class='fu'>torch_half</span><span class='op'>(</span><span class='op'>)</span>
+<span class="fu">torch_half</span><span class="op">(</span><span class="op">)</span>
 
-<span class='fu'>torch_uint8</span><span class='op'>(</span><span class='op'>)</span>
+<span class="fu">torch_uint8</span><span class="op">(</span><span class="op">)</span>
 
-<span class='fu'>torch_int8</span><span class='op'>(</span><span class='op'>)</span>
+<span class="fu">torch_int8</span><span class="op">(</span><span class="op">)</span>
 
-<span class='fu'>torch_int16</span><span class='op'>(</span><span class='op'>)</span>
+<span class="fu">torch_int16</span><span class="op">(</span><span class="op">)</span>
 
-<span class='fu'>torch_short</span><span class='op'>(</span><span class='op'>)</span>
+<span class="fu">torch_short</span><span class="op">(</span><span class="op">)</span>
 
-<span class='fu'>torch_int32</span><span class='op'>(</span><span class='op'>)</span>
+<span class="fu">torch_int32</span><span class="op">(</span><span class="op">)</span>
 
-<span class='fu'>torch_int</span><span class='op'>(</span><span class='op'>)</span>
+<span class="fu">torch_int</span><span class="op">(</span><span class="op">)</span>
 
-<span class='fu'>torch_int64</span><span class='op'>(</span><span class='op'>)</span>
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-    <h1>Eig</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_eig.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Eig</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_eig.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Eig</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_eig</span><span class='op'>(</span><span class='va'>self</span>, eigenvectors <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the square matrix of shape \((n \times n)\) for which the eigenvalues and eigenvectors        will be computed</p></td>
-    </tr>
-    <tr>
-      <th>eigenvectors</th>
-      <td><p>(bool) <code>TRUE</code> to compute both eigenvalues and eigenvectors;        otherwise, only eigenvalues will be computed</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_eig</span><span class="op">(</span><span class="va">self</span>, eigenvectors <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the square matrix of shape \((n \times n)\) for which the eigenvalues and eigenvectors        will be computed</p></dd>
+<dt>eigenvectors</dt>
+<dd><p>(bool) <code>TRUE</code> to compute both eigenvalues and eigenvectors;        otherwise, only eigenvalues will be computed</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     
-<pre>Since eigenvalues and eigenvectors might be complex, backward pass is supported only
+<div class="sourceCode"><pre><code>Since eigenvalues and eigenvectors might be complex, backward pass is supported only
 for [`torch_symeig`]
-</pre>
-
-    <h2 class="hasAnchor" id="eig-input-eigenvectors-false-out-null-gt-tensor-tensor-"><a class="anchor" href="#eig-input-eigenvectors-false-out-null-gt-tensor-tensor-"></a>eig(input, eigenvectors=False, out=NULL) -&gt; (Tensor, Tensor) </h2>
+</code></pre></div>
 
+    </div>
+    <div class="section level2">
+    <h2 id="eig-input-eigenvectors-false-out-null-gt-tensor-tensor-">eig(input, eigenvectors=False, out=NULL) -&gt; (Tensor, Tensor) <a class="anchor" aria-label="anchor" href="#eig-input-eigenvectors-false-out-null-gt-tensor-tensor-"></a></h2>
     
 
 
 <p>Computes the eigenvalues and eigenvectors of a real square matrix.</p>
+    </div>
 
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Einsum</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_einsum.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Einsum</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_einsum.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Einsum</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_einsum</span><span class='op'>(</span><span class='va'>equation</span>, <span class='va'>tensors</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>equation</th>
-      <td><p>(string) The equation is given in terms of lower case letters (indices) to be associated           with each dimension of the operands and result. The left hand side lists the operands           dimensions, separated by commas. There should be one index letter per tensor dimension.           The right hand side follows after <code>-&gt;</code> and gives the indices for the output.           If the <code>-&gt;</code> and right hand side are omitted, it implicitly defined as the alphabetically           sorted list of all indices appearing exactly once in the left hand side.           The indices not apprearing in the output are summed over after multiplying the operands           entries.           If an index appears several times for the same operand, a diagonal is taken.           Ellipses <code>...</code> represent a fixed number of dimensions. If the right hand side is inferred,           the ellipsis dimensions are at the beginning of the output.</p></td>
-    </tr>
-    <tr>
-      <th>tensors</th>
-      <td><p>(Tensor) The operands to compute the Einstein sum of.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="einsum-equation-operands-gt-tensor-"><a class="anchor" href="#einsum-equation-operands-gt-tensor-"></a>einsum(equation, *operands) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_einsum</span><span class="op">(</span><span class="va">equation</span>, <span class="va">tensors</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>equation</dt>
+<dd><p>(string) The equation is given in terms of lower case letters (indices) to be associated           with each dimension of the operands and result. The left hand side lists the operands           dimensions, separated by commas. There should be one index letter per tensor dimension.           The right hand side follows after <code>-&gt;</code> and gives the indices for the output.           If the <code>-&gt;</code> and right hand side are omitted, it implicitly defined as the alphabetically           sorted list of all indices appearing exactly once in the left hand side.           The indices not apprearing in the output are summed over after multiplying the operands           entries.           If an index appears several times for the same operand, a diagonal is taken.           Ellipses <code>...</code> represent a fixed number of dimensions. If the right hand side is inferred,           the ellipsis dimensions are at the beginning of the output.</p></dd>
+<dt>tensors</dt>
+<dd><p>(Tensor) The operands to compute the Einstein sum of.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="einsum-equation-operands-gt-tensor-">einsum(equation, *operands) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#einsum-equation-operands-gt-tensor-"></a></h2>
     
 
 
 <p>This function provides a way of computing multilinear expressions (i.e. sums of products) using the
 Einstein summation convention.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>5</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>y</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_einsum</span><span class='op'>(</span><span class='st'>'i,j-&gt;ij'</span>, <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span><span class='va'>x</span>, <span class='va'>y</span><span class='op'>)</span><span class='op'>)</span>  <span class='co'># outer product</span></span>
-<span class='r-in'><span class='va'>A</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3</span>,<span class='fl'>5</span>,<span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>l</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>2</span>,<span class='fl'>5</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>r</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>2</span>,<span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_einsum</span><span class='op'>(</span><span class='st'>'bn,anm,bm-&gt;ba'</span>, <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span><span class='va'>l</span>, <span class='va'>A</span>, <span class='va'>r</span><span class='op'>)</span><span class='op'>)</span> <span class='co'># compare torch_nn$functional$bilinear</span></span>
-<span class='r-in'><span class='va'>As</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3</span>,<span class='fl'>2</span>,<span class='fl'>5</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>Bs</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3</span>,<span class='fl'>5</span>,<span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_einsum</span><span class='op'>(</span><span class='st'>'bij,bjk-&gt;bik'</span>, <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span><span class='va'>As</span>, <span class='va'>Bs</span><span class='op'>)</span><span class='op'>)</span> <span class='co'># batch matrix multiplication</span></span>
-<span class='r-in'><span class='va'>A</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_einsum</span><span class='op'>(</span><span class='st'>'ii-&gt;i'</span>, <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span><span class='va'>A</span><span class='op'>)</span><span class='op'>)</span> <span class='co'># diagonal</span></span>
-<span class='r-in'><span class='va'>A</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span>, <span class='fl'>3</span>, <span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_einsum</span><span class='op'>(</span><span class='st'>'...ii-&gt;...i'</span>, <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span><span class='va'>A</span><span class='op'>)</span><span class='op'>)</span> <span class='co'># batch diagonal</span></span>
-<span class='r-in'><span class='va'>A</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>3</span>, <span class='fl'>4</span>, <span class='fl'>5</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_einsum</span><span class='op'>(</span><span class='st'>'...ij-&gt;...ji'</span>, <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span><span class='va'>A</span><span class='op'>)</span><span class='op'>)</span><span class='op'>$</span><span class='va'>shape</span> <span class='co'># batch permute</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [1] 2 3 5 4</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
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-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">x</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">5</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">y</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_einsum</span><span class="op">(</span><span class="st">'i,j-&gt;ij'</span>, <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">y</span><span class="op">)</span><span class="op">)</span>  <span class="co"># outer product</span></span>
+<span class="r-in"><span class="va">A</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>,<span class="fl">5</span>,<span class="fl">4</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">l</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>,<span class="fl">5</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">r</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>,<span class="fl">4</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_einsum</span><span class="op">(</span><span class="st">'bn,anm,bm-&gt;ba'</span>, <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">l</span>, <span class="va">A</span>, <span class="va">r</span><span class="op">)</span><span class="op">)</span> <span class="co"># compare torch_nn$functional$bilinear</span></span>
+<span class="r-in"><span class="va">As</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>,<span class="fl">2</span>,<span class="fl">5</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">Bs</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>,<span class="fl">5</span>,<span class="fl">4</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_einsum</span><span class="op">(</span><span class="st">'bij,bjk-&gt;bik'</span>, <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">As</span>, <span class="va">Bs</span><span class="op">)</span><span class="op">)</span> <span class="co"># batch matrix multiplication</span></span>
+<span class="r-in"><span class="va">A</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_einsum</span><span class="op">(</span><span class="st">'ii-&gt;i'</span>, <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">A</span><span class="op">)</span><span class="op">)</span> <span class="co"># diagonal</span></span>
+<span class="r-in"><span class="va">A</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span>, <span class="fl">3</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_einsum</span><span class="op">(</span><span class="st">'...ii-&gt;...i'</span>, <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">A</span><span class="op">)</span><span class="op">)</span> <span class="co"># batch diagonal</span></span>
+<span class="r-in"><span class="va">A</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">3</span>, <span class="fl">4</span>, <span class="fl">5</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_einsum</span><span class="op">(</span><span class="st">'...ij-&gt;...ji'</span>, <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">A</span><span class="op">)</span><span class="op">)</span><span class="op">$</span><span class="va">shape</span> <span class="co"># batch permute</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 2 3 5 4</span>
+</code></pre></div>
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+    
+  </div>
+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Empty</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/creation-ops.R'><code>R/creation-ops.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_empty.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Empty</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/creation-ops.R" class="external-link"><code>R/creation-ops.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a></small>
+      <div class="d-none name"><code>torch_empty.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Empty</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_empty</span><span class='op'>(</span>
-  <span class='va'>...</span>,
-  names <span class='op'>=</span> <span class='cn'>NULL</span>,
-  dtype <span class='op'>=</span> <span class='cn'>NULL</span>,
-  layout <span class='op'>=</span> <span class='fu'><a href='torch_layout.html'>torch_strided</a></span><span class='op'>(</span><span class='op'>)</span>,
-  device <span class='op'>=</span> <span class='cn'>NULL</span>,
-  requires_grad <span class='op'>=</span> <span class='cn'>FALSE</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>...</th>
-      <td><p>a sequence of integers defining the shape of the output tensor.</p></td>
-    </tr>
-    <tr>
-      <th>names</th>
-      <td><p>optional character vector naming each dimension.</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>).</p></td>
-    </tr>
-    <tr>
-      <th>layout</th>
-      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
-    </tr>
-    <tr>
-      <th>requires_grad</th>
-      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="empty-size-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-pin-memory-false-gt-tensor-"><a class="anchor" href="#empty-size-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-pin-memory-false-gt-tensor-"></a>empty(*size, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False, pin_memory=False) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_empty</span><span class="op">(</span>
+  <span class="va">...</span>,
+  names <span class="op">=</span> <span class="cn">NULL</span>,
+  dtype <span class="op">=</span> <span class="cn">NULL</span>,
+  layout <span class="op">=</span> <span class="fu"><a href="torch_layout.html">torch_strided</a></span><span class="op">(</span><span class="op">)</span>,
+  device <span class="op">=</span> <span class="cn">NULL</span>,
+  requires_grad <span class="op">=</span> <span class="cn">FALSE</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>...</dt>
+<dd><p>a sequence of integers defining the shape of the output tensor.</p></dd>
+<dt>names</dt>
+<dd><p>optional character vector naming each dimension.</p></dd>
+<dt>dtype</dt>
+<dd><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>).</p></dd>
+<dt>layout</dt>
+<dd><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></dd>
+<dt>device</dt>
+<dd><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></dd>
+<dt>requires_grad</dt>
+<dd><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="empty-size-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-pin-memory-false-gt-tensor-">empty(*size, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False, pin_memory=False) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#empty-size-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-pin-memory-false-gt-tensor-"></a></h2>
     
 
 
 <p>Returns a tensor filled with uninitialized data. The shape of the tensor is
 defined by the variable argument <code>size</code>.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='fu'>torch_empty</span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> 1e-05 *</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.0000  0.0000  0.0040</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   4.2010 -3266205805497548275712.0000  0.0000</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{2,3} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="fu">torch_empty</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  4.7275e+27  9.1066e-12  1.7592e+22</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  7.2143e+22  7.0308e+28  3.1986e+21</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2,3} ]</span>
+</code></pre></div>
+    </div>
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+    </nav></aside></div>
 
 
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Empty_like</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/creation-ops.R'><code>R/creation-ops.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_empty_like.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Empty_like</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/creation-ops.R" class="external-link"><code>R/creation-ops.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a></small>
+      <div class="d-none name"><code>torch_empty_like.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Empty_like</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_empty_like</span><span class='op'>(</span>
-  <span class='va'>input</span>,
-  dtype <span class='op'>=</span> <span class='cn'>NULL</span>,
-  layout <span class='op'>=</span> <span class='fu'><a href='torch_layout.html'>torch_strided</a></span><span class='op'>(</span><span class='op'>)</span>,
-  device <span class='op'>=</span> <span class='cn'>NULL</span>,
-  requires_grad <span class='op'>=</span> <span class='cn'>FALSE</span>,
-  memory_format <span class='op'>=</span> <span class='fu'><a href='torch_memory_format.html'>torch_preserve_format</a></span><span class='op'>(</span><span class='op'>)</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the size of <code>input</code> will determine size of the output tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned Tensor.        Default: if <code>NULL</code>, defaults to the dtype of <code>input</code>.</p></td>
-    </tr>
-    <tr>
-      <th>layout</th>
-      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned tensor.        Default: if <code>NULL</code>, defaults to the layout of <code>input</code>.</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, defaults to the device of <code>input</code>.</p></td>
-    </tr>
-    <tr>
-      <th>requires_grad</th>
-      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
-    </tr>
-    <tr>
-      <th>memory_format</th>
-      <td><p>(<code>torch.memory_format</code>, optional) the desired memory format of        returned Tensor. Default: <code>torch_preserve_format</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="empty-like-input-dtype-null-layout-null-device-null-requires-grad-false-memory-format-torch-preserve-format-gt-tensor-"><a class="anchor" href="#empty-like-input-dtype-null-layout-null-device-null-requires-grad-false-memory-format-torch-preserve-format-gt-tensor-"></a>empty_like(input, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, memory_format=torch.preserve_format) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_empty_like</span><span class="op">(</span>
+  <span class="va">input</span>,
+  dtype <span class="op">=</span> <span class="cn">NULL</span>,
+  layout <span class="op">=</span> <span class="fu"><a href="torch_layout.html">torch_strided</a></span><span class="op">(</span><span class="op">)</span>,
+  device <span class="op">=</span> <span class="cn">NULL</span>,
+  requires_grad <span class="op">=</span> <span class="cn">FALSE</span>,
+  memory_format <span class="op">=</span> <span class="fu"><a href="torch_memory_format.html">torch_preserve_format</a></span><span class="op">(</span><span class="op">)</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>(Tensor) the size of <code>input</code> will determine size of the output tensor.</p></dd>
+<dt>dtype</dt>
+<dd><p>(<code>torch.dtype</code>, optional) the desired data type of returned Tensor.        Default: if <code>NULL</code>, defaults to the dtype of <code>input</code>.</p></dd>
+<dt>layout</dt>
+<dd><p>(<code>torch.layout</code>, optional) the desired layout of returned tensor.        Default: if <code>NULL</code>, defaults to the layout of <code>input</code>.</p></dd>
+<dt>device</dt>
+<dd><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, defaults to the device of <code>input</code>.</p></dd>
+<dt>requires_grad</dt>
+<dd><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></dd>
+<dt>memory_format</dt>
+<dd><p>(<code>torch.memory_format</code>, optional) the desired memory format of        returned Tensor. Default: <code>torch_preserve_format</code>.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="empty-like-input-dtype-null-layout-null-device-null-requires-grad-false-memory-format-torch-preserve-format-gt-tensor-">empty_like(input, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, memory_format=torch.preserve_format) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#empty-like-input-dtype-null-layout-null-device-null-requires-grad-false-memory-format-torch-preserve-format-gt-tensor-"></a></h2>
     
 
 
 <p>Returns an uninitialized tensor with the same size as <code>input</code>.
 <code>torch_empty_like(input)</code> is equivalent to
 <code>torch_empty(input.size(), dtype=input.dtype, layout=input.layout, device=input.device)</code>.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='fu'><a href='torch_empty.html'>torch_empty</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span><span class='fl'>2</span>,<span class='fl'>3</span><span class='op'>)</span>, dtype <span class='op'>=</span> <span class='fu'><a href='torch_dtype.html'>torch_int64</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  2.3058e+18 -1.1529e+18  1.4067e+14</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1.4067e+14  1.4067e+14  0.0000e+00</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPULongType{2,3} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="fu"><a href="torch_empty.html">torch_empty</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="fl">2</span>,<span class="fl">3</span><span class="op">)</span>, dtype <span class="op">=</span> <span class="fu"><a href="torch_dtype.html">torch_int64</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  8.5899e+09  1.4035e+14  1.4035e+14</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.0000e+00  3.4360e+10  1.0000e+00</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPULongType{2,3} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Empty_strided</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/creation-ops.R'><code>R/creation-ops.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_empty_strided.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Empty_strided</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/creation-ops.R" class="external-link"><code>R/creation-ops.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a></small>
+      <div class="d-none name"><code>torch_empty_strided.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Empty_strided</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_empty_strided</span><span class='op'>(</span>
-  <span class='va'>size</span>,
-  <span class='va'>stride</span>,
-  dtype <span class='op'>=</span> <span class='cn'>NULL</span>,
-  layout <span class='op'>=</span> <span class='fu'><a href='torch_layout.html'>torch_strided</a></span><span class='op'>(</span><span class='op'>)</span>,
-  device <span class='op'>=</span> <span class='cn'>NULL</span>,
-  requires_grad <span class='op'>=</span> <span class='cn'>FALSE</span>,
-  pin_memory <span class='op'>=</span> <span class='cn'>FALSE</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>size</th>
-      <td><p>(tuple of ints) the shape of the output tensor</p></td>
-    </tr>
-    <tr>
-      <th>stride</th>
-      <td><p>(tuple of ints) the strides of the output tensor</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>).</p></td>
-    </tr>
-    <tr>
-      <th>layout</th>
-      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
-    </tr>
-    <tr>
-      <th>requires_grad</th>
-      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
-    </tr>
-    <tr>
-      <th>pin_memory</th>
-      <td><p>(bool, optional) If set, returned tensor would be allocated in        the pinned memory. Works only for CPU tensors. Default: <code>FALSE</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="empty-strided-size-stride-dtype-null-layout-null-device-null-requires-grad-false-pin-memory-false-gt-tensor-"><a class="anchor" href="#empty-strided-size-stride-dtype-null-layout-null-device-null-requires-grad-false-pin-memory-false-gt-tensor-"></a>empty_strided(size, stride, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, pin_memory=False) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_empty_strided</span><span class="op">(</span>
+  <span class="va">size</span>,
+  <span class="va">stride</span>,
+  dtype <span class="op">=</span> <span class="cn">NULL</span>,
+  layout <span class="op">=</span> <span class="fu"><a href="torch_layout.html">torch_strided</a></span><span class="op">(</span><span class="op">)</span>,
+  device <span class="op">=</span> <span class="cn">NULL</span>,
+  requires_grad <span class="op">=</span> <span class="cn">FALSE</span>,
+  pin_memory <span class="op">=</span> <span class="cn">FALSE</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>size</dt>
+<dd><p>(tuple of ints) the shape of the output tensor</p></dd>
+<dt>stride</dt>
+<dd><p>(tuple of ints) the strides of the output tensor</p></dd>
+<dt>dtype</dt>
+<dd><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>).</p></dd>
+<dt>layout</dt>
+<dd><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></dd>
+<dt>device</dt>
+<dd><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></dd>
+<dt>requires_grad</dt>
+<dd><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></dd>
+<dt>pin_memory</dt>
+<dd><p>(bool, optional) If set, returned tensor would be allocated in        the pinned memory. Works only for CPU tensors. Default: <code>FALSE</code>.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="empty-strided-size-stride-dtype-null-layout-null-device-null-requires-grad-false-pin-memory-false-gt-tensor-">empty_strided(size, stride, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, pin_memory=False) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#empty-strided-size-stride-dtype-null-layout-null-device-null-requires-grad-false-pin-memory-false-gt-tensor-"></a></h2>
     
 
 
@@ -241,50 +119,46 @@
 defined by the variable argument <code>size</code> and <code>stride</code> respectively.
 <code>torch_empty_strided(size, stride)</code> is equivalent to
 <code>torch_empty(size).as_strided(size, stride)</code>.</p>
-    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></a></h2>
     
 
 <p>More than one element of the created tensor may refer to a single memory
 location. As a result, in-place operations (especially ones that are
 vectorized) may result in incorrect behavior. If you need to write to
 the tensors, please clone them first.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'>torch_empty_strided</span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>3</span><span class='op'>)</span>, <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>2</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='va'>a</span><span class='op'>$</span><span class='fu'>stride</span><span class='op'>(</span><span class='fl'>1</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span><span class='op'>$</span><span class='fu'>size</span><span class='op'>(</span><span class='fl'>1</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [1] 2</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu">torch_empty_strided</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">3</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">2</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="va">a</span><span class="op">$</span><span class="fu">stride</span><span class="op">(</span><span class="fl">1</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span><span class="op">$</span><span class="fu">size</span><span class="op">(</span><span class="fl">1</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 2</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Eq</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_eq.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Eq</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_eq.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Eq</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_eq</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>other</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the tensor to compare</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Tensor or float) the tensor or value to compare
-Must be a <code>ByteTensor</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="eq-input-other-out-null-gt-tensor-"><a class="anchor" href="#eq-input-other-out-null-gt-tensor-"></a>eq(input, other, out=NULL) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_eq</span><span class="op">(</span><span class="va">self</span>, <span class="va">other</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the tensor to compare</p></dd>
+<dt>other</dt>
+<dd><p>(Tensor or float) the tensor or value to compare
+Must be a <code>ByteTensor</code></p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="eq-input-other-out-null-gt-tensor-">eq(input, other, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#eq-input-other-out-null-gt-tensor-"></a></h2>
     
 
 
 <p>Computes element-wise equality</p>
 <p>The second argument can be a number or a tensor whose shape is
 broadcastable  with the first argument.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='fu'>torch_eq</span><span class='op'>(</span><span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>,<span class='fl'>2</span>,<span class='fl'>3</span>,<span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span>, <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>3</span>, <span class='fl'>2</span>, <span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUBoolType{4} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="fu">torch_eq</span><span class="op">(</span><span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>,<span class="fl">2</span>,<span class="fl">3</span>,<span class="fl">4</span><span class="op">)</span><span class="op">)</span>, <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">3</span>, <span class="fl">2</span>, <span class="fl">4</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUBoolType{4} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Equal</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_equal.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Equal</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_equal.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Equal</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_equal</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>other</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>the input tensor</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>the other input tensor</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="equal-input-other-gt-bool-"><a class="anchor" href="#equal-input-other-gt-bool-"></a>equal(input, other) -&gt; bool </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_equal</span><span class="op">(</span><span class="va">self</span>, <span class="va">other</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>the input tensor</p></dd>
+<dt>other</dt>
+<dd><p>the other input tensor</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="equal-input-other-gt-bool-">equal(input, other) -&gt; bool <a class="anchor" aria-label="anchor" href="#equal-input-other-gt-bool-"></a></h2>
     
 
 
 <p><code>TRUE</code> if two tensors have the same size and elements, <code>FALSE</code> otherwise.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='fu'>torch_equal</span><span class='op'>(</span><span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>2</span><span class='op'>)</span><span class='op'>)</span>, <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>2</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [1] TRUE</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="fu">torch_equal</span><span class="op">(</span><span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">2</span><span class="op">)</span><span class="op">)</span>, <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">2</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] TRUE</span>
+</code></pre></div>
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index 4004bb232..077681179 100644
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Erf</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_erf.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Erf</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_erf.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Erf</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_erf</span><span class='op'>(</span><span class='va'>self</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="erf-input-out-null-gt-tensor-"><a class="anchor" href="#erf-input-out-null-gt-tensor-"></a>erf(input, out=NULL) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_erf</span><span class="op">(</span><span class="va">self</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="erf-input-out-null-gt-tensor-">erf(input, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#erf-input-out-null-gt-tensor-"></a></h2>
     
 
 
@@ -209,43 +99,38 @@
 <p>$$
     \mathrm{erf}(x) = \frac{2}{\sqrt{\pi}} \int_{0}^{x} e^{-t^2} dt
 $$</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='fu'>torch_erf</span><span class='op'>(</span><span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>0</span>, <span class='op'>-</span><span class='fl'>1.</span>, <span class='fl'>10.</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.0000</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.8427</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1.0000</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{3} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="fu">torch_erf</span><span class="op">(</span><span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="op">-</span><span class="fl">1.</span>, <span class="fl">10.</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.8427</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.0000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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+    <footer><div class="pkgdown-footer-left">
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+<div class="pkgdown-footer-right">
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diff --git a/reference/torch_erfc.html b/reference/torch_erfc.html
index 0004385b9..7582a4df6 100644
--- a/reference/torch_erfc.html
+++ b/reference/torch_erfc.html
@@ -1,207 +1,97 @@
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Erfc</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_erfc.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Erfc</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_erfc.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Erfc</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_erfc</span><span class='op'>(</span><span class='va'>self</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="erfc-input-out-null-gt-tensor-"><a class="anchor" href="#erfc-input-out-null-gt-tensor-"></a>erfc(input, out=NULL) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_erfc</span><span class="op">(</span><span class="va">self</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="erfc-input-out-null-gt-tensor-">erfc(input, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#erfc-input-out-null-gt-tensor-"></a></h2>
     
 
 
@@ -210,43 +100,38 @@ The complementary error function is defined as follows:</p>
 <p>$$
     \mathrm{erfc}(x) = 1 - \frac{2}{\sqrt{\pi}} \int_{0}^{x} e^{-t^2} dt
 $$</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='fu'>torch_erfc</span><span class='op'>(</span><span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>0</span>, <span class='op'>-</span><span class='fl'>1.</span>, <span class='fl'>10.</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1.0000e+00</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1.8427e+00</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1.4013e-45</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{3} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="fu">torch_erfc</span><span class="op">(</span><span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="op">-</span><span class="fl">1.</span>, <span class="fl">10.</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.0000e+00</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.8427e+00</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.4013e-45</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer>
-      <div class="copyright">
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+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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+<div class="pkgdown-footer-right">
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diff --git a/reference/torch_erfinv.html b/reference/torch_erfinv.html
index 08d8e0404..b428f7511 100644
--- a/reference/torch_erfinv.html
+++ b/reference/torch_erfinv.html
@@ -1,207 +1,97 @@
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Erfinv</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_erfinv.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Erfinv</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_erfinv.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Erfinv</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_erfinv</span><span class='op'>(</span><span class='va'>self</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="erfinv-input-out-null-gt-tensor-"><a class="anchor" href="#erfinv-input-out-null-gt-tensor-"></a>erfinv(input, out=NULL) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_erfinv</span><span class="op">(</span><span class="va">self</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="erfinv-input-out-null-gt-tensor-">erfinv(input, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#erfinv-input-out-null-gt-tensor-"></a></h2>
     
 
 
@@ -210,43 +100,38 @@ The inverse error function is defined in the range \((-1, 1)\) as:</p>
 <p>$$
     \mathrm{erfinv}(\mathrm{erf}(x)) = x
 $$</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='fu'>torch_erfinv</span><span class='op'>(</span><span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>0</span>, <span class='fl'>0.5</span>, <span class='op'>-</span><span class='fl'>1.</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.0000</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.4769</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>    -inf</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{3} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="fu">torch_erfinv</span><span class="op">(</span><span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">0.5</span>, <span class="op">-</span><span class="fl">1.</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.4769</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    -inf</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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-      <div class="copyright">
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+    <footer><div class="pkgdown-footer-left">
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+<div class="pkgdown-footer-right">
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diff --git a/reference/torch_exp.html b/reference/torch_exp.html
index da8875d2e..8a563a2df 100644
--- a/reference/torch_exp.html
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Exp</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_exp.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Exp</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_exp.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Exp</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_exp</span><span class='op'>(</span><span class='va'>self</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="exp-input-out-null-gt-tensor-"><a class="anchor" href="#exp-input-out-null-gt-tensor-"></a>exp(input, out=NULL) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_exp</span><span class="op">(</span><span class="va">self</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="exp-input-out-null-gt-tensor-">exp(input, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#exp-input-out-null-gt-tensor-"></a></h2>
     
 
 
@@ -210,42 +100,37 @@ of the input tensor <code>input</code>.</p>
 <p>$$
     y_{i} = e^{x_{i}}
 $$</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='fu'>torch_exp</span><span class='op'>(</span><span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>0</span>, <span class='fu'><a href='https://rdrr.io/r/base/Log.html'>log</a></span><span class='op'>(</span><span class='fl'>2</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  2</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{2} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="fu">torch_exp</span><span class="op">(</span><span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fu"><a href="https://rdrr.io/r/base/Log.html" class="external-link">log</a></span><span class="op">(</span><span class="fl">2</span><span class="op">)</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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-      <div class="copyright">
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+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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+<div class="pkgdown-footer-right">
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diff --git a/reference/torch_exp2.html b/reference/torch_exp2.html
index 03772a0c5..411da6f93 100644
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Exp2</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_exp2.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Exp2</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_exp2.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Exp2</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_exp2</span><span class='op'>(</span><span class='va'>self</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="exp-input-out-none-gt-tensor-"><a class="anchor" href="#exp-input-out-none-gt-tensor-"></a>exp2(input, *, out=None) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_exp2</span><span class="op">(</span><span class="va">self</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="exp-input-out-none-gt-tensor-">exp2(input, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#exp-input-out-none-gt-tensor-"></a></h2>
     
 
 
@@ -209,44 +99,39 @@
 <p>$$
     y_{i} = 2^{x_{i}}
 $$</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='fu'>torch_exp2</span><span class='op'>(</span><span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>0</span>, <span class='fu'><a href='https://rdrr.io/r/base/Log.html'>log2</a></span><span class='op'>(</span><span class='fl'>2.</span><span class='op'>)</span>, <span class='fl'>3</span>, <span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   2</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   8</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  16</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{4} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="fu">torch_exp2</span><span class="op">(</span><span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fu"><a href="https://rdrr.io/r/base/Log.html" class="external-link">log2</a></span><span class="op">(</span><span class="fl">2.</span><span class="op">)</span>, <span class="fl">3</span>, <span class="fl">4</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   2</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   8</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  16</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer>
-      <div class="copyright">
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+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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+<div class="pkgdown-footer-right">
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index ebca6aa31..d12d6f2bf 100644
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Expm1</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_expm1.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Expm1</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_expm1.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Expm1</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_expm1</span><span class='op'>(</span><span class='va'>self</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="expm-input-out-null-gt-tensor-"><a class="anchor" href="#expm-input-out-null-gt-tensor-"></a>expm1(input, out=NULL) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_expm1</span><span class="op">(</span><span class="va">self</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="expm-input-out-null-gt-tensor-">expm1(input, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#expm-input-out-null-gt-tensor-"></a></h2>
     
 
 
@@ -210,42 +100,37 @@ of <code>input</code>.</p>
 <p>$$
     y_{i} = e^{x_{i}} - 1
 $$</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='fu'>torch_expm1</span><span class='op'>(</span><span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>0</span>, <span class='fu'><a href='https://rdrr.io/r/base/Log.html'>log</a></span><span class='op'>(</span><span class='fl'>2</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{2} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="fu">torch_expm1</span><span class="op">(</span><span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fu"><a href="https://rdrr.io/r/base/Log.html" class="external-link">log</a></span><span class="op">(</span><span class="fl">2</span><span class="op">)</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer>
-      <div class="copyright">
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+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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+<div class="pkgdown-footer-right">
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index 640a52c29..8dee0302c 100644
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Eye</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/creation-ops.R'><code>R/creation-ops.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_eye.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Eye</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/creation-ops.R" class="external-link"><code>R/creation-ops.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a></small>
+      <div class="d-none name"><code>torch_eye.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Eye</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_eye</span><span class='op'>(</span>
-  <span class='va'>n</span>,
-  m <span class='op'>=</span> <span class='va'>n</span>,
-  dtype <span class='op'>=</span> <span class='cn'>NULL</span>,
-  layout <span class='op'>=</span> <span class='fu'><a href='torch_layout.html'>torch_strided</a></span><span class='op'>(</span><span class='op'>)</span>,
-  device <span class='op'>=</span> <span class='cn'>NULL</span>,
-  requires_grad <span class='op'>=</span> <span class='cn'>FALSE</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>n</th>
-      <td><p>(int) the number of rows</p></td>
-    </tr>
-    <tr>
-      <th>m</th>
-      <td><p>(int, optional) the number of columns with default being <code>n</code></p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>).</p></td>
-    </tr>
-    <tr>
-      <th>layout</th>
-      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
-    </tr>
-    <tr>
-      <th>requires_grad</th>
-      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="eye-n-m-null-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"><a class="anchor" href="#eye-n-m-null-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"></a>eye(n, m=NULL, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_eye</span><span class="op">(</span>
+  <span class="va">n</span>,
+  m <span class="op">=</span> <span class="va">n</span>,
+  dtype <span class="op">=</span> <span class="cn">NULL</span>,
+  layout <span class="op">=</span> <span class="fu"><a href="torch_layout.html">torch_strided</a></span><span class="op">(</span><span class="op">)</span>,
+  device <span class="op">=</span> <span class="cn">NULL</span>,
+  requires_grad <span class="op">=</span> <span class="cn">FALSE</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>n</dt>
+<dd><p>(int) the number of rows</p></dd>
+<dt>m</dt>
+<dd><p>(int, optional) the number of columns with default being <code>n</code></p></dd>
+<dt>dtype</dt>
+<dd><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>).</p></dd>
+<dt>layout</dt>
+<dd><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></dd>
+<dt>device</dt>
+<dd><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></dd>
+<dt>requires_grad</dt>
+<dd><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="eye-n-m-null-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-">eye(n, m=NULL, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#eye-n-m-null-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"></a></h2>
     
 
 
 <p>Returns a 2-D tensor with ones on the diagonal and zeros elsewhere.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='fu'>torch_eye</span><span class='op'>(</span><span class='fl'>3</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1  0  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0  1  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0  0  1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{3,3} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="fu">torch_eye</span><span class="op">(</span><span class="fl">3</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1  0  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0  1  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0  0  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3,3} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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-    <h1>Fft</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_fft.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Fft</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_fft</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>signal_ndim</span>, normalized <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor of at least <code>signal_ndim</code> <code>+ 1</code>        dimensions</p></td>
-    </tr>
-    <tr>
-      <th>signal_ndim</th>
-      <td><p>(int) the number of dimensions in each signal.        <code>signal_ndim</code> can only be 1, 2 or 3</p></td>
-    </tr>
-    <tr>
-      <th>normalized</th>
-      <td><p>(bool, optional) controls whether to return normalized results.        Default: <code>FALSE</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    
-<pre>For CUDA tensors, an LRU cache is used for cuFFT plans to speed up
-repeatedly running FFT methods on tensors of same geometry with same
-configuration. See cufft-plan-cache for more details on how to
-monitor and control the cache.
-</pre>
-
-    <h2 class="hasAnchor" id="fft-input-signal-ndim-normalized-false-gt-tensor-"><a class="anchor" href="#fft-input-signal-ndim-normalized-false-gt-tensor-"></a>fft(input, signal_ndim, normalized=False) -&gt; Tensor </h2>
-
-    
-
-
-<p>Complex-to-complex Discrete Fourier Transform</p>
-<p>This method computes the complex-to-complex discrete Fourier transform.
-Ignoring the batch dimensions, it computes the following expression:</p>
-<p>$$
-    X[\omega_1, \dots, \omega_d] =
-        \sum_{n_1=0}^{N_1-1} \dots \sum_{n_d=0}^{N_d-1} x[n_1, \dots, n_d]
-         e^{-j\ 2 \pi \sum_{i=0}^d \frac{\omega_i n_i}{N_i}},
-$$
-where \(d\) = <code>signal_ndim</code> is number of dimensions for the
-signal, and \(N_i\) is the size of signal dimension \(i\).</p>
-<p>This method supports 1D, 2D and 3D complex-to-complex transforms, indicated
-by <code>signal_ndim</code>. <code>input</code> must be a tensor with last dimension
-of size 2, representing the real and imaginary components of complex
-numbers, and should have at least <code>signal_ndim + 1</code> dimensions with optionally
-arbitrary number of leading batch dimensions. If <code>normalized</code> is set to
-<code>TRUE</code>, this normalizes the result by dividing it with
-\(\sqrt{\prod_{i=1}^K N_i}\) so that the operator is unitary.</p>
-<p>Returns the real and the imaginary parts together as one tensor of the same
-shape of <code>input</code>.</p>
-<p>The inverse of this function is <code><a href='torch_ifft.html'>torch_ifft</a></code>.</p>
-    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
-
-    
-
-<p>For CPU tensors, this method is currently only available with MKL. Use
-<code>torch_backends.mkl.is_available</code> to check if MKL is installed.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span>
-
-<span class='co'># unbatched 2D FFT</span>
-<span class='va'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span>, <span class='fl'>3</span>, <span class='fl'>2</span><span class='op'>)</span><span class='op'>)</span>
-<span class='fu'>torch_fft</span><span class='op'>(</span><span class='va'>x</span>, <span class='fl'>2</span><span class='op'>)</span>
-<span class='co'># batched 1D FFT</span>
-<span class='fu'>torch_fft</span><span class='op'>(</span><span class='va'>x</span>, <span class='fl'>1</span><span class='op'>)</span>
-<span class='co'># arbitrary number of batch dimensions, 2D FFT</span>
-<span class='va'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>3</span>, <span class='fl'>5</span>, <span class='fl'>5</span>, <span class='fl'>2</span><span class='op'>)</span><span class='op'>)</span>
-<span class='fu'>torch_fft</span><span class='op'>(</span><span class='va'>x</span>, <span class='fl'>2</span><span class='op'>)</span>
-
-<span class='op'>}</span>
-</div><div class='output co'>#&gt; torch_tensor
-#&gt; (1,1,1,.,.) = 
-#&gt;  -2.3428 -8.8931
-#&gt;  -0.9329  2.5940
-#&gt;   1.7995 -10.8052
-#&gt;  -1.1522 -1.0306
-#&gt;  -4.7890  3.4165
-#&gt; 
-#&gt; (2,1,1,.,.) = 
-#&gt;   4.3706 -6.0271
-#&gt;   3.2724 -6.5294
-#&gt;   2.1472  0.2018
-#&gt;  -6.2654  8.1258
-#&gt;   8.1230 -2.0722
-#&gt; 
-#&gt; (3,1,1,.,.) = 
-#&gt;  -9.4490 -0.3329
-#&gt;   1.2606 -2.8233
-#&gt;   2.9336 -2.3833
-#&gt;  -0.7245  1.5270
-#&gt;  -4.3890  6.6481
-#&gt; 
-#&gt; (1,2,1,.,.) = 
-#&gt;   2.8897 -0.2716
-#&gt;   0.1113  4.7869
-#&gt;   3.4957  8.8017
-#&gt;   6.7336 -2.2698
-#&gt;  -5.2185  1.3936
-#&gt; 
-#&gt; (2,2,1,.,.) = 
-#&gt;   4.5331 -11.0508
-#&gt; ... [the output was truncated (use n=-1 to disable)]
-#&gt; [ CPUFloatType{3,3,5,5,2} ]</div></pre>
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Fft</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/wrapers.R'><code>R/wrapers.R</code></a></small>
-    <div class="hidden name"><code>torch_fft_fft.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Fft</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
+      <div class="d-none name"><code>torch_fft_fft.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Computes the one dimensional discrete Fourier transform of input.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_fft_fft</span><span class='op'>(</span><span class='va'>self</span>, n <span class='op'>=</span> <span class='cn'>NULL</span>, dim <span class='op'>=</span> <span class='op'>-</span><span class='fl'>1L</span>, norm <span class='op'>=</span> <span class='cn'>NULL</span><span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_fft_fft</span><span class="op">(</span><span class="va">self</span>, n <span class="op">=</span> <span class="cn">NULL</span>, dim <span class="op">=</span> <span class="op">-</span><span class="fl">1L</span>, norm <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor</p></td>
-    </tr>
-    <tr>
-      <th>n</th>
-      <td><p>(int) Signal length. If given, the input will either be zero-padded
-or trimmed to this length before computing the FFT.</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int, optional) The dimension along which to take the one dimensional FFT.</p></td>
-    </tr>
-    <tr>
-      <th>norm</th>
-      <td><p>(str, optional) Normalization mode. For the forward transform, these
-correspond to:</p><ul>
-<li><p>"forward" - normalize by 1/n</p></li>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor</p></dd>
+<dt>n</dt>
+<dd><p>(int) Signal length. If given, the input will either be zero-padded
+or trimmed to this length before computing the FFT.</p></dd>
+<dt>dim</dt>
+<dd><p>(int, optional) The dimension along which to take the one dimensional FFT.</p></dd>
+<dt>norm</dt>
+<dd><p>(str, optional) Normalization mode. For the forward transform, these
+correspond to:</p><ul><li><p>"forward" - normalize by 1/n</p></li>
 <li><p>"backward" - no normalization</p></li>
 <li><p>"ortho" - normalize by 1/sqrt(n) (making the FFT orthonormal)
 Calling the backward transform (ifft()) with the same normalization mode will
 apply an overall normalization of 1/n between the two transforms. This is
 required to make IFFT the exact inverse.
 Default is "backward" (no normalization).</p></li>
-</ul></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
+</ul></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p>The Fourier domain representation of any real signal satisfies the Hermitian
 property: <code>X[i] = conj(X[-i]).</code> This function always returns both the positive
 and negative frequency terms even though, for real inputs, the negative
 frequencies are redundant. rfft() returns the more compact one-sided representation
 where only the positive frequencies are returned.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>t</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span><span class='op'>(</span>start <span class='op'>=</span> <span class='fl'>0</span>, end <span class='op'>=</span> <span class='fl'>3</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>t</span></span>
-<span class='r-in'><span class='fu'>torch_fft_fft</span><span class='op'>(</span><span class='va'>t</span>, norm <span class='op'>=</span> <span class='st'>"backward"</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  6</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -2</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -2</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -2</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUComplexFloatType{4} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">t</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_arange.html">torch_arange</a></span><span class="op">(</span>start <span class="op">=</span> <span class="fl">0</span>, end <span class="op">=</span> <span class="fl">3</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">t</span></span>
+<span class="r-in"><span class="fu">torch_fft_fft</span><span class="op">(</span><span class="va">t</span>, norm <span class="op">=</span> <span class="st">"backward"</span><span class="op">)</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  6</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -2</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -2</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -2</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUComplexFloatType{4} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Ifft</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/wrapers.R'><code>R/wrapers.R</code></a></small>
-    <div class="hidden name"><code>torch_fft_ifft.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Ifft</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
+      <div class="d-none name"><code>torch_fft_ifft.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Computes the one dimensional inverse discrete Fourier transform of input.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_fft_ifft</span><span class='op'>(</span><span class='va'>self</span>, n <span class='op'>=</span> <span class='cn'>NULL</span>, dim <span class='op'>=</span> <span class='op'>-</span><span class='fl'>1L</span>, norm <span class='op'>=</span> <span class='cn'>NULL</span><span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_fft_ifft</span><span class="op">(</span><span class="va">self</span>, n <span class="op">=</span> <span class="cn">NULL</span>, dim <span class="op">=</span> <span class="op">-</span><span class="fl">1L</span>, norm <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor</p></td>
-    </tr>
-    <tr>
-      <th>n</th>
-      <td><p>(int, optional) – Signal length. If given, the input will either be
-zero-padded or trimmed to this length before computing the IFFT.</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int, optional) – The dimension along which to take the one
-dimensional IFFT.</p></td>
-    </tr>
-    <tr>
-      <th>norm</th>
-      <td><p>(str, optional) – Normalization mode. For the backward transform,
-these correspond to:</p><ul>
-<li><p>"forward" - no normalization</p></li>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor</p></dd>
+<dt>n</dt>
+<dd><p>(int, optional) – Signal length. If given, the input will either be
+zero-padded or trimmed to this length before computing the IFFT.</p></dd>
+<dt>dim</dt>
+<dd><p>(int, optional) – The dimension along which to take the one
+dimensional IFFT.</p></dd>
+<dt>norm</dt>
+<dd><p>(str, optional) – Normalization mode. For the backward transform,
+these correspond to:</p><ul><li><p>"forward" - no normalization</p></li>
 <li><p>"backward" - normalize by 1/n</p></li>
 <li><p>"ortho" - normalize by 1/sqrt(n) (making the IFFT orthonormal)
 Calling the forward transform with the same normalization mode will apply an
 overall normalization of 1/n between the two transforms. This is required to
 make ifft() the exact inverse.
 Default is "backward" (normalize by 1/n).</p></li>
-</ul></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>t</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span><span class='op'>(</span>start <span class='op'>=</span> <span class='fl'>0</span>, end <span class='op'>=</span> <span class='fl'>3</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>t</span></span>
-<span class='r-in'><span class='va'>x</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_fft_fft.html'>torch_fft_fft</a></span><span class='op'>(</span><span class='va'>t</span>, norm <span class='op'>=</span> <span class='st'>"backward"</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_fft_ifft</span><span class='op'>(</span><span class='va'>x</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  2</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  3</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUComplexFloatType{4} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+</ul></dd>
+</dl></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">t</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_arange.html">torch_arange</a></span><span class="op">(</span>start <span class="op">=</span> <span class="fl">0</span>, end <span class="op">=</span> <span class="fl">3</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">t</span></span>
+<span class="r-in"><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_fft_fft.html">torch_fft_fft</a></span><span class="op">(</span><span class="va">t</span>, norm <span class="op">=</span> <span class="st">"backward"</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_fft_ifft</span><span class="op">(</span><span class="va">x</span><span class="op">)</span></span>
+<span class="r-in"></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  3</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUComplexFloatType{4} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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diff --git a/reference/torch_fft_irfft.html b/reference/torch_fft_irfft.html
index 13aa3b858..e2d985f20 100644
--- a/reference/torch_fft_irfft.html
+++ b/reference/torch_fft_irfft.html
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-<meta property="og:title" content="Irfft — torch_fft_irfft" />
-<meta property="og:description" content="Computes the inverse of torch_fft_rfft().
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Computes the inverse of torch_fft_rfft().
 Input is interpreted as a one-sided Hermitian signal in the Fourier domain,
 as produced by torch_fft_rfft(). By the Hermitian property, the output will
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+be real-valued."><title>Irfft — torch_fft_irfft • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.1.0/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.1.0/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Irfft — torch_fft_irfft"><meta property="og:description" content="Computes the inverse of torch_fft_rfft().
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+as produced by torch_fft_rfft(). By the Hermitian property, the output will
+be real-valued."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Irfft</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/wrapers.R'><code>R/wrapers.R</code></a></small>
-    <div class="hidden name"><code>torch_fft_irfft.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Irfft</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
+      <div class="d-none name"><code>torch_fft_irfft.Rd</code></div>
     </div>
 
-    <div class="ref-description">
-    <p>Computes the inverse of <code><a href='torch_fft_rfft.html'>torch_fft_rfft()</a></code>.
+    <div class="ref-description section level2">
+    <p>Computes the inverse of <code><a href="torch_fft_rfft.html">torch_fft_rfft()</a></code>.
 Input is interpreted as a one-sided Hermitian signal in the Fourier domain,
-as produced by <code><a href='torch_fft_rfft.html'>torch_fft_rfft()</a></code>. By the Hermitian property, the output will
+as produced by <code><a href="torch_fft_rfft.html">torch_fft_rfft()</a></code>. By the Hermitian property, the output will
 be real-valued.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_fft_irfft</span><span class='op'>(</span><span class='va'>self</span>, n <span class='op'>=</span> <span class='cn'>NULL</span>, dim <span class='op'>=</span> <span class='op'>-</span><span class='fl'>1L</span>, norm <span class='op'>=</span> <span class='cn'>NULL</span><span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_fft_irfft</span><span class="op">(</span><span class="va">self</span>, n <span class="op">=</span> <span class="cn">NULL</span>, dim <span class="op">=</span> <span class="op">-</span><span class="fl">1L</span>, norm <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor representing a half-Hermitian signal</p></td>
-    </tr>
-    <tr>
-      <th>n</th>
-      <td><p>(int) Output signal length. This determines the length of the output
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor representing a half-Hermitian signal</p></dd>
+<dt>n</dt>
+<dd><p>(int) Output signal length. This determines the length of the output
 signal. If given, the input will either be zero-padded or trimmed to this
-length before computing the real IFFT. Defaults to even output: <code>n=2*(input.size(dim) - 1)</code>.</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int, optional) – The dimension along which to take the one
-dimensional real IFFT.</p></td>
-    </tr>
-    <tr>
-      <th>norm</th>
-      <td><p>(str, optional) – Normalization mode. For the backward transform,
-these correspond to:</p><ul>
-<li><p>"forward" - no normalization</p></li>
+length before computing the real IFFT. Defaults to even output: <code>n=2*(input.size(dim) - 1)</code>.</p></dd>
+<dt>dim</dt>
+<dd><p>(int, optional) – The dimension along which to take the one
+dimensional real IFFT.</p></dd>
+<dt>norm</dt>
+<dd><p>(str, optional) – Normalization mode. For the backward transform,
+these correspond to:</p><ul><li><p>"forward" - no normalization</p></li>
 <li><p>"backward" - normalize by 1/n</p></li>
 <li><p>"ortho" - normalize by 1/sqrt(n) (making the real IFFT orthonormal)
-Calling the forward transform (<code><a href='torch_fft_rfft.html'>torch_fft_rfft()</a></code>) with the same normalization
+Calling the forward transform (<code><a href="torch_fft_rfft.html">torch_fft_rfft()</a></code>) with the same normalization
 mode will apply an overall normalization of 1/n between the two transforms.
 This is required to make irfft() the exact inverse.
 Default is "backward" (normalize by 1/n).</p></li>
-</ul></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
+</ul></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p>Some input frequencies must be real-valued to satisfy the Hermitian property.
 In these cases the imaginary component will be ignored. For example, any
 imaginary component in the zero-frequency term cannot be represented in a real
@@ -241,48 +127,43 @@ original data, as given by n. This is because each input shape could correspond
 to either an odd or even length signal. By default, the signal is assumed to be
 even length and odd signals will not round-trip properly. So, it is recommended
 to always pass the signal length n.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>t</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span><span class='op'>(</span>start <span class='op'>=</span> <span class='fl'>0</span>, end <span class='op'>=</span> <span class='fl'>4</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>x</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_fft_rfft.html'>torch_fft_rfft</a></span><span class='op'>(</span><span class='va'>t</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_fft_irfft</span><span class='op'>(</span><span class='va'>x</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_fft_irfft</span><span class='op'>(</span><span class='va'>x</span>, n <span class='op'>=</span> <span class='va'>t</span><span class='op'>$</span><span class='fu'>numel</span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  2</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  3</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  4</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{5} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">t</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_arange.html">torch_arange</a></span><span class="op">(</span>start <span class="op">=</span> <span class="fl">0</span>, end <span class="op">=</span> <span class="fl">4</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_fft_rfft.html">torch_fft_rfft</a></span><span class="op">(</span><span class="va">t</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_fft_irfft</span><span class="op">(</span><span class="va">x</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_fft_irfft</span><span class="op">(</span><span class="va">x</span>, n <span class="op">=</span> <span class="va">t</span><span class="op">$</span><span class="fu">numel</span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  3</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  4</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{5} ]</span>
+</code></pre></div>
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+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Rfft</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/wrapers.R'><code>R/wrapers.R</code></a></small>
-    <div class="hidden name"><code>torch_fft_rfft.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Rfft</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
+      <div class="d-none name"><code>torch_fft_rfft.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Computes the one dimensional Fourier transform of real-valued input.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_fft_rfft</span><span class='op'>(</span><span class='va'>self</span>, n <span class='op'>=</span> <span class='cn'>NULL</span>, dim <span class='op'>=</span> <span class='op'>-</span><span class='fl'>1L</span>, norm <span class='op'>=</span> <span class='cn'>NULL</span><span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_fft_rfft</span><span class="op">(</span><span class="va">self</span>, n <span class="op">=</span> <span class="cn">NULL</span>, dim <span class="op">=</span> <span class="op">-</span><span class="fl">1L</span>, norm <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor)  the real input tensor</p></td>
-    </tr>
-    <tr>
-      <th>n</th>
-      <td><p>(int) Signal length. If given, the input will either be zero-padded
-or trimmed to this length before computing the real FFT.</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int, optional) – The dimension along which to take the one
-dimensional real FFT.</p></td>
-    </tr>
-    <tr>
-      <th>norm</th>
-      <td><p>norm (str, optional) – Normalization mode. For the forward
-transform, these correspond to:</p><ul>
-<li><p>"forward" - normalize by 1/n</p></li>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor)  the real input tensor</p></dd>
+<dt>n</dt>
+<dd><p>(int) Signal length. If given, the input will either be zero-padded
+or trimmed to this length before computing the real FFT.</p></dd>
+<dt>dim</dt>
+<dd><p>(int, optional) – The dimension along which to take the one
+dimensional real FFT.</p></dd>
+<dt>norm</dt>
+<dd><p>norm (str, optional) – Normalization mode. For the forward
+transform, these correspond to:</p><ul><li><p>"forward" - normalize by 1/n</p></li>
 <li><p>"backward" - no normalization</p></li>
 <li><p>"ortho" - normalize by 1/sqrt(n) (making the FFT orthonormal)
-Calling the backward transform (<code><a href='torch_fft_irfft.html'>torch_fft_irfft()</a></code>) with the same
+Calling the backward transform (<code><a href="torch_fft_irfft.html">torch_fft_irfft()</a></code>) with the same
 normalization mode will apply an overall normalization of 1/n between the
 two transforms. This is required to make irfft() the exact inverse.
 Default is "backward" (no normalization).</p></li>
-</ul></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
+</ul></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>The FFT of a real signal is Hermitian-symmetric, <code>X[i] = conj(X[-i])</code> so the
 output contains only the positive frequencies below the Nyquist frequency.
-To compute the full output, use <code><a href='torch_fft_fft.html'>torch_fft_fft()</a></code>.</p>
+To compute the full output, use <code><a href="torch_fft_fft.html">torch_fft_fft()</a></code>.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>t</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span><span class='op'>(</span>start <span class='op'>=</span> <span class='fl'>0</span>, end <span class='op'>=</span> <span class='fl'>3</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_fft_rfft</span><span class='op'>(</span><span class='va'>t</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  6</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -2</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -2</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUComplexFloatType{3} ]</span>
-</pre>
-  </div>
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+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">t</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_arange.html">torch_arange</a></span><span class="op">(</span>start <span class="op">=</span> <span class="fl">0</span>, end <span class="op">=</span> <span class="fl">3</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_fft_rfft</span><span class="op">(</span><span class="va">t</span><span class="op">)</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  6</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -2</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -2</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUComplexFloatType{3} ]</span>
+</code></pre></div>
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Floating point type info</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/type-info.R'><code>R/type-info.R</code></a></small>
-    <div class="hidden name"><code>torch_finfo.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Floating point type info</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/type-info.R" class="external-link"><code>R/type-info.R</code></a></small>
+      <div class="d-none name"><code>torch_finfo.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>A list that represents the numerical properties of a
 floating point torch.dtype</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_finfo</span><span class='op'>(</span><span class='va'>dtype</span><span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_finfo</span><span class="op">(</span><span class="va">dtype</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>dtype</th>
-      <td><p>dtype to check information</p></td>
-    </tr>
-    </table>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>dtype</dt>
+<dd><p>dtype to check information</p></dd>
+</dl></div>
 
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diff --git a/reference/torch_fix.html b/reference/torch_fix.html
index 8acc99168..1184e59f4 100644
--- a/reference/torch_fix.html
+++ b/reference/torch_fix.html
@@ -1,237 +1,120 @@
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-    <h1>Fix</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
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+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_fix.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Fix</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_fix</span><span class='op'>(</span><span class='va'>self</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="fix-input-out-none-gt-tensor-"><a class="anchor" href="#fix-input-out-none-gt-tensor-"></a>fix(input, *, out=None) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_fix</span><span class="op">(</span><span class="va">self</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="fix-input-out-none-gt-tensor-">fix(input, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#fix-input-out-none-gt-tensor-"></a></h2>
     
 
 
-<p>Alias for <code><a href='torch_trunc.html'>torch_trunc()</a></code></p>
+<p>Alias for <code><a href="torch_trunc.html">torch_trunc()</a></code></p>
+    </div>
 
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diff --git a/reference/torch_flatten.html b/reference/torch_flatten.html
index 84ad35a0a..60a006ffa 100644
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Flatten</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_flatten.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Flatten</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_flatten.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Flatten</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_flatten</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>dims</span>, start_dim <span class='op'>=</span> <span class='fl'>1L</span>, end_dim <span class='op'>=</span> <span class='op'>-</span><span class='fl'>1L</span>, <span class='va'>out_dim</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dims</th>
-      <td><p>if tensor is named you can pass the name of the dimensions to
-flatten</p></td>
-    </tr>
-    <tr>
-      <th>start_dim</th>
-      <td><p>(int) the first dim to flatten</p></td>
-    </tr>
-    <tr>
-      <th>end_dim</th>
-      <td><p>(int) the last dim to flatten</p></td>
-    </tr>
-    <tr>
-      <th>out_dim</th>
-      <td><p>the name of the resulting dimension if a named tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="flatten-input-start-dim-end-dim-gt-tensor-"><a class="anchor" href="#flatten-input-start-dim-end-dim-gt-tensor-"></a>flatten(input, start_dim=0, end_dim=-1) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_flatten</span><span class="op">(</span><span class="va">self</span>, <span class="va">dims</span>, start_dim <span class="op">=</span> <span class="fl">1L</span>, end_dim <span class="op">=</span> <span class="op">-</span><span class="fl">1L</span>, <span class="va">out_dim</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+<dt>dims</dt>
+<dd><p>if tensor is named you can pass the name of the dimensions to
+flatten</p></dd>
+<dt>start_dim</dt>
+<dd><p>(int) the first dim to flatten</p></dd>
+<dt>end_dim</dt>
+<dd><p>(int) the last dim to flatten</p></dd>
+<dt>out_dim</dt>
+<dd><p>the name of the resulting dimension if a named tensor.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="flatten-input-start-dim-end-dim-gt-tensor-">flatten(input, start_dim=0, end_dim=-1) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#flatten-input-start-dim-end-dim-gt-tensor-"></a></h2>
     
 
 
 <p>Flattens a contiguous range of dims in a tensor.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>t</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>2</span><span class='op'>)</span>, ncol <span class='op'>=</span> <span class='fl'>2</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_flatten</span><span class='op'>(</span><span class='va'>t</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_flatten</span><span class='op'>(</span><span class='va'>t</span>, start_dim<span class='op'>=</span><span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1  2</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{1,2} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">t</span> <span class="op">=</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/matrix.html" class="external-link">matrix</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">2</span><span class="op">)</span>, ncol <span class="op">=</span> <span class="fl">2</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_flatten</span><span class="op">(</span><span class="va">t</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_flatten</span><span class="op">(</span><span class="va">t</span>, start_dim<span class="op">=</span><span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1  2</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{1,2} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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diff --git a/reference/torch_flip.html b/reference/torch_flip.html
index 100a8b343..55a3e1fa6 100644
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Flip</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_flip.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Flip</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_flip.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Flip</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_flip</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>dims</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dims</th>
-      <td><p>(a list or tuple) axis to flip on</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="flip-input-dims-gt-tensor-"><a class="anchor" href="#flip-input-dims-gt-tensor-"></a>flip(input, dims) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_flip</span><span class="op">(</span><span class="va">self</span>, <span class="va">dims</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+<dt>dims</dt>
+<dd><p>(a list or tuple) axis to flip on</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="flip-input-dims-gt-tensor-">flip(input, dims) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#flip-input-dims-gt-tensor-"></a></h2>
     
 
 
 <p>Reverse the order of a n-D tensor along given axis in dims.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>x</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>8</span><span class='op'>)</span><span class='op'>$</span><span class='fu'>view</span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>2</span>, <span class='fl'>2</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>x</span></span>
-<span class='r-in'><span class='fu'>torch_flip</span><span class='op'>(</span><span class='va'>x</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>2</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> (1,.,.) = </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   7  8</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   5  6</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> (2,.,.) = </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   3  4</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   1  2</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{2,2,2} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_arange.html">torch_arange</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">8</span><span class="op">)</span><span class="op">$</span><span class="fu">view</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">2</span>, <span class="fl">2</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">x</span></span>
+<span class="r-in"><span class="fu">torch_flip</span><span class="op">(</span><span class="va">x</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">2</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> (1,.,.) = </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   7  8</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   5  6</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> (2,.,.) = </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   3  4</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   1  2</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2,2,2} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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-      <div class="copyright">
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+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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+<div class="pkgdown-footer-right">
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Fliplr</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_fliplr.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Fliplr</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_fliplr.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Fliplr</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_fliplr</span><span class='op'>(</span><span class='va'>self</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) Must be at least 2-dimensional.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_fliplr</span><span class="op">(</span><span class="va">self</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) Must be at least 2-dimensional.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p>Equivalent to <code>input[,-1]</code>. Requires the array to be at least 2-D.</p>
-    <h2 class="hasAnchor" id="fliplr-input-gt-tensor-"><a class="anchor" href="#fliplr-input-gt-tensor-"></a>fliplr(input) -&gt; Tensor </h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="fliplr-input-gt-tensor-">fliplr(input) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#fliplr-input-gt-tensor-"></a></h2>
     
 
 
 <p>Flip array in the left/right direction, returning a new tensor.</p>
 <p>Flip the entries in each row in the left/right direction.
 Columns are preserved, but appear in a different order than before.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>x</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span><span class='op'>(</span>start <span class='op'>=</span> <span class='fl'>1</span>, end <span class='op'>=</span> <span class='fl'>4</span><span class='op'>)</span><span class='op'>$</span><span class='fu'>view</span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>2</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>x</span></span>
-<span class='r-in'><span class='fu'>torch_fliplr</span><span class='op'>(</span><span class='va'>x</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  2  1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  4  3</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{2,2} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_arange.html">torch_arange</a></span><span class="op">(</span>start <span class="op">=</span> <span class="fl">1</span>, end <span class="op">=</span> <span class="fl">4</span><span class="op">)</span><span class="op">$</span><span class="fu">view</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">2</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">x</span></span>
+<span class="r-in"><span class="fu">torch_fliplr</span><span class="op">(</span><span class="va">x</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  4  3</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2,2} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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diff --git a/reference/torch_flipud.html b/reference/torch_flipud.html
index 70cf61927..a1dbf0b08 100644
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Flipud</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_flipud.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Flipud</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_flipud.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Flipud</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_flipud</span><span class='op'>(</span><span class='va'>self</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) Must be at least 1-dimensional.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_flipud</span><span class="op">(</span><span class="va">self</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) Must be at least 1-dimensional.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p>Equivalent to <code>input[-1,]</code>. Requires the array to be at least 1-D.</p>
-    <h2 class="hasAnchor" id="flipud-input-gt-tensor-"><a class="anchor" href="#flipud-input-gt-tensor-"></a>flipud(input) -&gt; Tensor </h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="flipud-input-gt-tensor-">flipud(input) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#flipud-input-gt-tensor-"></a></h2>
     
 
 
 <p>Flip array in the up/down direction, returning a new tensor.</p>
 <p>Flip the entries in each column in the up/down direction.
 Rows are preserved, but appear in a different order than before.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>x</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span><span class='op'>(</span>start <span class='op'>=</span> <span class='fl'>1</span>, end <span class='op'>=</span> <span class='fl'>4</span><span class='op'>)</span><span class='op'>$</span><span class='fu'>view</span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>2</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>x</span></span>
-<span class='r-in'><span class='fu'>torch_flipud</span><span class='op'>(</span><span class='va'>x</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  3  4</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1  2</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{2,2} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_arange.html">torch_arange</a></span><span class="op">(</span>start <span class="op">=</span> <span class="fl">1</span>, end <span class="op">=</span> <span class="fl">4</span><span class="op">)</span><span class="op">$</span><span class="fu">view</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">2</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">x</span></span>
+<span class="r-in"><span class="fu">torch_flipud</span><span class="op">(</span><span class="va">x</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  3  4</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1  2</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2,2} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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+    <footer><div class="pkgdown-footer-left">
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diff --git a/reference/torch_floor.html b/reference/torch_floor.html
index c4ed392d5..6f94d6cc1 100644
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Floor</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_floor.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Floor</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_floor.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Floor</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_floor</span><span class='op'>(</span><span class='va'>self</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="floor-input-out-null-gt-tensor-"><a class="anchor" href="#floor-input-out-null-gt-tensor-"></a>floor(input, out=NULL) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_floor</span><span class="op">(</span><span class="va">self</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="floor-input-out-null-gt-tensor-">floor(input, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#floor-input-out-null-gt-tensor-"></a></h2>
     
 
 
@@ -210,46 +100,41 @@ the largest integer less than or equal to each element.</p>
 <p>$$
     \mbox{out}_{i} = \left\lfloor \mbox{input}_{i} \right\rfloor
 $$</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='fu'>torch_floor</span><span class='op'>(</span><span class='va'>a</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{4} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="fu">torch_floor</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer>
-      <div class="copyright">
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+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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+<div class="pkgdown-footer-right">
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diff --git a/reference/torch_floor_divide.html b/reference/torch_floor_divide.html
index 0b19aab2c..3265cc2bc 100644
--- a/reference/torch_floor_divide.html
+++ b/reference/torch_floor_divide.html
@@ -1,258 +1,141 @@
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Floor_divide</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_floor_divide.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Floor_divide</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_floor_divide.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Floor_divide</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_floor_divide</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>other</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the numerator tensor</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Tensor or Scalar) the denominator</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="floor-divide-input-other-out-null-gt-tensor-"><a class="anchor" href="#floor-divide-input-other-out-null-gt-tensor-"></a>floor_divide(input, other, out=NULL) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_floor_divide</span><span class="op">(</span><span class="va">self</span>, <span class="va">other</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the numerator tensor</p></dd>
+<dt>other</dt>
+<dd><p>(Tensor or Scalar) the denominator</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="floor-divide-input-other-out-null-gt-tensor-">floor_divide(input, other, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#floor-divide-input-other-out-null-gt-tensor-"></a></h2>
     
 
 
-<p>Return the division of the inputs rounded down to the nearest integer. See <code><a href='torch_div.html'>torch_div</a></code>
+<p>Return the division of the inputs rounded down to the nearest integer. See <code><a href="torch_div.html">torch_div</a></code>
 for type promotion and broadcasting rules.</p>
 <p>$$
     \mbox{{out}}_i = \left\lfloor \frac{{\mbox{{input}}_i}}{{\mbox{{other}}_i}} \right\rfloor
 $$</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4.0</span>, <span class='fl'>3.0</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>b</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>2.0</span>, <span class='fl'>2.0</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_floor_divide</span><span class='op'>(</span><span class='va'>a</span>, <span class='va'>b</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_floor_divide</span><span class='op'>(</span><span class='va'>a</span>, <span class='fl'>1.4</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  2</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  2</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{2} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4.0</span>, <span class="fl">3.0</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">b</span> <span class="op">=</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2.0</span>, <span class="fl">2.0</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_floor_divide</span><span class="op">(</span><span class="va">a</span>, <span class="va">b</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_floor_divide</span><span class="op">(</span><span class="va">a</span>, <span class="fl">1.4</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Fmod</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_fmod.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Fmod</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_fmod.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Fmod</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_fmod</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>other</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the dividend</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Tensor or float) the divisor, which may be either a number or a tensor of the same shape as the dividend</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="fmod-input-other-out-null-gt-tensor-"><a class="anchor" href="#fmod-input-other-out-null-gt-tensor-"></a>fmod(input, other, out=NULL) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_fmod</span><span class="op">(</span><span class="va">self</span>, <span class="va">other</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the dividend</p></dd>
+<dt>other</dt>
+<dd><p>(Tensor or float) the divisor, which may be either a number or a tensor of the same shape as the dividend</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="fmod-input-other-out-null-gt-tensor-">fmod(input, other, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#fmod-input-other-out-null-gt-tensor-"></a></h2>
     
 
 
@@ -214,46 +102,41 @@
 numbers. The remainder has the same sign as the dividend <code>input</code>.</p>
 <p>When <code>other</code> is a tensor, the shapes of <code>input</code> and
 <code>other</code> must be broadcastable .</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='fu'>torch_fmod</span><span class='op'>(</span><span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='op'>-</span><span class='fl'>3.</span>, <span class='op'>-</span><span class='fl'>2</span>, <span class='op'>-</span><span class='fl'>1</span>, <span class='fl'>1</span>, <span class='fl'>2</span>, <span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span>, <span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_fmod</span><span class='op'>(</span><span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1.</span>, <span class='fl'>2</span>, <span class='fl'>3</span>, <span class='fl'>4</span>, <span class='fl'>5</span><span class='op'>)</span><span class='op'>)</span>, <span class='fl'>1.5</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1.0000</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.5000</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.0000</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1.0000</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.5000</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{5} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="fu">torch_fmod</span><span class="op">(</span><span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="op">-</span><span class="fl">3.</span>, <span class="op">-</span><span class="fl">2</span>, <span class="op">-</span><span class="fl">1</span>, <span class="fl">1</span>, <span class="fl">2</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span>, <span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_fmod</span><span class="op">(</span><span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1.</span>, <span class="fl">2</span>, <span class="fl">3</span>, <span class="fl">4</span>, <span class="fl">5</span><span class="op">)</span><span class="op">)</span>, <span class="fl">1.5</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.0000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.5000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.0000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.5000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{5} ]</span>
+</code></pre></div>
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index 51d95d54d..3ebc54a9b 100644
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Frac</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_frac.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Frac</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_frac.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Frac</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_frac</span><span class='op'>(</span><span class='va'>self</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="frac-input-out-null-gt-tensor-"><a class="anchor" href="#frac-input-out-null-gt-tensor-"></a>frac(input, out=NULL) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_frac</span><span class="op">(</span><span class="va">self</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>the input tensor.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="frac-input-out-null-gt-tensor-">frac(input, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#frac-input-out-null-gt-tensor-"></a></h2>
     
 
 
@@ -209,43 +99,38 @@
 <p>$$
     \mbox{out}_{i} = \mbox{input}_{i} - \left\lfloor |\mbox{input}_{i}| \right\rfloor * \mbox{sgn}(\mbox{input}_{i})
 $$</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='fu'>torch_frac</span><span class='op'>(</span><span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>2.5</span>, <span class='op'>-</span><span class='fl'>3.2</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.0000</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.5000</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.2000</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{3} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="fu">torch_frac</span><span class="op">(</span><span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">2.5</span>, <span class="op">-</span><span class="fl">3.2</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.5000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.2000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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+        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
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+  </div>
+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Full</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/creation-ops.R'><code>R/creation-ops.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_full.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Full</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/creation-ops.R" class="external-link"><code>R/creation-ops.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a></small>
+      <div class="d-none name"><code>torch_full.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Full</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_full</span><span class='op'>(</span>
-  <span class='va'>size</span>,
-  <span class='va'>fill_value</span>,
-  names <span class='op'>=</span> <span class='cn'>NULL</span>,
-  dtype <span class='op'>=</span> <span class='cn'>NULL</span>,
-  layout <span class='op'>=</span> <span class='fu'><a href='torch_layout.html'>torch_strided</a></span><span class='op'>(</span><span class='op'>)</span>,
-  device <span class='op'>=</span> <span class='cn'>NULL</span>,
-  requires_grad <span class='op'>=</span> <span class='cn'>FALSE</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>size</th>
-      <td><p>(int...) a list, tuple, or <code>torch_Size</code> of integers defining the        shape of the output tensor.</p></td>
-    </tr>
-    <tr>
-      <th>fill_value</th>
-      <td><p>NA the number to fill the output tensor with.</p></td>
-    </tr>
-    <tr>
-      <th>names</th>
-      <td><p>optional names of the dimensions</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>).</p></td>
-    </tr>
-    <tr>
-      <th>layout</th>
-      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
-    </tr>
-    <tr>
-      <th>requires_grad</th>
-      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="full-size-fill-value-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"><a class="anchor" href="#full-size-fill-value-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"></a>full(size, fill_value, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_full</span><span class="op">(</span>
+  <span class="va">size</span>,
+  <span class="va">fill_value</span>,
+  names <span class="op">=</span> <span class="cn">NULL</span>,
+  dtype <span class="op">=</span> <span class="cn">NULL</span>,
+  layout <span class="op">=</span> <span class="fu"><a href="torch_layout.html">torch_strided</a></span><span class="op">(</span><span class="op">)</span>,
+  device <span class="op">=</span> <span class="cn">NULL</span>,
+  requires_grad <span class="op">=</span> <span class="cn">FALSE</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>size</dt>
+<dd><p>(int...) a list, tuple, or <code>torch_Size</code> of integers defining the        shape of the output tensor.</p></dd>
+<dt>fill_value</dt>
+<dd><p>NA the number to fill the output tensor with.</p></dd>
+<dt>names</dt>
+<dd><p>optional names of the dimensions</p></dd>
+<dt>dtype</dt>
+<dd><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>).</p></dd>
+<dt>layout</dt>
+<dd><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></dd>
+<dt>device</dt>
+<dd><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></dd>
+<dt>requires_grad</dt>
+<dd><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="full-size-fill-value-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-">full(size, fill_value, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#full-size-fill-value-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"></a></h2>
     
 
 
 <p>Returns a tensor of size <code>size</code> filled with <code>fill_value</code>.</p>
-    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></a></h2>
     
 
 <p>In PyTorch 1.5 a bool or integral <code>fill_value</code> will produce a warning if
@@ -247,42 +126,37 @@
 In a future PyTorch release, when <code>dtype</code> and <code>out</code> are not set
 a bool <code>fill_value</code> will return a tensor of torch.bool dtype,
 and an integral <code>fill_value</code> will return a tensor of torch.long dtype.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='fu'>torch_full</span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>3</span><span class='op'>)</span>, <span class='fl'>3.141592</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  3.1416  3.1416  3.1416</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  3.1416  3.1416  3.1416</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{2,3} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
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-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="fu">torch_full</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">3</span><span class="op">)</span>, <span class="fl">3.141592</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  3.1416  3.1416  3.1416</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  3.1416  3.1416  3.1416</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2,3} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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+  </div>
+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Full_like</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/creation-ops.R'><code>R/creation-ops.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_full_like.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Full_like</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/creation-ops.R" class="external-link"><code>R/creation-ops.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a></small>
+      <div class="d-none name"><code>torch_full_like.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Full_like</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_full_like</span><span class='op'>(</span>
-  <span class='va'>input</span>,
-  <span class='va'>fill_value</span>,
-  dtype <span class='op'>=</span> <span class='cn'>NULL</span>,
-  layout <span class='op'>=</span> <span class='fu'><a href='torch_layout.html'>torch_strided</a></span><span class='op'>(</span><span class='op'>)</span>,
-  device <span class='op'>=</span> <span class='cn'>NULL</span>,
-  requires_grad <span class='op'>=</span> <span class='cn'>FALSE</span>,
-  memory_format <span class='op'>=</span> <span class='fu'><a href='torch_memory_format.html'>torch_preserve_format</a></span><span class='op'>(</span><span class='op'>)</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the size of <code>input</code> will determine size of the output tensor.</p></td>
-    </tr>
-    <tr>
-      <th>fill_value</th>
-      <td><p>the number to fill the output tensor with.</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned Tensor.        Default: if <code>NULL</code>, defaults to the dtype of <code>input</code>.</p></td>
-    </tr>
-    <tr>
-      <th>layout</th>
-      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned tensor.        Default: if <code>NULL</code>, defaults to the layout of <code>input</code>.</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, defaults to the device of <code>input</code>.</p></td>
-    </tr>
-    <tr>
-      <th>requires_grad</th>
-      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
-    </tr>
-    <tr>
-      <th>memory_format</th>
-      <td><p>(<code>torch.memory_format</code>, optional) the desired memory format of        returned Tensor. Default: <code>torch_preserve_format</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="full-like-input-fill-value-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-"><a class="anchor" href="#full-like-input-fill-value-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-"></a>full_like(input, fill_value, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False, </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_full_like</span><span class="op">(</span>
+  <span class="va">input</span>,
+  <span class="va">fill_value</span>,
+  dtype <span class="op">=</span> <span class="cn">NULL</span>,
+  layout <span class="op">=</span> <span class="fu"><a href="torch_layout.html">torch_strided</a></span><span class="op">(</span><span class="op">)</span>,
+  device <span class="op">=</span> <span class="cn">NULL</span>,
+  requires_grad <span class="op">=</span> <span class="cn">FALSE</span>,
+  memory_format <span class="op">=</span> <span class="fu"><a href="torch_memory_format.html">torch_preserve_format</a></span><span class="op">(</span><span class="op">)</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>(Tensor) the size of <code>input</code> will determine size of the output tensor.</p></dd>
+<dt>fill_value</dt>
+<dd><p>the number to fill the output tensor with.</p></dd>
+<dt>dtype</dt>
+<dd><p>(<code>torch.dtype</code>, optional) the desired data type of returned Tensor.        Default: if <code>NULL</code>, defaults to the dtype of <code>input</code>.</p></dd>
+<dt>layout</dt>
+<dd><p>(<code>torch.layout</code>, optional) the desired layout of returned tensor.        Default: if <code>NULL</code>, defaults to the layout of <code>input</code>.</p></dd>
+<dt>device</dt>
+<dd><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, defaults to the device of <code>input</code>.</p></dd>
+<dt>requires_grad</dt>
+<dd><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></dd>
+<dt>memory_format</dt>
+<dd><p>(<code>torch.memory_format</code>, optional) the desired memory format of        returned Tensor. Default: <code>torch_preserve_format</code>.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="full-like-input-fill-value-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-">full_like(input, fill_value, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False, <a class="anchor" aria-label="anchor" href="#full-like-input-fill-value-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-"></a></h2>
     
 
 
@@ -241,32 +119,25 @@
 <p>Returns a tensor with the same size as <code>input</code> filled with <code>fill_value</code>.
 <code>torch_full_like(input, fill_value)</code> is equivalent to
 <code>torch_full(input.size(), fill_value, dtype=input.dtype, layout=input.layout, device=input.device)</code>.</p>
+    </div>
 
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+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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+<div class="pkgdown-footer-right">
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Gather</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_gather.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Gather</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_gather.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Gather</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_gather</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>dim</span>, <span class='va'>index</span>, sparse_grad <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the source tensor</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) the axis along which to index</p></td>
-    </tr>
-    <tr>
-      <th>index</th>
-      <td><p>(LongTensor) the indices of elements to gather</p></td>
-    </tr>
-    <tr>
-      <th>sparse_grad</th>
-      <td><p>(bool,optional) If <code>TRUE</code>, gradient w.r.t. <code>input</code> will be a sparse tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="gather-input-dim-index-sparse-grad-false-gt-tensor-"><a class="anchor" href="#gather-input-dim-index-sparse-grad-false-gt-tensor-"></a>gather(input, dim, index, sparse_grad=FALSE) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_gather</span><span class="op">(</span><span class="va">self</span>, <span class="va">dim</span>, <span class="va">index</span>, sparse_grad <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the source tensor</p></dd>
+<dt>dim</dt>
+<dd><p>(int) the axis along which to index</p></dd>
+<dt>index</dt>
+<dd><p>(LongTensor) the indices of elements to gather</p></dd>
+<dt>sparse_grad</dt>
+<dd><p>(bool,optional) If <code>TRUE</code>, gradient w.r.t. <code>input</code> will be a sparse tensor.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="gather-input-dim-index-sparse-grad-false-gt-tensor-">gather(input, dim, index, sparse_grad=FALSE) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#gather-input-dim-index-sparse-grad-false-gt-tensor-"></a></h2>
     
 
 
 <p>Gathers values along an axis specified by <code>dim</code>.</p>
-<p>For a 3-D tensor the output is specified by::</p><pre><span class='va'>out</span><span class='op'>[</span><span class='va'>i</span><span class='op'>]</span><span class='op'>[</span><span class='va'>j</span><span class='op'>]</span><span class='op'>[</span><span class='va'>k</span><span class='op'>]</span> <span class='op'>=</span> <span class='va'>input</span><span class='op'>[</span><span class='va'>index</span><span class='op'>[</span><span class='va'>i</span><span class='op'>]</span><span class='op'>[</span><span class='va'>j</span><span class='op'>]</span><span class='op'>[</span><span class='va'>k</span><span class='op'>]</span><span class='op'>]</span><span class='op'>[</span><span class='va'>j</span><span class='op'>]</span><span class='op'>[</span><span class='va'>k</span><span class='op'>]</span>  <span class='co'># if dim == 0</span>
-<span class='va'>out</span><span class='op'>[</span><span class='va'>i</span><span class='op'>]</span><span class='op'>[</span><span class='va'>j</span><span class='op'>]</span><span class='op'>[</span><span class='va'>k</span><span class='op'>]</span> <span class='op'>=</span> <span class='va'>input</span><span class='op'>[</span><span class='va'>i</span><span class='op'>]</span><span class='op'>[</span><span class='va'>index</span><span class='op'>[</span><span class='va'>i</span><span class='op'>]</span><span class='op'>[</span><span class='va'>j</span><span class='op'>]</span><span class='op'>[</span><span class='va'>k</span><span class='op'>]</span><span class='op'>]</span><span class='op'>[</span><span class='va'>k</span><span class='op'>]</span>  <span class='co'># if dim == 1</span>
-<span class='va'>out</span><span class='op'>[</span><span class='va'>i</span><span class='op'>]</span><span class='op'>[</span><span class='va'>j</span><span class='op'>]</span><span class='op'>[</span><span class='va'>k</span><span class='op'>]</span> <span class='op'>=</span> <span class='va'>input</span><span class='op'>[</span><span class='va'>i</span><span class='op'>]</span><span class='op'>[</span><span class='va'>j</span><span class='op'>]</span><span class='op'>[</span><span class='va'>index</span><span class='op'>[</span><span class='va'>i</span><span class='op'>]</span><span class='op'>[</span><span class='va'>j</span><span class='op'>]</span><span class='op'>[</span><span class='va'>k</span><span class='op'>]</span><span class='op'>]</span>  <span class='co'># if dim == 2</span>
-</pre>
+<p>For a 3-D tensor the output is specified by::</p><div class="sourceCode"><pre><code><span class="va">out</span><span class="op">[</span><span class="va">i</span><span class="op">]</span><span class="op">[</span><span class="va">j</span><span class="op">]</span><span class="op">[</span><span class="va">k</span><span class="op">]</span> <span class="op">=</span> <span class="va">input</span><span class="op">[</span><span class="va">index</span><span class="op">[</span><span class="va">i</span><span class="op">]</span><span class="op">[</span><span class="va">j</span><span class="op">]</span><span class="op">[</span><span class="va">k</span><span class="op">]</span><span class="op">]</span><span class="op">[</span><span class="va">j</span><span class="op">]</span><span class="op">[</span><span class="va">k</span><span class="op">]</span>  <span class="co"># if dim == 0</span>
+<span class="va">out</span><span class="op">[</span><span class="va">i</span><span class="op">]</span><span class="op">[</span><span class="va">j</span><span class="op">]</span><span class="op">[</span><span class="va">k</span><span class="op">]</span> <span class="op">=</span> <span class="va">input</span><span class="op">[</span><span class="va">i</span><span class="op">]</span><span class="op">[</span><span class="va">index</span><span class="op">[</span><span class="va">i</span><span class="op">]</span><span class="op">[</span><span class="va">j</span><span class="op">]</span><span class="op">[</span><span class="va">k</span><span class="op">]</span><span class="op">]</span><span class="op">[</span><span class="va">k</span><span class="op">]</span>  <span class="co"># if dim == 1</span>
+<span class="va">out</span><span class="op">[</span><span class="va">i</span><span class="op">]</span><span class="op">[</span><span class="va">j</span><span class="op">]</span><span class="op">[</span><span class="va">k</span><span class="op">]</span> <span class="op">=</span> <span class="va">input</span><span class="op">[</span><span class="va">i</span><span class="op">]</span><span class="op">[</span><span class="va">j</span><span class="op">]</span><span class="op">[</span><span class="va">index</span><span class="op">[</span><span class="va">i</span><span class="op">]</span><span class="op">[</span><span class="va">j</span><span class="op">]</span><span class="op">[</span><span class="va">k</span><span class="op">]</span><span class="op">]</span>  <span class="co"># if dim == 2</span></code></pre></div>
 
 <p>If <code>input</code> is an n-dimensional tensor with size
 \((x_0, x_1..., x_{i-1}, x_i, x_{i+1}, ..., x_{n-1})\)
 and <code>dim = i</code>, then <code>index</code> must be an \(n\)-dimensional tensor with
 size \((x_0, x_1, ..., x_{i-1}, y, x_{i+1}, ..., x_{n-1})\) where \(y \geq 1\)
 and <code>out</code> will have the same size as <code>index</code>.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>t</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>,<span class='fl'>2</span>,<span class='fl'>3</span>,<span class='fl'>4</span><span class='op'>)</span>, ncol <span class='op'>=</span> <span class='fl'>2</span>, byrow <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_gather</span><span class='op'>(</span><span class='va'>t</span>, <span class='fl'>2</span>, <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>,<span class='fl'>1</span>,<span class='fl'>2</span>,<span class='fl'>1</span><span class='op'>)</span>, ncol <span class='op'>=</span> <span class='fl'>2</span>, byrow<span class='op'>=</span><span class='cn'>TRUE</span><span class='op'>)</span>, dtype <span class='op'>=</span> <span class='fu'><a href='torch_dtype.html'>torch_int64</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1  1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  4  3</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{2,2} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">t</span> <span class="op">=</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/matrix.html" class="external-link">matrix</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>,<span class="fl">2</span>,<span class="fl">3</span>,<span class="fl">4</span><span class="op">)</span>, ncol <span class="op">=</span> <span class="fl">2</span>, byrow <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_gather</span><span class="op">(</span><span class="va">t</span>, <span class="fl">2</span>, <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/matrix.html" class="external-link">matrix</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>,<span class="fl">1</span>,<span class="fl">2</span>,<span class="fl">1</span><span class="op">)</span>, ncol <span class="op">=</span> <span class="fl">2</span>, byrow<span class="op">=</span><span class="cn">TRUE</span><span class="op">)</span>, dtype <span class="op">=</span> <span class="fu"><a href="torch_dtype.html">torch_int64</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  4  3</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2,2} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Gcd</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_gcd.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Gcd</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_gcd.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Gcd</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_gcd</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>other</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Tensor) the second input tensor</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_gcd</span><span class="op">(</span><span class="va">self</span>, <span class="va">other</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+<dt>other</dt>
+<dd><p>(Tensor) the second input tensor</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p>This defines \(gcd(0, 0) = 0\).</p>
-    <h2 class="hasAnchor" id="gcd-input-other-out-none-gt-tensor-"><a class="anchor" href="#gcd-input-other-out-none-gt-tensor-"></a>gcd(input, other, *, out=None) -&gt; Tensor </h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="gcd-input-other-out-none-gt-tensor-">gcd(input, other, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#gcd-input-other-out-none-gt-tensor-"></a></h2>
     
 
 
 <p>Computes the element-wise greatest common divisor (GCD) of <code>input</code> and <code>other</code>.</p>
 <p>Both <code>input</code> and <code>other</code> must have integer types.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'>torch</span><span class='fu'>::</span><span class='fu'><a href='cuda_is_available.html'>cuda_is_available</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>5</span>, <span class='fl'>10</span>, <span class='fl'>15</span><span class='op'>)</span>, dtype <span class='op'>=</span> <span class='fu'><a href='torch_dtype.html'>torch_long</a></span><span class='op'>(</span><span class='op'>)</span>, device <span class='op'>=</span> <span class='st'>"cuda"</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>b</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>4</span>, <span class='fl'>5</span><span class='op'>)</span>, dtype <span class='op'>=</span> <span class='fu'><a href='torch_dtype.html'>torch_long</a></span><span class='op'>(</span><span class='op'>)</span>, device <span class='op'>=</span> <span class='st'>"cuda"</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_gcd</span><span class='op'>(</span><span class='va'>a</span>, <span class='va'>b</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>c</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3L</span><span class='op'>)</span>, device <span class='op'>=</span> <span class='st'>"cuda"</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_gcd</span><span class='op'>(</span><span class='va'>a</span>, <span class='va'>c</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu">torch</span><span class="fu">::</span><span class="fu"><a href="cuda_is_available.html">cuda_is_available</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">5</span>, <span class="fl">10</span>, <span class="fl">15</span><span class="op">)</span>, dtype <span class="op">=</span> <span class="fu"><a href="torch_dtype.html">torch_long</a></span><span class="op">(</span><span class="op">)</span>, device <span class="op">=</span> <span class="st">"cuda"</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">b</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">4</span>, <span class="fl">5</span><span class="op">)</span>, dtype <span class="op">=</span> <span class="fu"><a href="torch_dtype.html">torch_long</a></span><span class="op">(</span><span class="op">)</span>, device <span class="op">=</span> <span class="st">"cuda"</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_gcd</span><span class="op">(</span><span class="va">a</span>, <span class="va">b</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">c</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3L</span><span class="op">)</span>, device <span class="op">=</span> <span class="st">"cuda"</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_gcd</span><span class="op">(</span><span class="va">a</span>, <span class="va">c</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Ge</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_ge.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Ge</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_ge.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Ge</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_ge</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>other</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the tensor to compare</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Tensor or float) the tensor or value to compare</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="ge-input-other-out-null-gt-tensor-"><a class="anchor" href="#ge-input-other-out-null-gt-tensor-"></a>ge(input, other, out=NULL) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_ge</span><span class="op">(</span><span class="va">self</span>, <span class="va">other</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the tensor to compare</p></dd>
+<dt>other</dt>
+<dd><p>(Tensor or float) the tensor or value to compare</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="ge-input-other-out-null-gt-tensor-">ge(input, other, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#ge-input-other-out-null-gt-tensor-"></a></h2>
     
 
 
 <p>Computes \(\mbox{input} \geq \mbox{other}\) element-wise.</p>
 <p>The second argument can be a number or a tensor whose shape is
 broadcastable  with the first argument.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='fu'>torch_ge</span><span class='op'>(</span><span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span><span class='op'>(</span><span class='fl'>1</span><span class='op'>:</span><span class='fl'>4</span>, ncol <span class='op'>=</span> <span class='fl'>2</span>, byrow<span class='op'>=</span><span class='cn'>TRUE</span><span class='op'>)</span><span class='op'>)</span>, </span>
-<span class='r-in'>         <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>,<span class='fl'>1</span>,<span class='fl'>4</span>,<span class='fl'>4</span><span class='op'>)</span>, ncol <span class='op'>=</span> <span class='fl'>2</span>, byrow<span class='op'>=</span><span class='cn'>TRUE</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1  1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0  1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUBoolType{2,2} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="fu">torch_ge</span><span class="op">(</span><span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/matrix.html" class="external-link">matrix</a></span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="fl">4</span>, ncol <span class="op">=</span> <span class="fl">2</span>, byrow<span class="op">=</span><span class="cn">TRUE</span><span class="op">)</span><span class="op">)</span>, </span>
+<span class="r-in">         <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/matrix.html" class="external-link">matrix</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>,<span class="fl">1</span>,<span class="fl">4</span>,<span class="fl">4</span><span class="op">)</span>, ncol <span class="op">=</span> <span class="fl">2</span>, byrow<span class="op">=</span><span class="cn">TRUE</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUBoolType{2,2} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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index 984ef2db5..066b979f0 100644
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="A torch_generator  is an object which manages the state of the algorithm
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Create a Generator object</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/generator.R'><code>R/generator.R</code></a></small>
-    <div class="hidden name"><code>torch_generator.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Create a Generator object</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/generator.R" class="external-link"><code>R/generator.R</code></a></small>
+      <div class="d-none name"><code>torch_generator.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>A <code>torch_generator</code>  is an object which manages the state of the algorithm
 that produces pseudo random numbers. Used as a keyword argument in many
 In-place random sampling functions.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_generator</span><span class='op'>(</span><span class='op'>)</span></pre>
-
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_generator</span><span class="op">(</span><span class="op">)</span></code></pre></div>
+    </div>
 
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='co'># Via string</span></span>
-<span class='r-in'><span class='va'>generator</span> <span class='op'>&lt;-</span> <span class='fu'>torch_generator</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>generator</span><span class='op'>$</span><span class='fu'>current_seed</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>generator</span><span class='op'>$</span><span class='fu'>set_current_seed</span><span class='op'>(</span><span class='fl'>1234567L</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>generator</span><span class='op'>$</span><span class='fu'>current_seed</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> integer64</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [1] 1234567</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="co"># Via string</span></span>
+<span class="r-in"><span class="va">generator</span> <span class="op">&lt;-</span> <span class="fu">torch_generator</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">generator</span><span class="op">$</span><span class="fu">current_seed</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">generator</span><span class="op">$</span><span class="fu">set_current_seed</span><span class="op">(</span><span class="fl">1234567L</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">generator</span><span class="op">$</span><span class="fu">current_seed</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> integer64</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 1234567</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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diff --git a/reference/torch_geqrf.html b/reference/torch_geqrf.html
index 49d5e83e3..c49f2ad36 100644
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-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Geqrf</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_geqrf.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Geqrf</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_geqrf.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Geqrf</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_geqrf</span><span class='op'>(</span><span class='va'>self</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input matrix</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="geqrf-input-out-null-gt-tensor-tensor-"><a class="anchor" href="#geqrf-input-out-null-gt-tensor-tensor-"></a>geqrf(input, out=NULL) -&gt; (Tensor, Tensor) </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_geqrf</span><span class="op">(</span><span class="va">self</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input matrix</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="geqrf-input-out-null-gt-tensor-tensor-">geqrf(input, out=NULL) -&gt; (Tensor, Tensor) <a class="anchor" aria-label="anchor" href="#geqrf-input-out-null-gt-tensor-tensor-"></a></h2>
     
 
 
 <p>This is a low-level function for calling LAPACK directly. This function
 returns a namedtuple (a, tau) as defined in <code>LAPACK documentation for geqrf</code>_ .</p>
-<p>You'll generally want to use <code><a href='torch_qr.html'>torch_qr</a></code> instead.</p>
+<p>You'll generally want to use <code><a href="torch_qr.html">torch_qr</a></code> instead.</p>
 <p>Computes a QR decomposition of <code>input</code>, but without constructing
 \(Q\) and \(R\) as explicit separate matrices.</p>
 <p>Rather, this directly calls the underlying LAPACK function <code>?geqrf</code>
 which produces a sequence of 'elementary reflectors'.</p>
 <p>See <code>LAPACK documentation for geqrf</code>_ for further details.</p>
+    </div>
 
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Ger</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_ger.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Ger</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_ger.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Ger</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_ger</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>vec2</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) 1-D input vector</p></td>
-    </tr>
-    <tr>
-      <th>vec2</th>
-      <td><p>(Tensor) 1-D input vector</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_ger</span><span class="op">(</span><span class="va">self</span>, <span class="va">vec2</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) 1-D input vector</p></dd>
+<dt>vec2</dt>
+<dd><p>(Tensor) 1-D input vector</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p>This function does not broadcast .</p>
-    <h2 class="hasAnchor" id="ger-input-vec-out-null-gt-tensor-"><a class="anchor" href="#ger-input-vec-out-null-gt-tensor-"></a>ger(input, vec2, out=NULL) -&gt; Tensor </h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="ger-input-vec-out-null-gt-tensor-">ger(input, vec2, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#ger-input-vec-out-null-gt-tensor-"></a></h2>
     
 
 
 <p>Outer product of <code>input</code> and <code>vec2</code>.
 If <code>input</code> is a vector of size \(n\) and <code>vec2</code> is a vector of
 size \(m\), then <code>out</code> must be a matrix of size \((n \times m)\).</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>v1</span> <span class='op'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span><span class='op'>(</span><span class='fl'>1.</span>, <span class='fl'>5.</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>v2</span> <span class='op'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span><span class='op'>(</span><span class='fl'>1.</span>, <span class='fl'>4.</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_ger</span><span class='op'>(</span><span class='va'>v1</span>, <span class='va'>v2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   1   2   3   4</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   2   4   6   8</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   3   6   9  12</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   4   8  12  16</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   5  10  15  20</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{5,4} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">v1</span> <span class="op">=</span> <span class="fu"><a href="torch_arange.html">torch_arange</a></span><span class="op">(</span><span class="fl">1.</span>, <span class="fl">5.</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">v2</span> <span class="op">=</span> <span class="fu"><a href="torch_arange.html">torch_arange</a></span><span class="op">(</span><span class="fl">1.</span>, <span class="fl">4.</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_ger</span><span class="op">(</span><span class="va">v1</span>, <span class="va">v2</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   1   2   3   4</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   2   4   6   8</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   3   6   9  12</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   4   8  12  16</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   5  10  15  20</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{5,4} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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diff --git a/reference/torch_greater.html b/reference/torch_greater.html
index ae92831b9..66fdd8bd5 100644
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-    <div class="hidden name"><code>torch_greater.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Greater</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_greater.Rd</code></div>
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-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Greater</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_greater</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>other</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the tensor to compare</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Tensor or float) the tensor or value to compare</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="greater-input-other-out-none-gt-tensor-"><a class="anchor" href="#greater-input-other-out-none-gt-tensor-"></a>greater(input, other, *, out=None) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_greater</span><span class="op">(</span><span class="va">self</span>, <span class="va">other</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the tensor to compare</p></dd>
+<dt>other</dt>
+<dd><p>(Tensor or float) the tensor or value to compare</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="greater-input-other-out-none-gt-tensor-">greater(input, other, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#greater-input-other-out-none-gt-tensor-"></a></h2>
     
 
 
-<p>Alias for <code><a href='torch_gt.html'>torch_gt()</a></code>.</p>
+<p>Alias for <code><a href="torch_gt.html">torch_gt()</a></code>.</p>
+    </div>
 
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index babb2c6c5..7180844f8 100644
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-    <h1>Greater_equal</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_greater_equal.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Greater_equal</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_greater_equal.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Greater_equal</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_greater_equal</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>other</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the tensor to compare</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Tensor or float) the tensor or value to compare</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="greater-equal-input-other-out-none-gt-tensor-"><a class="anchor" href="#greater-equal-input-other-out-none-gt-tensor-"></a>greater_equal(input, other, *, out=None) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_greater_equal</span><span class="op">(</span><span class="va">self</span>, <span class="va">other</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the tensor to compare</p></dd>
+<dt>other</dt>
+<dd><p>(Tensor or float) the tensor or value to compare</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="greater-equal-input-other-out-none-gt-tensor-">greater_equal(input, other, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#greater-equal-input-other-out-none-gt-tensor-"></a></h2>
     
 
 
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Gt</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_gt.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Gt</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_gt.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Gt</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_gt</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>other</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the tensor to compare</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Tensor or float) the tensor or value to compare</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="gt-input-other-out-null-gt-tensor-"><a class="anchor" href="#gt-input-other-out-null-gt-tensor-"></a>gt(input, other, out=NULL) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_gt</span><span class="op">(</span><span class="va">self</span>, <span class="va">other</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the tensor to compare</p></dd>
+<dt>other</dt>
+<dd><p>(Tensor or float) the tensor or value to compare</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="gt-input-other-out-null-gt-tensor-">gt(input, other, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#gt-input-other-out-null-gt-tensor-"></a></h2>
     
 
 
 <p>Computes \(\mbox{input} &gt; \mbox{other}\) element-wise.</p>
 <p>The second argument can be a number or a tensor whose shape is
 broadcastable  with the first argument.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='fu'>torch_gt</span><span class='op'>(</span><span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span><span class='op'>(</span><span class='fl'>1</span><span class='op'>:</span><span class='fl'>4</span>, ncol <span class='op'>=</span> <span class='fl'>2</span>, byrow<span class='op'>=</span><span class='cn'>TRUE</span><span class='op'>)</span><span class='op'>)</span>, </span>
-<span class='r-in'>         <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>,<span class='fl'>1</span>,<span class='fl'>4</span>,<span class='fl'>4</span><span class='op'>)</span>, ncol <span class='op'>=</span> <span class='fl'>2</span>, byrow<span class='op'>=</span><span class='cn'>TRUE</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0  1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUBoolType{2,2} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="fu">torch_gt</span><span class="op">(</span><span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/matrix.html" class="external-link">matrix</a></span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="fl">4</span>, ncol <span class="op">=</span> <span class="fl">2</span>, byrow<span class="op">=</span><span class="cn">TRUE</span><span class="op">)</span><span class="op">)</span>, </span>
+<span class="r-in">         <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/matrix.html" class="external-link">matrix</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>,<span class="fl">1</span>,<span class="fl">4</span>,<span class="fl">4</span><span class="op">)</span>, ncol <span class="op">=</span> <span class="fl">2</span>, byrow<span class="op">=</span><span class="cn">TRUE</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUBoolType{2,2} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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diff --git a/reference/torch_hamming_window.html b/reference/torch_hamming_window.html
index c7bdc69fa..eec41bdb5 100644
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Hamming_window</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/wrapers.R'><code>R/wrapers.R</code></a></small>
-    <div class="hidden name"><code>torch_hamming_window.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Hamming_window</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
+      <div class="d-none name"><code>torch_hamming_window.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Hamming_window</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_hamming_window</span><span class='op'>(</span>
-  <span class='va'>window_length</span>,
-  periodic <span class='op'>=</span> <span class='cn'>TRUE</span>,
-  alpha <span class='op'>=</span> <span class='fl'>0.54</span>,
-  beta <span class='op'>=</span> <span class='fl'>0.46</span>,
-  dtype <span class='op'>=</span> <span class='cn'>NULL</span>,
-  layout <span class='op'>=</span> <span class='fu'><a href='torch_layout.html'>torch_strided</a></span><span class='op'>(</span><span class='op'>)</span>,
-  device <span class='op'>=</span> <span class='cn'>NULL</span>,
-  requires_grad <span class='op'>=</span> <span class='cn'>FALSE</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>window_length</th>
-      <td><p>(int) the size of returned window</p></td>
-    </tr>
-    <tr>
-      <th>periodic</th>
-      <td><p>(bool, optional) If TRUE, returns a window to be used as periodic        function. If False, return a symmetric window.</p></td>
-    </tr>
-    <tr>
-      <th>alpha</th>
-      <td><p>(float, optional) The coefficient \(\alpha\) in the equation above</p></td>
-    </tr>
-    <tr>
-      <th>beta</th>
-      <td><p>(float, optional) The coefficient \(\beta\) in the equation above</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>). Only floating point types are supported.</p></td>
-    </tr>
-    <tr>
-      <th>layout</th>
-      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned window tensor. Only          <code>torch_strided</code> (dense layout) is supported.</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
-    </tr>
-    <tr>
-      <th>requires_grad</th>
-      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_hamming_window</span><span class="op">(</span>
+  <span class="va">window_length</span>,
+  periodic <span class="op">=</span> <span class="cn">TRUE</span>,
+  alpha <span class="op">=</span> <span class="fl">0.54</span>,
+  beta <span class="op">=</span> <span class="fl">0.46</span>,
+  dtype <span class="op">=</span> <span class="cn">NULL</span>,
+  layout <span class="op">=</span> <span class="fu"><a href="torch_layout.html">torch_strided</a></span><span class="op">(</span><span class="op">)</span>,
+  device <span class="op">=</span> <span class="cn">NULL</span>,
+  requires_grad <span class="op">=</span> <span class="cn">FALSE</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>window_length</dt>
+<dd><p>(int) the size of returned window</p></dd>
+<dt>periodic</dt>
+<dd><p>(bool, optional) If TRUE, returns a window to be used as periodic        function. If False, return a symmetric window.</p></dd>
+<dt>alpha</dt>
+<dd><p>(float, optional) The coefficient \(\alpha\) in the equation above</p></dd>
+<dt>beta</dt>
+<dd><p>(float, optional) The coefficient \(\beta\) in the equation above</p></dd>
+<dt>dtype</dt>
+<dd><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>). Only floating point types are supported.</p></dd>
+<dt>layout</dt>
+<dd><p>(<code>torch.layout</code>, optional) the desired layout of returned window tensor. Only          <code>torch_strided</code> (dense layout) is supported.</p></dd>
+<dt>device</dt>
+<dd><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></dd>
+<dt>requires_grad</dt>
+<dd><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     
-<pre>If `window_length` \eqn{=1}, the returned window contains a single value 1.
-</pre>
-
-<pre>This is a generalized version of `torch_hann_window`.
-</pre>
+<div class="sourceCode"><pre><code>If `window_length` \eqn{=1}, the returned window contains a single value 1.
+</code></pre></div>
 
-    <h2 class="hasAnchor" id="hamming-window-window-length-periodic-true-alpha-beta-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"><a class="anchor" href="#hamming-window-window-length-periodic-true-alpha-beta-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"></a>hamming_window(window_length, periodic=TRUE, alpha=0.54, beta=0.46, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor </h2>
+<div class="sourceCode"><pre><code>This is a generalized version of `torch_hann_window`.
+</code></pre></div>
 
+    </div>
+    <div class="section level2">
+    <h2 id="hamming-window-window-length-periodic-true-alpha-beta-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-">hamming_window(window_length, periodic=TRUE, alpha=0.54, beta=0.46, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#hamming-window-window-length-periodic-true-alpha-beta-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"></a></h2>
     
 
 
@@ -264,32 +141,25 @@ ready to be used as a periodic window with functions like
 above formula is in fact \(\mbox{window\_length} + 1\). Also, we always have
 <code>torch_hamming_window(L, periodic=TRUE)</code> equal to
 <code>torch_hamming_window(L + 1, periodic=False)[:-1])</code>.</p>
+    </div>
 
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-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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+<div class="pkgdown-footer-right">
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index 18356bdbc..4df70f651 100644
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Hann_window</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/wrapers.R'><code>R/wrapers.R</code></a></small>
-    <div class="hidden name"><code>torch_hann_window.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Hann_window</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
+      <div class="d-none name"><code>torch_hann_window.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Hann_window</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_hann_window</span><span class='op'>(</span>
-  <span class='va'>window_length</span>,
-  periodic <span class='op'>=</span> <span class='cn'>TRUE</span>,
-  dtype <span class='op'>=</span> <span class='cn'>NULL</span>,
-  layout <span class='op'>=</span> <span class='fu'><a href='torch_layout.html'>torch_strided</a></span><span class='op'>(</span><span class='op'>)</span>,
-  device <span class='op'>=</span> <span class='cn'>NULL</span>,
-  requires_grad <span class='op'>=</span> <span class='cn'>FALSE</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>window_length</th>
-      <td><p>(int) the size of returned window</p></td>
-    </tr>
-    <tr>
-      <th>periodic</th>
-      <td><p>(bool, optional) If TRUE, returns a window to be used as periodic        function. If False, return a symmetric window.</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>). Only floating point types are supported.</p></td>
-    </tr>
-    <tr>
-      <th>layout</th>
-      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned window tensor. Only          <code>torch_strided</code> (dense layout) is supported.</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
-    </tr>
-    <tr>
-      <th>requires_grad</th>
-      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_hann_window</span><span class="op">(</span>
+  <span class="va">window_length</span>,
+  periodic <span class="op">=</span> <span class="cn">TRUE</span>,
+  dtype <span class="op">=</span> <span class="cn">NULL</span>,
+  layout <span class="op">=</span> <span class="fu"><a href="torch_layout.html">torch_strided</a></span><span class="op">(</span><span class="op">)</span>,
+  device <span class="op">=</span> <span class="cn">NULL</span>,
+  requires_grad <span class="op">=</span> <span class="cn">FALSE</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>window_length</dt>
+<dd><p>(int) the size of returned window</p></dd>
+<dt>periodic</dt>
+<dd><p>(bool, optional) If TRUE, returns a window to be used as periodic        function. If False, return a symmetric window.</p></dd>
+<dt>dtype</dt>
+<dd><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>). Only floating point types are supported.</p></dd>
+<dt>layout</dt>
+<dd><p>(<code>torch.layout</code>, optional) the desired layout of returned window tensor. Only          <code>torch_strided</code> (dense layout) is supported.</p></dd>
+<dt>device</dt>
+<dd><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></dd>
+<dt>requires_grad</dt>
+<dd><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     
-<pre>If `window_length` \eqn{=1}, the returned window contains a single value 1.
-</pre>
-
-    <h2 class="hasAnchor" id="hann-window-window-length-periodic-true-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"><a class="anchor" href="#hann-window-window-length-periodic-true-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"></a>hann_window(window_length, periodic=TRUE, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor </h2>
+<div class="sourceCode"><pre><code>If `window_length` \eqn{=1}, the returned window contains a single value 1.
+</code></pre></div>
 
+    </div>
+    <div class="section level2">
+    <h2 id="hann-window-window-length-periodic-true-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-">hann_window(window_length, periodic=TRUE, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#hann-window-window-length-periodic-true-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"></a></h2>
     
 
 
@@ -252,32 +133,25 @@ ready to be used as a periodic window with functions like
 above formula is in fact \(\mbox{window\_length} + 1\). Also, we always have
 <code>torch_hann_window(L, periodic=TRUE)</code> equal to
 <code>torch_hann_window(L + 1, periodic=False)[:-1])</code>.</p>
+    </div>
 
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+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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diff --git a/reference/torch_heaviside.html b/reference/torch_heaviside.html
index 2dd4b8345..f95fb2c2f 100644
--- a/reference/torch_heaviside.html
+++ b/reference/torch_heaviside.html
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Heaviside</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_heaviside.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Heaviside</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_heaviside.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Heaviside</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_heaviside</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>values</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>values</th>
-      <td><p>(Tensor) The values to use where <code>input</code> is zero.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="heaviside-input-values-out-none-gt-tensor-"><a class="anchor" href="#heaviside-input-values-out-none-gt-tensor-"></a>heaviside(input, values, *, out=None) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_heaviside</span><span class="op">(</span><span class="va">self</span>, <span class="va">values</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+<dt>values</dt>
+<dd><p>(Tensor) The values to use where <code>input</code> is zero.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="heaviside-input-values-out-none-gt-tensor-">heaviside(input, values, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#heaviside-input-values-out-none-gt-tensor-"></a></h2>
     
 
 
@@ -218,47 +106,42 @@ The Heaviside step function is defined as:</p>
  1, &amp; \mbox{if input &gt; 0}
  \end{array}
 $$</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='op'>-</span><span class='fl'>1.5</span>, <span class='fl'>0</span>, <span class='fl'>2.0</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>values</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>0.5</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_heaviside</span><span class='op'>(</span><span class='va'>input</span>, <span class='va'>values</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>values</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1.2</span>, <span class='op'>-</span><span class='fl'>2.0</span>, <span class='fl'>3.5</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_heaviside</span><span class='op'>(</span><span class='va'>input</span>, <span class='va'>values</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -2</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{3} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="op">-</span><span class="fl">1.5</span>, <span class="fl">0</span>, <span class="fl">2.0</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">values</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.5</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_heaviside</span><span class="op">(</span><span class="va">input</span>, <span class="va">values</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">values</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1.2</span>, <span class="op">-</span><span class="fl">2.0</span>, <span class="fl">3.5</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_heaviside</span><span class="op">(</span><span class="va">input</span>, <span class="va">values</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -2</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3} ]</span>
+</code></pre></div>
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index 561207c71..d81f9f319 100644
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+        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Histc</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_histc.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Histc</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_histc.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Histc</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_histc</span><span class='op'>(</span><span class='va'>self</span>, bins <span class='op'>=</span> <span class='fl'>100L</span>, min <span class='op'>=</span> <span class='fl'>0L</span>, max <span class='op'>=</span> <span class='fl'>0L</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>bins</th>
-      <td><p>(int) number of histogram bins</p></td>
-    </tr>
-    <tr>
-      <th>min</th>
-      <td><p>(int) lower end of the range (inclusive)</p></td>
-    </tr>
-    <tr>
-      <th>max</th>
-      <td><p>(int) upper end of the range (inclusive)</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="histc-input-bins-min-max-out-null-gt-tensor-"><a class="anchor" href="#histc-input-bins-min-max-out-null-gt-tensor-"></a>histc(input, bins=100, min=0, max=0, out=NULL) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_histc</span><span class="op">(</span><span class="va">self</span>, bins <span class="op">=</span> <span class="fl">100L</span>, min <span class="op">=</span> <span class="fl">0L</span>, max <span class="op">=</span> <span class="fl">0L</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+<dt>bins</dt>
+<dd><p>(int) number of histogram bins</p></dd>
+<dt>min</dt>
+<dd><p>(int) lower end of the range (inclusive)</p></dd>
+<dt>max</dt>
+<dd><p>(int) upper end of the range (inclusive)</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="histc-input-bins-min-max-out-null-gt-tensor-">histc(input, bins=100, min=0, max=0, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#histc-input-bins-min-max-out-null-gt-tensor-"></a></h2>
     
 
 
@@ -221,44 +105,39 @@
 <p>The elements are sorted into equal width bins between <code>min</code> and
 <code>max</code>. If <code>min</code> and <code>max</code> are both zero, the minimum and
 maximum values of the data are used.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='fu'>torch_histc</span><span class='op'>(</span><span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1.</span>, <span class='fl'>2</span>, <span class='fl'>1</span><span class='op'>)</span><span class='op'>)</span>, bins<span class='op'>=</span><span class='fl'>4</span>, min<span class='op'>=</span><span class='fl'>0</span>, max<span class='op'>=</span><span class='fl'>3</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  2</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{4} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="fu">torch_histc</span><span class="op">(</span><span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1.</span>, <span class="fl">2</span>, <span class="fl">1</span><span class="op">)</span><span class="op">)</span>, bins<span class="op">=</span><span class="fl">4</span>, min<span class="op">=</span><span class="fl">0</span>, max<span class="op">=</span><span class="fl">3</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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+    
+  </div>
+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Hstack</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_hstack.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Hstack</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_hstack.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Hstack</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_hstack</span><span class='op'>(</span><span class='va'>tensors</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>tensors</th>
-      <td><p>(sequence of Tensors) sequence of tensors to concatenate</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="hstack-tensors-out-none-gt-tensor-"><a class="anchor" href="#hstack-tensors-out-none-gt-tensor-"></a>hstack(tensors, *, out=None) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_hstack</span><span class="op">(</span><span class="va">tensors</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>tensors</dt>
+<dd><p>(sequence of Tensors) sequence of tensors to concatenate</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="hstack-tensors-out-none-gt-tensor-">hstack(tensors, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#hstack-tensors-out-none-gt-tensor-"></a></h2>
     
 
 
 <p>Stack tensors in sequence horizontally (column wise).</p>
 <p>This is equivalent to concatenation along the first axis for 1-D tensors, and
 along the second axis for all other tensors.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>2</span>, <span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>b</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span>, <span class='fl'>5</span>, <span class='fl'>6</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_hstack</span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span><span class='va'>a</span>,<span class='va'>b</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/cbind.html'>rbind</a></span><span class='op'>(</span><span class='fl'>1</span>,<span class='fl'>2</span>,<span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>b</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/cbind.html'>rbind</a></span><span class='op'>(</span><span class='fl'>4</span>,<span class='fl'>5</span>,<span class='fl'>6</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_hstack</span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span><span class='va'>a</span>,<span class='va'>b</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1  4</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  2  5</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  3  6</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{3,2} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-      <h2 data-toc-skip>Contents</h2>
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+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">2</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">b</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span>, <span class="fl">5</span>, <span class="fl">6</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_hstack</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">a</span>,<span class="va">b</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/cbind.html" class="external-link">rbind</a></span><span class="op">(</span><span class="fl">1</span>,<span class="fl">2</span>,<span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">b</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/cbind.html" class="external-link">rbind</a></span><span class="op">(</span><span class="fl">4</span>,<span class="fl">5</span>,<span class="fl">6</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_hstack</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">a</span>,<span class="va">b</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1  4</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2  5</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  3  6</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3,2} ]</span>
+</code></pre></div>
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Hypot</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_hypot.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Hypot</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_hypot.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Hypot</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_hypot</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>other</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the first input tensor</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Tensor) the second input tensor</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="hypot-input-other-out-none-gt-tensor-"><a class="anchor" href="#hypot-input-other-out-none-gt-tensor-"></a>hypot(input, other, *, out=None) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_hypot</span><span class="op">(</span><span class="va">self</span>, <span class="va">other</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the first input tensor</p></dd>
+<dt>other</dt>
+<dd><p>(Tensor) the second input tensor</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="hypot-input-other-out-none-gt-tensor-">hypot(input, other, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#hypot-input-other-out-none-gt-tensor-"></a></h2>
     
 
 
@@ -215,43 +103,38 @@
 $$</p>
 <p>The shapes of <code>input</code> and <code>other</code> must be
 broadcastable .</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='fu'>torch_hypot</span><span class='op'>(</span><span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4.0</span><span class='op'>)</span><span class='op'>)</span>, <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3.0</span>, <span class='fl'>4.0</span>, <span class='fl'>5.0</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  5.0000</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  5.6569</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  6.4031</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{3} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="fu">torch_hypot</span><span class="op">(</span><span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4.0</span><span class="op">)</span><span class="op">)</span>, <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3.0</span>, <span class="fl">4.0</span>, <span class="fl">5.0</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  5.0000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  5.6569</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  6.4031</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer>
-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
 </div>
 
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+<div class="pkgdown-footer-right">
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-    <div class="hidden name"><code>torch_i0.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>I0</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_i0.Rd</code></div>
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-    <div class="ref-description">
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-    <pre class="usage"><span class='fu'>torch_i0</span><span class='op'>(</span><span class='va'>self</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="i-input-out-none-gt-tensor-"><a class="anchor" href="#i-input-out-none-gt-tensor-"></a>i0(input, *, out=None) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_i0</span><span class="op">(</span><span class="va">self</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="i-input-out-none-gt-tensor-">i0(input, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#i-input-out-none-gt-tensor-"></a></h2>
     
 
 
@@ -209,46 +99,41 @@
 <p>$$
 \mbox{out}_{i} = I_0(\mbox{input}_{i}) = \sum_{k=0}^{\infty} \frac{(\mbox{input}_{i}^2/4)^k}{(k!)^2}
 $$</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='fu'>torch_i0</span><span class='op'>(</span><span class='fu'><a href='torch_arange.html'>torch_arange</a></span><span class='op'>(</span>start <span class='op'>=</span> <span class='fl'>0</span>, end <span class='op'>=</span> <span class='fl'>5</span>, dtype<span class='op'>=</span><span class='fu'><a href='torch_dtype.html'>torch_float32</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   1.0000</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   1.2661</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   2.2796</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   4.8808</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  11.3019</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  27.2399</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{6} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="fu">torch_i0</span><span class="op">(</span><span class="fu"><a href="torch_arange.html">torch_arange</a></span><span class="op">(</span>start <span class="op">=</span> <span class="fl">0</span>, end <span class="op">=</span> <span class="fl">5</span>, dtype<span class="op">=</span><span class="fu"><a href="torch_dtype.html">torch_float32</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   1.0000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   1.2661</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   2.2796</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   4.8808</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  11.3019</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  27.2399</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{6} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer>
-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
 </div>
 
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+<div class="pkgdown-footer-right">
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diff --git a/reference/torch_ifft.html b/reference/torch_ifft.html
deleted file mode 100644
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-    <h1>Ifft</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_ifft.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Ifft</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_ifft</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>signal_ndim</span>, normalized <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor of at least <code>signal_ndim</code> <code>+ 1</code>        dimensions</p></td>
-    </tr>
-    <tr>
-      <th>signal_ndim</th>
-      <td><p>(int) the number of dimensions in each signal.        <code>signal_ndim</code> can only be 1, 2 or 3</p></td>
-    </tr>
-    <tr>
-      <th>normalized</th>
-      <td><p>(bool, optional) controls whether to return normalized results.        Default: <code>FALSE</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    
-<pre>For CUDA tensors, an LRU cache is used for cuFFT plans to speed up
-repeatedly running FFT methods on tensors of same geometry with same
-configuration. See cufft-plan-cache for more details on how to
-monitor and control the cache.
-</pre>
-
-    <h2 class="hasAnchor" id="ifft-input-signal-ndim-normalized-false-gt-tensor-"><a class="anchor" href="#ifft-input-signal-ndim-normalized-false-gt-tensor-"></a>ifft(input, signal_ndim, normalized=False) -&gt; Tensor </h2>
-
-    
-
-
-<p>Complex-to-complex Inverse Discrete Fourier Transform</p>
-<p>This method computes the complex-to-complex inverse discrete Fourier
-transform. Ignoring the batch dimensions, it computes the following
-expression:</p>
-<p>$$
-    X[\omega_1, \dots, \omega_d] =
-        \frac{1}{\prod_{i=1}^d N_i} \sum_{n_1=0}^{N_1-1} \dots \sum_{n_d=0}^{N_d-1} x[n_1, \dots, n_d]
-         e^{\ j\ 2 \pi \sum_{i=0}^d \frac{\omega_i n_i}{N_i}},
-$$
-where \(d\) = <code>signal_ndim</code> is number of dimensions for the
-signal, and \(N_i\) is the size of signal dimension \(i\).</p>
-<p>The argument specifications are almost identical with <code><a href='torch_fft.html'>torch_fft</a></code>.
-However, if <code>normalized</code> is set to <code>TRUE</code>, this instead returns the
-results multiplied by \(\sqrt{\prod_{i=1}^d N_i}\), to become a unitary
-operator. Therefore, to invert a <code><a href='torch_fft.html'>torch_fft</a></code>, the <code>normalized</code>
-argument should be set identically for <code><a href='torch_fft.html'>torch_fft</a></code>.</p>
-<p>Returns the real and the imaginary parts together as one tensor of the same
-shape of <code>input</code>.</p>
-<p>The inverse of this function is <code><a href='torch_fft.html'>torch_fft</a></code>.</p>
-    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
-
-    
-
-<p>For CPU tensors, this method is currently only available with MKL. Use
-<code>torch_backends.mkl.is_available</code> to check if MKL is installed.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span>
-
-<span class='va'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>3</span>, <span class='fl'>2</span><span class='op'>)</span><span class='op'>)</span>
-<span class='va'>x</span>
-<span class='va'>y</span> <span class='op'>=</span> <span class='fu'><a href='torch_fft.html'>torch_fft</a></span><span class='op'>(</span><span class='va'>x</span>, <span class='fl'>2</span><span class='op'>)</span>
-<span class='fu'>torch_ifft</span><span class='op'>(</span><span class='va'>y</span>, <span class='fl'>2</span><span class='op'>)</span>  <span class='co'># recover x</span>
-<span class='op'>}</span>
-</div><div class='output co'>#&gt; torch_tensor
-#&gt; (1,.,.) = 
-#&gt;  -0.1112  0.1976
-#&gt;  -0.6745 -0.6398
-#&gt;   0.5422 -0.5937
-#&gt; 
-#&gt; (2,.,.) = 
-#&gt;  -1.9813  0.5721
-#&gt;   2.0184 -0.7911
-#&gt;   1.0326 -0.0483
-#&gt; 
-#&gt; (3,.,.) = 
-#&gt;   0.2098  0.7124
-#&gt;   0.9631  0.2883
-#&gt;  -1.9407 -0.0044
-#&gt; [ CPUFloatType{3,3,2} ]</div></pre>
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-    </nav>
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diff --git a/reference/torch_iinfo.html b/reference/torch_iinfo.html
index 71c4821f4..46a571dad 100644
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-  <div class="col-md-9 contents">
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-    <h1>Integer type info</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/type-info.R'><code>R/type-info.R</code></a></small>
-    <div class="hidden name"><code>torch_iinfo.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Integer type info</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/type-info.R" class="external-link"><code>R/type-info.R</code></a></small>
+      <div class="d-none name"><code>torch_iinfo.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>A list that represents the numerical properties of a integer
 type.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_iinfo</span><span class='op'>(</span><span class='va'>dtype</span><span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_iinfo</span><span class="op">(</span><span class="va">dtype</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>dtype</th>
-      <td><p>dtype to get information from.</p></td>
-    </tr>
-    </table>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>dtype</dt>
+<dd><p>dtype to get information from.</p></dd>
+</dl></div>
 
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Imag</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_imag.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Imag</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_imag.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Imag</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_imag</span><span class='op'>(</span><span class='va'>self</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="imag-input-gt-tensor-"><a class="anchor" href="#imag-input-gt-tensor-"></a>imag(input) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_imag</span><span class="op">(</span><span class="va">self</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="imag-input-gt-tensor-">imag(input) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#imag-input-gt-tensor-"></a></h2>
     
 
 
 <p>Returns the imaginary part of the <code>input</code> tensor.</p>
-    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></a></h2>
     
 
 <p>Not yet implemented.</p>
 <p>$$
     \mbox{out}_{i} = imag(\mbox{input}_{i})
 $$</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='cn'>FALSE</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='fu'>torch_imag</span><span class='op'>(</span><span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='op'>-</span><span class='fl'>1</span> <span class='op'>+</span> <span class='fl'>1i</span>, <span class='op'>-</span><span class='fl'>2</span> <span class='op'>+</span> <span class='fl'>2i</span>, <span class='fl'>3</span> <span class='op'>-</span> <span class='fl'>3i</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
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-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="fu">torch_imag</span><span class="op">(</span><span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="op">-</span><span class="fl">1</span> <span class="op">+</span> <span class="fl">1i</span>, <span class="op">-</span><span class="fl">2</span> <span class="op">+</span> <span class="fl">2i</span>, <span class="fl">3</span> <span class="op">-</span> <span class="fl">3i</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
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index 8814a1f64..2534b2109 100644
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-    <h1>Index torch tensors</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/wrapers.R'><code>R/wrapers.R</code></a></small>
-    <div class="hidden name"><code>torch_index.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Index torch tensors</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
+      <div class="d-none name"><code>torch_index.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Helper functions to index tensors.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_index</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>indices</span><span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_index</span><span class="op">(</span><span class="va">self</span>, <span class="va">indices</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
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-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) Tensor that will be indexed.</p></td>
-    </tr>
-    <tr>
-      <th>indices</th>
-      <td><p>(<code>List[Tensor]</code>) List of indices. Indices are torch tensors with
-<code><a href='torch_dtype.html'>torch_long()</a></code> dtype.</p></td>
-    </tr>
-    </table>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) Tensor that will be indexed.</p></dd>
+<dt>indices</dt>
+<dd><p>(<code>List[Tensor]</code>) List of indices. Indices are torch tensors with
+<code><a href="torch_dtype.html">torch_long()</a></code> dtype.</p></dd>
+</dl></div>
 
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-    <h1>Modify values selected by <code>indices</code>.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/wrapers.R'><code>R/wrapers.R</code></a></small>
-    <div class="hidden name"><code>torch_index_put.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Modify values selected by <code>indices</code>.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
+      <div class="d-none name"><code>torch_index_put.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Modify values selected by <code>indices</code>.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_index_put</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>indices</span>, <span class='va'>values</span>, accumulate <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span></pre>
-
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-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) Tensor that will be indexed.</p></td>
-    </tr>
-    <tr>
-      <th>indices</th>
-      <td><p>(<code>List[Tensor]</code>) List of indices. Indices are torch tensors with
-<code><a href='torch_dtype.html'>torch_long()</a></code> dtype.</p></td>
-    </tr>
-    <tr>
-      <th>values</th>
-      <td><p>(Tensor) values that will be replaced the indexed location. Used
-for <code>torch_index_put</code> and <code>torch_index_put_</code>.</p></td>
-    </tr>
-    <tr>
-      <th>accumulate</th>
-      <td><p>(bool) Wether instead of replacing the current values with <code>values</code>,
-you want to add them.</p></td>
-    </tr>
-    </table>
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+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_index_put</span><span class="op">(</span><span class="va">self</span>, <span class="va">indices</span>, <span class="va">values</span>, accumulate <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
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+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) Tensor that will be indexed.</p></dd>
+<dt>indices</dt>
+<dd><p>(<code>List[Tensor]</code>) List of indices. Indices are torch tensors with
+<code><a href="torch_dtype.html">torch_long()</a></code> dtype.</p></dd>
+<dt>values</dt>
+<dd><p>(Tensor) values that will be replaced the indexed location. Used
+for <code>torch_index_put</code> and <code>torch_index_put_</code>.</p></dd>
+<dt>accumulate</dt>
+<dd><p>(bool) Wether instead of replacing the current values with <code>values</code>,
+you want to add them.</p></dd>
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/wrapers.R'><code>R/wrapers.R</code></a></small>
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     <p>In-place version of <code>torch_index_put</code>.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_index_put_</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>indices</span>, <span class='va'>values</span>, accumulate <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span></pre>
-
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-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) Tensor that will be indexed.</p></td>
-    </tr>
-    <tr>
-      <th>indices</th>
-      <td><p>(<code>List[Tensor]</code>) List of indices. Indices are torch tensors with
-<code><a href='torch_dtype.html'>torch_long()</a></code> dtype.</p></td>
-    </tr>
-    <tr>
-      <th>values</th>
-      <td><p>(Tensor) values that will be replaced the indexed location. Used
-for <code>torch_index_put</code> and <code>torch_index_put_</code>.</p></td>
-    </tr>
-    <tr>
-      <th>accumulate</th>
-      <td><p>(bool) Wether instead of replacing the current values with <code>values</code>,
-you want to add them.</p></td>
-    </tr>
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+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_index_put_</span><span class="op">(</span><span class="va">self</span>, <span class="va">indices</span>, <span class="va">values</span>, accumulate <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
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+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) Tensor that will be indexed.</p></dd>
+<dt>indices</dt>
+<dd><p>(<code>List[Tensor]</code>) List of indices. Indices are torch tensors with
+<code><a href="torch_dtype.html">torch_long()</a></code> dtype.</p></dd>
+<dt>values</dt>
+<dd><p>(Tensor) values that will be replaced the indexed location. Used
+for <code>torch_index_put</code> and <code>torch_index_put_</code>.</p></dd>
+<dt>accumulate</dt>
+<dd><p>(bool) Wether instead of replacing the current values with <code>values</code>,
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Index_select</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_index_select.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Index_select</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_index_select.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Index_select</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_index_select</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>dim</span>, <span class='va'>index</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) the dimension in which we index</p></td>
-    </tr>
-    <tr>
-      <th>index</th>
-      <td><p>(LongTensor) the 1-D tensor containing the indices to index</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_index_select</span><span class="op">(</span><span class="va">self</span>, <span class="va">dim</span>, <span class="va">index</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+<dt>dim</dt>
+<dd><p>(int) the dimension in which we index</p></dd>
+<dt>index</dt>
+<dd><p>(LongTensor) the 1-D tensor containing the indices to index</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p>The returned tensor does <strong>not</strong> use the same storage as the original
 tensor.  If <code>out</code> has a different shape than expected, we
 silently change it to the correct shape, reallocating the underlying
 storage if necessary.</p>
-    <h2 class="hasAnchor" id="index-select-input-dim-index-out-null-gt-tensor-"><a class="anchor" href="#index-select-input-dim-index-out-null-gt-tensor-"></a>index_select(input, dim, index, out=NULL) -&gt; Tensor </h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="index-select-input-dim-index-out-null-gt-tensor-">index_select(input, dim, index, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#index-select-input-dim-index-out-null-gt-tensor-"></a></h2>
     
 
 
@@ -224,47 +111,42 @@ storage if necessary.</p>
 <p>The returned tensor has the same number of dimensions as the original tensor
 (<code>input</code>).  The <code>dim</code>\ th dimension has the same size as the length
 of <code>index</code>; other dimensions have the same size as in the original tensor.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>x</span></span>
-<span class='r-in'><span class='va'>indices</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>3</span><span class='op'>)</span>, dtype <span class='op'>=</span> <span class='fu'><a href='torch_dtype.html'>torch_int64</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_index_select</span><span class='op'>(</span><span class='va'>x</span>, <span class='fl'>1</span>, <span class='va'>indices</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_index_select</span><span class='op'>(</span><span class='va'>x</span>, <span class='fl'>2</span>, <span class='va'>indices</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.8992  2.2558</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.6209 -0.2070</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.3402  0.5389</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{3,2} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">x</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">4</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">x</span></span>
+<span class="r-in"><span class="va">indices</span> <span class="op">=</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">3</span><span class="op">)</span>, dtype <span class="op">=</span> <span class="fu"><a href="torch_dtype.html">torch_int64</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_index_select</span><span class="op">(</span><span class="va">x</span>, <span class="fl">1</span>, <span class="va">indices</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_index_select</span><span class="op">(</span><span class="va">x</span>, <span class="fl">2</span>, <span class="va">indices</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -1.3321 -1.1262</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.2194 -0.5341</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.9468  1.1817</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3,2} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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diff --git a/reference/torch_install_path.html b/reference/torch_install_path.html
new file mode 100644
index 000000000..c5e27386a
--- /dev/null
+++ b/reference/torch_install_path.html
@@ -0,0 +1,113 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="A simple exported version of install_path
+Returns the torch installation path."><title>A simple exported version of install_path
+Returns the torch installation path. — torch_install_path • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.1.0/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.1.0/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.js"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="A simple exported version of install_path
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+      <img src="" class="logo" alt=""><h1>A simple exported version of install_path
+Returns the torch installation path.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/install.R" class="external-link"><code>R/install.R</code></a></small>
+      <div class="d-none name"><code>torch_install_path.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>A simple exported version of install_path
+Returns the torch installation path.</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_install_path</span><span class="op">(</span><span class="op">)</span></code></pre></div>
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diff --git a/reference/torch_inverse.html b/reference/torch_inverse.html
index 7a083e7fd..b27a45d2d 100644
--- a/reference/torch_inverse.html
+++ b/reference/torch_inverse.html
@@ -1,262 +1,148 @@
-<!-- Generated by pkgdown: do not edit by hand -->
 <!DOCTYPE html>
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Inverse</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_inverse.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Inverse</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_inverse.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Inverse</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_inverse</span><span class='op'>(</span><span class='va'>self</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor of size \((*, n, n)\) where <code>*</code> is zero or more                    batch dimensions</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_inverse</span><span class="op">(</span><span class="va">self</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor of size \((*, n, n)\) where <code>*</code> is zero or more                    batch dimensions</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     
-<pre>Irrespective of the original strides, the returned tensors will be
+<div class="sourceCode"><pre><code>Irrespective of the original strides, the returned tensors will be
 transposed, i.e. with strides like `input.contiguous().transpose(-2, -1).stride()`
-</pre>
-
-    <h2 class="hasAnchor" id="inverse-input-out-null-gt-tensor-"><a class="anchor" href="#inverse-input-out-null-gt-tensor-"></a>inverse(input, out=NULL) -&gt; Tensor </h2>
+</code></pre></div>
 
+    </div>
+    <div class="section level2">
+    <h2 id="inverse-input-out-null-gt-tensor-">inverse(input, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#inverse-input-out-null-gt-tensor-"></a></h2>
     
 
 
 <p>Takes the inverse of the square matrix <code>input</code>. <code>input</code> can be batches
 of 2D square tensors, in which case this function would return a tensor composed of
 individual inverses.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='cn'>FALSE</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_rand.html'>torch_rand</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span>, <span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>y</span> <span class='op'>=</span> <span class='fu'>torch_inverse</span><span class='op'>(</span><span class='va'>x</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>z</span> <span class='op'>=</span> <span class='fu'><a href='torch_mm.html'>torch_mm</a></span><span class='op'>(</span><span class='va'>x</span>, <span class='va'>y</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>z</span></span>
-<span class='r-in'><span class='fu'><a href='torch_max.html'>torch_max</a></span><span class='op'>(</span><span class='fu'><a href='torch_abs.html'>torch_abs</a></span><span class='op'>(</span><span class='va'>z</span> <span class='op'>-</span> <span class='fu'><a href='torch_eye.html'>torch_eye</a></span><span class='op'>(</span><span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span> <span class='co'># Max non-zero</span></span>
-<span class='r-in'><span class='co'># Batched inverse example</span></span>
-<span class='r-in'><span class='va'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>3</span>, <span class='fl'>4</span>, <span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>y</span> <span class='op'>=</span> <span class='fu'>torch_inverse</span><span class='op'>(</span><span class='va'>x</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>z</span> <span class='op'>=</span> <span class='fu'><a href='torch_matmul.html'>torch_matmul</a></span><span class='op'>(</span><span class='va'>x</span>, <span class='va'>y</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'><a href='torch_max.html'>torch_max</a></span><span class='op'>(</span><span class='fu'><a href='torch_abs.html'>torch_abs</a></span><span class='op'>(</span><span class='va'>z</span> <span class='op'>-</span> <span class='fu'><a href='torch_eye.html'>torch_eye</a></span><span class='op'>(</span><span class='fl'>4</span><span class='op'>)</span><span class='op'>$</span><span class='fu'>expand_as</span><span class='op'>(</span><span class='va'>x</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span> <span class='co'># Max non-zero</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">x</span> <span class="op">=</span> <span class="fu"><a href="torch_rand.html">torch_rand</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span>, <span class="fl">4</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">y</span> <span class="op">=</span> <span class="fu">torch_inverse</span><span class="op">(</span><span class="va">x</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">z</span> <span class="op">=</span> <span class="fu"><a href="torch_mm.html">torch_mm</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">y</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">z</span></span>
+<span class="r-in"><span class="fu"><a href="torch_max.html">torch_max</a></span><span class="op">(</span><span class="fu"><a href="torch_abs.html">torch_abs</a></span><span class="op">(</span><span class="va">z</span> <span class="op">-</span> <span class="fu"><a href="torch_eye.html">torch_eye</a></span><span class="op">(</span><span class="fl">4</span><span class="op">)</span><span class="op">)</span><span class="op">)</span> <span class="co"># Max non-zero</span></span>
+<span class="r-in"><span class="co"># Batched inverse example</span></span>
+<span class="r-in"><span class="va">x</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">3</span>, <span class="fl">4</span>, <span class="fl">4</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">y</span> <span class="op">=</span> <span class="fu">torch_inverse</span><span class="op">(</span><span class="va">x</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">z</span> <span class="op">=</span> <span class="fu"><a href="torch_matmul.html">torch_matmul</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">y</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu"><a href="torch_max.html">torch_max</a></span><span class="op">(</span><span class="fu"><a href="torch_abs.html">torch_abs</a></span><span class="op">(</span><span class="va">z</span> <span class="op">-</span> <span class="fu"><a href="torch_eye.html">torch_eye</a></span><span class="op">(</span><span class="fl">4</span><span class="op">)</span><span class="op">$</span><span class="fu">expand_as</span><span class="op">(</span><span class="va">x</span><span class="op">)</span><span class="op">)</span><span class="op">)</span> <span class="co"># Max non-zero</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
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diff --git a/reference/torch_irfft.html b/reference/torch_irfft.html
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-    <h1>Irfft</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_irfft.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Irfft</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_irfft</span><span class='op'>(</span>
-  <span class='va'>self</span>,
-  <span class='va'>signal_ndim</span>,
-  normalized <span class='op'>=</span> <span class='cn'>FALSE</span>,
-  onesided <span class='op'>=</span> <span class='cn'>TRUE</span>,
-  signal_sizes <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span><span class='op'>)</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor of at least <code>signal_ndim</code> <code>+ 1</code>        dimensions</p></td>
-    </tr>
-    <tr>
-      <th>signal_ndim</th>
-      <td><p>(int) the number of dimensions in each signal.        <code>signal_ndim</code> can only be 1, 2 or 3</p></td>
-    </tr>
-    <tr>
-      <th>normalized</th>
-      <td><p>(bool, optional) controls whether to return normalized results.        Default: <code>FALSE</code></p></td>
-    </tr>
-    <tr>
-      <th>onesided</th>
-      <td><p>(bool, optional) controls whether <code>input</code> was halfed to avoid        redundancy, e.g., by <code><a href='torch_rfft.html'>torch_rfft()</a></code>. Default: <code>TRUE</code></p></td>
-    </tr>
-    <tr>
-      <th>signal_sizes</th>
-      <td><p>(list or <code>torch.Size</code>, optional) the size of the original        signal (without batch dimension). Default: <code>NULL</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    
-<pre>Due to the conjugate symmetry, `input` do not need to contain the full
-complex frequency values. Roughly half of the values will be sufficient, as
-is the case when `input` is given by [`~torch.rfft`] with
-`rfft(signal, onesided=TRUE)`. In such case, set the `onesided`
-argument of this method to `TRUE`. Moreover, the original signal shape
-information can sometimes be lost, optionally set `signal_sizes` to be
-the size of the original signal (without the batch dimensions if in batched
-mode) to recover it with correct shape.
-
-Therefore, to invert an [torch_rfft()], the `normalized` and
-`onesided` arguments should be set identically for [torch_irfft()],
-and preferably a `signal_sizes` is given to avoid size mismatch. See the
-example below for a case of size mismatch.
-
-See [torch_rfft()] for details on conjugate symmetry.
-</pre>
-
-<p>The inverse of this function is <code><a href='torch_rfft.html'>torch_rfft()</a></code>.</p>
-<pre>For CUDA tensors, an LRU cache is used for cuFFT plans to speed up
-repeatedly running FFT methods on tensors of same geometry with same
-configuration. See cufft-plan-cache for more details on how to
-monitor and control the cache.
-</pre>
-
-    <h2 class="hasAnchor" id="irfft-input-signal-ndim-normalized-false-onesided-true-signal-sizes-null-gt-tensor-"><a class="anchor" href="#irfft-input-signal-ndim-normalized-false-onesided-true-signal-sizes-null-gt-tensor-"></a>irfft(input, signal_ndim, normalized=False, onesided=TRUE, signal_sizes=NULL) -&gt; Tensor </h2>
-
-    
-
-
-<p>Complex-to-real Inverse Discrete Fourier Transform</p>
-<p>This method computes the complex-to-real inverse discrete Fourier transform.
-It is mathematically equivalent with <code><a href='torch_ifft.html'>torch_ifft</a></code> with differences only in
-formats of the input and output.</p>
-<p>The argument specifications are almost identical with <code><a href='torch_ifft.html'>torch_ifft</a></code>.
-Similar to <code><a href='torch_ifft.html'>torch_ifft</a></code>, if <code>normalized</code> is set to <code>TRUE</code>,
-this normalizes the result by multiplying it with
-\(\sqrt{\prod_{i=1}^K N_i}\) so that the operator is unitary, where
-\(N_i\) is the size of signal dimension \(i\).</p>
-    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
-
-    
-
-<p>Generally speaking, input to this function should contain values
-following conjugate symmetry. Note that even if <code>onesided</code> is
-<code>TRUE</code>, often symmetry on some part is still needed. When this
-requirement is not satisfied, the behavior of <code>torch_irfft</code> is
-undefined. Since <code>torch_autograd.gradcheck</code> estimates numerical
-Jacobian with point perturbations, <code>torch_irfft</code> will almost
-certainly fail the check.</p>
-
-<p>For CPU tensors, this method is currently only available with MKL. Use
-<code>torch_backends.mkl.is_available</code> to check if MKL is installed.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span>
-
-<span class='va'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span>, <span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span>
-<span class='fu'><a href='torch_rfft.html'>torch_rfft</a></span><span class='op'>(</span><span class='va'>x</span>, <span class='fl'>2</span>, onesided<span class='op'>=</span><span class='cn'>TRUE</span><span class='op'>)</span>
-<span class='va'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span>, <span class='fl'>5</span><span class='op'>)</span><span class='op'>)</span>
-<span class='fu'><a href='torch_rfft.html'>torch_rfft</a></span><span class='op'>(</span><span class='va'>x</span>, <span class='fl'>2</span>, onesided<span class='op'>=</span><span class='cn'>TRUE</span><span class='op'>)</span>
-<span class='va'>y</span> <span class='op'>=</span> <span class='fu'><a href='torch_rfft.html'>torch_rfft</a></span><span class='op'>(</span><span class='va'>x</span>, <span class='fl'>2</span>, onesided<span class='op'>=</span><span class='cn'>TRUE</span><span class='op'>)</span>
-<span class='fu'>torch_irfft</span><span class='op'>(</span><span class='va'>y</span>, <span class='fl'>2</span>, onesided<span class='op'>=</span><span class='cn'>TRUE</span>, signal_sizes<span class='op'>=</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span>,<span class='fl'>5</span><span class='op'>)</span><span class='op'>)</span>  <span class='co'># recover x</span>
-<span class='op'>}</span>
-</div><div class='output co'>#&gt; torch_tensor
-#&gt; -0.0903  0.3919  0.0848 -0.6948  2.2436
-#&gt; -1.1448 -0.6908 -0.1444  0.0947  0.2402
-#&gt;  0.5605 -0.5919 -0.2863 -0.2805  1.7053
-#&gt; -0.3723 -1.4191 -1.0517 -0.4421  0.7274
-#&gt; [ CPUFloatType{4,5} ]</div></pre>
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diff --git a/reference/torch_is_complex.html b/reference/torch_is_complex.html
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-    <h1>Is_complex</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_is_complex.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Is_complex</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_is_complex.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Is_complex</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_is_complex</span><span class='op'>(</span><span class='va'>self</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the PyTorch tensor to test</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="is-complex-input-gt-bool-"><a class="anchor" href="#is-complex-input-gt-bool-"></a>is_complex(input) -&gt; (bool) </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_is_complex</span><span class="op">(</span><span class="va">self</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the PyTorch tensor to test</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="is-complex-input-gt-bool-">is_complex(input) -&gt; (bool) <a class="anchor" aria-label="anchor" href="#is-complex-input-gt-bool-"></a></h2>
     
 
 
 <p>Returns TRUE if the data type of <code>input</code> is a complex data type i.e.,
 one of <code>torch_complex64</code>, and <code>torch.complex128</code>.</p>
+    </div>
 
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index 3be80a6d9..de5cf3840 100644
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-    <h1>Is_floating_point</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_is_floating_point.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Is_floating_point</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_is_floating_point.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Is_floating_point</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_is_floating_point</span><span class='op'>(</span><span class='va'>self</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the PyTorch tensor to test</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="is-floating-point-input-gt-bool-"><a class="anchor" href="#is-floating-point-input-gt-bool-"></a>is_floating_point(input) -&gt; (bool) </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_is_floating_point</span><span class="op">(</span><span class="va">self</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the PyTorch tensor to test</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="is-floating-point-input-gt-bool-">is_floating_point(input) -&gt; (bool) <a class="anchor" aria-label="anchor" href="#is-floating-point-input-gt-bool-"></a></h2>
     
 
 
 <p>Returns TRUE if the data type of <code>input</code> is a floating point data type i.e.,
 one of <code>torch_float64</code>, <code>torch.float32</code> and <code>torch.float16</code>.</p>
+    </div>
 
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Is_nonzero</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_is_nonzero.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Is_nonzero</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_is_nonzero.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Is_nonzero</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_is_nonzero</span><span class='op'>(</span><span class='va'>self</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="is-nonzero-input-gt-bool-"><a class="anchor" href="#is-nonzero-input-gt-bool-"></a>is_nonzero(input) -&gt; (bool) </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_is_nonzero</span><span class="op">(</span><span class="va">self</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="is-nonzero-input-gt-bool-">is_nonzero(input) -&gt; (bool) <a class="anchor" aria-label="anchor" href="#is-nonzero-input-gt-bool-"></a></h2>
     
 
 
@@ -211,45 +101,40 @@ i.e. not equal to <code>torch_tensor(c(0))</code> or <code>torch_tensor(c(0))</c
 <code>torch_tensor(c(FALSE))</code>.
 Throws a <code>RuntimeError</code> if <code>torch_numel() != 1</code> (even in case
 of sparse tensors).</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='fu'>torch_is_nonzero</span><span class='op'>(</span><span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>0.</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_is_nonzero</span><span class='op'>(</span><span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1.5</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_is_nonzero</span><span class='op'>(</span><span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='cn'>FALSE</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_is_nonzero</span><span class='op'>(</span><span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='cn'>FALSE</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='fu'>torch_is_nonzero</span><span class='op'>(</span><span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>3</span>, <span class='fl'>5</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_is_nonzero</span><span class='op'>(</span><span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="fu">torch_is_nonzero</span><span class="op">(</span><span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_is_nonzero</span><span class="op">(</span><span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1.5</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_is_nonzero</span><span class="op">(</span><span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_is_nonzero</span><span class="op">(</span><span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="fu">torch_is_nonzero</span><span class="op">(</span><span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">3</span>, <span class="fl">5</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_is_nonzero</span><span class="op">(</span><span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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+<div class="pkgdown-footer-right">
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diff --git a/reference/torch_isclose.html b/reference/torch_isclose.html
index a78d75d53..b3c84f251 100644
--- a/reference/torch_isclose.html
+++ b/reference/torch_isclose.html
@@ -1,224 +1,106 @@
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Isclose</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_isclose.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Isclose</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_isclose.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Isclose</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_isclose</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>other</span>, rtol <span class='op'>=</span> <span class='fl'>1e-05</span>, atol <span class='op'>=</span> <span class='fl'>0</span>, equal_nan <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) first tensor to compare</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Tensor) second tensor to compare</p></td>
-    </tr>
-    <tr>
-      <th>rtol</th>
-      <td><p>(float, optional) relative tolerance. Default: 1e-05</p></td>
-    </tr>
-    <tr>
-      <th>atol</th>
-      <td><p>(float, optional) absolute tolerance. Default: 1e-08</p></td>
-    </tr>
-    <tr>
-      <th>equal_nan</th>
-      <td><p>(bool, optional) if <code>TRUE</code>, then two <code>NaN</code> s will be
-considered equal. Default: <code>FALSE</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="isclose-input-other-rtol-e-atol-e-equal-nan-false-gt-tensor-"><a class="anchor" href="#isclose-input-other-rtol-e-atol-e-equal-nan-false-gt-tensor-"></a>isclose(input, other, rtol=1e-05, atol=1e-08, equal_nan=FALSE) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_isclose</span><span class="op">(</span><span class="va">self</span>, <span class="va">other</span>, rtol <span class="op">=</span> <span class="fl">1e-05</span>, atol <span class="op">=</span> <span class="fl">1e-08</span>, equal_nan <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) first tensor to compare</p></dd>
+<dt>other</dt>
+<dd><p>(Tensor) second tensor to compare</p></dd>
+<dt>rtol</dt>
+<dd><p>(float, optional) relative tolerance. Default: 1e-05</p></dd>
+<dt>atol</dt>
+<dd><p>(float, optional) absolute tolerance. Default: 1e-08</p></dd>
+<dt>equal_nan</dt>
+<dd><p>(bool, optional) if <code>TRUE</code>, then two <code>NaN</code> s will be
+considered equal. Default: <code>FALSE</code></p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="isclose-input-other-rtol-e-atol-e-equal-nan-false-gt-tensor-">isclose(input, other, rtol=1e-05, atol=1e-08, equal_nan=FALSE) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#isclose-input-other-rtol-e-atol-e-equal-nan-false-gt-tensor-"></a></h2>
     
 
 
@@ -232,43 +114,38 @@ $$</p>
 and/or <code>other</code> are nonfinite they are close if and only if
 they are equal, with NaNs being considered equal to each other when
 <code>equal_nan</code> is TRUE.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='fu'>torch_isclose</span><span class='op'>(</span><span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1.</span>, <span class='fl'>2</span>, <span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span>, <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span> <span class='op'>+</span> <span class='fl'>1e-10</span>, <span class='fl'>3</span>, <span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_isclose</span><span class='op'>(</span><span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='cn'>Inf</span>, <span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span>, <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='cn'>Inf</span>, <span class='fl'>6</span><span class='op'>)</span><span class='op'>)</span>, rtol<span class='op'>=</span><span class='fl'>.5</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUBoolType{2} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="fu">torch_isclose</span><span class="op">(</span><span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1.</span>, <span class="fl">2</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span>, <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span> <span class="op">+</span> <span class="fl">1e-10</span>, <span class="fl">3</span>, <span class="fl">4</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_isclose</span><span class="op">(</span><span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="cn">Inf</span>, <span class="fl">4</span><span class="op">)</span><span class="op">)</span>, <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="cn">Inf</span>, <span class="fl">6</span><span class="op">)</span><span class="op">)</span>, rtol<span class="op">=</span><span class="fl">.5</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUBoolType{2} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
 </div>
 
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+<div class="pkgdown-footer-right">
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diff --git a/reference/torch_isfinite.html b/reference/torch_isfinite.html
index a6fa903ef..ea09c896d 100644
--- a/reference/torch_isfinite.html
+++ b/reference/torch_isfinite.html
@@ -1,250 +1,135 @@
-<!-- Generated by pkgdown: do not edit by hand -->
 <!DOCTYPE html>
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Isfinite</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_isfinite.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Isfinite</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_isfinite.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Isfinite</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_isfinite</span><span class='op'>(</span><span class='va'>self</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) A tensor to check</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="test-"><a class="anchor" href="#test-"></a>TEST </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_isfinite</span><span class="op">(</span><span class="va">self</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) A tensor to check</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="test-">TEST <a class="anchor" aria-label="anchor" href="#test-"></a></h2>
     
 
 
 <p>Returns a new tensor with boolean elements representing if each element is <code>Finite</code> or not.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='fu'>torch_isfinite</span><span class='op'>(</span><span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='cn'>Inf</span>, <span class='fl'>2</span>, <span class='op'>-</span><span class='cn'>Inf</span>, <span class='cn'>NaN</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUBoolType{5} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="fu">torch_isfinite</span><span class="op">(</span><span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="cn">Inf</span>, <span class="fl">2</span>, <span class="op">-</span><span class="cn">Inf</span>, <span class="cn">NaN</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUBoolType{5} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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index 262bf0faf..b395b48ba 100644
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Isinf</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_isinf.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Isinf</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_isinf.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Isinf</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_isinf</span><span class='op'>(</span><span class='va'>self</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) A tensor to check</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="test-"><a class="anchor" href="#test-"></a>TEST </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_isinf</span><span class="op">(</span><span class="va">self</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) A tensor to check</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="test-">TEST <a class="anchor" aria-label="anchor" href="#test-"></a></h2>
     
 
 
 <p>Returns a new tensor with boolean elements representing if each element is <code>+/-INF</code> or not.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='fu'>torch_isinf</span><span class='op'>(</span><span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='cn'>Inf</span>, <span class='fl'>2</span>, <span class='op'>-</span><span class='cn'>Inf</span>, <span class='cn'>NaN</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUBoolType{5} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="fu">torch_isinf</span><span class="op">(</span><span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="cn">Inf</span>, <span class="fl">2</span>, <span class="op">-</span><span class="cn">Inf</span>, <span class="cn">NaN</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUBoolType{5} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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diff --git a/reference/torch_isnan.html b/reference/torch_isnan.html
index 576edf78f..f67077bd7 100644
--- a/reference/torch_isnan.html
+++ b/reference/torch_isnan.html
@@ -1,248 +1,133 @@
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Isnan</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_isnan.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Isnan</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_isnan.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Isnan</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_isnan</span><span class='op'>(</span><span class='va'>self</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) A tensor to check</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="test-"><a class="anchor" href="#test-"></a>TEST </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_isnan</span><span class="op">(</span><span class="va">self</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) A tensor to check</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="test-">TEST <a class="anchor" aria-label="anchor" href="#test-"></a></h2>
     
 
 
 <p>Returns a new tensor with boolean elements representing if each element is <code>NaN</code> or not.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='fu'>torch_isnan</span><span class='op'>(</span><span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='cn'>NaN</span>, <span class='fl'>2</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUBoolType{3} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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-      <h2 data-toc-skip>Contents</h2>
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+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="fu">torch_isnan</span><span class="op">(</span><span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="cn">NaN</span>, <span class="fl">2</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUBoolType{3} ]</span>
+</code></pre></div>
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diff --git a/reference/torch_isneginf.html b/reference/torch_isneginf.html
index 57193d920..cd9d9c52a 100644
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Isneginf</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_isneginf.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Isneginf</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_isneginf.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Isneginf</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_isneginf</span><span class='op'>(</span><span class='va'>self</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="isneginf-input-out-none-gt-tensor-"><a class="anchor" href="#isneginf-input-out-none-gt-tensor-"></a>isneginf(input, *, out=None) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_isneginf</span><span class="op">(</span><span class="va">self</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="isneginf-input-out-none-gt-tensor-">isneginf(input, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#isneginf-input-out-none-gt-tensor-"></a></h2>
     
 
 
 <p>Tests if each element of <code>input</code> is negative infinity or not.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='op'>-</span><span class='cn'>Inf</span>, <span class='cn'>Inf</span>, <span class='fl'>1.2</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_isneginf</span><span class='op'>(</span><span class='va'>a</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUBoolType{3} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="op">-</span><span class="cn">Inf</span>, <span class="cn">Inf</span>, <span class="fl">1.2</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_isneginf</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUBoolType{3} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Isposinf</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_isposinf.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Isposinf</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_isposinf.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Isposinf</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_isposinf</span><span class='op'>(</span><span class='va'>self</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="isposinf-input-out-none-gt-tensor-"><a class="anchor" href="#isposinf-input-out-none-gt-tensor-"></a>isposinf(input, *, out=None) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_isposinf</span><span class="op">(</span><span class="va">self</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="isposinf-input-out-none-gt-tensor-">isposinf(input, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#isposinf-input-out-none-gt-tensor-"></a></h2>
     
 
 
 <p>Tests if each element of <code>input</code> is positive infinity or not.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='op'>-</span><span class='cn'>Inf</span>, <span class='cn'>Inf</span>, <span class='fl'>1.2</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_isposinf</span><span class='op'>(</span><span class='va'>a</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUBoolType{3} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="op">-</span><span class="cn">Inf</span>, <span class="cn">Inf</span>, <span class="fl">1.2</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_isposinf</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUBoolType{3} ]</span>
+</code></pre></div>
+    </div>
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index 21a36fa76..883c4ba01 100644
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Isreal</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_isreal.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Isreal</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_isreal.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Isreal</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_isreal</span><span class='op'>(</span><span class='va'>self</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="isreal-input-gt-tensor-"><a class="anchor" href="#isreal-input-gt-tensor-"></a>isreal(input) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_isreal</span><span class="op">(</span><span class="va">self</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="isreal-input-gt-tensor-">isreal(input) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#isreal-input-gt-tensor-"></a></h2>
     
 
 
 <p>Returns a new tensor with boolean elements representing if each element of <code>input</code> is real-valued or not.
 All real-valued types are considered real. Complex values are considered real when their imaginary part is 0.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='cn'>FALSE</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='fu'>torch_isreal</span><span class='op'>(</span><span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>1</span><span class='op'>+</span><span class='fl'>1i</span>, <span class='fl'>2</span><span class='op'>+</span><span class='fl'>0i</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
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-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="fu">torch_isreal</span><span class="op">(</span><span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">1</span><span class="op">+</span><span class="fl">1i</span>, <span class="fl">2</span><span class="op">+</span><span class="fl">0i</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
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index ecacb38b1..8fce0d07f 100644
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Istft</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_istft.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Istft</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_istft.Rd</code></div>
     </div>
 
-    <div class="ref-description">
-    <p>Inverse short time Fourier Transform. This is expected to be the inverse of <code><a href='torch_stft.html'>torch_stft()</a></code>.</p>
+    <div class="ref-description section level2">
+    <p>Inverse short time Fourier Transform. This is expected to be the inverse of <code><a href="torch_stft.html">torch_stft()</a></code>.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_istft</span><span class='op'>(</span>
-  <span class='va'>self</span>,
-  <span class='va'>n_fft</span>,
-  hop_length <span class='op'>=</span> <span class='cn'>NULL</span>,
-  win_length <span class='op'>=</span> <span class='cn'>NULL</span>,
-  window <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span><span class='op'>)</span>,
-  center <span class='op'>=</span> <span class='cn'>TRUE</span>,
-  normalized <span class='op'>=</span> <span class='cn'>FALSE</span>,
-  onesided <span class='op'>=</span> <span class='cn'>NULL</span>,
-  length <span class='op'>=</span> <span class='cn'>NULL</span>,
-  return_complex <span class='op'>=</span> <span class='cn'>FALSE</span>
-<span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_istft</span><span class="op">(</span>
+  <span class="va">self</span>,
+  <span class="va">n_fft</span>,
+  hop_length <span class="op">=</span> <span class="cn">NULL</span>,
+  win_length <span class="op">=</span> <span class="cn">NULL</span>,
+  window <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="op">)</span>,
+  center <span class="op">=</span> <span class="cn">TRUE</span>,
+  normalized <span class="op">=</span> <span class="cn">FALSE</span>,
+  onesided <span class="op">=</span> <span class="cn">NULL</span>,
+  length <span class="op">=</span> <span class="cn">NULL</span>,
+  return_complex <span class="op">=</span> <span class="cn">FALSE</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) The input tensor. Expected to be output of <code><a href='torch_stft.html'>torch_stft()</a></code>,
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) The input tensor. Expected to be output of <code><a href="torch_stft.html">torch_stft()</a></code>,
 can either be complex (<code>channel</code>, <code>fft_size</code>, <code>n_frame</code>), or real
 (<code>channel</code>, <code>fft_size</code>, <code>n_frame</code>, 2) where the <code>channel</code> dimension is
-optional.</p></td>
-    </tr>
-    <tr>
-      <th>n_fft</th>
-      <td><p>(int) Size of Fourier transform</p></td>
-    </tr>
-    <tr>
-      <th>hop_length</th>
-      <td><p>(Optional<code>[int]</code>) The distance between neighboring sliding window frames.
-(Default: <code>n_fft %% 4</code>)</p></td>
-    </tr>
-    <tr>
-      <th>win_length</th>
-      <td><p>(Optional<code>[int]</code>) The size of window frame and STFT filter.
-(Default: <code>n_fft</code>)</p></td>
-    </tr>
-    <tr>
-      <th>window</th>
-      <td><p>(Optional(torch.Tensor)) The optional window function.
-(Default: <code>torch_ones(win_length)</code>)</p></td>
-    </tr>
-    <tr>
-      <th>center</th>
-      <td><p>(bool) Whether <code>input</code> was padded on both sides so that the
+optional.</p></dd>
+<dt>n_fft</dt>
+<dd><p>(int) Size of Fourier transform</p></dd>
+<dt>hop_length</dt>
+<dd><p>(Optional<code>[int]</code>) The distance between neighboring sliding window frames.
+(Default: <code>n_fft %% 4</code>)</p></dd>
+<dt>win_length</dt>
+<dd><p>(Optional<code>[int]</code>) The size of window frame and STFT filter.
+(Default: <code>n_fft</code>)</p></dd>
+<dt>window</dt>
+<dd><p>(Optional(torch.Tensor)) The optional window function.
+(Default: <code>torch_ones(win_length)</code>)</p></dd>
+<dt>center</dt>
+<dd><p>(bool) Whether <code>input</code> was padded on both sides so that the
 \(t\)-th frame is centered at time \(t \times \mbox{hop\_length}\).
-(Default: <code>TRUE</code>)</p></td>
-    </tr>
-    <tr>
-      <th>normalized</th>
-      <td><p>(bool) Whether the STFT was normalized. (Default: <code>FALSE</code>)</p></td>
-    </tr>
-    <tr>
-      <th>onesided</th>
-      <td><p>(Optional(bool)) Whether the STFT was onesided.
-(Default: <code>TRUE</code> if <code>n_fft != fft_size</code> in the input size)</p></td>
-    </tr>
-    <tr>
-      <th>length</th>
-      <td><p>(Optional(int)]) The amount to trim the signal by (i.e. the
-original signal length). (Default: whole signal)</p></td>
-    </tr>
-    <tr>
-      <th>return_complex</th>
-      <td><p>(Optional(bool)) Whether the output should be complex,
+(Default: <code>TRUE</code>)</p></dd>
+<dt>normalized</dt>
+<dd><p>(bool) Whether the STFT was normalized. (Default: <code>FALSE</code>)</p></dd>
+<dt>onesided</dt>
+<dd><p>(Optional(bool)) Whether the STFT was onesided.
+(Default: <code>TRUE</code> if <code>n_fft != fft_size</code> in the input size)</p></dd>
+<dt>length</dt>
+<dd><p>(Optional(int)]) The amount to trim the signal by (i.e. the
+original signal length). (Default: whole signal)</p></dd>
+<dt>return_complex</dt>
+<dd><p>(Optional(bool)) Whether the output should be complex,
 or if the input should be assumed to derive from a real signal and window.
-Note that this is incompatible with <code>onesided=TRUE</code>. (Default: <code>FALSE</code>)</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
-
+Note that this is incompatible with <code>onesided=TRUE</code>. (Default: <code>FALSE</code>)</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>It has the same parameters (+ additional optional parameter of <code>length</code>) and it should return the
 least squares estimation of the original signal. The algorithm will check using the NOLA
 condition (nonzero overlap).</p>
 <p>Important consideration in the parameters <code>window</code> and <code>center</code> so that the envelop
 created by the summation of all the windows is never zero at certain point in time. Specifically,
 \(\sum_{t=-\infty}^{\infty} |w|^2(n-t\times hop_length) \neq 0\).</p>
-<p>Since <code><a href='torch_stft.html'>torch_stft()</a></code> discards elements at the end of the signal if they do not fit in a frame,
+<p>Since <code><a href="torch_stft.html">torch_stft()</a></code> discards elements at the end of the signal if they do not fit in a frame,
 <code>istft</code> may return a shorter signal than the original signal (can occur if <code>center</code> is FALSE
 since the signal isn't padded).</p>
 <p>If <code>center</code> is <code>TRUE</code>, then there will be padding e.g. <code>'constant'</code>, <code>'reflect'</code>, etc.
@@ -281,32 +153,25 @@ of right padding. These additional values could be zeros or a reflection of the
 (some loss of signal).</p>
 <p>D. W. Griffin and J. S. Lim, "Signal estimation from modified short-time Fourier transform,"
 IEEE Trans. ASSP, vol.32, no.2, pp.236-243, Apr. 1984.</p>
+    </div>
 
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+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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diff --git a/reference/torch_kaiser_window.html b/reference/torch_kaiser_window.html
index bb727125d..609873047 100644
--- a/reference/torch_kaiser_window.html
+++ b/reference/torch_kaiser_window.html
@@ -1,248 +1,127 @@
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Kaiser_window</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/wrapers.R'><code>R/wrapers.R</code></a></small>
-    <div class="hidden name"><code>torch_kaiser_window.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Kaiser_window</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
+      <div class="d-none name"><code>torch_kaiser_window.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Kaiser_window</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_kaiser_window</span><span class='op'>(</span>
-  <span class='va'>window_length</span>,
-  <span class='va'>periodic</span>,
-  <span class='va'>beta</span>,
-  dtype <span class='op'>=</span> <span class='fu'><a href='torch_dtype.html'>torch_float</a></span><span class='op'>(</span><span class='op'>)</span>,
-  layout <span class='op'>=</span> <span class='cn'>NULL</span>,
-  device <span class='op'>=</span> <span class='cn'>NULL</span>,
-  requires_grad <span class='op'>=</span> <span class='cn'>NULL</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>window_length</th>
-      <td><p>(int) length of the window.</p></td>
-    </tr>
-    <tr>
-      <th>periodic</th>
-      <td><p>(bool, optional) If TRUE, returns a periodic window suitable for use in spectral analysis.        If FALSE, returns a symmetric window suitable for use in filter design.</p></td>
-    </tr>
-    <tr>
-      <th>beta</th>
-      <td><p>(float, optional) shape parameter for the window.</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>). If <code>dtype</code> is not given, infer the data type from the other input        arguments. If any of <code>start</code>, <code>end</code>, or <code>stop</code> are floating-point, the        <code>dtype</code> is inferred to be the default dtype, see        <code>~torch.get_default_dtype</code>. Otherwise, the <code>dtype</code> is inferred to        be <code>torch.int64</code>.</p></td>
-    </tr>
-    <tr>
-      <th>layout</th>
-      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
-    </tr>
-    <tr>
-      <th>requires_grad</th>
-      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_kaiser_window</span><span class="op">(</span>
+  <span class="va">window_length</span>,
+  <span class="va">periodic</span>,
+  <span class="va">beta</span>,
+  dtype <span class="op">=</span> <span class="fu"><a href="torch_dtype.html">torch_float</a></span><span class="op">(</span><span class="op">)</span>,
+  layout <span class="op">=</span> <span class="cn">NULL</span>,
+  device <span class="op">=</span> <span class="cn">NULL</span>,
+  requires_grad <span class="op">=</span> <span class="cn">NULL</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>window_length</dt>
+<dd><p>(int) length of the window.</p></dd>
+<dt>periodic</dt>
+<dd><p>(bool, optional) If TRUE, returns a periodic window suitable for use in spectral analysis.        If FALSE, returns a symmetric window suitable for use in filter design.</p></dd>
+<dt>beta</dt>
+<dd><p>(float, optional) shape parameter for the window.</p></dd>
+<dt>dtype</dt>
+<dd><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>). If <code>dtype</code> is not given, infer the data type from the other input        arguments. If any of <code>start</code>, <code>end</code>, or <code>stop</code> are floating-point, the        <code>dtype</code> is inferred to be the default dtype, see        <code>~torch.get_default_dtype</code>. Otherwise, the <code>dtype</code> is inferred to        be <code>torch.int64</code>.</p></dd>
+<dt>layout</dt>
+<dd><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></dd>
+<dt>device</dt>
+<dd><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></dd>
+<dt>requires_grad</dt>
+<dd><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p>If <code>window_length</code> is one, then the returned window is a single element
 tensor containing a one.</p>
-    <h2 class="hasAnchor" id="kaiser-window-window-length-periodic-true-beta-dtype-none-layout-torch-strided-device-none-requires-grad-false-gt-tensor-"><a class="anchor" href="#kaiser-window-window-length-periodic-true-beta-dtype-none-layout-torch-strided-device-none-requires-grad-false-gt-tensor-"></a>kaiser_window(window_length, periodic=TRUE, beta=12.0, *, dtype=None, layout=torch.strided, device=None, requires_grad=FALSE) -&gt; Tensor </h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="kaiser-window-window-length-periodic-true-beta-dtype-none-layout-torch-strided-device-none-requires-grad-false-gt-tensor-">kaiser_window(window_length, periodic=TRUE, beta=12.0, *, dtype=None, layout=torch.strided, device=None, requires_grad=FALSE) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#kaiser-window-window-length-periodic-true-beta-dtype-none-layout-torch-strided-device-none-requires-grad-false-gt-tensor-"></a></h2>
     
 
 
 <p>Computes the Kaiser window with window length <code>window_length</code> and shape parameter <code>beta</code>.</p>
-<p>Let I_0 be the zeroth order modified Bessel function of the first kind (see <code><a href='torch_i0.html'>torch_i0()</a></code>) and
+<p>Let I_0 be the zeroth order modified Bessel function of the first kind (see <code><a href="torch_i0.html">torch_i0()</a></code>) and
 <code>N = L - 1</code> if <code>periodic</code> is FALSE and <code>L</code> if <code>periodic</code> is TRUE,
 where <code>L</code> is the <code>window_length</code>. This function computes:</p>
 <p>$$
@@ -251,33 +130,26 @@ $$</p>
 <p>Calling <code>torch_kaiser_window(L, B, periodic=TRUE)</code> is equivalent to calling
 <code>torch_kaiser_window(L + 1, B, periodic=FALSE)[:-1])</code>.
 The <code>periodic</code> argument is intended as a helpful shorthand
-to produce a periodic window as input to functions like <code><a href='torch_stft.html'>torch_stft()</a></code>.</p>
+to produce a periodic window as input to functions like <code><a href="torch_stft.html">torch_stft()</a></code>.</p>
+    </div>
 
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index d26d6b1ff..a992249a3 100644
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Kthvalue</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_kthvalue.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Kthvalue</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_kthvalue.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Kthvalue</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_kthvalue</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>k</span>, dim <span class='op'>=</span> <span class='op'>-</span><span class='fl'>1L</span>, keepdim <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>k</th>
-      <td><p>(int) k for the k-th smallest element</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int, optional) the dimension to find the kth value along</p></td>
-    </tr>
-    <tr>
-      <th>keepdim</th>
-      <td><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="kthvalue-input-k-dim-null-keepdim-false-out-null-gt-tensor-longtensor-"><a class="anchor" href="#kthvalue-input-k-dim-null-keepdim-false-out-null-gt-tensor-longtensor-"></a>kthvalue(input, k, dim=NULL, keepdim=False, out=NULL) -&gt; (Tensor, LongTensor) </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_kthvalue</span><span class="op">(</span><span class="va">self</span>, <span class="va">k</span>, dim <span class="op">=</span> <span class="op">-</span><span class="fl">1L</span>, keepdim <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+<dt>k</dt>
+<dd><p>(int) k for the k-th smallest element</p></dd>
+<dt>dim</dt>
+<dd><p>(int, optional) the dimension to find the kth value along</p></dd>
+<dt>keepdim</dt>
+<dd><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="kthvalue-input-k-dim-null-keepdim-false-out-null-gt-tensor-longtensor-">kthvalue(input, k, dim=NULL, keepdim=False, out=NULL) -&gt; (Tensor, LongTensor) <a class="anchor" aria-label="anchor" href="#kthvalue-input-k-dim-null-keepdim-false-out-null-gt-tensor-longtensor-"></a></h2>
     
 
 
@@ -224,55 +108,50 @@ smallest element of each row of the <code>input</code> tensor in the given dimen
 <p>If <code>keepdim</code> is <code>TRUE</code>, both the <code>values</code> and <code>indices</code> tensors
 are the same size as <code>input</code>, except in the dimension <code>dim</code> where
 they are of size 1. Otherwise, <code>dim</code> is squeezed
-(see <code><a href='torch_squeeze.html'>torch_squeeze</a></code>), resulting in both the <code>values</code> and
+(see <code><a href="torch_squeeze.html">torch_squeeze</a></code>), resulting in both the <code>values</code> and
 <code>indices</code> tensors having 1 fewer dimension than the <code>input</code> tensor.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>x</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>6</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>x</span></span>
-<span class='r-in'><span class='fu'>torch_kthvalue</span><span class='op'>(</span><span class='va'>x</span>, <span class='fl'>4</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>x</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span><span class='op'>(</span><span class='fl'>1</span>,<span class='fl'>6</span><span class='op'>)</span><span class='op'>$</span><span class='fu'>resize_</span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>2</span>,<span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>x</span></span>
-<span class='r-in'><span class='fu'>torch_kthvalue</span><span class='op'>(</span><span class='va'>x</span>, <span class='fl'>2</span>, <span class='fl'>1</span>, <span class='cn'>TRUE</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [[1]]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  4  5  6</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{1,3} ]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [[2]]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1  1  1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPULongType{1,3} ]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
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-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_arange.html">torch_arange</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">6</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">x</span></span>
+<span class="r-in"><span class="fu">torch_kthvalue</span><span class="op">(</span><span class="va">x</span>, <span class="fl">4</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_arange.html">torch_arange</a></span><span class="op">(</span><span class="fl">1</span>,<span class="fl">6</span><span class="op">)</span><span class="op">$</span><span class="fu">resize_</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>,<span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">x</span></span>
+<span class="r-in"><span class="fu">torch_kthvalue</span><span class="op">(</span><span class="va">x</span>, <span class="fl">2</span>, <span class="fl">1</span>, <span class="cn">TRUE</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [[1]]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  4  5  6</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{1,3} ]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [[2]]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1  1  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPULongType{1,3} ]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Creates the corresponding layout</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/layout.R'><code>R/layout.R</code></a></small>
-    <div class="hidden name"><code>torch_layout.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Creates the corresponding layout</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/layout.R" class="external-link"><code>R/layout.R</code></a></small>
+      <div class="d-none name"><code>torch_layout.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Creates the corresponding layout</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_strided</span><span class='op'>(</span><span class='op'>)</span>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_strided</span><span class="op">(</span><span class="op">)</span>
 
-<span class='fu'>torch_sparse_coo</span><span class='op'>(</span><span class='op'>)</span></pre>
+<span class="fu">torch_sparse_coo</span><span class="op">(</span><span class="op">)</span></code></pre></div>
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diff --git a/reference/torch_lcm.html b/reference/torch_lcm.html
index 298b5bb24..0de313363 100644
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@@ -1,257 +1,141 @@
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Lcm</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_lcm.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Lcm</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_lcm.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Lcm</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_lcm</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>other</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Tensor) the second input tensor</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_lcm</span><span class="op">(</span><span class="va">self</span>, <span class="va">other</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+<dt>other</dt>
+<dd><p>(Tensor) the second input tensor</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p>This defines \(lcm(0, 0) = 0\) and \(lcm(0, a) = 0\).</p>
-    <h2 class="hasAnchor" id="lcm-input-other-out-none-gt-tensor-"><a class="anchor" href="#lcm-input-other-out-none-gt-tensor-"></a>lcm(input, other, *, out=None) -&gt; Tensor </h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="lcm-input-other-out-none-gt-tensor-">lcm(input, other, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#lcm-input-other-out-none-gt-tensor-"></a></h2>
     
 
 
 <p>Computes the element-wise least common multiple (LCM) of <code>input</code> and <code>other</code>.</p>
 <p>Both <code>input</code> and <code>other</code> must have integer types.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'>torch</span><span class='fu'>::</span><span class='fu'><a href='cuda_is_available.html'>cuda_is_available</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>5</span>, <span class='fl'>10</span>, <span class='fl'>15</span><span class='op'>)</span>, dtype <span class='op'>=</span> <span class='fu'><a href='torch_dtype.html'>torch_long</a></span><span class='op'>(</span><span class='op'>)</span>, device <span class='op'>=</span> <span class='st'>"cuda"</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>b</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>4</span>, <span class='fl'>5</span><span class='op'>)</span>, dtype <span class='op'>=</span> <span class='fu'><a href='torch_dtype.html'>torch_long</a></span><span class='op'>(</span><span class='op'>)</span>, device <span class='op'>=</span> <span class='st'>"cuda"</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_lcm</span><span class='op'>(</span><span class='va'>a</span>, <span class='va'>b</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>c</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3L</span><span class='op'>)</span>, device <span class='op'>=</span> <span class='st'>"cuda"</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_lcm</span><span class='op'>(</span><span class='va'>a</span>, <span class='va'>c</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu">torch</span><span class="fu">::</span><span class="fu"><a href="cuda_is_available.html">cuda_is_available</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">5</span>, <span class="fl">10</span>, <span class="fl">15</span><span class="op">)</span>, dtype <span class="op">=</span> <span class="fu"><a href="torch_dtype.html">torch_long</a></span><span class="op">(</span><span class="op">)</span>, device <span class="op">=</span> <span class="st">"cuda"</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">b</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">4</span>, <span class="fl">5</span><span class="op">)</span>, dtype <span class="op">=</span> <span class="fu"><a href="torch_dtype.html">torch_long</a></span><span class="op">(</span><span class="op">)</span>, device <span class="op">=</span> <span class="st">"cuda"</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_lcm</span><span class="op">(</span><span class="va">a</span>, <span class="va">b</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">c</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3L</span><span class="op">)</span>, device <span class="op">=</span> <span class="st">"cuda"</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_lcm</span><span class="op">(</span><span class="va">a</span>, <span class="va">c</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Le</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_le.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Le</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_le.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Le</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_le</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>other</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the tensor to compare</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Tensor or float) the tensor or value to compare</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="le-input-other-out-null-gt-tensor-"><a class="anchor" href="#le-input-other-out-null-gt-tensor-"></a>le(input, other, out=NULL) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_le</span><span class="op">(</span><span class="va">self</span>, <span class="va">other</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the tensor to compare</p></dd>
+<dt>other</dt>
+<dd><p>(Tensor or float) the tensor or value to compare</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="le-input-other-out-null-gt-tensor-">le(input, other, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#le-input-other-out-null-gt-tensor-"></a></h2>
     
 
 
 <p>Computes \(\mbox{input} \leq \mbox{other}\) element-wise.</p>
 <p>The second argument can be a number or a tensor whose shape is
 broadcastable  with the first argument.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='fu'>torch_le</span><span class='op'>(</span><span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span><span class='op'>(</span><span class='fl'>1</span><span class='op'>:</span><span class='fl'>4</span>, ncol <span class='op'>=</span> <span class='fl'>2</span>, byrow<span class='op'>=</span><span class='cn'>TRUE</span><span class='op'>)</span><span class='op'>)</span>, </span>
-<span class='r-in'>         <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>,<span class='fl'>1</span>,<span class='fl'>4</span>,<span class='fl'>4</span><span class='op'>)</span>, ncol <span class='op'>=</span> <span class='fl'>2</span>, byrow<span class='op'>=</span><span class='cn'>TRUE</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1  1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUBoolType{2,2} ]</span>
-</pre>
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+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="fu">torch_le</span><span class="op">(</span><span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/matrix.html" class="external-link">matrix</a></span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="fl">4</span>, ncol <span class="op">=</span> <span class="fl">2</span>, byrow<span class="op">=</span><span class="cn">TRUE</span><span class="op">)</span><span class="op">)</span>, </span>
+<span class="r-in">         <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/matrix.html" class="external-link">matrix</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>,<span class="fl">1</span>,<span class="fl">4</span>,<span class="fl">4</span><span class="op">)</span>, ncol <span class="op">=</span> <span class="fl">2</span>, byrow<span class="op">=</span><span class="cn">TRUE</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUBoolType{2,2} ]</span>
+</code></pre></div>
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+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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+    
+  </div>
+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Lerp</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_lerp.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Lerp</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_lerp.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Lerp</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_lerp</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>end</span>, <span class='va'>weight</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the tensor with the starting points</p></td>
-    </tr>
-    <tr>
-      <th>end</th>
-      <td><p>(Tensor) the tensor with the ending points</p></td>
-    </tr>
-    <tr>
-      <th>weight</th>
-      <td><p>(float or tensor) the weight for the interpolation formula</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="lerp-input-end-weight-out-null-"><a class="anchor" href="#lerp-input-end-weight-out-null-"></a>lerp(input, end, weight, out=NULL) </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_lerp</span><span class="op">(</span><span class="va">self</span>, <span class="va">end</span>, <span class="va">weight</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the tensor with the starting points</p></dd>
+<dt>end</dt>
+<dd><p>(Tensor) the tensor with the ending points</p></dd>
+<dt>weight</dt>
+<dd><p>(float or tensor) the weight for the interpolation formula</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="lerp-input-end-weight-out-null-">lerp(input, end, weight, out=NULL) <a class="anchor" aria-label="anchor" href="#lerp-input-end-weight-out-null-"></a></h2>
     
 
 
@@ -221,49 +107,44 @@ $$
 The shapes of <code>start</code> and <code>end</code> must be
 broadcastable . If <code>weight</code> is a tensor, then
 the shapes of <code>weight</code>, <code>start</code>, and <code>end</code> must be broadcastable .</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>start</span> <span class='op'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>4</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>end</span> <span class='op'>=</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span><span class='op'>(</span><span class='fl'>4</span><span class='op'>)</span><span class='op'>$</span><span class='fu'>fill_</span><span class='op'>(</span><span class='fl'>10</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>start</span></span>
-<span class='r-in'><span class='va'>end</span></span>
-<span class='r-in'><span class='fu'>torch_lerp</span><span class='op'>(</span><span class='va'>start</span>, <span class='va'>end</span>, <span class='fl'>0.5</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_lerp</span><span class='op'>(</span><span class='va'>start</span>, <span class='va'>end</span>, <span class='fu'><a href='torch_full_like.html'>torch_full_like</a></span><span class='op'>(</span><span class='va'>start</span>, <span class='fl'>0.5</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  5.5000</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  6.0000</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  6.5000</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  7.0000</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{4} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">start</span> <span class="op">=</span> <span class="fu"><a href="torch_arange.html">torch_arange</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">4</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">end</span> <span class="op">=</span> <span class="fu"><a href="torch_empty.html">torch_empty</a></span><span class="op">(</span><span class="fl">4</span><span class="op">)</span><span class="op">$</span><span class="fu">fill_</span><span class="op">(</span><span class="fl">10</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">start</span></span>
+<span class="r-in"><span class="va">end</span></span>
+<span class="r-in"><span class="fu">torch_lerp</span><span class="op">(</span><span class="va">start</span>, <span class="va">end</span>, <span class="fl">0.5</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_lerp</span><span class="op">(</span><span class="va">start</span>, <span class="va">end</span>, <span class="fu"><a href="torch_full_like.html">torch_full_like</a></span><span class="op">(</span><span class="va">start</span>, <span class="fl">0.5</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  5.5000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  6.0000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  6.5000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  7.0000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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     </div>
 
-    <pre class="usage"><span class='fu'>torch_less</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>other</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the tensor to compare</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Tensor or float) the tensor or value to compare</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="less-input-other-out-none-gt-tensor-"><a class="anchor" href="#less-input-other-out-none-gt-tensor-"></a>less(input, other, *, out=None) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_less</span><span class="op">(</span><span class="va">self</span>, <span class="va">other</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the tensor to compare</p></dd>
+<dt>other</dt>
+<dd><p>(Tensor or float) the tensor or value to compare</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="less-input-other-out-none-gt-tensor-">less(input, other, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#less-input-other-out-none-gt-tensor-"></a></h2>
     
 
 
-<p>Alias for <code><a href='torch_lt.html'>torch_lt()</a></code>.</p>
+<p>Alias for <code><a href="torch_lt.html">torch_lt()</a></code>.</p>
+    </div>
 
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diff --git a/reference/torch_less_equal.html b/reference/torch_less_equal.html
index 207dfe739..ba1061781 100644
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Less_equal</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_less_equal.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Less_equal</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_less_equal.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Less_equal</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_less_equal</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>other</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the tensor to compare</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Tensor or float) the tensor or value to compare</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="less-equal-input-other-out-none-gt-tensor-"><a class="anchor" href="#less-equal-input-other-out-none-gt-tensor-"></a>less_equal(input, other, *, out=None) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_less_equal</span><span class="op">(</span><span class="va">self</span>, <span class="va">other</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the tensor to compare</p></dd>
+<dt>other</dt>
+<dd><p>(Tensor or float) the tensor or value to compare</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="less-equal-input-other-out-none-gt-tensor-">less_equal(input, other, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#less-equal-input-other-out-none-gt-tensor-"></a></h2>
     
 
 
-<p>Alias for <code><a href='torch_le.html'>torch_le()</a></code>.</p>
+<p>Alias for <code><a href="torch_le.html">torch_le()</a></code>.</p>
+    </div>
 
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diff --git a/reference/torch_lgamma.html b/reference/torch_lgamma.html
index c1bd51849..ff51bc9f0 100644
--- a/reference/torch_lgamma.html
+++ b/reference/torch_lgamma.html
@@ -1,207 +1,97 @@
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Lgamma</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_lgamma.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Lgamma</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_lgamma.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Lgamma</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_lgamma</span><span class='op'>(</span><span class='va'>self</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="lgamma-input-out-null-gt-tensor-"><a class="anchor" href="#lgamma-input-out-null-gt-tensor-"></a>lgamma(input, out=NULL) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_lgamma</span><span class="op">(</span><span class="va">self</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="lgamma-input-out-null-gt-tensor-">lgamma(input, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#lgamma-input-out-null-gt-tensor-"></a></h2>
     
 
 
@@ -209,45 +99,40 @@
 <p>$$
     \mbox{out}_{i} = \log \Gamma(\mbox{input}_{i})
 $$</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span><span class='op'>(</span><span class='fl'>0.5</span>, <span class='fl'>2</span>, <span class='fl'>0.5</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_lgamma</span><span class='op'>(</span><span class='va'>a</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.5724</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.0000</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.1208</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.0000</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{4} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_arange.html">torch_arange</a></span><span class="op">(</span><span class="fl">0.5</span>, <span class="fl">2</span>, <span class="fl">0.5</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_lgamma</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.5724</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.1208</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer>
-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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+<div class="pkgdown-footer-right">
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diff --git a/reference/torch_linspace.html b/reference/torch_linspace.html
index 9a32305ac..ed49a4bd2 100644
--- a/reference/torch_linspace.html
+++ b/reference/torch_linspace.html
@@ -1,283 +1,156 @@
-<!-- Generated by pkgdown: do not edit by hand -->
 <!DOCTYPE html>
-<html lang="en">
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Linspace</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/creation-ops.R'><code>R/creation-ops.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_linspace.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Linspace</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/creation-ops.R" class="external-link"><code>R/creation-ops.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a></small>
+      <div class="d-none name"><code>torch_linspace.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Linspace</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_linspace</span><span class='op'>(</span>
-  <span class='va'>start</span>,
-  <span class='va'>end</span>,
-  steps <span class='op'>=</span> <span class='fl'>100</span>,
-  dtype <span class='op'>=</span> <span class='cn'>NULL</span>,
-  layout <span class='op'>=</span> <span class='fu'><a href='torch_layout.html'>torch_strided</a></span><span class='op'>(</span><span class='op'>)</span>,
-  device <span class='op'>=</span> <span class='cn'>NULL</span>,
-  requires_grad <span class='op'>=</span> <span class='cn'>FALSE</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>start</th>
-      <td><p>(float) the starting value for the set of points</p></td>
-    </tr>
-    <tr>
-      <th>end</th>
-      <td><p>(float) the ending value for the set of points</p></td>
-    </tr>
-    <tr>
-      <th>steps</th>
-      <td><p>(int) number of points to sample between <code>start</code>        and <code>end</code>. Default: <code>100</code>.</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>).</p></td>
-    </tr>
-    <tr>
-      <th>layout</th>
-      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
-    </tr>
-    <tr>
-      <th>requires_grad</th>
-      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="linspace-start-end-steps-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"><a class="anchor" href="#linspace-start-end-steps-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"></a>linspace(start, end, steps=100, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_linspace</span><span class="op">(</span>
+  <span class="va">start</span>,
+  <span class="va">end</span>,
+  steps <span class="op">=</span> <span class="fl">100</span>,
+  dtype <span class="op">=</span> <span class="cn">NULL</span>,
+  layout <span class="op">=</span> <span class="fu"><a href="torch_layout.html">torch_strided</a></span><span class="op">(</span><span class="op">)</span>,
+  device <span class="op">=</span> <span class="cn">NULL</span>,
+  requires_grad <span class="op">=</span> <span class="cn">FALSE</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>start</dt>
+<dd><p>(float) the starting value for the set of points</p></dd>
+<dt>end</dt>
+<dd><p>(float) the ending value for the set of points</p></dd>
+<dt>steps</dt>
+<dd><p>(int) number of points to sample between <code>start</code>        and <code>end</code>. Default: <code>100</code>.</p></dd>
+<dt>dtype</dt>
+<dd><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>).</p></dd>
+<dt>layout</dt>
+<dd><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></dd>
+<dt>device</dt>
+<dd><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></dd>
+<dt>requires_grad</dt>
+<dd><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="linspace-start-end-steps-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-">linspace(start, end, steps=100, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#linspace-start-end-steps-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"></a></h2>
     
 
 
 <p>Returns a one-dimensional tensor of <code>steps</code>
 equally spaced points between <code>start</code> and <code>end</code>.</p>
 <p>The output tensor is 1-D of size <code>steps</code>.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='fu'>torch_linspace</span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>10</span>, steps<span class='op'>=</span><span class='fl'>5</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_linspace</span><span class='op'>(</span><span class='op'>-</span><span class='fl'>10</span>, <span class='fl'>10</span>, steps<span class='op'>=</span><span class='fl'>5</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_linspace</span><span class='op'>(</span>start<span class='op'>=</span><span class='op'>-</span><span class='fl'>10</span>, end<span class='op'>=</span><span class='fl'>10</span>, steps<span class='op'>=</span><span class='fl'>5</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_linspace</span><span class='op'>(</span>start<span class='op'>=</span><span class='op'>-</span><span class='fl'>10</span>, end<span class='op'>=</span><span class='fl'>10</span>, steps<span class='op'>=</span><span class='fl'>1</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -10</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{1} ]</span>
-</pre>
-  </div>
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+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="fu">torch_linspace</span><span class="op">(</span><span class="fl">3</span>, <span class="fl">10</span>, steps<span class="op">=</span><span class="fl">5</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_linspace</span><span class="op">(</span><span class="op">-</span><span class="fl">10</span>, <span class="fl">10</span>, steps<span class="op">=</span><span class="fl">5</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_linspace</span><span class="op">(</span>start<span class="op">=</span><span class="op">-</span><span class="fl">10</span>, end<span class="op">=</span><span class="fl">10</span>, steps<span class="op">=</span><span class="fl">5</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_linspace</span><span class="op">(</span>start<span class="op">=</span><span class="op">-</span><span class="fl">10</span>, end<span class="op">=</span><span class="fl">10</span>, steps<span class="op">=</span><span class="fl">1</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -10</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{1} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Loads a saved object</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/save.R'><code>R/save.R</code></a></small>
-    <div class="hidden name"><code>torch_load.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Loads a saved object</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/save.R" class="external-link"><code>R/save.R</code></a></small>
+      <div class="d-none name"><code>torch_load.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Loads a saved object</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_load</span><span class='op'>(</span><span class='va'>path</span>, device <span class='op'>=</span> <span class='st'>"cpu"</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>path</th>
-      <td><p>a path to the saved object</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>a device to load tensors to. By default we load to the <code>cpu</code> but you can also
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_load</span><span class="op">(</span><span class="va">path</span>, device <span class="op">=</span> <span class="st">"cpu"</span><span class="op">)</span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>path</dt>
+<dd><p>a path to the saved object</p></dd>
+<dt>device</dt>
+<dd><p>a device to load tensors to. By default we load to the <code>cpu</code> but you can also
 load them to any <code>cuda</code> device. If <code>NULL</code> then the device where the tensor has been saved will
-be reused.</p></td>
-    </tr>
-    </table>
+be reused.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
+    <div class="dont-index"><p>Other torch_save: 
+<code><a href="torch_save.html">torch_save</a>()</code></p></div>
+    </div>
 
-    <h2 class="hasAnchor" id="see-also"><a class="anchor" href="#see-also"></a>See also</h2>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
-    <div class='dont-index'><p>Other torch_save: 
-<code><a href='torch_save.html'>torch_save</a>()</code></p></div>
 
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+<div class="pkgdown-footer-right">
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diff --git a/reference/torch_log.html b/reference/torch_log.html
index 8e6aac914..cd9d37d7d 100644
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Log</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_log.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Log</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_log.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Log</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_log</span><span class='op'>(</span><span class='va'>self</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="log-input-out-null-gt-tensor-"><a class="anchor" href="#log-input-out-null-gt-tensor-"></a>log(input, out=NULL) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_log</span><span class="op">(</span><span class="va">self</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="log-input-out-null-gt-tensor-">log(input, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#log-input-out-null-gt-tensor-"></a></h2>
     
 
 
@@ -210,47 +100,42 @@ of <code>input</code>.</p>
 <p>$$
     y_{i} = \log_{e} (x_{i})
 $$</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>5</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='fu'>torch_log</span><span class='op'>(</span><span class='va'>a</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>     nan</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -2.3677</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>     nan</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.3859</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>     nan</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{5} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">5</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="fu">torch_log</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>     nan</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>     nan</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>     nan</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -4.7006</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>     nan</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{5} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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-      <div class="copyright">
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+    <footer><div class="pkgdown-footer-left">
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+<div class="pkgdown-footer-right">
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diff --git a/reference/torch_log10.html b/reference/torch_log10.html
index fd2e5cede..8ed56b224 100644
--- a/reference/torch_log10.html
+++ b/reference/torch_log10.html
@@ -1,207 +1,97 @@
-<!-- Generated by pkgdown: do not edit by hand -->
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Log10</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_log10.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Log10</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_log10.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Log10</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_log10</span><span class='op'>(</span><span class='va'>self</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="log-input-out-null-gt-tensor-"><a class="anchor" href="#log-input-out-null-gt-tensor-"></a>log10(input, out=NULL) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_log10</span><span class="op">(</span><span class="va">self</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="log-input-out-null-gt-tensor-">log10(input, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#log-input-out-null-gt-tensor-"></a></h2>
     
 
 
@@ -210,47 +100,42 @@ of <code>input</code>.</p>
 <p>$$
     y_{i} = \log_{10} (x_{i})
 $$</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_rand.html'>torch_rand</a></span><span class='op'>(</span><span class='fl'>5</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='fu'>torch_log10</span><span class='op'>(</span><span class='va'>a</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.1549</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.0672</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.1889</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.2191</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.4006</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{5} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_rand.html">torch_rand</a></span><span class="op">(</span><span class="fl">5</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="fu">torch_log10</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.1267</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.5351</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.1341</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.6519</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.5757</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{5} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer>
-      <div class="copyright">
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+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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+<div class="pkgdown-footer-right">
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diff --git a/reference/torch_log1p.html b/reference/torch_log1p.html
index dfa546d25..c1161a17a 100644
--- a/reference/torch_log1p.html
+++ b/reference/torch_log1p.html
@@ -1,211 +1,102 @@
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Log1p</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_log1p.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Log1p</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_log1p.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Log1p</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_log1p</span><span class='op'>(</span><span class='va'>self</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_log1p</span><span class="op">(</span><span class="va">self</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <p>This function is more accurate than <code><a href='torch_log.html'>torch_log</a></code> for small
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
+    <p>This function is more accurate than <code><a href="torch_log.html">torch_log</a></code> for small
 values of <code>input</code></p>
-    <h2 class="hasAnchor" id="log-p-input-out-null-gt-tensor-"><a class="anchor" href="#log-p-input-out-null-gt-tensor-"></a>log1p(input, out=NULL) -&gt; Tensor </h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="log-p-input-out-null-gt-tensor-">log1p(input, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#log-p-input-out-null-gt-tensor-"></a></h2>
     
 
 
@@ -213,47 +104,42 @@ values of <code>input</code></p>
 <p>$$
     y_i = \log_{e} (x_i + 1)
 $$</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>5</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='fu'>torch_log1p</span><span class='op'>(</span><span class='va'>a</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -1.3018</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.6516</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>     nan</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>     nan</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.2784</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{5} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">5</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="fu">torch_log1p</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.8064</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.6569</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -1.5694</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.9993</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.1102</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{5} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer>
-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
 </div>
 
-<div class="pkgdown">
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+<div class="pkgdown-footer-right">
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diff --git a/reference/torch_log2.html b/reference/torch_log2.html
index 94ef14b62..8fccfb67b 100644
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@@ -1,207 +1,97 @@
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Log2</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_log2.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Log2</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_log2.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Log2</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_log2</span><span class='op'>(</span><span class='va'>self</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="log-input-out-null-gt-tensor-"><a class="anchor" href="#log-input-out-null-gt-tensor-"></a>log2(input, out=NULL) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_log2</span><span class="op">(</span><span class="va">self</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="log-input-out-null-gt-tensor-">log2(input, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#log-input-out-null-gt-tensor-"></a></h2>
     
 
 
@@ -210,47 +100,42 @@ of <code>input</code>.</p>
 <p>$$
     y_{i} = \log_{2} (x_{i})
 $$</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_rand.html'>torch_rand</a></span><span class='op'>(</span><span class='fl'>5</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='fu'>torch_log2</span><span class='op'>(</span><span class='va'>a</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -1.6316</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -2.0255</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -3.1289</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.2529</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -1.2860</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{5} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_rand.html">torch_rand</a></span><span class="op">(</span><span class="fl">5</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="fu">torch_log2</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.2708</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.2082</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -3.5860</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -3.7349</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -3.9478</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{5} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer>
-      <div class="copyright">
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+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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+<div class="pkgdown-footer-right">
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diff --git a/reference/torch_logaddexp.html b/reference/torch_logaddexp.html
index df5d91f4b..0319c81c0 100644
--- a/reference/torch_logaddexp.html
+++ b/reference/torch_logaddexp.html
@@ -1,211 +1,99 @@
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Logaddexp</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_logaddexp.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Logaddexp</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_logaddexp.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Logaddexp</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_logaddexp</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>other</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Tensor) the second input tensor</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="logaddexp-input-other-out-none-gt-tensor-"><a class="anchor" href="#logaddexp-input-other-out-none-gt-tensor-"></a>logaddexp(input, other, *, out=None) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_logaddexp</span><span class="op">(</span><span class="va">self</span>, <span class="va">other</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+<dt>other</dt>
+<dd><p>(Tensor) the second input tensor</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="logaddexp-input-other-out-none-gt-tensor-">logaddexp(input, other, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#logaddexp-input-other-out-none-gt-tensor-"></a></h2>
     
 
 
@@ -215,47 +103,42 @@ in statistics where the calculated probabilities of events may be so small as to
 exceed the range of normal floating point numbers. In such cases the logarithm
 of the calculated probability is stored. This function allows adding
 probabilities stored in such a fashion.</p>
-<p>This op should be disambiguated with <code><a href='torch_logsumexp.html'>torch_logsumexp()</a></code> which performs a
+<p>This op should be disambiguated with <code><a href="torch_logsumexp.html">torch_logsumexp()</a></code> which performs a
 reduction on a single tensor.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='fu'>torch_logaddexp</span><span class='op'>(</span><span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='op'>-</span><span class='fl'>1.0</span><span class='op'>)</span><span class='op'>)</span>, <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='op'>-</span><span class='fl'>1.0</span>, <span class='op'>-</span><span class='fl'>2</span>, <span class='op'>-</span><span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_logaddexp</span><span class='op'>(</span><span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='op'>-</span><span class='fl'>100.0</span>, <span class='op'>-</span><span class='fl'>200</span>, <span class='op'>-</span><span class='fl'>300</span><span class='op'>)</span><span class='op'>)</span>, <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='op'>-</span><span class='fl'>1.0</span>, <span class='op'>-</span><span class='fl'>2</span>, <span class='op'>-</span><span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_logaddexp</span><span class='op'>(</span><span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1.0</span>, <span class='fl'>2000</span>, <span class='fl'>30000</span><span class='op'>)</span><span class='op'>)</span>, <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='op'>-</span><span class='fl'>1.0</span>, <span class='op'>-</span><span class='fl'>2</span>, <span class='op'>-</span><span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>      1.1269</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   2000.0000</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  30000.0000</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{3} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="fu">torch_logaddexp</span><span class="op">(</span><span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="op">-</span><span class="fl">1.0</span><span class="op">)</span><span class="op">)</span>, <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="op">-</span><span class="fl">1.0</span>, <span class="op">-</span><span class="fl">2</span>, <span class="op">-</span><span class="fl">3</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_logaddexp</span><span class="op">(</span><span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="op">-</span><span class="fl">100.0</span>, <span class="op">-</span><span class="fl">200</span>, <span class="op">-</span><span class="fl">300</span><span class="op">)</span><span class="op">)</span>, <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="op">-</span><span class="fl">1.0</span>, <span class="op">-</span><span class="fl">2</span>, <span class="op">-</span><span class="fl">3</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_logaddexp</span><span class="op">(</span><span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1.0</span>, <span class="fl">2000</span>, <span class="fl">30000</span><span class="op">)</span><span class="op">)</span>, <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="op">-</span><span class="fl">1.0</span>, <span class="op">-</span><span class="fl">2</span>, <span class="op">-</span><span class="fl">3</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>      1.1269</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   2000.0000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  30000.0000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Logaddexp2</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_logaddexp2.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Logaddexp2</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_logaddexp2.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Logaddexp2</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_logaddexp2</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>other</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Tensor) the second input tensor</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="logaddexp-input-other-out-none-gt-tensor-"><a class="anchor" href="#logaddexp-input-other-out-none-gt-tensor-"></a>logaddexp2(input, other, *, out=None) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_logaddexp2</span><span class="op">(</span><span class="va">self</span>, <span class="va">other</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+<dt>other</dt>
+<dd><p>(Tensor) the second input tensor</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="logaddexp-input-other-out-none-gt-tensor-">logaddexp2(input, other, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#logaddexp-input-other-out-none-gt-tensor-"></a></h2>
     
 
 
 <p>Logarithm of the sum of exponentiations of the inputs in base-2.</p>
 <p>Calculates pointwise \(\log_2\left(2^x + 2^y\right)\). See
-<code><a href='torch_logaddexp.html'>torch_logaddexp()</a></code> for more details.</p>
+<code><a href="torch_logaddexp.html">torch_logaddexp()</a></code> for more details.</p>
+    </div>
 
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index 33a047f6a..149fa76c4 100644
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Logcumsumexp</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_logcumsumexp.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Logcumsumexp</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_logcumsumexp.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Logcumsumexp</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_logcumsumexp</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>dim</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) the dimension to do the operation over</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="logcumsumexp-input-dim-out-none-gt-tensor-"><a class="anchor" href="#logcumsumexp-input-dim-out-none-gt-tensor-"></a>logcumsumexp(input, dim, *, out=None) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_logcumsumexp</span><span class="op">(</span><span class="va">self</span>, <span class="va">dim</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+<dt>dim</dt>
+<dd><p>(int) the dimension to do the operation over</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="logcumsumexp-input-dim-out-none-gt-tensor-">logcumsumexp(input, dim, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#logcumsumexp-input-dim-out-none-gt-tensor-"></a></h2>
     
 
 
@@ -215,51 +103,46 @@ elements of <code>input</code> in the dimension <code>dim</code>.</p>
 <p>$$
         \mbox{logcumsumexp}(x)_{ij} = \log \sum\limits_{j=0}^{i} \exp(x_{ij})
 $$</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>10</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_logcumsumexp</span><span class='op'>(</span><span class='va'>a</span>, dim<span class='op'>=</span><span class='fl'>1</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.8194</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.6000</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.8981</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1.1550</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1.5029</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1.6530</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1.9324</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  2.1454</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  2.4188</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  2.5271</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{10} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">10</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_logcumsumexp</span><span class="op">(</span><span class="va">a</span>, dim<span class="op">=</span><span class="fl">1</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.9025</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.7117</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.0725</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.1551</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.7743</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.7817</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.8073</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.9513</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.9586</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2.0104</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{10} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Logdet</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_logdet.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Logdet</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_logdet.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Logdet</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_logdet</span><span class='op'>(</span><span class='va'>self</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor of size <code>(*, n, n)</code> where <code>*</code> is zero or more                batch dimensions.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_logdet</span><span class="op">(</span><span class="va">self</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor of size <code>(*, n, n)</code> where <code>*</code> is zero or more                batch dimensions.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     
-<pre>Result is `-inf` if `input` has zero log determinant, and is `NaN` if
+<div class="sourceCode"><pre><code>Result is `-inf` if `input` has zero log determinant, and is `NaN` if
 `input` has negative determinant.
-</pre>
+</code></pre></div>
 
-<pre>Backward through `logdet` internally uses SVD results when `input`
+<div class="sourceCode"><pre><code>Backward through `logdet` internally uses SVD results when `input`
 is not invertible. In this case, double backward through `logdet` will
 be unstable in when `input` doesn't have distinct singular values. See
 `~torch.svd` for details.
-</pre>
-
-    <h2 class="hasAnchor" id="logdet-input-gt-tensor-"><a class="anchor" href="#logdet-input-gt-tensor-"></a>logdet(input) -&gt; Tensor </h2>
+</code></pre></div>
 
+    </div>
+    <div class="section level2">
+    <h2 id="logdet-input-gt-tensor-">logdet(input) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#logdet-input-gt-tensor-"></a></h2>
     
 
 
 <p>Calculates log determinant of a square matrix or batches of square matrices.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>A</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'><a href='torch_det.html'>torch_det</a></span><span class='op'>(</span><span class='va'>A</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_logdet</span><span class='op'>(</span><span class='va'>A</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>A</span></span>
-<span class='r-in'><span class='va'>A</span><span class='op'>$</span><span class='fu'>det</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>A</span><span class='op'>$</span><span class='fu'>det</span><span class='op'>(</span><span class='op'>)</span><span class='op'>$</span><span class='fu'>log</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> nan</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{} ]</span>
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+<span class="r-in"></span>
+<span class="r-in"><span class="va">A</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu"><a href="torch_det.html">torch_det</a></span><span class="op">(</span><span class="va">A</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_logdet</span><span class="op">(</span><span class="va">A</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">A</span></span>
+<span class="r-in"><span class="va">A</span><span class="op">$</span><span class="fu">det</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">A</span><span class="op">$</span><span class="fu">det</span><span class="op">(</span><span class="op">)</span><span class="op">$</span><span class="fu">log</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.336194</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{} ]</span>
+</code></pre></div>
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Logical_and</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_logical_and.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Logical_and</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_logical_and.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Logical_and</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_logical_and</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>other</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Tensor) the tensor to compute AND with</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="logical-and-input-other-out-null-gt-tensor-"><a class="anchor" href="#logical-and-input-other-out-null-gt-tensor-"></a>logical_and(input, other, out=NULL) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_logical_and</span><span class="op">(</span><span class="va">self</span>, <span class="va">other</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+<dt>other</dt>
+<dd><p>(Tensor) the tensor to compute AND with</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="logical-and-input-other-out-null-gt-tensor-">logical_and(input, other, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#logical-and-input-other-out-null-gt-tensor-"></a></h2>
     
 
 
 <p>Computes the element-wise logical AND of the given input tensors. Zeros are treated as <code>FALSE</code> and nonzeros are
 treated as <code>TRUE</code>.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='fu'>torch_logical_and</span><span class='op'>(</span><span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='cn'>TRUE</span>, <span class='cn'>FALSE</span>, <span class='cn'>TRUE</span><span class='op'>)</span><span class='op'>)</span>, <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='cn'>TRUE</span>, <span class='cn'>FALSE</span>, <span class='cn'>FALSE</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>0</span>, <span class='fl'>1</span>, <span class='fl'>10</span>, <span class='fl'>0</span><span class='op'>)</span>, dtype<span class='op'>=</span><span class='fu'><a href='torch_dtype.html'>torch_int8</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>b</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span>, <span class='fl'>0</span>, <span class='fl'>1</span>, <span class='fl'>0</span><span class='op'>)</span>, dtype<span class='op'>=</span><span class='fu'><a href='torch_dtype.html'>torch_int8</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_logical_and</span><span class='op'>(</span><span class='va'>a</span>, <span class='va'>b</span><span class='op'>)</span></span>
-<span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='cn'>FALSE</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='fu'>torch_logical_and</span><span class='op'>(</span><span class='va'>a</span>, <span class='va'>b</span>, out<span class='op'>=</span><span class='fu'><a href='torch_empty.html'>torch_empty</a></span><span class='op'>(</span><span class='fl'>4</span>, dtype<span class='op'>=</span><span class='fu'><a href='torch_dtype.html'>torch_bool</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
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+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="fu">torch_logical_and</span><span class="op">(</span><span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="cn">TRUE</span>, <span class="cn">FALSE</span>, <span class="cn">TRUE</span><span class="op">)</span><span class="op">)</span>, <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="cn">TRUE</span>, <span class="cn">FALSE</span>, <span class="cn">FALSE</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span>, <span class="fl">10</span>, <span class="fl">0</span><span class="op">)</span>, dtype<span class="op">=</span><span class="fu"><a href="torch_dtype.html">torch_int8</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">b</span> <span class="op">=</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span>, <span class="fl">0</span>, <span class="fl">1</span>, <span class="fl">0</span><span class="op">)</span>, dtype<span class="op">=</span><span class="fu"><a href="torch_dtype.html">torch_int8</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_logical_and</span><span class="op">(</span><span class="va">a</span>, <span class="va">b</span><span class="op">)</span></span>
+<span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="fu">torch_logical_and</span><span class="op">(</span><span class="va">a</span>, <span class="va">b</span>, out<span class="op">=</span><span class="fu"><a href="torch_empty.html">torch_empty</a></span><span class="op">(</span><span class="fl">4</span>, dtype<span class="op">=</span><span class="fu"><a href="torch_dtype.html">torch_bool</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
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-    <div class="page-header">
-    <h1>Logical_not</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_logical_not.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Logical_not</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a></small>
+      <div class="d-none name"><code>torch_logical_not.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Logical_not</p>
     </div>
 
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="logical-not-input-out-null-gt-tensor-"><a class="anchor" href="#logical-not-input-out-null-gt-tensor-"></a>logical_not(input, out=NULL) -&gt; Tensor </h2>
-
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="logical-not-input-out-null-gt-tensor-">logical_not(input, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#logical-not-input-out-null-gt-tensor-"></a></h2>
     
 
 
 <p>Computes the element-wise logical NOT of the given input tensor. If not specified, the output tensor will have the bool
 dtype. If the input tensor is not a bool tensor, zeros are treated as <code>FALSE</code> and non-zeros are treated as <code>TRUE</code>.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='fu'>torch_logical_not</span><span class='op'>(</span><span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='cn'>TRUE</span>, <span class='cn'>FALSE</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_logical_not</span><span class='op'>(</span><span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>0</span>, <span class='fl'>1</span>, <span class='op'>-</span><span class='fl'>10</span><span class='op'>)</span>, dtype<span class='op'>=</span><span class='fu'><a href='torch_dtype.html'>torch_int8</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_logical_not</span><span class='op'>(</span><span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>0.</span>, <span class='fl'>1.5</span>, <span class='op'>-</span><span class='fl'>10.</span><span class='op'>)</span>, dtype<span class='op'>=</span><span class='fu'><a href='torch_dtype.html'>torch_double</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUBoolType{3} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="fu">torch_logical_not</span><span class="op">(</span><span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="cn">TRUE</span>, <span class="cn">FALSE</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_logical_not</span><span class="op">(</span><span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span>, <span class="op">-</span><span class="fl">10</span><span class="op">)</span>, dtype<span class="op">=</span><span class="fu"><a href="torch_dtype.html">torch_int8</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_logical_not</span><span class="op">(</span><span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.</span>, <span class="fl">1.5</span>, <span class="op">-</span><span class="fl">10.</span><span class="op">)</span>, dtype<span class="op">=</span><span class="fu"><a href="torch_dtype.html">torch_double</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUBoolType{3} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Logical_or</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_logical_or.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Logical_or</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_logical_or.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Logical_or</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_logical_or</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>other</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Tensor) the tensor to compute OR with</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="logical-or-input-other-out-null-gt-tensor-"><a class="anchor" href="#logical-or-input-other-out-null-gt-tensor-"></a>logical_or(input, other, out=NULL) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_logical_or</span><span class="op">(</span><span class="va">self</span>, <span class="va">other</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+<dt>other</dt>
+<dd><p>(Tensor) the tensor to compute OR with</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="logical-or-input-other-out-null-gt-tensor-">logical_or(input, other, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#logical-or-input-other-out-null-gt-tensor-"></a></h2>
     
 
 
 <p>Computes the element-wise logical OR of the given input tensors. Zeros are treated as <code>FALSE</code> and nonzeros are
 treated as <code>TRUE</code>.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='fu'>torch_logical_or</span><span class='op'>(</span><span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='cn'>TRUE</span>, <span class='cn'>FALSE</span>, <span class='cn'>TRUE</span><span class='op'>)</span><span class='op'>)</span>, <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='cn'>TRUE</span>, <span class='cn'>FALSE</span>, <span class='cn'>FALSE</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>0</span>, <span class='fl'>1</span>, <span class='fl'>10</span>, <span class='fl'>0</span><span class='op'>)</span>, dtype<span class='op'>=</span><span class='fu'><a href='torch_dtype.html'>torch_int8</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>b</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span>, <span class='fl'>0</span>, <span class='fl'>1</span>, <span class='fl'>0</span><span class='op'>)</span>, dtype<span class='op'>=</span><span class='fu'><a href='torch_dtype.html'>torch_int8</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_logical_or</span><span class='op'>(</span><span class='va'>a</span>, <span class='va'>b</span><span class='op'>)</span></span>
-<span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='cn'>FALSE</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='fu'>torch_logical_or</span><span class='op'>(</span><span class='va'>a</span><span class='op'>$</span><span class='fu'>double</span><span class='op'>(</span><span class='op'>)</span>, <span class='va'>b</span><span class='op'>$</span><span class='fu'>double</span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_logical_or</span><span class='op'>(</span><span class='va'>a</span><span class='op'>$</span><span class='fu'>double</span><span class='op'>(</span><span class='op'>)</span>, <span class='va'>b</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_logical_or</span><span class='op'>(</span><span class='va'>a</span>, <span class='va'>b</span>, out<span class='op'>=</span><span class='fu'><a href='torch_empty.html'>torch_empty</a></span><span class='op'>(</span><span class='fl'>4</span>, dtype<span class='op'>=</span><span class='fu'><a href='torch_dtype.html'>torch_bool</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="fu">torch_logical_or</span><span class="op">(</span><span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="cn">TRUE</span>, <span class="cn">FALSE</span>, <span class="cn">TRUE</span><span class="op">)</span><span class="op">)</span>, <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="cn">TRUE</span>, <span class="cn">FALSE</span>, <span class="cn">FALSE</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span>, <span class="fl">10</span>, <span class="fl">0</span><span class="op">)</span>, dtype<span class="op">=</span><span class="fu"><a href="torch_dtype.html">torch_int8</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">b</span> <span class="op">=</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span>, <span class="fl">0</span>, <span class="fl">1</span>, <span class="fl">0</span><span class="op">)</span>, dtype<span class="op">=</span><span class="fu"><a href="torch_dtype.html">torch_int8</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_logical_or</span><span class="op">(</span><span class="va">a</span>, <span class="va">b</span><span class="op">)</span></span>
+<span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="fu">torch_logical_or</span><span class="op">(</span><span class="va">a</span><span class="op">$</span><span class="fu">double</span><span class="op">(</span><span class="op">)</span>, <span class="va">b</span><span class="op">$</span><span class="fu">double</span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_logical_or</span><span class="op">(</span><span class="va">a</span><span class="op">$</span><span class="fu">double</span><span class="op">(</span><span class="op">)</span>, <span class="va">b</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_logical_or</span><span class="op">(</span><span class="va">a</span>, <span class="va">b</span>, out<span class="op">=</span><span class="fu"><a href="torch_empty.html">torch_empty</a></span><span class="op">(</span><span class="fl">4</span>, dtype<span class="op">=</span><span class="fu"><a href="torch_dtype.html">torch_bool</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Logical_xor</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_logical_xor.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Logical_xor</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_logical_xor.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Logical_xor</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_logical_xor</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>other</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Tensor) the tensor to compute XOR with</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="logical-xor-input-other-out-null-gt-tensor-"><a class="anchor" href="#logical-xor-input-other-out-null-gt-tensor-"></a>logical_xor(input, other, out=NULL) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_logical_xor</span><span class="op">(</span><span class="va">self</span>, <span class="va">other</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+<dt>other</dt>
+<dd><p>(Tensor) the tensor to compute XOR with</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="logical-xor-input-other-out-null-gt-tensor-">logical_xor(input, other, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#logical-xor-input-other-out-null-gt-tensor-"></a></h2>
     
 
 
 <p>Computes the element-wise logical XOR of the given input tensors. Zeros are treated as <code>FALSE</code> and nonzeros are
 treated as <code>TRUE</code>.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='fu'>torch_logical_xor</span><span class='op'>(</span><span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='cn'>TRUE</span>, <span class='cn'>FALSE</span>, <span class='cn'>TRUE</span><span class='op'>)</span><span class='op'>)</span>, <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='cn'>TRUE</span>, <span class='cn'>FALSE</span>, <span class='cn'>FALSE</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>0</span>, <span class='fl'>1</span>, <span class='fl'>10</span>, <span class='fl'>0</span><span class='op'>)</span>, dtype<span class='op'>=</span><span class='fu'><a href='torch_dtype.html'>torch_int8</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>b</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span>, <span class='fl'>0</span>, <span class='fl'>1</span>, <span class='fl'>0</span><span class='op'>)</span>, dtype<span class='op'>=</span><span class='fu'><a href='torch_dtype.html'>torch_int8</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_logical_xor</span><span class='op'>(</span><span class='va'>a</span>, <span class='va'>b</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_logical_xor</span><span class='op'>(</span><span class='va'>a</span><span class='op'>$</span><span class='fu'>to</span><span class='op'>(</span>dtype<span class='op'>=</span><span class='fu'><a href='torch_dtype.html'>torch_double</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span>, <span class='va'>b</span><span class='op'>$</span><span class='fu'>to</span><span class='op'>(</span>dtype<span class='op'>=</span><span class='fu'><a href='torch_dtype.html'>torch_double</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_logical_xor</span><span class='op'>(</span><span class='va'>a</span><span class='op'>$</span><span class='fu'>to</span><span class='op'>(</span>dtype<span class='op'>=</span><span class='fu'><a href='torch_dtype.html'>torch_double</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span>, <span class='va'>b</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUBoolType{4} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="fu">torch_logical_xor</span><span class="op">(</span><span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="cn">TRUE</span>, <span class="cn">FALSE</span>, <span class="cn">TRUE</span><span class="op">)</span><span class="op">)</span>, <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="cn">TRUE</span>, <span class="cn">FALSE</span>, <span class="cn">FALSE</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span>, <span class="fl">10</span>, <span class="fl">0</span><span class="op">)</span>, dtype<span class="op">=</span><span class="fu"><a href="torch_dtype.html">torch_int8</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">b</span> <span class="op">=</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span>, <span class="fl">0</span>, <span class="fl">1</span>, <span class="fl">0</span><span class="op">)</span>, dtype<span class="op">=</span><span class="fu"><a href="torch_dtype.html">torch_int8</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_logical_xor</span><span class="op">(</span><span class="va">a</span>, <span class="va">b</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_logical_xor</span><span class="op">(</span><span class="va">a</span><span class="op">$</span><span class="fu">to</span><span class="op">(</span>dtype<span class="op">=</span><span class="fu"><a href="torch_dtype.html">torch_double</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span>, <span class="va">b</span><span class="op">$</span><span class="fu">to</span><span class="op">(</span>dtype<span class="op">=</span><span class="fu"><a href="torch_dtype.html">torch_double</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_logical_xor</span><span class="op">(</span><span class="va">a</span><span class="op">$</span><span class="fu">to</span><span class="op">(</span>dtype<span class="op">=</span><span class="fu"><a href="torch_dtype.html">torch_double</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span>, <span class="va">b</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUBoolType{4} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Logit</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_logit.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Logit</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_logit.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Logit</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_logit</span><span class='op'>(</span><span class='va'>self</span>, eps <span class='op'>=</span> <span class='cn'>NULL</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>eps</th>
-      <td><p>(float, optional) the epsilon for input clamp bound. Default: <code>None</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="logit-input-eps-none-out-none-gt-tensor-"><a class="anchor" href="#logit-input-eps-none-out-none-gt-tensor-"></a>logit(input, eps=None, *, out=None) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_logit</span><span class="op">(</span><span class="va">self</span>, eps <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+<dt>eps</dt>
+<dd><p>(float, optional) the epsilon for input clamp bound. Default: <code>None</code></p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="logit-input-eps-none-out-none-gt-tensor-">logit(input, eps=None, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#logit-input-eps-none-out-none-gt-tensor-"></a></h2>
     
 
 
@@ -221,47 +109,42 @@ When eps is None and <code>input</code> &lt; 0 or <code>input</code> &gt; 1, the
         1 - \mbox{eps} &amp; \mbox{if } x_{i} &gt; 1 - \mbox{eps}
     \end{array}
 $$</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_rand.html'>torch_rand</a></span><span class='op'>(</span><span class='fl'>5</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='fu'>torch_logit</span><span class='op'>(</span><span class='va'>a</span>, eps<span class='op'>=</span><span class='fl'>1e-6</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.3817</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.8498</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.8965</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1.3691</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -3.0468</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{5} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_rand.html">torch_rand</a></span><span class="op">(</span><span class="fl">5</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="fu">torch_logit</span><span class="op">(</span><span class="va">a</span>, eps<span class="op">=</span><span class="fl">1e-6</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -1.8133</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.6135</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -2.5095</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.1855</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.1235</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{5} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer>
-      <div class="copyright">
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+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Logspace</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/creation-ops.R'><code>R/creation-ops.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_logspace.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Logspace</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/creation-ops.R" class="external-link"><code>R/creation-ops.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a></small>
+      <div class="d-none name"><code>torch_logspace.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Logspace</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_logspace</span><span class='op'>(</span>
-  <span class='va'>start</span>,
-  <span class='va'>end</span>,
-  steps <span class='op'>=</span> <span class='fl'>100</span>,
-  base <span class='op'>=</span> <span class='fl'>10</span>,
-  dtype <span class='op'>=</span> <span class='cn'>NULL</span>,
-  layout <span class='op'>=</span> <span class='fu'><a href='torch_layout.html'>torch_strided</a></span><span class='op'>(</span><span class='op'>)</span>,
-  device <span class='op'>=</span> <span class='cn'>NULL</span>,
-  requires_grad <span class='op'>=</span> <span class='cn'>FALSE</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>start</th>
-      <td><p>(float) the starting value for the set of points</p></td>
-    </tr>
-    <tr>
-      <th>end</th>
-      <td><p>(float) the ending value for the set of points</p></td>
-    </tr>
-    <tr>
-      <th>steps</th>
-      <td><p>(int) number of points to sample between <code>start</code>        and <code>end</code>. Default: <code>100</code>.</p></td>
-    </tr>
-    <tr>
-      <th>base</th>
-      <td><p>(float) base of the logarithm function. Default: <code>10.0</code>.</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>).</p></td>
-    </tr>
-    <tr>
-      <th>layout</th>
-      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
-    </tr>
-    <tr>
-      <th>requires_grad</th>
-      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="logspace-start-end-steps-base-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"><a class="anchor" href="#logspace-start-end-steps-base-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"></a>logspace(start, end, steps=100, base=10.0, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_logspace</span><span class="op">(</span>
+  <span class="va">start</span>,
+  <span class="va">end</span>,
+  steps <span class="op">=</span> <span class="fl">100</span>,
+  base <span class="op">=</span> <span class="fl">10</span>,
+  dtype <span class="op">=</span> <span class="cn">NULL</span>,
+  layout <span class="op">=</span> <span class="fu"><a href="torch_layout.html">torch_strided</a></span><span class="op">(</span><span class="op">)</span>,
+  device <span class="op">=</span> <span class="cn">NULL</span>,
+  requires_grad <span class="op">=</span> <span class="cn">FALSE</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>start</dt>
+<dd><p>(float) the starting value for the set of points</p></dd>
+<dt>end</dt>
+<dd><p>(float) the ending value for the set of points</p></dd>
+<dt>steps</dt>
+<dd><p>(int) number of points to sample between <code>start</code>        and <code>end</code>. Default: <code>100</code>.</p></dd>
+<dt>base</dt>
+<dd><p>(float) base of the logarithm function. Default: <code>10.0</code>.</p></dd>
+<dt>dtype</dt>
+<dd><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>).</p></dd>
+<dt>layout</dt>
+<dd><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></dd>
+<dt>device</dt>
+<dd><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></dd>
+<dt>requires_grad</dt>
+<dd><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="logspace-start-end-steps-base-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-">logspace(start, end, steps=100, base=10.0, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#logspace-start-end-steps-base-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"></a></h2>
     
 
 
@@ -246,44 +122,39 @@
 logarithmically spaced with base <code>base</code> between
 \({\mbox{base}}^{\mbox{start}}\) and \({\mbox{base}}^{\mbox{end}}\).</p>
 <p>The output tensor is 1-D of size <code>steps</code>.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='fu'>torch_logspace</span><span class='op'>(</span>start<span class='op'>=</span><span class='op'>-</span><span class='fl'>10</span>, end<span class='op'>=</span><span class='fl'>10</span>, steps<span class='op'>=</span><span class='fl'>5</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_logspace</span><span class='op'>(</span>start<span class='op'>=</span><span class='fl'>0.1</span>, end<span class='op'>=</span><span class='fl'>1.0</span>, steps<span class='op'>=</span><span class='fl'>5</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_logspace</span><span class='op'>(</span>start<span class='op'>=</span><span class='fl'>0.1</span>, end<span class='op'>=</span><span class='fl'>1.0</span>, steps<span class='op'>=</span><span class='fl'>1</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_logspace</span><span class='op'>(</span>start<span class='op'>=</span><span class='fl'>2</span>, end<span class='op'>=</span><span class='fl'>2</span>, steps<span class='op'>=</span><span class='fl'>1</span>, base<span class='op'>=</span><span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  4</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{1} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="fu">torch_logspace</span><span class="op">(</span>start<span class="op">=</span><span class="op">-</span><span class="fl">10</span>, end<span class="op">=</span><span class="fl">10</span>, steps<span class="op">=</span><span class="fl">5</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_logspace</span><span class="op">(</span>start<span class="op">=</span><span class="fl">0.1</span>, end<span class="op">=</span><span class="fl">1.0</span>, steps<span class="op">=</span><span class="fl">5</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_logspace</span><span class="op">(</span>start<span class="op">=</span><span class="fl">0.1</span>, end<span class="op">=</span><span class="fl">1.0</span>, steps<span class="op">=</span><span class="fl">1</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_logspace</span><span class="op">(</span>start<span class="op">=</span><span class="fl">2</span>, end<span class="op">=</span><span class="fl">2</span>, steps<span class="op">=</span><span class="fl">1</span>, base<span class="op">=</span><span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  4</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{1} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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index 723f9c4fd..107ea3aaf 100644
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Logsumexp</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_logsumexp.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Logsumexp</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_logsumexp.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Logsumexp</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_logsumexp</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>dim</span>, keepdim <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int or tuple of ints) the dimension or dimensions to reduce.</p></td>
-    </tr>
-    <tr>
-      <th>keepdim</th>
-      <td><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="logsumexp-input-dim-keepdim-false-out-null-"><a class="anchor" href="#logsumexp-input-dim-keepdim-false-out-null-"></a>logsumexp(input, dim, keepdim=False, out=NULL) </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_logsumexp</span><span class="op">(</span><span class="va">self</span>, <span class="va">dim</span>, keepdim <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+<dt>dim</dt>
+<dd><p>(int or tuple of ints) the dimension or dimensions to reduce.</p></dd>
+<dt>keepdim</dt>
+<dd><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="logsumexp-input-dim-keepdim-false-out-null-">logsumexp(input, dim, keepdim=False, out=NULL) <a class="anchor" aria-label="anchor" href="#logsumexp-input-dim-keepdim-false-out-null-"></a></h2>
     
 
 
@@ -222,46 +108,41 @@ stabilized.</p>
 $$</p>
 <p>If <code>keepdim</code> is <code>TRUE</code>, the output tensor is of the same size
 as <code>input</code> except in the dimension(s) <code>dim</code> where it is of size 1.
-Otherwise, <code>dim</code> is squeezed (see <code><a href='torch_squeeze.html'>torch_squeeze</a></code>), resulting in the
+Otherwise, <code>dim</code> is squeezed (see <code><a href="torch_squeeze.html">torch_squeeze</a></code>), resulting in the
 output tensor having 1 (or <code>len(dim)</code>) fewer dimension(s).</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_logsumexp</span><span class='op'>(</span><span class='va'>a</span>, <span class='fl'>1</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1.5844</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.9297</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1.1732</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{3} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
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+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_logsumexp</span><span class="op">(</span><span class="va">a</span>, <span class="fl">1</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.7026</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.1355</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.7146</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Lstsq</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_lstsq.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Lstsq</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_lstsq.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Lstsq</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_lstsq</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>A</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the matrix \(B\)</p></td>
-    </tr>
-    <tr>
-      <th>A</th>
-      <td><p>(Tensor) the \(m\) by \(n\) matrix \(A\)</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_lstsq</span><span class="op">(</span><span class="va">self</span>, <span class="va">A</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the matrix \(B\)</p></dd>
+<dt>A</dt>
+<dd><p>(Tensor) the \(m\) by \(n\) matrix \(A\)</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     
-<pre>The case when \eqn{m &lt; n} is not supported on the GPU.
-</pre>
-
-    <h2 class="hasAnchor" id="lstsq-input-a-out-null-gt-tensor-"><a class="anchor" href="#lstsq-input-a-out-null-gt-tensor-"></a>lstsq(input, A, out=NULL) -&gt; Tensor </h2>
+<div class="sourceCode"><pre><code>The case when \eqn{m &lt; n} is not supported on the GPU.
+</code></pre></div>
 
+    </div>
+    <div class="section level2">
+    <h2 id="lstsq-input-a-out-null-gt-tensor-">lstsq(input, A, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#lstsq-input-a-out-null-gt-tensor-"></a></h2>
     
 
 
@@ -234,60 +123,55 @@ Returned tensor \(X\) has shape \((\mbox{max}(m, n) \times k)\). The first \(n\)
 rows of \(X\) contains the solution. If \(m \geq n\), the residual sum of squares
 for the solution in each column is given by the sum of squares of elements in the
 remaining \(m - n\) rows of that column.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>A</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/cbind.html'>rbind</a></span><span class='op'>(</span></span>
-<span class='r-in'> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>,<span class='fl'>1</span>,<span class='fl'>1</span><span class='op'>)</span>,</span>
-<span class='r-in'> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>2</span>,<span class='fl'>3</span>,<span class='fl'>4</span><span class='op'>)</span>,</span>
-<span class='r-in'> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3</span>,<span class='fl'>5</span>,<span class='fl'>2</span><span class='op'>)</span>,</span>
-<span class='r-in'> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span>,<span class='fl'>2</span>,<span class='fl'>5</span><span class='op'>)</span>,</span>
-<span class='r-in'> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>5</span>,<span class='fl'>4</span>,<span class='fl'>3</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>B</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/cbind.html'>rbind</a></span><span class='op'>(</span></span>
-<span class='r-in'> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='op'>-</span><span class='fl'>10</span>, <span class='op'>-</span><span class='fl'>3</span><span class='op'>)</span>,</span>
-<span class='r-in'> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>12</span>, <span class='fl'>14</span><span class='op'>)</span>,</span>
-<span class='r-in'> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>14</span>, <span class='fl'>12</span><span class='op'>)</span>,</span>
-<span class='r-in'> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>16</span>, <span class='fl'>16</span><span class='op'>)</span>,</span>
-<span class='r-in'> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>18</span>, <span class='fl'>16</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>out</span> <span class='op'>=</span> <span class='fu'>torch_lstsq</span><span class='op'>(</span><span class='va'>B</span>, <span class='va'>A</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>out</span><span class='op'>[[</span><span class='fl'>1</span><span class='op'>]</span><span class='op'>]</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   2.0000   1.0000</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   1.0000   1.0000</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   1.0000   2.0000</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  10.9635   4.8501</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   8.9332   5.2418</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{5,2} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">A</span> <span class="op">=</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/cbind.html" class="external-link">rbind</a></span><span class="op">(</span></span>
+<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>,<span class="fl">1</span>,<span class="fl">1</span><span class="op">)</span>,</span>
+<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>,<span class="fl">3</span>,<span class="fl">4</span><span class="op">)</span>,</span>
+<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>,<span class="fl">5</span>,<span class="fl">2</span><span class="op">)</span>,</span>
+<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span>,<span class="fl">2</span>,<span class="fl">5</span><span class="op">)</span>,</span>
+<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">5</span>,<span class="fl">4</span>,<span class="fl">3</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">B</span> <span class="op">=</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/cbind.html" class="external-link">rbind</a></span><span class="op">(</span></span>
+<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="op">-</span><span class="fl">10</span>, <span class="op">-</span><span class="fl">3</span><span class="op">)</span>,</span>
+<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">12</span>, <span class="fl">14</span><span class="op">)</span>,</span>
+<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">14</span>, <span class="fl">12</span><span class="op">)</span>,</span>
+<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">16</span>, <span class="fl">16</span><span class="op">)</span>,</span>
+<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">18</span>, <span class="fl">16</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">out</span> <span class="op">=</span> <span class="fu">torch_lstsq</span><span class="op">(</span><span class="va">B</span>, <span class="va">A</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">out</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   2.0000   1.0000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   1.0000   1.0000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   1.0000   2.0000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  10.9635   4.8501</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   8.9332   5.2418</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{5,2} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Lt</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_lt.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Lt</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_lt.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Lt</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_lt</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>other</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the tensor to compare</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Tensor or float) the tensor or value to compare</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="lt-input-other-out-null-gt-tensor-"><a class="anchor" href="#lt-input-other-out-null-gt-tensor-"></a>lt(input, other, out=NULL) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_lt</span><span class="op">(</span><span class="va">self</span>, <span class="va">other</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the tensor to compare</p></dd>
+<dt>other</dt>
+<dd><p>(Tensor or float) the tensor or value to compare</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="lt-input-other-out-null-gt-tensor-">lt(input, other, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#lt-input-other-out-null-gt-tensor-"></a></h2>
     
 
 
 <p>Computes \(\mbox{input} &lt; \mbox{other}\) element-wise.</p>
 <p>The second argument can be a number or a tensor whose shape is
 broadcastable  with the first argument.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='fu'>torch_lt</span><span class='op'>(</span><span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span><span class='op'>(</span><span class='fl'>1</span><span class='op'>:</span><span class='fl'>4</span>, ncol <span class='op'>=</span> <span class='fl'>2</span>, byrow<span class='op'>=</span><span class='cn'>TRUE</span><span class='op'>)</span><span class='op'>)</span>, </span>
-<span class='r-in'>         <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>,<span class='fl'>1</span>,<span class='fl'>4</span>,<span class='fl'>4</span><span class='op'>)</span>, ncol <span class='op'>=</span> <span class='fl'>2</span>, byrow<span class='op'>=</span><span class='cn'>TRUE</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUBoolType{2,2} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="fu">torch_lt</span><span class="op">(</span><span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/matrix.html" class="external-link">matrix</a></span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="fl">4</span>, ncol <span class="op">=</span> <span class="fl">2</span>, byrow<span class="op">=</span><span class="cn">TRUE</span><span class="op">)</span><span class="op">)</span>, </span>
+<span class="r-in">         <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/matrix.html" class="external-link">matrix</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>,<span class="fl">1</span>,<span class="fl">4</span>,<span class="fl">4</span><span class="op">)</span>, ncol <span class="op">=</span> <span class="fl">2</span>, byrow<span class="op">=</span><span class="cn">TRUE</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUBoolType{2,2} ]</span>
+</code></pre></div>
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+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>LU</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/wrapers.R'><code>R/wrapers.R</code></a></small>
-    <div class="hidden name"><code>torch_lu.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>LU</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
+      <div class="d-none name"><code>torch_lu.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Computes the LU factorization of a matrix or batches of matrices A. Returns a
 tuple containing the LU factorization and pivots of A. Pivoting is done if pivot
 is set to True.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_lu</span><span class='op'>(</span><span class='va'>A</span>, pivot <span class='op'>=</span> <span class='cn'>TRUE</span>, get_infos <span class='op'>=</span> <span class='cn'>FALSE</span>, out <span class='op'>=</span> <span class='cn'>NULL</span><span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_lu</span><span class="op">(</span><span class="va">A</span>, pivot <span class="op">=</span> <span class="cn">TRUE</span>, get_infos <span class="op">=</span> <span class="cn">FALSE</span>, out <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>A</th>
-      <td><p>(Tensor) the tensor to factor of size (<em>, m, n)(</em>,m,n)</p></td>
-    </tr>
-    <tr>
-      <th>pivot</th>
-      <td><p>(bool, optional) – controls whether pivoting is done. Default: TRUE</p></td>
-    </tr>
-    <tr>
-      <th>get_infos</th>
-      <td><p>(bool, optional) – if set to True, returns an info IntTensor. Default: FALSE</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(tuple, optional) – optional output tuple. If get_infos is True, then the elements
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>A</dt>
+<dd><p>(Tensor) the tensor to factor of size (<em>, m, n)(</em>,m,n)</p></dd>
+<dt>pivot</dt>
+<dd><p>(bool, optional) – controls whether pivoting is done. Default: TRUE</p></dd>
+<dt>get_infos</dt>
+<dd><p>(bool, optional) – if set to True, returns an info IntTensor. Default: FALSE</p></dd>
+<dt>out</dt>
+<dd><p>(tuple, optional) – optional output tuple. If get_infos is True, then the elements
 in the tuple are Tensor, IntTensor, and IntTensor. If get_infos is False, then the
-elements in the tuple are Tensor, IntTensor. Default: NULL</p></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>A</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>3</span>, <span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_lu</span><span class='op'>(</span><span class='va'>A</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [[1]]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> (1,.,.) = </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   2.0735  0.5737  1.3582</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  -0.4218  2.1589  0.4119</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  -0.6340  0.3636  1.2700</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> (2,.,.) = </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   1.7088 -1.0247 -0.6357</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   0.5663  1.2614  0.9340</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   0.4498 -0.5494  0.9051</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{2,3,3} ]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [[2]]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1  2  3</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1  2  3</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUIntType{2,3} ]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+elements in the tuple are Tensor, IntTensor. Default: NULL</p></dd>
+</dl></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">A</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">3</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_lu</span><span class="op">(</span><span class="va">A</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [[1]]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> (1,.,.) = </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.8885  0.4276  0.5209</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.5745  1.2627 -0.8762</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.8760 -0.0408  0.0814</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> (2,.,.) = </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.5752 -1.3390  1.0259</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.2306 -0.6162 -1.6195</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.1260  0.0925  0.3801</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2,3,3} ]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [[2]]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1  2  3</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2  3  3</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUIntType{2,3} ]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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-    <div class="page-header">
-    <h1>Lu_solve</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_lu_solve.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Lu_solve</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_lu_solve.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Lu_solve</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_lu_solve</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>LU_data</span>, <span class='va'>LU_pivots</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the RHS tensor of size \((*, m, k)\), where \(*\)                is zero or more batch dimensions.</p></td>
-    </tr>
-    <tr>
-      <th>LU_data</th>
-      <td><p>(Tensor) the pivoted LU factorization of A from <code>torch_lu</code> of size \((*, m, m)\),                       where \(*\) is zero or more batch dimensions.</p></td>
-    </tr>
-    <tr>
-      <th>LU_pivots</th>
-      <td><p>(IntTensor) the pivots of the LU factorization from <code>torch_lu</code> of size \((*, m)\),                           where \(*\) is zero or more batch dimensions.                           The batch dimensions of <code>LU_pivots</code> must be equal to the batch dimensions of                           <code>LU_data</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="lu-solve-input-lu-data-lu-pivots-out-null-gt-tensor-"><a class="anchor" href="#lu-solve-input-lu-data-lu-pivots-out-null-gt-tensor-"></a>lu_solve(input, LU_data, LU_pivots, out=NULL) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_lu_solve</span><span class="op">(</span><span class="va">self</span>, <span class="va">LU_data</span>, <span class="va">LU_pivots</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the RHS tensor of size \((*, m, k)\), where \(*\)                is zero or more batch dimensions.</p></dd>
+<dt>LU_data</dt>
+<dd><p>(Tensor) the pivoted LU factorization of A from <code>torch_lu</code> of size \((*, m, m)\),                       where \(*\) is zero or more batch dimensions.</p></dd>
+<dt>LU_pivots</dt>
+<dd><p>(IntTensor) the pivots of the LU factorization from <code>torch_lu</code> of size \((*, m)\),                           where \(*\) is zero or more batch dimensions.                           The batch dimensions of <code>LU_pivots</code> must be equal to the batch dimensions of                           <code>LU_data</code>.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="lu-solve-input-lu-data-lu-pivots-out-null-gt-tensor-">lu_solve(input, LU_data, LU_pivots, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#lu-solve-input-lu-data-lu-pivots-out-null-gt-tensor-"></a></h2>
     
 
 
 <p>Returns the LU solve of the linear system \(Ax = b\) using the partially pivoted
 LU factorization of A from <code>torch_lu</code>.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>A</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>3</span>, <span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>b</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>3</span>, <span class='fl'>1</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>out</span> <span class='op'>=</span> <span class='fu'><a href='torch_lu.html'>torch_lu</a></span><span class='op'>(</span><span class='va'>A</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>x</span> <span class='op'>=</span> <span class='fu'>torch_lu_solve</span><span class='op'>(</span><span class='va'>b</span>, <span class='va'>out</span><span class='op'>[[</span><span class='fl'>1</span><span class='op'>]</span><span class='op'>]</span>, <span class='va'>out</span><span class='op'>[[</span><span class='fl'>2</span><span class='op'>]</span><span class='op'>]</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'><a href='torch_norm.html'>torch_norm</a></span><span class='op'>(</span><span class='fu'><a href='torch_bmm.html'>torch_bmm</a></span><span class='op'>(</span><span class='va'>A</span>, <span class='va'>x</span><span class='op'>)</span> <span class='op'>-</span> <span class='va'>b</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> 1.58402e-07</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
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-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">A</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">3</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">b</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">3</span>, <span class="fl">1</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">out</span> <span class="op">=</span> <span class="fu"><a href="torch_lu.html">torch_lu</a></span><span class="op">(</span><span class="va">A</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">x</span> <span class="op">=</span> <span class="fu">torch_lu_solve</span><span class="op">(</span><span class="va">b</span>, <span class="va">out</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span>, <span class="va">out</span><span class="op">[[</span><span class="fl">2</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu"><a href="torch_norm.html">torch_norm</a></span><span class="op">(</span><span class="fu"><a href="torch_bmm.html">torch_bmm</a></span><span class="op">(</span><span class="va">A</span>, <span class="va">x</span><span class="op">)</span> <span class="op">-</span> <span class="va">b</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 1.18011e-06</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{} ]</span>
+</code></pre></div>
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Masked_select</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_masked_select.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Masked_select</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_masked_select.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Masked_select</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_masked_select</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>mask</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>mask</th>
-      <td><p>(BoolTensor) the tensor containing the binary mask to index with</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_masked_select</span><span class="op">(</span><span class="va">self</span>, <span class="va">mask</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+<dt>mask</dt>
+<dd><p>(BoolTensor) the tensor containing the binary mask to index with</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p>The returned tensor does <strong>not</strong> use the same storage
 as the original tensor</p>
-    <h2 class="hasAnchor" id="masked-select-input-mask-out-null-gt-tensor-"><a class="anchor" href="#masked-select-input-mask-out-null-gt-tensor-"></a>masked_select(input, mask, out=NULL) -&gt; Tensor </h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="masked-select-input-mask-out-null-gt-tensor-">masked_select(input, mask, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#masked-select-input-mask-out-null-gt-tensor-"></a></h2>
     
 
 
@@ -217,48 +106,44 @@ as the original tensor</p>
 the boolean mask <code>mask</code> which is a <code>BoolTensor</code>.</p>
 <p>The shapes of the <code>mask</code> tensor and the <code>input</code> tensor don't need
 to match, but they must be broadcastable .</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>x</span></span>
-<span class='r-in'><span class='va'>mask</span> <span class='op'>=</span> <span class='va'>x</span><span class='op'>$</span><span class='fu'>ge</span><span class='op'>(</span><span class='fl'>0.5</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>mask</span></span>
-<span class='r-in'><span class='fu'>torch_masked_select</span><span class='op'>(</span><span class='va'>x</span>, <span class='va'>mask</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.6521</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.7903</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1.2373</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.8047</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{4} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">x</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">4</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">x</span></span>
+<span class="r-in"><span class="va">mask</span> <span class="op">=</span> <span class="va">x</span><span class="op">$</span><span class="fu">ge</span><span class="op">(</span><span class="fl">0.5</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">mask</span></span>
+<span class="r-in"><span class="fu">torch_masked_select</span><span class="op">(</span><span class="va">x</span>, <span class="va">mask</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.0384</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.5945</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2.0981</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.5444</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.5651</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{5} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Matmul</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_matmul.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Matmul</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_matmul.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Matmul</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_matmul</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>other</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the first tensor to be multiplied</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Tensor) the second tensor to be multiplied</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_matmul</span><span class="op">(</span><span class="va">self</span>, <span class="va">other</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the first tensor to be multiplied</p></dd>
+<dt>other</dt>
+<dd><p>(Tensor) the second tensor to be multiplied</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     
-<pre>The 1-dimensional dot product version of this function does not support an `out` parameter.
-</pre>
-
-    <h2 class="hasAnchor" id="matmul-input-other-out-null-gt-tensor-"><a class="anchor" href="#matmul-input-other-out-null-gt-tensor-"></a>matmul(input, other, out=NULL) -&gt; Tensor </h2>
+<div class="sourceCode"><pre><code>The 1-dimensional dot product version of this function does not support an `out` parameter.
+</code></pre></div>
 
+    </div>
+    <div class="section level2">
+    <h2 id="matmul-input-other-out-null-gt-tensor-">matmul(input, other, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#matmul-input-other-out-null-gt-tensor-"></a></h2>
     
 
 
 <p>Matrix product of two tensors.</p>
-<p>The behavior depends on the dimensionality of the tensors as follows:</p><ul>
-<li><p>If both tensors are 1-dimensional, the dot product (scalar) is returned.</p></li>
+<p>The behavior depends on the dimensionality of the tensors as follows:</p><ul><li><p>If both tensors are 1-dimensional, the dot product (scalar) is returned.</p></li>
 <li><p>If both arguments are 2-dimensional, the matrix-matrix product is returned.</p></li>
 <li><p>If the first argument is 1-dimensional and the second argument is 2-dimensional,
 a 1 is prepended to its dimension for the purpose of the matrix multiply.
@@ -233,92 +121,85 @@ The non-matrix (i.e. batch) dimensions are broadcasted  (and thus
 must be broadcastable).  For example, if <code>input</code> is a
 \((j \times 1 \times n \times m)\) tensor and <code>other</code> is a \((k \times m \times p)\)
 tensor, <code>out</code> will be an \((j \times k \times n \times p)\) tensor.</p></li>
-</ul>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='co'># vector x vector</span></span>
-<span class='r-in'><span class='va'>tensor1</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>tensor2</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_matmul</span><span class='op'>(</span><span class='va'>tensor1</span>, <span class='va'>tensor2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='co'># matrix x vector</span></span>
-<span class='r-in'><span class='va'>tensor1</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>tensor2</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_matmul</span><span class='op'>(</span><span class='va'>tensor1</span>, <span class='va'>tensor2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='co'># batched matrix x broadcasted vector</span></span>
-<span class='r-in'><span class='va'>tensor1</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>10</span>, <span class='fl'>3</span>, <span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>tensor2</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_matmul</span><span class='op'>(</span><span class='va'>tensor1</span>, <span class='va'>tensor2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='co'># batched matrix x batched matrix</span></span>
-<span class='r-in'><span class='va'>tensor1</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>10</span>, <span class='fl'>3</span>, <span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>tensor2</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>10</span>, <span class='fl'>4</span>, <span class='fl'>5</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_matmul</span><span class='op'>(</span><span class='va'>tensor1</span>, <span class='va'>tensor2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='co'># batched matrix x broadcasted matrix</span></span>
-<span class='r-in'><span class='va'>tensor1</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>10</span>, <span class='fl'>3</span>, <span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>tensor2</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span>, <span class='fl'>5</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_matmul</span><span class='op'>(</span><span class='va'>tensor1</span>, <span class='va'>tensor2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> (1,.,.) = </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   1.0347  0.9383 -0.2128 -1.9383 -0.5239</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  -1.3438 -3.5077  3.2572  7.0626  1.2038</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   0.0628 -0.7234  0.9564  1.1917 -0.0163</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> (2,.,.) = </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  -0.5416  1.9320 -0.7307 -0.7391 -0.6483</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  -0.4493 -5.2712  3.2891  5.1073  0.6477</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   3.0317  0.1313  1.9297  0.5902  1.3069</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> (3,.,.) = </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   2.5301  1.3720 -0.0374 -4.3727 -1.4484</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   0.9692 -3.2186  2.2818  3.2848  1.6063</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   0.0259  0.2547 -0.5271 -1.6179 -0.8138</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> (4,.,.) = </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   0.3555 -0.4624  1.6977  1.9477 -0.0570</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  -1.6919  0.7828  1.0366  3.4411 -0.2882</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  -1.0362  0.2898 -2.1695 -2.0896 -0.1644</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> (5,.,.) = </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  -1.6252  1.1645 -0.9394  1.4195  0.5983</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  -7.7012  0.9219 -2.5478  4.4590 -1.2017</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   1.9651 -4.7349  2.1572  0.9658  0.9976</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> (6,.,.) = </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   1.2488 -1.6758  2.0117  1.9485  0.7541</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   1.4588  0.0704  0.1182 -2.4593 -0.8429</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  -0.1573  3.3905 -1.7889 -3.1360 -1.2563</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> ... [the output was truncated (use n=-1 to disable)]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{10,3,5} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+</ul></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="co"># vector x vector</span></span>
+<span class="r-in"><span class="va">tensor1</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">tensor2</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_matmul</span><span class="op">(</span><span class="va">tensor1</span>, <span class="va">tensor2</span><span class="op">)</span></span>
+<span class="r-in"><span class="co"># matrix x vector</span></span>
+<span class="r-in"><span class="va">tensor1</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">4</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">tensor2</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_matmul</span><span class="op">(</span><span class="va">tensor1</span>, <span class="va">tensor2</span><span class="op">)</span></span>
+<span class="r-in"><span class="co"># batched matrix x broadcasted vector</span></span>
+<span class="r-in"><span class="va">tensor1</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">10</span>, <span class="fl">3</span>, <span class="fl">4</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">tensor2</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_matmul</span><span class="op">(</span><span class="va">tensor1</span>, <span class="va">tensor2</span><span class="op">)</span></span>
+<span class="r-in"><span class="co"># batched matrix x batched matrix</span></span>
+<span class="r-in"><span class="va">tensor1</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">10</span>, <span class="fl">3</span>, <span class="fl">4</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">tensor2</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">10</span>, <span class="fl">4</span>, <span class="fl">5</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_matmul</span><span class="op">(</span><span class="va">tensor1</span>, <span class="va">tensor2</span><span class="op">)</span></span>
+<span class="r-in"><span class="co"># batched matrix x broadcasted matrix</span></span>
+<span class="r-in"><span class="va">tensor1</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">10</span>, <span class="fl">3</span>, <span class="fl">4</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">tensor2</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span>, <span class="fl">5</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_matmul</span><span class="op">(</span><span class="va">tensor1</span>, <span class="va">tensor2</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> (1,.,.) = </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.2467  0.0267  0.2256  0.3048 -0.2469</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.5704 -0.0074 -3.7815 -0.8250  3.3963</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.9025  0.3102 -0.3818 -1.0451  0.1589</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> (2,.,.) = </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -1.1851 -0.1799 -0.2034  4.6517  0.3584</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -3.3281  1.0199 -0.6615  3.3140 -0.0723</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.5760 -0.2987  1.7212 -0.4701 -1.3388</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> (3,.,.) = </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -2.2704  1.2708 -0.6654  1.7082 -0.3315</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   1.0337  0.0243 -0.2888 -2.8101  0.2049</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -3.8316  1.2946  2.1583  4.3194 -2.8303</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> (4,.,.) = </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.8220  0.9216  1.8024 -2.3606 -2.4297</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.8171  0.5321 -0.5868  0.0176  0.1392</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -1.2666  1.4857 -0.5426  1.4303 -0.6876</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> (5,.,.) = </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -2.3602  1.0924  0.5167  0.7857 -1.2332</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   1.9469 -1.1037  1.3619 -2.1359 -0.4112</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.7107 -0.6890  0.3136  1.5808  0.3235</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> (6,.,.) = </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.6544 -1.2847 -0.1382  0.8716  1.1478</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   4.1693 -2.7576  1.3553 -3.2945  0.8458</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.7586  0.1057  1.7404  1.0001 -1.6229</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> ... [the output was truncated (use n=-1 to disable)]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{10,3,5} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Matrix_exp</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_matrix_exp.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Matrix_exp</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_matrix_exp.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Matrix_exp</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_matrix_exp</span><span class='op'>(</span><span class='va'>self</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="matrix-power-input-gt-tensor-"><a class="anchor" href="#matrix-power-input-gt-tensor-"></a>matrix_power(input) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_matrix_exp</span><span class="op">(</span><span class="va">self</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="matrix-power-input-gt-tensor-">matrix_power(input) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#matrix-power-input-gt-tensor-"></a></h2>
     
 
 
@@ -214,49 +104,44 @@ $$</p>
 Bader, P.; Blanes, S.; Casas, F.
 Computing the Matrix Exponential with an Optimized Taylor Polynomial Approximation.
 Mathematics 2019, 7, 1174.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>2</span>, <span class='fl'>2</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span><span class='op'>[</span><span class='fl'>1</span>, , <span class='op'>]</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_eye.html'>torch_eye</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span><span class='op'>[</span><span class='fl'>2</span>, , <span class='op'>]</span> <span class='op'>&lt;-</span> <span class='fl'>2</span> <span class='op'>*</span> <span class='fu'><a href='torch_eye.html'>torch_eye</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='fu'>torch_matrix_exp</span><span class='op'>(</span><span class='va'>a</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>x</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/cbind.html'>rbind</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>0</span>, <span class='va'>pi</span><span class='op'>/</span><span class='fl'>3</span><span class='op'>)</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='op'>-</span><span class='va'>pi</span><span class='op'>/</span><span class='fl'>3</span>, <span class='fl'>0</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>x</span><span class='op'>$</span><span class='fu'>matrix_exp</span><span class='op'>(</span><span class='op'>)</span> <span class='co'># should be [[cos(pi/3), sin(pi/3)], [-sin(pi/3), cos(pi/3)]]</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.5000  0.8660</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.8660  0.5000</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{2,2} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">2</span>, <span class="fl">2</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span><span class="op">[</span><span class="fl">1</span>, , <span class="op">]</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_eye.html">torch_eye</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span><span class="op">[</span><span class="fl">2</span>, , <span class="op">]</span> <span class="op">&lt;-</span> <span class="fl">2</span> <span class="op">*</span> <span class="fu"><a href="torch_eye.html">torch_eye</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="fu">torch_matrix_exp</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/cbind.html" class="external-link">rbind</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="va">pi</span><span class="op">/</span><span class="fl">3</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="op">-</span><span class="va">pi</span><span class="op">/</span><span class="fl">3</span>, <span class="fl">0</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">x</span><span class="op">$</span><span class="fu">matrix_exp</span><span class="op">(</span><span class="op">)</span> <span class="co"># should be [[cos(pi/3), sin(pi/3)], [-sin(pi/3), cos(pi/3)]]</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.5000  0.8660</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.8660  0.5000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2,2} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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diff --git a/reference/torch_matrix_power.html b/reference/torch_matrix_power.html
index 9a53242c9..b6e8f111c 100644
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Matrix_power</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_matrix_power.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Matrix_power</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_matrix_power.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Matrix_power</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_matrix_power</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>n</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>n</th>
-      <td><p>(int) the power to raise the matrix to</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="matrix-power-input-n-gt-tensor-"><a class="anchor" href="#matrix-power-input-n-gt-tensor-"></a>matrix_power(input, n) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_matrix_power</span><span class="op">(</span><span class="va">self</span>, <span class="va">n</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+<dt>n</dt>
+<dd><p>(int) the power to raise the matrix to</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="matrix-power-input-n-gt-tensor-">matrix_power(input, n) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#matrix-power-input-n-gt-tensor-"></a></h2>
     
 
 
@@ -215,49 +103,45 @@ For batch of matrices, each individual matrix is raised to the power <code>n</co
 raised to the power <code>n</code>.  For a batch of matrices, the batched inverse
 (if invertible) is raised to the power <code>n</code>. If <code>n</code> is 0, then an identity matrix
 is returned.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>2</span>, <span class='fl'>2</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='fu'>torch_matrix_power</span><span class='op'>(</span><span class='va'>a</span>, <span class='fl'>3</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> (1,.,.) = </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   0.2868  0.0824</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  -0.3228 -0.3674</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> (2,.,.) = </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   0.2002  0.2288</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  -0.0559  0.2226</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{2,2,2} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">2</span>, <span class="fl">2</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="fu">torch_matrix_power</span><span class="op">(</span><span class="va">a</span>, <span class="fl">3</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> (1,.,.) = </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.01 *</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -8.2401 -109.0933</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   -37.7152 -470.9431</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> (2,.,.) = </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   1.0132 -0.6814</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   1.6588  0.0417</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2,2,2} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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-      <div class="copyright">
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+    <footer><div class="pkgdown-footer-left">
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+<div class="pkgdown-footer-right">
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diff --git a/reference/torch_matrix_rank.html b/reference/torch_matrix_rank.html
index 6cd0f464d..e5e55b79c 100644
--- a/reference/torch_matrix_rank.html
+++ b/reference/torch_matrix_rank.html
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Matrix_rank</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_matrix_rank.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Matrix_rank</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_matrix_rank.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Matrix_rank</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_matrix_rank</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>tol</span>, symmetric <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input 2-D tensor</p></td>
-    </tr>
-    <tr>
-      <th>tol</th>
-      <td><p>(float, optional) the tolerance value. Default: <code>NULL</code></p></td>
-    </tr>
-    <tr>
-      <th>symmetric</th>
-      <td><p>(bool, optional) indicates whether <code>input</code> is symmetric.                               Default: <code>FALSE</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="matrix-rank-input-tol-null-symmetric-false-gt-tensor-"><a class="anchor" href="#matrix-rank-input-tol-null-symmetric-false-gt-tensor-"></a>matrix_rank(input, tol=NULL, symmetric=False) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_matrix_rank</span><span class="op">(</span><span class="va">self</span>, <span class="va">tol</span>, symmetric <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input 2-D tensor</p></dd>
+<dt>tol</dt>
+<dd><p>(float, optional) the tolerance value. Default: <code>NULL</code></p></dd>
+<dt>symmetric</dt>
+<dd><p>(bool, optional) indicates whether <code>input</code> is symmetric.                               Default: <code>FALSE</code></p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="matrix-rank-input-tol-null-symmetric-false-gt-tensor-">matrix_rank(input, tol=NULL, symmetric=False) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#matrix-rank-input-tol-null-symmetric-false-gt-tensor-"></a></h2>
     
 
 
@@ -222,42 +108,37 @@ when <code>symmetric</code> is <code>TRUE</code>) are considered to be 0. If <co
 specified, <code>tol</code> is set to <code>S.max() * max(S.size()) * eps</code> where <code>S</code> is the
 singular values (or the eigenvalues when <code>symmetric</code> is <code>TRUE</code>), and <code>eps</code>
 is the epsilon value for the datatype of <code>input</code>.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_eye.html'>torch_eye</a></span><span class='op'>(</span><span class='fl'>10</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_matrix_rank</span><span class='op'>(</span><span class='va'>a</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> 10</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPULongType{} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_eye.html">torch_eye</a></span><span class="op">(</span><span class="fl">10</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_matrix_rank</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 10</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPULongType{} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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index 916dfc90b..67b357ab7 100644
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+  </div>
+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Max</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_max.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Max</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a></small>
+      <div class="d-none name"><code>torch_max.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Max</p>
     </div>
 
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) the dimension to reduce.</p></td>
-    </tr>
-    <tr>
-      <th>keepdim</th>
-      <td><p>(bool) whether the output tensor has <code>dim</code> retained or not. Default: <code>FALSE</code>.</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(tuple, optional) the result tuple of two output tensors (max, max_indices)</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Tensor) the second input tensor</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+<dt>dim</dt>
+<dd><p>(int) the dimension to reduce.</p></dd>
+<dt>keepdim</dt>
+<dd><p>(bool) whether the output tensor has <code>dim</code> retained or not. Default: <code>FALSE</code>.</p></dd>
+<dt>out</dt>
+<dd><p>(tuple, optional) the result tuple of two output tensors (max, max_indices)</p></dd>
+<dt>other</dt>
+<dd><p>(Tensor) the second input tensor</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p>When the shapes do not match, the shape of the returned output tensor
 follows the broadcasting rules .</p>
-    <h2 class="hasAnchor" id="max-input-gt-tensor-"><a class="anchor" href="#max-input-gt-tensor-"></a>max(input) -&gt; Tensor </h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="max-input-gt-tensor-">max(input) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#max-input-gt-tensor-"></a></h2>
     
 
 
 <p>Returns the maximum value of all elements in the <code>input</code> tensor.</p>
-    <h2 class="hasAnchor" id="max-input-dim-keepdim-false-out-null-gt-tensor-longtensor-"><a class="anchor" href="#max-input-dim-keepdim-false-out-null-gt-tensor-longtensor-"></a>max(input, dim, keepdim=False, out=NULL) -&gt; (Tensor, LongTensor) </h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="max-input-dim-keepdim-false-out-null-gt-tensor-longtensor-">max(input, dim, keepdim=False, out=NULL) -&gt; (Tensor, LongTensor) <a class="anchor" aria-label="anchor" href="#max-input-dim-keepdim-false-out-null-gt-tensor-longtensor-"></a></h2>
     
 
 
@@ -234,8 +115,9 @@ follows the broadcasting rules .</p>
 value of each row of the <code>input</code> tensor in the given dimension
 <code>dim</code>. And <code>indices</code> is the index location of each maximum value found
 (argmax).</p>
-    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></a></h2>
     
 
 <p><code>indices</code> does not necessarily contain the first occurrence of each
@@ -244,10 +126,11 @@ The exact implementation details are device-specific.
 Do not expect the same result when run on CPU and GPU in general.</p>
 <p>If <code>keepdim</code> is <code>TRUE</code>, the output tensors are of the same size
 as <code>input</code> except in the dimension <code>dim</code> where they are of size 1.
-Otherwise, <code>dim</code> is squeezed (see <code><a href='torch_squeeze.html'>torch_squeeze</a></code>), resulting
+Otherwise, <code>dim</code> is squeezed (see <code><a href="torch_squeeze.html">torch_squeeze</a></code>), resulting
 in the output tensors having 1 fewer dimension than <code>input</code>.</p>
-    <h2 class="hasAnchor" id="max-input-other-out-null-gt-tensor-"><a class="anchor" href="#max-input-other-out-null-gt-tensor-"></a>max(input, other, out=NULL) -&gt; Tensor </h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="max-input-other-out-null-gt-tensor-">max(input, other, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#max-input-other-out-null-gt-tensor-"></a></h2>
     
 
 
@@ -258,58 +141,53 @@ but they must be broadcastable .</p>
 <p>$$
     \mbox{out}_i = \max(\mbox{tensor}_i, \mbox{other}_i)
 $$</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='fu'>torch_max</span><span class='op'>(</span><span class='va'>a</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span>, <span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='fu'>torch_max</span><span class='op'>(</span><span class='va'>a</span>, dim <span class='op'>=</span> <span class='fl'>1</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='va'>b</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>b</span></span>
-<span class='r-in'><span class='fu'>torch_max</span><span class='op'>(</span><span class='va'>a</span>, other <span class='op'>=</span> <span class='va'>b</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.0297</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.4080</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.9195</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -1.6251</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{4} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="fu">torch_max</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span>
+<span class="r-in"></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span>, <span class="fl">4</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="fu">torch_max</span><span class="op">(</span><span class="va">a</span>, dim <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span>
+<span class="r-in"></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="va">b</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">b</span></span>
+<span class="r-in"><span class="fu">torch_max</span><span class="op">(</span><span class="va">a</span>, other <span class="op">=</span> <span class="va">b</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.0171</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.4346</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.5751</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.1117</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Maximum</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_maximum.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Maximum</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_maximum.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Maximum</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_maximum</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>other</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Tensor) the second input tensor</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_maximum</span><span class="op">(</span><span class="va">self</span>, <span class="va">other</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+<dt>other</dt>
+<dd><p>(Tensor) the second input tensor</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p>If one of the elements being compared is a NaN, then that element is returned.
 <code>torch_maximum()</code> is not supported for tensors with complex dtypes.</p>
-    <h2 class="hasAnchor" id="maximum-input-other-out-none-gt-tensor-"><a class="anchor" href="#maximum-input-other-out-none-gt-tensor-"></a>maximum(input, other, *, out=None) -&gt; Tensor </h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="maximum-input-other-out-none-gt-tensor-">maximum(input, other, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#maximum-input-other-out-none-gt-tensor-"></a></h2>
     
 
 
 <p>Computes the element-wise maximum of <code>input</code> and <code>other</code>.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>2</span>, <span class='op'>-</span><span class='fl'>1</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>b</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>0</span>, <span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_maximum</span><span class='op'>(</span><span class='va'>a</span>, <span class='va'>b</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  3</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  2</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  4</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{3} ]</span>
-</pre>
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-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">2</span>, <span class="op">-</span><span class="fl">1</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">b</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">0</span>, <span class="fl">4</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_maximum</span><span class="op">(</span><span class="va">a</span>, <span class="va">b</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  3</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  4</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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+  </div>
+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Mean</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_mean.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Mean</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_mean.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Mean</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_mean</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>dim</span>, keepdim <span class='op'>=</span> <span class='cn'>FALSE</span>, dtype <span class='op'>=</span> <span class='cn'>NULL</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int or tuple of ints) the dimension or dimensions to reduce.</p></td>
-    </tr>
-    <tr>
-      <th>keepdim</th>
-      <td><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>the resulting data type.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="mean-input-gt-tensor-"><a class="anchor" href="#mean-input-gt-tensor-"></a>mean(input) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_mean</span><span class="op">(</span><span class="va">self</span>, <span class="va">dim</span>, keepdim <span class="op">=</span> <span class="cn">FALSE</span>, dtype <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+<dt>dim</dt>
+<dd><p>(int or tuple of ints) the dimension or dimensions to reduce.</p></dd>
+<dt>keepdim</dt>
+<dd><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></dd>
+<dt>dtype</dt>
+<dd><p>the resulting data type.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="mean-input-gt-tensor-">mean(input) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#mean-input-gt-tensor-"></a></h2>
     
 
 
 <p>Returns the mean value of all elements in the <code>input</code> tensor.</p>
-    <h2 class="hasAnchor" id="mean-input-dim-keepdim-false-out-null-gt-tensor-"><a class="anchor" href="#mean-input-dim-keepdim-false-out-null-gt-tensor-"></a>mean(input, dim, keepdim=False, out=NULL) -&gt; Tensor </h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="mean-input-dim-keepdim-false-out-null-gt-tensor-">mean(input, dim, keepdim=False, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#mean-input-dim-keepdim-false-out-null-gt-tensor-"></a></h2>
     
 
 
@@ -228,51 +113,46 @@ dimension <code>dim</code>. If <code>dim</code> is a list of dimensions,
 reduce over all of them.</p>
 <p>If <code>keepdim</code> is <code>TRUE</code>, the output tensor is of the same size
 as <code>input</code> except in the dimension(s) <code>dim</code> where it is of size 1.
-Otherwise, <code>dim</code> is squeezed (see <code><a href='torch_squeeze.html'>torch_squeeze</a></code>), resulting in the
+Otherwise, <code>dim</code> is squeezed (see <code><a href="torch_squeeze.html">torch_squeeze</a></code>), resulting in the
 output tensor having 1 (or <code>len(dim)</code>) fewer dimension(s).</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='fu'>torch_mean</span><span class='op'>(</span><span class='va'>a</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span>, <span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='fu'>torch_mean</span><span class='op'>(</span><span class='va'>a</span>, <span class='fl'>1</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_mean</span><span class='op'>(</span><span class='va'>a</span>, <span class='fl'>1</span>, <span class='cn'>TRUE</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.2037  0.0189  0.6187  0.3729</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{1,4} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="fu">torch_mean</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span>
+<span class="r-in"></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span>, <span class="fl">4</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="fu">torch_mean</span><span class="op">(</span><span class="va">a</span>, <span class="fl">1</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_mean</span><span class="op">(</span><span class="va">a</span>, <span class="fl">1</span>, <span class="cn">TRUE</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.3247 -0.1993 -0.1385 -0.1443</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{1,4} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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-    <div class="page-header">
-    <h1>Median</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_median.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Median</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_median.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Median</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_median</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>dim</span>, keepdim <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) the dimension to reduce.</p></td>
-    </tr>
-    <tr>
-      <th>keepdim</th>
-      <td><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="median-input-gt-tensor-"><a class="anchor" href="#median-input-gt-tensor-"></a>median(input) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_median</span><span class="op">(</span><span class="va">self</span>, <span class="va">dim</span>, keepdim <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+<dt>dim</dt>
+<dd><p>(int) the dimension to reduce.</p></dd>
+<dt>keepdim</dt>
+<dd><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="median-input-gt-tensor-">median(input) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#median-input-gt-tensor-"></a></h2>
     
 
 
 <p>Returns the median value of all elements in the <code>input</code> tensor.</p>
-    <h2 class="hasAnchor" id="median-input-dim-keepdim-false-out-null-gt-tensor-longtensor-"><a class="anchor" href="#median-input-dim-keepdim-false-out-null-gt-tensor-longtensor-"></a>median(input, dim=-1, keepdim=False, out=NULL) -&gt; (Tensor, LongTensor) </h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="median-input-dim-keepdim-false-out-null-gt-tensor-longtensor-">median(input, dim=-1, keepdim=False, out=NULL) -&gt; (Tensor, LongTensor) <a class="anchor" aria-label="anchor" href="#median-input-dim-keepdim-false-out-null-gt-tensor-longtensor-"></a></h2>
     
 
 
@@ -225,65 +112,60 @@ value of each row of the <code>input</code> tensor in the given dimension
 <p>By default, <code>dim</code> is the last dimension of the <code>input</code> tensor.</p>
 <p>If <code>keepdim</code> is <code>TRUE</code>, the output tensors are of the same size
 as <code>input</code> except in the dimension <code>dim</code> where they are of size 1.
-Otherwise, <code>dim</code> is squeezed (see <code><a href='torch_squeeze.html'>torch_squeeze</a></code>), resulting in
+Otherwise, <code>dim</code> is squeezed (see <code><a href="torch_squeeze.html">torch_squeeze</a></code>), resulting in
 the outputs tensor having 1 fewer dimension than <code>input</code>.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='fu'>torch_median</span><span class='op'>(</span><span class='va'>a</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span>, <span class='fl'>5</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='fu'>torch_median</span><span class='op'>(</span><span class='va'>a</span>, <span class='fl'>1</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [[1]]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.4756</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.5334</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.3488</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -2.0512</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -1.3630</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{5} ]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [[2]]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  3</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  2</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  3</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  3</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  3</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPULongType{5} ]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="fu">torch_median</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span>
+<span class="r-in"></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span>, <span class="fl">5</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="fu">torch_median</span><span class="op">(</span><span class="va">a</span>, <span class="fl">1</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [[1]]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.1617</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.4404</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.3647</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.2535</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.2494</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{5} ]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [[2]]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  3</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPULongType{5} ]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer>
-      <div class="copyright">
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+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
 </div>
 
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+<div class="pkgdown-footer-right">
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diff --git a/reference/torch_memory_format.html b/reference/torch_memory_format.html
index 20e01894f..965d131d9 100644
--- a/reference/torch_memory_format.html
+++ b/reference/torch_memory_format.html
@@ -1,227 +1,111 @@
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-    <div class="page-header">
-    <h1>Meshgrid</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_meshgrid.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Meshgrid</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_meshgrid.Rd</code></div>
     </div>
 
-    <div class="ref-description">
-    <p>Meshgrid</p>
+    <div class="ref-description section level2">
+    <p>Take \(N\) tensors, each of which can be either scalar or 1-dimensional
+vector, and create \(N\) N-dimensional grids, where the \(i\) <code>th</code> grid is defined by
+expanding the \(i\) <code>th</code> input over dimensions defined by other inputs.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_meshgrid</span><span class='op'>(</span><span class='va'>tensors</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>tensors</th>
-      <td><p>(list of Tensor) list of scalars or 1 dimensional tensors. Scalars will be
-treated (1,).</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="test-"><a class="anchor" href="#test-"></a>TEST </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_meshgrid</span><span class="op">(</span><span class="va">tensors</span>, <span class="va">indexing</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>tensors</dt>
+<dd><p>(list of Tensor) list of scalars or 1 dimensional tensors. Scalars will be
+treated (1,).</p></dd>
+<dt>indexing</dt>
+<dd><p>(str, optional): the indexing mode, either “xy” or “ij”, defaults to “ij”.
+See warning for future changes.
+If “xy” is selected, the first dimension corresponds to the cardinality of
+the second input and the second dimension corresponds to the cardinality of the
+first input.
+If “ij” is selected, the dimensions are in the same order as the cardinality
+of the inputs.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></a></h2>
     
 
+<p>In the future <code>torch_meshgrid</code> will transition to indexing=’xy’ as the default.
+This <a href="https://github.com/pytorch/pytorch/issues/50276" class="external-link">issue</a> tracks this issue
+with the goal of migrating to NumPy’s behavior.</p>
+    </div>
 
-<p>Take \(N\) tensors, each of which can be either scalar or 1-dimensional
-vector, and create \(N\) N-dimensional grids, where the \(i\) <code>th</code> grid is defined by
-expanding the \(i\) <code>th</code> input over dimensions defined by other inputs.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>2</span>, <span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>y</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span>, <span class='fl'>5</span>, <span class='fl'>6</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>out</span> <span class='op'>=</span> <span class='fu'>torch_meshgrid</span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span><span class='va'>x</span>, <span class='va'>y</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>out</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [[1]]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1  1  1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  2  2  2</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  3  3  3</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{3,3} ]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [[2]]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  4  5  6</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  4  5  6</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  4  5  6</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{3,3} ]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">x</span> <span class="op">=</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">2</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">y</span> <span class="op">=</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span>, <span class="fl">5</span>, <span class="fl">6</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">out</span> <span class="op">=</span> <span class="fu">torch_meshgrid</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">y</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">out</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [[1]]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1  1  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2  2  2</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  3  3  3</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3,3} ]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [[2]]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  4  5  6</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  4  5  6</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  4  5  6</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3,3} ]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Min</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_min.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Min</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a></small>
+      <div class="d-none name"><code>torch_min.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Min</p>
     </div>
 
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) the dimension to reduce.</p></td>
-    </tr>
-    <tr>
-      <th>keepdim</th>
-      <td><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></td>
-    </tr>
-    <tr>
-      <th>out</th>
-      <td><p>(tuple, optional) the tuple of two output tensors (min, min_indices)</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Tensor) the second input tensor</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+<dt>dim</dt>
+<dd><p>(int) the dimension to reduce.</p></dd>
+<dt>keepdim</dt>
+<dd><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></dd>
+<dt>out</dt>
+<dd><p>(tuple, optional) the tuple of two output tensors (min, min_indices)</p></dd>
+<dt>other</dt>
+<dd><p>(Tensor) the second input tensor</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p>When the shapes do not match, the shape of the returned output tensor
 follows the broadcasting rules .</p>
-    <h2 class="hasAnchor" id="min-input-gt-tensor-"><a class="anchor" href="#min-input-gt-tensor-"></a>min(input) -&gt; Tensor </h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="min-input-gt-tensor-">min(input) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#min-input-gt-tensor-"></a></h2>
     
 
 
 <p>Returns the minimum value of all elements in the <code>input</code> tensor.</p>
-    <h2 class="hasAnchor" id="min-input-dim-keepdim-false-out-null-gt-tensor-longtensor-"><a class="anchor" href="#min-input-dim-keepdim-false-out-null-gt-tensor-longtensor-"></a>min(input, dim, keepdim=False, out=NULL) -&gt; (Tensor, LongTensor) </h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="min-input-dim-keepdim-false-out-null-gt-tensor-longtensor-">min(input, dim, keepdim=False, out=NULL) -&gt; (Tensor, LongTensor) <a class="anchor" aria-label="anchor" href="#min-input-dim-keepdim-false-out-null-gt-tensor-longtensor-"></a></h2>
     
 
 
@@ -234,8 +115,9 @@ follows the broadcasting rules .</p>
 value of each row of the <code>input</code> tensor in the given dimension
 <code>dim</code>. And <code>indices</code> is the index location of each minimum value found
 (argmin).</p>
-    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></a></h2>
     
 
 <p><code>indices</code> does not necessarily contain the first occurrence of each
@@ -244,10 +126,11 @@ The exact implementation details are device-specific.
 Do not expect the same result when run on CPU and GPU in general.</p>
 <p>If <code>keepdim</code> is <code>TRUE</code>, the output tensors are of the same size as
 <code>input</code> except in the dimension <code>dim</code> where they are of size 1.
-Otherwise, <code>dim</code> is squeezed (see <code><a href='torch_squeeze.html'>torch_squeeze</a></code>), resulting in
+Otherwise, <code>dim</code> is squeezed (see <code><a href="torch_squeeze.html">torch_squeeze</a></code>), resulting in
 the output tensors having 1 fewer dimension than <code>input</code>.</p>
-    <h2 class="hasAnchor" id="min-input-other-out-null-gt-tensor-"><a class="anchor" href="#min-input-other-out-null-gt-tensor-"></a>min(input, other, out=NULL) -&gt; Tensor </h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="min-input-other-out-null-gt-tensor-">min(input, other, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#min-input-other-out-null-gt-tensor-"></a></h2>
     
 
 
@@ -259,58 +142,53 @@ but they must be broadcastable .</p>
 <p>$$
     \mbox{out}_i = \min(\mbox{tensor}_i, \mbox{other}_i)
 $$</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='fu'>torch_min</span><span class='op'>(</span><span class='va'>a</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span>, <span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='fu'>torch_min</span><span class='op'>(</span><span class='va'>a</span>, dim <span class='op'>=</span> <span class='fl'>1</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='va'>b</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>b</span></span>
-<span class='r-in'><span class='fu'>torch_min</span><span class='op'>(</span><span class='va'>a</span>, other <span class='op'>=</span> <span class='va'>b</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -1.3048</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -2.2819</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.6150</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.1191</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{4} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="fu">torch_min</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span>
+<span class="r-in"></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span>, <span class="fl">4</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="fu">torch_min</span><span class="op">(</span><span class="va">a</span>, dim <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span>
+<span class="r-in"></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="va">b</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">b</span></span>
+<span class="r-in"><span class="fu">torch_min</span><span class="op">(</span><span class="va">a</span>, other <span class="op">=</span> <span class="va">b</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -1.4927</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.2869</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -1.2572</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.4288</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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-  <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p>
+<div class="pkgdown-footer-right">
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diff --git a/reference/torch_minimum.html b/reference/torch_minimum.html
index 90a7d4769..12f64632e 100644
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@@ -1,258 +1,142 @@
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Minimum</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_minimum.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Minimum</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_minimum.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Minimum</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_minimum</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>other</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Tensor) the second input tensor</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_minimum</span><span class="op">(</span><span class="va">self</span>, <span class="va">other</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+<dt>other</dt>
+<dd><p>(Tensor) the second input tensor</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p>If one of the elements being compared is a NaN, then that element is returned.
 <code>torch_minimum()</code> is not supported for tensors with complex dtypes.</p>
-    <h2 class="hasAnchor" id="minimum-input-other-out-none-gt-tensor-"><a class="anchor" href="#minimum-input-other-out-none-gt-tensor-"></a>minimum(input, other, *, out=None) -&gt; Tensor </h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="minimum-input-other-out-none-gt-tensor-">minimum(input, other, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#minimum-input-other-out-none-gt-tensor-"></a></h2>
     
 
 
 <p>Computes the element-wise minimum of <code>input</code> and <code>other</code>.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>2</span>, <span class='op'>-</span><span class='fl'>1</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>b</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>0</span>, <span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_minimum</span><span class='op'>(</span><span class='va'>a</span>, <span class='va'>b</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{3} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">2</span>, <span class="op">-</span><span class="fl">1</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">b</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">0</span>, <span class="fl">4</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_minimum</span><span class="op">(</span><span class="va">a</span>, <span class="va">b</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3} ]</span>
+</code></pre></div>
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+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Mm</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_mm.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Mm</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_mm.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Mm</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_mm</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>mat2</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the first matrix to be multiplied</p></td>
-    </tr>
-    <tr>
-      <th>mat2</th>
-      <td><p>(Tensor) the second matrix to be multiplied</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_mm</span><span class="op">(</span><span class="va">self</span>, <span class="va">mat2</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the first matrix to be multiplied</p></dd>
+<dt>mat2</dt>
+<dd><p>(Tensor) the second matrix to be multiplied</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p>This function does not broadcast .
-For broadcasting matrix products, see <code><a href='torch_matmul.html'>torch_matmul</a></code>.</p>
-    <h2 class="hasAnchor" id="mm-input-mat-out-null-gt-tensor-"><a class="anchor" href="#mm-input-mat-out-null-gt-tensor-"></a>mm(input, mat2, out=NULL) -&gt; Tensor </h2>
-
+For broadcasting matrix products, see <code><a href="torch_matmul.html">torch_matmul</a></code>.</p>
+    </div>
+    <div class="section level2">
+    <h2 id="mm-input-mat-out-null-gt-tensor-">mm(input, mat2, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#mm-input-mat-out-null-gt-tensor-"></a></h2>
     
 
 
 <p>Performs a matrix multiplication of the matrices <code>input</code> and <code>mat2</code>.</p>
 <p>If <code>input</code> is a \((n \times m)\) tensor, <code>mat2</code> is a
 \((m \times p)\) tensor, <code>out</code> will be a \((n \times p)\) tensor.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>mat1</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>mat2</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_mm</span><span class='op'>(</span><span class='va'>mat1</span>, <span class='va'>mat2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.5741  1.7918  0.1654</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.1197  0.0156 -0.0546</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{2,3} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">mat1</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">mat2</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_mm</span><span class="op">(</span><span class="va">mat1</span>, <span class="va">mat2</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.2582  1.1655 -0.0292</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -1.4012  0.7270  0.9580</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2,3} ]</span>
+</code></pre></div>
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+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Mode</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_mode.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Mode</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_mode.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Mode</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_mode</span><span class='op'>(</span><span class='va'>self</span>, dim <span class='op'>=</span> <span class='op'>-</span><span class='fl'>1L</span>, keepdim <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) the dimension to reduce.</p></td>
-    </tr>
-    <tr>
-      <th>keepdim</th>
-      <td><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_mode</span><span class="op">(</span><span class="va">self</span>, dim <span class="op">=</span> <span class="op">-</span><span class="fl">1L</span>, keepdim <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+<dt>dim</dt>
+<dd><p>(int) the dimension to reduce.</p></dd>
+<dt>keepdim</dt>
+<dd><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p>This function is not defined for <code>torch_cuda.Tensor</code> yet.</p>
-    <h2 class="hasAnchor" id="mode-input-dim-keepdim-false-out-null-gt-tensor-longtensor-"><a class="anchor" href="#mode-input-dim-keepdim-false-out-null-gt-tensor-longtensor-"></a>mode(input, dim=-1, keepdim=False, out=NULL) -&gt; (Tensor, LongTensor) </h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="mode-input-dim-keepdim-false-out-null-gt-tensor-longtensor-">mode(input, dim=-1, keepdim=False, out=NULL) -&gt; (Tensor, LongTensor) <a class="anchor" aria-label="anchor" href="#mode-input-dim-keepdim-false-out-null-gt-tensor-longtensor-"></a></h2>
     
 
 
@@ -223,52 +110,47 @@ in that row, and <code>indices</code> is the index location of each mode value f
 <p>By default, <code>dim</code> is the last dimension of the <code>input</code> tensor.</p>
 <p>If <code>keepdim</code> is <code>TRUE</code>, the output tensors are of the same size as
 <code>input</code> except in the dimension <code>dim</code> where they are of size 1.
-Otherwise, <code>dim</code> is squeezed (see <code><a href='torch_squeeze.html'>torch_squeeze</a></code>), resulting
+Otherwise, <code>dim</code> is squeezed (see <code><a href="torch_squeeze.html">torch_squeeze</a></code>), resulting
 in the output tensors having 1 fewer dimension than <code>input</code>.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randint.html'>torch_randint</a></span><span class='op'>(</span><span class='fl'>0</span>, <span class='fl'>50</span>, size <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span><span class='fl'>5</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='fu'>torch_mode</span><span class='op'>(</span><span class='va'>a</span>, <span class='fl'>1</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [[1]]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> 2</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{} ]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [[2]]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> 1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPULongType{} ]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_randint.html">torch_randint</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">50</span>, size <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="fl">5</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="fu">torch_mode</span><span class="op">(</span><span class="va">a</span>, <span class="fl">1</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [[1]]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 20</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{} ]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [[2]]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 3</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPULongType{} ]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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diff --git a/reference/torch_movedim.html b/reference/torch_movedim.html
index 2437e2963..686dab675 100644
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@@ -1,215 +1,101 @@
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Movedim</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/wrapers.R'><code>R/wrapers.R</code></a></small>
-    <div class="hidden name"><code>torch_movedim.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Movedim</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
+      <div class="d-none name"><code>torch_movedim.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Movedim</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_movedim</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>source</span>, <span class='va'>destination</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>source</th>
-      <td><p>(int or tuple of ints) Original positions of the dims to move. These must be unique.</p></td>
-    </tr>
-    <tr>
-      <th>destination</th>
-      <td><p>(int or tuple of ints) Destination positions for each of the original dims. These must also be unique.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="movedim-input-source-destination-gt-tensor-"><a class="anchor" href="#movedim-input-source-destination-gt-tensor-"></a>movedim(input, source, destination) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_movedim</span><span class="op">(</span><span class="va">self</span>, <span class="va">source</span>, <span class="va">destination</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+<dt>source</dt>
+<dd><p>(int or tuple of ints) Original positions of the dims to move. These must be unique.</p></dd>
+<dt>destination</dt>
+<dd><p>(int or tuple of ints) Destination positions for each of the original dims. These must also be unique.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="movedim-input-source-destination-gt-tensor-">movedim(input, source, destination) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#movedim-input-source-destination-gt-tensor-"></a></h2>
     
 
 
@@ -217,51 +103,46 @@
 to the position(s) in <code>destination</code>.</p>
 <p>Other dimensions of <code>input</code> that are not explicitly moved remain in
 their original order and appear at the positions not specified in <code>destination</code>.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>t</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3</span>,<span class='fl'>2</span>,<span class='fl'>1</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>t</span></span>
-<span class='r-in'><span class='fu'>torch_movedim</span><span class='op'>(</span><span class='va'>t</span>, <span class='fl'>2</span>, <span class='fl'>1</span><span class='op'>)</span><span class='op'>$</span><span class='va'>shape</span></span>
-<span class='r-in'><span class='fu'>torch_movedim</span><span class='op'>(</span><span class='va'>t</span>, <span class='fl'>2</span>, <span class='fl'>1</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_movedim</span><span class='op'>(</span><span class='va'>t</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>3</span><span class='op'>)</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>2</span><span class='op'>)</span><span class='op'>)</span><span class='op'>$</span><span class='va'>shape</span></span>
-<span class='r-in'><span class='fu'>torch_movedim</span><span class='op'>(</span><span class='va'>t</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>3</span><span class='op'>)</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>2</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> (1,.,.) = </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   0.5335  0.4904  0.2055</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> (2,.,.) = </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.01 *</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  -1.1249 -125.8257 -113.7139</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{2,1,3} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">t</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>,<span class="fl">2</span>,<span class="fl">1</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">t</span></span>
+<span class="r-in"><span class="fu">torch_movedim</span><span class="op">(</span><span class="va">t</span>, <span class="fl">2</span>, <span class="fl">1</span><span class="op">)</span><span class="op">$</span><span class="va">shape</span></span>
+<span class="r-in"><span class="fu">torch_movedim</span><span class="op">(</span><span class="va">t</span>, <span class="fl">2</span>, <span class="fl">1</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_movedim</span><span class="op">(</span><span class="va">t</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">3</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">2</span><span class="op">)</span><span class="op">)</span><span class="op">$</span><span class="va">shape</span></span>
+<span class="r-in"><span class="fu">torch_movedim</span><span class="op">(</span><span class="va">t</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">3</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">2</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> (1,.,.) = </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.01 *</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   4.1230 -105.8317 -20.6793</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> (2,.,.) = </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   1.3951  1.1874 -0.3547</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2,1,3} ]</span>
+</code></pre></div>
+    </div>
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_mul.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Mul</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_mul.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Mul</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_mul</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>other</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the first multiplicand tensor</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Tensor) the second multiplicand tensor</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="mul-input-other-out-null-"><a class="anchor" href="#mul-input-other-out-null-"></a>mul(input, other, out=NULL) </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_mul</span><span class="op">(</span><span class="va">self</span>, <span class="va">other</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the first multiplicand tensor</p></dd>
+<dt>other</dt>
+<dd><p>(Tensor) the second multiplicand tensor</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="mul-input-other-out-null-">mul(input, other, out=NULL) <a class="anchor" aria-label="anchor" href="#mul-input-other-out-null-"></a></h2>
     
 
 
@@ -225,53 +113,48 @@ broadcastable .</p>
 <p>$$
     \mbox{out}_i = \mbox{input}_i \times \mbox{other}_i
 $$</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='fu'>torch_mul</span><span class='op'>(</span><span class='va'>a</span>, <span class='fl'>100</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span>, <span class='fl'>1</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='va'>b</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>b</span></span>
-<span class='r-in'><span class='fu'>torch_mul</span><span class='op'>(</span><span class='va'>a</span>, <span class='va'>b</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1.4449 -1.3047  5.0607  3.9262</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.4151 -0.3748  1.4539  1.1280</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.5088  0.4595 -1.7822 -1.3827</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.2832 -0.2558  0.9921  0.7697</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{4,4} ]</span>
-</pre>
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+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="fu">torch_mul</span><span class="op">(</span><span class="va">a</span>, <span class="fl">100</span><span class="op">)</span></span>
+<span class="r-in"></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span>, <span class="fl">1</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="va">b</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">4</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">b</span></span>
+<span class="r-in"><span class="fu">torch_mul</span><span class="op">(</span><span class="va">a</span>, <span class="va">b</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.7258 -1.1372  0.7316  1.6862</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.4254  0.6666 -0.4288 -0.9884</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.1046 -0.1638  0.1054  0.2429</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.8322 -1.3040  0.8389  1.9336</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4,4} ]</span>
+</code></pre></div>
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+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Multinomial</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
+      <div class="d-none name"><code>torch_multinomial.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Multinomial</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_multinomial</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>num_samples</span>, replacement <span class='op'>=</span> <span class='cn'>FALSE</span>, generator <span class='op'>=</span> <span class='cn'>NULL</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor containing probabilities</p></td>
-    </tr>
-    <tr>
-      <th>num_samples</th>
-      <td><p>(int) number of samples to draw</p></td>
-    </tr>
-    <tr>
-      <th>replacement</th>
-      <td><p>(bool, optional) whether to draw with replacement or not</p></td>
-    </tr>
-    <tr>
-      <th>generator</th>
-      <td><p>(<code>torch.Generator</code>, optional) a pseudorandom number generator for sampling</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_multinomial</span><span class="op">(</span><span class="va">self</span>, <span class="va">num_samples</span>, replacement <span class="op">=</span> <span class="cn">FALSE</span>, generator <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor containing probabilities</p></dd>
+<dt>num_samples</dt>
+<dd><p>(int) number of samples to draw</p></dd>
+<dt>replacement</dt>
+<dd><p>(bool, optional) whether to draw with replacement or not</p></dd>
+<dt>generator</dt>
+<dd><p>(<code>torch.Generator</code>, optional) a pseudorandom number generator for sampling</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     
-<pre>The rows of `input` do not need to sum to one (in which case we use
+<div class="sourceCode"><pre><code>The rows of `input` do not need to sum to one (in which case we use
 the values as weights), but must be non-negative, finite and have
 a non-zero sum.
-</pre>
+</code></pre></div>
 
 <p>Indices are ordered from left to right according to when each was sampled
 (first samples are placed in first column).</p>
@@ -228,59 +112,55 @@ a non-zero sum.
 <p>If replacement is <code>TRUE</code>, samples are drawn with replacement.</p>
 <p>If not, they are drawn without replacement, which means that when a
 sample index is drawn for a row, it cannot be drawn again for that row.</p>
-<pre>When drawn without replacement, `num_samples` must be lower than
+<div class="sourceCode"><pre><code>When drawn without replacement, `num_samples` must be lower than
 number of non-zero elements in `input` (or the min number of non-zero
 elements in each row of `input` if it is a matrix).
-</pre>
-
-    <h2 class="hasAnchor" id="multinomial-input-num-samples-replacement-false-generator-null-out-null-gt-longtensor-"><a class="anchor" href="#multinomial-input-num-samples-replacement-false-generator-null-out-null-gt-longtensor-"></a>multinomial(input, num_samples, replacement=False, *, generator=NULL, out=NULL) -&gt; LongTensor </h2>
+</code></pre></div>
 
+    </div>
+    <div class="section level2">
+    <h2 id="multinomial-input-num-samples-replacement-false-generator-null-out-null-gt-longtensor-">multinomial(input, num_samples, replacement=False, *, generator=NULL, out=NULL) -&gt; LongTensor <a class="anchor" aria-label="anchor" href="#multinomial-input-num-samples-replacement-false-generator-null-out-null-gt-longtensor-"></a></h2>
     
 
 
 <p>Returns a tensor where each row contains <code>num_samples</code> indices sampled
 from the multinomial probability distribution located in the corresponding row
 of tensor <code>input</code>.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>weights</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>0</span>, <span class='fl'>10</span>, <span class='fl'>3</span>, <span class='fl'>0</span><span class='op'>)</span>, dtype<span class='op'>=</span><span class='fu'><a href='torch_dtype.html'>torch_float</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='co'># create a tensor of weights</span></span>
-<span class='r-in'><span class='fu'>torch_multinomial</span><span class='op'>(</span><span class='va'>weights</span>, <span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_multinomial</span><span class='op'>(</span><span class='va'>weights</span>, <span class='fl'>4</span>, replacement<span class='op'>=</span><span class='cn'>TRUE</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  2</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  3</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  2</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  2</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPULongType{4} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">weights</span> <span class="op">=</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">10</span>, <span class="fl">3</span>, <span class="fl">0</span><span class="op">)</span>, dtype<span class="op">=</span><span class="fu"><a href="torch_dtype.html">torch_float</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="co"># create a tensor of weights</span></span>
+<span class="r-in"><span class="fu">torch_multinomial</span><span class="op">(</span><span class="va">weights</span>, <span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_multinomial</span><span class="op">(</span><span class="va">weights</span>, <span class="fl">4</span>, replacement<span class="op">=</span><span class="cn">TRUE</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPULongType{4} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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index ddc8c471a..13add3982 100644
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-    <h1>Multiply</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_multiply.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Multiply</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_multiply.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Multiply</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_multiply</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>other</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the first multiplicand tensor</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Tensor) the second multiplicand tensor</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="multiply-input-other-out-none-"><a class="anchor" href="#multiply-input-other-out-none-"></a>multiply(input, other, *, out=None) </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_multiply</span><span class="op">(</span><span class="va">self</span>, <span class="va">other</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the first multiplicand tensor</p></dd>
+<dt>other</dt>
+<dd><p>(Tensor) the second multiplicand tensor</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="multiply-input-other-out-none-">multiply(input, other, *, out=None) <a class="anchor" aria-label="anchor" href="#multiply-input-other-out-none-"></a></h2>
     
 
 
-<p>Alias for <code><a href='torch_mul.html'>torch_mul()</a></code>.</p>
+<p>Alias for <code><a href="torch_mul.html">torch_mul()</a></code>.</p>
+    </div>
 
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     <p>Mv</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_mv</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>vec</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) matrix to be multiplied</p></td>
-    </tr>
-    <tr>
-      <th>vec</th>
-      <td><p>(Tensor) vector to be multiplied</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_mv</span><span class="op">(</span><span class="va">self</span>, <span class="va">vec</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) matrix to be multiplied</p></dd>
+<dt>vec</dt>
+<dd><p>(Tensor) vector to be multiplied</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p>This function does not broadcast .</p>
-    <h2 class="hasAnchor" id="mv-input-vec-out-null-gt-tensor-"><a class="anchor" href="#mv-input-vec-out-null-gt-tensor-"></a>mv(input, vec, out=NULL) -&gt; Tensor </h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="mv-input-vec-out-null-gt-tensor-">mv(input, vec, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#mv-input-vec-out-null-gt-tensor-"></a></h2>
     
 
 
@@ -216,44 +105,39 @@
 <code>vec</code>.</p>
 <p>If <code>input</code> is a \((n \times m)\) tensor, <code>vec</code> is a 1-D tensor of
 size \(m\), <code>out</code> will be 1-D of size \(n\).</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>mat</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>vec</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_mv</span><span class='op'>(</span><span class='va'>mat</span>, <span class='va'>vec</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.9527</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  2.5899</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{2} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">mat</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">vec</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_mv</span><span class="op">(</span><span class="va">mat</span>, <span class="va">vec</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.5577</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.0739</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer>
-      <div class="copyright">
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+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Mvlgamma</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_mvlgamma.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Mvlgamma</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_mvlgamma</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>p</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the tensor to compute the multivariate log-gamma function</p></td>
-    </tr>
-    <tr>
-      <th>p</th>
-      <td><p>(int) the number of dimensions</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="mvlgamma-input-p-gt-tensor-"><a class="anchor" href="#mvlgamma-input-p-gt-tensor-"></a>mvlgamma(input, p) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_mvlgamma</span><span class="op">(</span><span class="va">self</span>, <span class="va">p</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the tensor to compute the multivariate log-gamma function</p></dd>
+<dt>p</dt>
+<dd><p>(int) the number of dimensions</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="mvlgamma-input-p-gt-tensor-">mvlgamma(input, p) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#mvlgamma-input-p-gt-tensor-"></a></h2>
     
 
 
@@ -216,44 +104,39 @@
 $$
 where \(C = \log(\pi) \times \frac{p (p - 1)}{4}\) and \(\Gamma(\cdot)\) is the Gamma function.</p>
 <p>All elements must be greater than \(\frac{p - 1}{2}\), otherwise an error would be thrown.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span><span class='op'>$</span><span class='fu'>uniform_</span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='fu'>torch_mvlgamma</span><span class='op'>(</span><span class='va'>a</span>, <span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.5776  0.3896  0.7124</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.8808  0.4050  0.6894</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{2,3} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_empty.html">torch_empty</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span><span class="op">$</span><span class="fu">uniform_</span><span class="op">(</span><span class="fl">1</span>, <span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="fu">torch_mvlgamma</span><span class="op">(</span><span class="va">a</span>, <span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.5447  0.5908  0.4083</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.3935  0.3996  0.4849</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2,3} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer>
-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
 </div>
 
-<div class="pkgdown">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p>
+<div class="pkgdown-footer-right">
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index dd33d77ca..8ef331d5e 100644
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_nanquantile.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Nanquantile</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_nanquantile.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Nanquantile</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_nanquantile</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>q</span>, dim <span class='op'>=</span> <span class='cn'>NULL</span>, keepdim <span class='op'>=</span> <span class='cn'>FALSE</span>, <span class='va'>interpolation</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>q</th>
-      <td><p>(float or Tensor) a scalar or 1D tensor of quantile values in the range <code>[0, 1]</code></p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) the dimension to reduce.</p></td>
-    </tr>
-    <tr>
-      <th>keepdim</th>
-      <td><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></td>
-    </tr>
-    <tr>
-      <th>interpolation</th>
-      <td><p>The interpolation method.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="nanquantile-input-q-dim-none-keepdim-false-out-none-gt-tensor-"><a class="anchor" href="#nanquantile-input-q-dim-none-keepdim-false-out-none-gt-tensor-"></a>nanquantile(input, q, dim=None, keepdim=FALSE, *, out=None) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_nanquantile</span><span class="op">(</span><span class="va">self</span>, <span class="va">q</span>, dim <span class="op">=</span> <span class="cn">NULL</span>, keepdim <span class="op">=</span> <span class="cn">FALSE</span>, <span class="va">interpolation</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+<dt>q</dt>
+<dd><p>(float or Tensor) a scalar or 1D tensor of quantile values in the range <code>[0, 1]</code></p></dd>
+<dt>dim</dt>
+<dd><p>(int) the dimension to reduce.</p></dd>
+<dt>keepdim</dt>
+<dd><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></dd>
+<dt>interpolation</dt>
+<dd><p>The interpolation method.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="nanquantile-input-q-dim-none-keepdim-false-out-none-gt-tensor-">nanquantile(input, q, dim=None, keepdim=FALSE, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#nanquantile-input-q-dim-none-keepdim-false-out-none-gt-tensor-"></a></h2>
     
 
 
-<p>This is a variant of <code><a href='torch_quantile.html'>torch_quantile()</a></code> that "ignores" <code>NaN</code> values,
+<p>This is a variant of <code><a href="torch_quantile.html">torch_quantile()</a></code> that "ignores" <code>NaN</code> values,
 computing the quantiles <code>q</code> as if <code>NaN</code> values in <code>input</code> did
 not exist. If all values in a reduced row are <code>NaN</code> then the quantiles for
-that reduction will be <code>NaN</code>. See the documentation for <code><a href='torch_quantile.html'>torch_quantile()</a></code>.</p>
+that reduction will be <code>NaN</code>. See the documentation for <code><a href="torch_quantile.html">torch_quantile()</a></code>.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>t</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='cn'>NaN</span>, <span class='fl'>1</span>, <span class='fl'>2</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>t</span><span class='op'>$</span><span class='fu'>quantile</span><span class='op'>(</span><span class='fl'>0.5</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>t</span><span class='op'>$</span><span class='fu'>nanquantile</span><span class='op'>(</span><span class='fl'>0.5</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>t</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/cbind.html'>rbind</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='cn'>NaN</span>, <span class='cn'>NaN</span><span class='op'>)</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>2</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>t</span></span>
-<span class='r-in'><span class='va'>t</span><span class='op'>$</span><span class='fu'>nanquantile</span><span class='op'>(</span><span class='fl'>0.5</span>, dim<span class='op'>=</span><span class='fl'>1</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>t</span><span class='op'>$</span><span class='fu'>nanquantile</span><span class='op'>(</span><span class='fl'>0.5</span>, dim<span class='op'>=</span><span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_nanquantile</span><span class='op'>(</span><span class='va'>t</span>, <span class='fl'>0.5</span>, dim <span class='op'>=</span> <span class='fl'>1</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_nanquantile</span><span class='op'>(</span><span class='va'>t</span>, <span class='fl'>0.5</span>, dim <span class='op'>=</span> <span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>     nan  1.5000</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{1,2} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">t</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="cn">NaN</span>, <span class="fl">1</span>, <span class="fl">2</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">t</span><span class="op">$</span><span class="fu">quantile</span><span class="op">(</span><span class="fl">0.5</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">t</span><span class="op">$</span><span class="fu">nanquantile</span><span class="op">(</span><span class="fl">0.5</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">t</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/cbind.html" class="external-link">rbind</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="cn">NaN</span>, <span class="cn">NaN</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">2</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">t</span></span>
+<span class="r-in"><span class="va">t</span><span class="op">$</span><span class="fu">nanquantile</span><span class="op">(</span><span class="fl">0.5</span>, dim<span class="op">=</span><span class="fl">1</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">t</span><span class="op">$</span><span class="fu">nanquantile</span><span class="op">(</span><span class="fl">0.5</span>, dim<span class="op">=</span><span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_nanquantile</span><span class="op">(</span><span class="va">t</span>, <span class="fl">0.5</span>, dim <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_nanquantile</span><span class="op">(</span><span class="va">t</span>, <span class="fl">0.5</span>, dim <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>     nan  1.5000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{1,2} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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+    
+  </div>
+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Nansum</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_nansum.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Nansum</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_nansum.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Nansum</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_nansum</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>dim</span>, keepdim <span class='op'>=</span> <span class='cn'>FALSE</span>, dtype <span class='op'>=</span> <span class='cn'>NULL</span><span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_nansum</span><span class="op">(</span><span class="va">self</span>, <span class="va">dim</span>, keepdim <span class="op">=</span> <span class="cn">FALSE</span>, dtype <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int or tuple of ints) the dimension or dimensions to reduce.</p></td>
-    </tr>
-    <tr>
-      <th>keepdim</th>
-      <td><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>the desired data type of returned tensor. If specified, the
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+<dt>dim</dt>
+<dd><p>(int or tuple of ints) the dimension or dimensions to reduce.</p></dd>
+<dt>keepdim</dt>
+<dd><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></dd>
+<dt>dtype</dt>
+<dd><p>the desired data type of returned tensor. If specified, the
 input tensor is casted to dtype before the operation is performed. This is
-useful for preventing data type overflows. Default: <code>NULL</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="nansum-input-dtype-none-gt-tensor-"><a class="anchor" href="#nansum-input-dtype-none-gt-tensor-"></a>nansum(input, *, dtype=None) -&gt; Tensor </h2>
-
+useful for preventing data type overflows. Default: <code>NULL</code>.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="nansum-input-dtype-none-gt-tensor-">nansum(input, *, dtype=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#nansum-input-dtype-none-gt-tensor-"></a></h2>
     
 
 
 <p>Returns the sum of all elements, treating Not a Numbers (NaNs) as zero.</p>
-    <h2 class="hasAnchor" id="nansum-input-dim-keepdim-false-dtype-none-gt-tensor-"><a class="anchor" href="#nansum-input-dim-keepdim-false-dtype-none-gt-tensor-"></a>nansum(input, dim, keepdim=FALSE, *, dtype=None) -&gt; Tensor </h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="nansum-input-dim-keepdim-false-dtype-none-gt-tensor-">nansum(input, dim, keepdim=FALSE, *, dtype=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#nansum-input-dim-keepdim-false-dtype-none-gt-tensor-"></a></h2>
     
 
 
@@ -230,52 +115,47 @@ dimension <code>dim</code>, treating Not a Numbers (NaNs) as zero.
 If <code>dim</code> is a list of dimensions, reduce over all of them.</p>
 <p>If <code>keepdim</code> is <code>TRUE</code>, the output tensor is of the same size
 as <code>input</code> except in the dimension(s) <code>dim</code> where it is of size 1.
-Otherwise, <code>dim</code> is squeezed (see <code><a href='torch_squeeze.html'>torch_squeeze</a></code>), resulting in the
+Otherwise, <code>dim</code> is squeezed (see <code><a href="torch_squeeze.html">torch_squeeze</a></code>), resulting in the
 output tensor having 1 (or <code>len(dim)</code>) fewer dimension(s).</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1.</span>, <span class='fl'>2.</span>, <span class='cn'>NaN</span>, <span class='fl'>4.</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_nansum</span><span class='op'>(</span><span class='va'>a</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='fu'>torch_nansum</span><span class='op'>(</span><span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1.</span>, <span class='cn'>NaN</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/cbind.html'>rbind</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>2</span><span class='op'>)</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3.</span>, <span class='cn'>NaN</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_nansum</span><span class='op'>(</span><span class='va'>a</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_nansum</span><span class='op'>(</span><span class='va'>a</span>, dim<span class='op'>=</span><span class='fl'>1</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_nansum</span><span class='op'>(</span><span class='va'>a</span>, dim<span class='op'>=</span><span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  3</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  3</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{2} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1.</span>, <span class="fl">2.</span>, <span class="cn">NaN</span>, <span class="fl">4.</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_nansum</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span>
+<span class="r-in"></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="fu">torch_nansum</span><span class="op">(</span><span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1.</span>, <span class="cn">NaN</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/cbind.html" class="external-link">rbind</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">2</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3.</span>, <span class="cn">NaN</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_nansum</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_nansum</span><span class="op">(</span><span class="va">a</span>, dim<span class="op">=</span><span class="fl">1</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_nansum</span><span class="op">(</span><span class="va">a</span>, dim<span class="op">=</span><span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  3</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  3</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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+<div class="pkgdown-footer-right">
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diff --git a/reference/torch_narrow.html b/reference/torch_narrow.html
index 11133aa9a..ac2b0381d 100644
--- a/reference/torch_narrow.html
+++ b/reference/torch_narrow.html
@@ -1,264 +1,143 @@
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Narrow</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/wrapers.R'><code>R/wrapers.R</code></a></small>
-    <div class="hidden name"><code>torch_narrow.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Narrow</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
+      <div class="d-none name"><code>torch_narrow.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Narrow</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_narrow</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>dim</span>, <span class='va'>start</span>, <span class='va'>length</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the tensor to narrow</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) the dimension along which to narrow</p></td>
-    </tr>
-    <tr>
-      <th>start</th>
-      <td><p>(int) the starting dimension</p></td>
-    </tr>
-    <tr>
-      <th>length</th>
-      <td><p>(int) the distance to the ending dimension</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="narrow-input-dim-start-length-gt-tensor-"><a class="anchor" href="#narrow-input-dim-start-length-gt-tensor-"></a>narrow(input, dim, start, length) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_narrow</span><span class="op">(</span><span class="va">self</span>, <span class="va">dim</span>, <span class="va">start</span>, <span class="va">length</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the tensor to narrow</p></dd>
+<dt>dim</dt>
+<dd><p>(int) the dimension along which to narrow</p></dd>
+<dt>start</dt>
+<dd><p>(int) the starting dimension</p></dd>
+<dt>length</dt>
+<dd><p>(int) the distance to the ending dimension</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="narrow-input-dim-start-length-gt-tensor-">narrow(input, dim, start, length) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#narrow-input-dim-start-length-gt-tensor-"></a></h2>
     
 
 
 <p>Returns a new tensor that is a narrowed version of <code>input</code> tensor. The
 dimension <code>dim</code> is input from <code>start</code> to <code>start + length</code>. The
 returned tensor and <code>input</code> tensor share the same underlying storage.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span><span class='op'>:</span><span class='fl'>9</span><span class='op'>)</span>, ncol <span class='op'>=</span> <span class='fl'>3</span>, byrow<span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_narrow</span><span class='op'>(</span><span class='va'>x</span>, <span class='fl'>1</span>, <span class='fl'>1</span>, <span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_narrow</span><span class='op'>(</span><span class='va'>x</span>, <span class='fl'>2</span>, <span class='fl'>2</span>, <span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  2  3</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  5  6</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  8  9</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPULongType{3,2} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">x</span> <span class="op">=</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/matrix.html" class="external-link">matrix</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="fl">9</span><span class="op">)</span>, ncol <span class="op">=</span> <span class="fl">3</span>, byrow<span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_narrow</span><span class="op">(</span><span class="va">x</span>, <span class="fl">1</span>, <span class="fl">1</span>, <span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_narrow</span><span class="op">(</span><span class="va">x</span>, <span class="fl">2</span>, <span class="fl">2</span>, <span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2  3</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  5  6</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  8  9</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPULongType{3,2} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
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-    <h1>Ne</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_ne.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Ne</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_ne.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Ne</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_ne</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>other</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the tensor to compare</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Tensor or float) the tensor or value to compare</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="ne-input-other-out-null-gt-tensor-"><a class="anchor" href="#ne-input-other-out-null-gt-tensor-"></a>ne(input, other, out=NULL) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_ne</span><span class="op">(</span><span class="va">self</span>, <span class="va">other</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the tensor to compare</p></dd>
+<dt>other</dt>
+<dd><p>(Tensor or float) the tensor or value to compare</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="ne-input-other-out-null-gt-tensor-">ne(input, other, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#ne-input-other-out-null-gt-tensor-"></a></h2>
     
 
 
 <p>Computes \(input \neq other\) element-wise.</p>
 <p>The second argument can be a number or a tensor whose shape is
 broadcastable  with the first argument.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='fu'>torch_ne</span><span class='op'>(</span><span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span><span class='op'>(</span><span class='fl'>1</span><span class='op'>:</span><span class='fl'>4</span>, ncol <span class='op'>=</span> <span class='fl'>2</span>, byrow<span class='op'>=</span><span class='cn'>TRUE</span><span class='op'>)</span><span class='op'>)</span>, </span>
-<span class='r-in'>         <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/rep.html'>rep</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>,<span class='fl'>4</span><span class='op'>)</span>, each <span class='op'>=</span> <span class='fl'>2</span><span class='op'>)</span>, ncol <span class='op'>=</span> <span class='fl'>2</span>, byrow<span class='op'>=</span><span class='cn'>TRUE</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0  1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUBoolType{2,2} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="fu">torch_ne</span><span class="op">(</span><span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/matrix.html" class="external-link">matrix</a></span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="fl">4</span>, ncol <span class="op">=</span> <span class="fl">2</span>, byrow<span class="op">=</span><span class="cn">TRUE</span><span class="op">)</span><span class="op">)</span>, </span>
+<span class="r-in">         <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/matrix.html" class="external-link">matrix</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/rep.html" class="external-link">rep</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>,<span class="fl">4</span><span class="op">)</span>, each <span class="op">=</span> <span class="fl">2</span><span class="op">)</span>, ncol <span class="op">=</span> <span class="fl">2</span>, byrow<span class="op">=</span><span class="cn">TRUE</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUBoolType{2,2} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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diff --git a/reference/torch_neg.html b/reference/torch_neg.html
index df13508b3..1350c96fd 100644
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Neg</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_neg.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Neg</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_neg.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Neg</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_neg</span><span class='op'>(</span><span class='va'>self</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="neg-input-out-null-gt-tensor-"><a class="anchor" href="#neg-input-out-null-gt-tensor-"></a>neg(input, out=NULL) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_neg</span><span class="op">(</span><span class="va">self</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="neg-input-out-null-gt-tensor-">neg(input, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#neg-input-out-null-gt-tensor-"></a></h2>
     
 
 
@@ -209,47 +99,42 @@
 <p>$$
     \mbox{out} = -1 \times \mbox{input}
 $$</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>5</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='fu'>torch_neg</span><span class='op'>(</span><span class='va'>a</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -1.3974</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -1.0314</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1.5800</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.1683</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1.5489</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{5} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">5</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="fu">torch_neg</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -1.6512</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.5301</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.3428</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.3650</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.7776</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{5} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer>
-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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+<div class="pkgdown-footer-right">
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index 391f56ae1..f92e76b01 100644
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@@ -1,237 +1,120 @@
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_negative.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Negative</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_negative.Rd</code></div>
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-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Negative</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_negative</span><span class='op'>(</span><span class='va'>self</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="negative-input-out-none-gt-tensor-"><a class="anchor" href="#negative-input-out-none-gt-tensor-"></a>negative(input, *, out=None) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_negative</span><span class="op">(</span><span class="va">self</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="negative-input-out-none-gt-tensor-">negative(input, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#negative-input-out-none-gt-tensor-"></a></h2>
     
 
 
-<p>Alias for <code><a href='torch_neg.html'>torch_neg()</a></code></p>
+<p>Alias for <code><a href="torch_neg.html">torch_neg()</a></code></p>
+    </div>
 
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diff --git a/reference/torch_nextafter.html b/reference/torch_nextafter.html
index b5a9cb19d..b4fc141cd 100644
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Nextafter</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_nextafter.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Nextafter</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_nextafter.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Nextafter</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_nextafter</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>other</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the first input tensor</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Tensor) the second input tensor</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="nextafter-input-other-out-none-gt-tensor-"><a class="anchor" href="#nextafter-input-other-out-none-gt-tensor-"></a>nextafter(input, other, *, out=None) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_nextafter</span><span class="op">(</span><span class="va">self</span>, <span class="va">other</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the first input tensor</p></dd>
+<dt>other</dt>
+<dd><p>(Tensor) the second input tensor</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="nextafter-input-other-out-none-gt-tensor-">nextafter(input, other, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#nextafter-input-other-out-none-gt-tensor-"></a></h2>
     
 
 
 <p>Return the next floating-point value after <code>input</code> towards <code>other</code>, elementwise.</p>
 <p>The shapes of <code>input</code> and <code>other</code> must be
 broadcastable .</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>eps</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_finfo.html'>torch_finfo</a></span><span class='op'>(</span><span class='fu'><a href='torch_dtype.html'>torch_float32</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span><span class='op'>$</span><span class='va'>eps</span></span>
-<span class='r-in'><span class='fu'>torch_nextafter</span><span class='op'>(</span><span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>2</span><span class='op'>)</span><span class='op'>)</span>, <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>1</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>==</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='va'>eps</span> <span class='op'>+</span> <span class='fl'>1</span>, <span class='fl'>2</span> <span class='op'>-</span> <span class='va'>eps</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUBoolType{2} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">eps</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_finfo.html">torch_finfo</a></span><span class="op">(</span><span class="fu"><a href="torch_dtype.html">torch_float32</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span><span class="op">$</span><span class="va">eps</span></span>
+<span class="r-in"><span class="fu">torch_nextafter</span><span class="op">(</span><span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">2</span><span class="op">)</span><span class="op">)</span>, <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">1</span><span class="op">)</span><span class="op">)</span><span class="op">)</span> <span class="op">==</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="va">eps</span> <span class="op">+</span> <span class="fl">1</span>, <span class="fl">2</span> <span class="op">-</span> <span class="va">eps</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUBoolType{2} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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diff --git a/reference/torch_nonzero.html b/reference/torch_nonzero.html
index 312edeb7f..78d1ea27d 100644
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Nonzero</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/wrapers.R'><code>R/wrapers.R</code></a></small>
-    <div class="hidden name"><code>torch_nonzero.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Nonzero</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
+      <div class="d-none name"><code>torch_nonzero.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Nonzero elements of tensors.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_nonzero</span><span class='op'>(</span><span class='va'>self</span>, as_list <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_nonzero</span><span class="op">(</span><span class="va">self</span>, as_list <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>as_list</th>
-      <td><p>If <code>FALSE</code>, the output tensor containing indices. If <code>TRUE</code>, one
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+<dt>as_list</dt>
+<dd><p>If <code>FALSE</code>, the output tensor containing indices. If <code>TRUE</code>, one
 1-D tensor for each dimension, containing the indices of each nonzero element
 along that dimension.</p>
 <p><strong>When</strong> <code>as_list</code> <strong>is <code>FALSE</code> (default)</strong>:</p>
@@ -219,48 +109,40 @@ each containing the indices (in that dimension) of all non-zero elements of
 tensors of size \(z\), where \(z\) is the total number of
 non-zero elements in the <code>input</code> tensor.</p>
 <p>As a special case, when <code>input</code> has zero dimensions and a nonzero scalar
-value, it is treated as a one-dimensional tensor with one element.</p></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='fu'>torch_nonzero</span><span class='op'>(</span><span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>1</span>, <span class='fl'>1</span>, <span class='fl'>0</span>, <span class='fl'>1</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  2</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  3</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  5</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPULongType{4,1} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+value, it is treated as a one-dimensional tensor with one element.</p></dd>
+</dl></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="fu">torch_nonzero</span><span class="op">(</span><span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">1</span>, <span class="fl">1</span>, <span class="fl">0</span>, <span class="fl">1</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  3</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  5</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPULongType{4,1} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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diff --git a/reference/torch_norm.html b/reference/torch_norm.html
index c014b25d0..4d66088ec 100644
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Norm</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/wrapers.R'><code>R/wrapers.R</code></a></small>
-    <div class="hidden name"><code>torch_norm.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Norm</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
+      <div class="d-none name"><code>torch_norm.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Norm</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_norm</span><span class='op'>(</span><span class='va'>self</span>, p <span class='op'>=</span> <span class='fl'>2L</span>, <span class='va'>dim</span>, keepdim <span class='op'>=</span> <span class='cn'>FALSE</span>, <span class='va'>dtype</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor</p></td>
-    </tr>
-    <tr>
-      <th>p</th>
-      <td><p>(int, float, inf, -inf, 'fro', 'nuc', optional) the order of norm. Default: <code>'fro'</code>        The following norms can be calculated:        =====  ============================  ==========================        ord    matrix norm                   vector norm        =====  ============================  ==========================        NULL   Frobenius norm                2-norm        'fro'  Frobenius norm                --        'nuc'  nuclear norm                  --        Other  as vec norm when dim is NULL  sum(abs(x)<strong>ord)</strong>(1./ord)        =====  ============================  ==========================</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int, 2-tuple of ints, 2-list of ints, optional) If it is an int,        vector norm will be calculated, if it is 2-tuple of ints, matrix norm        will be calculated. If the value is NULL, matrix norm will be calculated        when the input tensor only has two dimensions, vector norm will be        calculated when the input tensor only has one dimension. If the input        tensor has more than two dimensions, the vector norm will be applied to        last dimension.</p></td>
-    </tr>
-    <tr>
-      <th>keepdim</th>
-      <td><p>(bool, optional) whether the output tensors have <code>dim</code>        retained or not. Ignored if <code>dim</code> = <code>NULL</code> and        <code>out</code> = <code>NULL</code>. Default: <code>FALSE</code>
-Ignored if        <code>dim</code> = <code>NULL</code> and <code>out</code> = <code>NULL</code>.</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of        returned tensor. If specified, the input tensor is casted to        'dtype' while performing the operation. Default: NULL.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="test-"><a class="anchor" href="#test-"></a>TEST </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_norm</span><span class="op">(</span><span class="va">self</span>, p <span class="op">=</span> <span class="fl">2L</span>, <span class="va">dim</span>, keepdim <span class="op">=</span> <span class="cn">FALSE</span>, <span class="va">dtype</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor</p></dd>
+<dt>p</dt>
+<dd><p>(int, float, inf, -inf, 'fro', 'nuc', optional) the order of norm. Default: <code>'fro'</code>        The following norms can be calculated:        =====  ============================  ==========================        ord    matrix norm                   vector norm        =====  ============================  ==========================        NULL   Frobenius norm                2-norm        'fro'  Frobenius norm                --        'nuc'  nuclear norm                  --        Other  as vec norm when dim is NULL  sum(abs(x)<strong>ord)</strong>(1./ord)        =====  ============================  ==========================</p></dd>
+<dt>dim</dt>
+<dd><p>(int, 2-tuple of ints, 2-list of ints, optional) If it is an int,        vector norm will be calculated, if it is 2-tuple of ints, matrix norm        will be calculated. If the value is NULL, matrix norm will be calculated        when the input tensor only has two dimensions, vector norm will be        calculated when the input tensor only has one dimension. If the input        tensor has more than two dimensions, the vector norm will be applied to        last dimension.</p></dd>
+<dt>keepdim</dt>
+<dd><p>(bool, optional) whether the output tensors have <code>dim</code>        retained or not. Ignored if <code>dim</code> = <code>NULL</code> and        <code>out</code> = <code>NULL</code>. Default: <code>FALSE</code>
+Ignored if        <code>dim</code> = <code>NULL</code> and <code>out</code> = <code>NULL</code>.</p></dd>
+<dt>dtype</dt>
+<dd><p>(<code>torch.dtype</code>, optional) the desired data type of        returned tensor. If specified, the input tensor is casted to        'dtype' while performing the operation. Default: NULL.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="test-">TEST <a class="anchor" aria-label="anchor" href="#test-"></a></h2>
     
 
 
 <p>Returns the matrix norm or vector norm of a given tensor.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>9</span>, dtype <span class='op'>=</span> <span class='fu'><a href='torch_dtype.html'>torch_float</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>b</span> <span class='op'>&lt;-</span> <span class='va'>a</span><span class='op'>$</span><span class='fu'>reshape</span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_norm</span><span class='op'>(</span><span class='va'>a</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_norm</span><span class='op'>(</span><span class='va'>b</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_norm</span><span class='op'>(</span><span class='va'>a</span>, <span class='cn'>Inf</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_norm</span><span class='op'>(</span><span class='va'>b</span>, <span class='cn'>Inf</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> 9</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_arange.html">torch_arange</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">9</span>, dtype <span class="op">=</span> <span class="fu"><a href="torch_dtype.html">torch_float</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">b</span> <span class="op">&lt;-</span> <span class="va">a</span><span class="op">$</span><span class="fu">reshape</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_norm</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_norm</span><span class="op">(</span><span class="va">b</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_norm</span><span class="op">(</span><span class="va">a</span>, <span class="cn">Inf</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_norm</span><span class="op">(</span><span class="va">b</span>, <span class="cn">Inf</span><span class="op">)</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 9</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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+      </ul><form class="form-inline my-2 my-lg-0" role="search">
+        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
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+    
+  </div>
+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Normal</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/wrapers.R'><code>R/wrapers.R</code></a></small>
-    <div class="hidden name"><code>torch_normal.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Normal</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
+      <div class="d-none name"><code>torch_normal.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Normal</p>
 <p>Normal distributed</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_normal</span><span class='op'>(</span><span class='va'>mean</span>, <span class='va'>std</span>, size <span class='op'>=</span> <span class='cn'>NULL</span>, generator <span class='op'>=</span> <span class='cn'>NULL</span>, <span class='va'>...</span><span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_normal</span><span class="op">(</span><span class="va">mean</span>, <span class="va">std</span>, size <span class="op">=</span> <span class="cn">NULL</span>, generator <span class="op">=</span> <span class="cn">NULL</span>, <span class="va">...</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>mean</th>
-      <td><p>(tensor or scalar double) Mean of the normal distribution.
-If this is a <code><a href='torch_tensor.html'>torch_tensor()</a></code> then the output has the same dim as <code>mean</code>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>mean</dt>
+<dd><p>(tensor or scalar double) Mean of the normal distribution.
+If this is a <code><a href="torch_tensor.html">torch_tensor()</a></code> then the output has the same dim as <code>mean</code>
 and it represents the per-element mean. If it's a scalar value, it's reused
-for all elements.</p></td>
-    </tr>
-    <tr>
-      <th>std</th>
-      <td><p>(tensor or scalar double) The standard deviation of the normal
-distribution. If this is a <code><a href='torch_tensor.html'>torch_tensor()</a></code> then the output has the same size as <code>std</code>
+for all elements.</p></dd>
+<dt>std</dt>
+<dd><p>(tensor or scalar double) The standard deviation of the normal
+distribution. If this is a <code><a href="torch_tensor.html">torch_tensor()</a></code> then the output has the same size as <code>std</code>
 and it represents the per-element standard deviation. If it's a scalar value,
-it's reused for all elements.</p></td>
-    </tr>
-    <tr>
-      <th>size</th>
-      <td><p>(integers, optional) only used if both <code>mean</code> and <code>std</code> are scalars.</p></td>
-    </tr>
-    <tr>
-      <th>generator</th>
-      <td><p>a random number generator created with <code><a href='torch_generator.html'>torch_generator()</a></code>. If <code>NULL</code>
-a default generator is used.</p></td>
-    </tr>
-    <tr>
-      <th>...</th>
-      <td><p>Tensor option parameters like <code>dtype</code>, <code>layout</code>, and <code>device</code>.
-Can only be used when <code>mean</code> and <code>std</code> are both scalar numerics.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
+it's reused for all elements.</p></dd>
+<dt>size</dt>
+<dd><p>(integers, optional) only used if both <code>mean</code> and <code>std</code> are scalars.</p></dd>
+<dt>generator</dt>
+<dd><p>a random number generator created with <code><a href="torch_generator.html">torch_generator()</a></code>. If <code>NULL</code>
+a default generator is used.</p></dd>
+<dt>...</dt>
+<dd><p>Tensor option parameters like <code>dtype</code>, <code>layout</code>, and <code>device</code>.
+Can only be used when <code>mean</code> and <code>std</code> are both scalar numerics.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p>When the shapes do not match, the shape of <code>mean</code>
 is used as the shape for the returned output tensor</p>
-    <h2 class="hasAnchor" id="normal-mean-std-gt-tensor-"><a class="anchor" href="#normal-mean-std-gt-tensor-"></a>normal(mean, std, *) -&gt; Tensor </h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="normal-mean-std-gt-tensor-">normal(mean, std, *) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#normal-mean-std-gt-tensor-"></a></h2>
     
 
 
@@ -243,66 +127,64 @@ each output element's normal distribution</p>
 each output element's normal distribution</p>
 <p>The shapes of <code>mean</code> and <code>std</code> don't need to match, but the
 total number of elements in each tensor need to be the same.</p>
-    <h2 class="hasAnchor" id="normal-mean-std-gt-tensor-"><a class="anchor" href="#normal-mean-std-gt-tensor-"></a>normal(mean=0.0, std) -&gt; Tensor </h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="normal-mean-std-gt-tensor--1">normal(mean=0.0, std) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#normal-mean-std-gt-tensor--1"></a></h2>
     
 
 
 <p>Similar to the function above, but the means are shared among all drawn
 elements.</p>
-    <h2 class="hasAnchor" id="normal-mean-std-gt-tensor-"><a class="anchor" href="#normal-mean-std-gt-tensor-"></a>normal(mean, std=1.0) -&gt; Tensor </h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="normal-mean-std-gt-tensor--2">normal(mean, std=1.0) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#normal-mean-std-gt-tensor--2"></a></h2>
     
 
 
 <p>Similar to the function above, but the standard-deviations are shared among
 all drawn elements.</p>
-    <h2 class="hasAnchor" id="normal-mean-std-size-gt-tensor-"><a class="anchor" href="#normal-mean-std-size-gt-tensor-"></a>normal(mean, std, size, *) -&gt; Tensor </h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="normal-mean-std-size-gt-tensor-">normal(mean, std, size, *) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#normal-mean-std-size-gt-tensor-"></a></h2>
     
 
 
 <p>Similar to the function above, but the means and standard deviations are shared
 among all drawn elements. The resulting tensor has size given by <code>size</code>.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='fu'>torch_normal</span><span class='op'>(</span>mean<span class='op'>=</span><span class='fl'>0</span>, std<span class='op'>=</span><span class='fu'><a href='torch_arange.html'>torch_arange</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>0</span>, <span class='op'>-</span><span class='fl'>0.1</span><span class='op'>)</span> <span class='op'>+</span> <span class='fl'>1e-6</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_normal</span><span class='op'>(</span>mean<span class='op'>=</span><span class='fl'>0.5</span>, std<span class='op'>=</span><span class='fu'><a href='torch_arange.html'>torch_arange</a></span><span class='op'>(</span><span class='fl'>1.</span>, <span class='fl'>6.</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_normal</span><span class='op'>(</span>mean<span class='op'>=</span><span class='fu'><a href='torch_arange.html'>torch_arange</a></span><span class='op'>(</span><span class='fl'>1.</span>, <span class='fl'>6.</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_normal</span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>3</span>, size<span class='op'>=</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -2.3561 -0.9092  1.8914  2.2451</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{1,4} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="fu">torch_normal</span><span class="op">(</span>mean<span class="op">=</span><span class="fl">0</span>, std<span class="op">=</span><span class="fu"><a href="torch_arange.html">torch_arange</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">0</span>, <span class="op">-</span><span class="fl">0.1</span><span class="op">)</span> <span class="op">+</span> <span class="fl">1e-6</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_normal</span><span class="op">(</span>mean<span class="op">=</span><span class="fl">0.5</span>, std<span class="op">=</span><span class="fu"><a href="torch_arange.html">torch_arange</a></span><span class="op">(</span><span class="fl">1.</span>, <span class="fl">6.</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_normal</span><span class="op">(</span>mean<span class="op">=</span><span class="fu"><a href="torch_arange.html">torch_arange</a></span><span class="op">(</span><span class="fl">1.</span>, <span class="fl">6.</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_normal</span><span class="op">(</span><span class="fl">2</span>, <span class="fl">3</span>, size<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">4</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.5876  3.8757  4.9848 -3.6009</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{1,4} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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-    <h1>Not_equal</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_not_equal.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Not_equal</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_not_equal.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Not_equal</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_not_equal</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>other</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the tensor to compare</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Tensor or float) the tensor or value to compare</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="not-equal-input-other-out-none-gt-tensor-"><a class="anchor" href="#not-equal-input-other-out-none-gt-tensor-"></a>not_equal(input, other, *, out=None) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_not_equal</span><span class="op">(</span><span class="va">self</span>, <span class="va">other</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the tensor to compare</p></dd>
+<dt>other</dt>
+<dd><p>(Tensor or float) the tensor or value to compare</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="not-equal-input-other-out-none-gt-tensor-">not_equal(input, other, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#not-equal-input-other-out-none-gt-tensor-"></a></h2>
     
 
 
-<p>Alias for <code><a href='torch_ne.html'>torch_ne()</a></code>.</p>
+<p>Alias for <code><a href="torch_ne.html">torch_ne()</a></code>.</p>
+    </div>
 
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Ones</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/creation-ops.R'><code>R/creation-ops.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_ones.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Ones</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/creation-ops.R" class="external-link"><code>R/creation-ops.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a></small>
+      <div class="d-none name"><code>torch_ones.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Ones</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_ones</span><span class='op'>(</span>
-  <span class='va'>...</span>,
-  names <span class='op'>=</span> <span class='cn'>NULL</span>,
-  dtype <span class='op'>=</span> <span class='cn'>NULL</span>,
-  layout <span class='op'>=</span> <span class='fu'><a href='torch_layout.html'>torch_strided</a></span><span class='op'>(</span><span class='op'>)</span>,
-  device <span class='op'>=</span> <span class='cn'>NULL</span>,
-  requires_grad <span class='op'>=</span> <span class='cn'>FALSE</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>...</th>
-      <td><p>(int...) a sequence of integers defining the shape of the output tensor.        Can be a variable number of arguments or a collection like a list or tuple.</p></td>
-    </tr>
-    <tr>
-      <th>names</th>
-      <td><p>optional names for the dimensions</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>).</p></td>
-    </tr>
-    <tr>
-      <th>layout</th>
-      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
-    </tr>
-    <tr>
-      <th>requires_grad</th>
-      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="ones-size-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"><a class="anchor" href="#ones-size-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"></a>ones(*size, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_ones</span><span class="op">(</span>
+  <span class="va">...</span>,
+  names <span class="op">=</span> <span class="cn">NULL</span>,
+  dtype <span class="op">=</span> <span class="cn">NULL</span>,
+  layout <span class="op">=</span> <span class="fu"><a href="torch_layout.html">torch_strided</a></span><span class="op">(</span><span class="op">)</span>,
+  device <span class="op">=</span> <span class="cn">NULL</span>,
+  requires_grad <span class="op">=</span> <span class="cn">FALSE</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>...</dt>
+<dd><p>(int...) a sequence of integers defining the shape of the output tensor.        Can be a variable number of arguments or a collection like a list or tuple.</p></dd>
+<dt>names</dt>
+<dd><p>optional names for the dimensions</p></dd>
+<dt>dtype</dt>
+<dd><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>).</p></dd>
+<dt>layout</dt>
+<dd><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></dd>
+<dt>device</dt>
+<dd><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></dd>
+<dt>requires_grad</dt>
+<dd><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="ones-size-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-">ones(*size, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#ones-size-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"></a></h2>
     
 
 
 <p>Returns a tensor filled with the scalar value <code>1</code>, with the shape defined
 by the variable argument <code>size</code>.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='fu'>torch_ones</span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_ones</span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>5</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{5} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="fu">torch_ones</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_ones</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">5</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{5} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer>
-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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-    <h1>Ones_like</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/creation-ops.R'><code>R/creation-ops.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_ones_like.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Ones_like</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/creation-ops.R" class="external-link"><code>R/creation-ops.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a></small>
+      <div class="d-none name"><code>torch_ones_like.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Ones_like</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_ones_like</span><span class='op'>(</span>
-  <span class='va'>input</span>,
-  dtype <span class='op'>=</span> <span class='cn'>NULL</span>,
-  layout <span class='op'>=</span> <span class='fu'><a href='torch_layout.html'>torch_strided</a></span><span class='op'>(</span><span class='op'>)</span>,
-  device <span class='op'>=</span> <span class='cn'>NULL</span>,
-  requires_grad <span class='op'>=</span> <span class='cn'>FALSE</span>,
-  memory_format <span class='op'>=</span> <span class='fu'><a href='torch_memory_format.html'>torch_preserve_format</a></span><span class='op'>(</span><span class='op'>)</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the size of <code>input</code> will determine size of the output tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned Tensor.        Default: if <code>NULL</code>, defaults to the dtype of <code>input</code>.</p></td>
-    </tr>
-    <tr>
-      <th>layout</th>
-      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned tensor.        Default: if <code>NULL</code>, defaults to the layout of <code>input</code>.</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, defaults to the device of <code>input</code>.</p></td>
-    </tr>
-    <tr>
-      <th>requires_grad</th>
-      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
-    </tr>
-    <tr>
-      <th>memory_format</th>
-      <td><p>(<code>torch.memory_format</code>, optional) the desired memory format of        returned Tensor. Default: <code>torch_preserve_format</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="ones-like-input-dtype-null-layout-null-device-null-requires-grad-false-memory-format-torch-preserve-format-gt-tensor-"><a class="anchor" href="#ones-like-input-dtype-null-layout-null-device-null-requires-grad-false-memory-format-torch-preserve-format-gt-tensor-"></a>ones_like(input, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, memory_format=torch.preserve_format) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_ones_like</span><span class="op">(</span>
+  <span class="va">input</span>,
+  dtype <span class="op">=</span> <span class="cn">NULL</span>,
+  layout <span class="op">=</span> <span class="fu"><a href="torch_layout.html">torch_strided</a></span><span class="op">(</span><span class="op">)</span>,
+  device <span class="op">=</span> <span class="cn">NULL</span>,
+  requires_grad <span class="op">=</span> <span class="cn">FALSE</span>,
+  memory_format <span class="op">=</span> <span class="fu"><a href="torch_memory_format.html">torch_preserve_format</a></span><span class="op">(</span><span class="op">)</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>(Tensor) the size of <code>input</code> will determine size of the output tensor.</p></dd>
+<dt>dtype</dt>
+<dd><p>(<code>torch.dtype</code>, optional) the desired data type of returned Tensor.        Default: if <code>NULL</code>, defaults to the dtype of <code>input</code>.</p></dd>
+<dt>layout</dt>
+<dd><p>(<code>torch.layout</code>, optional) the desired layout of returned tensor.        Default: if <code>NULL</code>, defaults to the layout of <code>input</code>.</p></dd>
+<dt>device</dt>
+<dd><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, defaults to the device of <code>input</code>.</p></dd>
+<dt>requires_grad</dt>
+<dd><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></dd>
+<dt>memory_format</dt>
+<dd><p>(<code>torch.memory_format</code>, optional) the desired memory format of        returned Tensor. Default: <code>torch_preserve_format</code>.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="ones-like-input-dtype-null-layout-null-device-null-requires-grad-false-memory-format-torch-preserve-format-gt-tensor-">ones_like(input, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, memory_format=torch.preserve_format) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#ones-like-input-dtype-null-layout-null-device-null-requires-grad-false-memory-format-torch-preserve-format-gt-tensor-"></a></h2>
     
 
 
 <p>Returns a tensor filled with the scalar value <code>1</code>, with the same size as
 <code>input</code>. <code>torch_ones_like(input)</code> is equivalent to
 <code>torch_ones(input.size(), dtype=input.dtype, layout=input.layout, device=input.device)</code>.</p>
-    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></a></h2>
     
 
 <p>As of 0.4, this function does not support an <code>out</code> keyword. As an alternative,
 the old <code>torch_ones_like(input, out=output)</code> is equivalent to
 <code>torch_ones(input.size(), out=output)</code>.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>=</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_ones_like</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1  1  1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1  1  1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{2,3} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">input</span> <span class="op">=</span> <span class="fu"><a href="torch_empty.html">torch_empty</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_ones_like</span><span class="op">(</span><span class="va">input</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1  1  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1  1  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2,3} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer>
-      <div class="copyright">
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+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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+<div class="pkgdown-footer-right">
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Orgqr</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_orgqr.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Orgqr</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_orgqr.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Orgqr</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_orgqr</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>input2</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the <code>a</code> from <code><a href='torch_geqrf.html'>torch_geqrf</a></code>.</p></td>
-    </tr>
-    <tr>
-      <th>input2</th>
-      <td><p>(Tensor) the <code>tau</code> from <code><a href='torch_geqrf.html'>torch_geqrf</a></code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="orgqr-input-input-gt-tensor-"><a class="anchor" href="#orgqr-input-input-gt-tensor-"></a>orgqr(input, input2) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_orgqr</span><span class="op">(</span><span class="va">self</span>, <span class="va">input2</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the <code>a</code> from <code><a href="torch_geqrf.html">torch_geqrf</a></code>.</p></dd>
+<dt>input2</dt>
+<dd><p>(Tensor) the <code>tau</code> from <code><a href="torch_geqrf.html">torch_geqrf</a></code>.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="orgqr-input-input-gt-tensor-">orgqr(input, input2) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#orgqr-input-input-gt-tensor-"></a></h2>
     
 
 
 <p>Computes the orthogonal matrix <code>Q</code> of a QR factorization, from the <code>(input, input2)</code>
-tuple returned by <code><a href='torch_geqrf.html'>torch_geqrf</a></code>.</p>
+tuple returned by <code><a href="torch_geqrf.html">torch_geqrf</a></code>.</p>
 <p>This directly calls the underlying LAPACK function <code>?orgqr</code>.
 See <code>LAPACK documentation for orgqr</code>_ for further details.</p>
+    </div>
 
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 <div class="row">
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-    <h1>Ormqr</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_ormqr.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Ormqr</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_ormqr.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Ormqr</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_ormqr</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>input2</span>, <span class='va'>input3</span>, left <span class='op'>=</span> <span class='cn'>TRUE</span>, transpose <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the <code>a</code> from <code><a href='torch_geqrf.html'>torch_geqrf</a></code>.</p></td>
-    </tr>
-    <tr>
-      <th>input2</th>
-      <td><p>(Tensor) the <code>tau</code> from <code><a href='torch_geqrf.html'>torch_geqrf</a></code>.</p></td>
-    </tr>
-    <tr>
-      <th>input3</th>
-      <td><p>(Tensor) the matrix to be multiplied.</p></td>
-    </tr>
-    <tr>
-      <th>left</th>
-      <td><p>see LAPACK documentation</p></td>
-    </tr>
-    <tr>
-      <th>transpose</th>
-      <td><p>see LAPACK documentation</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="ormqr-input-input-input-left-true-transpose-false-gt-tensor-"><a class="anchor" href="#ormqr-input-input-input-left-true-transpose-false-gt-tensor-"></a>ormqr(input, input2, input3, left=TRUE, transpose=False) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_ormqr</span><span class="op">(</span><span class="va">self</span>, <span class="va">input2</span>, <span class="va">input3</span>, left <span class="op">=</span> <span class="cn">TRUE</span>, transpose <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the <code>a</code> from <code><a href="torch_geqrf.html">torch_geqrf</a></code>.</p></dd>
+<dt>input2</dt>
+<dd><p>(Tensor) the <code>tau</code> from <code><a href="torch_geqrf.html">torch_geqrf</a></code>.</p></dd>
+<dt>input3</dt>
+<dd><p>(Tensor) the matrix to be multiplied.</p></dd>
+<dt>left</dt>
+<dd><p>see LAPACK documentation</p></dd>
+<dt>transpose</dt>
+<dd><p>see LAPACK documentation</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="ormqr-input-input-input-left-true-transpose-false-gt-tensor-">ormqr(input, input2, input3, left=TRUE, transpose=False) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#ormqr-input-input-input-left-true-transpose-false-gt-tensor-"></a></h2>
     
 
 
 <p>Multiplies <code>mat</code> (given by <code>input3</code>) by the orthogonal <code>Q</code> matrix of the QR factorization
-formed by <code><a href='torch_geqrf.html'>torch_geqrf()</a></code> that is represented by <code>(a, tau)</code> (given by (<code>input</code>, <code>input2</code>)).</p>
+formed by <code><a href="torch_geqrf.html">torch_geqrf()</a></code> that is represented by <code>(a, tau)</code> (given by (<code>input</code>, <code>input2</code>)).</p>
 <p>This directly calls the underlying LAPACK function <code>?ormqr</code>.
-See <a href='https://www.intel.com/content/www/us/en/develop/documentation/onemkl-developer-reference-c/top.html'>LAPACK documentation for ormqr</a> for further details.</p>
+See <a href="https://www.intel.com/content/www/us/en/develop/documentation/onemkl-developer-reference-c/top.html" class="external-link">LAPACK documentation for ormqr</a> for further details.</p>
+    </div>
 
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+      <div class="d-none name"><code>torch_outer.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Outer</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_outer</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>vec2</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) 1-D input vector</p></td>
-    </tr>
-    <tr>
-      <th>vec2</th>
-      <td><p>(Tensor) 1-D input vector</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_outer</span><span class="op">(</span><span class="va">self</span>, <span class="va">vec2</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) 1-D input vector</p></dd>
+<dt>vec2</dt>
+<dd><p>(Tensor) 1-D input vector</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p>This function does not broadcast.</p>
-    <h2 class="hasAnchor" id="outer-input-vec-out-none-gt-tensor-"><a class="anchor" href="#outer-input-vec-out-none-gt-tensor-"></a>outer(input, vec2, *, out=None) -&gt; Tensor </h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="outer-input-vec-out-none-gt-tensor-">outer(input, vec2, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#outer-input-vec-out-none-gt-tensor-"></a></h2>
     
 
 
 <p>Outer product of <code>input</code> and <code>vec2</code>.
 If <code>input</code> is a vector of size \(n\) and <code>vec2</code> is a vector of
 size \(m\), then <code>out</code> must be a matrix of size \((n \times m)\).</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>v1</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span><span class='op'>(</span><span class='fl'>1.</span>, <span class='fl'>5.</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>v2</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span><span class='op'>(</span><span class='fl'>1.</span>, <span class='fl'>4.</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_outer</span><span class='op'>(</span><span class='va'>v1</span>, <span class='va'>v2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   1   2   3   4</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   2   4   6   8</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   3   6   9  12</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   4   8  12  16</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   5  10  15  20</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{5,4} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">v1</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_arange.html">torch_arange</a></span><span class="op">(</span><span class="fl">1.</span>, <span class="fl">5.</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">v2</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_arange.html">torch_arange</a></span><span class="op">(</span><span class="fl">1.</span>, <span class="fl">4.</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_outer</span><span class="op">(</span><span class="va">v1</span>, <span class="va">v2</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   1   2   3   4</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   2   4   6   8</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   3   6   9  12</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   4   8  12  16</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   5  10  15  20</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{5,4} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer>
-      <div class="copyright">
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+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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-<div class="pkgdown">
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_pdist.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Pdist</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_pdist.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Pdist</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_pdist</span><span class='op'>(</span><span class='va'>self</span>, p <span class='op'>=</span> <span class='fl'>2L</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>NA input tensor of shape \(N \times M\).</p></td>
-    </tr>
-    <tr>
-      <th>p</th>
-      <td><p>NA p value for the p-norm distance to calculate between each vector pair        \(\in [0, \infty]\).</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="pdist-input-p-gt-tensor-"><a class="anchor" href="#pdist-input-p-gt-tensor-"></a>pdist(input, p=2) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_pdist</span><span class="op">(</span><span class="va">self</span>, p <span class="op">=</span> <span class="fl">2L</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>NA input tensor of shape \(N \times M\).</p></dd>
+<dt>p</dt>
+<dd><p>NA p value for the p-norm distance to calculate between each vector pair        \(\in [0, \infty]\).</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="pdist-input-p-gt-tensor-">pdist(input, p=2) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#pdist-input-p-gt-tensor-"></a></h2>
     
 
 
@@ -219,32 +107,25 @@ if the rows are contiguous.</p>
 equivalent to <code>scipy.spatial.distance.pdist(input, 'hamming') * M</code>.
 When \(p = \infty\), the closest scipy function is
 <code>scipy.spatial.distance.pdist(xn, lambda x, y: np.abs(x - y).max())</code>.</p>
+    </div>
 
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+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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+<div class="pkgdown-footer-right">
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-    <h1>Pinverse</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_pinverse.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Pinverse</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_pinverse.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Pinverse</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_pinverse</span><span class='op'>(</span><span class='va'>self</span>, rcond <span class='op'>=</span> <span class='fl'>0</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) The input tensor of size \((*, m, n)\) where \(*\) is zero or more batch dimensions</p></td>
-    </tr>
-    <tr>
-      <th>rcond</th>
-      <td><p>(float) A floating point value to determine the cutoff for small singular values.                   Default: 1e-15</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_pinverse</span><span class="op">(</span><span class="va">self</span>, rcond <span class="op">=</span> <span class="fl">1e-15</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) The input tensor of size \((*, m, n)\) where \(*\) is zero or more batch dimensions</p></dd>
+<dt>rcond</dt>
+<dd><p>(float) A floating point value to determine the cutoff for small singular values.                   Default: 1e-15</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     
-<pre>This method is implemented using the Singular Value Decomposition.
-</pre>
+<div class="sourceCode"><pre><code>This method is implemented using the Singular Value Decomposition.
+</code></pre></div>
 
-<pre>The pseudo-inverse is not necessarily a continuous function in the elements of the matrix `[1]`_.
+<div class="sourceCode"><pre><code>The pseudo-inverse is not necessarily a continuous function in the elements of the matrix `[1]`_.
 Therefore, derivatives are not always existent, and exist for a constant rank only `[2]`_.
 However, this method is backprop-able due to the implementation by using SVD results, and
 could be unstable. Double-backward will also be unstable due to the usage of SVD internally.
 See `~torch.svd` for more details.
-</pre>
-
-    <h2 class="hasAnchor" id="pinverse-input-rcond-e-gt-tensor-"><a class="anchor" href="#pinverse-input-rcond-e-gt-tensor-"></a>pinverse(input, rcond=1e-15) -&gt; Tensor </h2>
+</code></pre></div>
 
+    </div>
+    <div class="section level2">
+    <h2 id="pinverse-input-rcond-e-gt-tensor-">pinverse(input, rcond=1e-15) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#pinverse-input-rcond-e-gt-tensor-"></a></h2>
     
 
 
 <p>Calculates the pseudo-inverse (also known as the Moore-Penrose inverse) of a 2D tensor.
 Please look at <code>Moore-Penrose inverse</code>_ for more details</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>5</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>input</span></span>
-<span class='r-in'><span class='fu'>torch_pinverse</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span></span>
-<span class='r-in'><span class='co'># Batched pinverse example</span></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>2</span>,<span class='fl'>6</span>,<span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>b</span> <span class='op'>=</span> <span class='fu'>torch_pinverse</span><span class='op'>(</span><span class='va'>a</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'><a href='torch_matmul.html'>torch_matmul</a></span><span class='op'>(</span><span class='va'>b</span>, <span class='va'>a</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> (1,.,.) = </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   1.0000e+00 -4.0978e-08 -2.0489e-08</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   1.7881e-07  1.0000e+00 -5.9605e-08</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   2.0862e-07 -1.9372e-07  1.0000e+00</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> (2,.,.) = </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   1.0000e+00  3.7253e-09 -1.8626e-08</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   4.4703e-08  1.0000e+00 -4.4703e-08</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  -1.1921e-07  1.7881e-07  1.0000e+00</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{2,3,3} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">input</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">5</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">input</span></span>
+<span class="r-in"><span class="fu">torch_pinverse</span><span class="op">(</span><span class="va">input</span><span class="op">)</span></span>
+<span class="r-in"><span class="co"># Batched pinverse example</span></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>,<span class="fl">6</span>,<span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">b</span> <span class="op">=</span> <span class="fu">torch_pinverse</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu"><a href="torch_matmul.html">torch_matmul</a></span><span class="op">(</span><span class="va">b</span>, <span class="va">a</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> (1,.,.) = </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   1.0000 -0.0000  0.0000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.0000  1.0000  0.0000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.0000  0.0000  1.0000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> (2,.,.) = </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   1.0000e+00  1.1921e-07  1.3411e-07</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   1.5274e-07  1.0000e+00  6.7987e-08</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   1.4529e-07  1.0431e-07  1.0000e+00</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2,3,3} ]</span>
+</code></pre></div>
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Pixel_shuffle</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_pixel_shuffle.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Pixel_shuffle</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_pixel_shuffle.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Pixel_shuffle</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_pixel_shuffle</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>upscale_factor</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor</p></td>
-    </tr>
-    <tr>
-      <th>upscale_factor</th>
-      <td><p>(int) factor to increase spatial resolution by</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="rearranges-elements-in-a-tensor-of-shape-"><a class="anchor" href="#rearranges-elements-in-a-tensor-of-shape-"></a>Rearranges elements in a tensor of shape </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_pixel_shuffle</span><span class="op">(</span><span class="va">self</span>, <span class="va">upscale_factor</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor</p></dd>
+<dt>upscale_factor</dt>
+<dd><p>(int) factor to increase spatial resolution by</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="rearranges-elements-in-a-tensor-of-shape-">Rearranges elements in a tensor of shape <a class="anchor" aria-label="anchor" href="#rearranges-elements-in-a-tensor-of-shape-"></a></h2>
     <p>math:<code>(*, C \times r^2, H, W)</code> to a :</p>
 <p>Rearranges elements in a tensor of shape \((*, C \times r^2, H, W)\) to a
 tensor of shape \((*, C, H \times r, W \times r)\).</p>
 <p>See <code>~torch.nn.PixelShuffle</code> for details.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>9</span>, <span class='fl'>4</span>, <span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span> <span class='op'>=</span> <span class='fu'><a href='nnf_pixel_shuffle.html'>nnf_pixel_shuffle</a></span><span class='op'>(</span><span class='va'>input</span>, <span class='fl'>3</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span><span class='op'>(</span><span class='va'>output</span><span class='op'>$</span><span class='fu'>size</span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [1]  1  1 12 12</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">input</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">9</span>, <span class="fl">4</span>, <span class="fl">4</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span> <span class="op">=</span> <span class="fu"><a href="nnf_pixel_shuffle.html">nnf_pixel_shuffle</a></span><span class="op">(</span><span class="va">input</span>, <span class="fl">3</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">output</span><span class="op">$</span><span class="fu">size</span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1]  1  1 12 12</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Poisson</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_poisson.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Poisson</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_poisson.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Poisson</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_poisson</span><span class='op'>(</span><span class='va'>self</span>, generator <span class='op'>=</span> <span class='cn'>NULL</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor containing the rates of the Poisson distribution</p></td>
-    </tr>
-    <tr>
-      <th>generator</th>
-      <td><p>(<code>torch.Generator</code>, optional) a pseudorandom number generator for sampling</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="poisson-input-generator-null-gt-tensor-"><a class="anchor" href="#poisson-input-generator-null-gt-tensor-"></a>poisson(input *, generator=NULL) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_poisson</span><span class="op">(</span><span class="va">self</span>, generator <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor containing the rates of the Poisson distribution</p></dd>
+<dt>generator</dt>
+<dd><p>(<code>torch.Generator</code>, optional) a pseudorandom number generator for sampling</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="poisson-input-generator-null-gt-tensor-">poisson(input *, generator=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#poisson-input-generator-null-gt-tensor-"></a></h2>
     
 
 
@@ -215,45 +103,40 @@ element in <code>input</code> i.e.,</p>
 <p>$$
     \mbox{out}_i \sim \mbox{Poisson}(\mbox{input}_i)
 $$</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>rates</span> <span class='op'>=</span> <span class='fu'><a href='torch_rand.html'>torch_rand</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span>, <span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>*</span> <span class='fl'>5</span>  <span class='co'># rate parameter between 0 and 5</span></span>
-<span class='r-in'><span class='fu'>torch_poisson</span><span class='op'>(</span><span class='va'>rates</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  2  2  1  1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1  8  2  4</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0  0  5  1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0  3  3  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{4,4} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">rates</span> <span class="op">=</span> <span class="fu"><a href="torch_rand.html">torch_rand</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span>, <span class="fl">4</span><span class="op">)</span><span class="op">)</span> <span class="op">*</span> <span class="fl">5</span>  <span class="co"># rate parameter between 0 and 5</span></span>
+<span class="r-in"><span class="fu">torch_poisson</span><span class="op">(</span><span class="va">rates</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1  1  2  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1  6  0  6</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  4  4  4  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0  4  5  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4,4} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Polar</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_polar.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Polar</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_polar.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Polar</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_polar</span><span class='op'>(</span><span class='va'>abs</span>, <span class='va'>angle</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>abs</th>
-      <td><p>(Tensor) The absolute value the complex tensor. Must be float or
-double.</p></td>
-    </tr>
-    <tr>
-      <th>angle</th>
-      <td><p>(Tensor) The angle of the complex tensor. Must be same dtype as
-<code>abs</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="polar-abs-angle-out-none-gt-tensor-"><a class="anchor" href="#polar-abs-angle-out-none-gt-tensor-"></a>polar(abs, angle, *, out=None) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_polar</span><span class="op">(</span><span class="va">abs</span>, <span class="va">angle</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>abs</dt>
+<dd><p>(Tensor) The absolute value the complex tensor. Must be float or
+double.</p></dd>
+<dt>angle</dt>
+<dd><p>(Tensor) The angle of the complex tensor. Must be same dtype as
+<code>abs</code>.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="polar-abs-angle-out-none-gt-tensor-">polar(abs, angle, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#polar-abs-angle-out-none-gt-tensor-"></a></h2>
     
 
 
@@ -217,46 +105,41 @@ corresponding to the polar coordinates with absolute value <code>abs</code> and
 <p>$$
     \mbox{out} = \mbox{abs} \cdot \cos(\mbox{angle}) + \mbox{abs} \cdot \sin(\mbox{angle}) \cdot j
 $$</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>abs</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>2</span><span class='op'>)</span>, dtype<span class='op'>=</span><span class='fu'><a href='torch_dtype.html'>torch_float64</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>angle</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='va'>pi</span> <span class='op'>/</span> <span class='fl'>2</span>, <span class='fl'>5</span> <span class='op'>*</span> <span class='va'>pi</span> <span class='op'>/</span> <span class='fl'>4</span><span class='op'>)</span>, dtype<span class='op'>=</span><span class='fu'><a href='torch_dtype.html'>torch_float64</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>z</span> <span class='op'>&lt;-</span> <span class='fu'>torch_polar</span><span class='op'>(</span><span class='va'>abs</span>, <span class='va'>angle</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>z</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> 1e-17 *</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  6.1232</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -141421356237309520.0000</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUComplexDoubleType{2} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">abs</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">2</span><span class="op">)</span>, dtype<span class="op">=</span><span class="fu"><a href="torch_dtype.html">torch_float64</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">angle</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="va">pi</span> <span class="op">/</span> <span class="fl">2</span>, <span class="fl">5</span> <span class="op">*</span> <span class="va">pi</span> <span class="op">/</span> <span class="fl">4</span><span class="op">)</span>, dtype<span class="op">=</span><span class="fu"><a href="torch_dtype.html">torch_float64</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">z</span> <span class="op">&lt;-</span> <span class="fu">torch_polar</span><span class="op">(</span><span class="va">abs</span>, <span class="va">angle</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">z</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 1e-17 *</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  6.1232</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -141421356237309520.0000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUComplexDoubleType{2} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Polygamma</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/wrapers.R'><code>R/wrapers.R</code></a></small>
-    <div class="hidden name"><code>torch_polygamma.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Polygamma</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
+      <div class="d-none name"><code>torch_polygamma.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Polygamma</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_polygamma</span><span class='op'>(</span><span class='va'>n</span>, <span class='va'>input</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>n</th>
-      <td><p>(int) the order of the polygamma function</p></td>
-    </tr>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_polygamma</span><span class="op">(</span><span class="va">n</span>, <span class="va">input</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>n</dt>
+<dd><p>(int) the order of the polygamma function</p></dd>
+<dt>input</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     
-<pre>This function is not implemented for \eqn{n \geq 2}.
-</pre>
-
-    <h2 class="hasAnchor" id="polygamma-n-input-out-null-gt-tensor-"><a class="anchor" href="#polygamma-n-input-out-null-gt-tensor-"></a>polygamma(n, input, out=NULL) -&gt; Tensor </h2>
+<div class="sourceCode"><pre><code>This function is not implemented for \eqn{n \geq 2}.
+</code></pre></div>
 
+    </div>
+    <div class="section level2">
+    <h2 id="polygamma-n-input-out-null-gt-tensor-">polygamma(n, input, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#polygamma-n-input-out-null-gt-tensor-"></a></h2>
     
 
 
@@ -220,40 +109,35 @@
 <p>$$
     \psi^{(n)}(x) = \frac{d^{(n)}}{dx^{(n)}} \psi(x)
 $$</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='cn'>FALSE</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>0.5</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_polygamma</span><span class='op'>(</span><span class='fl'>1</span>, <span class='va'>a</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">0.5</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_polygamma</span><span class="op">(</span><span class="fl">1</span>, <span class="va">a</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Pow</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_pow.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Pow</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_pow.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Pow</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_pow</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>exponent</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(float) the scalar base value for the power operation</p></td>
-    </tr>
-    <tr>
-      <th>exponent</th>
-      <td><p>(float or tensor) the exponent value</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="pow-input-exponent-out-null-gt-tensor-"><a class="anchor" href="#pow-input-exponent-out-null-gt-tensor-"></a>pow(input, exponent, out=NULL) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_pow</span><span class="op">(</span><span class="va">self</span>, <span class="va">exponent</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(float) the scalar base value for the power operation</p></dd>
+<dt>exponent</dt>
+<dd><p>(float or tensor) the exponent value</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="pow-input-exponent-out-null-gt-tensor-">pow(input, exponent, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#pow-input-exponent-out-null-gt-tensor-"></a></h2>
     
 
 
@@ -223,8 +111,9 @@ When <code>exponent</code> is a tensor, the operation applied is:</p>
 $$
 When <code>exponent</code> is a tensor, the shapes of <code>input</code>
 and <code>exponent</code> must be broadcastable .</p>
-    <h2 class="hasAnchor" id="pow-self-exponent-out-null-gt-tensor-"><a class="anchor" href="#pow-self-exponent-out-null-gt-tensor-"></a>pow(self, exponent, out=NULL) -&gt; Tensor </h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="pow-self-exponent-out-null-gt-tensor-">pow(self, exponent, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#pow-self-exponent-out-null-gt-tensor-"></a></h2>
     
 
 
@@ -234,57 +123,52 @@ The returned tensor <code>out</code> is of the same shape as <code>exponent</cod
 <p>$$
     \mbox{out}_i = \mbox{self} ^ {\mbox{exponent}_i}
 $$</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='fu'>torch_pow</span><span class='op'>(</span><span class='va'>a</span>, <span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>exp</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>5</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>5</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='va'>exp</span></span>
-<span class='r-in'><span class='fu'>torch_pow</span><span class='op'>(</span><span class='va'>a</span>, <span class='va'>exp</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>exp</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>5</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>base</span> <span class='op'>&lt;-</span> <span class='fl'>2</span></span>
-<span class='r-in'><span class='fu'>torch_pow</span><span class='op'>(</span><span class='va'>base</span>, <span class='va'>exp</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   2</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   4</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   8</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  16</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  32</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{5} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="fu">torch_pow</span><span class="op">(</span><span class="va">a</span>, <span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">exp</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_arange.html">torch_arange</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">5</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_arange.html">torch_arange</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">5</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="va">exp</span></span>
+<span class="r-in"><span class="fu">torch_pow</span><span class="op">(</span><span class="va">a</span>, <span class="va">exp</span><span class="op">)</span></span>
+<span class="r-in"></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">exp</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_arange.html">torch_arange</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">5</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">base</span> <span class="op">&lt;-</span> <span class="fl">2</span></span>
+<span class="r-in"><span class="fu">torch_pow</span><span class="op">(</span><span class="va">base</span>, <span class="va">exp</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   2</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   4</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   8</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  16</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  32</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{5} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Prod</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_prod.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Prod</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_prod.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Prod</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_prod</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>dim</span>, keepdim <span class='op'>=</span> <span class='cn'>FALSE</span>, dtype <span class='op'>=</span> <span class='cn'>NULL</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) the dimension to reduce.</p></td>
-    </tr>
-    <tr>
-      <th>keepdim</th>
-      <td><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        If specified, the input tensor is casted to <code>dtype</code> before the operation        is performed. This is useful for preventing data type overflows. Default: NULL.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="prod-input-dtype-null-gt-tensor-"><a class="anchor" href="#prod-input-dtype-null-gt-tensor-"></a>prod(input, dtype=NULL) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_prod</span><span class="op">(</span><span class="va">self</span>, <span class="va">dim</span>, keepdim <span class="op">=</span> <span class="cn">FALSE</span>, dtype <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+<dt>dim</dt>
+<dd><p>(int) the dimension to reduce.</p></dd>
+<dt>keepdim</dt>
+<dd><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></dd>
+<dt>dtype</dt>
+<dd><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        If specified, the input tensor is casted to <code>dtype</code> before the operation        is performed. This is useful for preventing data type overflows. Default: NULL.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="prod-input-dtype-null-gt-tensor-">prod(input, dtype=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#prod-input-dtype-null-gt-tensor-"></a></h2>
     
 
 
 <p>Returns the product of all elements in the <code>input</code> tensor.</p>
-    <h2 class="hasAnchor" id="prod-input-dim-keepdim-false-dtype-null-gt-tensor-"><a class="anchor" href="#prod-input-dim-keepdim-false-dtype-null-gt-tensor-"></a>prod(input, dim, keepdim=False, dtype=NULL) -&gt; Tensor </h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="prod-input-dim-keepdim-false-dtype-null-gt-tensor-">prod(input, dim, keepdim=False, dtype=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#prod-input-dim-keepdim-false-dtype-null-gt-tensor-"></a></h2>
     
 
 
@@ -227,51 +112,46 @@
 dimension <code>dim</code>.</p>
 <p>If <code>keepdim</code> is <code>TRUE</code>, the output tensor is of the same size
 as <code>input</code> except in the dimension <code>dim</code> where it is of size 1.
-Otherwise, <code>dim</code> is squeezed (see <code><a href='torch_squeeze.html'>torch_squeeze</a></code>), resulting in
+Otherwise, <code>dim</code> is squeezed (see <code><a href="torch_squeeze.html">torch_squeeze</a></code>), resulting in
 the output tensor having 1 fewer dimension than <code>input</code>.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='fu'>torch_prod</span><span class='op'>(</span><span class='va'>a</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span>, <span class='fl'>2</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='fu'>torch_prod</span><span class='op'>(</span><span class='va'>a</span>, <span class='fl'>1</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.0500</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.1345</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{2} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="fu">torch_prod</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span>
+<span class="r-in"></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span>, <span class="fl">2</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="fu">torch_prod</span><span class="op">(</span><span class="va">a</span>, <span class="fl">1</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.4314</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.0025</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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-      <div class="copyright">
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+    <footer><div class="pkgdown-footer-left">
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+<div class="pkgdown-footer-right">
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diff --git a/reference/torch_promote_types.html b/reference/torch_promote_types.html
index 6bbea2290..64bf1a1dd 100644
--- a/reference/torch_promote_types.html
+++ b/reference/torch_promote_types.html
@@ -1,211 +1,99 @@
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Promote_types</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_promote_types.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Promote_types</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_promote_types.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Promote_types</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_promote_types</span><span class='op'>(</span><span class='va'>type1</span>, <span class='va'>type2</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>type1</th>
-      <td><p>(<code>torch.dtype</code>)</p></td>
-    </tr>
-    <tr>
-      <th>type2</th>
-      <td><p>(<code>torch.dtype</code>)</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="promote-types-type-type-gt-dtype-"><a class="anchor" href="#promote-types-type-type-gt-dtype-"></a>promote_types(type1, type2) -&gt; dtype </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_promote_types</span><span class="op">(</span><span class="va">type1</span>, <span class="va">type2</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>type1</dt>
+<dd><p>(<code>torch.dtype</code>)</p></dd>
+<dt>type2</dt>
+<dd><p>(<code>torch.dtype</code>)</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="promote-types-type-type-gt-dtype-">promote_types(type1, type2) -&gt; dtype <a class="anchor" aria-label="anchor" href="#promote-types-type-type-gt-dtype-"></a></h2>
     
 
 
@@ -213,40 +101,35 @@
 not smaller nor of lower kind than either <code>type1</code> or <code>type2</code>. See type promotion
 documentation  for more information on the type
 promotion logic.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='fu'>torch_promote_types</span><span class='op'>(</span><span class='fu'><a href='torch_dtype.html'>torch_int32</a></span><span class='op'>(</span><span class='op'>)</span>, <span class='fu'><a href='torch_dtype.html'>torch_float32</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_promote_types</span><span class='op'>(</span><span class='fu'><a href='torch_dtype.html'>torch_uint8</a></span><span class='op'>(</span><span class='op'>)</span>, <span class='fu'><a href='torch_dtype.html'>torch_long</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_Long</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="fu">torch_promote_types</span><span class="op">(</span><span class="fu"><a href="torch_dtype.html">torch_int32</a></span><span class="op">(</span><span class="op">)</span>, <span class="fu"><a href="torch_dtype.html">torch_float32</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_promote_types</span><span class="op">(</span><span class="fu"><a href="torch_dtype.html">torch_uint8</a></span><span class="op">(</span><span class="op">)</span>, <span class="fu"><a href="torch_dtype.html">torch_long</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_Long</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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-      <div class="copyright">
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+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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+<div class="pkgdown-footer-right">
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 <div class="row">
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-    <h1>Qr</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_qr.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Qr</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_qr.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Qr</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_qr</span><span class='op'>(</span><span class='va'>self</span>, some <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor of size \((*, m, n)\) where <code>*</code> is zero or more                batch dimensions consisting of matrices of dimension \(m \times n\).</p></td>
-    </tr>
-    <tr>
-      <th>some</th>
-      <td><p>(bool, optional) Set to <code>TRUE</code> for reduced QR decomposition and <code>FALSE</code> for                complete QR decomposition.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_qr</span><span class="op">(</span><span class="va">self</span>, some <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor of size \((*, m, n)\) where <code>*</code> is zero or more                batch dimensions consisting of matrices of dimension \(m \times n\).</p></dd>
+<dt>some</dt>
+<dd><p>(bool, optional) Set to <code>TRUE</code> for reduced QR decomposition and <code>FALSE</code> for                complete QR decomposition.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p>precision may be lost if the magnitudes of the elements of <code>input</code>
 are large</p>
 <p>While it should always give you a valid decomposition, it may not
 give you the same one across platforms - it will depend on your
 LAPACK implementation.</p>
-    <h2 class="hasAnchor" id="qr-input-some-true-out-null-gt-tensor-tensor-"><a class="anchor" href="#qr-input-some-true-out-null-gt-tensor-tensor-"></a>qr(input, some=TRUE, out=NULL) -&gt; (Tensor, Tensor) </h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="qr-input-some-true-out-null-gt-tensor-tensor-">qr(input, some=TRUE, out=NULL) -&gt; (Tensor, Tensor) <a class="anchor" aria-label="anchor" href="#qr-input-some-true-out-null-gt-tensor-tensor-"></a></h2>
     
 
 
@@ -222,48 +111,43 @@ with \(Q\) being an orthogonal matrix or batch of orthogonal matrices and
 \(R\) being an upper triangular matrix or batch of upper triangular matrices.</p>
 <p>If <code>some</code> is <code>TRUE</code>, then this function returns the thin (reduced) QR factorization.
 Otherwise, if <code>some</code> is <code>FALSE</code>, this function returns the complete QR factorization.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>12.</span>, <span class='op'>-</span><span class='fl'>51</span>, <span class='fl'>4</span>, <span class='fl'>6</span>, <span class='fl'>167</span>, <span class='op'>-</span><span class='fl'>68</span>, <span class='op'>-</span><span class='fl'>4</span>, <span class='fl'>24</span>, <span class='op'>-</span><span class='fl'>41</span><span class='op'>)</span>, ncol <span class='op'>=</span> <span class='fl'>3</span>, byrow <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>out</span> <span class='op'>=</span> <span class='fu'>torch_qr</span><span class='op'>(</span><span class='va'>a</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>q</span> <span class='op'>=</span> <span class='va'>out</span><span class='op'>[[</span><span class='fl'>1</span><span class='op'>]</span><span class='op'>]</span></span>
-<span class='r-in'><span class='va'>r</span> <span class='op'>=</span> <span class='va'>out</span><span class='op'>[[</span><span class='fl'>2</span><span class='op'>]</span><span class='op'>]</span></span>
-<span class='r-in'><span class='fu'><a href='torch_mm.html'>torch_mm</a></span><span class='op'>(</span><span class='va'>q</span>, <span class='va'>r</span><span class='op'>)</span><span class='op'>$</span><span class='fu'>round</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'><a href='torch_mm.html'>torch_mm</a></span><span class='op'>(</span><span class='va'>q</span><span class='op'>$</span><span class='fu'>t</span><span class='op'>(</span><span class='op'>)</span>, <span class='va'>q</span><span class='op'>)</span><span class='op'>$</span><span class='fu'>round</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1  0  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0  1  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0  0  1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{3,3} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/matrix.html" class="external-link">matrix</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">12.</span>, <span class="op">-</span><span class="fl">51</span>, <span class="fl">4</span>, <span class="fl">6</span>, <span class="fl">167</span>, <span class="op">-</span><span class="fl">68</span>, <span class="op">-</span><span class="fl">4</span>, <span class="fl">24</span>, <span class="op">-</span><span class="fl">41</span><span class="op">)</span>, ncol <span class="op">=</span> <span class="fl">3</span>, byrow <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">out</span> <span class="op">=</span> <span class="fu">torch_qr</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">q</span> <span class="op">=</span> <span class="va">out</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span></span>
+<span class="r-in"><span class="va">r</span> <span class="op">=</span> <span class="va">out</span><span class="op">[[</span><span class="fl">2</span><span class="op">]</span><span class="op">]</span></span>
+<span class="r-in"><span class="fu"><a href="torch_mm.html">torch_mm</a></span><span class="op">(</span><span class="va">q</span>, <span class="va">r</span><span class="op">)</span><span class="op">$</span><span class="fu">round</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu"><a href="torch_mm.html">torch_mm</a></span><span class="op">(</span><span class="va">q</span><span class="op">$</span><span class="fu">t</span><span class="op">(</span><span class="op">)</span>, <span class="va">q</span><span class="op">)</span><span class="op">$</span><span class="fu">round</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1  0  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0  1  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0  0  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3,3} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/qscheme.R'><code>R/qscheme.R</code></a></small>
-    <div class="hidden name"><code>torch_qscheme.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Creates the corresponding Scheme object</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/qscheme.R" class="external-link"><code>R/qscheme.R</code></a></small>
+      <div class="d-none name"><code>torch_qscheme.Rd</code></div>
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     <p>Creates the corresponding Scheme object</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_per_channel_affine</span><span class='op'>(</span><span class='op'>)</span>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_per_channel_affine</span><span class="op">(</span><span class="op">)</span>
 
-<span class='fu'>torch_per_tensor_affine</span><span class='op'>(</span><span class='op'>)</span>
+<span class="fu">torch_per_tensor_affine</span><span class="op">(</span><span class="op">)</span>
 
-<span class='fu'>torch_per_channel_symmetric</span><span class='op'>(</span><span class='op'>)</span>
+<span class="fu">torch_per_channel_symmetric</span><span class="op">(</span><span class="op">)</span>
 
-<span class='fu'>torch_per_tensor_symmetric</span><span class='op'>(</span><span class='op'>)</span></pre>
+<span class="fu">torch_per_tensor_symmetric</span><span class="op">(</span><span class="op">)</span></code></pre></div>
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diff --git a/reference/torch_quantile.html b/reference/torch_quantile.html
index efe48b63b..a98efcbb8 100644
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Quantile</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_quantile.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Quantile</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_quantile.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Quantile</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_quantile</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>q</span>, dim <span class='op'>=</span> <span class='cn'>NULL</span>, keepdim <span class='op'>=</span> <span class='cn'>FALSE</span>, <span class='va'>interpolation</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>q</th>
-      <td><p>(float or Tensor) a scalar or 1D tensor of quantile values in the range <code>[0, 1]</code></p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) the dimension to reduce.</p></td>
-    </tr>
-    <tr>
-      <th>keepdim</th>
-      <td><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></td>
-    </tr>
-    <tr>
-      <th>interpolation</th>
-      <td><p>The interpolation method.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="quantile-input-q-gt-tensor-"><a class="anchor" href="#quantile-input-q-gt-tensor-"></a>quantile(input, q) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_quantile</span><span class="op">(</span><span class="va">self</span>, <span class="va">q</span>, dim <span class="op">=</span> <span class="cn">NULL</span>, keepdim <span class="op">=</span> <span class="cn">FALSE</span>, <span class="va">interpolation</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+<dt>q</dt>
+<dd><p>(float or Tensor) a scalar or 1D tensor of quantile values in the range <code>[0, 1]</code></p></dd>
+<dt>dim</dt>
+<dd><p>(int) the dimension to reduce.</p></dd>
+<dt>keepdim</dt>
+<dd><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></dd>
+<dt>interpolation</dt>
+<dd><p>The interpolation method.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="quantile-input-q-gt-tensor-">quantile(input, q) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#quantile-input-q-gt-tensor-"></a></h2>
     
 
 
 <p>Returns the q-th quantiles of all elements in the <code>input</code> tensor, doing a linear
 interpolation when the q-th quantile lies between two data points.</p>
-    <h2 class="hasAnchor" id="quantile-input-q-dim-none-keepdim-false-out-none-gt-tensor-"><a class="anchor" href="#quantile-input-q-dim-none-keepdim-false-out-none-gt-tensor-"></a>quantile(input, q, dim=None, keepdim=FALSE, *, out=None) -&gt; Tensor </h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="quantile-input-q-dim-none-keepdim-false-out-none-gt-tensor-">quantile(input, q, dim=None, keepdim=FALSE, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#quantile-input-q-dim-none-keepdim-false-out-none-gt-tensor-"></a></h2>
     
 
 
@@ -234,52 +117,47 @@ data points. By default, <code>dim</code> is <code>None</code> resulting in the
 being flattened before computation.</p>
 <p>If <code>keepdim</code> is <code>TRUE</code>, the output dimensions are of the same size as <code>input</code>
 except in the dimensions being reduced (<code>dim</code> or all if <code>dim</code> is <code>NULL</code>) where they
-have size 1. Otherwise, the dimensions being reduced are squeezed (see <code><a href='torch_squeeze.html'>torch_squeeze</a></code>).
+have size 1. Otherwise, the dimensions being reduced are squeezed (see <code><a href="torch_squeeze.html">torch_squeeze</a></code>).
 If <code>q</code> is a 1D tensor, an extra dimension is prepended to the output tensor with the same
 size as <code>q</code> which represents the quantiles.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='va'>q</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>0</span>, <span class='fl'>0.5</span>, <span class='fl'>1</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_quantile</span><span class='op'>(</span><span class='va'>a</span>, <span class='va'>q</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='va'>q</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>0.25</span>, <span class='fl'>0.5</span>, <span class='fl'>0.75</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_quantile</span><span class='op'>(</span><span class='va'>a</span>, <span class='va'>q</span>, dim<span class='op'>=</span><span class='fl'>1</span>, keepdim<span class='op'>=</span><span class='cn'>TRUE</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_quantile</span><span class='op'>(</span><span class='va'>a</span>, <span class='va'>q</span>, dim<span class='op'>=</span><span class='fl'>1</span>, keepdim<span class='op'>=</span><span class='cn'>TRUE</span><span class='op'>)</span><span class='op'>$</span><span class='va'>shape</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [1] 3 1 3</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="va">q</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">0.5</span>, <span class="fl">1</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_quantile</span><span class="op">(</span><span class="va">a</span>, <span class="va">q</span><span class="op">)</span></span>
+<span class="r-in"></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="va">q</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.25</span>, <span class="fl">0.5</span>, <span class="fl">0.75</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_quantile</span><span class="op">(</span><span class="va">a</span>, <span class="va">q</span>, dim<span class="op">=</span><span class="fl">1</span>, keepdim<span class="op">=</span><span class="cn">TRUE</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_quantile</span><span class="op">(</span><span class="va">a</span>, <span class="va">q</span>, dim<span class="op">=</span><span class="fl">1</span>, keepdim<span class="op">=</span><span class="cn">TRUE</span><span class="op">)</span><span class="op">$</span><span class="va">shape</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 3 1 3</span>
+</code></pre></div>
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Quantize_per_channel</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_quantize_per_channel.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Quantize_per_channel</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_quantize_per_channel.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Quantize_per_channel</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_quantize_per_channel</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>scales</span>, <span class='va'>zero_points</span>, <span class='va'>axis</span>, <span class='va'>dtype</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) float tensor to quantize</p></td>
-    </tr>
-    <tr>
-      <th>scales</th>
-      <td><p>(Tensor) float 1D tensor of scales to use, size should match <code>input.size(axis)</code></p></td>
-    </tr>
-    <tr>
-      <th>zero_points</th>
-      <td><p>(int) integer 1D tensor of offset to use, size should match <code>input.size(axis)</code></p></td>
-    </tr>
-    <tr>
-      <th>axis</th>
-      <td><p>(int) dimension on which apply per-channel quantization</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>) the desired data type of returned tensor.        Has to be one of the quantized dtypes: <code>torch_quint8</code>, <code>torch.qint8</code>, <code>torch.qint32</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="quantize-per-channel-input-scales-zero-points-axis-dtype-gt-tensor-"><a class="anchor" href="#quantize-per-channel-input-scales-zero-points-axis-dtype-gt-tensor-"></a>quantize_per_channel(input, scales, zero_points, axis, dtype) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_quantize_per_channel</span><span class="op">(</span><span class="va">self</span>, <span class="va">scales</span>, <span class="va">zero_points</span>, <span class="va">axis</span>, <span class="va">dtype</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) float tensor to quantize</p></dd>
+<dt>scales</dt>
+<dd><p>(Tensor) float 1D tensor of scales to use, size should match <code>input.size(axis)</code></p></dd>
+<dt>zero_points</dt>
+<dd><p>(int) integer 1D tensor of offset to use, size should match <code>input.size(axis)</code></p></dd>
+<dt>axis</dt>
+<dd><p>(int) dimension on which apply per-channel quantization</p></dd>
+<dt>dtype</dt>
+<dd><p>(<code>torch.dtype</code>) the desired data type of returned tensor.        Has to be one of the quantized dtypes: <code>torch_quint8</code>, <code>torch.qint8</code>, <code>torch.qint32</code></p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="quantize-per-channel-input-scales-zero-points-axis-dtype-gt-tensor-">quantize_per_channel(input, scales, zero_points, axis, dtype) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#quantize-per-channel-input-scales-zero-points-axis-dtype-gt-tensor-"></a></h2>
     
 
 
 <p>Converts a float tensor to per-channel quantized tensor with given scales and zero points.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='op'>-</span><span class='fl'>1.0</span>, <span class='fl'>0.0</span>, <span class='fl'>1.0</span>, <span class='fl'>2.0</span><span class='op'>)</span>, ncol <span class='op'>=</span> <span class='fl'>2</span>, byrow <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_quantize_per_channel</span><span class='op'>(</span><span class='va'>x</span>, <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>0.1</span>, <span class='fl'>0.01</span><span class='op'>)</span><span class='op'>)</span>, </span>
-<span class='r-in'>                           <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>10L</span>, <span class='fl'>0L</span><span class='op'>)</span><span class='op'>)</span>, <span class='fl'>0</span>, <span class='fu'><a href='torch_dtype.html'>torch_quint8</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_quantize_per_channel</span><span class='op'>(</span><span class='va'>x</span>, <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>0.1</span>, <span class='fl'>0.01</span><span class='op'>)</span><span class='op'>)</span>, </span>
-<span class='r-in'>                           <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>10L</span>, <span class='fl'>0L</span><span class='op'>)</span><span class='op'>)</span>, <span class='fl'>0</span>, <span class='fu'><a href='torch_dtype.html'>torch_quint8</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span><span class='op'>$</span><span class='fu'>int_repr</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>    0   10</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  100  200</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUByteType{2,2} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">x</span> <span class="op">=</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/matrix.html" class="external-link">matrix</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="op">-</span><span class="fl">1.0</span>, <span class="fl">0.0</span>, <span class="fl">1.0</span>, <span class="fl">2.0</span><span class="op">)</span>, ncol <span class="op">=</span> <span class="fl">2</span>, byrow <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_quantize_per_channel</span><span class="op">(</span><span class="va">x</span>, <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.1</span>, <span class="fl">0.01</span><span class="op">)</span><span class="op">)</span>, </span>
+<span class="r-in">                           <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">10L</span>, <span class="fl">0L</span><span class="op">)</span><span class="op">)</span>, <span class="fl">0</span>, <span class="fu"><a href="torch_dtype.html">torch_quint8</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_quantize_per_channel</span><span class="op">(</span><span class="va">x</span>, <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.1</span>, <span class="fl">0.01</span><span class="op">)</span><span class="op">)</span>, </span>
+<span class="r-in">                           <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">10L</span>, <span class="fl">0L</span><span class="op">)</span><span class="op">)</span>, <span class="fl">0</span>, <span class="fu"><a href="torch_dtype.html">torch_quint8</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span><span class="op">$</span><span class="fu">int_repr</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    0   10</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  100  200</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUByteType{2,2} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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 <div class="row">
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-    <div class="page-header">
-    <h1>Quantize_per_tensor</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/wrapers.R'><code>R/wrapers.R</code></a></small>
-    <div class="hidden name"><code>torch_quantize_per_tensor.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Quantize_per_tensor</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
+      <div class="d-none name"><code>torch_quantize_per_tensor.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Quantize_per_tensor</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_quantize_per_tensor</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>scale</span>, <span class='va'>zero_point</span>, <span class='va'>dtype</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) float tensor to quantize</p></td>
-    </tr>
-    <tr>
-      <th>scale</th>
-      <td><p>(float) scale to apply in quantization formula</p></td>
-    </tr>
-    <tr>
-      <th>zero_point</th>
-      <td><p>(int) offset in integer value that maps to float zero</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>) the desired data type of returned tensor.        Has to be one of the quantized dtypes: <code>torch_quint8</code>, <code>torch.qint8</code>, <code>torch.qint32</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="quantize-per-tensor-input-scale-zero-point-dtype-gt-tensor-"><a class="anchor" href="#quantize-per-tensor-input-scale-zero-point-dtype-gt-tensor-"></a>quantize_per_tensor(input, scale, zero_point, dtype) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_quantize_per_tensor</span><span class="op">(</span><span class="va">self</span>, <span class="va">scale</span>, <span class="va">zero_point</span>, <span class="va">dtype</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) float tensor to quantize</p></dd>
+<dt>scale</dt>
+<dd><p>(float) scale to apply in quantization formula</p></dd>
+<dt>zero_point</dt>
+<dd><p>(int) offset in integer value that maps to float zero</p></dd>
+<dt>dtype</dt>
+<dd><p>(<code>torch.dtype</code>) the desired data type of returned tensor.        Has to be one of the quantized dtypes: <code>torch_quint8</code>, <code>torch.qint8</code>, <code>torch.qint32</code></p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="quantize-per-tensor-input-scale-zero-point-dtype-gt-tensor-">quantize_per_tensor(input, scale, zero_point, dtype) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#quantize-per-tensor-input-scale-zero-point-dtype-gt-tensor-"></a></h2>
     
 
 
 <p>Converts a float tensor to quantized tensor with given scale and zero point.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='fu'>torch_quantize_per_tensor</span><span class='op'>(</span><span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='op'>-</span><span class='fl'>1.0</span>, <span class='fl'>0.0</span>, <span class='fl'>1.0</span>, <span class='fl'>2.0</span><span class='op'>)</span><span class='op'>)</span>, <span class='fl'>0.1</span>, <span class='fl'>10</span>, <span class='fu'><a href='torch_dtype.html'>torch_quint8</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_quantize_per_tensor</span><span class='op'>(</span><span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='op'>-</span><span class='fl'>1.0</span>, <span class='fl'>0.0</span>, <span class='fl'>1.0</span>, <span class='fl'>2.0</span><span class='op'>)</span><span class='op'>)</span>, <span class='fl'>0.1</span>, <span class='fl'>10</span>, <span class='fu'><a href='torch_dtype.html'>torch_quint8</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span><span class='op'>$</span><span class='fu'>int_repr</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  10</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  20</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  30</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUByteType{4} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="fu">torch_quantize_per_tensor</span><span class="op">(</span><span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="op">-</span><span class="fl">1.0</span>, <span class="fl">0.0</span>, <span class="fl">1.0</span>, <span class="fl">2.0</span><span class="op">)</span><span class="op">)</span>, <span class="fl">0.1</span>, <span class="fl">10</span>, <span class="fu"><a href="torch_dtype.html">torch_quint8</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_quantize_per_tensor</span><span class="op">(</span><span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="op">-</span><span class="fl">1.0</span>, <span class="fl">0.0</span>, <span class="fl">1.0</span>, <span class="fl">2.0</span><span class="op">)</span><span class="op">)</span>, <span class="fl">0.1</span>, <span class="fl">10</span>, <span class="fu"><a href="torch_dtype.html">torch_quint8</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span><span class="op">$</span><span class="fu">int_repr</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  10</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  20</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  30</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUByteType{4} ]</span>
+</code></pre></div>
+    </div>
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Rad2deg</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_rad2deg.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Rad2deg</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_rad2deg.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Rad2deg</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_rad2deg</span><span class='op'>(</span><span class='va'>self</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="rad-deg-input-out-none-gt-tensor-"><a class="anchor" href="#rad-deg-input-out-none-gt-tensor-"></a>rad2deg(input, *, out=None) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_rad2deg</span><span class="op">(</span><span class="va">self</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="rad-deg-input-out-none-gt-tensor-">rad2deg(input, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#rad-deg-input-out-none-gt-tensor-"></a></h2>
     
 
 
 <p>Returns a new tensor with each of the elements of <code>input</code>
 converted from angles in radians to degrees.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/cbind.html'>rbind</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3.142</span>, <span class='op'>-</span><span class='fl'>3.142</span><span class='op'>)</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>6.283</span>, <span class='op'>-</span><span class='fl'>6.283</span><span class='op'>)</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1.570</span>, <span class='op'>-</span><span class='fl'>1.570</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_rad2deg</span><span class='op'>(</span><span class='va'>a</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  180.0233 -180.0233</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  359.9894 -359.9894</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   89.9544  -89.9544</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{3,2} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/cbind.html" class="external-link">rbind</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3.142</span>, <span class="op">-</span><span class="fl">3.142</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">6.283</span>, <span class="op">-</span><span class="fl">6.283</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1.570</span>, <span class="op">-</span><span class="fl">1.570</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_rad2deg</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  180.0233 -180.0233</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  359.9894 -359.9894</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   89.9544  -89.9544</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3,2} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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+    
+  </div>
+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Rand</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/creation-ops.R'><code>R/creation-ops.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_rand.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Rand</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/creation-ops.R" class="external-link"><code>R/creation-ops.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a></small>
+      <div class="d-none name"><code>torch_rand.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Rand</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_rand</span><span class='op'>(</span>
-  <span class='va'>...</span>,
-  names <span class='op'>=</span> <span class='cn'>NULL</span>,
-  dtype <span class='op'>=</span> <span class='cn'>NULL</span>,
-  layout <span class='op'>=</span> <span class='fu'><a href='torch_layout.html'>torch_strided</a></span><span class='op'>(</span><span class='op'>)</span>,
-  device <span class='op'>=</span> <span class='cn'>NULL</span>,
-  requires_grad <span class='op'>=</span> <span class='cn'>FALSE</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>...</th>
-      <td><p>(int...) a sequence of integers defining the shape of the output tensor.        Can be a variable number of arguments or a collection like a list or tuple.</p></td>
-    </tr>
-    <tr>
-      <th>names</th>
-      <td><p>optional dimension names</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>).</p></td>
-    </tr>
-    <tr>
-      <th>layout</th>
-      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
-    </tr>
-    <tr>
-      <th>requires_grad</th>
-      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="rand-size-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"><a class="anchor" href="#rand-size-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"></a>rand(*size, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_rand</span><span class="op">(</span>
+  <span class="va">...</span>,
+  names <span class="op">=</span> <span class="cn">NULL</span>,
+  dtype <span class="op">=</span> <span class="cn">NULL</span>,
+  layout <span class="op">=</span> <span class="fu"><a href="torch_layout.html">torch_strided</a></span><span class="op">(</span><span class="op">)</span>,
+  device <span class="op">=</span> <span class="cn">NULL</span>,
+  requires_grad <span class="op">=</span> <span class="cn">FALSE</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>...</dt>
+<dd><p>(int...) a sequence of integers defining the shape of the output tensor.        Can be a variable number of arguments or a collection like a list or tuple.</p></dd>
+<dt>names</dt>
+<dd><p>optional dimension names</p></dd>
+<dt>dtype</dt>
+<dd><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>).</p></dd>
+<dt>layout</dt>
+<dd><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></dd>
+<dt>device</dt>
+<dd><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></dd>
+<dt>requires_grad</dt>
+<dd><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="rand-size-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-">rand(*size, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#rand-size-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"></a></h2>
     
 
 
 <p>Returns a tensor filled with random numbers from a uniform distribution
 on the interval \([0, 1)\)</p>
 <p>The shape of the tensor is defined by the variable argument <code>size</code>.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='fu'>torch_rand</span><span class='op'>(</span><span class='fl'>4</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_rand</span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.4590  0.0315  0.9857</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.8921  0.0884  0.7842</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{2,3} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="fu">torch_rand</span><span class="op">(</span><span class="fl">4</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_rand</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.7649  0.7496  0.7544</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.8929  0.6352  0.6037</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2,3} ]</span>
+</code></pre></div>
+    </div>
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Rand_like</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/creation-ops.R'><code>R/creation-ops.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_rand_like.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Rand_like</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/creation-ops.R" class="external-link"><code>R/creation-ops.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a></small>
+      <div class="d-none name"><code>torch_rand_like.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Rand_like</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_rand_like</span><span class='op'>(</span>
-  <span class='va'>input</span>,
-  dtype <span class='op'>=</span> <span class='cn'>NULL</span>,
-  layout <span class='op'>=</span> <span class='fu'><a href='torch_layout.html'>torch_strided</a></span><span class='op'>(</span><span class='op'>)</span>,
-  device <span class='op'>=</span> <span class='cn'>NULL</span>,
-  requires_grad <span class='op'>=</span> <span class='cn'>FALSE</span>,
-  memory_format <span class='op'>=</span> <span class='fu'><a href='torch_memory_format.html'>torch_preserve_format</a></span><span class='op'>(</span><span class='op'>)</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the size of <code>input</code> will determine size of the output tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned Tensor.        Default: if <code>NULL</code>, defaults to the dtype of <code>input</code>.</p></td>
-    </tr>
-    <tr>
-      <th>layout</th>
-      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned tensor.        Default: if <code>NULL</code>, defaults to the layout of <code>input</code>.</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, defaults to the device of <code>input</code>.</p></td>
-    </tr>
-    <tr>
-      <th>requires_grad</th>
-      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
-    </tr>
-    <tr>
-      <th>memory_format</th>
-      <td><p>(<code>torch.memory_format</code>, optional) the desired memory format of        returned Tensor. Default: <code>torch_preserve_format</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="rand-like-input-dtype-null-layout-null-device-null-requires-grad-false-memory-format-torch-preserve-format-gt-tensor-"><a class="anchor" href="#rand-like-input-dtype-null-layout-null-device-null-requires-grad-false-memory-format-torch-preserve-format-gt-tensor-"></a>rand_like(input, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, memory_format=torch.preserve_format) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_rand_like</span><span class="op">(</span>
+  <span class="va">input</span>,
+  dtype <span class="op">=</span> <span class="cn">NULL</span>,
+  layout <span class="op">=</span> <span class="fu"><a href="torch_layout.html">torch_strided</a></span><span class="op">(</span><span class="op">)</span>,
+  device <span class="op">=</span> <span class="cn">NULL</span>,
+  requires_grad <span class="op">=</span> <span class="cn">FALSE</span>,
+  memory_format <span class="op">=</span> <span class="fu"><a href="torch_memory_format.html">torch_preserve_format</a></span><span class="op">(</span><span class="op">)</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>(Tensor) the size of <code>input</code> will determine size of the output tensor.</p></dd>
+<dt>dtype</dt>
+<dd><p>(<code>torch.dtype</code>, optional) the desired data type of returned Tensor.        Default: if <code>NULL</code>, defaults to the dtype of <code>input</code>.</p></dd>
+<dt>layout</dt>
+<dd><p>(<code>torch.layout</code>, optional) the desired layout of returned tensor.        Default: if <code>NULL</code>, defaults to the layout of <code>input</code>.</p></dd>
+<dt>device</dt>
+<dd><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, defaults to the device of <code>input</code>.</p></dd>
+<dt>requires_grad</dt>
+<dd><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></dd>
+<dt>memory_format</dt>
+<dd><p>(<code>torch.memory_format</code>, optional) the desired memory format of        returned Tensor. Default: <code>torch_preserve_format</code>.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="rand-like-input-dtype-null-layout-null-device-null-requires-grad-false-memory-format-torch-preserve-format-gt-tensor-">rand_like(input, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, memory_format=torch.preserve_format) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#rand-like-input-dtype-null-layout-null-device-null-requires-grad-false-memory-format-torch-preserve-format-gt-tensor-"></a></h2>
     
 
 
@@ -236,32 +116,25 @@
 random numbers from a uniform distribution on the interval \([0, 1)\).
 <code>torch_rand_like(input)</code> is equivalent to
 <code>torch_rand(input.size(), dtype=input.dtype, layout=input.layout, device=input.device)</code>.</p>
+    </div>
 
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+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Randint</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/creation-ops.R'><code>R/creation-ops.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_randint.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Randint</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/creation-ops.R" class="external-link"><code>R/creation-ops.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a></small>
+      <div class="d-none name"><code>torch_randint.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Randint</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_randint</span><span class='op'>(</span>
-  <span class='va'>low</span>,
-  <span class='va'>high</span>,
-  <span class='va'>size</span>,
-  generator <span class='op'>=</span> <span class='cn'>NULL</span>,
-  dtype <span class='op'>=</span> <span class='cn'>NULL</span>,
-  layout <span class='op'>=</span> <span class='fu'><a href='torch_layout.html'>torch_strided</a></span><span class='op'>(</span><span class='op'>)</span>,
-  device <span class='op'>=</span> <span class='cn'>NULL</span>,
-  requires_grad <span class='op'>=</span> <span class='cn'>FALSE</span>,
-  memory_format <span class='op'>=</span> <span class='fu'><a href='torch_memory_format.html'>torch_preserve_format</a></span><span class='op'>(</span><span class='op'>)</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>low</th>
-      <td><p>(int, optional) Lowest integer to be drawn from the distribution. Default: 0.</p></td>
-    </tr>
-    <tr>
-      <th>high</th>
-      <td><p>(int) One above the highest integer to be drawn from the distribution.</p></td>
-    </tr>
-    <tr>
-      <th>size</th>
-      <td><p>(tuple) a tuple defining the shape of the output tensor.</p></td>
-    </tr>
-    <tr>
-      <th>generator</th>
-      <td><p>(<code>torch.Generator</code>, optional) a pseudorandom number generator for sampling</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>).</p></td>
-    </tr>
-    <tr>
-      <th>layout</th>
-      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
-    </tr>
-    <tr>
-      <th>requires_grad</th>
-      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
-    </tr>
-    <tr>
-      <th>memory_format</th>
-      <td><p>memory format for the resulting tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="randint-low-high-size-generator-null-out-null-"><a class="anchor" href="#randint-low-high-size-generator-null-out-null-"></a>randint(low=0, high, size, *, generator=NULL, out=NULL, \ </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_randint</span><span class="op">(</span>
+  <span class="va">low</span>,
+  <span class="va">high</span>,
+  <span class="va">size</span>,
+  generator <span class="op">=</span> <span class="cn">NULL</span>,
+  dtype <span class="op">=</span> <span class="cn">NULL</span>,
+  layout <span class="op">=</span> <span class="fu"><a href="torch_layout.html">torch_strided</a></span><span class="op">(</span><span class="op">)</span>,
+  device <span class="op">=</span> <span class="cn">NULL</span>,
+  requires_grad <span class="op">=</span> <span class="cn">FALSE</span>,
+  memory_format <span class="op">=</span> <span class="fu"><a href="torch_memory_format.html">torch_preserve_format</a></span><span class="op">(</span><span class="op">)</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>low</dt>
+<dd><p>(int, optional) Lowest integer to be drawn from the distribution. Default: 0.</p></dd>
+<dt>high</dt>
+<dd><p>(int) One above the highest integer to be drawn from the distribution.</p></dd>
+<dt>size</dt>
+<dd><p>(tuple) a tuple defining the shape of the output tensor.</p></dd>
+<dt>generator</dt>
+<dd><p>(<code>torch.Generator</code>, optional) a pseudorandom number generator for sampling</p></dd>
+<dt>dtype</dt>
+<dd><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>).</p></dd>
+<dt>layout</dt>
+<dd><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></dd>
+<dt>device</dt>
+<dd><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></dd>
+<dt>requires_grad</dt>
+<dd><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></dd>
+<dt>memory_format</dt>
+<dd><p>memory format for the resulting tensor.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="randint-low-high-size-generator-null-out-null-">randint(low=0, high, size, *, generator=NULL, out=NULL, \ <a class="anchor" aria-label="anchor" href="#randint-low-high-size-generator-null-out-null-"></a></h2>
     
 
 
@@ -254,44 +128,39 @@ between <code>low</code> (inclusive) and <code>high</code> (exclusive).</p>
 <p>.. note:
 With the global dtype default (<code>torch_float32</code>), this function returns
 a tensor with dtype <code>torch_int64</code>.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='fu'>torch_randint</span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>5</span>, <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span><span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_randint</span><span class='op'>(</span><span class='fl'>0</span>, <span class='fl'>10</span>, size <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>2</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_randint</span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>10</span>, <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>2</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  9  3</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  4  8</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{2,2} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="fu">torch_randint</span><span class="op">(</span><span class="fl">3</span>, <span class="fl">5</span>, <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_randint</span><span class="op">(</span><span class="fl">0</span>, <span class="fl">10</span>, size <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">2</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_randint</span><span class="op">(</span><span class="fl">3</span>, <span class="fl">10</span>, <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">2</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  9  4</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  7  6</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2,2} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer>
-      <div class="copyright">
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+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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+<div class="pkgdown-footer-right">
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.2.</p>
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diff --git a/reference/torch_randint_like.html b/reference/torch_randint_like.html
index 1c05e5333..98a711792 100644
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+++ b/reference/torch_randint_like.html
@@ -1,239 +1,117 @@
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Randint_like</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/creation-ops.R'><code>R/creation-ops.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_randint_like.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Randint_like</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/creation-ops.R" class="external-link"><code>R/creation-ops.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a></small>
+      <div class="d-none name"><code>torch_randint_like.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Randint_like</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_randint_like</span><span class='op'>(</span>
-  <span class='va'>input</span>,
-  <span class='va'>low</span>,
-  <span class='va'>high</span>,
-  dtype <span class='op'>=</span> <span class='cn'>NULL</span>,
-  layout <span class='op'>=</span> <span class='fu'><a href='torch_layout.html'>torch_strided</a></span><span class='op'>(</span><span class='op'>)</span>,
-  device <span class='op'>=</span> <span class='cn'>NULL</span>,
-  requires_grad <span class='op'>=</span> <span class='cn'>FALSE</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the size of <code>input</code> will determine size of the output tensor.</p></td>
-    </tr>
-    <tr>
-      <th>low</th>
-      <td><p>(int, optional) Lowest integer to be drawn from the distribution. Default: 0.</p></td>
-    </tr>
-    <tr>
-      <th>high</th>
-      <td><p>(int) One above the highest integer to be drawn from the distribution.</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned Tensor.        Default: if <code>NULL</code>, defaults to the dtype of <code>input</code>.</p></td>
-    </tr>
-    <tr>
-      <th>layout</th>
-      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned tensor.        Default: if <code>NULL</code>, defaults to the layout of <code>input</code>.</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, defaults to the device of <code>input</code>.</p></td>
-    </tr>
-    <tr>
-      <th>requires_grad</th>
-      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="randint-like-input-low-high-dtype-null-layout-torch-strided-device-null-requires-grad-false-"><a class="anchor" href="#randint-like-input-low-high-dtype-null-layout-torch-strided-device-null-requires-grad-false-"></a>randint_like(input, low=0, high, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False, </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_randint_like</span><span class="op">(</span>
+  <span class="va">input</span>,
+  <span class="va">low</span>,
+  <span class="va">high</span>,
+  dtype <span class="op">=</span> <span class="cn">NULL</span>,
+  layout <span class="op">=</span> <span class="fu"><a href="torch_layout.html">torch_strided</a></span><span class="op">(</span><span class="op">)</span>,
+  device <span class="op">=</span> <span class="cn">NULL</span>,
+  requires_grad <span class="op">=</span> <span class="cn">FALSE</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>(Tensor) the size of <code>input</code> will determine size of the output tensor.</p></dd>
+<dt>low</dt>
+<dd><p>(int, optional) Lowest integer to be drawn from the distribution. Default: 0.</p></dd>
+<dt>high</dt>
+<dd><p>(int) One above the highest integer to be drawn from the distribution.</p></dd>
+<dt>dtype</dt>
+<dd><p>(<code>torch.dtype</code>, optional) the desired data type of returned Tensor.        Default: if <code>NULL</code>, defaults to the dtype of <code>input</code>.</p></dd>
+<dt>layout</dt>
+<dd><p>(<code>torch.layout</code>, optional) the desired layout of returned tensor.        Default: if <code>NULL</code>, defaults to the layout of <code>input</code>.</p></dd>
+<dt>device</dt>
+<dd><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, defaults to the device of <code>input</code>.</p></dd>
+<dt>requires_grad</dt>
+<dd><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="randint-like-input-low-high-dtype-null-layout-torch-strided-device-null-requires-grad-false-">randint_like(input, low=0, high, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False, <a class="anchor" aria-label="anchor" href="#randint-like-input-low-high-dtype-null-layout-torch-strided-device-null-requires-grad-false-"></a></h2>
     
 
 
@@ -244,32 +122,25 @@ random integers generated uniformly between <code>low</code> (inclusive) and
 <p>.. note:
 With the global dtype default (<code>torch_float32</code>), this function returns
 a tensor with dtype <code>torch_int64</code>.</p>
+    </div>
 
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+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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+<div class="pkgdown-footer-right">
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index 327da6da0..79a89765e 100644
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Randn</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/creation-ops.R'><code>R/creation-ops.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_randn.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Randn</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/creation-ops.R" class="external-link"><code>R/creation-ops.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a></small>
+      <div class="d-none name"><code>torch_randn.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Randn</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_randn</span><span class='op'>(</span>
-  <span class='va'>...</span>,
-  names <span class='op'>=</span> <span class='cn'>NULL</span>,
-  dtype <span class='op'>=</span> <span class='cn'>NULL</span>,
-  layout <span class='op'>=</span> <span class='fu'><a href='torch_layout.html'>torch_strided</a></span><span class='op'>(</span><span class='op'>)</span>,
-  device <span class='op'>=</span> <span class='cn'>NULL</span>,
-  requires_grad <span class='op'>=</span> <span class='cn'>FALSE</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>...</th>
-      <td><p>(int...) a sequence of integers defining the shape of the output tensor.        Can be a variable number of arguments or a collection like a list or tuple.</p></td>
-    </tr>
-    <tr>
-      <th>names</th>
-      <td><p>optional names for the dimensions</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>).</p></td>
-    </tr>
-    <tr>
-      <th>layout</th>
-      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
-    </tr>
-    <tr>
-      <th>requires_grad</th>
-      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="randn-size-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"><a class="anchor" href="#randn-size-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"></a>randn(*size, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_randn</span><span class="op">(</span>
+  <span class="va">...</span>,
+  names <span class="op">=</span> <span class="cn">NULL</span>,
+  dtype <span class="op">=</span> <span class="cn">NULL</span>,
+  layout <span class="op">=</span> <span class="fu"><a href="torch_layout.html">torch_strided</a></span><span class="op">(</span><span class="op">)</span>,
+  device <span class="op">=</span> <span class="cn">NULL</span>,
+  requires_grad <span class="op">=</span> <span class="cn">FALSE</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>...</dt>
+<dd><p>(int...) a sequence of integers defining the shape of the output tensor.        Can be a variable number of arguments or a collection like a list or tuple.</p></dd>
+<dt>names</dt>
+<dd><p>optional names for the dimensions</p></dd>
+<dt>dtype</dt>
+<dd><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>).</p></dd>
+<dt>layout</dt>
+<dd><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></dd>
+<dt>device</dt>
+<dd><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></dd>
+<dt>requires_grad</dt>
+<dd><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="randn-size-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-">randn(*size, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#randn-size-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"></a></h2>
     
 
 
@@ -239,43 +119,38 @@ distribution).</p>
     \mbox{out}_{i} \sim \mathcal{N}(0, 1)
 $$
 The shape of the tensor is defined by the variable argument <code>size</code>.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='fu'>torch_randn</span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_randn</span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.2382 -1.2102 -0.9142</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.7113 -0.5767  0.5976</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{2,3} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="fu">torch_randn</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_randn</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -2.5092 -0.5033  0.2772</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.2459 -0.3065 -1.1800</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2,3} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
 </div>
 
-<div class="pkgdown">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p>
+<div class="pkgdown-footer-right">
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.2.</p>
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diff --git a/reference/torch_randn_like.html b/reference/torch_randn_like.html
index 89d090e34..4510363a5 100644
--- a/reference/torch_randn_like.html
+++ b/reference/torch_randn_like.html
@@ -1,234 +1,114 @@
-<!-- Generated by pkgdown: do not edit by hand -->
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Randn_like</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/creation-ops.R'><code>R/creation-ops.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_randn_like.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Randn_like</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/creation-ops.R" class="external-link"><code>R/creation-ops.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a></small>
+      <div class="d-none name"><code>torch_randn_like.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Randn_like</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_randn_like</span><span class='op'>(</span>
-  <span class='va'>input</span>,
-  dtype <span class='op'>=</span> <span class='cn'>NULL</span>,
-  layout <span class='op'>=</span> <span class='fu'><a href='torch_layout.html'>torch_strided</a></span><span class='op'>(</span><span class='op'>)</span>,
-  device <span class='op'>=</span> <span class='cn'>NULL</span>,
-  requires_grad <span class='op'>=</span> <span class='cn'>FALSE</span>,
-  memory_format <span class='op'>=</span> <span class='fu'><a href='torch_memory_format.html'>torch_preserve_format</a></span><span class='op'>(</span><span class='op'>)</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the size of <code>input</code> will determine size of the output tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned Tensor.        Default: if <code>NULL</code>, defaults to the dtype of <code>input</code>.</p></td>
-    </tr>
-    <tr>
-      <th>layout</th>
-      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned tensor.        Default: if <code>NULL</code>, defaults to the layout of <code>input</code>.</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, defaults to the device of <code>input</code>.</p></td>
-    </tr>
-    <tr>
-      <th>requires_grad</th>
-      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
-    </tr>
-    <tr>
-      <th>memory_format</th>
-      <td><p>(<code>torch.memory_format</code>, optional) the desired memory format of        returned Tensor. Default: <code>torch_preserve_format</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="randn-like-input-dtype-null-layout-null-device-null-requires-grad-false-memory-format-torch-preserve-format-gt-tensor-"><a class="anchor" href="#randn-like-input-dtype-null-layout-null-device-null-requires-grad-false-memory-format-torch-preserve-format-gt-tensor-"></a>randn_like(input, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, memory_format=torch.preserve_format) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_randn_like</span><span class="op">(</span>
+  <span class="va">input</span>,
+  dtype <span class="op">=</span> <span class="cn">NULL</span>,
+  layout <span class="op">=</span> <span class="fu"><a href="torch_layout.html">torch_strided</a></span><span class="op">(</span><span class="op">)</span>,
+  device <span class="op">=</span> <span class="cn">NULL</span>,
+  requires_grad <span class="op">=</span> <span class="cn">FALSE</span>,
+  memory_format <span class="op">=</span> <span class="fu"><a href="torch_memory_format.html">torch_preserve_format</a></span><span class="op">(</span><span class="op">)</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>(Tensor) the size of <code>input</code> will determine size of the output tensor.</p></dd>
+<dt>dtype</dt>
+<dd><p>(<code>torch.dtype</code>, optional) the desired data type of returned Tensor.        Default: if <code>NULL</code>, defaults to the dtype of <code>input</code>.</p></dd>
+<dt>layout</dt>
+<dd><p>(<code>torch.layout</code>, optional) the desired layout of returned tensor.        Default: if <code>NULL</code>, defaults to the layout of <code>input</code>.</p></dd>
+<dt>device</dt>
+<dd><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, defaults to the device of <code>input</code>.</p></dd>
+<dt>requires_grad</dt>
+<dd><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></dd>
+<dt>memory_format</dt>
+<dd><p>(<code>torch.memory_format</code>, optional) the desired memory format of        returned Tensor. Default: <code>torch_preserve_format</code>.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="randn-like-input-dtype-null-layout-null-device-null-requires-grad-false-memory-format-torch-preserve-format-gt-tensor-">randn_like(input, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, memory_format=torch.preserve_format) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#randn-like-input-dtype-null-layout-null-device-null-requires-grad-false-memory-format-torch-preserve-format-gt-tensor-"></a></h2>
     
 
 
@@ -236,32 +116,25 @@
 random numbers from a normal distribution with mean 0 and variance 1.
 <code>torch_randn_like(input)</code> is equivalent to
 <code>torch_randn(input.size(), dtype=input.dtype, layout=input.layout, device=input.device)</code>.</p>
+    </div>
 
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+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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+<div class="pkgdown-footer-right">
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diff --git a/reference/torch_randperm.html b/reference/torch_randperm.html
index 217e729fb..7fb75b7d1 100644
--- a/reference/torch_randperm.html
+++ b/reference/torch_randperm.html
@@ -1,271 +1,148 @@
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Randperm</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/creation-ops.R'><code>R/creation-ops.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_randperm.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Randperm</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/creation-ops.R" class="external-link"><code>R/creation-ops.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a></small>
+      <div class="d-none name"><code>torch_randperm.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Randperm</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_randperm</span><span class='op'>(</span>
-  <span class='va'>n</span>,
-  dtype <span class='op'>=</span> <span class='fu'><a href='torch_dtype.html'>torch_int64</a></span><span class='op'>(</span><span class='op'>)</span>,
-  layout <span class='op'>=</span> <span class='fu'><a href='torch_layout.html'>torch_strided</a></span><span class='op'>(</span><span class='op'>)</span>,
-  device <span class='op'>=</span> <span class='cn'>NULL</span>,
-  requires_grad <span class='op'>=</span> <span class='cn'>FALSE</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>n</th>
-      <td><p>(int) the upper bound (exclusive)</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: <code>torch_int64</code>.</p></td>
-    </tr>
-    <tr>
-      <th>layout</th>
-      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
-    </tr>
-    <tr>
-      <th>requires_grad</th>
-      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="randperm-n-out-null-dtype-torch-int-layout-torch-strided-device-null-requires-grad-false-gt-longtensor-"><a class="anchor" href="#randperm-n-out-null-dtype-torch-int-layout-torch-strided-device-null-requires-grad-false-gt-longtensor-"></a>randperm(n, out=NULL, dtype=torch.int64, layout=torch.strided, device=NULL, requires_grad=False) -&gt; LongTensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_randperm</span><span class="op">(</span>
+  <span class="va">n</span>,
+  dtype <span class="op">=</span> <span class="fu"><a href="torch_dtype.html">torch_int64</a></span><span class="op">(</span><span class="op">)</span>,
+  layout <span class="op">=</span> <span class="fu"><a href="torch_layout.html">torch_strided</a></span><span class="op">(</span><span class="op">)</span>,
+  device <span class="op">=</span> <span class="cn">NULL</span>,
+  requires_grad <span class="op">=</span> <span class="cn">FALSE</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>n</dt>
+<dd><p>(int) the upper bound (exclusive)</p></dd>
+<dt>dtype</dt>
+<dd><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: <code>torch_int64</code>.</p></dd>
+<dt>layout</dt>
+<dd><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></dd>
+<dt>device</dt>
+<dd><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></dd>
+<dt>requires_grad</dt>
+<dd><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="randperm-n-out-null-dtype-torch-int-layout-torch-strided-device-null-requires-grad-false-gt-longtensor-">randperm(n, out=NULL, dtype=torch.int64, layout=torch.strided, device=NULL, requires_grad=False) -&gt; LongTensor <a class="anchor" aria-label="anchor" href="#randperm-n-out-null-dtype-torch-int-layout-torch-strided-device-null-requires-grad-false-gt-longtensor-"></a></h2>
     
 
 
 <p>Returns a random permutation of integers from <code>0</code> to <code>n - 1</code>.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='fu'>torch_randperm</span><span class='op'>(</span><span class='fl'>4</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  2</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  3</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPULongType{4} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="fu">torch_randperm</span><span class="op">(</span><span class="fl">4</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  3</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPULongType{4} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Range</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/creation-ops.R'><code>R/creation-ops.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_range.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Range</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/creation-ops.R" class="external-link"><code>R/creation-ops.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a></small>
+      <div class="d-none name"><code>torch_range.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Range</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_range</span><span class='op'>(</span>
-  <span class='va'>start</span>,
-  <span class='va'>end</span>,
-  step <span class='op'>=</span> <span class='fl'>1</span>,
-  dtype <span class='op'>=</span> <span class='cn'>NULL</span>,
-  layout <span class='op'>=</span> <span class='fu'><a href='torch_layout.html'>torch_strided</a></span><span class='op'>(</span><span class='op'>)</span>,
-  device <span class='op'>=</span> <span class='cn'>NULL</span>,
-  requires_grad <span class='op'>=</span> <span class='cn'>FALSE</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>start</th>
-      <td><p>(float) the starting value for the set of points. Default: <code>0</code>.</p></td>
-    </tr>
-    <tr>
-      <th>end</th>
-      <td><p>(float) the ending value for the set of points</p></td>
-    </tr>
-    <tr>
-      <th>step</th>
-      <td><p>(float) the gap between each pair of adjacent points. Default: <code>1</code>.</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>). If <code>dtype</code> is not given, infer the data type from the other input        arguments. If any of <code>start</code>, <code>end</code>, or <code>stop</code> are floating-point, the        <code>dtype</code> is inferred to be the default dtype, see        <code>~torch.get_default_dtype</code>. Otherwise, the <code>dtype</code> is inferred to        be <code>torch.int64</code>.</p></td>
-    </tr>
-    <tr>
-      <th>layout</th>
-      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
-    </tr>
-    <tr>
-      <th>requires_grad</th>
-      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="range-start-end-step-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"><a class="anchor" href="#range-start-end-step-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"></a>range(start=0, end, step=1, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_range</span><span class="op">(</span>
+  <span class="va">start</span>,
+  <span class="va">end</span>,
+  step <span class="op">=</span> <span class="fl">1</span>,
+  dtype <span class="op">=</span> <span class="cn">NULL</span>,
+  layout <span class="op">=</span> <span class="fu"><a href="torch_layout.html">torch_strided</a></span><span class="op">(</span><span class="op">)</span>,
+  device <span class="op">=</span> <span class="cn">NULL</span>,
+  requires_grad <span class="op">=</span> <span class="cn">FALSE</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>start</dt>
+<dd><p>(float) the starting value for the set of points. Default: <code>0</code>.</p></dd>
+<dt>end</dt>
+<dd><p>(float) the ending value for the set of points</p></dd>
+<dt>step</dt>
+<dd><p>(float) the gap between each pair of adjacent points. Default: <code>1</code>.</p></dd>
+<dt>dtype</dt>
+<dd><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>). If <code>dtype</code> is not given, infer the data type from the other input        arguments. If any of <code>start</code>, <code>end</code>, or <code>stop</code> are floating-point, the        <code>dtype</code> is inferred to be the default dtype, see        <code>~torch.get_default_dtype</code>. Otherwise, the <code>dtype</code> is inferred to        be <code>torch.int64</code>.</p></dd>
+<dt>layout</dt>
+<dd><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></dd>
+<dt>device</dt>
+<dd><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></dd>
+<dt>requires_grad</dt>
+<dd><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="range-start-end-step-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-">range(start=0, end, step=1, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#range-start-end-step-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"></a></h2>
     
 
 
@@ -243,55 +121,51 @@ the gap between two values in the tensor.</p>
 <p>$$
     \mbox{out}_{i+1} = \mbox{out}_i + \mbox{step}.
 $$</p>
-    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></a></h2>
     
 
-<p>This function is deprecated in favor of <code><a href='torch_arange.html'>torch_arange</a></code>.</p>
+<p>This function is deprecated in favor of <code><a href="torch_arange.html">torch_arange</a></code>.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='fu'>torch_range</span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>4</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_range</span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>4</span>, <span class='fl'>0.5</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-wrn co'><span class='r-pr'>#&gt;</span> <span class='warning'>Warning: </span>This function is deprecated in favor of torch_arange.</span>
-<span class='r-wrn co'><span class='r-pr'>#&gt;</span> <span class='warning'>Warning: </span>This function is deprecated in favor of torch_arange.</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1.0000</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1.5000</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  2.0000</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  2.5000</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  3.0000</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  3.5000</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  4.0000</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{7} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="fu">torch_range</span><span class="op">(</span><span class="fl">1</span>, <span class="fl">4</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_range</span><span class="op">(</span><span class="fl">1</span>, <span class="fl">4</span>, <span class="fl">0.5</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>This function is deprecated in favor of torch_arange.</span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>This function is deprecated in favor of torch_arange.</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.0000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.5000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2.0000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2.5000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  3.0000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  3.5000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  4.0000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{7} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer>
-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
 </div>
 
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+<div class="pkgdown-footer-right">
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+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_real.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Real</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_real</span><span class='op'>(</span><span class='va'>self</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="real-input-gt-tensor-"><a class="anchor" href="#real-input-gt-tensor-"></a>real(input) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_real</span><span class="op">(</span><span class="va">self</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="real-input-gt-tensor-">real(input) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#real-input-gt-tensor-"></a></h2>
     
 
 
 <p>Returns the real part of the <code>input</code> tensor. If
 <code>input</code> is a real (non-complex) tensor, this function just
 returns it.</p>
-    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></a></h2>
     
 
 <p>Not yet implemented for complex tensors.</p>
 <p>$$
     \mbox{out}_{i} = real(\mbox{input}_{i})
 $$</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='cn'>FALSE</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='fu'>torch_real</span><span class='op'>(</span><span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='op'>-</span><span class='fl'>1</span> <span class='op'>+</span> <span class='fl'>1i</span>, <span class='op'>-</span><span class='fl'>2</span> <span class='op'>+</span> <span class='fl'>2i</span>, <span class='fl'>3</span> <span class='op'>-</span> <span class='fl'>3i</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="fu">torch_real</span><span class="op">(</span><span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="op">-</span><span class="fl">1</span> <span class="op">+</span> <span class="fl">1i</span>, <span class="op">-</span><span class="fl">2</span> <span class="op">+</span> <span class="fl">2i</span>, <span class="fl">3</span> <span class="op">-</span> <span class="fl">3i</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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 </div>
 
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+<div class="pkgdown-footer-right">
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-    <pre class="usage"><span class='fu'>torch_reciprocal</span><span class='op'>(</span><span class='va'>self</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
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-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
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-    </table>
-
-    <h2 class="hasAnchor" id="reciprocal-input-out-null-gt-tensor-"><a class="anchor" href="#reciprocal-input-out-null-gt-tensor-"></a>reciprocal(input, out=NULL) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_reciprocal</span><span class="op">(</span><span class="va">self</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="reciprocal-input-out-null-gt-tensor-">reciprocal(input, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#reciprocal-input-out-null-gt-tensor-"></a></h2>
     
 
 
@@ -209,46 +99,41 @@
 <p>$$
     \mbox{out}_{i} = \frac{1}{\mbox{input}_{i}}
 $$</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='fu'>torch_reciprocal</span><span class='op'>(</span><span class='va'>a</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1.2193</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1.2546</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -3.2635</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -6.0441</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{4} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="fu">torch_reciprocal</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  15.0493</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   1.2671</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.8328</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.3938</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer>
-      <div class="copyright">
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+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
 </div>
 
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+<div class="pkgdown-footer-right">
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-    <h1>Creates the reduction objet</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/reduction.R'><code>R/reduction.R</code></a></small>
-    <div class="hidden name"><code>torch_reduction.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Creates the reduction objet</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/reduction.R" class="external-link"><code>R/reduction.R</code></a></small>
+      <div class="d-none name"><code>torch_reduction.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Creates the reduction objet</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_reduction_sum</span><span class='op'>(</span><span class='op'>)</span>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_reduction_sum</span><span class="op">(</span><span class="op">)</span>
 
-<span class='fu'>torch_reduction_mean</span><span class='op'>(</span><span class='op'>)</span>
+<span class="fu">torch_reduction_mean</span><span class="op">(</span><span class="op">)</span>
 
-<span class='fu'>torch_reduction_none</span><span class='op'>(</span><span class='op'>)</span></pre>
+<span class="fu">torch_reduction_none</span><span class="op">(</span><span class="op">)</span></code></pre></div>
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-    <h1>Relu</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_relu.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Relu</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_relu.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Relu</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_relu</span><span class='op'>(</span><span class='va'>self</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>the input tensor</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="relu-input-gt-tensor-"><a class="anchor" href="#relu-input-gt-tensor-"></a>relu(input) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_relu</span><span class="op">(</span><span class="va">self</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>the input tensor</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="relu-input-gt-tensor-">relu(input) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#relu-input-gt-tensor-"></a></h2>
     
 
 
 <p>Computes the relu tranformation.</p>
+    </div>
 
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_relu_.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Relu_</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_relu_.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Relu_</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_relu_</span><span class='op'>(</span><span class='va'>self</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>the input tensor</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="relu-input-gt-tensor-"><a class="anchor" href="#relu-input-gt-tensor-"></a>relu_(input) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_relu_</span><span class="op">(</span><span class="va">self</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>the input tensor</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="relu-input-gt-tensor-">relu_(input) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#relu-input-gt-tensor-"></a></h2>
     
 
 
-<p>In-place version of <code><a href='torch_relu.html'>torch_relu()</a></code>.</p>
+<p>In-place version of <code><a href="torch_relu.html">torch_relu()</a></code>.</p>
+    </div>
 
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Remainder</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_remainder.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Remainder</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_remainder.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Remainder</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_remainder</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>other</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the dividend</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Tensor or float) the divisor that may be either a number or a                               Tensor of the same shape as the dividend</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="remainder-input-other-out-null-gt-tensor-"><a class="anchor" href="#remainder-input-other-out-null-gt-tensor-"></a>remainder(input, other, out=NULL) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_remainder</span><span class="op">(</span><span class="va">self</span>, <span class="va">other</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the dividend</p></dd>
+<dt>other</dt>
+<dd><p>(Tensor or float) the divisor that may be either a number or a                               Tensor of the same shape as the dividend</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="remainder-input-other-out-null-gt-tensor-">remainder(input, other, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#remainder-input-other-out-null-gt-tensor-"></a></h2>
     
 
 
@@ -214,46 +102,41 @@
 numbers. The remainder has the same sign as the divisor.</p>
 <p>When <code>other</code> is a tensor, the shapes of <code>input</code> and
 <code>other</code> must be broadcastable .</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='fu'>torch_remainder</span><span class='op'>(</span><span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='op'>-</span><span class='fl'>3.</span>, <span class='op'>-</span><span class='fl'>2</span>, <span class='op'>-</span><span class='fl'>1</span>, <span class='fl'>1</span>, <span class='fl'>2</span>, <span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span>, <span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_remainder</span><span class='op'>(</span><span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1.</span>, <span class='fl'>2</span>, <span class='fl'>3</span>, <span class='fl'>4</span>, <span class='fl'>5</span><span class='op'>)</span><span class='op'>)</span>, <span class='fl'>1.5</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1.0000</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.5000</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.0000</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1.0000</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.5000</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{5} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="fu">torch_remainder</span><span class="op">(</span><span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="op">-</span><span class="fl">3.</span>, <span class="op">-</span><span class="fl">2</span>, <span class="op">-</span><span class="fl">1</span>, <span class="fl">1</span>, <span class="fl">2</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span>, <span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_remainder</span><span class="op">(</span><span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1.</span>, <span class="fl">2</span>, <span class="fl">3</span>, <span class="fl">4</span>, <span class="fl">5</span><span class="op">)</span><span class="op">)</span>, <span class="fl">1.5</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.0000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.5000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.0000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.5000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{5} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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-      <div class="copyright">
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+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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+<div class="pkgdown-footer-right">
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diff --git a/reference/torch_renorm.html b/reference/torch_renorm.html
index a8131124a..f2b819849 100644
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@@ -1,268 +1,148 @@
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Renorm</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_renorm.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Renorm</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_renorm.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Renorm</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_renorm</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>p</span>, <span class='va'>dim</span>, <span class='va'>maxnorm</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>p</th>
-      <td><p>(float) the power for the norm computation</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) the dimension to slice over to get the sub-tensors</p></td>
-    </tr>
-    <tr>
-      <th>maxnorm</th>
-      <td><p>(float) the maximum norm to keep each sub-tensor under</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_renorm</span><span class="op">(</span><span class="va">self</span>, <span class="va">p</span>, <span class="va">dim</span>, <span class="va">maxnorm</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+<dt>p</dt>
+<dd><p>(float) the power for the norm computation</p></dd>
+<dt>dim</dt>
+<dd><p>(int) the dimension to slice over to get the sub-tensors</p></dd>
+<dt>maxnorm</dt>
+<dd><p>(float) the maximum norm to keep each sub-tensor under</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p>If the norm of a row is lower than <code>maxnorm</code>, the row is unchanged</p>
-    <h2 class="hasAnchor" id="renorm-input-p-dim-maxnorm-out-null-gt-tensor-"><a class="anchor" href="#renorm-input-p-dim-maxnorm-out-null-gt-tensor-"></a>renorm(input, p, dim, maxnorm, out=NULL) -&gt; Tensor </h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="renorm-input-p-dim-maxnorm-out-null-gt-tensor-">renorm(input, p, dim, maxnorm, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#renorm-input-p-dim-maxnorm-out-null-gt-tensor-"></a></h2>
     
 
 
 <p>Returns a tensor where each sub-tensor of <code>input</code> along dimension
 <code>dim</code> is normalized such that the <code>p</code>-norm of the sub-tensor is lower
 than the value <code>maxnorm</code></p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_ones.html'>torch_ones</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>x</span><span class='op'>[</span><span class='fl'>2</span>,<span class='op'>]</span><span class='op'>$</span><span class='fu'>fill_</span><span class='op'>(</span><span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>x</span><span class='op'>[</span><span class='fl'>3</span>,<span class='op'>]</span><span class='op'>$</span><span class='fu'>fill_</span><span class='op'>(</span><span class='fl'>3</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>x</span></span>
-<span class='r-in'><span class='fu'>torch_renorm</span><span class='op'>(</span><span class='va'>x</span>, <span class='fl'>1</span>, <span class='fl'>1</span>, <span class='fl'>5</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1.0000  1.0000  1.0000</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1.6667  1.6667  1.6667</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1.6667  1.6667  1.6667</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{3,3} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">x</span> <span class="op">=</span> <span class="fu"><a href="torch_ones.html">torch_ones</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">x</span><span class="op">[</span><span class="fl">2</span>,<span class="op">]</span><span class="op">$</span><span class="fu">fill_</span><span class="op">(</span><span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">x</span><span class="op">[</span><span class="fl">3</span>,<span class="op">]</span><span class="op">$</span><span class="fu">fill_</span><span class="op">(</span><span class="fl">3</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">x</span></span>
+<span class="r-in"><span class="fu">torch_renorm</span><span class="op">(</span><span class="va">x</span>, <span class="fl">1</span>, <span class="fl">1</span>, <span class="fl">5</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.0000  1.0000  1.0000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.6667  1.6667  1.6667</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.6667  1.6667  1.6667</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3,3} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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index 0694bbc97..0205fe650 100644
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Repeat_interleave</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_repeat_interleave.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Repeat_interleave</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_repeat_interleave.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Repeat_interleave</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_repeat_interleave</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>repeats</span>, dim <span class='op'>=</span> <span class='cn'>NULL</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>repeats</th>
-      <td><p>(Tensor or int) The number of repetitions for each element.        repeats is broadcasted to fit the shape of the given axis.</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int, optional) The dimension along which to repeat values.        By default, use the flattened input array, and return a flat output        array.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="repeat-interleave-input-repeats-dim-null-gt-tensor-"><a class="anchor" href="#repeat-interleave-input-repeats-dim-null-gt-tensor-"></a>repeat_interleave(input, repeats, dim=NULL) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_repeat_interleave</span><span class="op">(</span><span class="va">self</span>, <span class="va">repeats</span>, dim <span class="op">=</span> <span class="cn">NULL</span>, output_size <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+<dt>repeats</dt>
+<dd><p>(Tensor or int) The number of repetitions for each element.        repeats is broadcasted to fit the shape of the given axis.</p></dd>
+<dt>dim</dt>
+<dd><p>(int, optional) The dimension along which to repeat values.        By default, use the flattened input array, and return a flat output        array.</p></dd>
+<dt>output_size</dt>
+<dd><p>(int, optional) – Total output size for the given axis
+( e.g. sum of repeats). If given, it will avoid stream syncronization needed
+to calculate output shape of the tensor.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="repeat-interleave-input-repeats-dim-null-gt-tensor-">repeat_interleave(input, repeats, dim=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#repeat-interleave-input-repeats-dim-null-gt-tensor-"></a></h2>
     
 
 
 <p>Repeat elements of a tensor.</p>
-    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></a></h2>
     
-<pre>This is different from `torch_Tensor.repeat` but similar to `numpy.repeat`.
-</pre>
-
-    <h2 class="hasAnchor" id="repeat-interleave-repeats-gt-tensor-"><a class="anchor" href="#repeat-interleave-repeats-gt-tensor-"></a>repeat_interleave(repeats) -&gt; Tensor </h2>
+<div class="sourceCode"><pre><code>This is different from `torch_Tensor.repeat` but similar to `numpy.repeat`.
+</code></pre></div>
 
+    </div>
+    <div class="section level2">
+    <h2 id="repeat-interleave-repeats-gt-tensor-">repeat_interleave(repeats) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#repeat-interleave-repeats-gt-tensor-"></a></h2>
     
 
 
 <p>If the <code>repeats</code> is <code>tensor([n1, n2, n3, ...])</code>, then the output will be
 <code>tensor([0, 0, ..., 1, 1, ..., 2, 2, ..., ...])</code> where <code>0</code> appears <code>n1</code> times,
 <code>1</code> appears <code>n2</code> times, <code>2</code> appears <code>n3</code> times, etc.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='cn'>FALSE</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>2</span>, <span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>x</span><span class='op'>$</span><span class='fu'>repeat_interleave</span><span class='op'>(</span><span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>y</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>2</span>, <span class='fl'>3</span>, <span class='fl'>4</span><span class='op'>)</span>, ncol <span class='op'>=</span> <span class='fl'>2</span>, byrow<span class='op'>=</span><span class='cn'>TRUE</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_repeat_interleave</span><span class='op'>(</span><span class='va'>y</span>, <span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_repeat_interleave</span><span class='op'>(</span><span class='va'>y</span>, <span class='fl'>3</span>, dim<span class='op'>=</span><span class='fl'>1</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_repeat_interleave</span><span class='op'>(</span><span class='va'>y</span>, <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>2</span><span class='op'>)</span><span class='op'>)</span>, dim<span class='op'>=</span><span class='fl'>1</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">x</span> <span class="op">=</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">2</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">x</span><span class="op">$</span><span class="fu">repeat_interleave</span><span class="op">(</span><span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">y</span> <span class="op">=</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/matrix.html" class="external-link">matrix</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">2</span>, <span class="fl">3</span>, <span class="fl">4</span><span class="op">)</span>, ncol <span class="op">=</span> <span class="fl">2</span>, byrow<span class="op">=</span><span class="cn">TRUE</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_repeat_interleave</span><span class="op">(</span><span class="va">y</span>, <span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_repeat_interleave</span><span class="op">(</span><span class="va">y</span>, <span class="fl">3</span>, dim<span class="op">=</span><span class="fl">1</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_repeat_interleave</span><span class="op">(</span><span class="va">y</span>, <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">2</span><span class="op">)</span><span class="op">)</span>, dim<span class="op">=</span><span class="fl">1</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Reshape</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_reshape.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Reshape</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_reshape.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Reshape</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_reshape</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>shape</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the tensor to be reshaped</p></td>
-    </tr>
-    <tr>
-      <th>shape</th>
-      <td><p>(tuple of ints) the new shape</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="reshape-input-shape-gt-tensor-"><a class="anchor" href="#reshape-input-shape-gt-tensor-"></a>reshape(input, shape) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_reshape</span><span class="op">(</span><span class="va">self</span>, <span class="va">shape</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the tensor to be reshaped</p></dd>
+<dt>shape</dt>
+<dd><p>(tuple of ints) the new shape</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="reshape-input-shape-gt-tensor-">reshape(input, shape) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#reshape-input-shape-gt-tensor-"></a></h2>
     
 
 
@@ -217,47 +105,42 @@ depend on the copying vs. viewing behavior.</p>
 <p>See <code>torch_Tensor.view</code> on when it is possible to return a view.</p>
 <p>A single dimension may be -1, in which case it's inferred from the remaining
 dimensions and the number of elements in <code>input</code>.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span><span class='op'>(</span><span class='fl'>0</span>, <span class='fl'>3</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_reshape</span><span class='op'>(</span><span class='va'>a</span>, <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>2</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>b</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>0</span>, <span class='fl'>1</span>, <span class='fl'>2</span>, <span class='fl'>3</span><span class='op'>)</span>, ncol <span class='op'>=</span> <span class='fl'>2</span>, byrow<span class='op'>=</span><span class='cn'>TRUE</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_reshape</span><span class='op'>(</span><span class='va'>b</span>, <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span><span class='op'>-</span><span class='fl'>1</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  2</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  3</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{4} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
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+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_arange.html">torch_arange</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">3</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_reshape</span><span class="op">(</span><span class="va">a</span>, <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">2</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">b</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/matrix.html" class="external-link">matrix</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span>, <span class="fl">2</span>, <span class="fl">3</span><span class="op">)</span>, ncol <span class="op">=</span> <span class="fl">2</span>, byrow<span class="op">=</span><span class="cn">TRUE</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_reshape</span><span class="op">(</span><span class="va">b</span>, <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="op">-</span><span class="fl">1</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  3</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4} ]</span>
+</code></pre></div>
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+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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+  </div>
+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Result_type</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/wrapers.R'><code>R/wrapers.R</code></a></small>
-    <div class="hidden name"><code>torch_result_type.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Result_type</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
+      <div class="d-none name"><code>torch_result_type.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Result_type</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_result_type</span><span class='op'>(</span><span class='va'>tensor1</span>, <span class='va'>tensor2</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>tensor1</th>
-      <td><p>(Tensor or Number) an input tensor or number</p></td>
-    </tr>
-    <tr>
-      <th>tensor2</th>
-      <td><p>(Tensor or Number) an input tensor or number</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="result-type-tensor-tensor-gt-dtype-"><a class="anchor" href="#result-type-tensor-tensor-gt-dtype-"></a>result_type(tensor1, tensor2) -&gt; dtype </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_result_type</span><span class="op">(</span><span class="va">tensor1</span>, <span class="va">tensor2</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>tensor1</dt>
+<dd><p>(Tensor or Number) an input tensor or number</p></dd>
+<dt>tensor2</dt>
+<dd><p>(Tensor or Number) an input tensor or number</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="result-type-tensor-tensor-gt-dtype-">result_type(tensor1, tensor2) -&gt; dtype <a class="anchor" aria-label="anchor" href="#result-type-tensor-tensor-gt-dtype-"></a></h2>
     
 
 
 <p>Returns the <code>torch_dtype</code> that would result from performing an arithmetic
 operation on the provided input tensors. See type promotion documentation
 for more information on the type promotion logic.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='fu'>torch_result_type</span><span class='op'>(</span>tensor1 <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>2</span><span class='op'>)</span>, dtype<span class='op'>=</span><span class='fu'><a href='torch_dtype.html'>torch_int</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span>, tensor2 <span class='op'>=</span> <span class='fl'>1</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_Float</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="fu">torch_result_type</span><span class="op">(</span>tensor1 <span class="op">=</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">2</span><span class="op">)</span>, dtype<span class="op">=</span><span class="fu"><a href="torch_dtype.html">torch_int</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span>, tensor2 <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_Float</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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diff --git a/reference/torch_rfft.html b/reference/torch_rfft.html
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-    <h1>Rfft</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_rfft.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Rfft</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>torch_rfft</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>signal_ndim</span>, normalized <span class='op'>=</span> <span class='cn'>FALSE</span>, onesided <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor of at least <code>signal_ndim</code> dimensions</p></td>
-    </tr>
-    <tr>
-      <th>signal_ndim</th>
-      <td><p>(int) the number of dimensions in each signal.        <code>signal_ndim</code> can only be 1, 2 or 3</p></td>
-    </tr>
-    <tr>
-      <th>normalized</th>
-      <td><p>(bool, optional) controls whether to return normalized results.        Default: <code>FALSE</code></p></td>
-    </tr>
-    <tr>
-      <th>onesided</th>
-      <td><p>(bool, optional) controls whether to return half of results to        avoid redundancy. Default: <code>TRUE</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
-    
-<pre>For CUDA tensors, an LRU cache is used for cuFFT plans to speed up
-repeatedly running FFT methods on tensors of same geometry with same
-configuration. See cufft-plan-cache for more details on how to
-monitor and control the cache.
-</pre>
-
-    <h2 class="hasAnchor" id="rfft-input-signal-ndim-normalized-false-onesided-true-gt-tensor-"><a class="anchor" href="#rfft-input-signal-ndim-normalized-false-onesided-true-gt-tensor-"></a>rfft(input, signal_ndim, normalized=False, onesided=TRUE) -&gt; Tensor </h2>
-
-    
-
-
-<p>Real-to-complex Discrete Fourier Transform</p>
-<p>This method computes the real-to-complex discrete Fourier transform. It is
-mathematically equivalent with <code><a href='torch_fft.html'>torch_fft</a></code> with differences only in
-formats of the input and output.</p>
-<p>This method supports 1D, 2D and 3D real-to-complex transforms, indicated
-by <code>signal_ndim</code>. <code>input</code> must be a tensor with at least
-<code>signal_ndim</code> dimensions with optionally arbitrary number of leading batch
-dimensions. If <code>normalized</code> is set to <code>TRUE</code>, this normalizes the result
-by dividing it with \(\sqrt{\prod_{i=1}^K N_i}\) so that the operator is
-unitary, where \(N_i\) is the size of signal dimension \(i\).</p>
-<p>The real-to-complex Fourier transform results follow conjugate symmetry:</p>
-<p>$$
-    X[\omega_1, \dots, \omega_d] = X^*[N_1 - \omega_1, \dots, N_d - \omega_d],
-$$
-where the index arithmetic is computed modulus the size of the corresponding
-dimension, \(\ ^*\) is the conjugate operator, and
-\(d\) = <code>signal_ndim</code>. <code>onesided</code> flag controls whether to avoid
-redundancy in the output results. If set to <code>TRUE</code> (default), the output will
-not be full complex result of shape \((*, 2)\), where \(*\) is the shape
-of <code>input</code>, but instead the last dimension will be halfed as of size
-\(\lfloor \frac{N_d}{2} \rfloor + 1\).</p>
-<p>The inverse of this function is <code><a href='torch_irfft.html'>torch_irfft</a></code>.</p>
-    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
-
-    
-
-<p>For CPU tensors, this method is currently only available with MKL. Use
-<code>torch_backends.mkl.is_available</code> to check if MKL is installed.</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span>
-
-<span class='va'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>5</span>, <span class='fl'>5</span><span class='op'>)</span><span class='op'>)</span>
-<span class='fu'>torch_rfft</span><span class='op'>(</span><span class='va'>x</span>, <span class='fl'>2</span><span class='op'>)</span>
-<span class='fu'>torch_rfft</span><span class='op'>(</span><span class='va'>x</span>, <span class='fl'>2</span>, onesided<span class='op'>=</span><span class='cn'>FALSE</span><span class='op'>)</span>
-<span class='op'>}</span>
-</div><div class='output co'>#&gt; torch_tensor
-#&gt; (1,.,.) = 
-#&gt;   11.1777   0.0000
-#&gt;    4.3071  -2.2901
-#&gt;    0.9876  -3.2964
-#&gt;    0.9876   3.2964
-#&gt;    4.3071   2.2901
-#&gt; 
-#&gt; (2,.,.) = 
-#&gt;   0.6483  1.7820
-#&gt;  -0.5132  3.5585
-#&gt;   0.4146 -0.9877
-#&gt;  -5.5350  2.7508
-#&gt;  -2.4733 -6.1138
-#&gt; 
-#&gt; (3,.,.) = 
-#&gt;  -1.0304 -2.7512
-#&gt;  -2.1111  0.2320
-#&gt;   5.2288 -1.1161
-#&gt;  -1.5376  1.7431
-#&gt;  -7.7964  2.7433
-#&gt; 
-#&gt; (4,.,.) = 
-#&gt;  -1.0304  2.7512
-#&gt;  -7.7964 -2.7433
-#&gt;  -1.5376 -1.7431
-#&gt;   5.2288  1.1161
-#&gt;  -2.1111 -0.2320
-#&gt; 
-#&gt; (5,.,.) = 
-#&gt;   0.6483 -1.7820
-#&gt; ... [the output was truncated (use n=-1 to disable)]
-#&gt; [ CPUFloatType{5,5,2} ]</div></pre>
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diff --git a/reference/torch_roll.html b/reference/torch_roll.html
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+  </div>
+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Roll</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_roll.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Roll</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_roll.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Roll</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_roll</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>shifts</span>, dims <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>shifts</th>
-      <td><p>(int or tuple of ints) The number of places by which the elements        of the tensor are shifted. If shifts is a tuple, dims must be a tuple of        the same size, and each dimension will be rolled by the corresponding        value</p></td>
-    </tr>
-    <tr>
-      <th>dims</th>
-      <td><p>(int or tuple of ints) Axis along which to roll</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="roll-input-shifts-dims-null-gt-tensor-"><a class="anchor" href="#roll-input-shifts-dims-null-gt-tensor-"></a>roll(input, shifts, dims=NULL) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_roll</span><span class="op">(</span><span class="va">self</span>, <span class="va">shifts</span>, dims <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+<dt>shifts</dt>
+<dd><p>(int or tuple of ints) The number of places by which the elements        of the tensor are shifted. If shifts is a tuple, dims must be a tuple of        the same size, and each dimension will be rolled by the corresponding        value</p></dd>
+<dt>dims</dt>
+<dd><p>(int or tuple of ints) Axis along which to roll</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="roll-input-shifts-dims-null-gt-tensor-">roll(input, shifts, dims=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#roll-input-shifts-dims-null-gt-tensor-"></a></h2>
     
 
 
@@ -217,48 +103,43 @@
 last position are re-introduced at the first position. If a dimension is not
 specified, the tensor will be flattened before rolling and then restored
 to the original shape.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>2</span>, <span class='fl'>3</span>, <span class='fl'>4</span>, <span class='fl'>5</span>, <span class='fl'>6</span>, <span class='fl'>7</span>, <span class='fl'>8</span><span class='op'>)</span><span class='op'>)</span><span class='op'>$</span><span class='fu'>view</span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span>, <span class='fl'>2</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>x</span></span>
-<span class='r-in'><span class='fu'>torch_roll</span><span class='op'>(</span><span class='va'>x</span>, <span class='fl'>1</span>, <span class='fl'>1</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_roll</span><span class='op'>(</span><span class='va'>x</span>, <span class='op'>-</span><span class='fl'>1</span>, <span class='fl'>1</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_roll</span><span class='op'>(</span><span class='va'>x</span>, shifts<span class='op'>=</span><span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>1</span><span class='op'>)</span>, dims<span class='op'>=</span><span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>2</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  6  5</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  8  7</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  2  1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  4  3</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{4,2} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">x</span> <span class="op">=</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">2</span>, <span class="fl">3</span>, <span class="fl">4</span>, <span class="fl">5</span>, <span class="fl">6</span>, <span class="fl">7</span>, <span class="fl">8</span><span class="op">)</span><span class="op">)</span><span class="op">$</span><span class="fu">view</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span>, <span class="fl">2</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">x</span></span>
+<span class="r-in"><span class="fu">torch_roll</span><span class="op">(</span><span class="va">x</span>, <span class="fl">1</span>, <span class="fl">1</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_roll</span><span class="op">(</span><span class="va">x</span>, <span class="op">-</span><span class="fl">1</span>, <span class="fl">1</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_roll</span><span class="op">(</span><span class="va">x</span>, shifts<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">1</span><span class="op">)</span>, dims<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">2</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  6  5</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  8  7</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  4  3</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4,2} ]</span>
+</code></pre></div>
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Rot90</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_rot90.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Rot90</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_rot90.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Rot90</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_rot90</span><span class='op'>(</span><span class='va'>self</span>, k <span class='op'>=</span> <span class='fl'>1L</span>, dims <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>0</span>, <span class='fl'>1</span><span class='op'>)</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>k</th>
-      <td><p>(int) number of times to rotate</p></td>
-    </tr>
-    <tr>
-      <th>dims</th>
-      <td><p>(a list or tuple) axis to rotate</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="rot-input-k-dims-gt-tensor-"><a class="anchor" href="#rot-input-k-dims-gt-tensor-"></a>rot90(input, k, dims) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_rot90</span><span class="op">(</span><span class="va">self</span>, k <span class="op">=</span> <span class="fl">1L</span>, dims <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span><span class="op">)</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+<dt>k</dt>
+<dd><p>(int) number of times to rotate</p></dd>
+<dt>dims</dt>
+<dd><p>(a list or tuple) axis to rotate</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="rot-input-k-dims-gt-tensor-">rot90(input, k, dims) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#rot-input-k-dims-gt-tensor-"></a></h2>
     
 
 
 <p>Rotate a n-D tensor by 90 degrees in the plane specified by dims axis.
 Rotation direction is from the first towards the second axis if k &gt; 0, and from the second towards the first for k &lt; 0.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>x</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>4</span><span class='op'>)</span><span class='op'>$</span><span class='fu'>view</span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>2</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>x</span></span>
-<span class='r-in'><span class='fu'>torch_rot90</span><span class='op'>(</span><span class='va'>x</span>, <span class='fl'>1</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>2</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>x</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>8</span><span class='op'>)</span><span class='op'>$</span><span class='fu'>view</span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>2</span>, <span class='fl'>2</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>x</span></span>
-<span class='r-in'><span class='fu'>torch_rot90</span><span class='op'>(</span><span class='va'>x</span>, <span class='fl'>1</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>2</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> (1,.,.) = </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   3  4</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   7  8</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> (2,.,.) = </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   1  2</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   5  6</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{2,2,2} ]</span>
-</pre>
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-      <h2 data-toc-skip>Contents</h2>
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+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_arange.html">torch_arange</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">4</span><span class="op">)</span><span class="op">$</span><span class="fu">view</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">2</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">x</span></span>
+<span class="r-in"><span class="fu">torch_rot90</span><span class="op">(</span><span class="va">x</span>, <span class="fl">1</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">2</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_arange.html">torch_arange</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">8</span><span class="op">)</span><span class="op">$</span><span class="fu">view</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">2</span>, <span class="fl">2</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">x</span></span>
+<span class="r-in"><span class="fu">torch_rot90</span><span class="op">(</span><span class="va">x</span>, <span class="fl">1</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">2</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> (1,.,.) = </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   3  4</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   7  8</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> (2,.,.) = </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   1  2</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   5  6</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2,2,2} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Round</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_round.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Round</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_round.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Round</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_round</span><span class='op'>(</span><span class='va'>self</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="round-input-out-null-gt-tensor-"><a class="anchor" href="#round-input-out-null-gt-tensor-"></a>round(input, out=NULL) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_round</span><span class="op">(</span><span class="va">self</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="round-input-out-null-gt-tensor-">round(input, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#round-input-out-null-gt-tensor-"></a></h2>
     
 
 
 <p>Returns a new tensor with each of the elements of <code>input</code> rounded
 to the closest integer.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='fu'>torch_round</span><span class='op'>(</span><span class='va'>a</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{4} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="fu">torch_round</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -2</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Rrelu_</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_rrelu_.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Rrelu_</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_rrelu_.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Rrelu_</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_rrelu_</span><span class='op'>(</span>
-  <span class='va'>self</span>,
-  lower <span class='op'>=</span> <span class='fl'>0.125</span>,
-  upper <span class='op'>=</span> <span class='fl'>0.333333</span>,
-  training <span class='op'>=</span> <span class='cn'>FALSE</span>,
-  generator <span class='op'>=</span> <span class='cn'>NULL</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>the input tensor</p></td>
-    </tr>
-    <tr>
-      <th>lower</th>
-      <td><p>lower bound of the uniform distribution. Default: 1/8</p></td>
-    </tr>
-    <tr>
-      <th>upper</th>
-      <td><p>upper bound of the uniform distribution. Default: 1/3</p></td>
-    </tr>
-    <tr>
-      <th>training</th>
-      <td><p>bool wether it's a training pass. DEfault: FALSE</p></td>
-    </tr>
-    <tr>
-      <th>generator</th>
-      <td><p>random number generator</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="rrelu-input-lower-upper-training-false-gt-tensor-"><a class="anchor" href="#rrelu-input-lower-upper-training-false-gt-tensor-"></a>rrelu_(input, lower=1./8, upper=1./3, training=False) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_rrelu_</span><span class="op">(</span>
+  <span class="va">self</span>,
+  lower <span class="op">=</span> <span class="fl">0.125</span>,
+  upper <span class="op">=</span> <span class="fl">0.333333333333333</span>,
+  training <span class="op">=</span> <span class="cn">FALSE</span>,
+  generator <span class="op">=</span> <span class="cn">NULL</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>the input tensor</p></dd>
+<dt>lower</dt>
+<dd><p>lower bound of the uniform distribution. Default: 1/8</p></dd>
+<dt>upper</dt>
+<dd><p>upper bound of the uniform distribution. Default: 1/3</p></dd>
+<dt>training</dt>
+<dd><p>bool wether it's a training pass. DEfault: FALSE</p></dd>
+<dt>generator</dt>
+<dd><p>random number generator</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="rrelu-input-lower-upper-training-false-gt-tensor-">rrelu_(input, lower=1./8, upper=1./3, training=False) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#rrelu-input-lower-upper-training-false-gt-tensor-"></a></h2>
     
 
 
 <p>In-place version of <code>torch_rrelu</code>.</p>
+    </div>
 
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diff --git a/reference/torch_rsqrt.html b/reference/torch_rsqrt.html
index a9f95e766..ce3f5f80a 100644
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+    
+  </div>
+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Rsqrt</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_rsqrt.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Rsqrt</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_rsqrt.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Rsqrt</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_rsqrt</span><span class='op'>(</span><span class='va'>self</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="rsqrt-input-out-null-gt-tensor-"><a class="anchor" href="#rsqrt-input-out-null-gt-tensor-"></a>rsqrt(input, out=NULL) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_rsqrt</span><span class="op">(</span><span class="va">self</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="rsqrt-input-out-null-gt-tensor-">rsqrt(input, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#rsqrt-input-out-null-gt-tensor-"></a></h2>
     
 
 
@@ -210,46 +100,41 @@ the elements of <code>input</code>.</p>
 <p>$$
     \mbox{out}_{i} = \frac{1}{\sqrt{\mbox{input}_{i}}}
 $$</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='fu'>torch_rsqrt</span><span class='op'>(</span><span class='va'>a</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>     nan</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>     nan</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1.0357</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>     nan</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{4} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="fu">torch_rsqrt</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>     nan</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.5397</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  4.0054</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.6926</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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 <div class="row">
-  <div class="col-md-9 contents">
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-    <h1>Saves an object to a disk file.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/save.R'><code>R/save.R</code></a></small>
-    <div class="hidden name"><code>torch_save.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Saves an object to a disk file.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/save.R" class="external-link"><code>R/save.R</code></a></small>
+      <div class="d-none name"><code>torch_save.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>This function is experimental, don't use for long
 term storage.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_save</span><span class='op'>(</span><span class='va'>obj</span>, <span class='va'>path</span>, <span class='va'>...</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>obj</th>
-      <td><p>the saved object</p></td>
-    </tr>
-    <tr>
-      <th>path</th>
-      <td><p>a connection or the name of the file to save.</p></td>
-    </tr>
-    <tr>
-      <th>...</th>
-      <td><p>not currently used.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="see-also"><a class="anchor" href="#see-also"></a>See also</h2>
-
-    <div class='dont-index'><p>Other torch_save: 
-<code><a href='torch_load.html'>torch_load</a>()</code></p></div>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_save</span><span class="op">(</span><span class="va">obj</span>, <span class="va">path</span>, <span class="va">...</span><span class="op">)</span></code></pre></div>
+    </div>
 
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-      <h2 data-toc-skip>Contents</h2>
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-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>obj</dt>
+<dd><p>the saved object</p></dd>
+<dt>path</dt>
+<dd><p>a connection or the name of the file to save.</p></dd>
+<dt>...</dt>
+<dd><p>not currently used.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
+    <div class="dont-index"><p>Other torch_save: 
+<code><a href="torch_load.html">torch_load</a>()</code></p></div>
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diff --git a/reference/torch_scalar_tensor.html b/reference/torch_scalar_tensor.html
index a3e19b44b..88440015e 100644
--- a/reference/torch_scalar_tensor.html
+++ b/reference/torch_scalar_tensor.html
@@ -1,243 +1,119 @@
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/creation-ops.R'><code>R/creation-ops.R</code></a></small>
-    <div class="hidden name"><code>torch_scalar_tensor.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Scalar tensor</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/creation-ops.R" class="external-link"><code>R/creation-ops.R</code></a></small>
+      <div class="d-none name"><code>torch_scalar_tensor.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Creates a singleton dimension tensor.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_scalar_tensor</span><span class='op'>(</span><span class='va'>value</span>, dtype <span class='op'>=</span> <span class='cn'>NULL</span>, device <span class='op'>=</span> <span class='cn'>NULL</span>, requires_grad <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>value</th>
-      <td><p>the value you want to use</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>).</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
-    </tr>
-    <tr>
-      <th>requires_grad</th>
-      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
-    </tr>
-    </table>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_scalar_tensor</span><span class="op">(</span><span class="va">value</span>, dtype <span class="op">=</span> <span class="cn">NULL</span>, device <span class="op">=</span> <span class="cn">NULL</span>, requires_grad <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>value</dt>
+<dd><p>the value you want to use</p></dd>
+<dt>dtype</dt>
+<dd><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>).</p></dd>
+<dt>device</dt>
+<dd><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></dd>
+<dt>requires_grad</dt>
+<dd><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></dd>
+</dl></div>
 
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+        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Searchsorted</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_searchsorted.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Searchsorted</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_searchsorted.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Searchsorted</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_searchsorted</span><span class='op'>(</span><span class='va'>sorted_sequence</span>, <span class='va'>self</span>, out_int32 <span class='op'>=</span> <span class='cn'>FALSE</span>, right <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span></pre>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_searchsorted</span><span class="op">(</span><span class="va">sorted_sequence</span>, <span class="va">self</span>, out_int32 <span class="op">=</span> <span class="cn">FALSE</span>, right <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>sorted_sequence</th>
-      <td><p>(Tensor) N-D or 1-D tensor, containing monotonically increasing
-sequence on the <em>innermost</em> dimension.</p></td>
-    </tr>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor or Scalar) N-D tensor or a Scalar containing the search value(s).</p></td>
-    </tr>
-    <tr>
-      <th>out_int32</th>
-      <td><p>(bool, optional) – indicate the output data type. <code><a href='torch_dtype.html'>torch_int32()</a></code>
-if True, <code><a href='torch_dtype.html'>torch_int64()</a></code> otherwise. Default value is FALSE, i.e. default output
-data type is <code><a href='torch_dtype.html'>torch_int64()</a></code>.</p></td>
-    </tr>
-    <tr>
-      <th>right</th>
-      <td><p>(bool, optional) – if False, return the first suitable location
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>sorted_sequence</dt>
+<dd><p>(Tensor) N-D or 1-D tensor, containing monotonically increasing
+sequence on the <em>innermost</em> dimension.</p></dd>
+<dt>self</dt>
+<dd><p>(Tensor or Scalar) N-D tensor or a Scalar containing the search value(s).</p></dd>
+<dt>out_int32</dt>
+<dd><p>(bool, optional) – indicate the output data type. <code><a href="torch_dtype.html">torch_int32()</a></code>
+if True, <code><a href="torch_dtype.html">torch_int64()</a></code> otherwise. Default value is FALSE, i.e. default output
+data type is <code><a href="torch_dtype.html">torch_int64()</a></code>.</p></dd>
+<dt>right</dt>
+<dd><p>(bool, optional) – if False, return the first suitable location
 that is found. If True, return the last such index. If no suitable index found,
 return 0 for non-numerical value (eg. nan, inf) or the size of boundaries
 (one pass the last index). In other words, if False, gets the lower bound index
 for each value in input from boundaries. If True, gets the upper bound index
-instead. Default value is False.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="searchsorted-sorted-sequence-values-out-int-false-right-false-out-none-gt-tensor-"><a class="anchor" href="#searchsorted-sorted-sequence-values-out-int-false-right-false-out-none-gt-tensor-"></a>searchsorted(sorted_sequence, values, *, out_int32=FALSE, right=FALSE, out=None) -&gt; Tensor </h2>
-
+instead. Default value is False.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="searchsorted-sorted-sequence-values-out-int-false-right-false-out-none-gt-tensor-">searchsorted(sorted_sequence, values, *, out_int32=FALSE, right=FALSE, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#searchsorted-sorted-sequence-values-out-int-false-right-false-out-none-gt-tensor-"></a></h2>
     
 
 
@@ -230,50 +114,45 @@ corresponding values in <code>values</code> were inserted before the indices, th
 corresponding <em>innermost</em> dimension within <code>sorted_sequence</code> would be preserved.
 Return a new tensor with the same size as <code>values</code>. If <code>right</code> is FALSE (default),
 then the left boundary of <code>sorted_sequence</code> is closed.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>sorted_sequence</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/cbind.html'>rbind</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>3</span>, <span class='fl'>5</span>, <span class='fl'>7</span>, <span class='fl'>9</span><span class='op'>)</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>4</span>, <span class='fl'>6</span>, <span class='fl'>8</span>, <span class='fl'>10</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>sorted_sequence</span></span>
-<span class='r-in'><span class='va'>values</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/cbind.html'>rbind</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>6</span>, <span class='fl'>9</span><span class='op'>)</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>6</span>, <span class='fl'>9</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>values</span></span>
-<span class='r-in'><span class='fu'>torch_searchsorted</span><span class='op'>(</span><span class='va'>sorted_sequence</span>, <span class='va'>values</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_searchsorted</span><span class='op'>(</span><span class='va'>sorted_sequence</span>, <span class='va'>values</span>, right<span class='op'>=</span><span class='cn'>TRUE</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>sorted_sequence_1d</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>3</span>, <span class='fl'>5</span>, <span class='fl'>7</span>, <span class='fl'>9</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>sorted_sequence_1d</span></span>
-<span class='r-in'><span class='fu'>torch_searchsorted</span><span class='op'>(</span><span class='va'>sorted_sequence_1d</span>, <span class='va'>values</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1  3  4</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1  3  4</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPULongType{2,3} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">sorted_sequence</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/cbind.html" class="external-link">rbind</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">3</span>, <span class="fl">5</span>, <span class="fl">7</span>, <span class="fl">9</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">4</span>, <span class="fl">6</span>, <span class="fl">8</span>, <span class="fl">10</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">sorted_sequence</span></span>
+<span class="r-in"><span class="va">values</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/cbind.html" class="external-link">rbind</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">6</span>, <span class="fl">9</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">6</span>, <span class="fl">9</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">values</span></span>
+<span class="r-in"><span class="fu">torch_searchsorted</span><span class="op">(</span><span class="va">sorted_sequence</span>, <span class="va">values</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_searchsorted</span><span class="op">(</span><span class="va">sorted_sequence</span>, <span class="va">values</span>, right<span class="op">=</span><span class="cn">TRUE</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">sorted_sequence_1d</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">3</span>, <span class="fl">5</span>, <span class="fl">7</span>, <span class="fl">9</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">sorted_sequence_1d</span></span>
+<span class="r-in"><span class="fu">torch_searchsorted</span><span class="op">(</span><span class="va">sorted_sequence_1d</span>, <span class="va">values</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1  3  4</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1  3  4</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPULongType{2,3} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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-    <h1>Selu</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_selu.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Selu</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_selu.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Selu</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_selu</span><span class='op'>(</span><span class='va'>self</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>the input tensor</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="selu-input-gt-tensor-"><a class="anchor" href="#selu-input-gt-tensor-"></a>selu(input) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_selu</span><span class="op">(</span><span class="va">self</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>the input tensor</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="selu-input-gt-tensor-">selu(input) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#selu-input-gt-tensor-"></a></h2>
     
 
 
 <p>Computes the selu transformation.</p>
+    </div>
 
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diff --git a/reference/torch_selu_.html b/reference/torch_selu_.html
index 8a224a316..ba35796e4 100644
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-    <div class="hidden name"><code>torch_selu_.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
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-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Selu_</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_selu_</span><span class='op'>(</span><span class='va'>self</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>the input tensor</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="selu-input-gt-tensor-"><a class="anchor" href="#selu-input-gt-tensor-"></a>selu_(input) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_selu_</span><span class="op">(</span><span class="va">self</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>the input tensor</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="selu-input-gt-tensor-">selu_(input) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#selu-input-gt-tensor-"></a></h2>
     
 
 
-<p>In-place version of <code><a href='torch_selu.html'>torch_selu()</a></code>.</p>
+<p>In-place version of <code><a href="torch_selu.html">torch_selu()</a></code>.</p>
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diff --git a/reference/torch_sgn.html b/reference/torch_sgn.html
index de990ddbe..92b9e5cf8 100644
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Sgn</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_sgn.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Sgn</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_sgn.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Sgn</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_sgn</span><span class='op'>(</span><span class='va'>self</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="sgn-input-out-none-gt-tensor-"><a class="anchor" href="#sgn-input-out-none-gt-tensor-"></a>sgn(input, *, out=None) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_sgn</span><span class="op">(</span><span class="va">self</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="sgn-input-out-none-gt-tensor-">sgn(input, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#sgn-input-out-none-gt-tensor-"></a></h2>
     
 
 
 <p>For complex tensors, this function returns a new tensor whose elemants have the same angle as that of the
 elements of <code>input</code> and absolute value 1. For a non-complex tensor, this function
-returns the signs of the elements of <code>input</code> (see <code><a href='torch_sign.html'>torch_sign</a></code>).</p>
+returns the signs of the elements of <code>input</code> (see <code><a href="torch_sign.html">torch_sign</a></code>).</p>
 <p>\(\mbox{out}_{i} = 0\), if \(|{\mbox{{input}}_i}| == 0\)
 \(\mbox{out}_{i} = \frac{{\mbox{{input}}_i}}{|{\mbox{{input}}_i}|}\), otherwise</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='cn'>FALSE</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>x</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3</span><span class='op'>+</span><span class='fl'>4i</span>, <span class='fl'>7</span><span class='op'>-</span><span class='fl'>24i</span>, <span class='fl'>0</span>, <span class='fl'>1</span><span class='op'>+</span><span class='fl'>2i</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>x</span><span class='op'>$</span><span class='fu'>sgn</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_sgn</span><span class='op'>(</span><span class='va'>x</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span><span class="op">+</span><span class="fl">4i</span>, <span class="fl">7</span><span class="op">-</span><span class="fl">24i</span>, <span class="fl">0</span>, <span class="fl">1</span><span class="op">+</span><span class="fl">2i</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">x</span><span class="op">$</span><span class="fu">sgn</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_sgn</span><span class="op">(</span><span class="va">x</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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index 3ae0fc898..dc23e2b8b 100644
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Sigmoid</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_sigmoid.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Sigmoid</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_sigmoid.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Sigmoid</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_sigmoid</span><span class='op'>(</span><span class='va'>self</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="sigmoid-input-out-null-gt-tensor-"><a class="anchor" href="#sigmoid-input-out-null-gt-tensor-"></a>sigmoid(input, out=NULL) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_sigmoid</span><span class="op">(</span><span class="va">self</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="sigmoid-input-out-null-gt-tensor-">sigmoid(input, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#sigmoid-input-out-null-gt-tensor-"></a></h2>
     
 
 
@@ -209,46 +99,41 @@
 <p>$$
     \mbox{out}_{i} = \frac{1}{1 + e^{-\mbox{input}_{i}}}
 $$</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='fu'>torch_sigmoid</span><span class='op'>(</span><span class='va'>a</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.2714</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.7690</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.8361</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.4737</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{4} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="fu">torch_sigmoid</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.5183</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.7670</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.5439</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.1101</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer>
-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
 </div>
 
-<div class="pkgdown">
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+<div class="pkgdown-footer-right">
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Sign</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_sign.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Sign</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_sign.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Sign</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_sign</span><span class='op'>(</span><span class='va'>self</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="sign-input-out-null-gt-tensor-"><a class="anchor" href="#sign-input-out-null-gt-tensor-"></a>sign(input, out=NULL) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_sign</span><span class="op">(</span><span class="va">self</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="sign-input-out-null-gt-tensor-">sign(input, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#sign-input-out-null-gt-tensor-"></a></h2>
     
 
 
@@ -209,46 +99,41 @@
 <p>$$
     \mbox{out}_{i} = \mbox{sgn}(\mbox{input}_{i})
 $$</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>0.7</span>, <span class='op'>-</span><span class='fl'>1.2</span>, <span class='fl'>0.</span>, <span class='fl'>2.3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='fu'>torch_sign</span><span class='op'>(</span><span class='va'>a</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{4} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.7</span>, <span class="op">-</span><span class="fl">1.2</span>, <span class="fl">0.</span>, <span class="fl">2.3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="fu">torch_sign</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer>
-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
 </div>
 
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+<div class="pkgdown-footer-right">
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diff --git a/reference/torch_signbit.html b/reference/torch_signbit.html
index 563a76653..f06789857 100644
--- a/reference/torch_signbit.html
+++ b/reference/torch_signbit.html
@@ -1,250 +1,135 @@
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Signbit</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_signbit.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Signbit</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_signbit.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Signbit</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_signbit</span><span class='op'>(</span><span class='va'>self</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="signbit-input-out-none-gt-tensor-"><a class="anchor" href="#signbit-input-out-none-gt-tensor-"></a>signbit(input, *, out=None) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_signbit</span><span class="op">(</span><span class="va">self</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="signbit-input-out-none-gt-tensor-">signbit(input, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#signbit-input-out-none-gt-tensor-"></a></h2>
     
 
 
 <p>Tests if each element of <code>input</code> has its sign bit set (is less than zero) or not.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>0.7</span>, <span class='op'>-</span><span class='fl'>1.2</span>, <span class='fl'>0.</span>, <span class='fl'>2.3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_signbit</span><span class='op'>(</span><span class='va'>a</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUBoolType{4} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.7</span>, <span class="op">-</span><span class="fl">1.2</span>, <span class="fl">0.</span>, <span class="fl">2.3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_signbit</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUBoolType{4} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Sin</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_sin.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Sin</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_sin.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Sin</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_sin</span><span class='op'>(</span><span class='va'>self</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="sin-input-out-null-gt-tensor-"><a class="anchor" href="#sin-input-out-null-gt-tensor-"></a>sin(input, out=NULL) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_sin</span><span class="op">(</span><span class="va">self</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="sin-input-out-null-gt-tensor-">sin(input, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#sin-input-out-null-gt-tensor-"></a></h2>
     
 
 
@@ -209,46 +99,41 @@
 <p>$$
     \mbox{out}_{i} = \sin(\mbox{input}_{i})
 $$</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='fu'>torch_sin</span><span class='op'>(</span><span class='va'>a</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.9870</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.1574</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.0487</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.1159</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{4} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="fu">torch_sin</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.3942</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.9255</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.2259</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.1994</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer>
-      <div class="copyright">
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+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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+<div class="pkgdown-footer-right">
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index 5fd8d047b..2e415fc92 100644
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Sinh</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_sinh.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Sinh</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_sinh.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Sinh</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_sinh</span><span class='op'>(</span><span class='va'>self</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="sinh-input-out-null-gt-tensor-"><a class="anchor" href="#sinh-input-out-null-gt-tensor-"></a>sinh(input, out=NULL) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_sinh</span><span class="op">(</span><span class="va">self</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="sinh-input-out-null-gt-tensor-">sinh(input, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#sinh-input-out-null-gt-tensor-"></a></h2>
     
 
 
@@ -210,46 +100,41 @@
 <p>$$
     \mbox{out}_{i} = \sinh(\mbox{input}_{i})
 $$</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='fu'>torch_sinh</span><span class='op'>(</span><span class='va'>a</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.9831</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.3219</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.1819</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.2323</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{4} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="fu">torch_sinh</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.2181</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.4530</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.3427</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -1.0866</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer>
-      <div class="copyright">
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+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Slogdet</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_slogdet.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Slogdet</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_slogdet.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Slogdet</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_slogdet</span><span class='op'>(</span><span class='va'>self</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor of size <code>(*, n, n)</code> where <code>*</code> is zero or more                batch dimensions.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_slogdet</span><span class="op">(</span><span class="va">self</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor of size <code>(*, n, n)</code> where <code>*</code> is zero or more                batch dimensions.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     
-<pre>If `input` has zero determinant, this returns `(0, -inf)`.
-</pre>
+<div class="sourceCode"><pre><code>If `input` has zero determinant, this returns `(0, -inf)`.
+</code></pre></div>
 
-<pre>Backward through `slogdet` internally uses SVD results when `input`
+<div class="sourceCode"><pre><code>Backward through `slogdet` internally uses SVD results when `input`
 is not invertible. In this case, double backward through `slogdet`
 will be unstable in when `input` doesn't have distinct singular values.
 See `~torch.svd` for details.
-</pre>
-
-    <h2 class="hasAnchor" id="slogdet-input-gt-tensor-tensor-"><a class="anchor" href="#slogdet-input-gt-tensor-tensor-"></a>slogdet(input) -&gt; (Tensor, Tensor) </h2>
+</code></pre></div>
 
+    </div>
+    <div class="section level2">
+    <h2 id="slogdet-input-gt-tensor-tensor-">slogdet(input) -&gt; (Tensor, Tensor) <a class="anchor" aria-label="anchor" href="#slogdet-input-gt-tensor-tensor-"></a></h2>
     
 
 
 <p>Calculates the sign and log absolute value of the determinant(s) of a square matrix or batches of square matrices.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>A</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>A</span></span>
-<span class='r-in'><span class='fu'><a href='torch_det.html'>torch_det</a></span><span class='op'>(</span><span class='va'>A</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'><a href='torch_logdet.html'>torch_logdet</a></span><span class='op'>(</span><span class='va'>A</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_slogdet</span><span class='op'>(</span><span class='va'>A</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [[1]]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{} ]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [[2]]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.143619</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{} ]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">A</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">A</span></span>
+<span class="r-in"><span class="fu"><a href="torch_det.html">torch_det</a></span><span class="op">(</span><span class="va">A</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu"><a href="torch_logdet.html">torch_logdet</a></span><span class="op">(</span><span class="va">A</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_slogdet</span><span class="op">(</span><span class="va">A</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [[1]]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{} ]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [[2]]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -5.06819</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{} ]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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-      <div class="copyright">
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+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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diff --git a/reference/torch_solve.html b/reference/torch_solve.html
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-    <h1>Solve</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_solve.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Solve</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_solve.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Solve</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_solve</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>A</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) input matrix \(B\) of size \((*, m, k)\) , where \(*\)                is zero or more batch dimensions.</p></td>
-    </tr>
-    <tr>
-      <th>A</th>
-      <td><p>(Tensor) input square matrix of size \((*, m, m)\), where                \(*\) is zero or more batch dimensions.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_solve</span><span class="op">(</span><span class="va">self</span>, <span class="va">A</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) input matrix \(B\) of size \((*, m, k)\) , where \(*\)                is zero or more batch dimensions.</p></dd>
+<dt>A</dt>
+<dd><p>(Tensor) input square matrix of size \((*, m, m)\), where                \(*\) is zero or more batch dimensions.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     
-<pre>Irrespective of the original strides, the returned matrices
+<div class="sourceCode"><pre><code>Irrespective of the original strides, the returned matrices
 `solution` and `LU` will be transposed, i.e. with strides like
 `B$contiguous()$transpose(-1, -2)$stride()` and
 `A$contiguous()$transpose(-1, -2)$stride()` respectively.
-</pre>
-
-    <h2 class="hasAnchor" id="solve-input-a-gt-tensor-tensor-"><a class="anchor" href="#solve-input-a-gt-tensor-tensor-"></a>solve(input, A) -&gt; (Tensor, Tensor) </h2>
+</code></pre></div>
 
+    </div>
+    <div class="section level2">
+    <h2 id="solve-input-a-gt-tensor-tensor-">solve(input, A) -&gt; (Tensor, Tensor) <a class="anchor" aria-label="anchor" href="#solve-input-a-gt-tensor-tensor-"></a></h2>
     
 
 
@@ -225,59 +114,54 @@ A, in order as a namedtuple <code>solution, LU</code>.</p>
 <p><code>torch_solve(B, A)</code> can take in 2D inputs <code>B, A</code> or inputs that are
 batches of 2D matrices. If the inputs are batches, then returns
 batched outputs <code>solution, LU</code>.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>A</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/cbind.html'>rbind</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>6.80</span>, <span class='op'>-</span><span class='fl'>2.11</span>,  <span class='fl'>5.66</span>,  <span class='fl'>5.97</span>,  <span class='fl'>8.23</span><span class='op'>)</span>,</span>
-<span class='r-in'>                      <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='op'>-</span><span class='fl'>6.05</span>, <span class='op'>-</span><span class='fl'>3.30</span>,  <span class='fl'>5.36</span>, <span class='op'>-</span><span class='fl'>4.44</span>,  <span class='fl'>1.08</span><span class='op'>)</span>,</span>
-<span class='r-in'>                      <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='op'>-</span><span class='fl'>0.45</span>,  <span class='fl'>2.58</span>, <span class='op'>-</span><span class='fl'>2.70</span>,  <span class='fl'>0.27</span>,  <span class='fl'>9.04</span><span class='op'>)</span>,</span>
-<span class='r-in'>                      <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>8.32</span>,  <span class='fl'>2.71</span>,  <span class='fl'>4.35</span>,  <span class='op'>-</span><span class='fl'>7.17</span>,  <span class='fl'>2.14</span><span class='op'>)</span>,</span>
-<span class='r-in'>                      <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='op'>-</span><span class='fl'>9.67</span>, <span class='op'>-</span><span class='fl'>5.14</span>, <span class='op'>-</span><span class='fl'>7.26</span>,  <span class='fl'>6.08</span>, <span class='op'>-</span><span class='fl'>6.87</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span><span class='op'>$</span><span class='fu'>t</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>B</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/cbind.html'>rbind</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4.02</span>,  <span class='fl'>6.19</span>, <span class='op'>-</span><span class='fl'>8.22</span>, <span class='op'>-</span><span class='fl'>7.57</span>, <span class='op'>-</span><span class='fl'>3.03</span><span class='op'>)</span>,</span>
-<span class='r-in'>                      <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='op'>-</span><span class='fl'>1.56</span>,  <span class='fl'>4.00</span>, <span class='op'>-</span><span class='fl'>8.67</span>,  <span class='fl'>1.75</span>,  <span class='fl'>2.86</span><span class='op'>)</span>,</span>
-<span class='r-in'>                      <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>9.81</span>, <span class='op'>-</span><span class='fl'>4.09</span>, <span class='op'>-</span><span class='fl'>4.57</span>, <span class='op'>-</span><span class='fl'>8.61</span>,  <span class='fl'>8.99</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span><span class='op'>$</span><span class='fu'>t</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>out</span> <span class='op'>=</span> <span class='fu'>torch_solve</span><span class='op'>(</span><span class='va'>B</span>, <span class='va'>A</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>X</span> <span class='op'>=</span> <span class='va'>out</span><span class='op'>[[</span><span class='fl'>1</span><span class='op'>]</span><span class='op'>]</span></span>
-<span class='r-in'><span class='va'>LU</span> <span class='op'>=</span> <span class='va'>out</span><span class='op'>[[</span><span class='fl'>2</span><span class='op'>]</span><span class='op'>]</span></span>
-<span class='r-in'><span class='fu'><a href='torch_dist.html'>torch_dist</a></span><span class='op'>(</span><span class='va'>B</span>, <span class='fu'><a href='torch_mm.html'>torch_mm</a></span><span class='op'>(</span><span class='va'>A</span>, <span class='va'>X</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='co'># Batched solver example</span></span>
-<span class='r-in'><span class='va'>A</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>3</span>, <span class='fl'>1</span>, <span class='fl'>4</span>, <span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>B</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>3</span>, <span class='fl'>1</span>, <span class='fl'>4</span>, <span class='fl'>6</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>out</span> <span class='op'>=</span> <span class='fu'>torch_solve</span><span class='op'>(</span><span class='va'>B</span>, <span class='va'>A</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>X</span> <span class='op'>=</span> <span class='va'>out</span><span class='op'>[[</span><span class='fl'>1</span><span class='op'>]</span><span class='op'>]</span></span>
-<span class='r-in'><span class='va'>LU</span> <span class='op'>=</span> <span class='va'>out</span><span class='op'>[[</span><span class='fl'>2</span><span class='op'>]</span><span class='op'>]</span></span>
-<span class='r-in'><span class='fu'><a href='torch_dist.html'>torch_dist</a></span><span class='op'>(</span><span class='va'>B</span>, <span class='va'>A</span><span class='op'>$</span><span class='fu'>matmul</span><span class='op'>(</span><span class='va'>X</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> 6.13625e-06</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">A</span> <span class="op">=</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/cbind.html" class="external-link">rbind</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">6.80</span>, <span class="op">-</span><span class="fl">2.11</span>,  <span class="fl">5.66</span>,  <span class="fl">5.97</span>,  <span class="fl">8.23</span><span class="op">)</span>,</span>
+<span class="r-in">                      <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="op">-</span><span class="fl">6.05</span>, <span class="op">-</span><span class="fl">3.30</span>,  <span class="fl">5.36</span>, <span class="op">-</span><span class="fl">4.44</span>,  <span class="fl">1.08</span><span class="op">)</span>,</span>
+<span class="r-in">                      <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="op">-</span><span class="fl">0.45</span>,  <span class="fl">2.58</span>, <span class="op">-</span><span class="fl">2.70</span>,  <span class="fl">0.27</span>,  <span class="fl">9.04</span><span class="op">)</span>,</span>
+<span class="r-in">                      <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">8.32</span>,  <span class="fl">2.71</span>,  <span class="fl">4.35</span>,  <span class="op">-</span><span class="fl">7.17</span>,  <span class="fl">2.14</span><span class="op">)</span>,</span>
+<span class="r-in">                      <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="op">-</span><span class="fl">9.67</span>, <span class="op">-</span><span class="fl">5.14</span>, <span class="op">-</span><span class="fl">7.26</span>,  <span class="fl">6.08</span>, <span class="op">-</span><span class="fl">6.87</span><span class="op">)</span><span class="op">)</span><span class="op">)</span><span class="op">$</span><span class="fu">t</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">B</span> <span class="op">=</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/cbind.html" class="external-link">rbind</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4.02</span>,  <span class="fl">6.19</span>, <span class="op">-</span><span class="fl">8.22</span>, <span class="op">-</span><span class="fl">7.57</span>, <span class="op">-</span><span class="fl">3.03</span><span class="op">)</span>,</span>
+<span class="r-in">                      <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="op">-</span><span class="fl">1.56</span>,  <span class="fl">4.00</span>, <span class="op">-</span><span class="fl">8.67</span>,  <span class="fl">1.75</span>,  <span class="fl">2.86</span><span class="op">)</span>,</span>
+<span class="r-in">                      <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">9.81</span>, <span class="op">-</span><span class="fl">4.09</span>, <span class="op">-</span><span class="fl">4.57</span>, <span class="op">-</span><span class="fl">8.61</span>,  <span class="fl">8.99</span><span class="op">)</span><span class="op">)</span><span class="op">)</span><span class="op">$</span><span class="fu">t</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">out</span> <span class="op">=</span> <span class="fu">torch_solve</span><span class="op">(</span><span class="va">B</span>, <span class="va">A</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">X</span> <span class="op">=</span> <span class="va">out</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span></span>
+<span class="r-in"><span class="va">LU</span> <span class="op">=</span> <span class="va">out</span><span class="op">[[</span><span class="fl">2</span><span class="op">]</span><span class="op">]</span></span>
+<span class="r-in"><span class="fu"><a href="torch_dist.html">torch_dist</a></span><span class="op">(</span><span class="va">B</span>, <span class="fu"><a href="torch_mm.html">torch_mm</a></span><span class="op">(</span><span class="va">A</span>, <span class="va">X</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="co"># Batched solver example</span></span>
+<span class="r-in"><span class="va">A</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">3</span>, <span class="fl">1</span>, <span class="fl">4</span>, <span class="fl">4</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">B</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">3</span>, <span class="fl">1</span>, <span class="fl">4</span>, <span class="fl">6</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">out</span> <span class="op">=</span> <span class="fu">torch_solve</span><span class="op">(</span><span class="va">B</span>, <span class="va">A</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">X</span> <span class="op">=</span> <span class="va">out</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span></span>
+<span class="r-in"><span class="va">LU</span> <span class="op">=</span> <span class="va">out</span><span class="op">[[</span><span class="fl">2</span><span class="op">]</span><span class="op">]</span></span>
+<span class="r-in"><span class="fu"><a href="torch_dist.html">torch_dist</a></span><span class="op">(</span><span class="va">B</span>, <span class="va">A</span><span class="op">$</span><span class="fu">matmul</span><span class="op">(</span><span class="va">X</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 1.22982e-05</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{} ]</span>
+</code></pre></div>
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Sort</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_sort.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Sort</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a></small>
+      <div class="d-none name"><code>torch_sort.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Sort</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_sort</span><span class='op'>(</span><span class='va'>self</span>, dim <span class='op'>=</span> <span class='op'>-</span><span class='fl'>1L</span>, descending <span class='op'>=</span> <span class='cn'>FALSE</span>, <span class='va'>stable</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int, optional) the dimension to sort along</p></td>
-    </tr>
-    <tr>
-      <th>descending</th>
-      <td><p>(bool, optional) controls the sorting order (ascending or descending)</p></td>
-    </tr>
-    <tr>
-      <th>stable</th>
-      <td><p>(bool, optional) – makes the sorting routine stable, which guarantees
-that the order of equivalent elements is preserved.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="sort-input-dim-descending-false-gt-tensor-longtensor-"><a class="anchor" href="#sort-input-dim-descending-false-gt-tensor-longtensor-"></a>sort(input, dim=-1, descending=FALSE) -&gt; (Tensor, LongTensor) </h2>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+<dt>dim</dt>
+<dd><p>(int, optional) the dimension to sort along</p></dd>
+<dt>descending</dt>
+<dd><p>(bool, optional) controls the sorting order (ascending or descending)</p></dd>
+<dt>stable</dt>
+<dd><p>(bool, optional) – makes the sorting routine stable, which guarantees
+that the order of equivalent elements is preserved.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="sort-input-dim-descending-false-gt-tensor-longtensor-">sort(input, dim=-1, descending=FALSE) -&gt; (Tensor, LongTensor) <a class="anchor" aria-label="anchor" href="#sort-input-dim-descending-false-gt-tensor-longtensor-"></a></h2>
     
 
 
@@ -226,56 +106,51 @@ order by value.</p>
 <p>A namedtuple of (values, indices) is returned, where the <code>values</code> are the
 sorted values and <code>indices</code> are the indices of the elements in the original
 <code>input</code> tensor.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>out</span> <span class='op'>=</span> <span class='fu'>torch_sort</span><span class='op'>(</span><span class='va'>x</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>out</span></span>
-<span class='r-in'><span class='va'>out</span> <span class='op'>=</span> <span class='fu'>torch_sort</span><span class='op'>(</span><span class='va'>x</span>, <span class='fl'>1</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>out</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [[1]]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -1.4982 -1.6099 -1.0541 -1.0672</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.2184 -0.7687 -0.5472 -0.9472</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.6616 -0.5947 -0.3772  0.9897</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{3,4} ]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [[2]]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1  2  2  1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0  0  1  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  2  1  0  2</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPULongType{3,4} ]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">x</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">4</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">out</span> <span class="op">=</span> <span class="fu">torch_sort</span><span class="op">(</span><span class="va">x</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">out</span></span>
+<span class="r-in"><span class="va">out</span> <span class="op">=</span> <span class="fu">torch_sort</span><span class="op">(</span><span class="va">x</span>, <span class="fl">1</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">out</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [[1]]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -1.9596  0.3040 -0.9921  0.1079</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -1.9441  1.0724 -0.9182  1.1927</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.5018  1.5238  1.6355  2.0199</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3,4} ]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [[2]]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  3  3  3  2</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2  2  2  3</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1  1  1  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPULongType{3,4} ]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Sparse_coo_tensor</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/wrapers.R'><code>R/wrapers.R</code></a></small>
-    <div class="hidden name"><code>torch_sparse_coo_tensor.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Sparse_coo_tensor</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
+      <div class="d-none name"><code>torch_sparse_coo_tensor.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Sparse_coo_tensor</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_sparse_coo_tensor</span><span class='op'>(</span>
-  <span class='va'>indices</span>,
-  <span class='va'>values</span>,
-  size <span class='op'>=</span> <span class='cn'>NULL</span>,
-  dtype <span class='op'>=</span> <span class='cn'>NULL</span>,
-  device <span class='op'>=</span> <span class='cn'>NULL</span>,
-  requires_grad <span class='op'>=</span> <span class='cn'>FALSE</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>indices</th>
-      <td><p>(array_like) Initial data for the tensor. Can be a list, tuple,        NumPy <code>ndarray</code>, scalar, and other types. Will be cast to a <code>torch_LongTensor</code>        internally. The indices are the coordinates of the non-zero values in the matrix, and thus        should be two-dimensional where the first dimension is the number of tensor dimensions and        the second dimension is the number of non-zero values.</p></td>
-    </tr>
-    <tr>
-      <th>values</th>
-      <td><p>(array_like) Initial values for the tensor. Can be a list, tuple,        NumPy <code>ndarray</code>, scalar, and other types.</p></td>
-    </tr>
-    <tr>
-      <th>size</th>
-      <td><p>(list, tuple, or <code>torch.Size</code>, optional) Size of the sparse tensor. If not        provided the size will be inferred as the minimum size big enough to hold all non-zero        elements.</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if NULL, infers data type from <code>values</code>.</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if NULL, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
-    </tr>
-    <tr>
-      <th>requires_grad</th>
-      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="sparse-coo-tensor-indices-values-size-null-dtype-null-device-null-requires-grad-false-gt-tensor-"><a class="anchor" href="#sparse-coo-tensor-indices-values-size-null-dtype-null-device-null-requires-grad-false-gt-tensor-"></a>sparse_coo_tensor(indices, values, size=NULL, dtype=NULL, device=NULL, requires_grad=False) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_sparse_coo_tensor</span><span class="op">(</span>
+  <span class="va">indices</span>,
+  <span class="va">values</span>,
+  size <span class="op">=</span> <span class="cn">NULL</span>,
+  dtype <span class="op">=</span> <span class="cn">NULL</span>,
+  device <span class="op">=</span> <span class="cn">NULL</span>,
+  requires_grad <span class="op">=</span> <span class="cn">FALSE</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>indices</dt>
+<dd><p>(array_like) Initial data for the tensor. Can be a list, tuple,        NumPy <code>ndarray</code>, scalar, and other types. Will be cast to a <code>torch_LongTensor</code>        internally. The indices are the coordinates of the non-zero values in the matrix, and thus        should be two-dimensional where the first dimension is the number of tensor dimensions and        the second dimension is the number of non-zero values.</p></dd>
+<dt>values</dt>
+<dd><p>(array_like) Initial values for the tensor. Can be a list, tuple,        NumPy <code>ndarray</code>, scalar, and other types.</p></dd>
+<dt>size</dt>
+<dd><p>(list, tuple, or <code>torch.Size</code>, optional) Size of the sparse tensor. If not        provided the size will be inferred as the minimum size big enough to hold all non-zero        elements.</p></dd>
+<dt>dtype</dt>
+<dd><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if NULL, infers data type from <code>values</code>.</p></dd>
+<dt>device</dt>
+<dd><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if NULL, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></dd>
+<dt>requires_grad</dt>
+<dd><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="sparse-coo-tensor-indices-values-size-null-dtype-null-device-null-requires-grad-false-gt-tensor-">sparse_coo_tensor(indices, values, size=NULL, dtype=NULL, device=NULL, requires_grad=False) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#sparse-coo-tensor-indices-values-size-null-dtype-null-device-null-requires-grad-false-gt-tensor-"></a></h2>
     
 
 
@@ -236,53 +116,48 @@
 with the given <code>values</code>. A sparse tensor can be <code>uncoalesced</code>, in that case, there are duplicate
 coordinates in the indices, and the value at that index is the sum of all duplicate value entries:
 <code>torch_sparse</code>_.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>i</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>2</span>, <span class='fl'>2</span>, <span class='fl'>3</span>, <span class='fl'>1</span>, <span class='fl'>3</span><span class='op'>)</span>, ncol <span class='op'>=</span> <span class='fl'>3</span>, byrow <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span>, dtype<span class='op'>=</span><span class='fu'><a href='torch_dtype.html'>torch_int64</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>v</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>4</span>, <span class='fl'>5</span><span class='op'>)</span>, dtype<span class='op'>=</span><span class='fu'><a href='torch_dtype.html'>torch_float32</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_sparse_coo_tensor</span><span class='op'>(</span><span class='va'>i</span>, <span class='va'>v</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_sparse_coo_tensor</span><span class='op'>(</span><span class='va'>i</span>, <span class='va'>v</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='co'># create empty sparse tensors</span></span>
-<span class='r-in'><span class='va'>S</span> <span class='op'>=</span> <span class='fu'>torch_sparse_coo_tensor</span><span class='op'>(</span></span>
-<span class='r-in'>  <span class='fu'><a href='torch_empty.html'>torch_empty</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>0</span><span class='op'>)</span>, dtype <span class='op'>=</span> <span class='fu'><a href='torch_dtype.html'>torch_int64</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span>, </span>
-<span class='r-in'>  <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/numeric.html'>numeric</a></span><span class='op'>(</span><span class='op'>)</span>, dtype <span class='op'>=</span> <span class='fu'><a href='torch_dtype.html'>torch_float32</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span>, </span>
-<span class='r-in'>  <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>S</span> <span class='op'>=</span> <span class='fu'>torch_sparse_coo_tensor</span><span class='op'>(</span></span>
-<span class='r-in'>  <span class='fu'><a href='torch_empty.html'>torch_empty</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>0</span><span class='op'>)</span>, dtype <span class='op'>=</span> <span class='fu'><a href='torch_dtype.html'>torch_int64</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span>, </span>
-<span class='r-in'>  <span class='fu'><a href='torch_empty.html'>torch_empty</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>0</span>, <span class='fl'>2</span><span class='op'>)</span><span class='op'>)</span>, </span>
-<span class='r-in'>  <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">i</span> <span class="op">=</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/matrix.html" class="external-link">matrix</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">2</span>, <span class="fl">2</span>, <span class="fl">3</span>, <span class="fl">1</span>, <span class="fl">3</span><span class="op">)</span>, ncol <span class="op">=</span> <span class="fl">3</span>, byrow <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span>, dtype<span class="op">=</span><span class="fu"><a href="torch_dtype.html">torch_int64</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">v</span> <span class="op">=</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">4</span>, <span class="fl">5</span><span class="op">)</span>, dtype<span class="op">=</span><span class="fu"><a href="torch_dtype.html">torch_float32</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_sparse_coo_tensor</span><span class="op">(</span><span class="va">i</span>, <span class="va">v</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_sparse_coo_tensor</span><span class="op">(</span><span class="va">i</span>, <span class="va">v</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">4</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="co"># create empty sparse tensors</span></span>
+<span class="r-in"><span class="va">S</span> <span class="op">=</span> <span class="fu">torch_sparse_coo_tensor</span><span class="op">(</span></span>
+<span class="r-in">  <span class="fu"><a href="torch_empty.html">torch_empty</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">0</span><span class="op">)</span>, dtype <span class="op">=</span> <span class="fu"><a href="torch_dtype.html">torch_int64</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span>, </span>
+<span class="r-in">  <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/numeric.html" class="external-link">numeric</a></span><span class="op">(</span><span class="op">)</span>, dtype <span class="op">=</span> <span class="fu"><a href="torch_dtype.html">torch_float32</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span>, </span>
+<span class="r-in">  <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">)</span></span>
+<span class="r-in"><span class="va">S</span> <span class="op">=</span> <span class="fu">torch_sparse_coo_tensor</span><span class="op">(</span></span>
+<span class="r-in">  <span class="fu"><a href="torch_empty.html">torch_empty</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">0</span><span class="op">)</span>, dtype <span class="op">=</span> <span class="fu"><a href="torch_dtype.html">torch_int64</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span>, </span>
+<span class="r-in">  <span class="fu"><a href="torch_empty.html">torch_empty</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">2</span><span class="op">)</span><span class="op">)</span>, </span>
+<span class="r-in">  <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
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+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Split</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/wrapers.R'><code>R/wrapers.R</code></a></small>
-    <div class="hidden name"><code>torch_split.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Split</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
+      <div class="d-none name"><code>torch_split.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Splits the tensor into chunks. Each chunk is a view of the original tensor.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_split</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>split_size</span>, dim <span class='op'>=</span> <span class='fl'>1L</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) tensor to split.</p></td>
-    </tr>
-    <tr>
-      <th>split_size</th>
-      <td><p>(int) size of a single chunk or
-list of sizes for each chunk</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) dimension along which to split the tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_split</span><span class="op">(</span><span class="va">self</span>, <span class="va">split_size</span>, dim <span class="op">=</span> <span class="fl">1L</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) tensor to split.</p></dd>
+<dt>split_size</dt>
+<dd><p>(int) size of a single chunk or
+list of sizes for each chunk</p></dd>
+<dt>dim</dt>
+<dd><p>(int) dimension along which to split the tensor.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>If <code>split_size</code> is an integer type, then <code>tensor</code> will
 be split into equally sized chunks (if possible). Last chunk will be smaller if
 the tensor size along the given dimension <code>dim</code> is not divisible by
@@ -218,32 +104,25 @@ the tensor size along the given dimension <code>dim</code> is not divisible by
 <p>If <code>split_size</code> is a list, then <code>tensor</code> will be split
 into <code>length(split_size)</code> chunks with sizes in <code>dim</code> according
 to <code>split_size_or_sections</code>.</p>
+    </div>
 
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diff --git a/reference/torch_sqrt.html b/reference/torch_sqrt.html
index 20e0ebdd9..3e14373b2 100644
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+++ b/reference/torch_sqrt.html
@@ -1,207 +1,97 @@
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Sqrt</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_sqrt.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Sqrt</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_sqrt.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Sqrt</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_sqrt</span><span class='op'>(</span><span class='va'>self</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="sqrt-input-out-null-gt-tensor-"><a class="anchor" href="#sqrt-input-out-null-gt-tensor-"></a>sqrt(input, out=NULL) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_sqrt</span><span class="op">(</span><span class="va">self</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="sqrt-input-out-null-gt-tensor-">sqrt(input, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#sqrt-input-out-null-gt-tensor-"></a></h2>
     
 
 
@@ -209,46 +99,41 @@
 <p>$$
     \mbox{out}_{i} = \sqrt{\mbox{input}_{i}}
 $$</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='fu'>torch_sqrt</span><span class='op'>(</span><span class='va'>a</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>     nan</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>     nan</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>     nan</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1.0019</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{4} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="fu">torch_sqrt</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>     nan</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>     nan</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>     nan</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.7801</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Square</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_square.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Square</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_square.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Square</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_square</span><span class='op'>(</span><span class='va'>self</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="square-input-out-null-gt-tensor-"><a class="anchor" href="#square-input-out-null-gt-tensor-"></a>square(input, out=NULL) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_square</span><span class="op">(</span><span class="va">self</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="square-input-out-null-gt-tensor-">square(input, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#square-input-out-null-gt-tensor-"></a></h2>
     
 
 
 <p>Returns a new tensor with the square of the elements of <code>input</code>.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='fu'>torch_square</span><span class='op'>(</span><span class='va'>a</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  2.0116</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.0732</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.3001</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1.1692</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{4} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="fu">torch_square</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2.0539</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.2019</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  3.3780</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0784</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4} ]</span>
+</code></pre></div>
+    </div>
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Squeeze</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_squeeze.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Squeeze</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_squeeze.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Squeeze</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_squeeze</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>dim</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int, optional) if given, the input will be squeezed only in           this dimension</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_squeeze</span><span class="op">(</span><span class="va">self</span>, <span class="va">dim</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+<dt>dim</dt>
+<dd><p>(int, optional) if given, the input will be squeezed only in           this dimension</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p>The returned tensor shares the storage with the input tensor,
 so changing the contents of one will change the contents of the other.</p>
-    <h2 class="hasAnchor" id="squeeze-input-dim-null-out-null-gt-tensor-"><a class="anchor" href="#squeeze-input-dim-null-out-null-gt-tensor-"></a>squeeze(input, dim=NULL, out=NULL) -&gt; Tensor </h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="squeeze-input-dim-null-out-null-gt-tensor-">squeeze(input, dim=NULL, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#squeeze-input-dim-null-out-null-gt-tensor-"></a></h2>
     
 
 
@@ -221,58 +110,53 @@ will be of shape: \((A \times B \times C \times D)\).</p>
 dimension. If <code>input</code> is of shape: \((A \times 1 \times B)\),
 <code>squeeze(input, 0)</code> leaves the tensor unchanged, but <code>squeeze(input, 1)</code>
 will squeeze the tensor to the shape \((A \times B)\).</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_zeros.html'>torch_zeros</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>1</span>, <span class='fl'>2</span>, <span class='fl'>1</span>, <span class='fl'>2</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>x</span></span>
-<span class='r-in'><span class='va'>y</span> <span class='op'>=</span> <span class='fu'>torch_squeeze</span><span class='op'>(</span><span class='va'>x</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>y</span></span>
-<span class='r-in'><span class='va'>y</span> <span class='op'>=</span> <span class='fu'>torch_squeeze</span><span class='op'>(</span><span class='va'>x</span>, <span class='fl'>1</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>y</span></span>
-<span class='r-in'><span class='va'>y</span> <span class='op'>=</span> <span class='fu'>torch_squeeze</span><span class='op'>(</span><span class='va'>x</span>, <span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>y</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> (1,1,.,.) = </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   0  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> (2,1,.,.) = </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   0  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> (1,2,.,.) = </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   0  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> (2,2,.,.) = </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   0  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{2,2,1,2} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">x</span> <span class="op">=</span> <span class="fu"><a href="torch_zeros.html">torch_zeros</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">1</span>, <span class="fl">2</span>, <span class="fl">1</span>, <span class="fl">2</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">x</span></span>
+<span class="r-in"><span class="va">y</span> <span class="op">=</span> <span class="fu">torch_squeeze</span><span class="op">(</span><span class="va">x</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">y</span></span>
+<span class="r-in"><span class="va">y</span> <span class="op">=</span> <span class="fu">torch_squeeze</span><span class="op">(</span><span class="va">x</span>, <span class="fl">1</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">y</span></span>
+<span class="r-in"><span class="va">y</span> <span class="op">=</span> <span class="fu">torch_squeeze</span><span class="op">(</span><span class="va">x</span>, <span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">y</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> (1,1,.,.) = </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> (2,1,.,.) = </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> (1,2,.,.) = </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> (2,2,.,.) = </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2,2,1,2} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Stack</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_stack.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Stack</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_stack.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Stack</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_stack</span><span class='op'>(</span><span class='va'>tensors</span>, dim <span class='op'>=</span> <span class='fl'>1L</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>tensors</th>
-      <td><p>(sequence of Tensors) sequence of tensors to concatenate</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) dimension to insert. Has to be between 0 and the number        of dimensions of concatenated tensors (inclusive)</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="stack-tensors-dim-out-null-gt-tensor-"><a class="anchor" href="#stack-tensors-dim-out-null-gt-tensor-"></a>stack(tensors, dim=0, out=NULL) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_stack</span><span class="op">(</span><span class="va">tensors</span>, dim <span class="op">=</span> <span class="fl">1L</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>tensors</dt>
+<dd><p>(sequence of Tensors) sequence of tensors to concatenate</p></dd>
+<dt>dim</dt>
+<dd><p>(int) dimension to insert. Has to be between 0 and the number        of dimensions of concatenated tensors (inclusive)</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="stack-tensors-dim-out-null-gt-tensor-">stack(tensors, dim=0, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#stack-tensors-dim-out-null-gt-tensor-"></a></h2>
     
 
 
 <p>Concatenates sequence of tensors along a new dimension.</p>
 <p>All tensors need to be of the same size.</p>
+    </div>
 
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diff --git a/reference/torch_std.html b/reference/torch_std.html
index a78b73d31..e4937f0a8 100644
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Std</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_std.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Std</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_std.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Std</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_std</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>dim</span>, <span class='va'>correction</span>, unbiased <span class='op'>=</span> <span class='cn'>TRUE</span>, keepdim <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int or tuple of ints) the dimension or dimensions to reduce.</p></td>
-    </tr>
-    <tr>
-      <th>correction</th>
-      <td><p>The type of correction.</p></td>
-    </tr>
-    <tr>
-      <th>unbiased</th>
-      <td><p>(bool) whether to use the unbiased estimation or not</p></td>
-    </tr>
-    <tr>
-      <th>keepdim</th>
-      <td><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="std-input-unbiased-true-gt-tensor-"><a class="anchor" href="#std-input-unbiased-true-gt-tensor-"></a>std(input, unbiased=TRUE) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_std</span><span class="op">(</span><span class="va">self</span>, <span class="va">dim</span>, <span class="va">correction</span>, unbiased <span class="op">=</span> <span class="cn">TRUE</span>, keepdim <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+<dt>dim</dt>
+<dd><p>(int or tuple of ints) the dimension or dimensions to reduce.</p></dd>
+<dt>correction</dt>
+<dd><p>The type of correction.</p></dd>
+<dt>unbiased</dt>
+<dd><p>(bool) whether to use the unbiased estimation or not</p></dd>
+<dt>keepdim</dt>
+<dd><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="std-input-unbiased-true-gt-tensor-">std(input, unbiased=TRUE) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#std-input-unbiased-true-gt-tensor-"></a></h2>
     
 
 
 <p>Returns the standard-deviation of all elements in the <code>input</code> tensor.</p>
 <p>If <code>unbiased</code> is <code>FALSE</code>, then the standard-deviation will be calculated
 via the biased estimator. Otherwise, Bessel's correction will be used.</p>
-    <h2 class="hasAnchor" id="std-input-dim-unbiased-true-keepdim-false-out-null-gt-tensor-"><a class="anchor" href="#std-input-dim-unbiased-true-keepdim-false-out-null-gt-tensor-"></a>std(input, dim, unbiased=TRUE, keepdim=False, out=NULL) -&gt; Tensor </h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="std-input-dim-unbiased-true-keepdim-false-out-null-gt-tensor-">std(input, dim, unbiased=TRUE, keepdim=False, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#std-input-dim-unbiased-true-keepdim-false-out-null-gt-tensor-"></a></h2>
     
 
 
@@ -234,55 +117,50 @@ dimension <code>dim</code>. If <code>dim</code> is a list of dimensions,
 reduce over all of them.</p>
 <p>If <code>keepdim</code> is <code>TRUE</code>, the output tensor is of the same size
 as <code>input</code> except in the dimension(s) <code>dim</code> where it is of size 1.
-Otherwise, <code>dim</code> is squeezed (see <code><a href='torch_squeeze.html'>torch_squeeze</a></code>), resulting in the
+Otherwise, <code>dim</code> is squeezed (see <code><a href="torch_squeeze.html">torch_squeeze</a></code>), resulting in the
 output tensor having 1 (or <code>len(dim)</code>) fewer dimension(s).</p>
 <p>If <code>unbiased</code> is <code>FALSE</code>, then the standard-deviation will be calculated
 via the biased estimator. Otherwise, Bessel's correction will be used.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='fu'>torch_std</span><span class='op'>(</span><span class='va'>a</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span>, <span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='fu'>torch_std</span><span class='op'>(</span><span class='va'>a</span>, dim<span class='op'>=</span><span class='fl'>1</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.5989</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.6307</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.3103</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.9362</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{4} ]</span>
-</pre>
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-      <h2 data-toc-skip>Contents</h2>
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+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="fu">torch_std</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span>
+<span class="r-in"></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span>, <span class="fl">4</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="fu">torch_std</span><span class="op">(</span><span class="va">a</span>, dim<span class="op">=</span><span class="fl">1</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.6745</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.8308</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.3051</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.9916</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Std_mean</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_std_mean.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Std_mean</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_std_mean.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Std_mean</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_std_mean</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>dim</span>, <span class='va'>correction</span>, unbiased <span class='op'>=</span> <span class='cn'>TRUE</span>, keepdim <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int or tuple of ints) the dimension or dimensions to reduce.</p></td>
-    </tr>
-    <tr>
-      <th>correction</th>
-      <td><p>The type of correction.</p></td>
-    </tr>
-    <tr>
-      <th>unbiased</th>
-      <td><p>(bool) whether to use the unbiased estimation or not</p></td>
-    </tr>
-    <tr>
-      <th>keepdim</th>
-      <td><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="std-mean-input-unbiased-true-gt-tensor-tensor-"><a class="anchor" href="#std-mean-input-unbiased-true-gt-tensor-tensor-"></a>std_mean(input, unbiased=TRUE) -&gt; (Tensor, Tensor) </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_std_mean</span><span class="op">(</span><span class="va">self</span>, <span class="va">dim</span>, <span class="va">correction</span>, unbiased <span class="op">=</span> <span class="cn">TRUE</span>, keepdim <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+<dt>dim</dt>
+<dd><p>(int or tuple of ints) the dimension or dimensions to reduce.</p></dd>
+<dt>correction</dt>
+<dd><p>The type of correction.</p></dd>
+<dt>unbiased</dt>
+<dd><p>(bool) whether to use the unbiased estimation or not</p></dd>
+<dt>keepdim</dt>
+<dd><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="std-mean-input-unbiased-true-gt-tensor-tensor-">std_mean(input, unbiased=TRUE) -&gt; (Tensor, Tensor) <a class="anchor" aria-label="anchor" href="#std-mean-input-unbiased-true-gt-tensor-tensor-"></a></h2>
     
 
 
 <p>Returns the standard-deviation and mean of all elements in the <code>input</code> tensor.</p>
 <p>If <code>unbiased</code> is <code>FALSE</code>, then the standard-deviation will be calculated
 via the biased estimator. Otherwise, Bessel's correction will be used.</p>
-    <h2 class="hasAnchor" id="std-mean-input-dim-unbiased-true-keepdim-false-gt-tensor-tensor-"><a class="anchor" href="#std-mean-input-dim-unbiased-true-keepdim-false-gt-tensor-tensor-"></a>std_mean(input, dim, unbiased=TRUE, keepdim=False) -&gt; (Tensor, Tensor) </h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="std-mean-input-dim-unbiased-true-keepdim-false-gt-tensor-tensor-">std_mean(input, dim, unbiased=TRUE, keepdim=False) -&gt; (Tensor, Tensor) <a class="anchor" aria-label="anchor" href="#std-mean-input-dim-unbiased-true-keepdim-false-gt-tensor-tensor-"></a></h2>
     
 
 
@@ -234,65 +117,60 @@ dimension <code>dim</code>. If <code>dim</code> is a list of dimensions,
 reduce over all of them.</p>
 <p>If <code>keepdim</code> is <code>TRUE</code>, the output tensor is of the same size
 as <code>input</code> except in the dimension(s) <code>dim</code> where it is of size 1.
-Otherwise, <code>dim</code> is squeezed (see <code><a href='torch_squeeze.html'>torch_squeeze</a></code>), resulting in the
+Otherwise, <code>dim</code> is squeezed (see <code><a href="torch_squeeze.html">torch_squeeze</a></code>), resulting in the
 output tensor having 1 (or <code>len(dim)</code>) fewer dimension(s).</p>
 <p>If <code>unbiased</code> is <code>FALSE</code>, then the standard-deviation will be calculated
 via the biased estimator. Otherwise, Bessel's correction will be used.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='fu'>torch_std_mean</span><span class='op'>(</span><span class='va'>a</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span>, <span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='fu'>torch_std_mean</span><span class='op'>(</span><span class='va'>a</span>, <span class='fl'>1</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [[1]]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.7103</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1.7148</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.3854</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.8706</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{4} ]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [[2]]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.0863</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -1.1478</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.0663</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.5215</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{4} ]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="fu">torch_std_mean</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span>
+<span class="r-in"></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span>, <span class="fl">4</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="fu">torch_std_mean</span><span class="op">(</span><span class="va">a</span>, <span class="fl">1</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [[1]]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.2808</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.9155</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.2249</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.5778</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4} ]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [[2]]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.0317</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.2710</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.1895</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.1946</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4} ]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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-      <div class="copyright">
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+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Stft</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/wrapers.R'><code>R/wrapers.R</code></a></small>
-    <div class="hidden name"><code>torch_stft.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Stft</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
+      <div class="d-none name"><code>torch_stft.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Stft</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_stft</span><span class='op'>(</span>
-  <span class='va'>input</span>,
-  <span class='va'>n_fft</span>,
-  hop_length <span class='op'>=</span> <span class='cn'>NULL</span>,
-  win_length <span class='op'>=</span> <span class='cn'>NULL</span>,
-  window <span class='op'>=</span> <span class='cn'>NULL</span>,
-  center <span class='op'>=</span> <span class='cn'>TRUE</span>,
-  pad_mode <span class='op'>=</span> <span class='st'>"reflect"</span>,
-  normalized <span class='op'>=</span> <span class='cn'>FALSE</span>,
-  onesided <span class='op'>=</span> <span class='cn'>TRUE</span>,
-  return_complex <span class='op'>=</span> <span class='cn'>NULL</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the input tensor</p></td>
-    </tr>
-    <tr>
-      <th>n_fft</th>
-      <td><p>(int) size of Fourier transform</p></td>
-    </tr>
-    <tr>
-      <th>hop_length</th>
-      <td><p>(int, optional) the distance between neighboring sliding window
-frames. Default: <code>NULL</code> (treated as equal to <code>floor(n_fft / 4)</code>)</p></td>
-    </tr>
-    <tr>
-      <th>win_length</th>
-      <td><p>(int, optional) the size of window frame and STFT filter.
-Default: <code>NULL</code>  (treated as equal to <code>n_fft</code>)</p></td>
-    </tr>
-    <tr>
-      <th>window</th>
-      <td><p>(Tensor, optional) the optional window function.
-Default: <code>NULL</code> (treated as window of all \(1\) s)</p></td>
-    </tr>
-    <tr>
-      <th>center</th>
-      <td><p>(bool, optional) whether to pad <code>input</code> on both sides so
-that the \(t\)-th frame is centered at time \(t \times \mbox{hop\_length}\).
-Default: <code>TRUE</code></p></td>
-    </tr>
-    <tr>
-      <th>pad_mode</th>
-      <td><p>(string, optional) controls the padding method used when
-<code>center</code> is <code>TRUE</code>. Default: <code>"reflect"</code></p></td>
-    </tr>
-    <tr>
-      <th>normalized</th>
-      <td><p>(bool, optional) controls whether to return the normalized
-STFT results Default: <code>FALSE</code></p></td>
-    </tr>
-    <tr>
-      <th>onesided</th>
-      <td><p>(bool, optional) controls whether to return half of results to
-avoid redundancy Default: <code>TRUE</code></p></td>
-    </tr>
-    <tr>
-      <th>return_complex</th>
-      <td><p>(bool, optional) controls whether to return complex tensors
-or not.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="short-time-fourier-transform-stft-"><a class="anchor" href="#short-time-fourier-transform-stft-"></a>Short-time Fourier transform (STFT). </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_stft</span><span class="op">(</span>
+  <span class="va">input</span>,
+  <span class="va">n_fft</span>,
+  hop_length <span class="op">=</span> <span class="cn">NULL</span>,
+  win_length <span class="op">=</span> <span class="cn">NULL</span>,
+  window <span class="op">=</span> <span class="cn">NULL</span>,
+  center <span class="op">=</span> <span class="cn">TRUE</span>,
+  pad_mode <span class="op">=</span> <span class="st">"reflect"</span>,
+  normalized <span class="op">=</span> <span class="cn">FALSE</span>,
+  onesided <span class="op">=</span> <span class="cn">TRUE</span>,
+  return_complex <span class="op">=</span> <span class="cn">NULL</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>(Tensor) the input tensor</p></dd>
+<dt>n_fft</dt>
+<dd><p>(int) size of Fourier transform</p></dd>
+<dt>hop_length</dt>
+<dd><p>(int, optional) the distance between neighboring sliding window
+frames. Default: <code>NULL</code> (treated as equal to <code>floor(n_fft / 4)</code>)</p></dd>
+<dt>win_length</dt>
+<dd><p>(int, optional) the size of window frame and STFT filter.
+Default: <code>NULL</code>  (treated as equal to <code>n_fft</code>)</p></dd>
+<dt>window</dt>
+<dd><p>(Tensor, optional) the optional window function.
+Default: <code>NULL</code> (treated as window of all \(1\) s)</p></dd>
+<dt>center</dt>
+<dd><p>(bool, optional) whether to pad <code>input</code> on both sides so
+that the \(t\)-th frame is centered at time \(t \times \mbox{hop\_length}\).
+Default: <code>TRUE</code></p></dd>
+<dt>pad_mode</dt>
+<dd><p>(string, optional) controls the padding method used when
+<code>center</code> is <code>TRUE</code>. Default: <code>"reflect"</code></p></dd>
+<dt>normalized</dt>
+<dd><p>(bool, optional) controls whether to return the normalized
+STFT results Default: <code>FALSE</code></p></dd>
+<dt>onesided</dt>
+<dd><p>(bool, optional) controls whether to return half of results to
+avoid redundancy Default: <code>TRUE</code></p></dd>
+<dt>return_complex</dt>
+<dd><p>(bool, optional) controls whether to return complex tensors
+or not.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="short-time-fourier-transform-stft-">Short-time Fourier transform (STFT). <a class="anchor" aria-label="anchor" href="#short-time-fourier-transform-stft-"></a></h2>
     
 
 
-<p>Short-time Fourier transform (STFT).</p><pre>Ignoring the optional batch dimension, this method computes the following
+<p>Short-time Fourier transform (STFT).</p><div class="sourceCode"><pre><code>Ignoring the optional batch dimension, this method computes the following
 expression:
-</pre>
+</code></pre></div>
 
 <p>$$
         X[m, \omega] = \sum_{k = 0}^{\mbox{win\_length-1}}%
@@ -272,7 +144,7 @@ expression:
 $$
 where \(m\) is the index of the sliding window, and \(\omega\) is
 the frequency that \(0 \leq \omega &lt; \mbox{n\_fft}\). When
-<code>onesided</code> is the default value <code>TRUE</code>,</p><pre>* `input` must be either a 1-D time sequence or a 2-D batch of time
+<code>onesided</code> is the default value <code>TRUE</code>,</p><div class="sourceCode"><pre><code>* `input` must be either a 1-D time sequence or a 2-D batch of time
   sequences.
 
 * If `hop_length` is `NULL` (default), it is treated as equal to
@@ -310,40 +182,34 @@ batch size of `input`, \eqn{N} is the number of frequencies where
 STFT is applied, \eqn{T} is the total number of frames used, and each pair
 in the last dimension represents a complex number as the real part and the
 imaginary part.
-</pre>
-
-    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
+</code></pre></div>
 
+    </div>
+    <div class="section level2">
+    <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></a></h2>
     
 
 <p>This function changed signature at version 0.4.1. Calling with the
 previous signature may cause error or return incorrect result.</p>
+    </div>
 
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+    <footer><div class="pkgdown-footer-left">
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+    
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Sub</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_sub.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Sub</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_sub.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Sub</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_sub</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>other</span>, alpha <span class='op'>=</span> <span class='fl'>1L</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Tensor or Scalar) the tensor or scalar to subtract from <code>input</code></p></td>
-    </tr>
-    <tr>
-      <th>alpha</th>
-      <td><p>the scalar multiplier for other</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="sub-input-other-alpha-out-none-gt-tensor-"><a class="anchor" href="#sub-input-other-alpha-out-none-gt-tensor-"></a>sub(input, other, *, alpha=1, out=None) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_sub</span><span class="op">(</span><span class="va">self</span>, <span class="va">other</span>, alpha <span class="op">=</span> <span class="fl">1L</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+<dt>other</dt>
+<dd><p>(Tensor or Scalar) the tensor or scalar to subtract from <code>input</code></p></dd>
+<dt>alpha</dt>
+<dd><p>the scalar multiplier for other</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="sub-input-other-alpha-out-none-gt-tensor-">sub(input, other, *, alpha=1, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#sub-input-other-alpha-out-none-gt-tensor-"></a></h2>
     
 
 
@@ -219,44 +105,39 @@
 $$</p>
 <p>Supports broadcasting to a common shape ,
 type promotion , and integer, float, and complex inputs.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>2</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>b</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>0</span>, <span class='fl'>1</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_sub</span><span class='op'>(</span><span class='va'>a</span>, <span class='va'>b</span>, alpha<span class='op'>=</span><span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{2} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">2</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">b</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_sub</span><span class="op">(</span><span class="va">a</span>, <span class="va">b</span>, alpha<span class="op">=</span><span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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index 4ce0a412c..8db19089d 100644
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-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_subtract.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Subtract</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
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     <p>Subtract</p>
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-    <pre class="usage"><span class='fu'>torch_subtract</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>other</span>, alpha <span class='op'>=</span> <span class='fl'>1L</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
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-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Tensor or Scalar) the tensor or scalar to subtract from <code>input</code></p></td>
-    </tr>
-    <tr>
-      <th>alpha</th>
-      <td><p>the scalar multiplier for other</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="subtract-input-other-alpha-out-none-gt-tensor-"><a class="anchor" href="#subtract-input-other-alpha-out-none-gt-tensor-"></a>subtract(input, other, *, alpha=1, out=None) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_subtract</span><span class="op">(</span><span class="va">self</span>, <span class="va">other</span>, alpha <span class="op">=</span> <span class="fl">1L</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+<dt>other</dt>
+<dd><p>(Tensor or Scalar) the tensor or scalar to subtract from <code>input</code></p></dd>
+<dt>alpha</dt>
+<dd><p>the scalar multiplier for other</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="subtract-input-other-alpha-out-none-gt-tensor-">subtract(input, other, *, alpha=1, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#subtract-input-other-alpha-out-none-gt-tensor-"></a></h2>
     
 
 
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+    
+  </div>
+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Sum</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_sum.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Sum</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_sum.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Sum</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_sum</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>dim</span>, keepdim <span class='op'>=</span> <span class='cn'>FALSE</span>, dtype <span class='op'>=</span> <span class='cn'>NULL</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int or tuple of ints) the dimension or dimensions to reduce.</p></td>
-    </tr>
-    <tr>
-      <th>keepdim</th>
-      <td><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        If specified, the input tensor is casted to <code>dtype</code> before the operation        is performed. This is useful for preventing data type overflows. Default: NULL.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="sum-input-dtype-null-gt-tensor-"><a class="anchor" href="#sum-input-dtype-null-gt-tensor-"></a>sum(input, dtype=NULL) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_sum</span><span class="op">(</span><span class="va">self</span>, <span class="va">dim</span>, keepdim <span class="op">=</span> <span class="cn">FALSE</span>, dtype <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+<dt>dim</dt>
+<dd><p>(int or tuple of ints) the dimension or dimensions to reduce.</p></dd>
+<dt>keepdim</dt>
+<dd><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></dd>
+<dt>dtype</dt>
+<dd><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        If specified, the input tensor is casted to <code>dtype</code> before the operation        is performed. This is useful for preventing data type overflows. Default: NULL.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="sum-input-dtype-null-gt-tensor-">sum(input, dtype=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#sum-input-dtype-null-gt-tensor-"></a></h2>
     
 
 
 <p>Returns the sum of all elements in the <code>input</code> tensor.</p>
-    <h2 class="hasAnchor" id="sum-input-dim-keepdim-false-dtype-null-gt-tensor-"><a class="anchor" href="#sum-input-dim-keepdim-false-dtype-null-gt-tensor-"></a>sum(input, dim, keepdim=False, dtype=NULL) -&gt; Tensor </h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="sum-input-dim-keepdim-false-dtype-null-gt-tensor-">sum(input, dim, keepdim=False, dtype=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#sum-input-dim-keepdim-false-dtype-null-gt-tensor-"></a></h2>
     
 
 
@@ -228,57 +113,52 @@ dimension <code>dim</code>. If <code>dim</code> is a list of dimensions,
 reduce over all of them.</p>
 <p>If <code>keepdim</code> is <code>TRUE</code>, the output tensor is of the same size
 as <code>input</code> except in the dimension(s) <code>dim</code> where it is of size 1.
-Otherwise, <code>dim</code> is squeezed (see <code><a href='torch_squeeze.html'>torch_squeeze</a></code>), resulting in the
+Otherwise, <code>dim</code> is squeezed (see <code><a href="torch_squeeze.html">torch_squeeze</a></code>), resulting in the
 output tensor having 1 (or <code>len(dim)</code>) fewer dimension(s).</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='fu'>torch_sum</span><span class='op'>(</span><span class='va'>a</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span>, <span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='fu'>torch_sum</span><span class='op'>(</span><span class='va'>a</span>, <span class='fl'>1</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>b</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>4</span> <span class='op'>*</span> <span class='fl'>5</span> <span class='op'>*</span> <span class='fl'>6</span><span class='op'>)</span><span class='op'>$</span><span class='fu'>view</span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span>, <span class='fl'>5</span>, <span class='fl'>6</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_sum</span><span class='op'>(</span><span class='va'>b</span>, <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>1</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1160</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1180</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1200</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1220</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1240</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1260</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{6} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="fu">torch_sum</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span>
+<span class="r-in"></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span>, <span class="fl">4</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="fu">torch_sum</span><span class="op">(</span><span class="va">a</span>, <span class="fl">1</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">b</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_arange.html">torch_arange</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">4</span> <span class="op">*</span> <span class="fl">5</span> <span class="op">*</span> <span class="fl">6</span><span class="op">)</span><span class="op">$</span><span class="fu">view</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span>, <span class="fl">5</span>, <span class="fl">6</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_sum</span><span class="op">(</span><span class="va">b</span>, <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">1</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1160</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1180</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1200</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1220</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1240</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1260</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{6} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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-      <div class="copyright">
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+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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+<div class="pkgdown-footer-right">
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+  </div>
+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Svd</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_svd.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Svd</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_svd.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Svd</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_svd</span><span class='op'>(</span><span class='va'>self</span>, some <span class='op'>=</span> <span class='cn'>TRUE</span>, compute_uv <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor of size \((*, m, n)\) where <code>*</code> is zero or more                    batch dimensions consisting of \(m \times n\) matrices.</p></td>
-    </tr>
-    <tr>
-      <th>some</th>
-      <td><p>(bool, optional) controls the shape of returned <code>U</code> and <code>V</code></p></td>
-    </tr>
-    <tr>
-      <th>compute_uv</th>
-      <td><p>(bool, optional) option whether to compute <code>U</code> and <code>V</code> or not</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_svd</span><span class="op">(</span><span class="va">self</span>, some <span class="op">=</span> <span class="cn">TRUE</span>, compute_uv <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor of size \((*, m, n)\) where <code>*</code> is zero or more                    batch dimensions consisting of \(m \times n\) matrices.</p></dd>
+<dt>some</dt>
+<dd><p>(bool, optional) controls the shape of returned <code>U</code> and <code>V</code></p></dd>
+<dt>compute_uv</dt>
+<dd><p>(bool, optional) option whether to compute <code>U</code> and <code>V</code> or not</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p>The singular values are returned in descending order. If <code>input</code> is a batch of matrices,
 then the singular values of each matrix in the batch is returned in descending order.</p>
 <p>The implementation of SVD on CPU uses the LAPACK routine <code>?gesdd</code> (a divide-and-conquer
@@ -228,8 +114,9 @@ and <code>V[..., :, min(m, n):]</code> will be ignored in backward as those vect
 can be arbitrary bases of the subspaces.</p>
 <p>When <code>compute_uv</code> = <code>FALSE</code>, backward cannot be performed since <code>U</code> and <code>V</code>
 from the forward pass is required for the backward operation.</p>
-    <h2 class="hasAnchor" id="svd-input-some-true-compute-uv-true-gt-tensor-tensor-tensor-"><a class="anchor" href="#svd-input-some-true-compute-uv-true-gt-tensor-tensor-tensor-"></a>svd(input, some=TRUE, compute_uv=TRUE) -&gt; (Tensor, Tensor, Tensor) </h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="svd-input-some-true-compute-uv-true-gt-tensor-tensor-tensor-">svd(input, some=TRUE, compute_uv=TRUE) -&gt; (Tensor, Tensor, Tensor) <a class="anchor" aria-label="anchor" href="#svd-input-some-true-compute-uv-true-gt-tensor-tensor-tensor-"></a></h2>
     
 
 
@@ -241,53 +128,48 @@ i.e., if the last two dimensions of <code>input</code> are <code>m</code> and <c
 <code>U</code> and <code>V</code> matrices will contain only \(min(n, m)\) orthonormal columns.</p>
 <p>If <code>compute_uv</code> is <code>FALSE</code>, the returned <code>U</code> and <code>V</code> matrices will be zero matrices
 of shape \((m \times m)\) and \((n \times n)\) respectively. <code>some</code> will be ignored here.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>5</span>, <span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='va'>out</span> <span class='op'>=</span> <span class='fu'>torch_svd</span><span class='op'>(</span><span class='va'>a</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>u</span> <span class='op'>=</span> <span class='va'>out</span><span class='op'>[[</span><span class='fl'>1</span><span class='op'>]</span><span class='op'>]</span></span>
-<span class='r-in'><span class='va'>s</span> <span class='op'>=</span> <span class='va'>out</span><span class='op'>[[</span><span class='fl'>2</span><span class='op'>]</span><span class='op'>]</span></span>
-<span class='r-in'><span class='va'>v</span> <span class='op'>=</span> <span class='va'>out</span><span class='op'>[[</span><span class='fl'>3</span><span class='op'>]</span><span class='op'>]</span></span>
-<span class='r-in'><span class='fu'><a href='torch_dist.html'>torch_dist</a></span><span class='op'>(</span><span class='va'>a</span>, <span class='fu'><a href='torch_mm.html'>torch_mm</a></span><span class='op'>(</span><span class='fu'><a href='torch_mm.html'>torch_mm</a></span><span class='op'>(</span><span class='va'>u</span>, <span class='fu'><a href='torch_diag.html'>torch_diag</a></span><span class='op'>(</span><span class='va'>s</span><span class='op'>)</span><span class='op'>)</span>, <span class='va'>v</span><span class='op'>$</span><span class='fu'>t</span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a_big</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>7</span>, <span class='fl'>5</span>, <span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>out</span> <span class='op'>=</span> <span class='fu'>torch_svd</span><span class='op'>(</span><span class='va'>a_big</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>u</span> <span class='op'>=</span> <span class='va'>out</span><span class='op'>[[</span><span class='fl'>1</span><span class='op'>]</span><span class='op'>]</span></span>
-<span class='r-in'><span class='va'>s</span> <span class='op'>=</span> <span class='va'>out</span><span class='op'>[[</span><span class='fl'>2</span><span class='op'>]</span><span class='op'>]</span></span>
-<span class='r-in'><span class='va'>v</span> <span class='op'>=</span> <span class='va'>out</span><span class='op'>[[</span><span class='fl'>3</span><span class='op'>]</span><span class='op'>]</span></span>
-<span class='r-in'><span class='fu'><a href='torch_dist.html'>torch_dist</a></span><span class='op'>(</span><span class='va'>a_big</span>, <span class='fu'><a href='torch_matmul.html'>torch_matmul</a></span><span class='op'>(</span><span class='fu'><a href='torch_matmul.html'>torch_matmul</a></span><span class='op'>(</span><span class='va'>u</span>, <span class='fu'><a href='torch_diag_embed.html'>torch_diag_embed</a></span><span class='op'>(</span><span class='va'>s</span><span class='op'>)</span><span class='op'>)</span>, <span class='va'>v</span><span class='op'>$</span><span class='fu'>transpose</span><span class='op'>(</span><span class='op'>-</span><span class='fl'>2</span>, <span class='op'>-</span><span class='fl'>1</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> 3.21306e-06</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">5</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="va">out</span> <span class="op">=</span> <span class="fu">torch_svd</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">u</span> <span class="op">=</span> <span class="va">out</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span></span>
+<span class="r-in"><span class="va">s</span> <span class="op">=</span> <span class="va">out</span><span class="op">[[</span><span class="fl">2</span><span class="op">]</span><span class="op">]</span></span>
+<span class="r-in"><span class="va">v</span> <span class="op">=</span> <span class="va">out</span><span class="op">[[</span><span class="fl">3</span><span class="op">]</span><span class="op">]</span></span>
+<span class="r-in"><span class="fu"><a href="torch_dist.html">torch_dist</a></span><span class="op">(</span><span class="va">a</span>, <span class="fu"><a href="torch_mm.html">torch_mm</a></span><span class="op">(</span><span class="fu"><a href="torch_mm.html">torch_mm</a></span><span class="op">(</span><span class="va">u</span>, <span class="fu"><a href="torch_diag.html">torch_diag</a></span><span class="op">(</span><span class="va">s</span><span class="op">)</span><span class="op">)</span>, <span class="va">v</span><span class="op">$</span><span class="fu">t</span><span class="op">(</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a_big</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">7</span>, <span class="fl">5</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">out</span> <span class="op">=</span> <span class="fu">torch_svd</span><span class="op">(</span><span class="va">a_big</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">u</span> <span class="op">=</span> <span class="va">out</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span></span>
+<span class="r-in"><span class="va">s</span> <span class="op">=</span> <span class="va">out</span><span class="op">[[</span><span class="fl">2</span><span class="op">]</span><span class="op">]</span></span>
+<span class="r-in"><span class="va">v</span> <span class="op">=</span> <span class="va">out</span><span class="op">[[</span><span class="fl">3</span><span class="op">]</span><span class="op">]</span></span>
+<span class="r-in"><span class="fu"><a href="torch_dist.html">torch_dist</a></span><span class="op">(</span><span class="va">a_big</span>, <span class="fu"><a href="torch_matmul.html">torch_matmul</a></span><span class="op">(</span><span class="fu"><a href="torch_matmul.html">torch_matmul</a></span><span class="op">(</span><span class="va">u</span>, <span class="fu"><a href="torch_diag_embed.html">torch_diag_embed</a></span><span class="op">(</span><span class="va">s</span><span class="op">)</span><span class="op">)</span>, <span class="va">v</span><span class="op">$</span><span class="fu">transpose</span><span class="op">(</span><span class="op">-</span><span class="fl">2</span>, <span class="op">-</span><span class="fl">1</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2.9447e-06</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Symeig</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_symeig.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Symeig</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_symeig.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Symeig</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_symeig</span><span class='op'>(</span><span class='va'>self</span>, eigenvectors <span class='op'>=</span> <span class='cn'>FALSE</span>, upper <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor of size \((*, n, n)\) where <code>*</code> is zero or more                    batch dimensions consisting of symmetric matrices.</p></td>
-    </tr>
-    <tr>
-      <th>eigenvectors</th>
-      <td><p>(boolean, optional) controls whether eigenvectors have to be computed</p></td>
-    </tr>
-    <tr>
-      <th>upper</th>
-      <td><p>(boolean, optional) controls whether to consider upper-triangular or lower-triangular region</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_symeig</span><span class="op">(</span><span class="va">self</span>, eigenvectors <span class="op">=</span> <span class="cn">FALSE</span>, upper <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor of size \((*, n, n)\) where <code>*</code> is zero or more                    batch dimensions consisting of symmetric matrices.</p></dd>
+<dt>eigenvectors</dt>
+<dd><p>(boolean, optional) controls whether eigenvectors have to be computed</p></dd>
+<dt>upper</dt>
+<dd><p>(boolean, optional) controls whether to consider upper-triangular or lower-triangular region</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p>The eigenvalues are returned in ascending order. If <code>input</code> is a batch of matrices,
 then the eigenvalues of each matrix in the batch is returned in ascending order.</p>
 <p>Irrespective of the original strides, the returned matrix <code>V</code> will
@@ -217,8 +103,9 @@ be transposed, i.e. with strides <code>V.contiguous().transpose(-1, -2).stride()
 <p>Extra care needs to be taken when backward through outputs. Such
 operation is really only stable when all eigenvalues are distinct.
 Otherwise, <code>NaN</code> can appear as the gradients are not properly defined.</p>
-    <h2 class="hasAnchor" id="symeig-input-eigenvectors-false-upper-true-gt-tensor-tensor-"><a class="anchor" href="#symeig-input-eigenvectors-false-upper-true-gt-tensor-tensor-"></a>symeig(input, eigenvectors=False, upper=TRUE) -&gt; (Tensor, Tensor) </h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="symeig-input-eigenvectors-false-upper-true-gt-tensor-tensor-">symeig(input, eigenvectors=False, upper=TRUE) -&gt; (Tensor, Tensor) <a class="anchor" aria-label="anchor" href="#symeig-input-eigenvectors-false-upper-true-gt-tensor-tensor-"></a></h2>
     
 
 
@@ -234,52 +121,47 @@ both eigenvalues and eigenvectors are computed.</p>
 <p>Since the input matrix <code>input</code> is supposed to be symmetric,
 only the upper triangular portion is used by default.</p>
 <p>If <code>upper</code> is <code>FALSE</code>, then lower triangular portion is used.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>5</span>, <span class='fl'>5</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='va'>a</span> <span class='op'>+</span> <span class='va'>a</span><span class='op'>$</span><span class='fu'>t</span><span class='op'>(</span><span class='op'>)</span>  <span class='co'># To make a symmetric</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='va'>o</span> <span class='op'>=</span> <span class='fu'>torch_symeig</span><span class='op'>(</span><span class='va'>a</span>, eigenvectors<span class='op'>=</span><span class='cn'>TRUE</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>e</span> <span class='op'>=</span> <span class='va'>o</span><span class='op'>[[</span><span class='fl'>1</span><span class='op'>]</span><span class='op'>]</span></span>
-<span class='r-in'><span class='va'>v</span> <span class='op'>=</span> <span class='va'>o</span><span class='op'>[[</span><span class='fl'>2</span><span class='op'>]</span><span class='op'>]</span></span>
-<span class='r-in'><span class='va'>e</span></span>
-<span class='r-in'><span class='va'>v</span></span>
-<span class='r-in'><span class='va'>a_big</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>5</span>, <span class='fl'>2</span>, <span class='fl'>2</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a_big</span> <span class='op'>=</span> <span class='va'>a_big</span> <span class='op'>+</span> <span class='va'>a_big</span><span class='op'>$</span><span class='fu'>transpose</span><span class='op'>(</span><span class='op'>-</span><span class='fl'>2</span>, <span class='op'>-</span><span class='fl'>1</span><span class='op'>)</span>  <span class='co'># To make a_big symmetric</span></span>
-<span class='r-in'><span class='va'>o</span> <span class='op'>=</span> <span class='va'>a_big</span><span class='op'>$</span><span class='fu'>symeig</span><span class='op'>(</span>eigenvectors<span class='op'>=</span><span class='cn'>TRUE</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>e</span> <span class='op'>=</span> <span class='va'>o</span><span class='op'>[[</span><span class='fl'>1</span><span class='op'>]</span><span class='op'>]</span></span>
-<span class='r-in'><span class='va'>v</span> <span class='op'>=</span> <span class='va'>o</span><span class='op'>[[</span><span class='fl'>2</span><span class='op'>]</span><span class='op'>]</span></span>
-<span class='r-in'><span class='fu'><a href='torch_allclose.html'>torch_allclose</a></span><span class='op'>(</span><span class='fu'><a href='torch_matmul.html'>torch_matmul</a></span><span class='op'>(</span><span class='va'>v</span>, <span class='fu'><a href='torch_matmul.html'>torch_matmul</a></span><span class='op'>(</span><span class='va'>e</span><span class='op'>$</span><span class='fu'>diag_embed</span><span class='op'>(</span><span class='op'>)</span>, <span class='va'>v</span><span class='op'>$</span><span class='fu'>transpose</span><span class='op'>(</span><span class='op'>-</span><span class='fl'>2</span>, <span class='op'>-</span><span class='fl'>1</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span>, <span class='va'>a_big</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [1] TRUE</span>
-</pre>
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+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">5</span>, <span class="fl">5</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="va">a</span> <span class="op">+</span> <span class="va">a</span><span class="op">$</span><span class="fu">t</span><span class="op">(</span><span class="op">)</span>  <span class="co"># To make a symmetric</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="va">o</span> <span class="op">=</span> <span class="fu">torch_symeig</span><span class="op">(</span><span class="va">a</span>, eigenvectors<span class="op">=</span><span class="cn">TRUE</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">e</span> <span class="op">=</span> <span class="va">o</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span></span>
+<span class="r-in"><span class="va">v</span> <span class="op">=</span> <span class="va">o</span><span class="op">[[</span><span class="fl">2</span><span class="op">]</span><span class="op">]</span></span>
+<span class="r-in"><span class="va">e</span></span>
+<span class="r-in"><span class="va">v</span></span>
+<span class="r-in"><span class="va">a_big</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">5</span>, <span class="fl">2</span>, <span class="fl">2</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a_big</span> <span class="op">=</span> <span class="va">a_big</span> <span class="op">+</span> <span class="va">a_big</span><span class="op">$</span><span class="fu">transpose</span><span class="op">(</span><span class="op">-</span><span class="fl">2</span>, <span class="op">-</span><span class="fl">1</span><span class="op">)</span>  <span class="co"># To make a_big symmetric</span></span>
+<span class="r-in"><span class="va">o</span> <span class="op">=</span> <span class="va">a_big</span><span class="op">$</span><span class="fu">symeig</span><span class="op">(</span>eigenvectors<span class="op">=</span><span class="cn">TRUE</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">e</span> <span class="op">=</span> <span class="va">o</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span></span>
+<span class="r-in"><span class="va">v</span> <span class="op">=</span> <span class="va">o</span><span class="op">[[</span><span class="fl">2</span><span class="op">]</span><span class="op">]</span></span>
+<span class="r-in"><span class="fu"><a href="torch_allclose.html">torch_allclose</a></span><span class="op">(</span><span class="fu"><a href="torch_matmul.html">torch_matmul</a></span><span class="op">(</span><span class="va">v</span>, <span class="fu"><a href="torch_matmul.html">torch_matmul</a></span><span class="op">(</span><span class="va">e</span><span class="op">$</span><span class="fu">diag_embed</span><span class="op">(</span><span class="op">)</span>, <span class="va">v</span><span class="op">$</span><span class="fu">transpose</span><span class="op">(</span><span class="op">-</span><span class="fl">2</span>, <span class="op">-</span><span class="fl">1</span><span class="op">)</span><span class="op">)</span><span class="op">)</span>, <span class="va">a_big</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] TRUE</span>
+</code></pre></div>
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+      </ul></div>
 
+    
+  </div>
+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>T</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_t.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>T</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_t.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>T</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_t</span><span class='op'>(</span><span class='va'>self</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="t-input-gt-tensor-"><a class="anchor" href="#t-input-gt-tensor-"></a>t(input) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_t</span><span class="op">(</span><span class="va">self</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="t-input-gt-tensor-">t(input) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#t-input-gt-tensor-"></a></h2>
     
 
 
@@ -209,51 +99,46 @@
 and 1.</p>
 <p>0-D and 1-D tensors are returned as is. When input is a 2-D tensor this
 is equivalent to <code>transpose(input, 0, 1)</code>.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>2</span>,<span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>x</span></span>
-<span class='r-in'><span class='fu'>torch_t</span><span class='op'>(</span><span class='va'>x</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>x</span></span>
-<span class='r-in'><span class='fu'>torch_t</span><span class='op'>(</span><span class='va'>x</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>x</span></span>
-<span class='r-in'><span class='fu'>torch_t</span><span class='op'>(</span><span class='va'>x</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1.3232 -0.1109</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -2.2801 -1.1550</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.5056 -0.3579</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{3,2} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">x</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>,<span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">x</span></span>
+<span class="r-in"><span class="fu">torch_t</span><span class="op">(</span><span class="va">x</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">x</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">x</span></span>
+<span class="r-in"><span class="fu">torch_t</span><span class="op">(</span><span class="va">x</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">x</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">x</span></span>
+<span class="r-in"><span class="fu">torch_t</span><span class="op">(</span><span class="va">x</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.1711 -0.0716</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.7940  0.5677</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.7778 -0.0998</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3,2} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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+    <footer><div class="pkgdown-footer-left">
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Take</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_take.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Take</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_take.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Take</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_take</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>index</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>index</th>
-      <td><p>(LongTensor) the indices into tensor</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="take-input-index-gt-tensor-"><a class="anchor" href="#take-input-index-gt-tensor-"></a>take(input, index) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_take</span><span class="op">(</span><span class="va">self</span>, <span class="va">index</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+<dt>index</dt>
+<dd><p>(LongTensor) the indices into tensor</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="take-input-index-gt-tensor-">take(input, index) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#take-input-index-gt-tensor-"></a></h2>
     
 
 
 <p>Returns a new tensor with the elements of <code>input</code> at the given indices.
 The input tensor is treated as if it were viewed as a 1-D tensor. The result
 takes the same shape as the indices.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>src</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span>,<span class='fl'>3</span>,<span class='fl'>5</span>,<span class='fl'>6</span>,<span class='fl'>7</span>,<span class='fl'>8</span><span class='op'>)</span>, ncol <span class='op'>=</span> <span class='fl'>3</span>, byrow <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_take</span><span class='op'>(</span><span class='va'>src</span>, <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>2</span>, <span class='fl'>5</span><span class='op'>)</span>, dtype <span class='op'>=</span> <span class='fu'><a href='torch_dtype.html'>torch_int64</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  4</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  3</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  7</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{3} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">src</span> <span class="op">=</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/matrix.html" class="external-link">matrix</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span>,<span class="fl">3</span>,<span class="fl">5</span>,<span class="fl">6</span>,<span class="fl">7</span>,<span class="fl">8</span><span class="op">)</span>, ncol <span class="op">=</span> <span class="fl">3</span>, byrow <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_take</span><span class="op">(</span><span class="va">src</span>, <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">2</span>, <span class="fl">5</span><span class="op">)</span>, dtype <span class="op">=</span> <span class="fu"><a href="torch_dtype.html">torch_int64</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  4</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  3</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  7</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3} ]</span>
+</code></pre></div>
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+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Tan</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_tan.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Tan</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_tan.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Tan</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_tan</span><span class='op'>(</span><span class='va'>self</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="tan-input-out-null-gt-tensor-"><a class="anchor" href="#tan-input-out-null-gt-tensor-"></a>tan(input, out=NULL) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_tan</span><span class="op">(</span><span class="va">self</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="tan-input-out-null-gt-tensor-">tan(input, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#tan-input-out-null-gt-tensor-"></a></h2>
     
 
 
@@ -209,46 +99,41 @@
 <p>$$
     \mbox{out}_{i} = \tan(\mbox{input}_{i})
 $$</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='fu'>torch_tan</span><span class='op'>(</span><span class='va'>a</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -38.1641</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   1.2463</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  -0.0535</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  -2.7100</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{4} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="fu">torch_tan</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.0686</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0617</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.5475</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.8126</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Tanh</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_tanh.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Tanh</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_tanh.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Tanh</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_tanh</span><span class='op'>(</span><span class='va'>self</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="tanh-input-out-null-gt-tensor-"><a class="anchor" href="#tanh-input-out-null-gt-tensor-"></a>tanh(input, out=NULL) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_tanh</span><span class="op">(</span><span class="va">self</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="tanh-input-out-null-gt-tensor-">tanh(input, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#tanh-input-out-null-gt-tensor-"></a></h2>
     
 
 
@@ -210,46 +100,41 @@ of <code>input</code>.</p>
 <p>$$
     \mbox{out}_{i} = \tanh(\mbox{input}_{i})
 $$</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='fu'>torch_tanh</span><span class='op'>(</span><span class='va'>a</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.5002</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.6798</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.9154</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.7033</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{4} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="fu">torch_tanh</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.7301</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.1744</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.9210</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.6822</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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+    
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Converts R objects to a torch tensor</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/tensor.R'><code>R/tensor.R</code></a></small>
-    <div class="hidden name"><code>torch_tensor.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Converts R objects to a torch tensor</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/tensor.R" class="external-link"><code>R/tensor.R</code></a></small>
+      <div class="d-none name"><code>torch_tensor.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Converts R objects to a torch tensor</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_tensor</span><span class='op'>(</span>
-  <span class='va'>data</span>,
-  dtype <span class='op'>=</span> <span class='cn'>NULL</span>,
-  device <span class='op'>=</span> <span class='cn'>NULL</span>,
-  requires_grad <span class='op'>=</span> <span class='cn'>FALSE</span>,
-  pin_memory <span class='op'>=</span> <span class='cn'>FALSE</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>data</th>
-      <td><p>an R atomic vector, matrix or array</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>a <a href='torch_dtype.html'>torch_dtype</a> instance</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>a device creted with <code><a href='torch_device.html'>torch_device()</a></code></p></td>
-    </tr>
-    <tr>
-      <th>requires_grad</th>
-      <td><p>if autograd should record operations on the returned tensor.</p></td>
-    </tr>
-    <tr>
-      <th>pin_memory</th>
-      <td><p>If set, returned tensor would be allocated in the pinned memory.</p></td>
-    </tr>
-    </table>
-
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_tensor</span><span class="op">(</span>
+  <span class="va">data</span>,
+  dtype <span class="op">=</span> <span class="cn">NULL</span>,
+  device <span class="op">=</span> <span class="cn">NULL</span>,
+  requires_grad <span class="op">=</span> <span class="cn">FALSE</span>,
+  pin_memory <span class="op">=</span> <span class="cn">FALSE</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='fu'>torch_tensor</span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>,<span class='fl'>2</span>,<span class='fl'>3</span>,<span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_tensor</span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>,<span class='fl'>2</span>,<span class='fl'>3</span>,<span class='fl'>4</span><span class='op'>)</span>, dtype <span class='op'>=</span> <span class='fu'><a href='torch_dtype.html'>torch_int</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  2</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  3</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  4</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUIntType{4} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>data</dt>
+<dd><p>an R atomic vector, matrix or array</p></dd>
+<dt>dtype</dt>
+<dd><p>a <a href="torch_dtype.html">torch_dtype</a> instance</p></dd>
+<dt>device</dt>
+<dd><p>a device creted with <code><a href="torch_device.html">torch_device()</a></code></p></dd>
+<dt>requires_grad</dt>
+<dd><p>if autograd should record operations on the returned tensor.</p></dd>
+<dt>pin_memory</dt>
+<dd><p>If set, returned tensor would be allocated in the pinned memory.</p></dd>
+</dl></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="fu">torch_tensor</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">2</span>, <span class="fl">3</span>, <span class="fl">4</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_tensor</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">2</span>, <span class="fl">3</span>, <span class="fl">4</span><span class="op">)</span>, dtype <span class="op">=</span> <span class="fu"><a href="torch_dtype.html">torch_int</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  3</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  4</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUIntType{4} ]</span>
+</code></pre></div>
+    </div>
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Tensordot</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/wrapers.R'><code>R/wrapers.R</code></a></small>
-    <div class="hidden name"><code>torch_tensordot.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Tensordot</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
+      <div class="d-none name"><code>torch_tensordot.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Returns a contraction of a and b over multiple dimensions.
 <code>tensordot</code> implements a generalized matrix product.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_tensordot</span><span class='op'>(</span><span class='va'>a</span>, <span class='va'>b</span>, dims <span class='op'>=</span> <span class='fl'>2</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>a</th>
-      <td><p>(Tensor) Left tensor to contract</p></td>
-    </tr>
-    <tr>
-      <th>b</th>
-      <td><p>(Tensor) Right tensor to contract</p></td>
-    </tr>
-    <tr>
-      <th>dims</th>
-      <td><p>(int or tuple of two lists of integers) number of dimensions to     contract or explicit lists of dimensions for <code>a</code> and     <code>b</code> respectively</p></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span><span class='op'>(</span>start <span class='op'>=</span> <span class='fl'>1</span>, end <span class='op'>=</span> <span class='fl'>60</span><span class='op'>)</span><span class='op'>$</span><span class='fu'>reshape</span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>4</span>, <span class='fl'>5</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>b</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span><span class='op'>(</span>start <span class='op'>=</span> <span class='fl'>1</span>, end <span class='op'>=</span> <span class='fl'>24</span><span class='op'>)</span><span class='op'>$</span><span class='fu'>reshape</span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span>, <span class='fl'>3</span>, <span class='fl'>2</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_tensordot</span><span class='op'>(</span><span class='va'>a</span>, <span class='va'>b</span>, dims <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>1</span><span class='op'>)</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>2</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='cn'>FALSE</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>4</span>, <span class='fl'>5</span>, device<span class='op'>=</span><span class='st'>'cuda'</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>b</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>4</span>, <span class='fl'>5</span>, <span class='fl'>6</span>, device<span class='op'>=</span><span class='st'>'cuda'</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>c</span> <span class='op'>=</span> <span class='fu'>torch_tensordot</span><span class='op'>(</span><span class='va'>a</span>, <span class='va'>b</span>, dims<span class='op'>=</span><span class='fl'>2</span><span class='op'>)</span><span class='op'>$</span><span class='fu'>cpu</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_tensordot</span><span class="op">(</span><span class="va">a</span>, <span class="va">b</span>, dims <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>a</dt>
+<dd><p>(Tensor) Left tensor to contract</p></dd>
+<dt>b</dt>
+<dd><p>(Tensor) Right tensor to contract</p></dd>
+<dt>dims</dt>
+<dd><p>(int or tuple of two lists of integers) number of dimensions to     contract or explicit lists of dimensions for <code>a</code> and     <code>b</code> respectively</p></dd>
+</dl></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_arange.html">torch_arange</a></span><span class="op">(</span>start <span class="op">=</span> <span class="fl">1</span>, end <span class="op">=</span> <span class="fl">60</span><span class="op">)</span><span class="op">$</span><span class="fu">reshape</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">4</span>, <span class="fl">5</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">b</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_arange.html">torch_arange</a></span><span class="op">(</span>start <span class="op">=</span> <span class="fl">1</span>, end <span class="op">=</span> <span class="fl">24</span><span class="op">)</span><span class="op">$</span><span class="fu">reshape</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span>, <span class="fl">3</span>, <span class="fl">2</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_tensordot</span><span class="op">(</span><span class="va">a</span>, <span class="va">b</span>, dims <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">1</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">2</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">4</span>, <span class="fl">5</span>, device<span class="op">=</span><span class="st">'cuda'</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">b</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">4</span>, <span class="fl">5</span>, <span class="fl">6</span>, device<span class="op">=</span><span class="st">'cuda'</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">c</span> <span class="op">=</span> <span class="fu">torch_tensordot</span><span class="op">(</span><span class="va">a</span>, <span class="va">b</span>, dims<span class="op">=</span><span class="fl">2</span><span class="op">)</span><span class="op">$</span><span class="fu">cpu</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
+    </div>
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diff --git a/reference/torch_threshold_.html b/reference/torch_threshold_.html
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+  <main id="main" class="col-md-9"><div class="page-header">
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-    <div class="ref-description">
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     <p>Threshold_</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_threshold_</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>threshold</span>, <span class='va'>value</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>input tensor</p></td>
-    </tr>
-    <tr>
-      <th>threshold</th>
-      <td><p>The value to threshold at</p></td>
-    </tr>
-    <tr>
-      <th>value</th>
-      <td><p>The value to replace with</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="threshold-input-threshold-value-gt-tensor-"><a class="anchor" href="#threshold-input-threshold-value-gt-tensor-"></a>threshold_(input, threshold, value) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_threshold_</span><span class="op">(</span><span class="va">self</span>, <span class="va">threshold</span>, <span class="va">value</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>input tensor</p></dd>
+<dt>threshold</dt>
+<dd><p>The value to threshold at</p></dd>
+<dt>value</dt>
+<dd><p>The value to replace with</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="threshold-input-threshold-value-gt-tensor-">threshold_(input, threshold, value) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#threshold-input-threshold-value-gt-tensor-"></a></h2>
     
 
 
 <p>In-place version of <code>torch_threshold</code>.</p>
+    </div>
 
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Topk</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/wrapers.R'><code>R/wrapers.R</code></a></small>
-    <div class="hidden name"><code>torch_topk.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Topk</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
+      <div class="d-none name"><code>torch_topk.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Topk</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_topk</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>k</span>, dim <span class='op'>=</span> <span class='op'>-</span><span class='fl'>1L</span>, largest <span class='op'>=</span> <span class='cn'>TRUE</span>, sorted <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>k</th>
-      <td><p>(int) the k in "top-k"</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int, optional) the dimension to sort along</p></td>
-    </tr>
-    <tr>
-      <th>largest</th>
-      <td><p>(bool, optional) controls whether to return largest or           smallest elements</p></td>
-    </tr>
-    <tr>
-      <th>sorted</th>
-      <td><p>(bool, optional) controls whether to return the elements           in sorted order</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="topk-input-k-dim-null-largest-true-sorted-true-gt-tensor-longtensor-"><a class="anchor" href="#topk-input-k-dim-null-largest-true-sorted-true-gt-tensor-longtensor-"></a>topk(input, k, dim=NULL, largest=TRUE, sorted=TRUE) -&gt; (Tensor, LongTensor) </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_topk</span><span class="op">(</span><span class="va">self</span>, <span class="va">k</span>, dim <span class="op">=</span> <span class="op">-</span><span class="fl">1L</span>, largest <span class="op">=</span> <span class="cn">TRUE</span>, sorted <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+<dt>k</dt>
+<dd><p>(int) the k in "top-k"</p></dd>
+<dt>dim</dt>
+<dd><p>(int, optional) the dimension to sort along</p></dd>
+<dt>largest</dt>
+<dd><p>(bool, optional) controls whether to return largest or           smallest elements</p></dd>
+<dt>sorted</dt>
+<dd><p>(bool, optional) controls whether to return the elements           in sorted order</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="topk-input-k-dim-null-largest-true-sorted-true-gt-tensor-longtensor-">topk(input, k, dim=NULL, largest=TRUE, sorted=TRUE) -&gt; (Tensor, LongTensor) <a class="anchor" aria-label="anchor" href="#topk-input-k-dim-null-largest-true-sorted-true-gt-tensor-longtensor-"></a></h2>
     
 
 
@@ -229,54 +111,49 @@ a given dimension.</p>
 of the elements in the original <code>input</code> tensor.</p>
 <p>The boolean option <code>sorted</code> if <code>TRUE</code>, will make sure that the returned
 <code>k</code> elements are themselves sorted</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span><span class='op'>(</span><span class='fl'>1.</span>, <span class='fl'>6.</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>x</span></span>
-<span class='r-in'><span class='fu'>torch_topk</span><span class='op'>(</span><span class='va'>x</span>, <span class='fl'>3</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [[1]]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  6</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  5</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  4</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{3} ]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [[2]]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  6</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  5</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  4</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPULongType{3} ]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">x</span> <span class="op">=</span> <span class="fu"><a href="torch_arange.html">torch_arange</a></span><span class="op">(</span><span class="fl">1.</span>, <span class="fl">6.</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">x</span></span>
+<span class="r-in"><span class="fu">torch_topk</span><span class="op">(</span><span class="va">x</span>, <span class="fl">3</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [[1]]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  6</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  5</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  4</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3} ]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [[2]]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  6</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  5</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  4</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPULongType{3} ]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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+    <footer><div class="pkgdown-footer-left">
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index 0324fd1b4..fc0223e95 100644
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@@ -1,248 +1,133 @@
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Trace</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_trace.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Trace</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_trace.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Trace</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_trace</span><span class='op'>(</span><span class='va'>self</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>the input tensor</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="trace-input-gt-tensor-"><a class="anchor" href="#trace-input-gt-tensor-"></a>trace(input) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_trace</span><span class="op">(</span><span class="va">self</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>the input tensor</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="trace-input-gt-tensor-">trace(input) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#trace-input-gt-tensor-"></a></h2>
     
 
 
 <p>Returns the sum of the elements of the diagonal of the input 2-D matrix.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>x</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>9</span><span class='op'>)</span><span class='op'>$</span><span class='fu'>view</span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>x</span></span>
-<span class='r-in'><span class='fu'>torch_trace</span><span class='op'>(</span><span class='va'>x</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> 15</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_arange.html">torch_arange</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">9</span><span class="op">)</span><span class="op">$</span><span class="fu">view</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">x</span></span>
+<span class="r-in"><span class="fu">torch_trace</span><span class="op">(</span><span class="va">x</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 15</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{} ]</span>
+</code></pre></div>
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diff --git a/reference/torch_transpose.html b/reference/torch_transpose.html
index 8579eb3de..0722e283e 100644
--- a/reference/torch_transpose.html
+++ b/reference/torch_transpose.html
@@ -1,215 +1,101 @@
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Transpose</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_transpose.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Transpose</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_transpose.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Transpose</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_transpose</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>dim0</span>, <span class='va'>dim1</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dim0</th>
-      <td><p>(int) the first dimension to be transposed</p></td>
-    </tr>
-    <tr>
-      <th>dim1</th>
-      <td><p>(int) the second dimension to be transposed</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="transpose-input-dim-dim-gt-tensor-"><a class="anchor" href="#transpose-input-dim-dim-gt-tensor-"></a>transpose(input, dim0, dim1) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_transpose</span><span class="op">(</span><span class="va">self</span>, <span class="va">dim0</span>, <span class="va">dim1</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+<dt>dim0</dt>
+<dd><p>(int) the first dimension to be transposed</p></dd>
+<dt>dim1</dt>
+<dd><p>(int) the second dimension to be transposed</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="transpose-input-dim-dim-gt-tensor-">transpose(input, dim0, dim1) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#transpose-input-dim-dim-gt-tensor-"></a></h2>
     
 
 
@@ -218,45 +104,40 @@ The given dimensions <code>dim0</code> and <code>dim1</code> are swapped.</p>
 <p>The resulting <code>out</code> tensor shares it's underlying storage with the
 <code>input</code> tensor, so changing the content of one would change the content
 of the other.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>x</span></span>
-<span class='r-in'><span class='fu'>torch_transpose</span><span class='op'>(</span><span class='va'>x</span>, <span class='fl'>1</span>, <span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.4189  1.6814</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -1.3838 -0.7911</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1.9548  0.4426</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{3,2} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">x</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">x</span></span>
+<span class="r-in"><span class="fu">torch_transpose</span><span class="op">(</span><span class="va">x</span>, <span class="fl">1</span>, <span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.3229  1.7494</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.3605  0.4216</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -1.0440  0.4398</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3,2} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer>
-      <div class="copyright">
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+    <footer><div class="pkgdown-footer-left">
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+<div class="pkgdown-footer-right">
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diff --git a/reference/torch_trapz.html b/reference/torch_trapz.html
index 9bd055f11..c7ef967c9 100644
--- a/reference/torch_trapz.html
+++ b/reference/torch_trapz.html
@@ -1,269 +1,149 @@
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Trapz</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_trapz.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Trapz</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_trapz.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Trapz</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_trapz</span><span class='op'>(</span><span class='va'>y</span>, dx <span class='op'>=</span> <span class='fl'>1L</span>, <span class='va'>x</span>, dim <span class='op'>=</span> <span class='op'>-</span><span class='fl'>1L</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>y</th>
-      <td><p>(Tensor) The values of the function to integrate</p></td>
-    </tr>
-    <tr>
-      <th>dx</th>
-      <td><p>(float) The distance between points at which <code>y</code> is sampled.</p></td>
-    </tr>
-    <tr>
-      <th>x</th>
-      <td><p>(Tensor) The points at which the function <code>y</code> is sampled.        If <code>x</code> is not in ascending order, intervals on which it is decreasing        contribute negatively to the estimated integral (i.e., the convention        \(\int_a^b f = -\int_b^a f\) is followed).</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) The dimension along which to integrate.        By default, use the last dimension.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="trapz-y-x-dim-gt-tensor-"><a class="anchor" href="#trapz-y-x-dim-gt-tensor-"></a>trapz(y, x, *, dim=-1) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_trapz</span><span class="op">(</span><span class="va">y</span>, dx <span class="op">=</span> <span class="fl">1L</span>, <span class="va">x</span>, dim <span class="op">=</span> <span class="op">-</span><span class="fl">1L</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>y</dt>
+<dd><p>(Tensor) The values of the function to integrate</p></dd>
+<dt>dx</dt>
+<dd><p>(float) The distance between points at which <code>y</code> is sampled.</p></dd>
+<dt>x</dt>
+<dd><p>(Tensor) The points at which the function <code>y</code> is sampled.        If <code>x</code> is not in ascending order, intervals on which it is decreasing        contribute negatively to the estimated integral (i.e., the convention        \(\int_a^b f = -\int_b^a f\) is followed).</p></dd>
+<dt>dim</dt>
+<dd><p>(int) The dimension along which to integrate.        By default, use the last dimension.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="trapz-y-x-dim-gt-tensor-">trapz(y, x, *, dim=-1) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#trapz-y-x-dim-gt-tensor-"></a></h2>
     
 
 
 <p>Estimate \(\int y\,dx\) along <code>dim</code>, using the trapezoid rule.</p>
-    <h2 class="hasAnchor" id="trapz-y-dx-dim-gt-tensor-"><a class="anchor" href="#trapz-y-dx-dim-gt-tensor-"></a>trapz(y, *, dx=1, dim=-1) -&gt; Tensor </h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="trapz-y-dx-dim-gt-tensor-">trapz(y, *, dx=1, dim=-1) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#trapz-y-dx-dim-gt-tensor-"></a></h2>
     
 
 
 <p>As above, but the sample points are spaced uniformly at a distance of <code>dx</code>.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>y</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>y</span></span>
-<span class='r-in'><span class='va'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>3</span>, <span class='fl'>4</span>, <span class='fl'>1</span>, <span class='fl'>2</span>, <span class='fl'>3</span><span class='op'>)</span>, ncol <span class='op'>=</span> <span class='fl'>3</span>, byrow<span class='op'>=</span><span class='cn'>TRUE</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_trapz</span><span class='op'>(</span><span class='va'>y</span>, x <span class='op'>=</span> <span class='va'>x</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.1849</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.5930</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{2} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">y</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">y</span></span>
+<span class="r-in"><span class="va">x</span> <span class="op">=</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/matrix.html" class="external-link">matrix</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">3</span>, <span class="fl">4</span>, <span class="fl">1</span>, <span class="fl">2</span>, <span class="fl">3</span><span class="op">)</span>, ncol <span class="op">=</span> <span class="fl">3</span>, byrow<span class="op">=</span><span class="cn">TRUE</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_trapz</span><span class="op">(</span><span class="va">y</span>, x <span class="op">=</span> <span class="va">x</span><span class="op">)</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.5575</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.3415</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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index f2940a32d..8792cb8db 100644
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Triangular_solve</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_triangular_solve.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Triangular_solve</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_triangular_solve.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Triangular_solve</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_triangular_solve</span><span class='op'>(</span>
-  <span class='va'>self</span>,
-  <span class='va'>A</span>,
-  upper <span class='op'>=</span> <span class='cn'>TRUE</span>,
-  transpose <span class='op'>=</span> <span class='cn'>FALSE</span>,
-  unitriangular <span class='op'>=</span> <span class='cn'>FALSE</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) multiple right-hand sides of size \((*, m, k)\) where                \(*\) is zero of more batch dimensions (\(b\))</p></td>
-    </tr>
-    <tr>
-      <th>A</th>
-      <td><p>(Tensor) the input triangular coefficient matrix of size \((*, m, m)\)                where \(*\) is zero or more batch dimensions</p></td>
-    </tr>
-    <tr>
-      <th>upper</th>
-      <td><p>(bool, optional) whether to solve the upper-triangular system        of equations (default) or the lower-triangular system of equations. Default: <code>TRUE</code>.</p></td>
-    </tr>
-    <tr>
-      <th>transpose</th>
-      <td><p>(bool, optional) whether \(A\) should be transposed before        being sent into the solver. Default: <code>FALSE</code>.</p></td>
-    </tr>
-    <tr>
-      <th>unitriangular</th>
-      <td><p>(bool, optional) whether \(A\) is unit triangular.        If TRUE, the diagonal elements of \(A\) are assumed to be        1 and not referenced from \(A\). Default: <code>FALSE</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="triangular-solve-input-a-upper-true-transpose-false-unitriangular-false-gt-tensor-tensor-"><a class="anchor" href="#triangular-solve-input-a-upper-true-transpose-false-unitriangular-false-gt-tensor-tensor-"></a>triangular_solve(input, A, upper=TRUE, transpose=False, unitriangular=False) -&gt; (Tensor, Tensor) </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_triangular_solve</span><span class="op">(</span>
+  <span class="va">self</span>,
+  <span class="va">A</span>,
+  upper <span class="op">=</span> <span class="cn">TRUE</span>,
+  transpose <span class="op">=</span> <span class="cn">FALSE</span>,
+  unitriangular <span class="op">=</span> <span class="cn">FALSE</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) multiple right-hand sides of size \((*, m, k)\) where                \(*\) is zero of more batch dimensions (\(b\))</p></dd>
+<dt>A</dt>
+<dd><p>(Tensor) the input triangular coefficient matrix of size \((*, m, m)\)                where \(*\) is zero or more batch dimensions</p></dd>
+<dt>upper</dt>
+<dd><p>(bool, optional) whether to solve the upper-triangular system        of equations (default) or the lower-triangular system of equations. Default: <code>TRUE</code>.</p></dd>
+<dt>transpose</dt>
+<dd><p>(bool, optional) whether \(A\) should be transposed before        being sent into the solver. Default: <code>FALSE</code>.</p></dd>
+<dt>unitriangular</dt>
+<dd><p>(bool, optional) whether \(A\) is unit triangular.        If TRUE, the diagonal elements of \(A\) are assumed to be        1 and not referenced from \(A\). Default: <code>FALSE</code>.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="triangular-solve-input-a-upper-true-transpose-false-unitriangular-false-gt-tensor-tensor-">triangular_solve(input, A, upper=TRUE, transpose=False, unitriangular=False) -&gt; (Tensor, Tensor) <a class="anchor" aria-label="anchor" href="#triangular-solve-input-a-upper-true-transpose-false-unitriangular-false-gt-tensor-tensor-"></a></h2>
     
 
 
@@ -234,54 +116,49 @@ with the default keyword arguments.</p>
 <p><code>torch_triangular_solve(b, A)</code> can take in 2D inputs <code>b, A</code> or inputs that are
 batches of 2D matrices. If the inputs are batches, then returns
 batched outputs <code>X</code></p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>A</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>2</span><span class='op'>)</span><span class='op'>)</span><span class='op'>$</span><span class='fu'>triu</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>A</span></span>
-<span class='r-in'><span class='va'>b</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>b</span></span>
-<span class='r-in'><span class='fu'>torch_triangular_solve</span><span class='op'>(</span><span class='va'>b</span>, <span class='va'>A</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [[1]]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  2.2703 -2.7713  3.2675</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.2852  3.9275 -3.6138</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{2,3} ]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [[2]]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.5396  0.5246</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.0000  0.3622</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{2,2} ]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">A</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">2</span><span class="op">)</span><span class="op">)</span><span class="op">$</span><span class="fu">triu</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">A</span></span>
+<span class="r-in"><span class="va">b</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">b</span></span>
+<span class="r-in"><span class="fu">torch_triangular_solve</span><span class="op">(</span><span class="va">b</span>, <span class="va">A</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [[1]]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.3981 -2.4839  0.2372</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.6423  4.8577 -0.3119</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2,3} ]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [[2]]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.9897 -0.5224</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0000 -0.3678</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2,2} ]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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-    <h1>Tril</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_tril.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Tril</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_tril.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Tril</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_tril</span><span class='op'>(</span><span class='va'>self</span>, diagonal <span class='op'>=</span> <span class='fl'>0L</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>diagonal</th>
-      <td><p>(int, optional) the diagonal to consider</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="tril-input-diagonal-out-null-gt-tensor-"><a class="anchor" href="#tril-input-diagonal-out-null-gt-tensor-"></a>tril(input, diagonal=0, out=NULL) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_tril</span><span class="op">(</span><span class="va">self</span>, diagonal <span class="op">=</span> <span class="fl">0L</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+<dt>diagonal</dt>
+<dd><p>(int, optional) the diagonal to consider</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="tril-input-diagonal-out-null-gt-tensor-">tril(input, diagonal=0, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#tril-input-diagonal-out-null-gt-tensor-"></a></h2>
     
 
 
@@ -220,50 +108,45 @@ diagonal, and similarly a negative value excludes just as many diagonals below
 the main diagonal. The main diagonal are the set of indices
 \(\lbrace (i, i) \rbrace\) for \(i \in [0, \min\{d_{1}, d_{2}\} - 1]\) where
 \(d_{1}, d_{2}\) are the dimensions of the matrix.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='fu'>torch_tril</span><span class='op'>(</span><span class='va'>a</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>b</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span>, <span class='fl'>6</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>b</span></span>
-<span class='r-in'><span class='fu'>torch_tril</span><span class='op'>(</span><span class='va'>b</span>, diagonal<span class='op'>=</span><span class='fl'>1</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_tril</span><span class='op'>(</span><span class='va'>b</span>, diagonal<span class='op'>=</span><span class='op'>-</span><span class='fl'>1</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.0000  0.0000  0.0000  0.0000  0.0000  0.0000</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1.3372  0.0000  0.0000  0.0000  0.0000  0.0000</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.9854 -1.7864  0.0000  0.0000  0.0000  0.0000</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.9134  0.6498  1.7714  0.0000  0.0000  0.0000</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{4,6} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="fu">torch_tril</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">b</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span>, <span class="fl">6</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">b</span></span>
+<span class="r-in"><span class="fu">torch_tril</span><span class="op">(</span><span class="va">b</span>, diagonal<span class="op">=</span><span class="fl">1</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_tril</span><span class="op">(</span><span class="va">b</span>, diagonal<span class="op">=</span><span class="op">-</span><span class="fl">1</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0000  0.0000  0.0000  0.0000  0.0000  0.0000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.3068  0.0000  0.0000  0.0000  0.0000  0.0000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.9373 -0.9743  0.0000  0.0000  0.0000  0.0000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -1.0832  0.4385 -0.7025  0.0000  0.0000  0.0000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4,6} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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+    
+  </div>
+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Tril_indices</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/wrapers.R'><code>R/wrapers.R</code></a></small>
-    <div class="hidden name"><code>torch_tril_indices.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Tril_indices</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
+      <div class="d-none name"><code>torch_tril_indices.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Tril_indices</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_tril_indices</span><span class='op'>(</span>
-  <span class='va'>row</span>,
-  <span class='va'>col</span>,
-  offset <span class='op'>=</span> <span class='fl'>0</span>,
-  dtype <span class='op'>=</span> <span class='fu'><a href='torch_dtype.html'>torch_long</a></span><span class='op'>(</span><span class='op'>)</span>,
-  device <span class='op'>=</span> <span class='st'>"cpu"</span>,
-  layout <span class='op'>=</span> <span class='fu'><a href='torch_layout.html'>torch_strided</a></span><span class='op'>(</span><span class='op'>)</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>row</th>
-      <td><p>(<code>int</code>) number of rows in the 2-D matrix.</p></td>
-    </tr>
-    <tr>
-      <th>col</th>
-      <td><p>(<code>int</code>) number of columns in the 2-D matrix.</p></td>
-    </tr>
-    <tr>
-      <th>offset</th>
-      <td><p>(<code>int</code>) diagonal offset from the main diagonal.        Default: if not provided, 0.</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, <code>torch_long</code>.</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
-    </tr>
-    <tr>
-      <th>layout</th>
-      <td><p>(<code>torch.layout</code>, optional) currently only support <code>torch_strided</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_tril_indices</span><span class="op">(</span>
+  <span class="va">row</span>,
+  <span class="va">col</span>,
+  offset <span class="op">=</span> <span class="fl">0</span>,
+  dtype <span class="op">=</span> <span class="fu"><a href="torch_dtype.html">torch_long</a></span><span class="op">(</span><span class="op">)</span>,
+  device <span class="op">=</span> <span class="st">"cpu"</span>,
+  layout <span class="op">=</span> <span class="fu"><a href="torch_layout.html">torch_strided</a></span><span class="op">(</span><span class="op">)</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>row</dt>
+<dd><p>(<code>int</code>) number of rows in the 2-D matrix.</p></dd>
+<dt>col</dt>
+<dd><p>(<code>int</code>) number of columns in the 2-D matrix.</p></dd>
+<dt>offset</dt>
+<dd><p>(<code>int</code>) diagonal offset from the main diagonal.        Default: if not provided, 0.</p></dd>
+<dt>dtype</dt>
+<dd><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, <code>torch_long</code>.</p></dd>
+<dt>device</dt>
+<dd><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></dd>
+<dt>layout</dt>
+<dd><p>(<code>torch.layout</code>, optional) currently only support <code>torch_strided</code>.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     
-<pre>When running on CUDA, `row * col` must be less than \eqn{2^{59}} to
+<div class="sourceCode"><pre><code>When running on CUDA, `row * col` must be less than \eqn{2^{59}} to
 prevent overflow during calculation.
-</pre>
-
-    <h2 class="hasAnchor" id="tril-indices-row-col-offset-dtype-torch-long-device-cpu-layout-torch-strided-gt-tensor-"><a class="anchor" href="#tril-indices-row-col-offset-dtype-torch-long-device-cpu-layout-torch-strided-gt-tensor-"></a>tril_indices(row, col, offset=0, dtype=torch.long, device='cpu', layout=torch.strided) -&gt; Tensor </h2>
+</code></pre></div>
 
+    </div>
+    <div class="section level2">
+    <h2 id="tril-indices-row-col-offset-dtype-torch-long-device-cpu-layout-torch-strided-gt-tensor-">tril_indices(row, col, offset=0, dtype=torch.long, device='cpu', layout=torch.strided) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#tril-indices-row-col-offset-dtype-torch-long-device-cpu-layout-torch-strided-gt-tensor-"></a></h2>
     
 
 
@@ -252,44 +133,39 @@ diagonal, and similarly a negative value excludes just as many diagonals below
 the main diagonal. The main diagonal are the set of indices
 \(\lbrace (i, i) \rbrace\) for \(i \in [0, \min\{d_{1}, d_{2}\} - 1]\)
 where \(d_{1}, d_{2}\) are the dimensions of the matrix.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='cn'>FALSE</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'>torch_tril_indices</span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>3</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'>torch_tril_indices</span><span class='op'>(</span><span class='fl'>4</span>, <span class='fl'>3</span>, <span class='op'>-</span><span class='fl'>1</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'>torch_tril_indices</span><span class='op'>(</span><span class='fl'>4</span>, <span class='fl'>3</span>, <span class='fl'>1</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu">torch_tril_indices</span><span class="op">(</span><span class="fl">3</span>, <span class="fl">3</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu">torch_tril_indices</span><span class="op">(</span><span class="fl">4</span>, <span class="fl">3</span>, <span class="op">-</span><span class="fl">1</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu">torch_tril_indices</span><span class="op">(</span><span class="fl">4</span>, <span class="fl">3</span>, <span class="fl">1</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Triu</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_triu.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Triu</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_triu.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Triu</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_triu</span><span class='op'>(</span><span class='va'>self</span>, diagonal <span class='op'>=</span> <span class='fl'>0L</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>diagonal</th>
-      <td><p>(int, optional) the diagonal to consider</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="triu-input-diagonal-out-null-gt-tensor-"><a class="anchor" href="#triu-input-diagonal-out-null-gt-tensor-"></a>triu(input, diagonal=0, out=NULL) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_triu</span><span class="op">(</span><span class="va">self</span>, diagonal <span class="op">=</span> <span class="fl">0L</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+<dt>diagonal</dt>
+<dd><p>(int, optional) the diagonal to consider</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="triu-input-diagonal-out-null-gt-tensor-">triu(input, diagonal=0, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#triu-input-diagonal-out-null-gt-tensor-"></a></h2>
     
 
 
@@ -220,52 +108,47 @@ diagonal, and similarly a negative value includes just as many diagonals below
 the main diagonal. The main diagonal are the set of indices
 \(\lbrace (i, i) \rbrace\) for \(i \in [0, \min\{d_{1}, d_{2}\} - 1]\) where
 \(d_{1}, d_{2}\) are the dimensions of the matrix.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='fu'>torch_triu</span><span class='op'>(</span><span class='va'>a</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_triu</span><span class='op'>(</span><span class='va'>a</span>, diagonal<span class='op'>=</span><span class='fl'>1</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_triu</span><span class='op'>(</span><span class='va'>a</span>, diagonal<span class='op'>=</span><span class='op'>-</span><span class='fl'>1</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>b</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span>, <span class='fl'>6</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>b</span></span>
-<span class='r-in'><span class='fu'>torch_triu</span><span class='op'>(</span><span class='va'>b</span>, diagonal<span class='op'>=</span><span class='fl'>1</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_triu</span><span class='op'>(</span><span class='va'>b</span>, diagonal<span class='op'>=</span><span class='op'>-</span><span class='fl'>1</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.4149 -0.1725 -0.3772 -0.3026 -0.0993 -0.3575</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1.2546  0.3994 -0.0510  0.4780  0.2357 -0.8355</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.0000 -0.4546  0.8069  0.1239 -1.0831 -0.9744</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.0000  0.0000 -1.7586  1.2715  0.5882 -0.0973</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{4,6} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="fu">torch_triu</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_triu</span><span class="op">(</span><span class="va">a</span>, diagonal<span class="op">=</span><span class="fl">1</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_triu</span><span class="op">(</span><span class="va">a</span>, diagonal<span class="op">=</span><span class="op">-</span><span class="fl">1</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">b</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span>, <span class="fl">6</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">b</span></span>
+<span class="r-in"><span class="fu">torch_triu</span><span class="op">(</span><span class="va">b</span>, diagonal<span class="op">=</span><span class="fl">1</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_triu</span><span class="op">(</span><span class="va">b</span>, diagonal<span class="op">=</span><span class="op">-</span><span class="fl">1</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.5404  0.2572  0.2881  0.0915 -0.9819  0.8741</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.1111  0.0094  1.0896  1.9976 -1.2385  0.2527</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0000 -0.5850  0.3817 -1.5193  1.4128  0.6110</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0000  0.0000  0.9613  0.5291 -0.7027  2.6263</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4,6} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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index cfb9bbf4c..b531abf41 100644
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Triu_indices</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/wrapers.R'><code>R/wrapers.R</code></a></small>
-    <div class="hidden name"><code>torch_triu_indices.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Triu_indices</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
+      <div class="d-none name"><code>torch_triu_indices.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Triu_indices</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_triu_indices</span><span class='op'>(</span>
-  <span class='va'>row</span>,
-  <span class='va'>col</span>,
-  offset <span class='op'>=</span> <span class='fl'>0</span>,
-  dtype <span class='op'>=</span> <span class='fu'><a href='torch_dtype.html'>torch_long</a></span><span class='op'>(</span><span class='op'>)</span>,
-  device <span class='op'>=</span> <span class='st'>"cpu"</span>,
-  layout <span class='op'>=</span> <span class='fu'><a href='torch_layout.html'>torch_strided</a></span><span class='op'>(</span><span class='op'>)</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>row</th>
-      <td><p>(<code>int</code>) number of rows in the 2-D matrix.</p></td>
-    </tr>
-    <tr>
-      <th>col</th>
-      <td><p>(<code>int</code>) number of columns in the 2-D matrix.</p></td>
-    </tr>
-    <tr>
-      <th>offset</th>
-      <td><p>(<code>int</code>) diagonal offset from the main diagonal.        Default: if not provided, 0.</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, <code>torch_long</code>.</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
-    </tr>
-    <tr>
-      <th>layout</th>
-      <td><p>(<code>torch.layout</code>, optional) currently only support <code>torch_strided</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_triu_indices</span><span class="op">(</span>
+  <span class="va">row</span>,
+  <span class="va">col</span>,
+  offset <span class="op">=</span> <span class="fl">0</span>,
+  dtype <span class="op">=</span> <span class="fu"><a href="torch_dtype.html">torch_long</a></span><span class="op">(</span><span class="op">)</span>,
+  device <span class="op">=</span> <span class="st">"cpu"</span>,
+  layout <span class="op">=</span> <span class="fu"><a href="torch_layout.html">torch_strided</a></span><span class="op">(</span><span class="op">)</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>row</dt>
+<dd><p>(<code>int</code>) number of rows in the 2-D matrix.</p></dd>
+<dt>col</dt>
+<dd><p>(<code>int</code>) number of columns in the 2-D matrix.</p></dd>
+<dt>offset</dt>
+<dd><p>(<code>int</code>) diagonal offset from the main diagonal.        Default: if not provided, 0.</p></dd>
+<dt>dtype</dt>
+<dd><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, <code>torch_long</code>.</p></dd>
+<dt>device</dt>
+<dd><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></dd>
+<dt>layout</dt>
+<dd><p>(<code>torch.layout</code>, optional) currently only support <code>torch_strided</code>.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     
-<pre>When running on CUDA, `row * col` must be less than \eqn{2^{59}} to
+<div class="sourceCode"><pre><code>When running on CUDA, `row * col` must be less than \eqn{2^{59}} to
 prevent overflow during calculation.
-</pre>
-
-    <h2 class="hasAnchor" id="triu-indices-row-col-offset-dtype-torch-long-device-cpu-layout-torch-strided-gt-tensor-"><a class="anchor" href="#triu-indices-row-col-offset-dtype-torch-long-device-cpu-layout-torch-strided-gt-tensor-"></a>triu_indices(row, col, offset=0, dtype=torch.long, device='cpu', layout=torch.strided) -&gt; Tensor </h2>
+</code></pre></div>
 
+    </div>
+    <div class="section level2">
+    <h2 id="triu-indices-row-col-offset-dtype-torch-long-device-cpu-layout-torch-strided-gt-tensor-">triu_indices(row, col, offset=0, dtype=torch.long, device='cpu', layout=torch.strided) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#triu-indices-row-col-offset-dtype-torch-long-device-cpu-layout-torch-strided-gt-tensor-"></a></h2>
     
 
 
@@ -252,44 +133,39 @@ diagonal, and similarly a negative value includes just as many diagonals below
 the main diagonal. The main diagonal are the set of indices
 \(\lbrace (i, i) \rbrace\) for \(i \in [0, \min\{d_{1}, d_{2}\} - 1]\)
 where \(d_{1}, d_{2}\) are the dimensions of the matrix.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='cn'>FALSE</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'>torch_triu_indices</span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>3</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'>torch_triu_indices</span><span class='op'>(</span><span class='fl'>4</span>, <span class='fl'>3</span>, <span class='op'>-</span><span class='fl'>1</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'>torch_triu_indices</span><span class='op'>(</span><span class='fl'>4</span>, <span class='fl'>3</span>, <span class='fl'>1</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu">torch_triu_indices</span><span class="op">(</span><span class="fl">3</span>, <span class="fl">3</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu">torch_triu_indices</span><span class="op">(</span><span class="fl">4</span>, <span class="fl">3</span>, <span class="op">-</span><span class="fl">1</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu">torch_triu_indices</span><span class="op">(</span><span class="fl">4</span>, <span class="fl">3</span>, <span class="fl">1</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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+    <footer><div class="pkgdown-footer-left">
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diff --git a/reference/torch_true_divide.html b/reference/torch_true_divide.html
index e6d0eed2f..5caafdd13 100644
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>TRUE_divide</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_true_divide.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>TRUE_divide</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_true_divide.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>TRUE_divide</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_true_divide</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>other</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the dividend</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Tensor or Scalar) the divisor</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="true-divide-dividend-divisor-gt-tensor-"><a class="anchor" href="#true-divide-dividend-divisor-gt-tensor-"></a>true_divide(dividend, divisor) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_true_divide</span><span class="op">(</span><span class="va">self</span>, <span class="va">other</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the dividend</p></dd>
+<dt>other</dt>
+<dd><p>(Tensor or Scalar) the divisor</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="true-divide-dividend-divisor-gt-tensor-">true_divide(dividend, divisor) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#true-divide-dividend-divisor-gt-tensor-"></a></h2>
     
 
 
 <p>Performs "true division" that always computes the division
 in floating point. Analogous to division in Python 3 and equivalent to
-<code><a href='torch_div.html'>torch_div</a></code> except when both inputs have bool or integer scalar types,
+<code><a href="torch_div.html">torch_div</a></code> except when both inputs have bool or integer scalar types,
 in which case they are cast to the default (floating) scalar type before the division.</p>
 <p>$$
     \mbox{out}_i = \frac{\mbox{dividend}_i}{\mbox{divisor}}
 $$</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>dividend</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>5</span>, <span class='fl'>3</span><span class='op'>)</span>, dtype<span class='op'>=</span><span class='fu'><a href='torch_dtype.html'>torch_int</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>divisor</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>2</span><span class='op'>)</span>, dtype<span class='op'>=</span><span class='fu'><a href='torch_dtype.html'>torch_int</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_true_divide</span><span class='op'>(</span><span class='va'>dividend</span>, <span class='va'>divisor</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_true_divide</span><span class='op'>(</span><span class='va'>dividend</span>, <span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  2.5000</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1.5000</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{2} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">dividend</span> <span class="op">=</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">5</span>, <span class="fl">3</span><span class="op">)</span>, dtype<span class="op">=</span><span class="fu"><a href="torch_dtype.html">torch_int</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">divisor</span> <span class="op">=</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">2</span><span class="op">)</span>, dtype<span class="op">=</span><span class="fu"><a href="torch_dtype.html">torch_int</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_true_divide</span><span class="op">(</span><span class="va">dividend</span>, <span class="va">divisor</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_true_divide</span><span class="op">(</span><span class="va">dividend</span>, <span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2.5000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.5000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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+  </div>
+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Trunc</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_trunc.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Trunc</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_trunc.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Trunc</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_trunc</span><span class='op'>(</span><span class='va'>self</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="trunc-input-out-null-gt-tensor-"><a class="anchor" href="#trunc-input-out-null-gt-tensor-"></a>trunc(input, out=NULL) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_trunc</span><span class="op">(</span><span class="va">self</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="trunc-input-out-null-gt-tensor-">trunc(input, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#trunc-input-out-null-gt-tensor-"></a></h2>
     
 
 
 <p>Returns a new tensor with the truncated integer values of
 the elements of <code>input</code>.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='fu'>torch_trunc</span><span class='op'>(</span><span class='va'>a</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  2</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{4} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="fu">torch_trunc</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4} ]</span>
+</code></pre></div>
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+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Unbind</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_unbind.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Unbind</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_unbind.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Unbind</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_unbind</span><span class='op'>(</span><span class='va'>self</span>, dim <span class='op'>=</span> <span class='fl'>1L</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the tensor to unbind</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) dimension to remove</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="unbind-input-dim-gt-seq-"><a class="anchor" href="#unbind-input-dim-gt-seq-"></a>unbind(input, dim=0) -&gt; seq </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_unbind</span><span class="op">(</span><span class="va">self</span>, dim <span class="op">=</span> <span class="fl">1L</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the tensor to unbind</p></dd>
+<dt>dim</dt>
+<dd><p>(int) dimension to remove</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="unbind-input-dim-gt-seq-">unbind(input, dim=0) -&gt; seq <a class="anchor" aria-label="anchor" href="#unbind-input-dim-gt-seq-"></a></h2>
     
 
 
 <p>Removes a tensor dimension.</p>
 <p>Returns a tuple of all slices along a given dimension, already without it.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='fu'>torch_unbind</span><span class='op'>(</span><span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span><span class='op'>(</span><span class='fl'>1</span><span class='op'>:</span><span class='fl'>9</span>, ncol <span class='op'>=</span> <span class='fl'>3</span>, byrow<span class='op'>=</span><span class='cn'>TRUE</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [[1]]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  2</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  3</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPULongType{3} ]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [[2]]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  4</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  5</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  6</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPULongType{3} ]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [[3]]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  7</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  8</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  9</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPULongType{3} ]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
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+<span class="r-in"></span>
+<span class="r-in"><span class="fu">torch_unbind</span><span class="op">(</span><span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/matrix.html" class="external-link">matrix</a></span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="fl">9</span>, ncol <span class="op">=</span> <span class="fl">3</span>, byrow<span class="op">=</span><span class="cn">TRUE</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [[1]]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  3</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPULongType{3} ]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [[2]]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  4</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  5</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  6</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPULongType{3} ]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [[3]]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  7</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  8</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  9</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPULongType{3} ]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+</code></pre></div>
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Unique_consecutive</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_unique_consecutive.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Unique_consecutive</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_unique_consecutive.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Unique_consecutive</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_unique_consecutive</span><span class='op'>(</span>
-  <span class='va'>self</span>,
-  return_inverse <span class='op'>=</span> <span class='cn'>FALSE</span>,
-  return_counts <span class='op'>=</span> <span class='cn'>FALSE</span>,
-  dim <span class='op'>=</span> <span class='cn'>NULL</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor</p></td>
-    </tr>
-    <tr>
-      <th>return_inverse</th>
-      <td><p>(bool) Whether to also return the indices for where        elements in the original input ended up in the returned unique list.</p></td>
-    </tr>
-    <tr>
-      <th>return_counts</th>
-      <td><p>(bool) Whether to also return the counts for each unique        element.</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) the dimension to apply unique. If <code>NULL</code>, the unique of the        flattened input is returned. default: <code>NULL</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="test-"><a class="anchor" href="#test-"></a>TEST </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_unique_consecutive</span><span class="op">(</span>
+  <span class="va">self</span>,
+  return_inverse <span class="op">=</span> <span class="cn">FALSE</span>,
+  return_counts <span class="op">=</span> <span class="cn">FALSE</span>,
+  dim <span class="op">=</span> <span class="cn">NULL</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor</p></dd>
+<dt>return_inverse</dt>
+<dd><p>(bool) Whether to also return the indices for where        elements in the original input ended up in the returned unique list.</p></dd>
+<dt>return_counts</dt>
+<dd><p>(bool) Whether to also return the counts for each unique        element.</p></dd>
+<dt>dim</dt>
+<dd><p>(int) the dimension to apply unique. If <code>NULL</code>, the unique of the        flattened input is returned. default: <code>NULL</code></p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="test-">TEST <a class="anchor" aria-label="anchor" href="#test-"></a></h2>
     
 
 
-<p>Eliminates all but the first element from every consecutive group of equivalent elements.</p><pre>.. note:: This function is different from [`torch_unique`] in the sense that this function
+<p>Eliminates all but the first element from every consecutive group of equivalent elements.</p><div class="sourceCode"><pre><code>.. note:: This function is different from [`torch_unique`] in the sense that this function
     only eliminates consecutive duplicate values. This semantics is similar to `std::unique`
     in C++.
-</pre>
+</code></pre></div>
 
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>1</span>, <span class='fl'>2</span>, <span class='fl'>2</span>, <span class='fl'>3</span>, <span class='fl'>1</span>, <span class='fl'>1</span>, <span class='fl'>2</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span> <span class='op'>=</span> <span class='fu'>torch_unique_consecutive</span><span class='op'>(</span><span class='va'>x</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>output</span></span>
-<span class='r-in'><span class='fu'>torch_unique_consecutive</span><span class='op'>(</span><span class='va'>x</span>, return_inverse<span class='op'>=</span><span class='cn'>TRUE</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_unique_consecutive</span><span class='op'>(</span><span class='va'>x</span>, return_counts<span class='op'>=</span><span class='cn'>TRUE</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [[1]]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  2</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  3</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  2</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{5} ]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [[2]]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPULongType{0} ]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [[3]]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  2</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  2</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  2</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPULongType{5} ]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">x</span> <span class="op">=</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">1</span>, <span class="fl">2</span>, <span class="fl">2</span>, <span class="fl">3</span>, <span class="fl">1</span>, <span class="fl">1</span>, <span class="fl">2</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span> <span class="op">=</span> <span class="fu">torch_unique_consecutive</span><span class="op">(</span><span class="va">x</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">output</span></span>
+<span class="r-in"><span class="fu">torch_unique_consecutive</span><span class="op">(</span><span class="va">x</span>, return_inverse<span class="op">=</span><span class="cn">TRUE</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_unique_consecutive</span><span class="op">(</span><span class="va">x</span>, return_counts<span class="op">=</span><span class="cn">TRUE</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [[1]]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  3</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{5} ]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [[2]]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPULongType{0} ]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [[3]]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPULongType{5} ]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Unsafe_chunk</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_unsafe_chunk.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Unsafe_chunk</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_unsafe_chunk.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Unsafe_chunk</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_unsafe_chunk</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>chunks</span>, dim <span class='op'>=</span> <span class='fl'>1L</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the tensor to split</p></td>
-    </tr>
-    <tr>
-      <th>chunks</th>
-      <td><p>(int) number of chunks to return</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) dimension along which to split the tensor</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="unsafe-chunk-input-chunks-dim-gt-list-of-tensors-"><a class="anchor" href="#unsafe-chunk-input-chunks-dim-gt-list-of-tensors-"></a>unsafe_chunk(input, chunks, dim=0) -&gt; List of Tensors </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_unsafe_chunk</span><span class="op">(</span><span class="va">self</span>, <span class="va">chunks</span>, dim <span class="op">=</span> <span class="fl">1L</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the tensor to split</p></dd>
+<dt>chunks</dt>
+<dd><p>(int) number of chunks to return</p></dd>
+<dt>dim</dt>
+<dd><p>(int) dimension along which to split the tensor</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="unsafe-chunk-input-chunks-dim-gt-list-of-tensors-">unsafe_chunk(input, chunks, dim=0) -&gt; List of Tensors <a class="anchor" aria-label="anchor" href="#unsafe-chunk-input-chunks-dim-gt-list-of-tensors-"></a></h2>
     
 
 
-<p>Works like <code><a href='torch_chunk.html'>torch_chunk()</a></code> but without enforcing the autograd restrictions
+<p>Works like <code><a href="torch_chunk.html">torch_chunk()</a></code> but without enforcing the autograd restrictions
 on inplace modification of the outputs.</p>
-    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></a></h2>
     
 
 <p>This function is safe to use as long as only the input, or only the outputs
@@ -224,32 +111,25 @@ are modified inplace after calling this function. It is user's
 responsibility to ensure that is the case. If both the input and one or more
 of the outputs are modified inplace, gradients computed by autograd will be
 silently incorrect.</p>
+    </div>
 
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-    <div class="page-header">
-    <h1>Unsafe_split</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_unsafe_split.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Unsafe_split</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_unsafe_split.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Unsafe_split</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_unsafe_split</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>split_size</span>, dim <span class='op'>=</span> <span class='fl'>1L</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) tensor to split.</p></td>
-    </tr>
-    <tr>
-      <th>split_size</th>
-      <td><p>(int) size of a single chunk or
-list of sizes for each chunk</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) dimension along which to split the tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="unsafe-split-tensor-split-size-or-sections-dim-gt-list-of-tensors-"><a class="anchor" href="#unsafe-split-tensor-split-size-or-sections-dim-gt-list-of-tensors-"></a>unsafe_split(tensor, split_size_or_sections, dim=0) -&gt; List of Tensors </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_unsafe_split</span><span class="op">(</span><span class="va">self</span>, <span class="va">split_size</span>, dim <span class="op">=</span> <span class="fl">1L</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) tensor to split.</p></dd>
+<dt>split_size</dt>
+<dd><p>(int) size of a single chunk or
+list of sizes for each chunk</p></dd>
+<dt>dim</dt>
+<dd><p>(int) dimension along which to split the tensor.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="unsafe-split-tensor-split-size-or-sections-dim-gt-list-of-tensors-">unsafe_split(tensor, split_size_or_sections, dim=0) -&gt; List of Tensors <a class="anchor" aria-label="anchor" href="#unsafe-split-tensor-split-size-or-sections-dim-gt-list-of-tensors-"></a></h2>
     
 
 
-<p>Works like <code><a href='torch_split.html'>torch_split()</a></code> but without enforcing the autograd restrictions
+<p>Works like <code><a href="torch_split.html">torch_split()</a></code> but without enforcing the autograd restrictions
 on inplace modification of the outputs.</p>
-    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></a></h2>
     
 
 <p>This function is safe to use as long as only the input, or only the outputs
@@ -225,32 +112,25 @@ are modified inplace after calling this function. It is user's
 responsibility to ensure that is the case. If both the input and one or more
 of the outputs are modified inplace, gradients computed by autograd will be
 silently incorrect.</p>
+    </div>
 
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer>
-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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-  <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p>
+<div class="pkgdown-footer-right">
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.2.</p>
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+    </footer></div>
 
   
 
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index 8b2c889c2..f47ab2a26 100644
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Unsqueeze</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_unsqueeze.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Unsqueeze</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_unsqueeze.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Unsqueeze</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_unsqueeze</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>dim</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int) the index at which to insert the singleton dimension</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="unsqueeze-input-dim-gt-tensor-"><a class="anchor" href="#unsqueeze-input-dim-gt-tensor-"></a>unsqueeze(input, dim) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_unsqueeze</span><span class="op">(</span><span class="va">self</span>, <span class="va">dim</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+<dt>dim</dt>
+<dd><p>(int) the index at which to insert the singleton dimension</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="unsqueeze-input-dim-gt-tensor-">unsqueeze(input, dim) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#unsqueeze-input-dim-gt-tensor-"></a></h2>
     
 
 
@@ -215,46 +103,41 @@ specified position.</p>
 <p>A <code>dim</code> value within the range <code>[-input.dim() - 1, input.dim() + 1)</code>
 can be used. Negative <code>dim</code> will correspond to <code>unsqueeze</code>
 applied at <code>dim</code> = <code>dim + input.dim() + 1</code>.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>2</span>, <span class='fl'>3</span>, <span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_unsqueeze</span><span class='op'>(</span><span class='va'>x</span>, <span class='fl'>1</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_unsqueeze</span><span class='op'>(</span><span class='va'>x</span>, <span class='fl'>2</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  2</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  3</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  4</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{4,1} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">x</span> <span class="op">=</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">2</span>, <span class="fl">3</span>, <span class="fl">4</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_unsqueeze</span><span class="op">(</span><span class="va">x</span>, <span class="fl">1</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_unsqueeze</span><span class="op">(</span><span class="va">x</span>, <span class="fl">2</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  3</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  4</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4,1} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer>
-      <div class="copyright">
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+    <footer><div class="pkgdown-footer-left">
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-    <h1>Vander</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/wrapers.R'><code>R/wrapers.R</code></a></small>
-    <div class="hidden name"><code>torch_vander.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Vander</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
+      <div class="d-none name"><code>torch_vander.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Vander</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_vander</span><span class='op'>(</span><span class='va'>x</span>, N <span class='op'>=</span> <span class='cn'>NULL</span>, increasing <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>x</th>
-      <td><p>(Tensor) 1-D input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>N</th>
-      <td><p>(int, optional) Number of columns in the output. If N is not specified,
-a square array is returned \((N = len(x))\).</p></td>
-    </tr>
-    <tr>
-      <th>increasing</th>
-      <td><p>(bool, optional) Order of the powers of the columns. If TRUE,
-the powers increase from left to right, if FALSE (the default) they are reversed.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="vander-x-n-none-increasing-false-gt-tensor-"><a class="anchor" href="#vander-x-n-none-increasing-false-gt-tensor-"></a>vander(x, N=None, increasing=FALSE) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_vander</span><span class="op">(</span><span class="va">x</span>, N <span class="op">=</span> <span class="cn">NULL</span>, increasing <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>x</dt>
+<dd><p>(Tensor) 1-D input tensor.</p></dd>
+<dt>N</dt>
+<dd><p>(int, optional) Number of columns in the output. If N is not specified,
+a square array is returned \((N = len(x))\).</p></dd>
+<dt>increasing</dt>
+<dd><p>(bool, optional) Order of the powers of the columns. If TRUE,
+the powers increase from left to right, if FALSE (the default) they are reversed.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="vander-x-n-none-increasing-false-gt-tensor-">vander(x, N=None, increasing=FALSE) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#vander-x-n-none-increasing-false-gt-tensor-"></a></h2>
     
 
 
@@ -222,47 +108,42 @@ If increasing is TRUE, the order of the columns is reversed
 \(x^0, x^1, ..., x^{(N-1)}\). Such a
 matrix with a geometric progression in each row is
 named for Alexandre-Theophile Vandermonde.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>x</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>2</span>, <span class='fl'>3</span>, <span class='fl'>5</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_vander</span><span class='op'>(</span><span class='va'>x</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_vander</span><span class='op'>(</span><span class='va'>x</span>, N<span class='op'>=</span><span class='fl'>3</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_vander</span><span class='op'>(</span><span class='va'>x</span>, N<span class='op'>=</span><span class='fl'>3</span>, increasing<span class='op'>=</span><span class='cn'>TRUE</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   1   1   1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   1   2   4</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   1   3   9</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   1   5  25</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{4,3} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">2</span>, <span class="fl">3</span>, <span class="fl">5</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_vander</span><span class="op">(</span><span class="va">x</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_vander</span><span class="op">(</span><span class="va">x</span>, N<span class="op">=</span><span class="fl">3</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_vander</span><span class="op">(</span><span class="va">x</span>, N<span class="op">=</span><span class="fl">3</span>, increasing<span class="op">=</span><span class="cn">TRUE</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   1   1   1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   1   2   4</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   1   3   9</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   1   5  25</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4,3} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer>
-      <div class="copyright">
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+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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+<div class="pkgdown-footer-right">
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-    <h1>Var</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_var.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Var</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_var.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Var</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_var</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>dim</span>, <span class='va'>correction</span>, unbiased <span class='op'>=</span> <span class='cn'>TRUE</span>, keepdim <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int or tuple of ints) the dimension or dimensions to reduce.</p></td>
-    </tr>
-    <tr>
-      <th>correction</th>
-      <td><p>The type of correction.</p></td>
-    </tr>
-    <tr>
-      <th>unbiased</th>
-      <td><p>(bool) whether to use the unbiased estimation or not</p></td>
-    </tr>
-    <tr>
-      <th>keepdim</th>
-      <td><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="var-input-unbiased-true-gt-tensor-"><a class="anchor" href="#var-input-unbiased-true-gt-tensor-"></a>var(input, unbiased=TRUE) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_var</span><span class="op">(</span><span class="va">self</span>, <span class="va">dim</span>, <span class="va">correction</span>, unbiased <span class="op">=</span> <span class="cn">TRUE</span>, keepdim <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+<dt>dim</dt>
+<dd><p>(int or tuple of ints) the dimension or dimensions to reduce.</p></dd>
+<dt>correction</dt>
+<dd><p>The type of correction.</p></dd>
+<dt>unbiased</dt>
+<dd><p>(bool) whether to use the unbiased estimation or not</p></dd>
+<dt>keepdim</dt>
+<dd><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="var-input-unbiased-true-gt-tensor-">var(input, unbiased=TRUE) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#var-input-unbiased-true-gt-tensor-"></a></h2>
     
 
 
 <p>Returns the variance of all elements in the <code>input</code> tensor.</p>
 <p>If <code>unbiased</code> is <code>FALSE</code>, then the variance will be calculated via the
 biased estimator. Otherwise, Bessel's correction will be used.</p>
-    <h2 class="hasAnchor" id="var-input-dim-keepdim-false-unbiased-true-out-null-gt-tensor-"><a class="anchor" href="#var-input-dim-keepdim-false-unbiased-true-out-null-gt-tensor-"></a>var(input, dim, keepdim=False, unbiased=TRUE, out=NULL) -&gt; Tensor </h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="var-input-dim-keepdim-false-unbiased-true-out-null-gt-tensor-">var(input, dim, keepdim=False, unbiased=TRUE, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#var-input-dim-keepdim-false-unbiased-true-out-null-gt-tensor-"></a></h2>
     
 
 
@@ -233,55 +116,50 @@ biased estimator. Otherwise, Bessel's correction will be used.</p>
 dimension <code>dim</code>.</p>
 <p>If <code>keepdim</code> is <code>TRUE</code>, the output tensor is of the same size
 as <code>input</code> except in the dimension(s) <code>dim</code> where it is of size 1.
-Otherwise, <code>dim</code> is squeezed (see <code><a href='torch_squeeze.html'>torch_squeeze</a></code>), resulting in the
+Otherwise, <code>dim</code> is squeezed (see <code><a href="torch_squeeze.html">torch_squeeze</a></code>), resulting in the
 output tensor having 1 (or <code>len(dim)</code>) fewer dimension(s).</p>
 <p>If <code>unbiased</code> is <code>FALSE</code>, then the variance will be calculated via the
 biased estimator. Otherwise, Bessel's correction will be used.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='fu'>torch_var</span><span class='op'>(</span><span class='va'>a</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span>, <span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='fu'>torch_var</span><span class='op'>(</span><span class='va'>a</span>, <span class='fl'>1</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.3675</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.2884</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.4979</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1.3222</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{4} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="fu">torch_var</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span>
+<span class="r-in"></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span>, <span class="fl">4</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="fu">torch_var</span><span class="op">(</span><span class="va">a</span>, <span class="fl">1</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.6636</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.3532</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.7643</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.6631</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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index 90f036f56..555f2df26 100644
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+    
+  </div>
+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Var_mean</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_var_mean.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Var_mean</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_var_mean.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Var_mean</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_var_mean</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>dim</span>, <span class='va'>correction</span>, unbiased <span class='op'>=</span> <span class='cn'>TRUE</span>, keepdim <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dim</th>
-      <td><p>(int or tuple of ints) the dimension or dimensions to reduce.</p></td>
-    </tr>
-    <tr>
-      <th>correction</th>
-      <td><p>The type of correction.</p></td>
-    </tr>
-    <tr>
-      <th>unbiased</th>
-      <td><p>(bool) whether to use the unbiased estimation or not</p></td>
-    </tr>
-    <tr>
-      <th>keepdim</th>
-      <td><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="var-mean-input-unbiased-true-gt-tensor-tensor-"><a class="anchor" href="#var-mean-input-unbiased-true-gt-tensor-tensor-"></a>var_mean(input, unbiased=TRUE) -&gt; (Tensor, Tensor) </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_var_mean</span><span class="op">(</span><span class="va">self</span>, <span class="va">dim</span>, <span class="va">correction</span>, unbiased <span class="op">=</span> <span class="cn">TRUE</span>, keepdim <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+<dt>dim</dt>
+<dd><p>(int or tuple of ints) the dimension or dimensions to reduce.</p></dd>
+<dt>correction</dt>
+<dd><p>The type of correction.</p></dd>
+<dt>unbiased</dt>
+<dd><p>(bool) whether to use the unbiased estimation or not</p></dd>
+<dt>keepdim</dt>
+<dd><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="var-mean-input-unbiased-true-gt-tensor-tensor-">var_mean(input, unbiased=TRUE) -&gt; (Tensor, Tensor) <a class="anchor" aria-label="anchor" href="#var-mean-input-unbiased-true-gt-tensor-tensor-"></a></h2>
     
 
 
 <p>Returns the variance and mean of all elements in the <code>input</code> tensor.</p>
 <p>If <code>unbiased</code> is <code>FALSE</code>, then the variance will be calculated via the
 biased estimator. Otherwise, Bessel's correction will be used.</p>
-    <h2 class="hasAnchor" id="var-mean-input-dim-keepdim-false-unbiased-true-gt-tensor-tensor-"><a class="anchor" href="#var-mean-input-dim-keepdim-false-unbiased-true-gt-tensor-tensor-"></a>var_mean(input, dim, keepdim=False, unbiased=TRUE) -&gt; (Tensor, Tensor) </h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="var-mean-input-dim-keepdim-false-unbiased-true-gt-tensor-tensor-">var_mean(input, dim, keepdim=False, unbiased=TRUE) -&gt; (Tensor, Tensor) <a class="anchor" aria-label="anchor" href="#var-mean-input-dim-keepdim-false-unbiased-true-gt-tensor-tensor-"></a></h2>
     
 
 
@@ -233,65 +116,60 @@ biased estimator. Otherwise, Bessel's correction will be used.</p>
 dimension <code>dim</code>.</p>
 <p>If <code>keepdim</code> is <code>TRUE</code>, the output tensor is of the same size
 as <code>input</code> except in the dimension(s) <code>dim</code> where it is of size 1.
-Otherwise, <code>dim</code> is squeezed (see <code><a href='torch_squeeze.html'>torch_squeeze</a></code>), resulting in the
+Otherwise, <code>dim</code> is squeezed (see <code><a href="torch_squeeze.html">torch_squeeze</a></code>), resulting in the
 output tensor having 1 (or <code>len(dim)</code>) fewer dimension(s).</p>
 <p>If <code>unbiased</code> is <code>FALSE</code>, then the variance will be calculated via the
 biased estimator. Otherwise, Bessel's correction will be used.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='fu'>torch_var_mean</span><span class='op'>(</span><span class='va'>a</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span>, <span class='fl'>4</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span></span>
-<span class='r-in'><span class='fu'>torch_var_mean</span><span class='op'>(</span><span class='va'>a</span>, <span class='fl'>1</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [[1]]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1.8915</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.8592</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.0342</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.5564</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{4} ]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [[2]]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.1077</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.3606</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0.1758</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> -0.3331</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{4} ]</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> </span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="fu">torch_var_mean</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span>
+<span class="r-in"></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span>, <span class="fl">4</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span></span>
+<span class="r-in"><span class="fu">torch_var_mean</span><span class="op">(</span><span class="va">a</span>, <span class="fl">1</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [[1]]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.9536</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.6696</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.4329</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.9070</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4} ]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [[2]]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.8302</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.1983</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.2831</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.6986</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4} ]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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-  <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p>
+<div class="pkgdown-footer-right">
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.2.</p>
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diff --git a/reference/torch_vdot.html b/reference/torch_vdot.html
index d88442664..81c81c6d3 100644
--- a/reference/torch_vdot.html
+++ b/reference/torch_vdot.html
@@ -1,259 +1,143 @@
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Vdot</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_vdot.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Vdot</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_vdot.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Vdot</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_vdot</span><span class='op'>(</span><span class='va'>self</span>, <span class='va'>other</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) first tensor in the dot product. Its conjugate is used
-if it's complex.</p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Tensor) second tensor in the dot product.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_vdot</span><span class="op">(</span><span class="va">self</span>, <span class="va">other</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) first tensor in the dot product. Its conjugate is used
+if it's complex.</p></dd>
+<dt>other</dt>
+<dd><p>(Tensor) second tensor in the dot product.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p>This function does not broadcast .</p>
-    <h2 class="hasAnchor" id="vdot-input-other-out-none-gt-tensor-"><a class="anchor" href="#vdot-input-other-out-none-gt-tensor-"></a>vdot(input, other, *, out=None) -&gt; Tensor </h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="vdot-input-other-out-none-gt-tensor-">vdot(input, other, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#vdot-input-other-out-none-gt-tensor-"></a></h2>
     
 
 
 <p>Computes the dot product (inner product) of two tensors. The vdot(a, b) function
 handles complex numbers differently than dot(a, b). If the first argument is complex,
 the complex conjugate of the first argument is used for the calculation of the dot product.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='fu'>torch_vdot</span><span class='op'>(</span><span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span>, <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>1</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='cn'>FALSE</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span><span class='fl'>1</span> <span class='op'>+</span><span class='fl'>2i</span>, <span class='fl'>3</span> <span class='op'>-</span> <span class='fl'>1i</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>b</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span><span class='fl'>2</span> <span class='op'>+</span><span class='fl'>1i</span>, <span class='fl'>4</span> <span class='op'>-</span> <span class='fl'>0i</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_vdot</span><span class='op'>(</span><span class='va'>a</span>, <span class='va'>b</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_vdot</span><span class='op'>(</span><span class='va'>b</span>, <span class='va'>a</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="fu">torch_vdot</span><span class="op">(</span><span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span>, <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">1</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="fl">1</span> <span class="op">+</span><span class="fl">2i</span>, <span class="fl">3</span> <span class="op">-</span> <span class="fl">1i</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">b</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="fl">2</span> <span class="op">+</span><span class="fl">1i</span>, <span class="fl">4</span> <span class="op">-</span> <span class="fl">0i</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_vdot</span><span class="op">(</span><span class="va">a</span>, <span class="va">b</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_vdot</span><span class="op">(</span><span class="va">b</span>, <span class="va">a</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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index 5c79fd6d5..3d23f6528 100644
--- a/reference/torch_view_as_complex.html
+++ b/reference/torch_view_as_complex.html
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>View_as_complex</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_view_as_complex.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>View_as_complex</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_view_as_complex.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>View_as_complex</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_view_as_complex</span><span class='op'>(</span><span class='va'>self</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="view-as-complex-input-gt-tensor-"><a class="anchor" href="#view-as-complex-input-gt-tensor-"></a>view_as_complex(input) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_view_as_complex</span><span class="op">(</span><span class="va">self</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="view-as-complex-input-gt-tensor-">view_as_complex(input) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#view-as-complex-input-gt-tensor-"></a></h2>
     
 
 
@@ -210,50 +100,46 @@ tensor of <code>size</code> \(m1, m2, \dots, mi, 2\), this function returns a
 new complex tensor of <code>size</code> \(m1, m2, \dots, mi\) where the last
 dimension of the input tensor is expected to represent the real and imaginary
 components of complex numbers.</p>
-    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></a></h2>
     
 
 <p>torch_view_as_complex is only supported for tensors with
-<code>torch_dtype</code> <code><a href='torch_dtype.html'>torch_float64()</a></code> and <code><a href='torch_dtype.html'>torch_float32()</a></code>.  The input is
+<code>torch_dtype</code> <code><a href="torch_dtype.html">torch_float64()</a></code> and <code><a href="torch_dtype.html">torch_float32()</a></code>.  The input is
 expected to have the last dimension of <code>size</code> 2. In addition, the
 tensor must have a <code>stride</code> of 1 for its last dimension. The strides of all
 other dimensions must be even numbers.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='cn'>FALSE</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>x</span><span class='op'>=</span><span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span>, <span class='fl'>2</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>x</span></span>
-<span class='r-in'><span class='fu'>torch_view_as_complex</span><span class='op'>(</span><span class='va'>x</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">x</span><span class="op">=</span><span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span>, <span class="fl">2</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">x</span></span>
+<span class="r-in"><span class="fu">torch_view_as_complex</span><span class="op">(</span><span class="va">x</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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diff --git a/reference/torch_view_as_real.html b/reference/torch_view_as_real.html
index 7f07a3063..9c322d0f3 100644
--- a/reference/torch_view_as_real.html
+++ b/reference/torch_view_as_real.html
@@ -1,207 +1,97 @@
-<!-- Generated by pkgdown: do not edit by hand -->
 <!DOCTYPE html>
-<html lang="en">
-  <head>
-  <meta charset="utf-8">
-<meta http-equiv="X-UA-Compatible" content="IE=edge">
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-
-<title>View_as_real — torch_view_as_real • torch</title>
-
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>View_as_real</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_view_as_real.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>View_as_real</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_view_as_real.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>View_as_real</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_view_as_real</span><span class='op'>(</span><span class='va'>self</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) the input tensor.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="view-as-real-input-gt-tensor-"><a class="anchor" href="#view-as-real-input-gt-tensor-"></a>view_as_real(input) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_view_as_real</span><span class="op">(</span><span class="va">self</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>self</dt>
+<dd><p>(Tensor) the input tensor.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="view-as-real-input-gt-tensor-">view_as_real(input) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#view-as-real-input-gt-tensor-"></a></h2>
     
 
 
@@ -209,47 +99,43 @@
 <code>size</code> \(m1, m2, \dots, mi\), this function returns a new
 real tensor of size \(m1, m2, \dots, mi, 2\), where the last dimension of size 2
 represents the real and imaginary components of complex numbers.</p>
-    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></a></h2>
     
 
 <p><code>torch_view_as_real()</code> is only supported for tensors with <code>complex dtypes</code>.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='cn'>FALSE</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>x</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>4</span>, dtype<span class='op'>=</span><span class='fu'>torch_cfloat</span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>x</span></span>
-<span class='r-in'><span class='fu'>torch_view_as_real</span><span class='op'>(</span><span class='va'>x</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">4</span>, dtype<span class="op">=</span><span class="fu">torch_cfloat</span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">x</span></span>
+<span class="r-in"><span class="fu">torch_view_as_real</span><span class="op">(</span><span class="va">x</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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-      <div class="copyright">
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+    <footer><div class="pkgdown-footer-left">
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+<div class="pkgdown-footer-right">
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diff --git a/reference/torch_vstack.html b/reference/torch_vstack.html
index 7252b6eb5..ce5184bc7 100644
--- a/reference/torch_vstack.html
+++ b/reference/torch_vstack.html
@@ -1,258 +1,143 @@
-<!-- Generated by pkgdown: do not edit by hand -->
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Vstack</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_vstack.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Vstack</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_vstack.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Vstack</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_vstack</span><span class='op'>(</span><span class='va'>tensors</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>tensors</th>
-      <td><p>(sequence of Tensors) sequence of tensors to concatenate</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="vstack-tensors-out-none-gt-tensor-"><a class="anchor" href="#vstack-tensors-out-none-gt-tensor-"></a>vstack(tensors, *, out=None) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_vstack</span><span class="op">(</span><span class="va">tensors</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>tensors</dt>
+<dd><p>(sequence of Tensors) sequence of tensors to concatenate</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="vstack-tensors-out-none-gt-tensor-">vstack(tensors, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#vstack-tensors-out-none-gt-tensor-"></a></h2>
     
 
 
 <p>Stack tensors in sequence vertically (row wise).</p>
 <p>This is equivalent to concatenation along the first axis after all 1-D tensors
-have been reshaped by <code><a href='torch_atleast_2d.html'>torch_atleast_2d()</a></code>.</p>
+have been reshaped by <code><a href="torch_atleast_2d.html">torch_atleast_2d()</a></code>.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>, <span class='fl'>2</span>, <span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>b</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>4</span>, <span class='fl'>5</span>, <span class='fl'>6</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_vstack</span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span><span class='va'>a</span>,<span class='va'>b</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>a</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/cbind.html'>rbind</a></span><span class='op'>(</span><span class='fl'>1</span>,<span class='fl'>2</span>,<span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>b</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/cbind.html'>rbind</a></span><span class='op'>(</span><span class='fl'>4</span>,<span class='fl'>5</span>,<span class='fl'>6</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_vstack</span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span><span class='va'>a</span>,<span class='va'>b</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  1</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  2</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  3</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  4</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  5</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  6</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{6,1} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">2</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">b</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span>, <span class="fl">5</span>, <span class="fl">6</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_vstack</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">a</span>,<span class="va">b</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/cbind.html" class="external-link">rbind</a></span><span class="op">(</span><span class="fl">1</span>,<span class="fl">2</span>,<span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">b</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/cbind.html" class="external-link">rbind</a></span><span class="op">(</span><span class="fl">4</span>,<span class="fl">5</span>,<span class="fl">6</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_vstack</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">a</span>,<span class="va">b</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  3</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  4</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  5</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  6</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{6,1} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Where</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
-    <div class="hidden name"><code>torch_where.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Where</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_where.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Where</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_where</span><span class='op'>(</span><span class='va'>condition</span>, <span class='va'>self</span>, <span class='va'>other</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>condition</th>
-      <td><p>(BoolTensor) When TRUE (nonzero), yield x, otherwise yield y</p></td>
-    </tr>
-    <tr>
-      <th>self</th>
-      <td><p>(Tensor) values selected at indices where <code>condition</code> is <code>TRUE</code></p></td>
-    </tr>
-    <tr>
-      <th>other</th>
-      <td><p>(Tensor) values selected at indices where <code>condition</code> is <code>FALSE</code></p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_where</span><span class="op">(</span><span class="va">condition</span>, <span class="va">self</span>, <span class="va">other</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>condition</dt>
+<dd><p>(BoolTensor) When TRUE (nonzero), yield x, otherwise yield y</p></dd>
+<dt>self</dt>
+<dd><p>(Tensor) values selected at indices where <code>condition</code> is <code>TRUE</code></p></dd>
+<dt>other</dt>
+<dd><p>(Tensor) values selected at indices where <code>condition</code> is <code>FALSE</code></p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     
-<pre>The tensors `condition`, `x`, `y` must be broadcastable .
-</pre>
-
-<p>See also <code><a href='torch_nonzero.html'>torch_nonzero()</a></code>.</p>
-    <h2 class="hasAnchor" id="where-condition-x-y-gt-tensor-"><a class="anchor" href="#where-condition-x-y-gt-tensor-"></a>where(condition, x, y) -&gt; Tensor </h2>
+<div class="sourceCode"><pre><code>The tensors `condition`, `x`, `y` must be broadcastable .
+</code></pre></div>
 
+<p>See also <code><a href="torch_nonzero.html">torch_nonzero()</a></code>.</p>
+    </div>
+    <div class="section level2">
+    <h2 id="where-condition-x-y-gt-tensor-">where(condition, x, y) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#where-condition-x-y-gt-tensor-"></a></h2>
     
 
 
@@ -229,53 +116,49 @@
     \end{array}
     \right.
 $$</p>
-    <h2 class="hasAnchor" id="where-condition-gt-tuple-of-longtensor-"><a class="anchor" href="#where-condition-gt-tuple-of-longtensor-"></a>where(condition) -&gt; tuple of LongTensor </h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="where-condition-gt-tuple-of-longtensor-">where(condition) -&gt; tuple of LongTensor <a class="anchor" aria-label="anchor" href="#where-condition-gt-tuple-of-longtensor-"></a></h2>
     
 
 
 <p><code>torch_where(condition)</code> is identical to
 <code>torch_nonzero(condition, as_tuple=TRUE)</code>.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='cn'>FALSE</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>2</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>y</span> <span class='op'>=</span> <span class='fu'><a href='torch_ones.html'>torch_ones</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>2</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>x</span></span>
-<span class='r-in'><span class='fu'>torch_where</span><span class='op'>(</span><span class='va'>x</span> <span class='op'>&gt;</span> <span class='fl'>0</span>, <span class='va'>x</span>, <span class='va'>y</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-in'></span>
-<span class='r-in'></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-</pre>
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+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">x</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">2</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">y</span> <span class="op">=</span> <span class="fu"><a href="torch_ones.html">torch_ones</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">2</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">x</span></span>
+<span class="r-in"><span class="fu">torch_where</span><span class="op">(</span><span class="va">x</span> <span class="op">&gt;</span> <span class="fl">0</span>, <span class="va">x</span>, <span class="va">y</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-in"></span>
+<span class="r-in"></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="op">}</span></span>
+</code></pre></div>
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index 88ac7b77b..2690fb83a 100644
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Zeros</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/creation-ops.R'><code>R/creation-ops.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_zeros.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Zeros</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/creation-ops.R" class="external-link"><code>R/creation-ops.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a></small>
+      <div class="d-none name"><code>torch_zeros.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Zeros</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_zeros</span><span class='op'>(</span>
-  <span class='va'>...</span>,
-  names <span class='op'>=</span> <span class='cn'>NULL</span>,
-  dtype <span class='op'>=</span> <span class='cn'>NULL</span>,
-  layout <span class='op'>=</span> <span class='fu'><a href='torch_layout.html'>torch_strided</a></span><span class='op'>(</span><span class='op'>)</span>,
-  device <span class='op'>=</span> <span class='cn'>NULL</span>,
-  requires_grad <span class='op'>=</span> <span class='cn'>FALSE</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>...</th>
-      <td><p>a sequence of integers defining the shape of the output tensor.        Can be a variable number of arguments or a collection like a list or tuple.</p></td>
-    </tr>
-    <tr>
-      <th>names</th>
-      <td><p>optional dimension names</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>).</p></td>
-    </tr>
-    <tr>
-      <th>layout</th>
-      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
-    </tr>
-    <tr>
-      <th>requires_grad</th>
-      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="zeros-size-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"><a class="anchor" href="#zeros-size-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"></a>zeros(*size, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_zeros</span><span class="op">(</span>
+  <span class="va">...</span>,
+  names <span class="op">=</span> <span class="cn">NULL</span>,
+  dtype <span class="op">=</span> <span class="cn">NULL</span>,
+  layout <span class="op">=</span> <span class="fu"><a href="torch_layout.html">torch_strided</a></span><span class="op">(</span><span class="op">)</span>,
+  device <span class="op">=</span> <span class="cn">NULL</span>,
+  requires_grad <span class="op">=</span> <span class="cn">FALSE</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>...</dt>
+<dd><p>a sequence of integers defining the shape of the output tensor.        Can be a variable number of arguments or a collection like a list or tuple.</p></dd>
+<dt>names</dt>
+<dd><p>optional dimension names</p></dd>
+<dt>dtype</dt>
+<dd><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>).</p></dd>
+<dt>layout</dt>
+<dd><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></dd>
+<dt>device</dt>
+<dd><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></dd>
+<dt>requires_grad</dt>
+<dd><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="zeros-size-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-">zeros(*size, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#zeros-size-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"></a></h2>
     
 
 
 <p>Returns a tensor filled with the scalar value <code>0</code>, with the shape defined
 by the variable argument <code>size</code>.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='fu'>torch_zeros</span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_zeros</span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>5</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{5} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="fu">torch_zeros</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_zeros</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">5</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{5} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer>
-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Zeros_like</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/creation-ops.R'><code>R/creation-ops.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
-    <div class="hidden name"><code>torch_zeros_like.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Zeros_like</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/creation-ops.R" class="external-link"><code>R/creation-ops.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a></small>
+      <div class="d-none name"><code>torch_zeros_like.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Zeros_like</p>
     </div>
 
-    <pre class="usage"><span class='fu'>torch_zeros_like</span><span class='op'>(</span>
-  <span class='va'>input</span>,
-  dtype <span class='op'>=</span> <span class='cn'>NULL</span>,
-  layout <span class='op'>=</span> <span class='fu'><a href='torch_layout.html'>torch_strided</a></span><span class='op'>(</span><span class='op'>)</span>,
-  device <span class='op'>=</span> <span class='cn'>NULL</span>,
-  requires_grad <span class='op'>=</span> <span class='cn'>FALSE</span>,
-  memory_format <span class='op'>=</span> <span class='fu'><a href='torch_memory_format.html'>torch_preserve_format</a></span><span class='op'>(</span><span class='op'>)</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>input</th>
-      <td><p>(Tensor) the size of <code>input</code> will determine size of the output tensor.</p></td>
-    </tr>
-    <tr>
-      <th>dtype</th>
-      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned Tensor.        Default: if <code>NULL</code>, defaults to the dtype of <code>input</code>.</p></td>
-    </tr>
-    <tr>
-      <th>layout</th>
-      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned tensor.        Default: if <code>NULL</code>, defaults to the layout of <code>input</code>.</p></td>
-    </tr>
-    <tr>
-      <th>device</th>
-      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, defaults to the device of <code>input</code>.</p></td>
-    </tr>
-    <tr>
-      <th>requires_grad</th>
-      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
-    </tr>
-    <tr>
-      <th>memory_format</th>
-      <td><p>(<code>torch.memory_format</code>, optional) the desired memory format of        returned Tensor. Default: <code>torch_preserve_format</code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="zeros-like-input-dtype-null-layout-null-device-null-requires-grad-false-memory-format-torch-preserve-format-gt-tensor-"><a class="anchor" href="#zeros-like-input-dtype-null-layout-null-device-null-requires-grad-false-memory-format-torch-preserve-format-gt-tensor-"></a>zeros_like(input, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, memory_format=torch.preserve_format) -&gt; Tensor </h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">torch_zeros_like</span><span class="op">(</span>
+  <span class="va">input</span>,
+  dtype <span class="op">=</span> <span class="cn">NULL</span>,
+  layout <span class="op">=</span> <span class="fu"><a href="torch_layout.html">torch_strided</a></span><span class="op">(</span><span class="op">)</span>,
+  device <span class="op">=</span> <span class="cn">NULL</span>,
+  requires_grad <span class="op">=</span> <span class="cn">FALSE</span>,
+  memory_format <span class="op">=</span> <span class="fu"><a href="torch_memory_format.html">torch_preserve_format</a></span><span class="op">(</span><span class="op">)</span>
+<span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>input</dt>
+<dd><p>(Tensor) the size of <code>input</code> will determine size of the output tensor.</p></dd>
+<dt>dtype</dt>
+<dd><p>(<code>torch.dtype</code>, optional) the desired data type of returned Tensor.        Default: if <code>NULL</code>, defaults to the dtype of <code>input</code>.</p></dd>
+<dt>layout</dt>
+<dd><p>(<code>torch.layout</code>, optional) the desired layout of returned tensor.        Default: if <code>NULL</code>, defaults to the layout of <code>input</code>.</p></dd>
+<dt>device</dt>
+<dd><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, defaults to the device of <code>input</code>.</p></dd>
+<dt>requires_grad</dt>
+<dd><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></dd>
+<dt>memory_format</dt>
+<dd><p>(<code>torch.memory_format</code>, optional) the desired memory format of        returned Tensor. Default: <code>torch_preserve_format</code>.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="zeros-like-input-dtype-null-layout-null-device-null-requires-grad-false-memory-format-torch-preserve-format-gt-tensor-">zeros_like(input, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, memory_format=torch.preserve_format) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#zeros-like-input-dtype-null-layout-null-device-null-requires-grad-false-memory-format-torch-preserve-format-gt-tensor-"></a></h2>
     
 
 
 <p>Returns a tensor filled with the scalar value <code>0</code>, with the same size as
 <code>input</code>. <code>torch_zeros_like(input)</code> is equivalent to
 <code>torch_zeros(input.size(), dtype=input.dtype, layout=input.layout, device=input.device)</code>.</p>
-    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
-
+    </div>
+    <div class="section level2">
+    <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></a></h2>
     
 
 <p>As of 0.4, this function does not support an <code>out</code> keyword. As an alternative,
 the old <code>torch_zeros_like(input, out=output)</code> is equivalent to
 <code>torch_zeros(input.size(), out=output)</code>.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>input</span> <span class='op'>=</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>2</span>, <span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>torch_zeros_like</span><span class='op'>(</span><span class='va'>input</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0  0  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  0  0  0</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{2,3} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">input</span> <span class="op">=</span> <span class="fu"><a href="torch_empty.html">torch_empty</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">torch_zeros_like</span><span class="op">(</span><span class="va">input</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0  0  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0  0  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2,3} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer>
-      <div class="copyright">
-  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Daniel Falbel, Javier Luraschi.</p>
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-  <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p>
+<div class="pkgdown-footer-right">
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+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Context-manager that enable anomaly detection for the autograd engine.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/autograd.R" class="external-link"><code>R/autograd.R</code></a></small>
+      <div class="d-none name"><code>with_detect_anomaly.Rd</code></div>
+    </div>
 
-      </header>
+    <div class="ref-description section level2">
+    <p>This does two things:</p>
+    </div>
 
-<div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Context-manager that enable anomaly detection for the autograd engine.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/autograd.R'><code>R/autograd.R</code></a></small>
-    <div class="hidden name"><code>with_detect_anomaly.Rd</code></div>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">with_detect_anomaly</span><span class="op">(</span><span class="va">code</span><span class="op">)</span></code></pre></div>
     </div>
 
-    <div class="ref-description">
-    <p>This does two things:</p><ul>
-<li><p>Running the forward pass with detection enabled will allow the backward
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>code</dt>
+<dd><p>Cod that will be execued in the detect anomaly context.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
+    
+<ul><li><p>Running the forward pass with detection enabled will allow the backward
 pass to print the traceback of the forward operation that created the failing
 backward function.</p></li>
 <li><p>Any backward computation that generate "nan" value will raise an error.</p></li>
-</ul>
-
-    </div>
-
-    <pre class="usage"><span class='fu'>with_detect_anomaly</span><span class='op'>(</span><span class='va'>code</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>code</th>
-      <td><p>Cod that will be execued in the detect anomaly context.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
-
+</ul></div>
+    <div class="section level2">
+    <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></a></h2>
     
 
 <p>This mode should be enabled only for debugging as the different tests
 will slow down your program execution.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>x</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>2</span>, requires_grad <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>y</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span><span class='op'>(</span><span class='fl'>1</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>b</span> <span class='op'>&lt;-</span> <span class='op'>(</span><span class='va'>x</span><span class='op'>^</span><span class='va'>y</span><span class='op'>)</span><span class='op'>$</span><span class='fu'>sum</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>y</span><span class='op'>$</span><span class='fu'>add_</span><span class='op'>(</span><span class='fl'>1</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='kw'><a href='https://rdrr.io/r/base/try.html'>try</a></span><span class='op'>(</span><span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>b</span><span class='op'>$</span><span class='fu'>backward</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='fu'>with_detect_anomaly</span><span class='op'>(</span><span class='op'>{</span></span>
-<span class='r-in'>  <span class='va'>b</span><span class='op'>$</span><span class='fu'>backward</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span><span class='op'>)</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> Error in (function (self, inputs, gradient, retain_graph, create_graph)  : </span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>   one of the variables needed for gradient computation has been modified by an inplace operation: [CPUFloatType [1]] is at version 1; expected version 0 instead. Hint: enable anomaly detection to find the operation that failed to compute its gradient, with torch.autograd.set_detect_anomaly(True).</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">2</span>, requires_grad <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">y</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">1</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">b</span> <span class="op">&lt;-</span> <span class="op">(</span><span class="va">x</span><span class="op">^</span><span class="va">y</span><span class="op">)</span><span class="op">$</span><span class="fu">sum</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">y</span><span class="op">$</span><span class="fu">add_</span><span class="op">(</span><span class="fl">1</span><span class="op">)</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="kw"><a href="https://rdrr.io/r/base/try.html" class="external-link">try</a></span><span class="op">(</span><span class="op">{</span></span>
+<span class="r-in">  <span class="va">b</span><span class="op">$</span><span class="fu">backward</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"></span>
+<span class="r-in">  <span class="fu">with_detect_anomaly</span><span class="op">(</span><span class="op">{</span></span>
+<span class="r-in">    <span class="va">b</span><span class="op">$</span><span class="fu">backward</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in">  <span class="op">}</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Error in (function (self, inputs, gradient, retain_graph, create_graph)  : </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   one of the variables needed for gradient computation has been modified by an inplace operation: [CPUFloatType [1]] is at version 1; expected version 0 instead. Hint: enable anomaly detection to find the operation that failed to compute its gradient, with torch.autograd.set_detect_anomaly(True).</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Exception raised from unpack at ../torch/csrc/autograd/saved_variable.cpp:164 (most recent call first):</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> frame #0: c10::Error::Error(c10::SourceLocation, std::__1::basic_string&lt;char, std::__1::char_traits&lt;char&gt;, std::__1::allocator&lt;char&gt; &gt;) + 98 (0x10bfce522 in libc10.dylib)</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> frame #1: c10::detail::torchCheckFail(char const*, char const*, unsigned int, std::__1::basic_string&lt;char, std::__1::char_traits&lt;char&gt;, std::__1::allocator&lt;char&gt; &gt; const&amp;) + 106 (0x10bfccc3a in libc10.dylib)</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> frame #2: torch::autograd::SavedVariable::unpack(std::__1::shared_ptr&lt;torch::autograd::Node&gt;) const + 2546 (0x1135a7f82 in libtorch_cpu.dylib)</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> frame #3: torch::autograd::generated::PowBackward1::apply(std::__1::vector&lt;at::Tensor, std::__1::allocator&lt;at::Tensor&gt; &gt;&amp;&amp;) + 237 (0x112c6475d in libtorch_cpu.dylib)</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> frame #4: torch::autograd::Node::operator()(std::__1::vector&lt;at::Tensor, std::__1::allocator&lt;at::Tensor&gt; &gt;&amp;&amp;) + 407 (0x11356c237 in libtorch_cpu.dylib)</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> frame #5: torch::autograd::Engine::evaluate_function(std::__1::shared_ptr&lt;torch::autograd::GraphTask&gt;&amp;, torch::autograd::Node*, torch::autograd::InputBuffer&amp;, std::__1::shared_ptr&lt;torch::autograd::ReadyQueue&gt; const&amp;) + 1897 (0x113561b99 in libtorch_cpu.dylib)</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> frame #6: torch::autograd::Engine::thread_main(std::__1::shared_ptr&lt;torch::autograd::GraphTask&gt; const&amp;) + 1961 (0x113560dc9 in libtorch_cpu.dylib)</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> frame #7: torch::autograd::Engine::execute_with_graph_task(std::__1::shared_ptr&lt;torch::autograd::GraphTask&gt; const&amp;, std::__1::shared_ptr&lt;torch::autograd::Node&gt;, torch::autograd::InputBuffer&amp;&amp;) + 1139 (0x11356ac23 in libtorch_cpu.dylib)</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> frame #8: torch::autograd::Engine::execute(std::__1::vector&lt;torch::autograd::Edge, std::__1::allocator&lt;torch::autograd::Edge&gt; &gt; const&amp;, std::__1::vector&lt;at::Tensor, std::__1::allocator&lt;at::Tensor&gt; &gt; const&amp;, bool, bool, bool, std::__1::vector&lt;torch::autograd::Edge, std::__1::allocator&lt;torch::autograd::Edge&gt; &gt; const&amp;) + 2339 (0x1135687e3 in libtorch_cpu.dylib)</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> frame #9: torch::autograd::run_backward(std::__1::vector&lt;at::Tensor, std::__1::allocator&lt;at::Tensor&gt; &gt; const&amp;, std::__1::vector&lt;at::Tensor, std::__1::allocator&lt;at::Tensor&gt; &gt; const&amp;, bool, bool, std::__1::vector&lt;at::Tensor, std::__1::allocator&lt;at::Tensor&gt; &gt; const&amp;, bool, bool) + 2167 (0x1135533b7 in libtorch_cpu.dylib)</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> frame #10: torch::autograd::backward(std::__1::vector&lt;at::Tensor, std::__1::allocator&lt;at::Tensor&gt; &gt; const&amp;, std::__1::vector&lt;at::Tensor, std::__1::allocator&lt;at::Tensor&gt; &gt; const&amp;, c10::optional&lt;bool&gt;, bool, std::__1::vector&lt;at::Tensor, std::__1::allocator&lt;at::Tensor&gt; &gt; const&amp;) + 100 (0x113553914 in libtorch_cpu.dylib)</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> frame #11: torch::autograd::VariableHooks::_backward(at::Tensor const&amp;, c10::ArrayRef&lt;at::Tensor&gt;, c10::optional&lt;at::Tensor&gt; const&amp;, c10::optional&lt;bool&gt;, bool) const + 455 (0x1135acab7 in libtorch_cpu.dylib)</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> frame #12: at::Tensor::_backward(c10::ArrayRef&lt;at::Tensor&gt;, c10::optional&lt;at::Tensor&gt; const&amp;, c10::optional&lt;bool&gt;, bool) const + 75 (0x110900f9b in libtorch_cpu.dylib)</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> frame #13: _lantern_Tensor__backward_tensor_tensorlist_tensor_bool_bool + 286 (0x10b9ca90e in liblantern.dylib)</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> frame #14: std::__1::__function::__func&lt;cpp_torch_method__backward_self_Tensor_inputs_TensorList(XPtrTorchTensor, XPtrTorchTensorList, XPtrTorchOptionalTensor, XPtrTorchoptional_bool, XPtrTorchbool)::$_0, std::__1::allocator&lt;cpp_torch_method__backward_self_Tensor_inputs_TensorList(XPtrTorchTensor, XPtrTorchTensorList, XPtrTorchOptionalTensor, XPtrTorchoptional_bool, XPtrTorchbool)::$_0&gt;, void ()&gt;::operator()() + 53 (0x10b1c7d85 in torchpkg.so)</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> frame #15: std::__1::packaged_task&lt;void ()&gt;::operator()() + 72 (0x10b1c6628 in torchpkg.so)</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> frame #16: void* std::__1::__thread_proxy&lt;std::__1::tuple&lt;std::__1::unique_ptr&lt;std::__1::__thread_struct, std::__1::default_delete&lt;std::__1::__thread_struct&gt; &gt;, (anonymous namespace)::schedule_backward_task(std::__1::packaged_task&lt;void ()&gt;&amp;&amp;)::$_7&gt; &gt;(void*) + 132 (0x10b1c64a4 in torchpkg.so)</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> frame #17: _pthread_start + 224 (0x7fff2045b8fc in libsystem_pthread.dylib)</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> frame #18: thread_start + 15 (0x7fff20457443 in libsystem_pthread.dylib)</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
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diff --git a/reference/with_enable_grad.html b/reference/with_enable_grad.html
index b72193413..f27491e3f 100644
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@@ -1,254 +1,139 @@
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 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Enable grad</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/autograd.R'><code>R/autograd.R</code></a></small>
-    <div class="hidden name"><code>with_enable_grad.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Enable grad</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/autograd.R" class="external-link"><code>R/autograd.R</code></a></small>
+      <div class="d-none name"><code>with_enable_grad.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Context-manager that enables gradient calculation.
-Enables gradient calculation, if it has been disabled via <a href='with_no_grad.html'>with_no_grad</a>.</p>
+Enables gradient calculation, if it has been disabled via <a href="with_no_grad.html">with_no_grad</a>.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>with_enable_grad</span><span class='op'>(</span><span class='va'>code</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>code</th>
-      <td><p>code to be executed with gradient recording.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">with_enable_grad</span><span class="op">(</span><span class="va">code</span><span class="op">)</span></code></pre></div>
+    </div>
 
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>code</dt>
+<dd><p>code to be executed with gradient recording.</p></dd>
+</dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>This context manager is thread local; it will not affect computation in
 other threads.</p>
+    </div>
 
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='va'>x</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fl'>1</span>, requires_grad<span class='op'>=</span><span class='cn'>TRUE</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'><a href='with_no_grad.html'>with_no_grad</a></span><span class='op'>(</span><span class='op'>{</span></span>
-<span class='r-in'>  <span class='fu'>with_enable_grad</span><span class='op'>(</span><span class='op'>{</span></span>
-<span class='r-in'>    <span class='va'>y</span> <span class='op'>=</span> <span class='va'>x</span> <span class='op'>*</span> <span class='fl'>2</span></span>
-<span class='r-in'>  <span class='op'>}</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>y</span><span class='op'>$</span><span class='fu'>backward</span><span class='op'>(</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>x</span><span class='op'>$</span><span class='va'>grad</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span>  2</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ CPUFloatType{1} ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"></span>
+<span class="r-in"><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fl">1</span>, requires_grad <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu"><a href="with_no_grad.html">with_no_grad</a></span><span class="op">(</span><span class="op">{</span></span>
+<span class="r-in">  <span class="fu">with_enable_grad</span><span class="op">(</span><span class="op">{</span></span>
+<span class="r-in">    <span class="va">y</span> <span class="op">&lt;-</span> <span class="va">x</span> <span class="op">*</span> <span class="fl">2</span></span>
+<span class="r-in">  <span class="op">}</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">y</span><span class="op">$</span><span class="fu">backward</span><span class="op">(</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">x</span><span class="op">$</span><span class="va">grad</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{1} ]</span>
+</code></pre></div>
+    </div>
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diff --git a/reference/with_no_grad.html b/reference/with_no_grad.html
index b008d2c40..f15ab760c 100644
--- a/reference/with_no_grad.html
+++ b/reference/with_no_grad.html
@@ -1,244 +1,127 @@
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+</nav><div class="container template-reference-topic">
 <div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Temporarily modify gradient recording.</h1>
-    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/autograd.R'><code>R/autograd.R</code></a></small>
-    <div class="hidden name"><code>with_no_grad.Rd</code></div>
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Temporarily modify gradient recording.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/autograd.R" class="external-link"><code>R/autograd.R</code></a></small>
+      <div class="d-none name"><code>with_no_grad.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Temporarily modify gradient recording.</p>
     </div>
 
-    <pre class="usage"><span class='fu'>with_no_grad</span><span class='op'>(</span><span class='va'>code</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>code</th>
-      <td><p>code to be executed with no gradient recording.</p></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='r-in'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span></span>
-<span class='r-in'><span class='va'>x</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/stats/Uniform.html'>runif</a></span><span class='op'>(</span><span class='fl'>5</span><span class='op'>)</span>, requires_grad <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span></span>
-<span class='r-in'><span class='fu'>with_no_grad</span><span class='op'>(</span><span class='op'>{</span></span>
-<span class='r-in'>  <span class='va'>x</span><span class='op'>$</span><span class='fu'>sub_</span><span class='op'>(</span><span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/numeric.html'>as.numeric</a></span><span class='op'>(</span><span class='fl'>1</span><span class='op'>:</span><span class='fl'>5</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span></span>
-<span class='r-in'><span class='op'>}</span><span class='op'>)</span></span>
-<span class='r-in'><span class='va'>x</span></span>
-<span class='r-in'><span class='va'>x</span><span class='op'>$</span><span class='va'>grad</span></span>
-<span class='r-in'></span>
-<span class='r-in'><span class='op'>}</span></span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> torch_tensor</span>
-<span class='r-out co'><span class='r-pr'>#&gt;</span> [ Tensor (undefined) ]</span>
-</pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">with_no_grad</span><span class="op">(</span><span class="va">code</span><span class="op">)</span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>code</dt>
+<dd><p>code to be executed with no gradient recording.</p></dd>
+</dl></div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span class="r-in"><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/stats/Uniform.html" class="external-link">runif</a></span><span class="op">(</span><span class="fl">5</span><span class="op">)</span>, requires_grad <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">with_no_grad</span><span class="op">(</span><span class="op">{</span></span>
+<span class="r-in">  <span class="va">x</span><span class="op">$</span><span class="fu">sub_</span><span class="op">(</span><span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/numeric.html" class="external-link">as.numeric</a></span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="fl">5</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span class="r-in"><span class="op">}</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">x</span></span>
+<span class="r-in"><span class="va">x</span><span class="op">$</span><span class="va">grad</span></span>
+<span class="r-in"><span class="op">}</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ Tensor (undefined) ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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diff --git a/search.json b/search.json
new file mode 100644
index 000000000..602a205d6
--- /dev/null
+++ b/search.json
@@ -0,0 +1 @@
+[{"path":"https://torch.mlverse.org/docs/CONTRIBUTING.html","id":null,"dir":"","previous_headings":"","what":"Contributing to torch","title":"Contributing to torch","text":"outlines propose change torch. detailed info contributing , tidyverse packages, please see development contributing guide.","code":""},{"path":"https://torch.mlverse.org/docs/CONTRIBUTING.html","id":"fixing-typos","dir":"","previous_headings":"","what":"Fixing typos","title":"Contributing to torch","text":"can fix typos, spelling mistakes, grammatical errors documentation directly using GitHub web interface, long changes made source file. generally means ’ll need edit roxygen2 comments .R, .Rd file. can find .R file generates .Rd reading comment first line. See also Documentation section.","code":""},{"path":"https://torch.mlverse.org/docs/CONTRIBUTING.html","id":"filing-bugs","dir":"","previous_headings":"","what":"Filing bugs","title":"Contributing to torch","text":"find bug torch please open issue . Please, provide detailed information reproduce bug. great also provide reprex.","code":""},{"path":"https://torch.mlverse.org/docs/CONTRIBUTING.html","id":"feature-requests","dir":"","previous_headings":"","what":"Feature requests","title":"Contributing to torch","text":"Feel free open issues add feature-request tag. Try searching ’s already open issue feature-request, case ’s better comment upvote intead opening new one.","code":""},{"path":"https://torch.mlverse.org/docs/CONTRIBUTING.html","id":"examples","dir":"","previous_headings":"","what":"Examples","title":"Contributing to torch","text":"welcome contributed examples. feel free open PR new examples. examples placed vignettes/examples folder. examples .R file .Rmd file name just renders code. See mnist-mlp.R mnist-mlp.Rmd One must able run example without manually downloading dataset/file. also add entry _pkgdown.yaml file.","code":""},{"path":"https://torch.mlverse.org/docs/CONTRIBUTING.html","id":"code-contributions","dir":"","previous_headings":"","what":"Code contributions","title":"Contributing to torch","text":"many open issues github repo ’s one item want work , can comment ask directions.","code":""},{"path":"https://torch.mlverse.org/docs/CONTRIBUTING.html","id":"code-style","dir":"","previous_headings":"Code contributions","what":"Code style","title":"Contributing to torch","text":"New R code follow tidyverse style guide. can use styler package apply styles, simply run tools/style.sh script, also formats code removes whitespaces. Please don’t restyle code nothing PR. New C/C++ code follow Google style guide. can use clang-format apply styles, simply run tools/style.sh script, also formats code removes whitespaces. Please don’t restyle code nothing PR. use roxygen2, Markdown syntax, build documentation package. use testthat unit tests. Contributions test cases included easier accept.","code":""},{"path":"https://torch.mlverse.org/docs/CONTRIBUTING.html","id":"requirements","dir":"","previous_headings":"Code contributions","what":"Requirements","title":"Contributing to torch","text":"R installation R Tools compilation (Windows) devtools package CMake compile lantern binaries","code":""},{"path":"https://torch.mlverse.org/docs/CONTRIBUTING.html","id":"workflow","dir":"","previous_headings":"Code contributions","what":"Workflow","title":"Contributing to torch","text":"use devtools toolchain development, steps must done setiing . first time clone repository, must run: compile Lantern binaries download LibTorch copy binaries deps folder working directory. command must run everytime modify lantern code. ie. code lives lantern/src. can run load torch test interactively. run test suite.","code":"source(\"tools/buildlantern.R\") devtools::load_all() devtools::test()"},{"path":"https://torch.mlverse.org/docs/CONTRIBUTING.html","id":"documentation","dir":"","previous_headings":"","what":"Documentation","title":"Contributing to torch","text":"use roxygen2 generate documentation. order update docs, edit file R directory. regenerate preview docs, use custom tools/document.R script, need patch roxygen2 avoid running examples CRAN.","code":""},{"path":"https://torch.mlverse.org/docs/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"MIT License","title":"MIT License","text":"Copyright (c) 2020 Daniel Falbel Permission hereby granted, free charge, person obtaining copy software associated documentation files (“Software”), deal Software without restriction, including without limitation rights use, copy, modify, merge, publish, distribute, sublicense, /sell copies Software, permit persons Software furnished , subject following conditions: copyright notice permission notice shall included copies substantial portions Software. SOFTWARE PROVIDED “”, WITHOUT WARRANTY KIND, EXPRESS IMPLIED, INCLUDING LIMITED WARRANTIES MERCHANTABILITY, FITNESS PARTICULAR PURPOSE NONINFRINGEMENT. EVENT SHALL AUTHORS COPYRIGHT HOLDERS LIABLE CLAIM, DAMAGES LIABILITY, WHETHER ACTION CONTRACT, TORT OTHERWISE, ARISING , CONNECTION SOFTWARE USE DEALINGS SOFTWARE.","code":""},{"path":"https://torch.mlverse.org/docs/articles/distributions.html","id":"basic-univariate-distributions","dir":"Articles","previous_headings":"","what":"Basic univariate distributions","title":"Distributions","text":"Let’s start create new instance normal distribution: can draw samples : , draw multiple samples: can evaluate log probability values: , evaluate multiple log probabilities:","code":"n <- distr_normal(loc = 0, scale = 1) n #> torch_Normal () n$sample() #> torch_tensor #>  0.3550 #> [ CPUFloatType{1} ] n$sample(3) #> torch_tensor #> -0.7894 #>  0.3833 #> -0.6563 #> [ CPUFloatType{3,1} ] n$log_prob(0) #> torch_tensor #> -0.9189 #> [ CPUFloatType{1} ] log(dnorm(0)) # equivalent R code #> [1] -0.9189385 n$log_prob(c(0, 2, 4)) #> torch_tensor #> -0.9189 #> -2.9189 #> -8.9189 #> [ CPUFloatType{3} ]"},{"path":"https://torch.mlverse.org/docs/articles/distributions.html","id":"multiple-distributions","dir":"Articles","previous_headings":"","what":"Multiple distributions","title":"Distributions","text":"distribution can take tensor ’s parameters: object represents 3 independent Bernoulli distributions, one element tensor. can sample single observation: , batch n observations:","code":"b <- distr_bernoulli(probs = torch_tensor(c(0.25, 0.5, 0.75))) b #> torch_Bernoulli () b$sample() #> torch_tensor #>  0 #>  1 #>  1 #> [ CPUFloatType{3} ] b$sample(6) #> torch_tensor #>  0  1  0 #>  0  0  1 #>  0  0  1 #>  0  1  1 #>  0  0  1 #>  0  0  1 #> [ CPUFloatType{6,3} ]"},{"path":"https://torch.mlverse.org/docs/articles/distributions.html","id":"using-distributions-within-models","dir":"Articles","previous_headings":"","what":"Using distributions within models","title":"Distributions","text":"log_prob method distributions can differentiated, thus, distributions can used train models torch. Let’s implement Gaussian linear model, first let’s simulate data plot:  can now define model: can now train model : can see parameter estimates : quickly compare glm() function:","code":"x <- torch_randn(100, 1) y <- 2*x + 1 + torch_randn(100, 1) plot(as.numeric(x), as.numeric(y)) GaussianLinear <- nn_module(   initialize = function() {     # this linear predictor will estimate the mean of the normal distribution     self$linear <- nn_linear(1, 1)     # this parameter will hold the estimate of the variability     self$scale <- nn_parameter(torch_ones(1))   },   forward = function(x) {     # we estimate the mean     loc <- self$linear(x)     # return a normal distribution     distr_normal(loc, self$scale)   } )  model <- GaussianLinear() opt <- optim_sgd(model$parameters, lr = 0.1)  for (i in 1:100) {   opt$zero_grad()   d <- model(x)   loss <- torch_mean(-d$log_prob(y))   loss$backward()   opt$step()   if (i %% 10 == 0)     cat(\"iter: \", i, \" loss: \", loss$item(), \"\\n\") } #> iter:  10  loss:  1.809854  #> iter:  20  loss:  1.606322  #> iter:  30  loss:  1.46065  #> iter:  40  loss:  1.399433  #> iter:  50  loss:  1.39078  #> iter:  60  loss:  1.390136  #> iter:  70  loss:  1.390089  #> iter:  80  loss:  1.390086  #> iter:  90  loss:  1.390085  #> iter:  100  loss:  1.390085 model$parameters #> $linear.weight #> torch_tensor #>  1.9772 #> [ CPUFloatType{1,1} ][ requires_grad = TRUE ] #>  #> $linear.bias #> torch_tensor #>  1.0390 #> [ CPUFloatType{1} ][ requires_grad = TRUE ] #>  #> $scale #> torch_tensor #>  0.9716 #> [ CPUFloatType{1} ][ requires_grad = TRUE ] summary(glm(as.numeric(y) ~ as.numeric(x))) #>  #> Call: #> glm(formula = as.numeric(y) ~ as.numeric(x)) #>  #> Deviance Residuals:  #>     Min       1Q   Median       3Q      Max   #> -2.5311  -0.6277  -0.1177   0.5544   3.3037   #>  #> Coefficients: #>               Estimate Std. Error t value Pr(>|t|)     #> (Intercept)    1.03900    0.09844   10.55   <2e-16 *** #> as.numeric(x)  1.97723    0.09392   21.05   <2e-16 *** #> --- #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #>  #> (Dispersion parameter for gaussian family taken to be 0.963191) #>  #>     Null deviance: 521.260  on 99  degrees of freedom #> Residual deviance:  94.393  on 98  degrees of freedom #> AIC: 284.02 #>  #> Number of Fisher Scoring iterations: 2"},{"path":"https://torch.mlverse.org/docs/articles/extending-autograd.html","id":"note","dir":"Articles","previous_headings":"","what":"Note","title":"Extending Autograd","text":"’s user’s responsibility use special functions forward’s ctx properly order ensure new autograd_function works properly autograd engine. save_for_backward() must used saving input ouput forward used later backward. mark_dirty() must used mark input modified inplace forward function. mark_non_differentiable() must used tell engine output differentiable.","code":""},{"path":"https://torch.mlverse.org/docs/articles/extending-autograd.html","id":"examples","dir":"Articles","previous_headings":"","what":"Examples","title":"Extending Autograd","text":"can find code linear function: , give additional example function parametrized non-Tensor arguments:","code":"linear <- autograd_function(   forward = function(ctx, input, weight, bias = NULL) {     ctx$save_for_backward(input = input, weight = weight, bias = bias)     output <- input$mm(weight$t())     if (!is.null(bias))       output <- output + bias$unsqueeze(0)$expand_as(output)          output   },   backward = function(ctx, grad_output) {          s <- ctx$saved_variables          grads <- list(       input = NULL,       weight = NULL,       bias = NULL     )          if (ctx$needs_input_grad$input)       grads$input <- grad_output$mm(s$weight)          if (ctx$needs_input_grad$weight)       grads$weight <- grad_output$t()$mm(s$input)          if (!is.null(s$bias) && ctx$needs_input_grad$bias)       grads$bias <- grad_output$sum(dim = 0)          grads   } ) mul_constant <- autograd_function(   forward = function(ctx, tensor, constant) {     ctx$save_for_backward(constant = constant)     tensor * constant   },   backward = function(ctx, grad_output) {     v <- ctx$saved_variables     list(       tensor = grad_output * v$constant     )   } ) x <- torch_tensor(1, requires_grad = TRUE) o <- mul_constant(x, 2) o$backward() x$grad #> torch_tensor #>  2 #> [ CPUFloatType{1} ]"},{"path":"https://torch.mlverse.org/docs/articles/indexing.html","id":"single-element-indexing","dir":"Articles","previous_headings":"","what":"Single element indexing","title":"Indexing tensors","text":"Single element indexing 1-D tensors works mostly expected. Like R, 1-based. Unlike R though, accepts negative indices indexing end array. (R, negative indices used remove elements.) can also subset matrices higher dimensions arrays using syntax: Note one indexes multidimensional tensor fewer indices dimensions, one gets error, unlike R flatten array. example:","code":"x <- torch_tensor(1:10) x[1] #> torch_tensor #> 1 #> [ CPULongType{} ] x[-1] #> torch_tensor #> 10 #> [ CPULongType{} ] x <- x$reshape(shape = c(2,5)) x #> torch_tensor #>   1   2   3   4   5 #>   6   7   8   9  10 #> [ CPULongType{2,5} ] x[1,3] #> torch_tensor #> 3 #> [ CPULongType{} ] x[1,-1] #> torch_tensor #> 5 #> [ CPULongType{} ] x[1] #> torch_tensor #>  1 #>  2 #>  3 #>  4 #>  5 #> [ CPULongType{5} ]"},{"path":"https://torch.mlverse.org/docs/articles/indexing.html","id":"slicing-and-striding","dir":"Articles","previous_headings":"","what":"Slicing and striding","title":"Indexing tensors","text":"possible slice stride arrays extract sub-arrays number dimensions, different sizes original. best illustrated examples: can also use 1:10:2 syntax means: range 1 10, take every second item. example: Another special syntax N, meaning size specified dimension. Note: slicing behavior relies Non Standard Evaluation. requires expression passed [ exactly resulting R vector. allow dynamic dynamic indices, can create new slice using slc function. example: equivalent :","code":"x <- torch_tensor(1:10) x #> torch_tensor #>   1 #>   2 #>   3 #>   4 #>   5 #>   6 #>   7 #>   8 #>   9 #>  10 #> [ CPULongType{10} ] x[2:5] #> torch_tensor #>  2 #>  3 #>  4 #>  5 #> [ CPULongType{4} ] x[1:(-7)] #> torch_tensor #>  1 #>  2 #>  3 #>  4 #> [ CPULongType{4} ] x[1:5:2] #> torch_tensor #>  1 #>  3 #>  5 #> [ CPULongType{3} ] x[5:N] #> torch_tensor #>   5 #>   6 #>   7 #>   8 #>   9 #>  10 #> [ CPULongType{6} ] x[1:5:2] #> torch_tensor #>  1 #>  3 #>  5 #> [ CPULongType{3} ] x[slc(start = 1, end = 5, step = 2)] #> torch_tensor #>  1 #>  3 #>  5 #> [ CPULongType{3} ]"},{"path":"https://torch.mlverse.org/docs/articles/indexing.html","id":"getting-the-complete-dimension","dir":"Articles","previous_headings":"","what":"Getting the complete dimension","title":"Indexing tensors","text":"Like R, can take elements dimension leaving index empty. Consider matrix: following syntax give first row: give first 2 columns:","code":"x <- torch_randn(2, 3) x #> torch_tensor #>  0.4345  2.2531 -0.8595 #> -1.8210 -0.9156 -1.1016 #> [ CPUFloatType{2,3} ] x[1,] #> torch_tensor #>  0.4345 #>  2.2531 #> -0.8595 #> [ CPUFloatType{3} ] x[,1:2] #> torch_tensor #>  0.4345  2.2531 #> -1.8210 -0.9156 #> [ CPUFloatType{2,2} ]"},{"path":"https://torch.mlverse.org/docs/articles/indexing.html","id":"dropping-dimensions","dir":"Articles","previous_headings":"","what":"Dropping dimensions","title":"Indexing tensors","text":"default, indexing single integer, dimension dropped avoid singleton dimension: can optionally use drop = FALSE argument avoid dropping dimension.","code":"x <- torch_randn(2, 3) x[1,]$shape #> [1] 3 x[1,,drop = FALSE]$shape #> [1] 1 3"},{"path":"https://torch.mlverse.org/docs/articles/indexing.html","id":"adding-a-new-dimension","dir":"Articles","previous_headings":"","what":"Adding a new dimension","title":"Indexing tensors","text":"’s possible add new dimension tensor using index-like syntax: can also use NULL instead newaxis:","code":"x <- torch_tensor(c(10)) x$shape #> [1] 1 x[, newaxis]$shape #> [1] 1 1 x[, newaxis, newaxis]$shape #> [1] 1 1 1 x[,NULL]$shape #> [1] 1 1"},{"path":"https://torch.mlverse.org/docs/articles/indexing.html","id":"dealing-with-variable-number-of-indices","dir":"Articles","previous_headings":"","what":"Dealing with variable number of indices","title":"Indexing tensors","text":"Sometimes don’t know many dimensions tensor , know last available dimension, first one. subsume others, can use ..:","code":"z <- torch_tensor(1:125)$reshape(c(5,5,5)) z[1,..] #> torch_tensor #>   1   2   3   4   5 #>   6   7   8   9  10 #>  11  12  13  14  15 #>  16  17  18  19  20 #>  21  22  23  24  25 #> [ CPULongType{5,5} ] z[..,1] #> torch_tensor #>    1    6   11   16   21 #>   26   31   36   41   46 #>   51   56   61   66   71 #>   76   81   86   91   96 #>  101  106  111  116  121 #> [ CPULongType{5,5} ]"},{"path":"https://torch.mlverse.org/docs/articles/indexing.html","id":"indexing-with-vectors","dir":"Articles","previous_headings":"","what":"Indexing with vectors","title":"Indexing tensors","text":"Vector indexing also supported care must taken regarding performance , general much less performant slice based indexing. Note: Starting version 0.5.0, vector indexing torch follows R semantics, prior behavior similar numpy’s advanced indexing. use old behavior, consider using ?torch_index, ?torch_index_put torch_index_put_. can also use boolean vectors, example: examples also work index long boolean tensors, instead R vectors. ’s also possible index multi-dimensional boolean tensors:","code":"x <- torch_randn(4,4) x[c(1,3), c(1,3)] #> torch_tensor #>  0.3352  0.8809 #> -0.0952 -0.3883 #> [ CPUFloatType{2,2} ] x[c(TRUE, FALSE, TRUE, FALSE), c(TRUE, FALSE, TRUE, FALSE)] #> torch_tensor #>  0.3352  0.8809 #> -0.0952 -0.3883 #> [ CPUFloatType{2,2} ] x <- torch_tensor(rbind(   c(1,2,3),   c(4,5,6) )) x[x>3] #> torch_tensor #>  4 #>  5 #>  6 #> [ CPUFloatType{3} ]"},{"path":[]},{"path":"https://torch.mlverse.org/docs/articles/installation.html","id":"cpu","dir":"Articles","previous_headings":"Windows","what":"CPU","title":"Installation","text":"don’t GPU want install CPU version torch, can install : Windows distributions don’t Visual Studio runtime pre-installed observe error like: See instructions install .","code":"install.packages(\"torch\") Error in cpp_lantern_init(normalizePath(install_path())): C:\\Users\\User\\Documents\\R\\R-4.0.2\\library\\torch\\deps\\lantern.dll - The specified module could not be found."},{"path":"https://torch.mlverse.org/docs/articles/installation.html","id":"gpu","dir":"Articles","previous_headings":"Windows","what":"GPU","title":"Installation","text":"Since version 0.1.1 torch supports GPU installation Windows. order use GPU’s torch need : CUDA compatible NVIDIA GPU. can find CUDA compatible GPU . properly installed NVIDIA CUDA toolkit version 10.2 11.1. CUDA v11.1, follow installation instructions . Note: currently support latest CUDA version 11. installed cuDNN version 7.6 (using CUDA v10.2) cuDNN 8.0 (using CUDA 11.1). Follow installation instructions available . installed pre-requisites can install torch : followed default installation locations detect CUDA software installed automatically download GPU enabled Lantern binaries. can also specify CUDA env var something like Sys.setenv(CUDA=\"11.1\") want force specific version CUDA toolkit.","code":"install.packages(\"torch\")"},{"path":[]},{"path":"https://torch.mlverse.org/docs/articles/installation.html","id":"cpu-1","dir":"Articles","previous_headings":"MacOS","what":"CPU","title":"Installation","text":"support CPU builds torch MacOS. MacOS can install torch :","code":"install.packages(\"torch\")"},{"path":[]},{"path":"https://torch.mlverse.org/docs/articles/installation.html","id":"cpu-2","dir":"Articles","previous_headings":"Linux","what":"CPU","title":"Installation","text":"install cpu version torch can run:","code":"install.packages(\"torch\")"},{"path":"https://torch.mlverse.org/docs/articles/installation.html","id":"gpu-1","dir":"Articles","previous_headings":"Linux","what":"GPU","title":"Installation","text":"install GPU version torch linux must verify : NVIDIA CUDA compatible GPU. can find CUDA compatible GPU . correctly installed NVIDIA CUDA Toolkit versions 10.2 11.1, follow instructions . installed cuDNN version 7.6 - (CUDA 10.2) 8.0 CUDA 11.1. Follow installation instructions available . installed pre-requisites can install torch : followed default installation locations detect CUDA software installed automatically download GPU enabled Lantern binaries. can also specify CUDA env var something like Sys.setenv(CUDA=\"10.2\") want force specific version CUDA toolkit.","code":"install.packages(\"torch\")"},{"path":[]},{"path":"https://torch.mlverse.org/docs/articles/installation.html","id":"large-file-download-timeout","dir":"Articles","previous_headings":"Troubleshooting","what":"Large file download timeout","title":"Installation","text":"encounter timeout library download, , downloads end-warning : means encounter download timeout. , increase timeout value install_torch() like","code":"Warning messages: 1: In utils::download.file(library_url, temp_file) :   downloaded length 44901568 != reported length 141774525 2: In utils::download.file(library_url, temp_file) :   URL '...': Timeout of 60 seconds was reached 3: Failed to install Torch, manually run install_torch(). download from 'https://download.pytorch.org/libtorch/cpu/libtorch-macos-1.7.1.zip' failed install_torch(timeout = 600)"},{"path":"https://torch.mlverse.org/docs/articles/installation.html","id":"file-based-download","dir":"Articles","previous_headings":"Troubleshooting","what":"File based download","title":"Installation","text":"cases reach download servers machine intend install torch , last resort install Torch Lantern library files. done 3 steps : 1- get download URLs files. 2- save files machine filesystem. use /tmp/ example . 3- install torch files","code":"get_install_libs_url(type = \"10.2\") install_torch_from_file(libtorch = \"file:///tmp/libtorch-cxx11-abi-shared-with-deps-1.7.1%2Bcu101.zip\",                         liblantern = \"file:///tmp/Linux-gpu-101.zip\")"},{"path":"https://torch.mlverse.org/docs/articles/loading-data.html","id":"datasets-and-data-loaders","dir":"Articles","previous_headings":"","what":"Datasets and data loaders","title":"Loading data","text":"Central data ingestion preprocessing datasets data loaders. dataset object holds data use, data loader object load data dataset providing way access subsets data. using datasets data loaders process clearly organizing data passing components torch package, model training. Built torch premade datasets commonly used machine learning, MNIST handwriting dataset (mnist_dataset()). prebuilt datasets relate image recognition natural language processing. example use MNIST dataset dataloader. First, minst_dataset() function used create ds Dataset object. dataloader dl created query data. Finally, dataloader used coro::loop() iterate batches data: See vignettes/examples/mnist-cnn.R complete example. common situation unique set data isn’t included package ’ll need make custom Dataset subclass using dataset() function. custom Dataset subclass abstract R6 container data. need know information particular dataset, iterate . minimum, using dataset() create custom Dataset class ’ll want define following: name - convenience, keep track type data initialize - member function defining create object class. parameters, objects class , can give specific parameters usually different objects different data. .getitem - member function called dataloader goes pull new batch data. can include preprocessing function needed. Note function called extremely frequently, ’s advantageous make fast. .length - return amount data dataset, helpful users.","code":"# Create a dataset from included data ds <- mnist_dataset(   dir,    download = TRUE,    transform = function(x) {     x <- x$to(dtype = torch_float())/256     x[newaxis,..]   } )  # Create the loader to query the data in batches dl <- dataloader(ds, batch_size = 32, shuffle = TRUE)  coro::loop(for (b in dl)) { # use the data from each batch `b` here # ... })"},{"path":"https://torch.mlverse.org/docs/articles/loading-data.html","id":"example-of-using-a-custom-dataset","dir":"Articles","previous_headings":"","what":"Example of using a custom Dataset","title":"Loading data","text":"may sound complicated base logic steps–complexity often comes data involved preprocessing . show create Dataset class train Allison Horst's penguins. addition, number helper functions can defined. , assume penguins already loaded, preprocessing consists removing lines NA values, transforming factors numbers starting 0, converting R data types torch tensors. .getitem, essentially decide data going used: variables besides species go x, predictor, species constitute y, target. Predictor target returned list, accessed batch[[1]] batch[[2]] training. Let’s create dataset , query ’s length, look first item: able iterate tuxes, need data loader (override default batch size 1): Calling .length() data loader (opposed dataset) return number batches : can create iterator inspect first batch: train network, can use coro::loop() iterate batches.","code":"library(palmerpenguins) library(magrittr)  penguins #> # A tibble: 344 × 8 #>    species island    bill_length_mm bill_depth_mm flipper_length_mm body_mass_g #>    <fct>   <fct>              <dbl>         <dbl>             <int>       <int> #>  1 Adelie  Torgersen           39.1          18.7               181        3750 #>  2 Adelie  Torgersen           39.5          17.4               186        3800 #>  3 Adelie  Torgersen           40.3          18                 195        3250 #>  4 Adelie  Torgersen           NA            NA                  NA          NA #>  5 Adelie  Torgersen           36.7          19.3               193        3450 #>  6 Adelie  Torgersen           39.3          20.6               190        3650 #>  7 Adelie  Torgersen           38.9          17.8               181        3625 #>  8 Adelie  Torgersen           39.2          19.6               195        4675 #>  9 Adelie  Torgersen           34.1          18.1               193        3475 #> 10 Adelie  Torgersen           42            20.2               190        4250 #> # … with 334 more rows, and 2 more variables: sex <fct>, year <int> penguins_dataset <- dataset(      name = \"penguins_dataset\",      initialize = function() {     self$data <- self$prepare_penguin_data()   },      .getitem = function(index) {          x <- self$data[index, 2:-1]     y <- self$data[index, 1]$to(torch_long())          list(x, y)   },      .length = function() {     self$data$size()[[1]]   },      prepare_penguin_data = function() {          input <- na.omit(penguins)      # conveniently, the categorical data are already factors     input$species <- as.numeric(input$species)     input$island <- as.numeric(input$island)     input$sex <- as.numeric(input$sex)          input <- as.matrix(input)     torch_tensor(input)   } ) tuxes <- penguins_dataset() tuxes$.length() #> [1] 333 tuxes$.getitem(1) #> [[1]] #> torch_tensor #>     3.0000 #>    39.1000 #>    18.7000 #>   181.0000 #>  3750.0000 #>     2.0000 #>  2007.0000 #> [ CPUFloatType{7} ] #>  #> [[2]] #> torch_tensor #> 1 #> [ CPULongType{} ] dl <- tuxes %>% dataloader(batch_size = 8) dl$.length() #> [1] 42 iter <- dl$.iter() b <- iter$.next() b #> [[1]] #> torch_tensor #>     3.0000    39.1000    18.7000   181.0000  3750.0000     2.0000  2007.0000 #>     3.0000    39.5000    17.4000   186.0000  3800.0000     1.0000  2007.0000 #>     3.0000    40.3000    18.0000   195.0000  3250.0000     1.0000  2007.0000 #>     3.0000    36.7000    19.3000   193.0000  3450.0000     1.0000  2007.0000 #>     3.0000    39.3000    20.6000   190.0000  3650.0000     2.0000  2007.0000 #>     3.0000    38.9000    17.8000   181.0000  3625.0000     1.0000  2007.0000 #>     3.0000    39.2000    19.6000   195.0000  4675.0000     2.0000  2007.0000 #>     3.0000    41.1000    17.6000   182.0000  3200.0000     1.0000  2007.0000 #> [ CPUFloatType{8,7} ] #>  #> [[2]] #> torch_tensor #>  1 #>  1 #>  1 #>  1 #>  1 #>  1 #>  1 #>  1 #> [ CPULongType{8} ]"},{"path":"https://torch.mlverse.org/docs/articles/loading-data.html","id":"training-with-data-loaders","dir":"Articles","previous_headings":"Example of using a custom Dataset","what":"Training with data loaders","title":"Loading data","text":"example network simple. (reality, want treat island categorical variable , either one-hot-encode embed .) still need optimizer: ’re ready train: example trained deep learning model using dataset() define custom class loaded batches data loader. using dataset data loader able write code split data preprocessing setup model training .","code":"net <- nn_module(   \"PenguinNet\",   initialize = function() {     self$fc1 <- nn_linear(7, 32)     self$fc2 <- nn_linear(32, 3)   },   forward = function(x) {     x %>%        self$fc1() %>%        nnf_relu() %>%        self$fc2() %>%        nnf_log_softmax(dim = 1)   } )  model <- net() optimizer <- optim_sgd(model$parameters, lr = 0.01) for (epoch in 1:10) {      l <- c()      coro::loop(for (b in dl) {     optimizer$zero_grad()     output <- model(b[[1]])     loss <- nnf_nll_loss(output, b[[2]])     loss$backward()     optimizer$step()     l <- c(l, loss$item())   })      cat(sprintf(\"Loss at epoch %d: %3f\\n\", epoch, mean(l))) } #> Loss at epoch 1: 306.894899 #> Loss at epoch 2: 2.068251 #> Loss at epoch 3: 2.068251 #> Loss at epoch 4: 2.068251 #> Loss at epoch 5: 2.068251 #> Loss at epoch 6: 2.068251 #> Loss at epoch 7: 2.068251 #> Loss at epoch 8: 2.068251 #> Loss at epoch 9: 2.068251 #> Loss at epoch 10: 2.068251"},{"path":"https://torch.mlverse.org/docs/articles/loading-data.html","id":"notes-on-efficiency","dir":"Articles","previous_headings":"","what":"Notes on efficiency","title":"Loading data","text":"using datasets data loaders may find certain conditions code running slowly ’d expect. situations overhead using dataloaders datasets can impact overall performance. may change time R/C++ integration Torch improves, now workarounds:","code":""},{"path":"https://torch.mlverse.org/docs/articles/loading-data.html","id":"use--getbatch-instead-of--getitem","dir":"Articles","previous_headings":"Notes on efficiency","what":"Use .getbatch() instead of .getitem()","title":"Loading data","text":"default dataloader use .getitem() member function pull single datapoint individually. can speed switching using .getbatch() pull datapoints batch : many instances change exactly replace just .getitem .getbatch since often .getitem function written handle vectors indices. penguins example .getitem function used index select rows, work fine vector instead","code":"penguins_dataset_batching <- dataset(      name = \"penguins_dataset_batching\",      initialize = function() {     self$data <- self$prepare_penguin_data()   },      # the only change is that this went from .getitem to .getbatch   .getbatch = function(index) {          x <- self$data[index, 2:-1]     y <- self$data[index, 1]$to(torch_long())          list(x, y)   },      .length = function() {     self$data$size()[[1]]   },      prepare_penguin_data = function() {          input <- na.omit(penguins)      # conveniently, the categorical data are already factors     input$species <- as.numeric(input$species)     input$island <- as.numeric(input$island)     input$sex <- as.numeric(input$sex)          input <- as.matrix(input)     torch_tensor(input)   } )"},{"path":"https://torch.mlverse.org/docs/articles/loading-data.html","id":"remove-dataset-dataloader-and-manually-define-the-function-calls","dir":"Articles","previous_headings":"Notes on efficiency","what":"Remove dataset dataloader and manually define the function calls","title":"Loading data","text":"switching .getbatch provide benefit expecting also remove dataset entirely manually pass data. point trading readability code convenience speed.","code":"input <- na.omit(penguins)  # conveniently, the categorical data are already factors input$species <- as.numeric(input$species) input$island <- as.numeric(input$island) input$sex <- as.numeric(input$sex)  input <- as.matrix(input) input <- torch_tensor(input)  data_x <- input[, 2:-1] data_y <- input[, 1]$to(torch_long())  batch_size <- 8 num_data_points <- data_y$size(1) num_batches <- floor(num_data_points/batch_size)  for(epoch in 1:10){    # rearrange the data each epoch   permute <- torch_randperm(num_data_points) + 1L   data_x <- data_x[permute]   data_y <- data_y[permute]      # manually loop through the batches   for(batch_idx in 1:num_batches){      # here index is a vector of the indices in the batch     index <- (batch_size*(batch_idx-1) + 1):(batch_idx*batch_size)          x <- data_x[index]     y <- data_y[index]$to(torch_long())      optimizer$zero_grad()     output <- model(x)     loss <- nnf_nll_loss(output, y)     loss$backward()     optimizer$step()     l <- c(l, loss$item())   }      cat(sprintf(\"Loss at epoch %d: %3f\\n\", epoch, mean(l))) }"},{"path":"https://torch.mlverse.org/docs/articles/python-to-r.html","id":"loading-models-saved-in-python","dir":"Articles","previous_headings":"","what":"Loading models saved in python","title":"Python to R","text":"Currently way load models python rewrite model architecture R. parameter names must identical. complete example Python R shown . extension Serialization vignette. artificial neural net implemented Python. Note final line uses torch.save(). ```{python eval=FALSE] import torch import numpy np #Make data madeUpData_x = np.random.rand(1000,100) madeUpData_y = np.random.rand(1000) #Convert categorical madeUpData_y = madeUpData_y.round() train_py_X = torch.from_numpy(madeUpData_x).float() train_py_Y = torch.from_numpy(madeUpData_y).float() #Note class must replicated identically R class simpleMLP(torch.nn.Module): def init(self): super(simpleMLP, self).__init__() self.modelFit = torch.nn.Sequential( torch.nn.Linear(100,20), torch.nn.ReLU(), torch.nn.Linear(20,1), torch.nn.Sigmoid()) model = simpleMLP() def modelTrainer(data_X,data_Y,model): criterion = torch.nn.BCELoss() optimizer = torch.optim.SGD(model.parameters(), lr=0.001, momentum=0.9) modelTrainer(data_X = train_py_X,data_Y = train_py_Y,model = model) #—————————————————————– #save model #Note model.state_dict() comes ordered dictionary #code converts dictionary stateDict = dict(model.state_dict()) #Note argument _use_new_zipfile_serialization torch.save(stateDict,f=“path/babyTest.pth”, _use_new_zipfile_serialization=True)","code":"def forward(self, x):     x =self.modelFit(x)      return x for epoch in range(100):      optimizer.zero_grad()      yhat = model(data_X)      loss = criterion(yhat,data_Y.unsqueeze(1))      loss.backward()     optimizer.step() Once we have a saved .pth object we can load this into R. An example use case  would be training a model in Python then using Shiny to develop a GUI for predictions from a trained model.   ```r library(torch)  #Make up some test data #note that proper installation of torch will yield no errors when we run #this code y <- torch_tensor(array(runif(8),dim = c(2,2,2)),dtype = torch_float64())  #Note the identical names between the Python class definition and our #class definition simpleMLP <- torch::nn_module(   \"simpleMLP\",   initialize = function(){          self$modelFit <- nn_sequential(nn_linear(100,20),                                    nn_relu(),                                    nn_linear(20,1),                                    nn_sigmoid())        },   forward = function(x){     self$modelFit(x)     } )   model <- simpleMLP()  state_dict <- torch::load_state_dict(\"p/babyTest.pth\") model$load_state_dict(state_dict)  #Note that the dtype set in R has to match the made up data from Python #More generally if reading new data into R you must ensure that it matches the  #dtype that the model was trained with in Python newData = torch_tensor(array(rnorm(n=1000),dim=c(10,100)),dtype=torch_float32())  predictMe <- model(newData)"},{"path":"https://torch.mlverse.org/docs/articles/serialization.html","id":"saving-tensors","dir":"Articles","previous_headings":"","what":"Saving tensors","title":"Serialization","text":"can save object type torch_tensor disk using:","code":"x <- torch_randn(10, 10) torch_save(x, \"tensor.pt\") x_ <- torch_load(\"tensor.pt\")  torch_allclose(x, x_) #> [1] TRUE"},{"path":"https://torch.mlverse.org/docs/articles/serialization.html","id":"saving-modules","dir":"Articles","previous_headings":"","what":"Saving modules","title":"Serialization","text":"torch_save torch_load functions also work nn_modules objects. saving nn_module, object serialized including model structure ’s state.","code":"module <- nn_module(   \"my_module\",   initialize = function() {     self$fc1 <- nn_linear(10, 10)     self$fc2 <- nn_linear(10, 1)   },   forward = function(x) {     x %>%        self$fc1() %>%        self$fc2()   } )  model <- module() torch_save(model, \"model.pt\") model_ <- torch_load(\"model.pt\")  # input tensor x <- torch_randn(50, 10) torch_allclose(model(x), model_(x)) #> [1] TRUE"},{"path":"https://torch.mlverse.org/docs/articles/serialization.html","id":"loading-models-saved-in-python","dir":"Articles","previous_headings":"","what":"Loading models saved in python","title":"Serialization","text":"Currently way load models python rewrite model architecture R. parameter names must identical. can save PyTorch model state_dict using: can reload state dict R reload model : can find working examples torchvision. example AlexNet model.","code":"torch.save(model, fpath, _use_new_zipfile_serialization=True) state_dict <- load_state_dict(fpath) model <- Model() model$load_state_dict(state_dict)"},{"path":"https://torch.mlverse.org/docs/articles/serialization.html","id":"saving-optimizer-state","dir":"Articles","previous_headings":"","what":"Saving optimizer state","title":"Serialization","text":"can save state optimizers can continue training exact position. order use state_dict() load_state_dict() methods optimizer combined torch_save:","code":"model <- nn_linear(1, 1) opt <- optim_adam(model$parameters)  train_x <- torch_randn(100, 1) train_y <- torch_randn(100, 1)  loss <- nnf_mse_loss(model(train_x), train_y) loss$backward() opt$step() #> NULL  # Now let's save the optimizer state tmp <- tempfile() torch_save(opt$state_dict(), tmp)  # And now let's create a new optimizer and load back opt2 <- optim_adam(model$parameters) opt2$load_state_dict(torch_load(tmp))"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"numpy_t","dir":"Articles > Tensor","previous_headings":"","what":"numpy_T","title":"Tensor objects","text":"Tensor dimensions reversed. n number dimensions x, x$numpy_T() equivalent x$permute(n, n-1, ..., 1).","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"abs","dir":"Articles > Tensor","previous_headings":"","what":"abs","title":"Tensor objects","text":"abs() -> Tensor See ?torch_abs","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"abs_","dir":"Articles > Tensor","previous_headings":"","what":"abs_","title":"Tensor objects","text":"abs_() -> Tensor -place version $abs","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"absolute","dir":"Articles > Tensor","previous_headings":"","what":"absolute","title":"Tensor objects","text":"absolute() -> Tensor Alias [$abs()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"absolute_","dir":"Articles > Tensor","previous_headings":"","what":"absolute_","title":"Tensor objects","text":"absolute_() -> Tensor -place version $absolute Alias [$abs_()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"acos","dir":"Articles > Tensor","previous_headings":"","what":"acos","title":"Tensor objects","text":"acos() -> Tensor See ?torch_acos","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"acos_","dir":"Articles > Tensor","previous_headings":"","what":"acos_","title":"Tensor objects","text":"acos_() -> Tensor -place version $acos","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"acosh","dir":"Articles > Tensor","previous_headings":"","what":"acosh","title":"Tensor objects","text":"acosh() -> Tensor See ?torch_acosh","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"acosh_","dir":"Articles > Tensor","previous_headings":"","what":"acosh_","title":"Tensor objects","text":"acosh_() -> Tensor -place version $acosh","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"add","dir":"Articles > Tensor","previous_headings":"","what":"add","title":"Tensor objects","text":"add(, *, alpha=1) -> Tensor Add scalar tensor self tensor. alpha specified, element scaled alpha used. tensor, shape must broadcastable shape underlying tensor See ?torch_add","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"add_","dir":"Articles > Tensor","previous_headings":"","what":"add_","title":"Tensor objects","text":"add_(, *, alpha=1) -> Tensor -place version $add","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"addbmm","dir":"Articles > Tensor","previous_headings":"","what":"addbmm","title":"Tensor objects","text":"addbmm(batch1, batch2, *, beta=1, alpha=1) -> Tensor See ?torch_addbmm","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"addbmm_","dir":"Articles > Tensor","previous_headings":"","what":"addbmm_","title":"Tensor objects","text":"addbmm_(batch1, batch2, *, beta=1, alpha=1) -> Tensor -place version $addbmm","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"addcdiv","dir":"Articles > Tensor","previous_headings":"","what":"addcdiv","title":"Tensor objects","text":"addcdiv(tensor1, tensor2, *, value=1) -> Tensor See ?torch_addcdiv","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"addcdiv_","dir":"Articles > Tensor","previous_headings":"","what":"addcdiv_","title":"Tensor objects","text":"addcdiv_(tensor1, tensor2, *, value=1) -> Tensor -place version $addcdiv","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"addcmul","dir":"Articles > Tensor","previous_headings":"","what":"addcmul","title":"Tensor objects","text":"addcmul(tensor1, tensor2, *, value=1) -> Tensor See ?torch_addcmul","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"addcmul_","dir":"Articles > Tensor","previous_headings":"","what":"addcmul_","title":"Tensor objects","text":"addcmul_(tensor1, tensor2, *, value=1) -> Tensor -place version $addcmul","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"addmm","dir":"Articles > Tensor","previous_headings":"","what":"addmm","title":"Tensor objects","text":"addmm(mat1, mat2, *, beta=1, alpha=1) -> Tensor See ?torch_addmm","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"addmm_","dir":"Articles > Tensor","previous_headings":"","what":"addmm_","title":"Tensor objects","text":"addmm_(mat1, mat2, *, beta=1, alpha=1) -> Tensor -place version $addmm","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"addmv","dir":"Articles > Tensor","previous_headings":"","what":"addmv","title":"Tensor objects","text":"addmv(mat, vec, *, beta=1, alpha=1) -> Tensor See ?torch_addmv","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"addmv_","dir":"Articles > Tensor","previous_headings":"","what":"addmv_","title":"Tensor objects","text":"addmv_(mat, vec, *, beta=1, alpha=1) -> Tensor -place version $addmv","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"addr","dir":"Articles > Tensor","previous_headings":"","what":"addr","title":"Tensor objects","text":"addr(vec1, vec2, *, beta=1, alpha=1) -> Tensor See ?torch_addr","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"addr_","dir":"Articles > Tensor","previous_headings":"","what":"addr_","title":"Tensor objects","text":"addr_(vec1, vec2, *, beta=1, alpha=1) -> Tensor -place version $addr","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"align_as","dir":"Articles > Tensor","previous_headings":"","what":"align_as","title":"Tensor objects","text":"align_as() -> Tensor Permutes dimensions self tensor match dimension order tensor, adding size-one dims new names. operation useful explicit broadcasting names (see examples). dims self must named order use method. resulting tensor view original tensor. dimension names self must present $names. may contain named dimensions self$names; output tensor size-one dimension new names. align tensor specific order, use $align_to.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples","dir":"Articles > Tensor","previous_headings":"align_as","what":"Examples:","title":"Tensor objects","text":"","code":"# Example 1: Applying a mask mask <- torch_randint(low = 0, high = 2, size = c(127, 128), dtype=torch_bool())$refine_names(c('W', 'H')) imgs <- torch_randn(32, 128, 127, 3, names=c('N', 'H', 'W', 'C')) imgs$masked_fill_(mask$align_as(imgs), 0)  # Example 2: Applying a per-channel-scale scale_channels <- function(input, scale) {   scale <- scale$refine_names(\"C\")   input * scale$align_as(input) }  num_channels <- 3 scale <- torch_randn(num_channels, names='C') imgs <- torch_rand(32, 128, 128, num_channels, names=c('N', 'H', 'W', 'C')) more_imgs = torch_rand(32, num_channels, 128, 128, names=c('N', 'C', 'H', 'W')) videos = torch_randn(3, num_channels, 128, 128, 128, names=c('N', 'C', 'H', 'W', 'D'))  # scale_channels is agnostic to the dimension order of the input scale_channels(imgs, scale) scale_channels(more_imgs, scale) scale_channels(videos, scale)"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"warning","dir":"Articles > Tensor","previous_headings":"align_as","what":"Warning:","title":"Tensor objects","text":"named tensor API experimental subject change.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"align_to","dir":"Articles > Tensor","previous_headings":"","what":"align_to","title":"Tensor objects","text":"Permutes dimensions self tensor match order specified names, adding size-one dims new names. dims self must named order use method. resulting tensor view original tensor. dimension names self must present names. names may contain additional names self$names; output tensor size-one dimension new names.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments","dir":"Articles > Tensor","previous_headings":"align_to","what":"Arguments:","title":"Tensor objects","text":"names (iterable str): desired dimension ordering output tensor. May contain one Ellipsis expanded unmentioned dim names self.","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"warning-1","dir":"Articles > Tensor","previous_headings":"align_to > Examples:","what":"Warning:","title":"Tensor objects","text":"named tensor API experimental subject change.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"all","dir":"Articles > Tensor","previous_headings":"","what":"all","title":"Tensor objects","text":"() -> bool Returns TRUE elements tensor TRUE, FALSE otherwise.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-2","dir":"Articles > Tensor","previous_headings":"all","what":"Examples:","title":"Tensor objects","text":"(dim, keepdim=FALSE, =NULL) -> Tensor Returns TRUE elements row tensor given dimension dim TRUE, FALSE otherwise. keepdim TRUE, output tensor size input except dimension dim size 1. Otherwise, dim squeezed (see ?torch_squeeze()), resulting output tensor 1 fewer dimension input.","code":"a <- torch_rand(1, 2)$to(dtype = torch_bool()) a a$all()"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-1","dir":"Articles > Tensor","previous_headings":"all","what":"Arguments:","title":"Tensor objects","text":"dim (int): dimension reduce keepdim (bool): whether output tensor dim retained (Tensor, optional): output tensor","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-3","dir":"Articles > Tensor","previous_headings":"all","what":"Examples:","title":"Tensor objects","text":"","code":"a <- torch_rand(4, 2)$to(dtype = torch_bool()) a a$all(dim=2) a$all(dim=1)"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"allclose","dir":"Articles > Tensor","previous_headings":"","what":"allclose","title":"Tensor objects","text":"allclose(, rtol=1e-05, atol=1e-08, equal_nan=FALSE) -> Tensor See ?torch_allclose","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"angle","dir":"Articles > Tensor","previous_headings":"","what":"angle","title":"Tensor objects","text":"angle() -> Tensor See ?torch_angle","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"any","dir":"Articles > Tensor","previous_headings":"","what":"any","title":"Tensor objects","text":"() -> bool Returns TRUE elements tensor TRUE, FALSE otherwise.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-4","dir":"Articles > Tensor","previous_headings":"any","what":"Examples:","title":"Tensor objects","text":"(dim, keepdim=FALSE, =NULL) -> Tensor Returns TRUE elements row tensor given dimension dim TRUE, FALSE otherwise. keepdim TRUE, output tensor size input except dimension dim size 1. Otherwise, dim squeezed (see ?torch_squeeze()), resulting output tensor 1 fewer dimension input.","code":"a <- torch_rand(1, 2)$to(dtype = torch_bool()) a a$any()"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-2","dir":"Articles > Tensor","previous_headings":"any","what":"Arguments:","title":"Tensor objects","text":"dim (int): dimension reduce keepdim (bool): whether output tensor dim retained (Tensor, optional): output tensor","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-5","dir":"Articles > Tensor","previous_headings":"any","what":"Examples:","title":"Tensor objects","text":"","code":"a <- torch_randn(4, 2) < 0 a a$any(2) a$any(1)"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"apply_","dir":"Articles > Tensor","previous_headings":"","what":"apply_","title":"Tensor objects","text":"apply_(callable) -> Tensor Applies function callable element tensor, replacing element value returned callable.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"note","dir":"Articles > Tensor","previous_headings":"apply_","what":"Note:","title":"Tensor objects","text":"function works CPU tensors used code sections require high performance.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"argmax","dir":"Articles > Tensor","previous_headings":"","what":"argmax","title":"Tensor objects","text":"argmax(dim=NULL, keepdim=FALSE) -> LongTensor See ?torch_argmax","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"argmin","dir":"Articles > Tensor","previous_headings":"","what":"argmin","title":"Tensor objects","text":"argmin(dim=NULL, keepdim=FALSE) -> LongTensor See ?torch_argmin","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"argsort","dir":"Articles > Tensor","previous_headings":"","what":"argsort","title":"Tensor objects","text":"argsort(dim=-1, descending=FALSE) -> LongTensor See ?torch_argsort","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"as_strided","dir":"Articles > Tensor","previous_headings":"","what":"as_strided","title":"Tensor objects","text":"as_strided(size, stride, storage_offset=0) -> Tensor See [torch_as_strided()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"as_subclass","dir":"Articles > Tensor","previous_headings":"","what":"as_subclass","title":"Tensor objects","text":"as_subclass(cls) -> Tensor Makes cls instance data pointer self. Changes output mirror changes self, output stays attached autograd graph. cls must subclass Tensor.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"asin","dir":"Articles > Tensor","previous_headings":"","what":"asin","title":"Tensor objects","text":"asin() -> Tensor See ?torch_asin","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"asin_","dir":"Articles > Tensor","previous_headings":"","what":"asin_","title":"Tensor objects","text":"asin_() -> Tensor -place version $asin","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"asinh","dir":"Articles > Tensor","previous_headings":"","what":"asinh","title":"Tensor objects","text":"asinh() -> Tensor See ?torch_asinh","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"asinh_","dir":"Articles > Tensor","previous_headings":"","what":"asinh_","title":"Tensor objects","text":"asinh_() -> Tensor -place version $asinh","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"atan","dir":"Articles > Tensor","previous_headings":"","what":"atan","title":"Tensor objects","text":"atan() -> Tensor See ?torch_atan","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"atan2","dir":"Articles > Tensor","previous_headings":"","what":"atan2","title":"Tensor objects","text":"atan2() -> Tensor See [torch_atan2()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"atan2_","dir":"Articles > Tensor","previous_headings":"","what":"atan2_","title":"Tensor objects","text":"atan2_() -> Tensor -place version $atan2","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"atan_","dir":"Articles > Tensor","previous_headings":"","what":"atan_","title":"Tensor objects","text":"atan_() -> Tensor -place version $atan","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"atanh","dir":"Articles > Tensor","previous_headings":"","what":"atanh","title":"Tensor objects","text":"atanh() -> Tensor See ?torch_atanh","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"atanh_","dir":"Articles > Tensor","previous_headings":"","what":"atanh_","title":"Tensor objects","text":"-place version $atanh","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"backward","dir":"Articles > Tensor","previous_headings":"","what":"backward","title":"Tensor objects","text":"Computes gradient current tensor w.r.t. graph leaves. graph differentiated using chain rule. tensor non-scalar (.e. data one element) requires gradient, function additionally requires specifying gradient. tensor matching type location, contains gradient differentiated function w.r.t. self. function accumulates gradients leaves - might need zero $grad attributes set NULL calling . See Default gradient layouts<default-grad-layouts> details memory layout accumulated gradients.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-3","dir":"Articles > Tensor","previous_headings":"backward","what":"Arguments:","title":"Tensor objects","text":"gradient (Tensor NULL): Gradient w.r.t. tensor. tensor, automatically converted Tensor require grad unless create_graph TRUE. NULL values can specified scalar Tensors ones don’t require grad. NULL value acceptable argument optional. retain_graph (bool, optional): FALSE, graph used compute grads freed. Note nearly cases setting option TRUE needed often can worked around much efficient way. Defaults value create_graph. create_graph (bool, optional): TRUE, graph derivative constructed, allowing compute higher order derivative products. Defaults FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"baddbmm","dir":"Articles > Tensor","previous_headings":"","what":"baddbmm","title":"Tensor objects","text":"baddbmm(batch1, batch2, *, beta=1, alpha=1) -> Tensor See ?torch_baddbmm","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"baddbmm_","dir":"Articles > Tensor","previous_headings":"","what":"baddbmm_","title":"Tensor objects","text":"baddbmm_(batch1, batch2, *, beta=1, alpha=1) -> Tensor -place version $baddbmm","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"bernoulli","dir":"Articles > Tensor","previous_headings":"","what":"bernoulli","title":"Tensor objects","text":"bernoulli(*, generator=NULL) -> Tensor Returns result tensor \\(\\texttt{result[]}\\) independently sampled \\(\\text{Bernoulli}(\\texttt{self[]})\\). self must floating point dtype, result dtype. See ?torch_bernoulli","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"bernoulli_","dir":"Articles > Tensor","previous_headings":"","what":"bernoulli_","title":"Tensor objects","text":"bernoulli_(p=0.5, *, generator=NULL) -> Tensor Fills location self independent sample \\(\\text{Bernoulli}(\\texttt{p})\\). self can integral dtype. bernoulli_(p_tensor, *, generator=NULL) -> Tensor p_tensor tensor containing probabilities used drawing binary random number. \\(\\text{}^{th}\\) element self tensor set value sampled \\(\\text{Bernoulli}(\\texttt{p\\_tensor[]})\\). self can integral dtype, p_tensor must floating point dtype. See also $bernoulli ?torch_bernoulli","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"bfloat16","dir":"Articles > Tensor","previous_headings":"","what":"bfloat16","title":"Tensor objects","text":"bfloat16(memory_format=torch_preserve_format) -> Tensor self$bfloat16() equivalent self$(torch_bfloat16). See [()].","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-4","dir":"Articles > Tensor","previous_headings":"bfloat16","what":"Arguments:","title":"Tensor objects","text":"memory_format (torch_memory_format, optional): desired memory format returned Tensor. Default: torch_preserve_format.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"bincount","dir":"Articles > Tensor","previous_headings":"","what":"bincount","title":"Tensor objects","text":"bincount(weights=NULL, minlength=0) -> Tensor See ?torch_bincount","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"bitwise_and","dir":"Articles > Tensor","previous_headings":"","what":"bitwise_and","title":"Tensor objects","text":"bitwise_and() -> Tensor See [torch_bitwise_and()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"bitwise_and_","dir":"Articles > Tensor","previous_headings":"","what":"bitwise_and_","title":"Tensor objects","text":"bitwise_and_() -> Tensor -place version $bitwise_and","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"bitwise_not","dir":"Articles > Tensor","previous_headings":"","what":"bitwise_not","title":"Tensor objects","text":"bitwise_not() -> Tensor See [torch_bitwise_not()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"bitwise_not_","dir":"Articles > Tensor","previous_headings":"","what":"bitwise_not_","title":"Tensor objects","text":"bitwise_not_() -> Tensor -place version $bitwise_not","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"bitwise_or","dir":"Articles > Tensor","previous_headings":"","what":"bitwise_or","title":"Tensor objects","text":"bitwise_or() -> Tensor See [torch_bitwise_or()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"bitwise_or_","dir":"Articles > Tensor","previous_headings":"","what":"bitwise_or_","title":"Tensor objects","text":"bitwise_or_() -> Tensor -place version $bitwise_or","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"bitwise_xor","dir":"Articles > Tensor","previous_headings":"","what":"bitwise_xor","title":"Tensor objects","text":"bitwise_xor() -> Tensor See [torch_bitwise_xor()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"bitwise_xor_","dir":"Articles > Tensor","previous_headings":"","what":"bitwise_xor_","title":"Tensor objects","text":"bitwise_xor_() -> Tensor -place version $bitwise_xor","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"bmm","dir":"Articles > Tensor","previous_headings":"","what":"bmm","title":"Tensor objects","text":"bmm(batch2) -> Tensor See ?torch_bmm","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"bool","dir":"Articles > Tensor","previous_headings":"","what":"bool","title":"Tensor objects","text":"bool(memory_format=torch_preserve_format) -> Tensor self$bool() equivalent self$(torch_bool). See [()].","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-5","dir":"Articles > Tensor","previous_headings":"bool","what":"Arguments:","title":"Tensor objects","text":"memory_format (torch_memory_format, optional): desired memory format returned Tensor. Default: torch_preserve_format.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"byte","dir":"Articles > Tensor","previous_headings":"","what":"byte","title":"Tensor objects","text":"byte(memory_format=torch_preserve_format) -> Tensor self$byte() equivalent self$(torch_uint8). See [()].","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-6","dir":"Articles > Tensor","previous_headings":"byte","what":"Arguments:","title":"Tensor objects","text":"memory_format (torch_memory_format, optional): desired memory format returned Tensor. Default: torch_preserve_format.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"cauchy_","dir":"Articles > Tensor","previous_headings":"","what":"cauchy_","title":"Tensor objects","text":"cauchy_(median=0, sigma=1, *, generator=NULL) -> Tensor Fills tensor numbers drawn Cauchy distribution: \\[ f(x) = \\dfrac{1}{\\pi} \\dfrac{\\sigma}{(x - \\text{median})^2 + \\sigma^2} \\]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"ceil","dir":"Articles > Tensor","previous_headings":"","what":"ceil","title":"Tensor objects","text":"ceil() -> Tensor See ?torch_ceil","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"ceil_","dir":"Articles > Tensor","previous_headings":"","what":"ceil_","title":"Tensor objects","text":"ceil_() -> Tensor -place version $ceil","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"char","dir":"Articles > Tensor","previous_headings":"","what":"char","title":"Tensor objects","text":"char(memory_format=torch_preserve_format) -> Tensor self$char() equivalent self$(torch_int8). See [()].","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-7","dir":"Articles > Tensor","previous_headings":"char","what":"Arguments:","title":"Tensor objects","text":"memory_format (torch_memory_format, optional): desired memory format returned Tensor. Default: torch_preserve_format.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"cholesky","dir":"Articles > Tensor","previous_headings":"","what":"cholesky","title":"Tensor objects","text":"cholesky(upper=FALSE) -> Tensor See ?torch_cholesky","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"cholesky_inverse","dir":"Articles > Tensor","previous_headings":"","what":"cholesky_inverse","title":"Tensor objects","text":"cholesky_inverse(upper=FALSE) -> Tensor See [torch_cholesky_inverse()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"cholesky_solve","dir":"Articles > Tensor","previous_headings":"","what":"cholesky_solve","title":"Tensor objects","text":"cholesky_solve(input2, upper=FALSE) -> Tensor See [torch_cholesky_solve()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"chunk","dir":"Articles > Tensor","previous_headings":"","what":"chunk","title":"Tensor objects","text":"chunk(chunks, dim=0) -> List Tensors See ?torch_chunk","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"clamp","dir":"Articles > Tensor","previous_headings":"","what":"clamp","title":"Tensor objects","text":"clamp(min, max) -> Tensor See ?torch_clamp","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"clamp_","dir":"Articles > Tensor","previous_headings":"","what":"clamp_","title":"Tensor objects","text":"clamp_(min, max) -> Tensor -place version $clamp","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"clone","dir":"Articles > Tensor","previous_headings":"","what":"clone","title":"Tensor objects","text":"clone(memory_format=torch_preserve_format()) -> Tensor Returns copy self tensor. copy size data type self.","code":"x <- torch_tensor(1) y <- x$clone()  x$add_(1) y"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"note-1","dir":"Articles > Tensor","previous_headings":"clone","what":"Note:","title":"Tensor objects","text":"Unlike copy_(), function recorded computation graph. Gradients propagating cloned tensor propagate original tensor.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-8","dir":"Articles > Tensor","previous_headings":"clone","what":"Arguments:","title":"Tensor objects","text":"memory_format (torch_memory_format, optional): desired memory format returned Tensor. Default: torch_preserve_format.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"conj","dir":"Articles > Tensor","previous_headings":"","what":"conj","title":"Tensor objects","text":"conj() -> Tensor See ?torch_conj","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"contiguous","dir":"Articles > Tensor","previous_headings":"","what":"contiguous","title":"Tensor objects","text":"contiguous(memory_format=torch_contiguous_format) -> Tensor Returns contiguous memory tensor containing data self tensor. self tensor already specified memory format, function returns self tensor.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-9","dir":"Articles > Tensor","previous_headings":"contiguous","what":"Arguments:","title":"Tensor objects","text":"memory_format (torch_memory_format, optional): desired memory format returned Tensor. Default: torch_contiguous_format.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"copy_","dir":"Articles > Tensor","previous_headings":"","what":"copy_","title":"Tensor objects","text":"copy_(src, non_blocking=FALSE) -> Tensor Copies elements src self tensor returns self. src tensor must broadcastable self tensor. may different data type reside different device.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-10","dir":"Articles > Tensor","previous_headings":"copy_","what":"Arguments:","title":"Tensor objects","text":"src (Tensor): source tensor copy non_blocking (bool): TRUE copy CPU GPU, copy may occur asynchronously respect host. cases, argument effect.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"cos","dir":"Articles > Tensor","previous_headings":"","what":"cos","title":"Tensor objects","text":"cos() -> Tensor See ?torch_cos","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"cos_","dir":"Articles > Tensor","previous_headings":"","what":"cos_","title":"Tensor objects","text":"cos_() -> Tensor -place version $cos","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"cosh","dir":"Articles > Tensor","previous_headings":"","what":"cosh","title":"Tensor objects","text":"cosh() -> Tensor See ?torch_cosh","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"cosh_","dir":"Articles > Tensor","previous_headings":"","what":"cosh_","title":"Tensor objects","text":"cosh_() -> Tensor -place version $cosh","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"cpu","dir":"Articles > Tensor","previous_headings":"","what":"cpu","title":"Tensor objects","text":"cpu(memory_format=torch_preserve_format) -> Tensor Returns copy object CPU memory. object already CPU memory correct device, copy performed original object returned.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-11","dir":"Articles > Tensor","previous_headings":"cpu","what":"Arguments:","title":"Tensor objects","text":"memory_format (torch_memory_format, optional): desired memory format returned Tensor. Default: torch_preserve_format.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"cross","dir":"Articles > Tensor","previous_headings":"","what":"cross","title":"Tensor objects","text":"cross(, dim=-1) -> Tensor See ?torch_cross","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"cuda","dir":"Articles > Tensor","previous_headings":"","what":"cuda","title":"Tensor objects","text":"cuda(device=NULL, non_blocking=FALSE, memory_format=torch_preserve_format) -> Tensor Returns copy object CUDA memory. object already CUDA memory correct device, copy performed original object returned.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-12","dir":"Articles > Tensor","previous_headings":"cuda","what":"Arguments:","title":"Tensor objects","text":"device (torch_device): destination GPU device. Defaults current CUDA device. non_blocking (bool): TRUE source pinned memory, copy asynchronous respect host. Otherwise, argument effect. Default: FALSE. memory_format (torch_memory_format, optional): desired memory format returned Tensor. Default: torch_preserve_format.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"cummax","dir":"Articles > Tensor","previous_headings":"","what":"cummax","title":"Tensor objects","text":"cummax(dim) -> (Tensor, Tensor) See ?torch_cummax","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"cummin","dir":"Articles > Tensor","previous_headings":"","what":"cummin","title":"Tensor objects","text":"cummin(dim) -> (Tensor, Tensor) See ?torch_cummin","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"cumprod","dir":"Articles > Tensor","previous_headings":"","what":"cumprod","title":"Tensor objects","text":"cumprod(dim, dtype=NULL) -> Tensor See ?torch_cumprod","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"cumsum","dir":"Articles > Tensor","previous_headings":"","what":"cumsum","title":"Tensor objects","text":"cumsum(dim, dtype=NULL) -> Tensor See ?torch_cumsum","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"data_ptr","dir":"Articles > Tensor","previous_headings":"","what":"data_ptr","title":"Tensor objects","text":"data_ptr() -> int Returns address first element self tensor.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"deg2rad","dir":"Articles > Tensor","previous_headings":"","what":"deg2rad","title":"Tensor objects","text":"deg2rad() -> Tensor See [torch_deg2rad()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"deg2rad_","dir":"Articles > Tensor","previous_headings":"","what":"deg2rad_","title":"Tensor objects","text":"deg2rad_() -> Tensor -place version $deg2rad","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"dense_dim","dir":"Articles > Tensor","previous_headings":"","what":"dense_dim","title":"Tensor objects","text":"dense_dim() -> int self sparse COO tensor (.e., torch_sparse_coo layout), returns number dense dimensions. Otherwise, throws error. See also $sparse_dim.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"dequantize","dir":"Articles > Tensor","previous_headings":"","what":"dequantize","title":"Tensor objects","text":"dequantize() -> Tensor Given quantized Tensor, dequantize return dequantized float Tensor.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"det","dir":"Articles > Tensor","previous_headings":"","what":"det","title":"Tensor objects","text":"det() -> Tensor See ?torch_det","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"detach","dir":"Articles > Tensor","previous_headings":"","what":"detach","title":"Tensor objects","text":"Returns new Tensor, detached current graph. result never require gradient.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"note-2","dir":"Articles > Tensor","previous_headings":"detach","what":"Note:","title":"Tensor objects","text":"Returned Tensor shares storage original one. -place modifications either seen, may trigger errors correctness checks. IMPORTANT NOTE: Previously, -place size / stride / storage changes (resize_ / resize_as_ / set_ / transpose_) returned tensor also update original tensor. Now, -place changes update original tensor anymore, instead trigger error. sparse tensors: -place indices / values changes (zero_ / copy_ / add_) returned tensor update original tensor anymore, instead trigger error.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"detach_","dir":"Articles > Tensor","previous_headings":"","what":"detach_","title":"Tensor objects","text":"Detaches Tensor graph created , making leaf. Views detached -place.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"device","dir":"Articles > Tensor","previous_headings":"","what":"device","title":"Tensor objects","text":"torch_device Tensor .","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"diag","dir":"Articles > Tensor","previous_headings":"","what":"diag","title":"Tensor objects","text":"diag(diagonal=0) -> Tensor See ?torch_diag","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"diag_embed","dir":"Articles > Tensor","previous_headings":"","what":"diag_embed","title":"Tensor objects","text":"diag_embed(offset=0, dim1=-2, dim2=-1) -> Tensor See [torch_diag_embed()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"diagflat","dir":"Articles > Tensor","previous_headings":"","what":"diagflat","title":"Tensor objects","text":"diagflat(offset=0) -> Tensor See ?torch_diagflat","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"diagonal","dir":"Articles > Tensor","previous_headings":"","what":"diagonal","title":"Tensor objects","text":"diagonal(offset=0, dim1=0, dim2=1) -> Tensor See ?torch_diagonal","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"digamma","dir":"Articles > Tensor","previous_headings":"","what":"digamma","title":"Tensor objects","text":"digamma() -> Tensor See ?torch_digamma","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"digamma_","dir":"Articles > Tensor","previous_headings":"","what":"digamma_","title":"Tensor objects","text":"digamma_() -> Tensor -place version $digamma","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"dim","dir":"Articles > Tensor","previous_headings":"","what":"dim","title":"Tensor objects","text":"dim() -> int Returns number dimensions self tensor.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"dist","dir":"Articles > Tensor","previous_headings":"","what":"dist","title":"Tensor objects","text":"dist(, p=2) -> Tensor See ?torch_dist","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"div","dir":"Articles > Tensor","previous_headings":"","what":"div","title":"Tensor objects","text":"div(value) -> Tensor See ?torch_div","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"div_","dir":"Articles > Tensor","previous_headings":"","what":"div_","title":"Tensor objects","text":"div_(value) -> Tensor -place version $div","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"dot","dir":"Articles > Tensor","previous_headings":"","what":"dot","title":"Tensor objects","text":"dot(tensor2) -> Tensor See ?torch_dot","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"double","dir":"Articles > Tensor","previous_headings":"","what":"double","title":"Tensor objects","text":"double(memory_format=torch_preserve_format) -> Tensor self$double() equivalent self$(torch_float64). See [()].","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-13","dir":"Articles > Tensor","previous_headings":"double","what":"Arguments:","title":"Tensor objects","text":"memory_format (torch_memory_format, optional): desired memory format returned Tensor. Default: torch_preserve_format.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"eig","dir":"Articles > Tensor","previous_headings":"","what":"eig","title":"Tensor objects","text":"eig(eigenvectors=FALSE) -> (Tensor, Tensor) See ?torch_eig","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"element_size","dir":"Articles > Tensor","previous_headings":"","what":"element_size","title":"Tensor objects","text":"element_size() -> int Returns size bytes individual element.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-6","dir":"Articles > Tensor","previous_headings":"element_size","what":"Examples:","title":"Tensor objects","text":"","code":"torch_tensor(c(1))$element_size()"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"eq","dir":"Articles > Tensor","previous_headings":"","what":"eq","title":"Tensor objects","text":"eq() -> Tensor See ?torch_eq","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"eq_","dir":"Articles > Tensor","previous_headings":"","what":"eq_","title":"Tensor objects","text":"eq_() -> Tensor -place version $eq","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"equal","dir":"Articles > Tensor","previous_headings":"","what":"equal","title":"Tensor objects","text":"equal() -> bool See ?torch_equal","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"erf","dir":"Articles > Tensor","previous_headings":"","what":"erf","title":"Tensor objects","text":"erf() -> Tensor See ?torch_erf","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"erf_","dir":"Articles > Tensor","previous_headings":"","what":"erf_","title":"Tensor objects","text":"erf_() -> Tensor -place version $erf","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"erfc","dir":"Articles > Tensor","previous_headings":"","what":"erfc","title":"Tensor objects","text":"erfc() -> Tensor See ?torch_erfc","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"erfc_","dir":"Articles > Tensor","previous_headings":"","what":"erfc_","title":"Tensor objects","text":"erfc_() -> Tensor -place version $erfc","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"erfinv","dir":"Articles > Tensor","previous_headings":"","what":"erfinv","title":"Tensor objects","text":"erfinv() -> Tensor See ?torch_erfinv","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"erfinv_","dir":"Articles > Tensor","previous_headings":"","what":"erfinv_","title":"Tensor objects","text":"erfinv_() -> Tensor -place version $erfinv","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"exp","dir":"Articles > Tensor","previous_headings":"","what":"exp","title":"Tensor objects","text":"exp() -> Tensor See ?torch_exp","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"exp_","dir":"Articles > Tensor","previous_headings":"","what":"exp_","title":"Tensor objects","text":"exp_() -> Tensor -place version $exp","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"expand","dir":"Articles > Tensor","previous_headings":"","what":"expand","title":"Tensor objects","text":"expand(*sizes) -> Tensor Returns new view self tensor singleton dimensions expanded larger size. Passing -1 size dimension means changing size dimension. Tensor can also expanded larger number dimensions, new ones appended front. new dimensions, size set -1. Expanding tensor allocate new memory, creates new view existing tensor dimension size one expanded larger size setting stride 0. dimension size 1 can expanded arbitrary value without allocating new memory.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-14","dir":"Articles > Tensor","previous_headings":"expand","what":"Arguments:","title":"Tensor objects","text":"sizes (torch_Size int…): desired expanded size","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"warning-2","dir":"Articles > Tensor","previous_headings":"expand","what":"Warning:","title":"Tensor objects","text":"one element expanded tensor may refer single memory location. result, -place operations (especially ones vectorized) may result incorrect behavior. need write tensors, please clone first.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-7","dir":"Articles > Tensor","previous_headings":"expand","what":"Examples:","title":"Tensor objects","text":"","code":"x <- torch_tensor(matrix(c(1,2,3), ncol = 1)) x$size() x$expand(c(3, 4)) x$expand(c(-1, 4))  # -1 means not changing the size of that dimension"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"expand_as","dir":"Articles > Tensor","previous_headings":"","what":"expand_as","title":"Tensor objects","text":"expand_as() -> Tensor Expand tensor size . self$expand_as() equivalent self$expand(.size()). Please see $expand information expand.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-15","dir":"Articles > Tensor","previous_headings":"expand_as","what":"Arguments:","title":"Tensor objects","text":"(`$): result tensor size .","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"expm1","dir":"Articles > Tensor","previous_headings":"","what":"expm1","title":"Tensor objects","text":"expm1() -> Tensor See [torch_expm1()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"expm1_","dir":"Articles > Tensor","previous_headings":"","what":"expm1_","title":"Tensor objects","text":"expm1_() -> Tensor -place version $expm1","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"exponential_","dir":"Articles > Tensor","previous_headings":"","what":"exponential_","title":"Tensor objects","text":"exponential_(lambd=1, *, generator=NULL) -> Tensor Fills self tensor elements drawn exponential distribution: \\[ f(x) = \\lambda e^{-\\lambda x} \\]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"fft","dir":"Articles > Tensor","previous_headings":"","what":"fft","title":"Tensor objects","text":"fft(signal_ndim, normalized=FALSE) -> Tensor See ?torch_fft","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"fill_","dir":"Articles > Tensor","previous_headings":"","what":"fill_","title":"Tensor objects","text":"fill_(value) -> Tensor Fills self tensor specified value.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"fill_diagonal_","dir":"Articles > Tensor","previous_headings":"","what":"fill_diagonal_","title":"Tensor objects","text":"fill_diagonal_(fill_value, wrap=FALSE) -> Tensor Fill main diagonal tensor least 2-dimensions. dims>2, dimensions input must equal length. function modifies input tensor -place, returns input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-16","dir":"Articles > Tensor","previous_headings":"fill_diagonal_","what":"Arguments:","title":"Tensor objects","text":"fill_value (Scalar): fill value wrap (bool): diagonal ‘wrapped’ N columns tall matrices.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-8","dir":"Articles > Tensor","previous_headings":"fill_diagonal_","what":"Examples:","title":"Tensor objects","text":"","code":"a <- torch_zeros(3, 3) a$fill_diagonal_(5) b <- torch_zeros(7, 3) b$fill_diagonal_(5) c <- torch_zeros(7, 3) c$fill_diagonal_(5, wrap=TRUE)"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"flatten","dir":"Articles > Tensor","previous_headings":"","what":"flatten","title":"Tensor objects","text":"flatten(input, start_dim=0, end_dim=-1) -> Tensor see ?torch_flatten","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"flip","dir":"Articles > Tensor","previous_headings":"","what":"flip","title":"Tensor objects","text":"flip(dims) -> Tensor See ?torch_flip","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"fliplr","dir":"Articles > Tensor","previous_headings":"","what":"fliplr","title":"Tensor objects","text":"fliplr() -> Tensor See ?torch_fliplr","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"flipud","dir":"Articles > Tensor","previous_headings":"","what":"flipud","title":"Tensor objects","text":"flipud() -> Tensor See ?torch_flipud","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"float","dir":"Articles > Tensor","previous_headings":"","what":"float","title":"Tensor objects","text":"float(memory_format=torch_preserve_format) -> Tensor self$float() equivalent self$(torch_float32). See [()].","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-17","dir":"Articles > Tensor","previous_headings":"float","what":"Arguments:","title":"Tensor objects","text":"memory_format (torch_memory_format, optional): desired memory format returned Tensor. Default: torch_preserve_format.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"floor","dir":"Articles > Tensor","previous_headings":"","what":"floor","title":"Tensor objects","text":"floor() -> Tensor See ?torch_floor","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"floor_","dir":"Articles > Tensor","previous_headings":"","what":"floor_","title":"Tensor objects","text":"floor_() -> Tensor -place version $floor","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"floor_divide","dir":"Articles > Tensor","previous_headings":"","what":"floor_divide","title":"Tensor objects","text":"floor_divide(value) -> Tensor See [torch_floor_divide()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"floor_divide_","dir":"Articles > Tensor","previous_headings":"","what":"floor_divide_","title":"Tensor objects","text":"floor_divide_(value) -> Tensor -place version $floor_divide","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"fmod","dir":"Articles > Tensor","previous_headings":"","what":"fmod","title":"Tensor objects","text":"fmod(divisor) -> Tensor See ?torch_fmod","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"fmod_","dir":"Articles > Tensor","previous_headings":"","what":"fmod_","title":"Tensor objects","text":"fmod_(divisor) -> Tensor -place version $fmod","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"frac","dir":"Articles > Tensor","previous_headings":"","what":"frac","title":"Tensor objects","text":"frac() -> Tensor See ?torch_frac","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"frac_","dir":"Articles > Tensor","previous_headings":"","what":"frac_","title":"Tensor objects","text":"frac_() -> Tensor -place version $frac","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"gather","dir":"Articles > Tensor","previous_headings":"","what":"gather","title":"Tensor objects","text":"gather(dim, index) -> Tensor See ?torch_gather","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"ge","dir":"Articles > Tensor","previous_headings":"","what":"ge","title":"Tensor objects","text":"ge() -> Tensor See ?torch_ge","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"ge_","dir":"Articles > Tensor","previous_headings":"","what":"ge_","title":"Tensor objects","text":"ge_() -> Tensor -place version $ge","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"geometric_","dir":"Articles > Tensor","previous_headings":"","what":"geometric_","title":"Tensor objects","text":"geometric_(p, *, generator=NULL) -> Tensor Fills self tensor elements drawn geometric distribution: \\[ f(X=k) = p^{k - 1} (1 - p) \\]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"geqrf","dir":"Articles > Tensor","previous_headings":"","what":"geqrf","title":"Tensor objects","text":"geqrf() -> (Tensor, Tensor) See ?torch_geqrf","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"ger","dir":"Articles > Tensor","previous_headings":"","what":"ger","title":"Tensor objects","text":"ger(vec2) -> Tensor See ?torch_ger","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"get_device","dir":"Articles > Tensor","previous_headings":"","what":"get_device","title":"Tensor objects","text":"get_device() -> Device ordinal (Integer) CUDA tensors, function returns device ordinal GPU tensor resides. CPU tensors, error thrown.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-9","dir":"Articles > Tensor","previous_headings":"get_device","what":"Examples:","title":"Tensor objects","text":"","code":"x <- torch_randn(3, 4, 5, device='cuda:0') x$get_device() x$cpu()$get_device()  # RuntimeError: get_device is not implemented for type torch_FloatTensor"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"grad","dir":"Articles > Tensor","previous_headings":"","what":"grad","title":"Tensor objects","text":"attribute NULL default becomes Tensor first time call backward computes gradients self. attribute contain gradients computed future calls [backward()] accumulate (add) gradients .","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"gt","dir":"Articles > Tensor","previous_headings":"","what":"gt","title":"Tensor objects","text":"gt() -> Tensor See ?torch_gt","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"gt_","dir":"Articles > Tensor","previous_headings":"","what":"gt_","title":"Tensor objects","text":"gt_() -> Tensor -place version $gt","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"half","dir":"Articles > Tensor","previous_headings":"","what":"half","title":"Tensor objects","text":"half(memory_format=torch_preserve_format) -> Tensor self$half() equivalent self$(torch_float16). See [()].","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-18","dir":"Articles > Tensor","previous_headings":"half","what":"Arguments:","title":"Tensor objects","text":"memory_format (torch_memory_format, optional): desired memory format returned Tensor. Default: torch_preserve_format.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"hardshrink","dir":"Articles > Tensor","previous_headings":"","what":"hardshrink","title":"Tensor objects","text":"hardshrink(lambd=0.5) -> Tensor See [torch_nn.functional.hardshrink()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"has_names","dir":"Articles > Tensor","previous_headings":"","what":"has_names","title":"Tensor objects","text":"TRUE tensor’s dimensions named. Otherwise, FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"histc","dir":"Articles > Tensor","previous_headings":"","what":"histc","title":"Tensor objects","text":"histc(bins=100, min=0, max=0) -> Tensor See ?torch_histc","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"ifft","dir":"Articles > Tensor","previous_headings":"","what":"ifft","title":"Tensor objects","text":"ifft(signal_ndim, normalized=FALSE) -> Tensor See ?torch_ifft","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"imag","dir":"Articles > Tensor","previous_headings":"","what":"imag","title":"Tensor objects","text":"Returns new tensor containing imaginary values self tensor. returned tensor self share underlying storage.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"warning-3","dir":"Articles > Tensor","previous_headings":"imag","what":"Warning:","title":"Tensor objects","text":"[imag()] supported tensors complex dtypes.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-10","dir":"Articles > Tensor","previous_headings":"imag","what":"Examples:","title":"Tensor objects","text":"","code":"x <- torch_randn(4, dtype=torch_cfloat()) x x$imag"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"index_add","dir":"Articles > Tensor","previous_headings":"","what":"index_add","title":"Tensor objects","text":"index_add(tensor1, dim, index, tensor2) -> Tensor --place version $index_add_. tensor1 corresponds self $index_add_.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"index_add_","dir":"Articles > Tensor","previous_headings":"","what":"index_add_","title":"Tensor objects","text":"index_add_(dim, index, tensor) -> Tensor Accumulate elements tensor self tensor adding indices order given index. example, dim == 0 index[] == j,  th row tensor added j th row self. dim th dimension tensor must size length index (must vector), dimensions must match self, error raised.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"note-3","dir":"Articles > Tensor","previous_headings":"index_add_","what":"Note:","title":"Tensor objects","text":"circumstances using CUDA backend CuDNN, operator may select nondeterministic algorithm increase performance. undesirable, can try make operation deterministic (potentially performance cost) setting torch_backends.cudnn.deterministic = TRUE.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-19","dir":"Articles > Tensor","previous_headings":"index_add_","what":"Arguments:","title":"Tensor objects","text":"dim (int): dimension along index index (LongTensor): indices tensor select tensor (Tensor): tensor containing values add","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-11","dir":"Articles > Tensor","previous_headings":"index_add_","what":"Examples:","title":"Tensor objects","text":"","code":"x <- torch_ones(5, 3) t <- torch_tensor(matrix(1:9, ncol = 3), dtype=torch_float()) index <- torch_tensor(c(1L, 4L, 3L)) x$index_add_(1, index, t)"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"index_copy","dir":"Articles > Tensor","previous_headings":"","what":"index_copy","title":"Tensor objects","text":"index_copy(tensor1, dim, index, tensor2) -> Tensor --place version $index_copy_. tensor1 corresponds self $index_copy_.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"index_copy_","dir":"Articles > Tensor","previous_headings":"","what":"index_copy_","title":"Tensor objects","text":"index_copy_(dim, index, tensor) -> Tensor Copies elements tensor self tensor selecting indices order given index. example, dim == 0 index[] == j,  th row tensor copied j th row self. dim th dimension tensor must size length index (must vector), dimensions must match self, error raised.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-20","dir":"Articles > Tensor","previous_headings":"index_copy_","what":"Arguments:","title":"Tensor objects","text":"dim (int): dimension along index index (LongTensor): indices tensor select tensor (Tensor): tensor containing values copy","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-12","dir":"Articles > Tensor","previous_headings":"index_copy_","what":"Examples:","title":"Tensor objects","text":"","code":"x <- torch_zeros(5, 3) t <- torch_tensor(matrix(1:9, ncol = 3), dtype=torch_float()) index <- torch_tensor(c(1, 5, 3)) x$index_copy_(1, index, t)"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"index_fill","dir":"Articles > Tensor","previous_headings":"","what":"index_fill","title":"Tensor objects","text":"index_fill(tensor1, dim, index, value) -> Tensor --place version $index_fill_. tensor1 corresponds self $index_fill_.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"index_fill_","dir":"Articles > Tensor","previous_headings":"","what":"index_fill_","title":"Tensor objects","text":"index_fill_(dim, index, val) -> Tensor Fills elements self tensor value val selecting indices order given index.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-21","dir":"Articles > Tensor","previous_headings":"index_fill_","what":"Arguments:","title":"Tensor objects","text":"dim (int): dimension along index index (LongTensor): indices self tensor fill val (float): value fill ","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-13","dir":"Articles > Tensor","previous_headings":"index_fill_","what":"Examples:","title":"Tensor objects","text":"","code":"x <- torch_tensor(matrix(1:9, ncol = 3), dtype=torch_float()) index <- torch_tensor(c(1, 3), dtype = torch_long()) x$index_fill_(1, index, -1)"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"index_put","dir":"Articles > Tensor","previous_headings":"","what":"index_put","title":"Tensor objects","text":"index_put(tensor1, indices, value, accumulate=FALSE) -> Tensor -place version $index_put_. tensor1 corresponds self $index_put_.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"index_put_","dir":"Articles > Tensor","previous_headings":"","what":"index_put_","title":"Tensor objects","text":"index_put_(indices, value, accumulate=FALSE) -> Tensor Puts values tensor value tensor self using indices specified indices (tuple Tensors). expression tensor.index_put_(indices, value) equivalent tensor[indices] = value. Returns self. accumulate TRUE, elements value added self. accumulate FALSE, behavior undefined indices contain duplicate elements.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-22","dir":"Articles > Tensor","previous_headings":"index_put_","what":"Arguments:","title":"Tensor objects","text":"indices (tuple LongTensor): tensors used index self. value (Tensor): tensor dtype self. accumulate (bool): whether accumulate self","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"index_select","dir":"Articles > Tensor","previous_headings":"","what":"index_select","title":"Tensor objects","text":"index_select(dim, index) -> Tensor See [torch_index_select()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"indices","dir":"Articles > Tensor","previous_headings":"","what":"indices","title":"Tensor objects","text":"indices() -> Tensor self sparse COO tensor (.e., torch_sparse_coo layout), returns view contained indices tensor. Otherwise, throws error. See also Tensor.values.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"note-4","dir":"Articles > Tensor","previous_headings":"indices","what":"Note:","title":"Tensor objects","text":"method can called coalesced sparse tensor. See Tensor.coalesce details.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"int","dir":"Articles > Tensor","previous_headings":"","what":"int","title":"Tensor objects","text":"int(memory_format=torch_preserve_format) -> Tensor self$int() equivalent self$(torch_int32). See [()].","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-23","dir":"Articles > Tensor","previous_headings":"int","what":"Arguments:","title":"Tensor objects","text":"memory_format (torch_memory_format, optional): desired memory format returned Tensor. Default: torch_preserve_format.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"int_repr","dir":"Articles > Tensor","previous_headings":"","what":"int_repr","title":"Tensor objects","text":"int_repr() -> Tensor Given quantized Tensor, self$int_repr() returns CPU Tensor uint8_t data type stores underlying uint8_t values given Tensor.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"inverse","dir":"Articles > Tensor","previous_headings":"","what":"inverse","title":"Tensor objects","text":"inverse() -> Tensor See ?torch_inverse","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"irfft","dir":"Articles > Tensor","previous_headings":"","what":"irfft","title":"Tensor objects","text":"irfft(signal_ndim, normalized=FALSE, onesided=TRUE, signal_sizes=NULL) -> Tensor See ?torch_irfft","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"is_complex","dir":"Articles > Tensor","previous_headings":"","what":"is_complex","title":"Tensor objects","text":"is_complex() -> bool Returns TRUE data type self complex data type.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"is_contiguous","dir":"Articles > Tensor","previous_headings":"","what":"is_contiguous","title":"Tensor objects","text":"is_contiguous(memory_format=torch_contiguous_format) -> bool Returns TRUE self tensor contiguous memory order specified memory format.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-24","dir":"Articles > Tensor","previous_headings":"is_contiguous","what":"Arguments:","title":"Tensor objects","text":"memory_format (torch_memory_format, optional): Specifies memory allocation order. Default: torch_contiguous_format.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"is_cuda","dir":"Articles > Tensor","previous_headings":"","what":"is_cuda","title":"Tensor objects","text":"TRUE Tensor stored GPU, FALSE otherwise.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"is_floating_point","dir":"Articles > Tensor","previous_headings":"","what":"is_floating_point","title":"Tensor objects","text":"is_floating_point() -> bool Returns TRUE data type self floating point data type.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"is_leaf","dir":"Articles > Tensor","previous_headings":"","what":"is_leaf","title":"Tensor objects","text":"Tensors requires_grad FALSE leaf Tensors convention. Tensors requires_grad TRUE, leaf Tensors created user. means result operation grad_fn NULL. leaf Tensors grad populated call [backward()]. get grad populated non-leaf Tensors, can use [retain_grad()].","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-14","dir":"Articles > Tensor","previous_headings":"is_leaf","what":"Examples:","title":"Tensor objects","text":"","code":"a <- torch_rand(10, requires_grad=TRUE) a$is_leaf  # b <- torch_rand(10, requires_grad=TRUE)$cuda() # b$is_leaf() # FALSE # b was created by the operation that cast a cpu Tensor into a cuda Tensor  c <- torch_rand(10, requires_grad=TRUE) + 2 c$is_leaf # c was created by the addition operation  # d <- torch_rand(10)$cuda() # d$is_leaf() # TRUE # d does not require gradients and so has no operation creating it (that is tracked by the autograd engine)  # e <- torch_rand(10)$cuda()$requires_grad_() # e$is_leaf() # TRUE # e requires gradients and has no operations creating it  # f <- torch_rand(10, requires_grad=TRUE, device=\"cuda\") # f$is_leaf # TRUE # f requires grad, has no operation creating it"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"is_meta","dir":"Articles > Tensor","previous_headings":"","what":"is_meta","title":"Tensor objects","text":"TRUE Tensor meta tensor, FALSE otherwise. Meta tensors like normal tensors, carry data.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"is_pinned","dir":"Articles > Tensor","previous_headings":"","what":"is_pinned","title":"Tensor objects","text":"Returns true tensor resides pinned memory.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"is_quantized","dir":"Articles > Tensor","previous_headings":"","what":"is_quantized","title":"Tensor objects","text":"TRUE Tensor quantized, FALSE otherwise.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"is_set_to","dir":"Articles > Tensor","previous_headings":"","what":"is_set_to","title":"Tensor objects","text":"is_set_to(tensor) -> bool Returns TRUE object refers THTensor object Torch C API given tensor.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"is_shared","dir":"Articles > Tensor","previous_headings":"","what":"is_shared","title":"Tensor objects","text":"Checks tensor shared memory. always TRUE CUDA tensors.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"is_signed","dir":"Articles > Tensor","previous_headings":"","what":"is_signed","title":"Tensor objects","text":"is_signed() -> bool Returns TRUE data type self signed data type.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"isclose","dir":"Articles > Tensor","previous_headings":"","what":"isclose","title":"Tensor objects","text":"isclose(, rtol=1e-05, atol=1e-08, equal_nan=FALSE) -> Tensor See ?torch_isclose","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"isfinite","dir":"Articles > Tensor","previous_headings":"","what":"isfinite","title":"Tensor objects","text":"isfinite() -> Tensor See ?torch_isfinite","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"isinf","dir":"Articles > Tensor","previous_headings":"","what":"isinf","title":"Tensor objects","text":"isinf() -> Tensor See ?torch_isinf","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"isnan","dir":"Articles > Tensor","previous_headings":"","what":"isnan","title":"Tensor objects","text":"isnan() -> Tensor See ?torch_isnan","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"istft","dir":"Articles > Tensor","previous_headings":"","what":"istft","title":"Tensor objects","text":"See ?torch_istft ## item item() -> number Returns value tensor standard Python number. works tensors one element. cases, see $tolist. operation differentiable.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-15","dir":"Articles > Tensor","previous_headings":"istft","what":"Examples:","title":"Tensor objects","text":"","code":"x <- torch_tensor(1.0) x$item()"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"kthvalue","dir":"Articles > Tensor","previous_headings":"","what":"kthvalue","title":"Tensor objects","text":"kthvalue(k, dim=NULL, keepdim=FALSE) -> (Tensor, LongTensor) See ?torch_kthvalue","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"le","dir":"Articles > Tensor","previous_headings":"","what":"le","title":"Tensor objects","text":"le() -> Tensor See ?torch_le","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"le_","dir":"Articles > Tensor","previous_headings":"","what":"le_","title":"Tensor objects","text":"le_() -> Tensor -place version $le","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"lerp","dir":"Articles > Tensor","previous_headings":"","what":"lerp","title":"Tensor objects","text":"lerp(end, weight) -> Tensor See ?torch_lerp","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"lerp_","dir":"Articles > Tensor","previous_headings":"","what":"lerp_","title":"Tensor objects","text":"lerp_(end, weight) -> Tensor -place version $lerp","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"lgamma","dir":"Articles > Tensor","previous_headings":"","what":"lgamma","title":"Tensor objects","text":"lgamma() -> Tensor See ?torch_lgamma","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"lgamma_","dir":"Articles > Tensor","previous_headings":"","what":"lgamma_","title":"Tensor objects","text":"lgamma_() -> Tensor -place version $lgamma","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"log","dir":"Articles > Tensor","previous_headings":"","what":"log","title":"Tensor objects","text":"log() -> Tensor See ?torch_log","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"log10","dir":"Articles > Tensor","previous_headings":"","what":"log10","title":"Tensor objects","text":"log10() -> Tensor See [torch_log10()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"log10_","dir":"Articles > Tensor","previous_headings":"","what":"log10_","title":"Tensor objects","text":"log10_() -> Tensor -place version $log10","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"log1p","dir":"Articles > Tensor","previous_headings":"","what":"log1p","title":"Tensor objects","text":"log1p() -> Tensor See [torch_log1p()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"log1p_","dir":"Articles > Tensor","previous_headings":"","what":"log1p_","title":"Tensor objects","text":"log1p_() -> Tensor -place version $log1p","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"log2","dir":"Articles > Tensor","previous_headings":"","what":"log2","title":"Tensor objects","text":"log2() -> Tensor See [torch_log2()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"log2_","dir":"Articles > Tensor","previous_headings":"","what":"log2_","title":"Tensor objects","text":"log2_() -> Tensor -place version $log2","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"log_","dir":"Articles > Tensor","previous_headings":"","what":"log_","title":"Tensor objects","text":"log_() -> Tensor -place version $log","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"log_normal_","dir":"Articles > Tensor","previous_headings":"","what":"log_normal_","title":"Tensor objects","text":"log_normal_(mean=1, std=2, *, generator=NULL) Fills self tensor numbers samples log-normal distribution parameterized given mean \\mu standard deviation \\sigma. Note mean std mean standard deviation underlying normal distribution, returned distribution: \\[ f(x) = \\dfrac{1}{x \\sigma \\sqrt{2\\pi}}\\ e^{-\\frac{(\\ln x - \\mu)^2}{2\\sigma^2}} \\]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"logaddexp","dir":"Articles > Tensor","previous_headings":"","what":"logaddexp","title":"Tensor objects","text":"logaddexp() -> Tensor See ?torch_logaddexp","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"logaddexp2","dir":"Articles > Tensor","previous_headings":"","what":"logaddexp2","title":"Tensor objects","text":"logaddexp2() -> Tensor See [torch_logaddexp2()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"logcumsumexp","dir":"Articles > Tensor","previous_headings":"","what":"logcumsumexp","title":"Tensor objects","text":"logcumsumexp(dim) -> Tensor See ?torch_logcumsumexp","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"logdet","dir":"Articles > Tensor","previous_headings":"","what":"logdet","title":"Tensor objects","text":"logdet() -> Tensor See ?torch_logdet","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"logical_and","dir":"Articles > Tensor","previous_headings":"","what":"logical_and","title":"Tensor objects","text":"logical_and() -> Tensor See [torch_logical_and()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"logical_and_","dir":"Articles > Tensor","previous_headings":"","what":"logical_and_","title":"Tensor objects","text":"logical_and_() -> Tensor -place version $logical_and","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"logical_not","dir":"Articles > Tensor","previous_headings":"","what":"logical_not","title":"Tensor objects","text":"logical_not() -> Tensor See [torch_logical_not()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"logical_not_","dir":"Articles > Tensor","previous_headings":"","what":"logical_not_","title":"Tensor objects","text":"logical_not_() -> Tensor -place version $logical_not","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"logical_or","dir":"Articles > Tensor","previous_headings":"","what":"logical_or","title":"Tensor objects","text":"logical_or() -> Tensor See [torch_logical_or()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"logical_or_","dir":"Articles > Tensor","previous_headings":"","what":"logical_or_","title":"Tensor objects","text":"logical_or_() -> Tensor -place version $logical_or","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"logical_xor","dir":"Articles > Tensor","previous_headings":"","what":"logical_xor","title":"Tensor objects","text":"logical_xor() -> Tensor See [torch_logical_xor()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"logical_xor_","dir":"Articles > Tensor","previous_headings":"","what":"logical_xor_","title":"Tensor objects","text":"logical_xor_() -> Tensor -place version $logical_xor","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"logsumexp","dir":"Articles > Tensor","previous_headings":"","what":"logsumexp","title":"Tensor objects","text":"logsumexp(dim, keepdim=FALSE) -> Tensor See ?torch_logsumexp","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"long","dir":"Articles > Tensor","previous_headings":"","what":"long","title":"Tensor objects","text":"long(memory_format=torch_preserve_format) -> Tensor self$long() equivalent self$(torch_int64). See [()].","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-25","dir":"Articles > Tensor","previous_headings":"long","what":"Arguments:","title":"Tensor objects","text":"memory_format (torch_memory_format, optional): desired memory format returned Tensor. Default: torch_preserve_format.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"lstsq","dir":"Articles > Tensor","previous_headings":"","what":"lstsq","title":"Tensor objects","text":"lstsq() -> (Tensor, Tensor) See ?torch_lstsq","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"lt","dir":"Articles > Tensor","previous_headings":"","what":"lt","title":"Tensor objects","text":"lt() -> Tensor See ?torch_lt","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"lt_","dir":"Articles > Tensor","previous_headings":"","what":"lt_","title":"Tensor objects","text":"lt_() -> Tensor -place version $lt","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"lu","dir":"Articles > Tensor","previous_headings":"","what":"lu","title":"Tensor objects","text":"See ?torch_lu ## lu_solve lu_solve(LU_data, LU_pivots) -> Tensor See [torch_lu_solve()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"map_","dir":"Articles > Tensor","previous_headings":"","what":"map_","title":"Tensor objects","text":"map_(tensor, callable) Applies callable element self tensor given tensor stores results self tensor. self tensor given tensor must broadcastable. callable signature: callable(, b) -> number","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"masked_fill","dir":"Articles > Tensor","previous_headings":"","what":"masked_fill","title":"Tensor objects","text":"masked_fill(mask, value) -> Tensor --place version $masked_fill_","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"masked_fill_","dir":"Articles > Tensor","previous_headings":"","what":"masked_fill_","title":"Tensor objects","text":"masked_fill_(mask, value) Fills elements self tensor value mask TRUE. shape mask must broadcastable <broadcasting-semantics> shape underlying tensor.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-26","dir":"Articles > Tensor","previous_headings":"masked_fill_","what":"Arguments:","title":"Tensor objects","text":"mask (BoolTensor): boolean mask value (float): value fill ","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"masked_scatter","dir":"Articles > Tensor","previous_headings":"","what":"masked_scatter","title":"Tensor objects","text":"masked_scatter(mask, tensor) -> Tensor --place version $masked_scatter_","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"masked_scatter_","dir":"Articles > Tensor","previous_headings":"","what":"masked_scatter_","title":"Tensor objects","text":"masked_scatter_(mask, source) Copies elements source self tensor positions mask TRUE. shape mask must :ref:broadcastable <broadcasting-semantics> shape underlying tensor. source least many elements number ones mask","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-27","dir":"Articles > Tensor","previous_headings":"masked_scatter_","what":"Arguments:","title":"Tensor objects","text":"mask (BoolTensor): boolean mask source (Tensor): tensor copy ","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"note-5","dir":"Articles > Tensor","previous_headings":"masked_scatter_","what":"Note:","title":"Tensor objects","text":"mask operates self tensor, given source tensor.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"masked_select","dir":"Articles > Tensor","previous_headings":"","what":"masked_select","title":"Tensor objects","text":"masked_select(mask) -> Tensor See [torch_masked_select()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"matmul","dir":"Articles > Tensor","previous_headings":"","what":"matmul","title":"Tensor objects","text":"matmul(tensor2) -> Tensor See ?torch_matmul","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"matrix_power","dir":"Articles > Tensor","previous_headings":"","what":"matrix_power","title":"Tensor objects","text":"matrix_power(n) -> Tensor See [torch_matrix_power()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"max","dir":"Articles > Tensor","previous_headings":"","what":"max","title":"Tensor objects","text":"max(dim=NULL, keepdim=FALSE) -> Tensor (Tensor, Tensor) See ?torch_max","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"mean","dir":"Articles > Tensor","previous_headings":"","what":"mean","title":"Tensor objects","text":"mean(dim=NULL, keepdim=FALSE) -> Tensor (Tensor, Tensor) See ?torch_mean","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"median","dir":"Articles > Tensor","previous_headings":"","what":"median","title":"Tensor objects","text":"median(dim=NULL, keepdim=FALSE) -> (Tensor, LongTensor) See ?torch_median","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"min","dir":"Articles > Tensor","previous_headings":"","what":"min","title":"Tensor objects","text":"min(dim=NULL, keepdim=FALSE) -> Tensor (Tensor, Tensor) See ?torch_min","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"mm","dir":"Articles > Tensor","previous_headings":"","what":"mm","title":"Tensor objects","text":"mm(mat2) -> Tensor See ?torch_mm","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"mode","dir":"Articles > Tensor","previous_headings":"","what":"mode","title":"Tensor objects","text":"mode(dim=NULL, keepdim=FALSE) -> (Tensor, LongTensor) See ?torch_mode","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"mul","dir":"Articles > Tensor","previous_headings":"","what":"mul","title":"Tensor objects","text":"mul(value) -> Tensor See ?torch_mul","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"mul_","dir":"Articles > Tensor","previous_headings":"","what":"mul_","title":"Tensor objects","text":"mul_(value) -place version $mul","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"multinomial","dir":"Articles > Tensor","previous_headings":"","what":"multinomial","title":"Tensor objects","text":"multinomial(num_samples, replacement=FALSE, *, generator=NULL) -> Tensor See ?torch_multinomial","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"mv","dir":"Articles > Tensor","previous_headings":"","what":"mv","title":"Tensor objects","text":"mv(vec) -> Tensor See ?torch_mv","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"mvlgamma","dir":"Articles > Tensor","previous_headings":"","what":"mvlgamma","title":"Tensor objects","text":"mvlgamma(p) -> Tensor See ?torch_mvlgamma","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"mvlgamma_","dir":"Articles > Tensor","previous_headings":"","what":"mvlgamma_","title":"Tensor objects","text":"mvlgamma_(p) -> Tensor -place version $mvlgamma","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"names","dir":"Articles > Tensor","previous_headings":"","what":"names","title":"Tensor objects","text":"Stores names tensor’s dimensions. names[idx] corresponds name tensor dimension idx. Names either string dimension named NULL dimension unnamed. Dimension names may contain characters underscore. Furthermore, dimension name must valid Python variable name (.e., start underscore). Tensors may two named dimensions name.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"warning-4","dir":"Articles > Tensor","previous_headings":"names","what":"Warning:","title":"Tensor objects","text":"named tensor API experimental subject change.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"narrow","dir":"Articles > Tensor","previous_headings":"","what":"narrow","title":"Tensor objects","text":"narrow(dimension, start, length) -> Tensor See ?torch_narrow","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-16","dir":"Articles > Tensor","previous_headings":"narrow","what":"Examples:","title":"Tensor objects","text":"","code":"x <- torch_tensor(matrix(1:9, ncol = 3)) x$narrow(1, 1, 3) x$narrow(1, 1, 2)"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"narrow_copy","dir":"Articles > Tensor","previous_headings":"","what":"narrow_copy","title":"Tensor objects","text":"narrow_copy(dimension, start, length) -> Tensor Tensor.narrow except returning copy rather shared storage. primarily sparse tensors, shared-storage narrow method. Calling narrow_copy` withdimemsion > self\\(sparse_dim()`` return copy relevant dense dimension narrowed, ``self\\)shape`` updated accordingly.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"ndim","dir":"Articles > Tensor","previous_headings":"","what":"ndim","title":"Tensor objects","text":"Alias $dim()","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"ndimension","dir":"Articles > Tensor","previous_headings":"","what":"ndimension","title":"Tensor objects","text":"ndimension() -> int Alias $dim()","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"ne","dir":"Articles > Tensor","previous_headings":"","what":"ne","title":"Tensor objects","text":"ne() -> Tensor See ?torch_ne","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"ne_","dir":"Articles > Tensor","previous_headings":"","what":"ne_","title":"Tensor objects","text":"ne_() -> Tensor -place version $ne","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"neg","dir":"Articles > Tensor","previous_headings":"","what":"neg","title":"Tensor objects","text":"neg() -> Tensor See ?torch_neg","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"neg_","dir":"Articles > Tensor","previous_headings":"","what":"neg_","title":"Tensor objects","text":"neg_() -> Tensor -place version $neg","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"nelement","dir":"Articles > Tensor","previous_headings":"","what":"nelement","title":"Tensor objects","text":"nelement() -> int Alias $numel","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"new_empty","dir":"Articles > Tensor","previous_headings":"","what":"new_empty","title":"Tensor objects","text":"new_empty(size, dtype=NULL, device=NULL, requires_grad=FALSE) -> Tensor Returns Tensor size size filled uninitialized data. default, returned Tensor torch_dtype torch_device tensor.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-28","dir":"Articles > Tensor","previous_headings":"new_empty","what":"Arguments:","title":"Tensor objects","text":"dtype (torch_dtype, optional): desired type returned tensor. Default: NULL, torch_dtype tensor. device (torch_device, optional): desired device returned tensor. Default: NULL, torch_device tensor. requires_grad (bool, optional): autograd record operations returned tensor. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-17","dir":"Articles > Tensor","previous_headings":"new_empty","what":"Examples:","title":"Tensor objects","text":"","code":"tensor <- torch_ones(5) tensor$new_empty(c(2, 3))"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"new_full","dir":"Articles > Tensor","previous_headings":"","what":"new_full","title":"Tensor objects","text":"new_full(size, fill_value, dtype=NULL, device=NULL, requires_grad=FALSE) -> Tensor Returns Tensor size size filled fill_value. default, returned Tensor torch_dtype torch_device tensor.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-29","dir":"Articles > Tensor","previous_headings":"new_full","what":"Arguments:","title":"Tensor objects","text":"fill_value (scalar): number fill output tensor . dtype (torch_dtype, optional): desired type returned tensor. Default: NULL, torch_dtype tensor. device (torch_device, optional): desired device returned tensor. Default: NULL, torch_device tensor. requires_grad (bool, optional): autograd record operations returned tensor. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-18","dir":"Articles > Tensor","previous_headings":"new_full","what":"Examples:","title":"Tensor objects","text":"","code":"tensor <- torch_ones(c(2), dtype=torch_float64()) tensor$new_full(c(3, 4), 3.141592)"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"new_ones","dir":"Articles > Tensor","previous_headings":"","what":"new_ones","title":"Tensor objects","text":"new_ones(size, dtype=NULL, device=NULL, requires_grad=FALSE) -> Tensor Returns Tensor size size filled 1. default, returned Tensor torch_dtype torch_device tensor.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-30","dir":"Articles > Tensor","previous_headings":"new_ones","what":"Arguments:","title":"Tensor objects","text":"size (int…): list, tuple, torch_Size integers defining shape output tensor. dtype (torch_dtype, optional): desired type returned tensor. Default: NULL, torch_dtype tensor. device (torch_device, optional): desired device returned tensor. Default: NULL, torch_device tensor. requires_grad (bool, optional): autograd record operations returned tensor. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-19","dir":"Articles > Tensor","previous_headings":"new_ones","what":"Examples:","title":"Tensor objects","text":"","code":"tensor <- torch_tensor(c(2), dtype=torch_int32()) tensor$new_ones(c(2, 3))"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"new_tensor","dir":"Articles > Tensor","previous_headings":"","what":"new_tensor","title":"Tensor objects","text":"new_tensor(data, dtype=NULL, device=NULL, requires_grad=FALSE) -> Tensor Returns new Tensor data tensor data. default, returned Tensor torch_dtype torch_device tensor.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"warning-5","dir":"Articles > Tensor","previous_headings":"new_tensor","what":"Warning:","title":"Tensor objects","text":"new_tensor always copies data(). Tensordata` want avoid copy, use [$requires_grad_()] [$detach()]. numpy array want avoid copy, use [torch_from_numpy()]. data tensor x, [new_tensor()()] reads ‘data’ whatever passed, constructs leaf variable. Therefore tensor$new_tensor(x) equivalent x$clone()$detach() tensor$new_tensor(x, requires_grad=TRUE) equivalent x$clone()$detach()$requires_grad_(TRUE). equivalents using clone() detach() recommended.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-31","dir":"Articles > Tensor","previous_headings":"new_tensor","what":"Arguments:","title":"Tensor objects","text":"data (array_like): returned Tensor copies data. dtype (torch_dtype, optional): desired type returned tensor. Default: NULL, torch_dtype tensor. device (torch_device, optional): desired device returned tensor. Default: NULL, torch_device tensor. requires_grad (bool, optional): autograd record operations returned tensor. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-20","dir":"Articles > Tensor","previous_headings":"new_tensor","what":"Examples:","title":"Tensor objects","text":"","code":"tensor <- torch_ones(c(2), dtype=torch_int8) data <- matrix(1:4, ncol = 2) tensor$new_tensor(data)"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"new_zeros","dir":"Articles > Tensor","previous_headings":"","what":"new_zeros","title":"Tensor objects","text":"new_zeros(size, dtype=NULL, device=NULL, requires_grad=FALSE) -> Tensor Returns Tensor size size filled 0. default, returned Tensor torch_dtype torch_device tensor.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-32","dir":"Articles > Tensor","previous_headings":"new_zeros","what":"Arguments:","title":"Tensor objects","text":"size (int…): list, tuple, torch_Size integers defining shape output tensor. dtype (torch_dtype, optional): desired type returned tensor. Default: NULL, torch_dtype tensor. device (torch_device, optional): desired device returned tensor. Default: NULL, torch_device tensor. requires_grad (bool, optional): autograd record operations returned tensor. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-21","dir":"Articles > Tensor","previous_headings":"new_zeros","what":"Examples:","title":"Tensor objects","text":"","code":"tensor <- torch_tensor(c(1), dtype=torch_float64()) tensor$new_zeros(c(2, 3))"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"nonzero","dir":"Articles > Tensor","previous_headings":"","what":"nonzero","title":"Tensor objects","text":"nonzero() -> LongTensor See ?torch_nonzero","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"norm","dir":"Articles > Tensor","previous_headings":"","what":"norm","title":"Tensor objects","text":"See ?torch_norm ## normal_ normal_(mean=0, std=1, *, generator=NULL) -> Tensor Fills self tensor elements samples normal distribution parameterized mean std.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"numel","dir":"Articles > Tensor","previous_headings":"","what":"numel","title":"Tensor objects","text":"numel() -> int See ?torch_numel","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"numpy","dir":"Articles > Tensor","previous_headings":"","what":"numpy","title":"Tensor objects","text":"numpy() -> numpy.ndarray Returns self tensor NumPy :class:ndarray. tensor returned ndarray share underlying storage. Changes self tensor reflected :class:ndarray vice versa.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"orgqr","dir":"Articles > Tensor","previous_headings":"","what":"orgqr","title":"Tensor objects","text":"orgqr(input2) -> Tensor See ?torch_orgqr","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"ormqr","dir":"Articles > Tensor","previous_headings":"","what":"ormqr","title":"Tensor objects","text":"ormqr(input2, input3, left=TRUE, transpose=FALSE) -> Tensor See ?torch_ormqr","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"permute","dir":"Articles > Tensor","previous_headings":"","what":"permute","title":"Tensor objects","text":"permute(*dims) -> Tensor Returns view original tensor dimensions permuted.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-33","dir":"Articles > Tensor","previous_headings":"permute","what":"Arguments:","title":"Tensor objects","text":"dims (int…): desired ordering dimensions","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-22","dir":"Articles > Tensor","previous_headings":"permute","what":"Examples:","title":"Tensor objects","text":"","code":"x <- torch_randn(2, 3, 5) x$size() x$permute(c(3, 1, 2))$size()"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"pin_memory","dir":"Articles > Tensor","previous_headings":"","what":"pin_memory","title":"Tensor objects","text":"pin_memory() -> Tensor Copies tensor pinned memory, ’s already pinned.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"pinverse","dir":"Articles > Tensor","previous_headings":"","what":"pinverse","title":"Tensor objects","text":"pinverse() -> Tensor See ?torch_pinverse","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"polygamma","dir":"Articles > Tensor","previous_headings":"","what":"polygamma","title":"Tensor objects","text":"polygamma(n) -> Tensor See ?torch_polygamma","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"polygamma_","dir":"Articles > Tensor","previous_headings":"","what":"polygamma_","title":"Tensor objects","text":"polygamma_(n) -> Tensor -place version $polygamma","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"pow","dir":"Articles > Tensor","previous_headings":"","what":"pow","title":"Tensor objects","text":"pow(exponent) -> Tensor See ?torch_pow","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"pow_","dir":"Articles > Tensor","previous_headings":"","what":"pow_","title":"Tensor objects","text":"pow_(exponent) -> Tensor -place version $pow","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"prod","dir":"Articles > Tensor","previous_headings":"","what":"prod","title":"Tensor objects","text":"prod(dim=NULL, keepdim=FALSE, dtype=NULL) -> Tensor See ?torch_prod","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"put_","dir":"Articles > Tensor","previous_headings":"","what":"put_","title":"Tensor objects","text":"put_(indices, tensor, accumulate=FALSE) -> Tensor Copies elements tensor positions specified indices. purpose indexing, self tensor treated 1-D tensor. accumulate TRUE, elements tensor added self. accumulate FALSE, behavior undefined indices contain duplicate elements.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-34","dir":"Articles > Tensor","previous_headings":"put_","what":"Arguments:","title":"Tensor objects","text":"indices (LongTensor): indices self tensor (Tensor): tensor containing values copy accumulate (bool): whether accumulate self","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-23","dir":"Articles > Tensor","previous_headings":"put_","what":"Examples:","title":"Tensor objects","text":"","code":"src <- torch_tensor(matrix(3:8, ncol = 3)) src$put_(torch_tensor(1:2), torch_tensor(9:10))"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"q_per_channel_axis","dir":"Articles > Tensor","previous_headings":"","what":"q_per_channel_axis","title":"Tensor objects","text":"q_per_channel_axis() -> int Given Tensor quantized linear (affine) per-channel quantization, returns index dimension per-channel quantization applied.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"q_per_channel_scales","dir":"Articles > Tensor","previous_headings":"","what":"q_per_channel_scales","title":"Tensor objects","text":"q_per_channel_scales() -> Tensor Given Tensor quantized linear (affine) per-channel quantization, returns Tensor scales underlying quantizer. number elements matches corresponding dimensions (q_per_channel_axis) tensor.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"q_per_channel_zero_points","dir":"Articles > Tensor","previous_headings":"","what":"q_per_channel_zero_points","title":"Tensor objects","text":"q_per_channel_zero_points() -> Tensor Given Tensor quantized linear (affine) per-channel quantization, returns tensor zero_points underlying quantizer. number elements matches corresponding dimensions (q_per_channel_axis) tensor.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"q_scale","dir":"Articles > Tensor","previous_headings":"","what":"q_scale","title":"Tensor objects","text":"q_scale() -> float Given Tensor quantized linear(affine) quantization, returns scale underlying quantizer().","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"q_zero_point","dir":"Articles > Tensor","previous_headings":"","what":"q_zero_point","title":"Tensor objects","text":"q_zero_point() -> int Given Tensor quantized linear(affine) quantization, returns zero_point underlying quantizer().","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"qr","dir":"Articles > Tensor","previous_headings":"","what":"qr","title":"Tensor objects","text":"qr(=TRUE) -> (Tensor, Tensor) See ?torch_qr","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"qscheme","dir":"Articles > Tensor","previous_headings":"","what":"qscheme","title":"Tensor objects","text":"qscheme() -> torch_qscheme Returns quantization scheme given QTensor.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"rad2deg","dir":"Articles > Tensor","previous_headings":"","what":"rad2deg","title":"Tensor objects","text":"rad2deg() -> Tensor See [torch_rad2deg()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"rad2deg_","dir":"Articles > Tensor","previous_headings":"","what":"rad2deg_","title":"Tensor objects","text":"rad2deg_() -> Tensor -place version $rad2deg","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"random_","dir":"Articles > Tensor","previous_headings":"","what":"random_","title":"Tensor objects","text":"random_(=0, =NULL, *, generator=NULL) -> Tensor Fills self tensor numbers sampled discrete uniform distribution [, - 1]. specified, values usually bounded self tensor’s data type. However, floating point types, unspecified, range [0, 2^mantissa] ensure every value representable. example, torch_tensor(1, dtype=torch_double).random_() uniform [0, 2^53].","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"real","dir":"Articles > Tensor","previous_headings":"","what":"real","title":"Tensor objects","text":"Returns new tensor containing real values self tensor. returned tensor self share underlying storage.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"warning-6","dir":"Articles > Tensor","previous_headings":"real","what":"Warning:","title":"Tensor objects","text":"[real()] supported tensors complex dtypes.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-24","dir":"Articles > Tensor","previous_headings":"real","what":"Examples:","title":"Tensor objects","text":"","code":"x <- torch_randn(4, dtype=torch_cfloat()) x x$real"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"reciprocal","dir":"Articles > Tensor","previous_headings":"","what":"reciprocal","title":"Tensor objects","text":"reciprocal() -> Tensor See ?torch_reciprocal","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"reciprocal_","dir":"Articles > Tensor","previous_headings":"","what":"reciprocal_","title":"Tensor objects","text":"reciprocal_() -> Tensor -place version $reciprocal","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"record_stream","dir":"Articles > Tensor","previous_headings":"","what":"record_stream","title":"Tensor objects","text":"record_stream(stream) Ensures tensor memory reused another tensor current work queued stream complete.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"note-6","dir":"Articles > Tensor","previous_headings":"record_stream","what":"Note:","title":"Tensor objects","text":"caching allocator aware stream tensor allocated. Due awareness, already correctly manages life cycle tensors one stream. tensor used stream different stream origin, allocator might reuse memory unexpectedly. Calling method lets allocator know streams used tensor.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"refine_names","dir":"Articles > Tensor","previous_headings":"","what":"refine_names","title":"Tensor objects","text":"Refines dimension names self according names. Refining special case renaming “lifts” unnamed dimensions. NULL dim can refined name; named dim can refined name. named tensors can coexist unnamed tensors, refining names gives nice way write named-tensor-aware code works named unnamed tensors. names may contain one Ellipsis (...). Ellipsis expanded greedily; expanded -place fill names length self$dim() using names corresponding indices self$names.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-35","dir":"Articles > Tensor","previous_headings":"refine_names","what":"Arguments:","title":"Tensor objects","text":"names (iterable str): desired names output tensor. May contain one Ellipsis.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-25","dir":"Articles > Tensor","previous_headings":"refine_names","what":"Examples:","title":"Tensor objects","text":"","code":"imgs <- torch_randn(32, 3, 128, 128) named_imgs <- imgs$refine_names(c('N', 'C', 'H', 'W')) named_imgs$names"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"register_hook","dir":"Articles > Tensor","previous_headings":"","what":"register_hook","title":"Tensor objects","text":"Registers backward hook. hook called every time gradient respect Tensor computed. hook following signature:: hook(grad) -> Tensor NULL hook modify argument, can optionally return new gradient used place grad. function returns handle method handle$remove() removes hook module.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"example","dir":"Articles > Tensor","previous_headings":"register_hook","what":"Example","title":"Tensor objects","text":"","code":"v <- torch_tensor(c(0., 0., 0.), requires_grad=TRUE) h <- v$register_hook(function(grad) grad * 2)  # double the gradient v$backward(torch_tensor(c(1., 2., 3.))) v$grad h$remove()"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"remainder","dir":"Articles > Tensor","previous_headings":"","what":"remainder","title":"Tensor objects","text":"remainder(divisor) -> Tensor See ?torch_remainder","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"remainder_","dir":"Articles > Tensor","previous_headings":"","what":"remainder_","title":"Tensor objects","text":"remainder_(divisor) -> Tensor -place version $remainder","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"rename","dir":"Articles > Tensor","previous_headings":"","what":"rename","title":"Tensor objects","text":"Renames dimension names self. two main usages: self$rename(**rename_map) returns view tensor dims renamed specified mapping rename_map. self$rename(*names) returns view tensor, renaming dimensions positionally using names. Use self$rename(NULL) drop names tensor. One specify positional args names keyword args rename_map.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-26","dir":"Articles > Tensor","previous_headings":"rename","what":"Examples:","title":"Tensor objects","text":"","code":"imgs <- torch_rand(2, 3, 5, 7, names=c('N', 'C', 'H', 'W')) renamed_imgs <- imgs$rename(c(\"Batch\", \"Channels\", \"Height\", \"Width\"))"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"rename_","dir":"Articles > Tensor","previous_headings":"","what":"rename_","title":"Tensor objects","text":"-place version $rename.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"renorm","dir":"Articles > Tensor","previous_headings":"","what":"renorm","title":"Tensor objects","text":"renorm(p, dim, maxnorm) -> Tensor See ?torch_renorm","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"renorm_","dir":"Articles > Tensor","previous_headings":"","what":"renorm_","title":"Tensor objects","text":"renorm_(p, dim, maxnorm) -> Tensor -place version $renorm","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"repeat","dir":"Articles > Tensor","previous_headings":"","what":"repeat","title":"Tensor objects","text":"repeat(*sizes) -> Tensor Repeats tensor along specified dimensions. Unlike $expand, function copies tensor’s data.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-36","dir":"Articles > Tensor","previous_headings":"repeat","what":"Arguments:","title":"Tensor objects","text":"sizes (torch_Size int…): number times repeat tensor along dimension","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-27","dir":"Articles > Tensor","previous_headings":"repeat","what":"Examples:","title":"Tensor objects","text":"","code":"x <- torch_tensor(c(1, 2, 3)) x$`repeat`(c(4, 2)) x$`repeat`(c(4, 2, 1))$size()"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"repeat_interleave","dir":"Articles > Tensor","previous_headings":"","what":"repeat_interleave","title":"Tensor objects","text":"repeat_interleave(repeats, dim=NULL) -> Tensor See [torch_repeat_interleave()].","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"requires_grad","dir":"Articles > Tensor","previous_headings":"","what":"requires_grad","title":"Tensor objects","text":"TRUE gradients need computed Tensor, FALSE otherwise.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"note-7","dir":"Articles > Tensor","previous_headings":"requires_grad","what":"Note:","title":"Tensor objects","text":"fact gradients need computed Tensor mean grad attribute populated, see is_leaf details.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"requires_grad_","dir":"Articles > Tensor","previous_headings":"","what":"requires_grad_","title":"Tensor objects","text":"requires_grad_(requires_grad=TRUE) -> Tensor Change autograd record operations tensor: sets tensor’s requires_grad attribute -place. Returns tensor. [requires_grad_()]’s main use case tell autograd begin recording operations Tensor tensor. tensor requires_grad=FALSE (obtained DataLoader, required preprocessing initialization), tensor.requires_grad_() makes autograd begin record operations tensor.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-37","dir":"Articles > Tensor","previous_headings":"requires_grad_","what":"Arguments:","title":"Tensor objects","text":"requires_grad (bool): autograd record operations tensor. Default: TRUE.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-28","dir":"Articles > Tensor","previous_headings":"requires_grad_","what":"Examples:","title":"Tensor objects","text":"","code":"# Let's say we want to preprocess some saved weights and use # the result as new weights. saved_weights <- c(0.1, 0.2, 0.3, 0.25) loaded_weights <- torch_tensor(saved_weights) weights <- preprocess(loaded_weights)  # some function weights  # Now, start to record operations done to weights weights$requires_grad_() out <- weights$pow(2)$sum() out$backward() weights$grad"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"reshape","dir":"Articles > Tensor","previous_headings":"","what":"reshape","title":"Tensor objects","text":"reshape(*shape) -> Tensor Returns tensor data number elements self specified shape. method returns view shape compatible current shape. See $view possible return view. See ?torch_reshape","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-38","dir":"Articles > Tensor","previous_headings":"reshape","what":"Arguments:","title":"Tensor objects","text":"shape (tuple ints int…): desired shape","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"reshape_as","dir":"Articles > Tensor","previous_headings":"","what":"reshape_as","title":"Tensor objects","text":"reshape_as() -> Tensor Returns tensor shape . self$reshape_as() equivalent self$reshape(.sizes()). method returns view .sizes() compatible current shape. See $view possible return view. Please see reshape information reshape.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-39","dir":"Articles > Tensor","previous_headings":"reshape_as","what":"Arguments:","title":"Tensor objects","text":"(`$): result tensor shape .","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"resize_","dir":"Articles > Tensor","previous_headings":"","what":"resize_","title":"Tensor objects","text":"resize_(*sizes, memory_format=torch_contiguous_format) -> Tensor Resizes self tensor specified size. number elements larger current storage size, underlying storage resized fit new number elements. number elements smaller, underlying storage changed. Existing elements preserved new memory uninitialized.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"warning-7","dir":"Articles > Tensor","previous_headings":"resize_","what":"Warning:","title":"Tensor objects","text":"low-level method. storage reinterpreted C-contiguous, ignoring current strides (unless target size equals current size, case tensor left unchanged). purposes, instead want use $view(), checks contiguity, $reshape(), copies data needed. change size -place custom strides, see $set_().","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-40","dir":"Articles > Tensor","previous_headings":"resize_","what":"Arguments:","title":"Tensor objects","text":"sizes (torch_Size int…): desired size memory_format (torch_memory_format, optional): desired memory format Tensor. Default: torch_contiguous_format. Note memory format self going unaffected self$size() matches sizes.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-29","dir":"Articles > Tensor","previous_headings":"resize_","what":"Examples:","title":"Tensor objects","text":"","code":"x <- torch_tensor(matrix(1:6, ncol = 2)) x$resize_(c(2, 2))"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"resize_as_","dir":"Articles > Tensor","previous_headings":"","what":"resize_as_","title":"Tensor objects","text":"resize_as_(tensor, memory_format=torch_contiguous_format) -> Tensor Resizes self tensor size specified tensor. equivalent self$resize_(tensor.size()).","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-41","dir":"Articles > Tensor","previous_headings":"resize_as_","what":"Arguments:","title":"Tensor objects","text":"memory_format (torch_memory_format, optional): desired memory format Tensor. Default: torch_contiguous_format. Note memory format self going unaffected self$size() matches tensor.size().","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"retain_grad","dir":"Articles > Tensor","previous_headings":"","what":"retain_grad","title":"Tensor objects","text":"Enables $grad attribute non-leaf Tensors.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"rfft","dir":"Articles > Tensor","previous_headings":"","what":"rfft","title":"Tensor objects","text":"rfft(signal_ndim, normalized=FALSE, onesided=TRUE) -> Tensor See ?torch_rfft","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"roll","dir":"Articles > Tensor","previous_headings":"","what":"roll","title":"Tensor objects","text":"roll(shifts, dims) -> Tensor See ?torch_roll","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"rot90","dir":"Articles > Tensor","previous_headings":"","what":"rot90","title":"Tensor objects","text":"rot90(k, dims) -> Tensor See [torch_rot90()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"round","dir":"Articles > Tensor","previous_headings":"","what":"round","title":"Tensor objects","text":"round() -> Tensor See ?torch_round","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"round_","dir":"Articles > Tensor","previous_headings":"","what":"round_","title":"Tensor objects","text":"round_() -> Tensor -place version $round","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"rsqrt","dir":"Articles > Tensor","previous_headings":"","what":"rsqrt","title":"Tensor objects","text":"rsqrt() -> Tensor See ?torch_rsqrt","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"rsqrt_","dir":"Articles > Tensor","previous_headings":"","what":"rsqrt_","title":"Tensor objects","text":"rsqrt_() -> Tensor -place version $rsqrt","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"scatter","dir":"Articles > Tensor","previous_headings":"","what":"scatter","title":"Tensor objects","text":"scatter(dim, index, src) -> Tensor --place version $scatter_","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"scatter_","dir":"Articles > Tensor","previous_headings":"","what":"scatter_","title":"Tensor objects","text":"scatter_(dim, index, src) -> Tensor Writes values tensor src self indices specified index tensor. value src, output index specified index src dimension != dim corresponding value index dimension = dim. 3-D tensor, self updated : reverse operation manner described $gather. self, index src (Tensor) number dimensions. also required index.size(d) <= src.size(d) dimensions d, index.size(d) <= self$size(d) dimensions d != dim. Moreover, $gather, values index must 0 self$size(dim) - 1 inclusive, values row along specified dimension dim must unique.","code":"self[index[i][j][k]][j][k] = src[i][j][k]  # if dim == 0 self[i][index[i][j][k]][k] = src[i][j][k]  # if dim == 1 self[i][j][index[i][j][k]] = src[i][j][k]  # if dim == 2"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-42","dir":"Articles > Tensor","previous_headings":"scatter_","what":"Arguments:","title":"Tensor objects","text":"dim (int): axis along index index (LongTensor): indices elements scatter, can either empty size src. empty, operation returns identity src (Tensor): source element(s) scatter, incase value specified value (float): source element(s) scatter, incase src specified","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-30","dir":"Articles > Tensor","previous_headings":"scatter_","what":"Examples:","title":"Tensor objects","text":"","code":"x <- torch_rand(2, 5) x torch_zeros(3, 5)$scatter_(         1,          torch_tensor(rbind(c(2, 3, 3, 1, 1), c(3, 1, 1, 2, 3)), x) )  z <- torch_zeros(2, 4)$scatter_(         2,          torch_tensor(matrix(3:4, ncol = 1)), 1.23 )"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"scatter_add","dir":"Articles > Tensor","previous_headings":"","what":"scatter_add","title":"Tensor objects","text":"scatter_add(dim, index, src) -> Tensor --place version $scatter_add_","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"scatter_add_","dir":"Articles > Tensor","previous_headings":"","what":"scatter_add_","title":"Tensor objects","text":"scatter_add_(dim, index, src) -> Tensor Adds values tensor self indices specified index tensor similar fashion ~$scatter_. value src, added index self specified index src dimension != dim corresponding value index dimension = dim. 3-D tensor, self updated :: self, index src number dimensions. also required index.size(d) <= src.size(d) dimensions d, index.size(d) <= self$size(d) dimensions d != dim.","code":"self[index[i][j][k]][j][k] += src[i][j][k]  # if dim == 0 self[i][index[i][j][k]][k] += src[i][j][k]  # if dim == 1 self[i][j][index[i][j][k]] += src[i][j][k]  # if dim == 2"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"note-8","dir":"Articles > Tensor","previous_headings":"scatter_add_","what":"Note:","title":"Tensor objects","text":"circumstances using CUDA backend CuDNN, operator may select nondeterministic algorithm increase performance. undesirable, can try make operation deterministic (potentially performance cost) setting torch_backends.cudnn.deterministic = TRUE.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-43","dir":"Articles > Tensor","previous_headings":"scatter_add_","what":"Arguments:","title":"Tensor objects","text":"dim (int): axis along index index (LongTensor): indices elements scatter add, can either empty size src. empty, operation returns identity. src (Tensor): source elements scatter add","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-31","dir":"Articles > Tensor","previous_headings":"scatter_add_","what":"Examples:","title":"Tensor objects","text":"","code":"x <- torch_rand(2, 5) x torch_ones(3, 5)$scatter_add_(1, torch_tensor(rbind(c(0, 1, 2, 0, 0), c(2, 0, 0, 1, 2))), x)"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"select","dir":"Articles > Tensor","previous_headings":"","what":"select","title":"Tensor objects","text":"select(dim, index) -> Tensor Slices self tensor along selected dimension given index. function returns view original tensor given dimension removed.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-44","dir":"Articles > Tensor","previous_headings":"select","what":"Arguments:","title":"Tensor objects","text":"dim (int): dimension slice index (int): index select ","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"note-9","dir":"Articles > Tensor","previous_headings":"select","what":"Note:","title":"Tensor objects","text":"select equivalent slicing. example, tensor$select(0, index) equivalent tensor[index] tensor$select(2, index) equivalent tensor[:,:,index].","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"set_","dir":"Articles > Tensor","previous_headings":"","what":"set_","title":"Tensor objects","text":"set_(source=NULL, storage_offset=0, size=NULL, stride=NULL) -> Tensor Sets underlying storage, size, strides. source tensor, self tensor share storage size strides source. Changes elements one tensor reflected .","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-45","dir":"Articles > Tensor","previous_headings":"set_","what":"Arguments:","title":"Tensor objects","text":"source (Tensor Storage): tensor storage use storage_offset (int, optional): offset storage size (torch_Size, optional): desired size. Defaults size source. stride (tuple, optional): desired stride. Defaults C-contiguous strides.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"share_memory_","dir":"Articles > Tensor","previous_headings":"","what":"share_memory_","title":"Tensor objects","text":"Moves underlying storage shared memory. -op underlying storage already shared memory CUDA tensors. Tensors shared memory resized.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"short","dir":"Articles > Tensor","previous_headings":"","what":"short","title":"Tensor objects","text":"short(memory_format=torch_preserve_format) -> Tensor self$short() equivalent self$(torch_int16). See [()].","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-46","dir":"Articles > Tensor","previous_headings":"short","what":"Arguments:","title":"Tensor objects","text":"memory_format (torch_memory_format, optional): desired memory format returned Tensor. Default: torch_preserve_format.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"sigmoid","dir":"Articles > Tensor","previous_headings":"","what":"sigmoid","title":"Tensor objects","text":"sigmoid() -> Tensor See ?torch_sigmoid","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"sigmoid_","dir":"Articles > Tensor","previous_headings":"","what":"sigmoid_","title":"Tensor objects","text":"sigmoid_() -> Tensor -place version $sigmoid","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"sign","dir":"Articles > Tensor","previous_headings":"","what":"sign","title":"Tensor objects","text":"sign() -> Tensor See ?torch_sign","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"sign_","dir":"Articles > Tensor","previous_headings":"","what":"sign_","title":"Tensor objects","text":"sign_() -> Tensor -place version $sign","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"sin","dir":"Articles > Tensor","previous_headings":"","what":"sin","title":"Tensor objects","text":"sin() -> Tensor See ?torch_sin","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"sin_","dir":"Articles > Tensor","previous_headings":"","what":"sin_","title":"Tensor objects","text":"sin_() -> Tensor -place version $sin","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"sinh","dir":"Articles > Tensor","previous_headings":"","what":"sinh","title":"Tensor objects","text":"sinh() -> Tensor See ?torch_sinh","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"sinh_","dir":"Articles > Tensor","previous_headings":"","what":"sinh_","title":"Tensor objects","text":"sinh_() -> Tensor -place version $sinh","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"size","dir":"Articles > Tensor","previous_headings":"","what":"size","title":"Tensor objects","text":"size() -> torch_Size Returns size self tensor. returned value subclass tuple.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-32","dir":"Articles > Tensor","previous_headings":"size","what":"Examples:","title":"Tensor objects","text":"","code":"torch_empty(3, 4, 5)$size()"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"slogdet","dir":"Articles > Tensor","previous_headings":"","what":"slogdet","title":"Tensor objects","text":"slogdet() -> (Tensor, Tensor) See ?torch_slogdet","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"solve","dir":"Articles > Tensor","previous_headings":"","what":"solve","title":"Tensor objects","text":"solve() -> Tensor, Tensor See ?torch_solve","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"sort","dir":"Articles > Tensor","previous_headings":"","what":"sort","title":"Tensor objects","text":"sort(dim=-1, descending=FALSE) -> (Tensor, LongTensor) See ?torch_sort","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"sparse_dim","dir":"Articles > Tensor","previous_headings":"","what":"sparse_dim","title":"Tensor objects","text":"sparse_dim() -> int self sparse COO tensor (.e., torch_sparse_coo layout), returns number sparse dimensions. Otherwise, throws error. See also Tensor.dense_dim.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"sparse_mask","dir":"Articles > Tensor","previous_headings":"","what":"sparse_mask","title":"Tensor objects","text":"sparse_mask(input, mask) -> Tensor Returns new SparseTensor values Tensor input filtered indices mask values ignored. input mask must shape.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-47","dir":"Articles > Tensor","previous_headings":"sparse_mask","what":"Arguments:","title":"Tensor objects","text":"input (Tensor): input Tensor mask (SparseTensor): SparseTensor filter input based indices","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"split","dir":"Articles > Tensor","previous_headings":"","what":"split","title":"Tensor objects","text":"See ?torch_split","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"sqrt","dir":"Articles > Tensor","previous_headings":"","what":"sqrt","title":"Tensor objects","text":"sqrt() -> Tensor See ?torch_sqrt","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"sqrt_","dir":"Articles > Tensor","previous_headings":"","what":"sqrt_","title":"Tensor objects","text":"sqrt_() -> Tensor -place version $sqrt","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"square","dir":"Articles > Tensor","previous_headings":"","what":"square","title":"Tensor objects","text":"square() -> Tensor See ?torch_square","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"square_","dir":"Articles > Tensor","previous_headings":"","what":"square_","title":"Tensor objects","text":"square_() -> Tensor -place version $square","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"squeeze","dir":"Articles > Tensor","previous_headings":"","what":"squeeze","title":"Tensor objects","text":"squeeze(dim=NULL) -> Tensor See ?torch_squeeze","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"squeeze_","dir":"Articles > Tensor","previous_headings":"","what":"squeeze_","title":"Tensor objects","text":"squeeze_(dim=NULL) -> Tensor -place version $squeeze","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"std","dir":"Articles > Tensor","previous_headings":"","what":"std","title":"Tensor objects","text":"std(dim=NULL, unbiased=TRUE, keepdim=FALSE) -> Tensor See ?torch_std","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"stft","dir":"Articles > Tensor","previous_headings":"","what":"stft","title":"Tensor objects","text":"See ?torch_stft","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"storage","dir":"Articles > Tensor","previous_headings":"","what":"storage","title":"Tensor objects","text":"storage() -> torch_Storage Returns underlying storage.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"storage_offset","dir":"Articles > Tensor","previous_headings":"","what":"storage_offset","title":"Tensor objects","text":"storage_offset() -> int Returns self tensor’s offset underlying storage terms number storage elements (bytes).","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-33","dir":"Articles > Tensor","previous_headings":"storage_offset","what":"Examples:","title":"Tensor objects","text":"","code":"x <- torch_tensor(c(1, 2, 3, 4, 5)) x$storage_offset() x[3:N]$storage_offset()"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"storage_type","dir":"Articles > Tensor","previous_headings":"","what":"storage_type","title":"Tensor objects","text":"storage_type() -> type Returns type underlying storage.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"stride","dir":"Articles > Tensor","previous_headings":"","what":"stride","title":"Tensor objects","text":"stride(dim) -> tuple int Returns stride self tensor. Stride jump necessary go one element next one specified dimension dim. tuple strides returned argument passed . Otherwise, integer value returned stride particular dimension dim.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-48","dir":"Articles > Tensor","previous_headings":"stride","what":"Arguments:","title":"Tensor objects","text":"dim (int, optional): desired dimension stride required","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-34","dir":"Articles > Tensor","previous_headings":"stride","what":"Examples:","title":"Tensor objects","text":"","code":"x <- torch_tensor(matrix(1:10, nrow = 2)) x$stride() x$stride(1) x$stride(-1)"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"sub","dir":"Articles > Tensor","previous_headings":"","what":"sub","title":"Tensor objects","text":"sub(, *, alpha=1) -> Tensor Subtracts scalar tensor self tensor. alpha specified, element scaled alpha used. tensor, shape must broadcastable <broadcasting-semantics> shape underlying tensor.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"sub_","dir":"Articles > Tensor","previous_headings":"","what":"sub_","title":"Tensor objects","text":"sub_(, *, alpha=1) -> Tensor -place version $sub","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"sum","dir":"Articles > Tensor","previous_headings":"","what":"sum","title":"Tensor objects","text":"sum(dim=NULL, keepdim=FALSE, dtype=NULL) -> Tensor See ?torch_sum","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"sum_to_size","dir":"Articles > Tensor","previous_headings":"","what":"sum_to_size","title":"Tensor objects","text":"sum_to_size(*size) -> Tensor Sum tensor size. size must broadcastable tensor size.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-49","dir":"Articles > Tensor","previous_headings":"sum_to_size","what":"Arguments:","title":"Tensor objects","text":"size (int…): sequence integers defining shape output tensor.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"svd","dir":"Articles > Tensor","previous_headings":"","what":"svd","title":"Tensor objects","text":"svd(=TRUE, compute_uv=TRUE) -> (Tensor, Tensor, Tensor) See ?torch_svd","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"symeig","dir":"Articles > Tensor","previous_headings":"","what":"symeig","title":"Tensor objects","text":"symeig(eigenvectors=FALSE, upper=TRUE) -> (Tensor, Tensor) See ?torch_symeig","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"t","dir":"Articles > Tensor","previous_headings":"","what":"t","title":"Tensor objects","text":"t() -> Tensor See ?torch_t","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"t_","dir":"Articles > Tensor","previous_headings":"","what":"t_","title":"Tensor objects","text":"t_() -> Tensor -place version $t","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"take","dir":"Articles > Tensor","previous_headings":"","what":"take","title":"Tensor objects","text":"take(indices) -> Tensor See ?torch_take","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"tan","dir":"Articles > Tensor","previous_headings":"","what":"tan","title":"Tensor objects","text":"tan() -> Tensor See ?torch_tan","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"tan_","dir":"Articles > Tensor","previous_headings":"","what":"tan_","title":"Tensor objects","text":"tan_() -> Tensor -place version $tan","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"tanh","dir":"Articles > Tensor","previous_headings":"","what":"tanh","title":"Tensor objects","text":"tanh() -> Tensor See ?torch_tanh","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"tanh_","dir":"Articles > Tensor","previous_headings":"","what":"tanh_","title":"Tensor objects","text":"tanh_() -> Tensor -place version $tanh","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"to","dir":"Articles > Tensor","previous_headings":"","what":"to","title":"Tensor objects","text":"(*args, **kwargs) -> Tensor Performs Tensor dtype /device conversion. torch_dtype :class:torch_device inferred arguments self$(*args, **kwargs).","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"note-10","dir":"Articles > Tensor","previous_headings":"to","what":"Note:","title":"Tensor objects","text":"self Tensor already correct torch_dtype :class:torch_device, self returned. Otherwise, returned tensor copy self desired torch_dtype :class:torch_device. ways call : (dtype, non_blocking=FALSE, copy=FALSE, memory_format=torch_preserve_format) -> Tensor Returns Tensor specified dtype","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-50","dir":"Articles > Tensor","previous_headings":"to","what":"Arguments:","title":"Tensor objects","text":"memory_format (torch_memory_format, optional): desired memory format returned Tensor. Default: torch_preserve_format. (device=NULL, dtype=NULL, non_blocking=FALSE, copy=FALSE, memory_format=torch_preserve_format) -> Tensor Returns Tensor specified device (optional) dtype. dtype NULL inferred self$dtype. non_blocking, tries convert asynchronously respect host possible, e.g., converting CPU Tensor pinned memory CUDA Tensor. copy set, new Tensor created even Tensor already matches desired conversion.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-51","dir":"Articles > Tensor","previous_headings":"to","what":"Arguments:","title":"Tensor objects","text":"memory_format (torch_memory_format, optional): desired memory format returned Tensor. Default: torch_preserve_format. function:: (, non_blocking=FALSE, copy=FALSE) -> Tensor Returns Tensor torch_dtype :class:torch_device Tensor . non_blocking, tries convert asynchronously respect host possible, e.g., converting CPU Tensor pinned memory CUDA Tensor. copy set, new Tensor created even Tensor already matches desired conversion.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-35","dir":"Articles > Tensor","previous_headings":"to","what":"Examples:","title":"Tensor objects","text":"","code":"tensor <- torch_randn(2, 2)  # Initially dtype=float32, device=cpu tensor$to(dtype = torch_float64())  other <- torch_randn(1, dtype=torch_float64()) tensor$to(other = other, non_blocking=TRUE)"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"to_mkldnn","dir":"Articles > Tensor","previous_headings":"","what":"to_mkldnn","title":"Tensor objects","text":"to_mkldnn() -> Tensor Returns copy tensor torch_mkldnn layout.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"to_sparse","dir":"Articles > Tensor","previous_headings":"","what":"to_sparse","title":"Tensor objects","text":"to_sparse(sparseDims) -> Tensor Returns sparse copy tensor. PyTorch supports sparse tensors coordinate format <sparse-docs>.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-52","dir":"Articles > Tensor","previous_headings":"to_sparse","what":"Arguments:","title":"Tensor objects","text":"sparseDims (int, optional): number sparse dimensions include new sparse tensor","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"tolist","dir":"Articles > Tensor","previous_headings":"","what":"tolist","title":"Tensor objects","text":"tolist() -> list number Returns tensor (nested) list. scalars, standard Python number returned, just like $item. Tensors automatically moved CPU first necessary. operation differentiable.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"topk","dir":"Articles > Tensor","previous_headings":"","what":"topk","title":"Tensor objects","text":"topk(k, dim=NULL, largest=TRUE, sorted=TRUE) -> (Tensor, LongTensor) See ?torch_topk","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"trace","dir":"Articles > Tensor","previous_headings":"","what":"trace","title":"Tensor objects","text":"trace() -> Tensor See ?torch_trace","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"transpose","dir":"Articles > Tensor","previous_headings":"","what":"transpose","title":"Tensor objects","text":"transpose(dim0, dim1) -> Tensor See ?torch_transpose","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"transpose_","dir":"Articles > Tensor","previous_headings":"","what":"transpose_","title":"Tensor objects","text":"transpose_(dim0, dim1) -> Tensor -place version $transpose","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"triangular_solve","dir":"Articles > Tensor","previous_headings":"","what":"triangular_solve","title":"Tensor objects","text":"triangular_solve(, upper=TRUE, transpose=FALSE, unitriangular=FALSE) -> (Tensor, Tensor) See [torch_triangular_solve()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"tril","dir":"Articles > Tensor","previous_headings":"","what":"tril","title":"Tensor objects","text":"tril(k=0) -> Tensor See ?torch_tril","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"tril_","dir":"Articles > Tensor","previous_headings":"","what":"tril_","title":"Tensor objects","text":"tril_(k=0) -> Tensor -place version $tril","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"triu","dir":"Articles > Tensor","previous_headings":"","what":"triu","title":"Tensor objects","text":"triu(k=0) -> Tensor See ?torch_triu","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"triu_","dir":"Articles > Tensor","previous_headings":"","what":"triu_","title":"Tensor objects","text":"triu_(k=0) -> Tensor -place version $triu","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"true_divide","dir":"Articles > Tensor","previous_headings":"","what":"true_divide","title":"Tensor objects","text":"true_divide(value) -> Tensor See [torch_true_divide()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"true_divide_","dir":"Articles > Tensor","previous_headings":"","what":"true_divide_","title":"Tensor objects","text":"true_divide_(value) -> Tensor -place version $true_divide_","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"trunc","dir":"Articles > Tensor","previous_headings":"","what":"trunc","title":"Tensor objects","text":"trunc() -> Tensor See ?torch_trunc","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"trunc_","dir":"Articles > Tensor","previous_headings":"","what":"trunc_","title":"Tensor objects","text":"trunc_() -> Tensor -place version $trunc","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"type","dir":"Articles > Tensor","previous_headings":"","what":"type","title":"Tensor objects","text":"type(dtype=NULL, non_blocking=FALSE, **kwargs) -> str Tensor Returns type dtype provided, else casts object specified type. already correct type, copy performed original object returned.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-53","dir":"Articles > Tensor","previous_headings":"type","what":"Arguments:","title":"Tensor objects","text":"dtype (type string): desired type non_blocking (bool): TRUE, source pinned memory destination GPU vice versa, copy performed asynchronously respect host. Otherwise, argument effect. **kwargs: compatibility, may contain key async place non_blocking argument. async arg deprecated.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"type_as","dir":"Articles > Tensor","previous_headings":"","what":"type_as","title":"Tensor objects","text":"type_as(tensor) -> Tensor Returns tensor cast type given tensor. -op tensor already correct type. equivalent self$type(tensor.type())","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-54","dir":"Articles > Tensor","previous_headings":"type_as","what":"Arguments:","title":"Tensor objects","text":"tensor (Tensor): tensor desired type","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"unbind","dir":"Articles > Tensor","previous_headings":"","what":"unbind","title":"Tensor objects","text":"unbind(dim=0) -> seq See ?torch_unbind","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"unflatten","dir":"Articles > Tensor","previous_headings":"","what":"unflatten","title":"Tensor objects","text":"Unflattens named dimension dim, viewing shape specified namedshape.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-55","dir":"Articles > Tensor","previous_headings":"unflatten","what":"Arguments:","title":"Tensor objects","text":"namedshape: (iterable (name, size) tuples).","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"unfold","dir":"Articles > Tensor","previous_headings":"","what":"unfold","title":"Tensor objects","text":"unfold(dimension, size, step) -> Tensor Returns view original tensor contains slices size size self tensor dimension dimension. Step two slices given step. sizedim size dimension dimension self, size dimension dimension returned tensor (sizedim - size) / step + 1. additional dimension size size appended returned tensor.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-56","dir":"Articles > Tensor","previous_headings":"unfold","what":"Arguments:","title":"Tensor objects","text":"dimension (int): dimension unfolding happens size (int): size slice unfolded step (int): step slice","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"uniform_","dir":"Articles > Tensor","previous_headings":"","what":"uniform_","title":"Tensor objects","text":"uniform_(=0, =1) -> Tensor Fills self tensor numbers sampled continuous uniform distribution: \\[ P(x) = \\dfrac{1}{\\text{} - \\text{}} \\]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"unique","dir":"Articles > Tensor","previous_headings":"","what":"unique","title":"Tensor objects","text":"Returns unique elements input tensor. See ?torch_unique","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"unique_consecutive","dir":"Articles > Tensor","previous_headings":"","what":"unique_consecutive","title":"Tensor objects","text":"Eliminates first element every consecutive group equivalent elements. See [torch_unique_consecutive()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"unsqueeze","dir":"Articles > Tensor","previous_headings":"","what":"unsqueeze","title":"Tensor objects","text":"unsqueeze(dim) -> Tensor See ?torch_unsqueeze","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"unsqueeze_","dir":"Articles > Tensor","previous_headings":"","what":"unsqueeze_","title":"Tensor objects","text":"unsqueeze_(dim) -> Tensor -place version $unsqueeze","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"values","dir":"Articles > Tensor","previous_headings":"","what":"values","title":"Tensor objects","text":"values() -> Tensor self sparse COO tensor (.e., torch_sparse_coo layout), returns view contained values tensor. Otherwise, throws error.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"note-11","dir":"Articles > Tensor","previous_headings":"values","what":"Note:","title":"Tensor objects","text":"method can called coalesced sparse tensor. See Tensor$coalesce details.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"var","dir":"Articles > Tensor","previous_headings":"","what":"var","title":"Tensor objects","text":"var(dim=NULL, unbiased=TRUE, keepdim=FALSE) -> Tensor See ?torch_var","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"view","dir":"Articles > Tensor","previous_headings":"","what":"view","title":"Tensor objects","text":"view(*shape) -> Tensor Returns new tensor data self tensor different shape. returned tensor shares data must number elements, may different size. tensor viewed, new view size must compatible original size stride, .e., new view dimension must either subspace original dimension, span across original dimensions d, d+1, \\dots, d+k satisfy following contiguity-like condition \\forall = d, \\dots, d+k-1, \\[ \\text{stride}[] = \\text{stride}[+1] \\times \\text{size}[+1] \\] Otherwise, possible view self tensor shape without copying (e.g., via contiguous). unclear whether view can performed, advisable use :meth:reshape, returns view shapes compatible, copies (equivalent calling contiguous) otherwise.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-57","dir":"Articles > Tensor","previous_headings":"view","what":"Arguments:","title":"Tensor objects","text":"shape (torch_Size int…): desired size","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"view_as","dir":"Articles > Tensor","previous_headings":"","what":"view_as","title":"Tensor objects","text":"view_as() -> Tensor View tensor size . self$view_as() equivalent self$view(.size()). Please see $view information view.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-58","dir":"Articles > Tensor","previous_headings":"view_as","what":"Arguments:","title":"Tensor objects","text":"(`$): result tensor size .","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"where","dir":"Articles > Tensor","previous_headings":"","what":"where","title":"Tensor objects","text":"(condition, y) -> Tensor self$(condition, y) equivalent torch_where(condition, self, y). See ?torch_where","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"zero_","dir":"Articles > Tensor","previous_headings":"","what":"zero_","title":"Tensor objects","text":"zero_() -> Tensor Fills self tensor zeros.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor-creation.html","id":"from-r-objects","dir":"Articles","previous_headings":"","what":"From R objects","title":"Creating tensors","text":"can create tensors R objects using torch_tensor function. torch_tensor function takes R vector, matrix array creates equivalent torch_tensor. can see examples : default, create tensors cpu device, converting R datatype corresponding torch dtype. Note currently, numeric boolean types supported. can always modify dtype device converting R object torch tensor. example: options available creating tensor : requires_grad: boolean indicating want autograd record operations automatic differentiation. pin_memory: – set, tensor returned allocated pinned memory. Works CPU tensors. options available functions can used create new tensors, including factory functions listed next section.","code":"torch_tensor(c(1,2,3)) #> torch_tensor #>  1 #>  2 #>  3 #> [ CPUFloatType{3} ]  # conform to row-major indexing used in torch torch_tensor(matrix(1:10, ncol = 5, nrow = 2, byrow = TRUE)) #> torch_tensor #>   1   2   3   4   5 #>   6   7   8   9  10 #> [ CPULongType{2,5} ] torch_tensor(array(runif(12), dim = c(2, 2, 3))) #> torch_tensor #> (1,.,.) =  #>   0.9997  0.0685  0.2189 #>   0.0015  0.2691  0.5970 #>  #> (2,.,.) =  #>   0.4444  0.7189  0.2738 #>   0.0837  0.3057  0.9979 #> [ CPUFloatType{2,2,3} ] torch_tensor(1, dtype = torch_long()) #> torch_tensor #>  1 #> [ CPULongType{1} ] torch_tensor(1, device = \"cpu\", dtype = torch_float64()) #> torch_tensor #>  1 #> [ CPUDoubleType{1} ]"},{"path":"https://torch.mlverse.org/docs/articles/tensor-creation.html","id":"using-creation-functions","dir":"Articles","previous_headings":"","what":"Using creation functions","title":"Creating tensors","text":"can also use torch_* functions listed create torch tensors using algorithm. example, torch_randn function create tensors using normal distribution mean 0 standard deviation 1. can use ... argument pass size dimensions. example, code create normally distributed tensor shape 5x3. Another example torch_ones, creates tensor filled ones. full list functions can used bulk-create tensors torch: torch_arange: Returns tensor sequence integers, torch_empty: Returns tensor uninitialized values, torch_eye: Returns identity matrix, torch_full: Returns tensor filled single value, torch_linspace: Returns tensor values linearly spaced interval, torch_logspace: Returns tensor values logarithmically spaced interval, torch_ones: Returns tensor filled ones, torch_rand: Returns tensor filled values drawn uniform distribution [0, 1). torch_randint: Returns tensor integers randomly drawn interval, torch_randn: Returns tensor filled values drawn unit normal distribution, torch_randperm: Returns tensor filled random permutation integers interval, torch_zeros: Returns tensor filled zeros.","code":"x <- torch_randn(5, 3) x #> torch_tensor #> -2.1127  1.5563 -1.4043 #> -0.5640 -2.7906 -0.4972 #>  1.1102 -1.4511 -0.7686 #>  0.2785 -1.1359 -1.4382 #>  0.9929 -0.5821  0.2325 #> [ CPUFloatType{5,3} ] x <- torch_ones(2, 4, dtype = torch_int64(), device = \"cpu\") x #> torch_tensor #>  1  1  1  1 #>  1  1  1  1 #> [ CPULongType{2,4} ]"},{"path":"https://torch.mlverse.org/docs/articles/tensor-creation.html","id":"conversion","dir":"Articles","previous_headings":"","what":"Conversion","title":"Creating tensors","text":"tensor exists can convert dtypes move different device method. example: can also copy tensor GPU using:","code":"x <- torch_tensor(1) y <- x$to(dtype = torch_int32()) x #> torch_tensor #>  1 #> [ CPUFloatType{1} ] y #> torch_tensor #>  1 #> [ CPUIntType{1} ] x <- torch_tensor(1) y <- x$cuda())"},{"path":[]},{"path":"https://torch.mlverse.org/docs/articles/torchscript.html","id":"tracing","dir":"Articles","previous_headings":"Creating TorchScript programs","what":"Tracing","title":"TorchScript","text":"TorchScript programs can created R using tracing. using tracing, code automatically converted subset Python recording actual operators tensors simply executing discarding surrounding R code. Currently tracing supported way create TorchScript programs R code. example, let’s use jit_trace function create TorchScript program. pass regular R function example inputs. jit_trace function executed R function example input recorded torch operations occurred execution create graph. graph call intermediate representation TorchScript programs, can inspected : traced function can now invoked regular R function: ’s also possible trace nn_modules() defined R, example: using jit_trace nn_module forward method traced. can use jit_trace_module function pass example inputs methods. Traced modules look like normal nn_modules(), can called way:","code":"fn <- function(x) {   torch_relu(x) }  traced_fn <- jit_trace(fn, torch_tensor(c(-1, 0, 1))) traced_fn$graph #> graph(%0 : Float(3, strides=[1], requires_grad=0, device=cpu)): #>   %1 : Float(3, strides=[1], requires_grad=0, device=cpu) = aten::relu(%0) #>   return (%1) traced_fn(torch_randn(3)) #> torch_tensor #>  0.3837 #>  0.0000 #>  2.4086 #> [ CPUFloatType{3} ] module <- nn_module(   initialize = function() {     self$linear1 <- nn_linear(10, 10)     self$linear2 <- nn_linear(10, 1)   },   forward = function(x) {     x %>%        self$linear1() %>%        nnf_relu() %>%        self$linear2()   } ) traced_module <- jit_trace(module(), torch_randn(10, 10)) traced_module(torch_randn(3, 10)) #> torch_tensor #>  0.1978 #>  0.1542 #>  0.1632 #> [ CPUFloatType{3,1} ][ grad_fn = <AddBackward0> ]"},{"path":"https://torch.mlverse.org/docs/articles/torchscript.html","id":"limitations-of-tracing","dir":"Articles","previous_headings":"Creating TorchScript programs > Tracing","what":"Limitations of tracing","title":"TorchScript","text":"Tracing record control flow like -statements loops. control flow constant across module, fine often inlines control flow decisions. sometimes control flow actually part model . instance, recurrent network loop (possibly dynamic) length input sequence. example: returned ScriptModule, operations different behaviors training eval modes always behave mode tracing, matter mode ScriptModule . example: Tracing proegrams can take tensors lists tensors input return tensors lists tensors. example:","code":"# fn does does an operation for each dimension of a tensor fn <- function(x) {   x %>%      torch_unbind(dim = 1) %>%      lapply(function(x) x$sum()) %>%      torch_stack(dim = 1) } # we trace using as an example a tensor with size (10, 5, 5) traced_fn <- jit_trace(fn, torch_randn(10, 5, 5)) # applying it with a tensor with different size returns an error. traced_fn(torch_randn(11, 5, 5)) #> Error in cpp_call_traced_fn(ptr, inputs): The following operation failed in the TorchScript interpreter. #> Traceback of TorchScript (most recent call last): #> RuntimeError: Expected 10 elements in a list but found 11 traced_dropout <- jit_trace(nn_dropout(), torch_ones(5,5)) traced_dropout(torch_ones(3,3)) #> torch_tensor #>  2  2  0 #>  0  2  0 #>  2  0  0 #> [ CPUFloatType{3,3} ] traced_dropout$eval() # even after setting to eval mode, dropout is applied traced_dropout(torch_ones(3,3)) #> torch_tensor #>  2  2  2 #>  0  0  2 #>  2  0  0 #> [ CPUFloatType{3,3} ] fn <- function(x, y) {   x + y } jit_trace(fn, torch_tensor(1), 1) #> Error in cpp_trace_function(tr_fn, list(...), .compilation_unit, strict, : Only tensors or (possibly nested) dict or tuples of tensors can be inputs to traced functions. Got float #> Exception raised from addInput at ../torch/csrc/jit/frontend/tracer.cpp:408 (most recent call first): #> frame #0: c10::Error::Error(c10::SourceLocation, std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char> >) + 98 (0x1135cb522 in libc10.dylib) #> frame #1: c10::detail::torchCheckFail(char const*, char const*, unsigned int, std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char> > const&) + 106 (0x1135c9c3a in libc10.dylib) #> frame #2: torch::jit::tracer::addInput(std::__1::shared_ptr<torch::jit::tracer::TracingState> const&, c10::IValue const&, std::__1::shared_ptr<c10::Type> const&, torch::jit::Value*) + 6951 (0x1191e58e7 in libtorch_cpu.dylib) #> frame #3: torch::jit::tracer::addInput(std::__1::shared_ptr<torch::jit::tracer::TracingState> const&, c10::IValue const&, std::__1::shared_ptr<c10::Type> const&, torch::jit::Value*) + 4216 (0x1191e4e38 in libtorch_cpu.dylib) #> frame #4: torch::jit::tracer::trace(std::__1::vector<c10::IValue, std::__1::allocator<c10::IValue> >, std::__1::function<std::__1::vector<c10::IValue, std::__1::allocator<c10::IValue> > (std::__1::vector<c10::IValue, std::__1::allocator<c10::IValue> >)> const&, std::__1::function<std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char> > (at::Tensor const&)>, bool, bool, torch::jit::Module*, std::__1::vector<std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char> >, std::__1::allocator<std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char> > > > const&) + 1034 (0x1191e244a in libtorch_cpu.dylib) #> frame #5: _lantern_trace_fn + 541 (0x1160f146d in liblantern.dylib) #> frame #6: cpp_trace_function(Rcpp::Function_Impl<Rcpp::PreserveStorage>, XPtrTorchStack, XPtrTorchCompilationUnit, XPtrTorchstring, bool, XPtrTorchScriptModule, bool, bool) + 547 (0x1156609a3 in torchpkg.so) #> frame #7: _torch_cpp_trace_function + 727 (0x1154e4357 in torchpkg.so) #> frame #8: R_doDotCall + 2679 (0x10d98d737 in libR.dylib) #> frame #9: do_dotcall + 334 (0x10d98ebfe in libR.dylib) #> frame #10: bcEval + 28581 (0x10d9c5c05 in libR.dylib) #> frame #11: Rf_eval + 385 (0x10d9be5b1 in libR.dylib) #> frame #12: R_execClosure + 2169 (0x10d9de429 in libR.dylib) #> frame #13: Rf_applyClosure + 471 (0x10d9dd217 in libR.dylib) #> frame #14: bcEval + 26782 (0x10d9c54fe in libR.dylib) #> frame #15: Rf_eval + 385 (0x10d9be5b1 in libR.dylib) #> frame #16: R_execClosure + 2169 (0x10d9de429 in libR.dylib) #> frame #17: Rf_applyClosure + 471 (0x10d9dd217 in libR.dylib) #> frame #18: Rf_eval + 1595 (0x10d9bea6b in libR.dylib) #> frame #19: do_eval + 625 (0x10d9e2461 in libR.dylib) #> frame #20: bcEval + 28581 (0x10d9c5c05 in libR.dylib) #> frame #21: Rf_eval + 385 (0x10d9be5b1 in libR.dylib) #> frame #22: R_execClosure + 2169 (0x10d9de429 in libR.dylib) #> frame #23: Rf_applyClosure + 471 (0x10d9dd217 in libR.dylib) #> frame #24: bcEval + 26782 (0x10d9c54fe in libR.dylib) #> frame #25: Rf_eval + 385 (0x10d9be5b1 in libR.dylib) #> frame #26: forcePromise + 172 (0x10d9dc92c in libR.dylib) #> frame #27: Rf_eval + 1124 (0x10d9be894 in libR.dylib) #> frame #28: do_withVisible + 57 (0x10d9e2ad9 in libR.dylib) #> frame #29: do_internal + 362 (0x10da262fa in libR.dylib) #> frame #30: bcEval + 29053 (0x10d9c5ddd in libR.dylib) #> frame #31: Rf_eval + 385 (0x10d9be5b1 in libR.dylib) #> frame #32: R_execClosure + 2169 (0x10d9de429 in libR.dylib) #> frame #33: Rf_applyClosure + 471 (0x10d9dd217 in libR.dylib) #> frame #34: bcEval + 26782 (0x10d9c54fe in libR.dylib) #> frame #35: Rf_eval + 385 (0x10d9be5b1 in libR.dylib) #> frame #36: forcePromise + 172 (0x10d9dc92c in libR.dylib) #> frame #37: getvar + 778 (0x10d9e7f7a in libR.dylib) #> frame #38: bcEval + 15063 (0x10d9c2737 in libR.dylib) #> frame #39: Rf_eval + 385 (0x10d9be5b1 in libR.dylib) #> frame #40: R_execClosure + 2169 (0x10d9de429 in libR.dylib) #> frame #41: Rf_applyClosure + 471 (0x10d9dd217 in libR.dylib) #> frame #42: bcEval + 26782 (0x10d9c54fe in libR.dylib) #> frame #43: Rf_eval + 385 (0x10d9be5b1 in libR.dylib) #> frame #44: forcePromise + 172 (0x10d9dc92c in libR.dylib) #> frame #45: getvar + 778 (0x10d9e7f7a in libR.dylib) #> frame #46: bcEval + 15063 (0x10d9c2737 in libR.dylib) #> frame #47: Rf_eval + 385 (0x10d9be5b1 in libR.dylib) #> frame #48: forcePromise + 172 (0x10d9dc92c in libR.dylib) #> frame #49: getvar + 778 (0x10d9e7f7a in libR.dylib) #> frame #50: bcEval + 15063 (0x10d9c2737 in libR.dylib) #> frame #51: Rf_eval + 385 (0x10d9be5b1 in libR.dylib) #> frame #52: forcePromise + 172 (0x10d9dc92c in libR.dylib) #> frame #53: getvar + 778 (0x10d9e7f7a in libR.dylib) #> frame #54: bcEval + 15063 (0x10d9c2737 in libR.dylib) #> frame #55: Rf_eval + 385 (0x10d9be5b1 in libR.dylib) #> frame #56: forcePromise + 172 (0x10d9dc92c in libR.dylib) #> frame #57: getvar + 778 (0x10d9e7f7a in libR.dylib) #> frame #58: bcEval + 15063 (0x10d9c2737 in libR.dylib) #> frame #59: Rf_eval + 385 (0x10d9be5b1 in libR.dylib) #> frame #60: forcePromise + 172 (0x10d9dc92c in libR.dylib) #> frame #61: getvar + 778 (0x10d9e7f7a in libR.dylib) #> frame #62: bcEval + 15063 (0x10d9c2737 in libR.dylib) #> frame #63: Rf_eval + 385 (0x10d9be5b1 in libR.dylib) #> :"},{"path":"https://torch.mlverse.org/docs/articles/torchscript.html","id":"compiling-torchscript","dir":"Articles","previous_headings":"Creating TorchScript programs","what":"Compiling TorchScript","title":"TorchScript","text":"’s also possible create TorchScript programs compiling TorchScript code. TorchScript code looks lot like standard python code. example:","code":"tr <- jit_compile(\" def fn (x: Tensor):   return torch.relu(x)  \") tr$fn(torch_tensor(c(-1, 0, 1))) #> torch_tensor #>  0 #>  0 #>  1 #> [ CPUFloatType{3} ]"},{"path":"https://torch.mlverse.org/docs/articles/torchscript.html","id":"serializing-and-loading","dir":"Articles","previous_headings":"","what":"Serializing and loading","title":"TorchScript","text":"TorchScript programs can serialized using jit_save function loaded back disk jit_load. example: Loaded programs can executed usual: Note can load TorchScript programs created libraries different torch R. Eg, TorchScript program can created PyTorch torch.jit.trace torch.jit.script, run R. R objects automatically converted TorchScript counterpart following Types table document. However, sometimes ’s necessary make type annotations jit_tuple() jit_scalar() disambiguate conversion.","code":"fn <- function(x) {   torch_relu(x) } tr_fn <- jit_trace(fn, torch_tensor(1)) jit_save(tr_fn, \"path.pt\") loaded <- jit_load(\"path.pt\") loaded(torch_tensor(c(-1, 0, 1))) #> torch_tensor #>  0 #>  0 #>  1 #> [ CPUFloatType{3} ]"},{"path":"https://torch.mlverse.org/docs/articles/torchscript.html","id":"types","dir":"Articles","previous_headings":"","what":"Types","title":"TorchScript","text":"following table lists TorchScript types convert back R.","code":""},{"path":"https://torch.mlverse.org/docs/articles/using-autograd.html","id":"automatic-differentiation-with-autograd","dir":"Articles","previous_headings":"","what":"Automatic differentiation with autograd","title":"Using autograd","text":"Torch uses module called autograd record operations performed tensors, store done obtain respective gradients. actions stored functions, functions applied order gradient output (normally, loss) respect tensors calculated: starting output node propagating gradients back network. form reverse mode automatic differentiation. users, can see bit implementation. prerequisite “recording” happen, tensors created requires_grad = TRUE. E.g. clear, tensor respect gradients calculated – normally, tensor representing weight bias, input data 1. now perform operation tensor, assigning result y find y now non-empty grad_fn tells torch compute gradient y respect x: Actual computation gradients triggered calling backward() output tensor. executed, x now non-empty field grad stores gradient y respect x: longer chain computations, can peek torch builds graph backward operations. slightly complex example. call retain_grad() y z just demonstration purposes; default, intermediate gradients – course computed – aren’t stored, order save memory. Starting $grad_fn, can follow graph back leaf nodes: calling $backward(), tensors graph respective gradients created. Without calls retain_grad , z$grad y$grad empty: Thus acquainted autograd, ’re ready modify example.","code":"x <- torch_ones(2,2, requires_grad = TRUE) y <- x$mean() y$grad_fn #> MeanBackward0 y$backward() x$grad #> torch_tensor #>  0.2500  0.2500 #>  0.2500  0.2500 #> [ CPUFloatType{2,2} ] x1 <- torch_ones(2,2, requires_grad = TRUE) x2 <- torch_tensor(1.1, requires_grad = TRUE) y <- x1 * (x2 + 2) y$retain_grad() z <- y$pow(2) * 3 z$retain_grad() out <- z$mean() # how to compute the gradient for mean, the last operation executed out$grad_fn #> MeanBackward0 # how to compute the gradient for the multiplication by 3 in z = y$pow(2) * 3 out$grad_fn$next_functions #> [[1]] #> MulBackward1 # how to compute the gradient for pow in z = y.pow(2) * 3 out$grad_fn$next_functions[[1]]$next_functions #> [[1]] #> PowBackward0 # how to compute the gradient for the multiplication in y = x * (x + 2) out$grad_fn$next_functions[[1]]$next_functions[[1]]$next_functions #> [[1]] #> MulBackward0 # how to compute the gradient for the two branches of y = x * (x + 2), # where the left branch is a leaf node (AccumulateGrad for x1) out$grad_fn$next_functions[[1]]$next_functions[[1]]$next_functions[[1]]$next_functions #> [[1]] #> torch::autograd::AccumulateGrad #> [[2]] #> AddBackward1 # here we arrive at the other leaf node (AccumulateGrad for x2) out$grad_fn$next_functions[[1]]$next_functions[[1]]$next_functions[[1]]$next_functions[[2]]$next_functions #> [[1]] #> torch::autograd::AccumulateGrad out$backward() z$grad #> torch_tensor #>  0.2500  0.2500 #>  0.2500  0.2500 #> [ CPUFloatType{2,2} ] y$grad #> torch_tensor #>  4.6500  4.6500 #>  4.6500  4.6500 #> [ CPUFloatType{2,2} ] x2$grad #> torch_tensor #>  18.6000 #> [ CPUFloatType{1} ] x1$grad #> torch_tensor #>  14.4150  14.4150 #>  14.4150  14.4150 #> [ CPUFloatType{2,2} ]"},{"path":"https://torch.mlverse.org/docs/articles/using-autograd.html","id":"the-simple-network-now-using-autograd","dir":"Articles","previous_headings":"","what":"The simple network, now using autograd","title":"Using autograd","text":"single new line calling loss$backward(), now number lines (manual backprop) gone: still manually compute forward pass, still manually update weights. last two chapters section, ’ll see parts logic can made modular reusable, well.","code":"### generate training data ----------------------------------------------------- # input dimensionality (number of input features) d_in <- 3 # output dimensionality (number of predicted features) d_out <- 1 # number of observations in training set n <- 100 # create random data x <- torch_randn(n, d_in) y <- x[,1]*0.2 - x[..,2]*1.3 - x[..,3]*0.5 + torch_randn(n) y <- y$unsqueeze(dim = 1) ### initialize weights --------------------------------------------------------- # dimensionality of hidden layer d_hidden <- 32 # weights connecting input to hidden layer w1 <- torch_randn(d_in, d_hidden, requires_grad = TRUE) # weights connecting hidden to output layer w2 <- torch_randn(d_hidden, d_out, requires_grad = TRUE) # hidden layer bias b1 <- torch_zeros(1, d_hidden, requires_grad = TRUE) # output layer bias b2 <- torch_zeros(1, d_out,requires_grad = TRUE) ### network parameters --------------------------------------------------------- learning_rate <- 1e-4 ### training loop -------------------------------------------------------------- for (t in 1:200) {      ### -------- Forward pass --------      y_pred <- x$mm(w1)$add(b1)$clamp(min = 0)$mm(w2)$add(b2)     ### -------- compute loss --------      loss <- (y_pred - y)$pow(2)$mean()     if (t %% 10 == 0) cat(t, as_array(loss), \"\\n\")     ### -------- Backpropagation --------      # compute the gradient of loss with respect to all tensors with requires_grad = True.     loss$backward()       ### -------- Update weights --------           # Wrap in torch.no_grad() because this is a part we DON'T want to record for automatic gradient computation     with_no_grad({              w1$sub_(learning_rate * w1$grad)       w2$sub_(learning_rate * w2$grad)       b1$sub_(learning_rate * b1$grad)       b2$sub_(learning_rate * b2$grad)              # Zero the gradients after every pass, because they'd accumulate otherwise       w1$grad$zero_()       w2$grad$zero_()       b1$grad$zero_()       b2$grad$zero_()          })      } #> 10 263.5121  #> 20 223.0612  #> 30 190.0824  #> 40 162.9376  #> 50 140.4214  #> 60 121.7117  #> 70 106.0199  #> 80 92.77345  #> 90 81.55437  #> 100 71.98052  #> 110 63.83845  #> 120 56.86206  #> 130 50.81776  #> 140 45.57465  #> 150 41.01472  #> 160 37.03083  #> 170 33.56329  #> 180 30.53346  #> 190 27.87165  #> 200 25.53565"},{"path":"https://torch.mlverse.org/docs/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Daniel Falbel. Author, maintainer, copyright holder. Javier Luraschi. Author. Dmitriy Selivanov. Contributor. Athos Damiani. Contributor. Christophe Regouby. Contributor. Krzysztof Joachimiak. Contributor. Hamada S. Badr. Contributor. RStudio. Copyright holder.","code":""},{"path":"https://torch.mlverse.org/docs/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Falbel D, Luraschi J (2022). torch: Tensors Neural Networks 'GPU' Acceleration. https://torch.mlverse.org/docs, https://github.com/mlverse/torch.","code":"@Manual{,   title = {torch: Tensors and Neural Networks with 'GPU' Acceleration},   author = {Daniel Falbel and Javier Luraschi},   year = {2022},   note = {https://torch.mlverse.org/docs, https://github.com/mlverse/torch}, }"},{"path":[]},{"path":"https://torch.mlverse.org/docs/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Tensors and Neural Networks with GPU Acceleration","text":"torch can installed CRAN : can also install development version : first package load additional software installed.","code":"install.packages(\"torch\") remotes::install_github(\"mlverse/torch\")"},{"path":"https://torch.mlverse.org/docs/index.html","id":"installation-with-docker","dir":"","previous_headings":"","what":"Installation with Docker","title":"Tensors and Neural Networks with GPU Acceleration","text":"like install Docker, please read following document. way installation Docker","code":""},{"path":"https://torch.mlverse.org/docs/index.html","id":"examples","dir":"","previous_headings":"","what":"Examples","title":"Tensors and Neural Networks with GPU Acceleration","text":"can create torch tensors R objects torch_tensor function convert back R objects as_array.","code":"library(torch) x <- array(runif(8), dim = c(2, 2, 2)) y <- torch_tensor(x, dtype = torch_float64()) y #> torch_tensor #> (1,.,.) =  #>   0.7658  0.6123 #>   0.3150  0.4639 #>  #> (2,.,.) =  #>   0.0604  0.0290 #>   0.9553  0.6541 #> [ CPUDoubleType{2,2,2} ] identical(x, as_array(y)) #> [1] TRUE"},{"path":"https://torch.mlverse.org/docs/index.html","id":"simple-autograd-example","dir":"","previous_headings":"Examples","what":"Simple Autograd Example","title":"Tensors and Neural Networks with GPU Acceleration","text":"following snippet let torch, using autograd feature, calculate derivatives:","code":"x <- torch_tensor(1, requires_grad = TRUE) w <- torch_tensor(2, requires_grad = TRUE) b <- torch_tensor(3, requires_grad = TRUE) y <- w * x + b y$backward() x$grad #> torch_tensor #>  2 #> [ CPUFloatType{1} ] w$grad #> torch_tensor #>  1 #> [ CPUFloatType{1} ] b$grad #> torch_tensor #>  1 #> [ CPUFloatType{1} ]"},{"path":"https://torch.mlverse.org/docs/index.html","id":"contributing","dir":"","previous_headings":"","what":"Contributing","title":"Tensors and Neural Networks with GPU Acceleration","text":"matter current skills ’s possible contribute torch development. See contributing guide information.","code":""},{"path":"https://torch.mlverse.org/docs/reference/AutogradContext.html","id":null,"dir":"Reference","previous_headings":"","what":"Class representing the context. — AutogradContext","title":"Class representing the context. — AutogradContext","text":"Class representing context. Class representing context.","code":""},{"path":"https://torch.mlverse.org/docs/reference/AutogradContext.html","id":"public-fields","dir":"Reference","previous_headings":"","what":"Public fields","title":"Class representing the context. — AutogradContext","text":"ptr (Dev related) pointer context c++ object.","code":""},{"path":"https://torch.mlverse.org/docs/reference/AutogradContext.html","id":"active-bindings","dir":"Reference","previous_headings":"","what":"Active bindings","title":"Class representing the context. — AutogradContext","text":"needs_input_grad boolean listing arguments forward whether require_grad. saved_variables list objects saved backward via save_for_backward.","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/AutogradContext.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"Class representing the context. — AutogradContext","text":"AutogradContext$new() AutogradContext$save_for_backward() AutogradContext$mark_non_differentiable() AutogradContext$mark_dirty() AutogradContext$clone()","code":""},{"path":"https://torch.mlverse.org/docs/reference/AutogradContext.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"Class representing the context. — AutogradContext","text":"(Dev related) Initializes context. user related.","code":""},{"path":"https://torch.mlverse.org/docs/reference/AutogradContext.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Class representing the context. — AutogradContext","text":"","code":"AutogradContext$new(   ptr,   env,   argument_names = NULL,   argument_needs_grad = NULL )"},{"path":"https://torch.mlverse.org/docs/reference/AutogradContext.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Class representing the context. — AutogradContext","text":"ptr pointer c++ object env environment encloses forward backward argument_names names forward arguments argument_needs_grad whether argument forward needs grad.","code":""},{"path":"https://torch.mlverse.org/docs/reference/AutogradContext.html","id":"method-save-for-backward-","dir":"Reference","previous_headings":"","what":"Method save_for_backward()","title":"Class representing the context. — AutogradContext","text":"Saves given objects future call backward(). called , inside forward() method. Later, saved objects can accessed saved_variables attribute. returning user, check made ensure weren’t used -place operation modified content. Arguments can also kind R object.","code":""},{"path":"https://torch.mlverse.org/docs/reference/AutogradContext.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"Class representing the context. — AutogradContext","text":"","code":"AutogradContext$save_for_backward(...)"},{"path":"https://torch.mlverse.org/docs/reference/AutogradContext.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"Class representing the context. — AutogradContext","text":"... kind R object saved backward pass. common pass named arguments.","code":""},{"path":"https://torch.mlverse.org/docs/reference/AutogradContext.html","id":"method-mark-non-differentiable-","dir":"Reference","previous_headings":"","what":"Method mark_non_differentiable()","title":"Class representing the context. — AutogradContext","text":"Marks outputs non-differentiable. called , inside forward() method, arguments outputs. mark outputs requiring gradients, increasing efficiency backward computation. still need accept gradient output backward(), ’s always going zero tensor shape shape corresponding output. used e.g. indices returned max Function.","code":""},{"path":"https://torch.mlverse.org/docs/reference/AutogradContext.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"Class representing the context. — AutogradContext","text":"","code":"AutogradContext$mark_non_differentiable(...)"},{"path":"https://torch.mlverse.org/docs/reference/AutogradContext.html","id":"arguments-2","dir":"Reference","previous_headings":"","what":"Arguments","title":"Class representing the context. — AutogradContext","text":"... non-differentiable outputs.","code":""},{"path":"https://torch.mlverse.org/docs/reference/AutogradContext.html","id":"method-mark-dirty-","dir":"Reference","previous_headings":"","what":"Method mark_dirty()","title":"Class representing the context. — AutogradContext","text":"Marks given tensors modified -place operation. called , inside forward() method, arguments inputs. Every tensor ’s modified -place call forward() given function, ensure correctness checks. doesn’t matter whether function called modification.","code":""},{"path":"https://torch.mlverse.org/docs/reference/AutogradContext.html","id":"usage-3","dir":"Reference","previous_headings":"","what":"Usage","title":"Class representing the context. — AutogradContext","text":"","code":"AutogradContext$mark_dirty(...)"},{"path":"https://torch.mlverse.org/docs/reference/AutogradContext.html","id":"arguments-3","dir":"Reference","previous_headings":"","what":"Arguments","title":"Class representing the context. — AutogradContext","text":"... tensors modified -place.","code":""},{"path":"https://torch.mlverse.org/docs/reference/AutogradContext.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"Class representing the context. — AutogradContext","text":"objects class cloneable method.","code":""},{"path":"https://torch.mlverse.org/docs/reference/AutogradContext.html","id":"usage-4","dir":"Reference","previous_headings":"","what":"Usage","title":"Class representing the context. — AutogradContext","text":"","code":"AutogradContext$clone(deep = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/AutogradContext.html","id":"arguments-4","dir":"Reference","previous_headings":"","what":"Arguments","title":"Class representing the context. — AutogradContext","text":"deep Whether make deep clone.","code":""},{"path":"https://torch.mlverse.org/docs/reference/Constraint.html","id":null,"dir":"Reference","previous_headings":"","what":"Abstract base class for constraints. — Constraint","title":"Abstract base class for constraints. — Constraint","text":"Abstract base class constraints. Abstract base class constraints.","code":""},{"path":"https://torch.mlverse.org/docs/reference/Constraint.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Abstract base class for constraints. — Constraint","text":"constraint object represents region variable valid, e.g. within variable can optimized.","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/Constraint.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"Abstract base class for constraints. — Constraint","text":"Constraint$check() Constraint$print() Constraint$clone()","code":""},{"path":"https://torch.mlverse.org/docs/reference/Constraint.html","id":"method-check-","dir":"Reference","previous_headings":"","what":"Method check()","title":"Abstract base class for constraints. — Constraint","text":"Returns byte tensor sample_shape + batch_shape indicating whether event value satisfies constraint.","code":""},{"path":"https://torch.mlverse.org/docs/reference/Constraint.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Abstract base class for constraints. — Constraint","text":"","code":"Constraint$check(value)"},{"path":"https://torch.mlverse.org/docs/reference/Constraint.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Abstract base class for constraints. — Constraint","text":"value event value checked.","code":""},{"path":"https://torch.mlverse.org/docs/reference/Constraint.html","id":"method-print-","dir":"Reference","previous_headings":"","what":"Method print()","title":"Abstract base class for constraints. — Constraint","text":"Define print method constraints,","code":""},{"path":"https://torch.mlverse.org/docs/reference/Constraint.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"Abstract base class for constraints. — Constraint","text":"","code":"Constraint$print()"},{"path":"https://torch.mlverse.org/docs/reference/Constraint.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"Abstract base class for constraints. — Constraint","text":"objects class cloneable method.","code":""},{"path":"https://torch.mlverse.org/docs/reference/Constraint.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"Abstract base class for constraints. — Constraint","text":"","code":"Constraint$clone(deep = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/Constraint.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"Abstract base class for constraints. — Constraint","text":"deep Whether make deep clone.","code":""},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":null,"dir":"Reference","previous_headings":"","what":"Generic R6 class representing distributions — Distribution","title":"Generic R6 class representing distributions — Distribution","text":"Distribution abstract base class probability distributions. Note: Python, adding torch.Size objects works concatenation Try example: torch.Size((2, 1)) + torch.Size((1,))","code":""},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"public-fields","dir":"Reference","previous_headings":"","what":"Public fields","title":"Generic R6 class representing distributions — Distribution","text":".validate_args whether validate arguments has_rsample whether rsample has_enumerate_support whether enumerate support","code":""},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"active-bindings","dir":"Reference","previous_headings":"","what":"Active bindings","title":"Generic R6 class representing distributions — Distribution","text":"batch_shape Returns shape parameters batched. event_shape Returns shape single sample (without batching). Returns dictionary argument names torch_Constraint objects satisfied argument distribution. Args tensors need appear dict. support Returns torch_Constraint object representing distribution's support. mean Returns mean distribution variance Returns variance distribution stddev Returns standard deviation distribution TODO: consider different message","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"Generic R6 class representing distributions — Distribution","text":"Distribution$new() Distribution$expand() Distribution$sample() Distribution$rsample() Distribution$log_prob() Distribution$cdf() Distribution$icdf() Distribution$enumerate_support() Distribution$entropy() Distribution$perplexity() Distribution$.extended_shape() Distribution$.validate_sample() Distribution$print() Distribution$clone()","code":""},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"Generic R6 class representing distributions — Distribution","text":"Initializes distribution class.","code":""},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generic R6 class representing distributions — Distribution","text":"","code":"Distribution$new(batch_shape = NULL, event_shape = NULL, validate_args = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generic R6 class representing distributions — Distribution","text":"batch_shape shape parameters batched. event_shape shape single sample (without batching). validate_args whether validate arguments . Validation can time consuming might want disable .","code":""},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"method-expand-","dir":"Reference","previous_headings":"","what":"Method expand()","title":"Generic R6 class representing distributions — Distribution","text":"Returns new distribution instance (populates existing instance provided derived class) batch dimensions expanded batch_shape. method calls expand distribution’s parameters. , allocate new memory expanded distribution instance. Additionally, repeat args checking parameter broadcasting initialize, instance first created.","code":""},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"Generic R6 class representing distributions — Distribution","text":"","code":"Distribution$expand(batch_shape, .instance = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generic R6 class representing distributions — Distribution","text":"batch_shape desired expanded size. .instance new instance provided subclasses need override expand.","code":""},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"method-sample-","dir":"Reference","previous_headings":"","what":"Method sample()","title":"Generic R6 class representing distributions — Distribution","text":"Generates sample_shape shaped sample sample_shape shaped batch samples distribution parameters batched.","code":""},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"Generic R6 class representing distributions — Distribution","text":"","code":"Distribution$sample(sample_shape = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"arguments-2","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generic R6 class representing distributions — Distribution","text":"sample_shape shape want sample.","code":""},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"method-rsample-","dir":"Reference","previous_headings":"","what":"Method rsample()","title":"Generic R6 class representing distributions — Distribution","text":"Generates sample_shape shaped reparameterized sample sample_shape shaped batch reparameterized samples distribution parameters batched.","code":""},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"usage-3","dir":"Reference","previous_headings":"","what":"Usage","title":"Generic R6 class representing distributions — Distribution","text":"","code":"Distribution$rsample(sample_shape = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"arguments-3","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generic R6 class representing distributions — Distribution","text":"sample_shape shape want sample.","code":""},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"method-log-prob-","dir":"Reference","previous_headings":"","what":"Method log_prob()","title":"Generic R6 class representing distributions — Distribution","text":"Returns log probability density/mass function evaluated value.","code":""},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"usage-4","dir":"Reference","previous_headings":"","what":"Usage","title":"Generic R6 class representing distributions — Distribution","text":"","code":"Distribution$log_prob(value)"},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"arguments-4","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generic R6 class representing distributions — Distribution","text":"value values evaluate density .","code":""},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"method-cdf-","dir":"Reference","previous_headings":"","what":"Method cdf()","title":"Generic R6 class representing distributions — Distribution","text":"Returns cumulative density/mass function evaluated value.","code":""},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"usage-5","dir":"Reference","previous_headings":"","what":"Usage","title":"Generic R6 class representing distributions — Distribution","text":"","code":"Distribution$cdf(value)"},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"arguments-5","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generic R6 class representing distributions — Distribution","text":"value values evaluate density .","code":""},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"method-icdf-","dir":"Reference","previous_headings":"","what":"Method icdf()","title":"Generic R6 class representing distributions — Distribution","text":"Returns inverse cumulative density/mass function evaluated value. @description Returns tensor containing values supported discrete distribution. result enumerate dimension 0, shape result (cardinality,) + batch_shape + event_shape (event_shape = ()univariate distributions). Note enumerates batched tensors lock-steplist(c(0, 0), c(1, 1), ...). expand=FALSE, enumeration happens along dim 0, remaining batch dimensions singleton dimensions, list(c(0), c(1), ...)`.","code":""},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"usage-6","dir":"Reference","previous_headings":"","what":"Usage","title":"Generic R6 class representing distributions — Distribution","text":"","code":"Distribution$icdf(value)"},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"arguments-6","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generic R6 class representing distributions — Distribution","text":"value values evaluate density .","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"usage-7","dir":"Reference","previous_headings":"","what":"Usage","title":"Generic R6 class representing distributions — Distribution","text":"","code":"Distribution$enumerate_support(expand = TRUE)"},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"arguments-7","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generic R6 class representing distributions — Distribution","text":"expand (bool): whether expand support batch dims match distribution's batch_shape.","code":""},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"returns","dir":"Reference","previous_headings":"","what":"Returns","title":"Generic R6 class representing distributions — Distribution","text":"Tensor iterating dimension 0.","code":""},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"method-entropy-","dir":"Reference","previous_headings":"","what":"Method entropy()","title":"Generic R6 class representing distributions — Distribution","text":"Returns entropy distribution, batched batch_shape.","code":""},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"usage-8","dir":"Reference","previous_headings":"","what":"Usage","title":"Generic R6 class representing distributions — Distribution","text":"","code":"Distribution$entropy()"},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"returns-1","dir":"Reference","previous_headings":"","what":"Returns","title":"Generic R6 class representing distributions — Distribution","text":"Tensor shape batch_shape.","code":""},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"method-perplexity-","dir":"Reference","previous_headings":"","what":"Method perplexity()","title":"Generic R6 class representing distributions — Distribution","text":"Returns perplexity distribution, batched batch_shape.","code":""},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"usage-9","dir":"Reference","previous_headings":"","what":"Usage","title":"Generic R6 class representing distributions — Distribution","text":"","code":"Distribution$perplexity()"},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"returns-2","dir":"Reference","previous_headings":"","what":"Returns","title":"Generic R6 class representing distributions — Distribution","text":"Tensor shape batch_shape.","code":""},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"method-extended-shape-","dir":"Reference","previous_headings":"","what":"Method .extended_shape()","title":"Generic R6 class representing distributions — Distribution","text":"Returns size sample returned distribution, given sample_shape. Note, batch event shapes distribution instance fixed time construction. empty, returned shape upcast (1,).","code":""},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"usage-10","dir":"Reference","previous_headings":"","what":"Usage","title":"Generic R6 class representing distributions — Distribution","text":"","code":"Distribution$.extended_shape(sample_shape = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"arguments-8","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generic R6 class representing distributions — Distribution","text":"sample_shape (torch_Size): size sample drawn.","code":""},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"method-validate-sample-","dir":"Reference","previous_headings":"","what":"Method .validate_sample()","title":"Generic R6 class representing distributions — Distribution","text":"Argument validation distribution methods log_prob, cdf icdf. rightmost dimensions value scored via methods must agree distribution's batch event shapes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"usage-11","dir":"Reference","previous_headings":"","what":"Usage","title":"Generic R6 class representing distributions — Distribution","text":"","code":"Distribution$.validate_sample(value)"},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"arguments-9","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generic R6 class representing distributions — Distribution","text":"value (Tensor): tensor whose log probability computed log_prob method.","code":""},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"method-print-","dir":"Reference","previous_headings":"","what":"Method print()","title":"Generic R6 class representing distributions — Distribution","text":"Prints distribution instance.","code":""},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"usage-12","dir":"Reference","previous_headings":"","what":"Usage","title":"Generic R6 class representing distributions — Distribution","text":"","code":"Distribution$print()"},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"Generic R6 class representing distributions — Distribution","text":"objects class cloneable method.","code":""},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"usage-13","dir":"Reference","previous_headings":"","what":"Usage","title":"Generic R6 class representing distributions — Distribution","text":"","code":"Distribution$clone(deep = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"arguments-10","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generic R6 class representing distributions — Distribution","text":"deep Whether make deep clone.","code":""},{"path":"https://torch.mlverse.org/docs/reference/as_array.html","id":null,"dir":"Reference","previous_headings":"","what":"Converts to array — as_array","title":"Converts to array — as_array","text":"Converts array","code":""},{"path":"https://torch.mlverse.org/docs/reference/as_array.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Converts to array — as_array","text":"","code":"as_array(x)"},{"path":"https://torch.mlverse.org/docs/reference/as_array.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Converts to array — as_array","text":"x object converted array","code":""},{"path":"https://torch.mlverse.org/docs/reference/autograd_backward.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the sum of gradients of given tensors w.r.t. graph leaves. — autograd_backward","title":"Computes the sum of gradients of given tensors w.r.t. graph leaves. — autograd_backward","text":"graph differentiated using chain rule. tensors non-scalar (.e. data one element) require gradient, Jacobian-vector product computed, case function additionally requires specifying grad_tensors. sequence matching length, contains “vector” Jacobian-vector product, usually gradient differentiated function w.r.t. corresponding tensors (None acceptable value tensors don’t need gradient tensors).","code":""},{"path":"https://torch.mlverse.org/docs/reference/autograd_backward.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the sum of gradients of given tensors w.r.t. graph leaves. — autograd_backward","text":"","code":"autograd_backward(   tensors,   grad_tensors = NULL,   retain_graph = create_graph,   create_graph = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/autograd_backward.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the sum of gradients of given tensors w.r.t. graph leaves. — autograd_backward","text":"tensors (list Tensor) – Tensors derivative computed. grad_tensors (list (Tensor NULL)) – “vector” Jacobian-vector product, usually gradients w.r.t. element corresponding tensors. NULLvalues can specified scalar Tensors ones don’t require grad. aNULL` value acceptable grad_tensors, argument optional. retain_graph (bool, optional) – FALSE, graph used compute grad freed. Note nearly cases setting option TRUE needed often can worked around much efficient way. Defaults value create_graph. create_graph (bool, optional) – TRUE, graph derivative constructed, allowing compute higher order derivative products. Defaults FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/autograd_backward.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Computes the sum of gradients of given tensors w.r.t. graph leaves. — autograd_backward","text":"function accumulates gradients leaves - might need zero calling .","code":""},{"path":"https://torch.mlverse.org/docs/reference/autograd_backward.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the sum of gradients of given tensors w.r.t. graph leaves. — autograd_backward","text":"","code":"if (torch_is_installed()) { x <- torch_tensor(1, requires_grad = TRUE) y <- 2 * x  a <- torch_tensor(1, requires_grad = TRUE) b <- 3 * a  autograd_backward(list(y, b)) }"},{"path":"https://torch.mlverse.org/docs/reference/autograd_function.html","id":null,"dir":"Reference","previous_headings":"","what":"Records operation history and defines formulas for differentiating ops. — autograd_function","title":"Records operation history and defines formulas for differentiating ops. — autograd_function","text":"Every operation performed Tensor's creates new function object, performs computation, records happened. history retained form DAG functions, edges denoting data dependencies (input <- output). , backward called, graph processed topological ordering, calling backward() methods Function object, passing returned gradients next Function's.","code":""},{"path":"https://torch.mlverse.org/docs/reference/autograd_function.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Records operation history and defines formulas for differentiating ops. — autograd_function","text":"","code":"autograd_function(forward, backward)"},{"path":"https://torch.mlverse.org/docs/reference/autograd_function.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Records operation history and defines formulas for differentiating ops. — autograd_function","text":"forward Performs operation. must accept context ctx first argument, followed number arguments (tensors types). context can used store tensors can retrieved backward pass. See AutogradContext information context methods. backward Defines formula differentiating operation. must accept context ctx first argument, followed many outputs forward() return, return named list. argument gradient w.r.t given output, element returned list gradient w.r.t. corresponding input. context can used retrieve tensors saved forward pass. also attribute ctx$needs_input_grad named list booleans representing whether input needs gradient. E.g., backward() ctx$needs_input_grad$input = TRUE input argument forward() needs gradient computated w.r.t. output. See AutogradContext information context methods.","code":""},{"path":"https://torch.mlverse.org/docs/reference/autograd_function.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Records operation history and defines formulas for differentiating ops. — autograd_function","text":"","code":"if (torch_is_installed()) {  exp2 <- autograd_function(   forward = function(ctx, i) {     result <- i$exp()     ctx$save_for_backward(result = result)     result   },   backward = function(ctx, grad_output) {     list(i = grad_output * ctx$saved_variable$result)   } ) }"},{"path":"https://torch.mlverse.org/docs/reference/autograd_grad.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes and returns the sum of gradients of outputs w.r.t. the inputs. — autograd_grad","title":"Computes and returns the sum of gradients of outputs w.r.t. the inputs. — autograd_grad","text":"grad_outputs list length matching output containing “vector” Jacobian-vector product, usually pre-computed gradients w.r.t. outputs. output doesn’t require_grad, gradient can None).","code":""},{"path":"https://torch.mlverse.org/docs/reference/autograd_grad.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes and returns the sum of gradients of outputs w.r.t. the inputs. — autograd_grad","text":"","code":"autograd_grad(   outputs,   inputs,   grad_outputs = NULL,   retain_graph = create_graph,   create_graph = FALSE,   allow_unused = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/autograd_grad.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes and returns the sum of gradients of outputs w.r.t. the inputs. — autograd_grad","text":"outputs (sequence Tensor) – outputs differentiated function. inputs (sequence Tensor) – Inputs w.r.t. gradient returned (accumulated .grad). grad_outputs (sequence Tensor) – “vector” Jacobian-vector product. Usually gradients w.r.t. output. None values can specified scalar Tensors ones don’t require grad. None value acceptable grad_tensors, argument optional. Default: None. retain_graph (bool, optional) – FALSE, graph used compute grad freed. Note nearly cases setting option TRUE needed often can worked around much efficient way. Defaults value create_graph. create_graph (bool, optional) – TRUE, graph derivative constructed, allowing compute higher order derivative products. Default: FALSE`. allow_unused (bool, optional) – FALSE, specifying inputs used computing outputs (therefore grad always zero) error. Defaults FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/autograd_grad.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Computes and returns the sum of gradients of outputs w.r.t. the inputs. — autograd_grad","text":"only_inputs TRUE, function return list gradients w.r.t specified inputs. ’s FALSE, gradient w.r.t. remaining leaves still computed, accumulated .grad attribute.","code":""},{"path":"https://torch.mlverse.org/docs/reference/autograd_grad.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes and returns the sum of gradients of outputs w.r.t. the inputs. — autograd_grad","text":"","code":"if (torch_is_installed()) { w <- torch_tensor(0.5, requires_grad = TRUE) b <- torch_tensor(0.9, requires_grad = TRUE) x <- torch_tensor(runif(100)) y <- 2 * x + 1 loss <- (y - (w * x + b))^2 loss <- loss$mean()  o <- autograd_grad(loss, list(w, b)) o } #> [[1]] #> torch_tensor #> -1.0233 #> [ CPUFloatType{1} ] #>  #> [[2]] #> torch_tensor #> -1.6507 #> [ CPUFloatType{1} ] #>"},{"path":"https://torch.mlverse.org/docs/reference/autograd_set_grad_mode.html","id":null,"dir":"Reference","previous_headings":"","what":"Set grad mode — autograd_set_grad_mode","title":"Set grad mode — autograd_set_grad_mode","text":"Sets disables gradient history.","code":""},{"path":"https://torch.mlverse.org/docs/reference/autograd_set_grad_mode.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set grad mode — autograd_set_grad_mode","text":"","code":"autograd_set_grad_mode(enabled)"},{"path":"https://torch.mlverse.org/docs/reference/autograd_set_grad_mode.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set grad mode — autograd_set_grad_mode","text":"enabled bool wether enable disable gradient recording.","code":""},{"path":"https://torch.mlverse.org/docs/reference/backends_cudnn_is_available.html","id":null,"dir":"Reference","previous_headings":"","what":"CuDNN is available — backends_cudnn_is_available","title":"CuDNN is available — backends_cudnn_is_available","text":"CuDNN available","code":""},{"path":"https://torch.mlverse.org/docs/reference/backends_cudnn_is_available.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CuDNN is available — backends_cudnn_is_available","text":"","code":"backends_cudnn_is_available()"},{"path":"https://torch.mlverse.org/docs/reference/backends_cudnn_version.html","id":null,"dir":"Reference","previous_headings":"","what":"CuDNN version — backends_cudnn_version","title":"CuDNN version — backends_cudnn_version","text":"CuDNN version","code":""},{"path":"https://torch.mlverse.org/docs/reference/backends_cudnn_version.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CuDNN version — backends_cudnn_version","text":"","code":"backends_cudnn_version()"},{"path":"https://torch.mlverse.org/docs/reference/backends_mkl_is_available.html","id":null,"dir":"Reference","previous_headings":"","what":"MKL is available — backends_mkl_is_available","title":"MKL is available — backends_mkl_is_available","text":"MKL available","code":""},{"path":"https://torch.mlverse.org/docs/reference/backends_mkl_is_available.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"MKL is available — backends_mkl_is_available","text":"","code":"backends_mkl_is_available()"},{"path":"https://torch.mlverse.org/docs/reference/backends_mkl_is_available.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"MKL is available — backends_mkl_is_available","text":"Returns whether LibTorch built MKL support.","code":""},{"path":"https://torch.mlverse.org/docs/reference/backends_mkldnn_is_available.html","id":null,"dir":"Reference","previous_headings":"","what":"MKLDNN is available — backends_mkldnn_is_available","title":"MKLDNN is available — backends_mkldnn_is_available","text":"MKLDNN available","code":""},{"path":"https://torch.mlverse.org/docs/reference/backends_mkldnn_is_available.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"MKLDNN is available — backends_mkldnn_is_available","text":"","code":"backends_mkldnn_is_available()"},{"path":"https://torch.mlverse.org/docs/reference/backends_mkldnn_is_available.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"MKLDNN is available — backends_mkldnn_is_available","text":"Returns whether LibTorch built MKL-DNN support.","code":""},{"path":"https://torch.mlverse.org/docs/reference/backends_openmp_is_available.html","id":null,"dir":"Reference","previous_headings":"","what":"OpenMP is available — backends_openmp_is_available","title":"OpenMP is available — backends_openmp_is_available","text":"OpenMP available","code":""},{"path":"https://torch.mlverse.org/docs/reference/backends_openmp_is_available.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"OpenMP is available — backends_openmp_is_available","text":"","code":"backends_openmp_is_available()"},{"path":"https://torch.mlverse.org/docs/reference/backends_openmp_is_available.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"OpenMP is available — backends_openmp_is_available","text":"Returns whether LibTorch built OpenMP support.","code":""},{"path":"https://torch.mlverse.org/docs/reference/broadcast_all.html","id":null,"dir":"Reference","previous_headings":"","what":"Given a list of values (possibly containing numbers), returns a list where each\nvalue is broadcasted based on the following rules: — broadcast_all","title":"Given a list of values (possibly containing numbers), returns a list where each\nvalue is broadcasted based on the following rules: — broadcast_all","text":"Raises value_error: values numeric instance, torch.*Tensor instance, instance implementing torch_function TODO: add has_torch_function((v,)) See: https://github.com/pytorch/pytorch/blob/master/torch/distributions/utils.py","code":""},{"path":"https://torch.mlverse.org/docs/reference/broadcast_all.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Given a list of values (possibly containing numbers), returns a list where each\nvalue is broadcasted based on the following rules: — broadcast_all","text":"","code":"broadcast_all(values)"},{"path":"https://torch.mlverse.org/docs/reference/broadcast_all.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Given a list of values (possibly containing numbers), returns a list where each\nvalue is broadcasted based on the following rules: — broadcast_all","text":"values List : torch.*Tensor instances broadcasted per _broadcasting-semantics. numeric instances (scalars) upcast tensors size type first tensor passed values.  values scalars, upcasted scalar Tensors. values (list numeric, torch.*Tensor objects implementing torch_function)","code":""},{"path":"https://torch.mlverse.org/docs/reference/call_torch_function.html","id":null,"dir":"Reference","previous_headings":"","what":"Call a (Potentially Unexported) Torch Function — call_torch_function","title":"Call a (Potentially Unexported) Torch Function — call_torch_function","text":"function allows calling function prefixed torch_, including unexported functions potentially valuable uses yet user-friendly R wrapper function. Therefore, function used extreme caution. Make sure understand function expects input. may helpful read torch source code help , well documentation corresponding function Pytorch C++ API. Generally development advanced use .","code":""},{"path":"https://torch.mlverse.org/docs/reference/call_torch_function.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Call a (Potentially Unexported) Torch Function — call_torch_function","text":"","code":"call_torch_function(name, ..., quiet = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/call_torch_function.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Call a (Potentially Unexported) Torch Function — call_torch_function","text":"name Name function call string. start \"torch_\" ... list arguments pass function. Argument splicing !!! supported. quiet TRUE, suppress warnings valuable information dangers function.","code":""},{"path":"https://torch.mlverse.org/docs/reference/call_torch_function.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Call a (Potentially Unexported) Torch Function — call_torch_function","text":"return value calling function name arguments ...","code":""},{"path":"https://torch.mlverse.org/docs/reference/call_torch_function.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Call a (Potentially Unexported) Torch Function — call_torch_function","text":"","code":"if (torch_is_installed()) { ## many unexported functions do 'backward' calculations (e.g. derivatives) ## These could be used as a part of custom autograd functions for example. x <- torch_randn(10, requires_grad = TRUE) y <- torch_tanh(x) ## calculate backwards gradient using standard torch method y$backward(torch_ones_like(x)) x$grad ## we can get the same result by calling the unexported `torch_tanh_backward()` ## function. The first argument is 1 to setup the Jacobian-vector product. ## see https://pytorch.org/blog/overview-of-pytorch-autograd-engine/ for details. call_torch_function(\"torch_tanh_backward\", 1, y) all.equal(call_torch_function(\"torch_tanh_backward\", 1, y, quiet = TRUE), x$grad) } #> Warning: Because this function allows access to unexported functions, please use with caution, and #>             only if you are sure know what you are doing. Unexported functions will expect inputs that #>             are more C++-like than R-like. For example, they will expect all indexes to be 0-based instead #>             of 1-based. In addition unexported functions may be subject to removal from the API without #>             warning. Set quiet = TRUE to silence this warning. #> [1] TRUE"},{"path":"https://torch.mlverse.org/docs/reference/contrib_sort_vertices.html","id":null,"dir":"Reference","previous_headings":"","what":"Contrib sort vertices — contrib_sort_vertices","title":"Contrib sort vertices — contrib_sort_vertices","text":"Based implementation Rotated_IoU","code":""},{"path":"https://torch.mlverse.org/docs/reference/contrib_sort_vertices.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Contrib sort vertices — contrib_sort_vertices","text":"","code":"contrib_sort_vertices(vertices, mask, num_valid)"},{"path":"https://torch.mlverse.org/docs/reference/contrib_sort_vertices.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Contrib sort vertices — contrib_sort_vertices","text":"vertices Tensor vertices. mask tensors containing masks. num_valid integer tensors.","code":""},{"path":"https://torch.mlverse.org/docs/reference/contrib_sort_vertices.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Contrib sort vertices — contrib_sort_vertices","text":"tensors CUDA device function can used.","code":""},{"path":"https://torch.mlverse.org/docs/reference/contrib_sort_vertices.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Contrib sort vertices — contrib_sort_vertices","text":"function make part official torch API.","code":""},{"path":"https://torch.mlverse.org/docs/reference/contrib_sort_vertices.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Contrib sort vertices — contrib_sort_vertices","text":"","code":"if (torch_is_installed()) { if (cuda_is_available()) {   v <- torch_randn(8, 1024, 24, 2)$cuda()   mean <- torch_mean(v, dim = 2, keepdim = TRUE)   v <- v - mean   m <- (torch_rand(8, 1024, 24) > 0.8)$cuda()   nv <- torch_sum(m$to(dtype = torch_int()), dim = -1)$to(dtype = torch_int())$cuda()   result <- contrib_sort_vertices(v, m, nv) } }"},{"path":"https://torch.mlverse.org/docs/reference/cuda_current_device.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns the index of a currently selected device. — cuda_current_device","title":"Returns the index of a currently selected device. — cuda_current_device","text":"Returns index currently selected device.","code":""},{"path":"https://torch.mlverse.org/docs/reference/cuda_current_device.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns the index of a currently selected device. — cuda_current_device","text":"","code":"cuda_current_device()"},{"path":"https://torch.mlverse.org/docs/reference/cuda_device_count.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns the number of GPUs available. — cuda_device_count","title":"Returns the number of GPUs available. — cuda_device_count","text":"Returns number GPUs available.","code":""},{"path":"https://torch.mlverse.org/docs/reference/cuda_device_count.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns the number of GPUs available. — cuda_device_count","text":"","code":"cuda_device_count()"},{"path":"https://torch.mlverse.org/docs/reference/cuda_get_device_capability.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns the major and minor CUDA capability of device — cuda_get_device_capability","title":"Returns the major and minor CUDA capability of device — cuda_get_device_capability","text":"Returns major minor CUDA capability device","code":""},{"path":"https://torch.mlverse.org/docs/reference/cuda_get_device_capability.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns the major and minor CUDA capability of device — cuda_get_device_capability","text":"","code":"cuda_get_device_capability(device = cuda_current_device())"},{"path":"https://torch.mlverse.org/docs/reference/cuda_get_device_capability.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns the major and minor CUDA capability of device — cuda_get_device_capability","text":"device Integer value CUDA device return capabilities .","code":""},{"path":"https://torch.mlverse.org/docs/reference/cuda_is_available.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns a bool indicating if CUDA is currently available. — cuda_is_available","title":"Returns a bool indicating if CUDA is currently available. — cuda_is_available","text":"Returns bool indicating CUDA currently available.","code":""},{"path":"https://torch.mlverse.org/docs/reference/cuda_is_available.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns a bool indicating if CUDA is currently available. — cuda_is_available","text":"","code":"cuda_is_available()"},{"path":"https://torch.mlverse.org/docs/reference/cuda_memory_stats.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns a dictionary of CUDA memory allocator statistics for a given device. — cuda_memory_stats","title":"Returns a dictionary of CUDA memory allocator statistics for a given device. — cuda_memory_stats","text":"return value function dictionary statistics, non-negative integer.","code":""},{"path":"https://torch.mlverse.org/docs/reference/cuda_memory_stats.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns a dictionary of CUDA memory allocator statistics for a given device. — cuda_memory_stats","text":"","code":"cuda_memory_stats(device = cuda_current_device())  cuda_memory_summary(device = cuda_current_device())"},{"path":"https://torch.mlverse.org/docs/reference/cuda_memory_stats.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns a dictionary of CUDA memory allocator statistics for a given device. — cuda_memory_stats","text":"device Integer value CUDA device return capabilities .","code":""},{"path":"https://torch.mlverse.org/docs/reference/cuda_memory_stats.html","id":"core-statistics","dir":"Reference","previous_headings":"","what":"Core statistics","title":"Returns a dictionary of CUDA memory allocator statistics for a given device. — cuda_memory_stats","text":"\"allocated.,large_pool,small_pool.current,peak,allocated,freed\": number allocation requests received memory allocator. \"allocated_bytes.,large_pool,small_pool.current,peak,allocated,freed\": amount allocated memory. \"segment.,large_pool,small_pool.current,peak,allocated,freed\": number reserved segments cudaMalloc(). \"reserved_bytes.,large_pool,small_pool.current,peak,allocated,freed\": amount reserved memory. \"active.,large_pool,small_pool.current,peak,allocated,freed\": number active memory blocks. \"active_bytes.,large_pool,small_pool.current,peak,allocated,freed\": amount active memory. \"inactive_split.,large_pool,small_pool.current,peak,allocated,freed\": number inactive, non-releasable memory blocks. \"inactive_split_bytes.,large_pool,small_pool.current,peak,allocated,freed\": amount inactive, non-releasable memory. core statistics, values broken follows. Pool type: : combined statistics across memory pools. large_pool: statistics large allocation pool (October 2019, size >= 1MB allocations). small_pool: statistics small allocation pool (October 2019, size < 1MB allocations). Metric type: current: current value metric. peak: maximum value metric. allocated: historical total increase metric. freed: historical total decrease metric.","code":""},{"path":"https://torch.mlverse.org/docs/reference/cuda_memory_stats.html","id":"additional-metrics","dir":"Reference","previous_headings":"","what":"Additional metrics","title":"Returns a dictionary of CUDA memory allocator statistics for a given device. — cuda_memory_stats","text":"\"num_alloc_retries\": number failed cudaMalloc calls result cache flush retry. \"num_ooms\": number --memory errors thrown.","code":""},{"path":"https://torch.mlverse.org/docs/reference/dataloader.html","id":null,"dir":"Reference","previous_headings":"","what":"Data loader. Combines a dataset and a sampler, and provides\nsingle- or multi-process iterators over the dataset. — dataloader","title":"Data loader. Combines a dataset and a sampler, and provides\nsingle- or multi-process iterators over the dataset. — dataloader","text":"Data loader. Combines dataset sampler, provides single- multi-process iterators dataset.","code":""},{"path":"https://torch.mlverse.org/docs/reference/dataloader.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data loader. Combines a dataset and a sampler, and provides\nsingle- or multi-process iterators over the dataset. — dataloader","text":"","code":"dataloader(   dataset,   batch_size = 1,   shuffle = FALSE,   sampler = NULL,   batch_sampler = NULL,   num_workers = 0,   collate_fn = NULL,   pin_memory = FALSE,   drop_last = FALSE,   timeout = -1,   worker_init_fn = NULL,   worker_globals = NULL,   worker_packages = NULL )"},{"path":"https://torch.mlverse.org/docs/reference/dataloader.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Data loader. Combines a dataset and a sampler, and provides\nsingle- or multi-process iterators over the dataset. — dataloader","text":"dataset (Dataset): dataset load data. batch_size (int, optional): many samples per batch load (default: 1). shuffle (bool, optional): set TRUE data reshuffled every epoch (default: FALSE). sampler (Sampler, optional): defines strategy draw samples dataset. specified, shuffle must False. batch_sampler (Sampler, optional): like sampler, returns batch indices time. Mutually exclusive batch_size, shuffle, sampler, drop_last. num_workers (int, optional): many subprocesses use data loading. 0 means data loaded main process. (default: 0) collate_fn (callable, optional): merges list samples form mini-batch. pin_memory (bool, optional): TRUE, data loader copy tensors CUDA pinned memory returning .  data elements custom type, collate_fn returns batch custom type see example . drop_last (bool, optional): set TRUE drop last incomplete batch, dataset size divisible batch size. FALSE size dataset divisible batch size, last batch smaller. (default: FALSE) timeout (numeric, optional): positive, timeout value collecting batch workers. -1 means timeout. (default: -1) worker_init_fn (callable, optional): NULL, called worker subprocess worker id (int [1, num_workers]) input, seeding data loading. (default: NULL) worker_globals (list character vector, optional) used num_workers > 0. character vector, objects names copied global environment workers. named list, list copied attached worker global environment. Notice objects copied worker initialization. worker_packages (character vector, optional) used num_workers > 0 optional character vector naming packages loaded worker.","code":""},{"path":"https://torch.mlverse.org/docs/reference/dataloader.html","id":"parallel-data-loading","dir":"Reference","previous_headings":"","what":"Parallel data loading","title":"Data loader. Combines a dataset and a sampler, and provides\nsingle- or multi-process iterators over the dataset. — dataloader","text":"using num_workers > 0 data loading happen parallel worker. Note batches taken parallel observations. worker initialization  process happens following order: num_workers R sessions initialized. worker perform following actions: torch library loaded. random seed set using set.seed() using torch_manual_seed. packages passed worker_packages argument loaded. objects passed trough worker_globals parameters copied global environment. worker_init function ran id argument. dataset fetcher copied worker.","code":""},{"path":"https://torch.mlverse.org/docs/reference/dataloader_make_iter.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates an iterator from a DataLoader — dataloader_make_iter","title":"Creates an iterator from a DataLoader — dataloader_make_iter","text":"Creates iterator DataLoader","code":""},{"path":"https://torch.mlverse.org/docs/reference/dataloader_make_iter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates an iterator from a DataLoader — dataloader_make_iter","text":"","code":"dataloader_make_iter(dataloader)"},{"path":"https://torch.mlverse.org/docs/reference/dataloader_make_iter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates an iterator from a DataLoader — dataloader_make_iter","text":"dataloader dataloader object.","code":""},{"path":"https://torch.mlverse.org/docs/reference/dataloader_next.html","id":null,"dir":"Reference","previous_headings":"","what":"Get the next element of a dataloader iterator — dataloader_next","title":"Get the next element of a dataloader iterator — dataloader_next","text":"Get next element dataloader iterator","code":""},{"path":"https://torch.mlverse.org/docs/reference/dataloader_next.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get the next element of a dataloader iterator — dataloader_next","text":"","code":"dataloader_next(iter, completed = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/dataloader_next.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get the next element of a dataloader iterator — dataloader_next","text":"iter DataLoader iter created dataloader_make_iter. completed returned value iterator exhausted.","code":""},{"path":"https://torch.mlverse.org/docs/reference/dataset.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper function to create an function that generates R6 instances of class dataset — dataset","title":"Helper function to create an function that generates R6 instances of class dataset — dataset","text":"datasets represent map keys data samples subclass class. subclasses overwrite .getitem() method, supports fetching data sample given key. Subclasses also optionally overwrite .length(), expected return size dataset (e.g. number samples) used many sampler implementations default options dataloader().","code":""},{"path":"https://torch.mlverse.org/docs/reference/dataset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper function to create an function that generates R6 instances of class dataset — dataset","text":"","code":"dataset(   name = NULL,   inherit = Dataset,   ...,   private = NULL,   active = NULL,   parent_env = parent.frame() )"},{"path":"https://torch.mlverse.org/docs/reference/dataset.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper function to create an function that generates R6 instances of class dataset — dataset","text":"name name dataset. also used class . inherit can optionally inherit dataset creating new dataset. ... public methods dataset class private passed R6::R6Class(). active passed R6::R6Class(). parent_env environment use parent newly-created objects.","code":""},{"path":"https://torch.mlverse.org/docs/reference/dataset.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper function to create an function that generates R6 instances of class dataset — dataset","text":"output function f class dataset_generator. Calling f()creates new instance R6 class dataset. R6 class stored enclosing environment f can also accessed fs attribute Dataset.","code":""},{"path":"https://torch.mlverse.org/docs/reference/dataset.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Helper function to create an function that generates R6 instances of class dataset — dataset","text":"dataloader()  default constructs index sampler yields integral indices.  make work map-style dataset non-integral indices/keys, custom sampler must provided.","code":""},{"path":"https://torch.mlverse.org/docs/reference/dataset.html","id":"get-a-batch-of-observations","dir":"Reference","previous_headings":"","what":"Get a batch of observations","title":"Helper function to create an function that generates R6 instances of class dataset — dataset","text":"default datasets iterated returning observation/item individually. Often possible optimized implementation take batch observations (eg, subsetting tensor multiple indexes faster subsetting index), case can implement .getbatch method used instead .getitem getting batch observations within dataloader. .getbatch must work batches size larger equal 1. see vignette(\"loading-data\").","code":""},{"path":"https://torch.mlverse.org/docs/reference/dataset_subset.html","id":null,"dir":"Reference","previous_headings":"","what":"Dataset Subset — dataset_subset","title":"Dataset Subset — dataset_subset","text":"Subset dataset specified indices.","code":""},{"path":"https://torch.mlverse.org/docs/reference/dataset_subset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Dataset Subset — dataset_subset","text":"","code":"dataset_subset(dataset, indices)"},{"path":"https://torch.mlverse.org/docs/reference/dataset_subset.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Dataset Subset — dataset_subset","text":"dataset (Dataset): whole Dataset indices (sequence): Indices whole set selected subset","code":""},{"path":"https://torch.mlverse.org/docs/reference/default_dtype.html","id":null,"dir":"Reference","previous_headings":"","what":"Gets and sets the default floating point dtype. — torch_set_default_dtype","title":"Gets and sets the default floating point dtype. — torch_set_default_dtype","text":"Gets sets default floating point dtype.","code":""},{"path":"https://torch.mlverse.org/docs/reference/default_dtype.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Gets and sets the default floating point dtype. — torch_set_default_dtype","text":"","code":"torch_set_default_dtype(d)  torch_get_default_dtype()"},{"path":"https://torch.mlverse.org/docs/reference/default_dtype.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Gets and sets the default floating point dtype. — torch_set_default_dtype","text":"d default floating point dtype set. Initially set torch_float().","code":""},{"path":"https://torch.mlverse.org/docs/reference/distr_bernoulli.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates a Bernoulli distribution parameterized by probs\nor logits (but not both).\nSamples are binary (0 or 1). They take the value 1 with probability p\nand 0 with probability 1 - p. — distr_bernoulli","title":"Creates a Bernoulli distribution parameterized by probs\nor logits (but not both).\nSamples are binary (0 or 1). They take the value 1 with probability p\nand 0 with probability 1 - p. — distr_bernoulli","text":"Creates Bernoulli distribution parameterized probs logits (). Samples binary (0 1). take value 1 probability p 0 probability 1 - p.","code":""},{"path":"https://torch.mlverse.org/docs/reference/distr_bernoulli.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates a Bernoulli distribution parameterized by probs\nor logits (but not both).\nSamples are binary (0 or 1). They take the value 1 with probability p\nand 0 with probability 1 - p. — distr_bernoulli","text":"","code":"distr_bernoulli(probs = NULL, logits = NULL, validate_args = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/distr_bernoulli.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates a Bernoulli distribution parameterized by probs\nor logits (but not both).\nSamples are binary (0 or 1). They take the value 1 with probability p\nand 0 with probability 1 - p. — distr_bernoulli","text":"probs (numeric torch_tensor): probability sampling 1 logits (numeric torch_tensor): log-odds sampling 1 validate_args whether validate arguments .","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/distr_bernoulli.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Creates a Bernoulli distribution parameterized by probs\nor logits (but not both).\nSamples are binary (0 or 1). They take the value 1 with probability p\nand 0 with probability 1 - p. — distr_bernoulli","text":"","code":"if (torch_is_installed()) { m <- distr_bernoulli(0.3) m$sample() # 30% chance 1; 70% chance 0 } #> torch_tensor #>  0 #> [ CPUFloatType{1} ]"},{"path":"https://torch.mlverse.org/docs/reference/distr_categorical.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates a categorical distribution parameterized by either probs or\nlogits (but not both). — distr_categorical","title":"Creates a categorical distribution parameterized by either probs or\nlogits (but not both). — distr_categorical","text":"Creates categorical distribution parameterized either probs logits ().","code":""},{"path":"https://torch.mlverse.org/docs/reference/distr_categorical.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates a categorical distribution parameterized by either probs or\nlogits (but not both). — distr_categorical","text":"","code":"distr_categorical(probs = NULL, logits = NULL, validate_args = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/distr_categorical.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates a categorical distribution parameterized by either probs or\nlogits (but not both). — distr_categorical","text":"probs (Tensor): event probabilities logits (Tensor): event log probabilities (unnormalized) validate_args Additional arguments","code":""},{"path":"https://torch.mlverse.org/docs/reference/distr_categorical.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Creates a categorical distribution parameterized by either probs or\nlogits (but not both). — distr_categorical","text":"equivalent distribution torch_multinomial() samples . Samples integers \\(\\{0, \\ldots, K-1\\}\\) K probs$size(-1). probs 1-dimensional length-K, element relative probability sampling class index. probs N-dimensional, first N-1 dimensions treated batch relative probability vectors. probs argument must non-negative, finite non-zero sum, normalized sum 1 along last dimension. attr:probs return normalized value. logits argument interpreted unnormalized log probabilities can therefore real number. likewise normalized resulting probabilities sum 1 along last dimension. attr:logits return normalized value. See also: torch_multinomial()","code":""},{"path":"https://torch.mlverse.org/docs/reference/distr_categorical.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Creates a categorical distribution parameterized by either probs or\nlogits (but not both). — distr_categorical","text":"","code":"if (torch_is_installed()) { m <- distr_categorical(torch_tensor(c(0.25, 0.25, 0.25, 0.25))) m$sample() # equal probability of 1,2,3,4 } #> torch_tensor #> 3 #> [ CPULongType{} ]"},{"path":"https://torch.mlverse.org/docs/reference/distr_chi2.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates a Chi2 distribution parameterized by shape parameter df.\nThis is exactly equivalent to distr_gamma(alpha=0.5*df, beta=0.5) — distr_chi2","title":"Creates a Chi2 distribution parameterized by shape parameter df.\nThis is exactly equivalent to distr_gamma(alpha=0.5*df, beta=0.5) — distr_chi2","text":"Creates Chi2 distribution parameterized shape parameter df. exactly equivalent distr_gamma(alpha=0.5*df, beta=0.5)","code":""},{"path":"https://torch.mlverse.org/docs/reference/distr_chi2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates a Chi2 distribution parameterized by shape parameter df.\nThis is exactly equivalent to distr_gamma(alpha=0.5*df, beta=0.5) — distr_chi2","text":"","code":"distr_chi2(df, validate_args = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/distr_chi2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates a Chi2 distribution parameterized by shape parameter df.\nThis is exactly equivalent to distr_gamma(alpha=0.5*df, beta=0.5) — distr_chi2","text":"df (float torch_tensor): shape parameter distribution validate_args whether validate arguments .","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/distr_chi2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Creates a Chi2 distribution parameterized by shape parameter df.\nThis is exactly equivalent to distr_gamma(alpha=0.5*df, beta=0.5) — distr_chi2","text":"","code":"if (torch_is_installed()) { m <- distr_chi2(torch_tensor(1.0)) m$sample() # Chi2 distributed with shape df=1 torch_tensor(0.1046) } #> torch_tensor #>  0.1046 #> [ CPUFloatType{1} ]"},{"path":"https://torch.mlverse.org/docs/reference/distr_gamma.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates a Gamma distribution parameterized by shape concentration and rate. — distr_gamma","title":"Creates a Gamma distribution parameterized by shape concentration and rate. — distr_gamma","text":"Creates Gamma distribution parameterized shape concentration rate.","code":""},{"path":"https://torch.mlverse.org/docs/reference/distr_gamma.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates a Gamma distribution parameterized by shape concentration and rate. — distr_gamma","text":"","code":"distr_gamma(concentration, rate, validate_args = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/distr_gamma.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates a Gamma distribution parameterized by shape concentration and rate. — distr_gamma","text":"concentration (float Tensor): shape parameter distribution (often referred alpha) rate (float Tensor): rate = 1 / scale distribution (often referred beta) validate_args whether validate arguments .","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/distr_gamma.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Creates a Gamma distribution parameterized by shape concentration and rate. — distr_gamma","text":"","code":"if (torch_is_installed()) { m <- distr_gamma(torch_tensor(1.0), torch_tensor(1.0)) m$sample() # Gamma distributed with concentration=1 and rate=1 } #> torch_tensor #>  2.2154 #> [ CPUFloatType{1} ]"},{"path":"https://torch.mlverse.org/docs/reference/distr_mixture_same_family.html","id":null,"dir":"Reference","previous_headings":"","what":"Mixture of components in the same family — distr_mixture_same_family","title":"Mixture of components in the same family — distr_mixture_same_family","text":"MixtureSameFamily distribution implements (batch ) mixture distribution component different parameterizations distribution type. parameterized Categorical selecting distribution\" (k component) component distribution, .e., Distribution rightmost batch shape (equal [k]) indexes (batch ) component.","code":""},{"path":"https://torch.mlverse.org/docs/reference/distr_mixture_same_family.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mixture of components in the same family — distr_mixture_same_family","text":"","code":"distr_mixture_same_family(   mixture_distribution,   component_distribution,   validate_args = NULL )"},{"path":"https://torch.mlverse.org/docs/reference/distr_mixture_same_family.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mixture of components in the same family — distr_mixture_same_family","text":"mixture_distribution torch_distributions.Categorical-like instance. Manages probability selecting component. number categories must match rightmost batch dimension component_distribution. Must either scalar batch_shape batch_shape matching component_distribution.batch_shape[:-1] component_distribution torch_distributions.Distribution-like instance. Right-batch dimension indexes component. validate_args Additional arguments","code":""},{"path":"https://torch.mlverse.org/docs/reference/distr_mixture_same_family.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Mixture of components in the same family — distr_mixture_same_family","text":"","code":"if (torch_is_installed()) { # Construct Gaussian Mixture Model in 1D consisting of 5 equally # weighted normal distributions mix <- distr_categorical(torch_ones(5)) comp <- distr_normal(torch_randn(5), torch_rand(5)) gmm <- distr_mixture_same_family(mix, comp) }"},{"path":"https://torch.mlverse.org/docs/reference/distr_multivariate_normal.html","id":null,"dir":"Reference","previous_headings":"","what":"Gaussian distribution — distr_multivariate_normal","title":"Gaussian distribution — distr_multivariate_normal","text":"Creates multivariate normal (also called Gaussian) distribution parameterized mean vector covariance matrix.","code":""},{"path":"https://torch.mlverse.org/docs/reference/distr_multivariate_normal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Gaussian distribution — distr_multivariate_normal","text":"","code":"distr_multivariate_normal(   loc,   covariance_matrix = NULL,   precision_matrix = NULL,   scale_tril = NULL,   validate_args = NULL )"},{"path":"https://torch.mlverse.org/docs/reference/distr_multivariate_normal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Gaussian distribution — distr_multivariate_normal","text":"loc (Tensor): mean distribution covariance_matrix (Tensor): positive-definite covariance matrix precision_matrix (Tensor): positive-definite precision matrix scale_tril (Tensor): lower-triangular factor covariance, positive-valued diagonal validate_args Bool wether validate arguments .","code":""},{"path":"https://torch.mlverse.org/docs/reference/distr_multivariate_normal.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Gaussian distribution — distr_multivariate_normal","text":"multivariate normal distribution can parameterized either terms positive definite covariance matrix \\(\\mathbf{\\Sigma}\\) positive definite precision matrix \\(\\mathbf{\\Sigma}^{-1}\\) lower-triangular matrix \\(\\mathbf{L}\\) positive-valued diagonal entries, \\(\\mathbf{\\Sigma} = \\mathbf{L}\\mathbf{L}^\\top\\). triangular matrix can obtained via e.g. Cholesky decomposition covariance.","code":""},{"path":"https://torch.mlverse.org/docs/reference/distr_multivariate_normal.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Gaussian distribution — distr_multivariate_normal","text":"one covariance_matrix precision_matrix scale_tril can specified. Using scale_tril efficient: computations internally based scale_tril. covariance_matrix precision_matrix passed instead, used compute corresponding lower triangular matrices using Cholesky decomposition.","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/distr_multivariate_normal.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Gaussian distribution — distr_multivariate_normal","text":"","code":"if (torch_is_installed()) { m <- distr_multivariate_normal(torch_zeros(2), torch_eye(2)) m$sample() # normally distributed with mean=`[0,0]` and covariance_matrix=`I` } #> torch_tensor #>  0.3365 #>  1.0608 #> [ CPUFloatType{2} ]"},{"path":"https://torch.mlverse.org/docs/reference/distr_normal.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates a normal (also called Gaussian) distribution parameterized by\nloc and scale. — distr_normal","title":"Creates a normal (also called Gaussian) distribution parameterized by\nloc and scale. — distr_normal","text":"Creates normal (also called Gaussian) distribution parameterized loc scale.","code":""},{"path":"https://torch.mlverse.org/docs/reference/distr_normal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates a normal (also called Gaussian) distribution parameterized by\nloc and scale. — distr_normal","text":"","code":"distr_normal(loc, scale, validate_args = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/distr_normal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates a normal (also called Gaussian) distribution parameterized by\nloc and scale. — distr_normal","text":"loc (float Tensor): mean distribution (often referred mu) scale (float Tensor): standard deviation distribution (often referred sigma) validate_args Additional arguments","code":""},{"path":"https://torch.mlverse.org/docs/reference/distr_normal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Creates a normal (also called Gaussian) distribution parameterized by\nloc and scale. — distr_normal","text":"Object torch_Normal class","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/distr_normal.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Creates a normal (also called Gaussian) distribution parameterized by\nloc and scale. — distr_normal","text":"","code":"if (torch_is_installed()) { m <- distr_normal(loc = 0, scale = 1) m$sample() # normally distributed with loc=0 and scale=1 } #> torch_tensor #> -0.2166 #> [ CPUFloatType{1} ]"},{"path":"https://torch.mlverse.org/docs/reference/distr_poisson.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates a Poisson distribution parameterized by rate, the rate parameter. — distr_poisson","title":"Creates a Poisson distribution parameterized by rate, the rate parameter. — distr_poisson","text":"Samples nonnegative integers, pmf given $$ \\mbox{rate}^{k} \\frac{e^{-\\mbox{rate}}}{k!} $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/distr_poisson.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates a Poisson distribution parameterized by rate, the rate parameter. — distr_poisson","text":"","code":"distr_poisson(rate, validate_args = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/distr_poisson.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates a Poisson distribution parameterized by rate, the rate parameter. — distr_poisson","text":"rate (numeric, torch_tensor): rate parameter validate_args whether validate arguments .","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/distr_poisson.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Creates a Poisson distribution parameterized by rate, the rate parameter. — distr_poisson","text":"","code":"if (torch_is_installed()) { m <- distr_poisson(torch_tensor(4)) m$sample() } #> torch_tensor #>  4 #> [ CPUFloatType{1} ]"},{"path":"https://torch.mlverse.org/docs/reference/enumerate.dataloader.html","id":null,"dir":"Reference","previous_headings":"","what":"Enumerate an iterator — enumerate.dataloader","title":"Enumerate an iterator — enumerate.dataloader","text":"Enumerate iterator","code":""},{"path":"https://torch.mlverse.org/docs/reference/enumerate.dataloader.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Enumerate an iterator — enumerate.dataloader","text":"","code":"# S3 method for dataloader enumerate(x, max_len = 1e+06, ...)"},{"path":"https://torch.mlverse.org/docs/reference/enumerate.dataloader.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Enumerate an iterator — enumerate.dataloader","text":"x generator enumerate. max_len maximum number iterations. ... passed specific methods.","code":""},{"path":"https://torch.mlverse.org/docs/reference/enumerate.html","id":null,"dir":"Reference","previous_headings":"","what":"Enumerate an iterator — enumerate","title":"Enumerate an iterator — enumerate","text":"Enumerate iterator","code":""},{"path":"https://torch.mlverse.org/docs/reference/enumerate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Enumerate an iterator — enumerate","text":"","code":"enumerate(x, ...)"},{"path":"https://torch.mlverse.org/docs/reference/enumerate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Enumerate an iterator — enumerate","text":"x generator enumerate. ... passed specific methods.","code":""},{"path":"https://torch.mlverse.org/docs/reference/get_install_libs_url.html","id":null,"dir":"Reference","previous_headings":"","what":"List of files to download — get_install_libs_url","title":"List of files to download — get_install_libs_url","text":"List Torch Lantern files download local files order proceed install_torch_from_file().","code":""},{"path":"https://torch.mlverse.org/docs/reference/get_install_libs_url.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"List of files to download — get_install_libs_url","text":"","code":"get_install_libs_url(   version = \"1.10.2\",   type = install_type(version = version) )"},{"path":"https://torch.mlverse.org/docs/reference/get_install_libs_url.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"List of files to download — get_install_libs_url","text":"version Torch version install. type installation type Torch. Valid values \"cpu\" 'CUDA' version.","code":""},{"path":"https://torch.mlverse.org/docs/reference/install_torch.html","id":null,"dir":"Reference","previous_headings":"","what":"Install Torch — install_torch","title":"Install Torch — install_torch","text":"Installs Torch dependencies.","code":""},{"path":"https://torch.mlverse.org/docs/reference/install_torch.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Install Torch — install_torch","text":"","code":"install_torch(   version = \"1.10.2\",   type = install_type(version = version),   reinstall = FALSE,   path = install_path(),   timeout = 360,   ... )"},{"path":"https://torch.mlverse.org/docs/reference/install_torch.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Install Torch — install_torch","text":"version Torch version install. type installation type Torch. Valid values \"cpu\" 'CUDA' version. reinstall Re-install Torch even already installed? path Optional path install check already existing installation. timeout Optional timeout seconds large file download. ... optional arguments (like `load` manual installation).","code":""},{"path":"https://torch.mlverse.org/docs/reference/install_torch.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Install Torch — install_torch","text":"using path install specific location, make sure TORCH_HOME environment variable set path reuse installation. TORCH_INSTALL environment variable can set 0 prevent auto-installing torch TORCH_LOAD set 0 avoid loading dependencies automatically. environment variables meant advanced use cases troubleshooting . timeout error occurs library archive download, length downloaded files differ reported length, increase timeout value help.","code":""},{"path":"https://torch.mlverse.org/docs/reference/install_torch_from_file.html","id":null,"dir":"Reference","previous_headings":"","what":"Install Torch from files — install_torch_from_file","title":"Install Torch from files — install_torch_from_file","text":"Installs Torch dependencies files.","code":""},{"path":"https://torch.mlverse.org/docs/reference/install_torch_from_file.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Install Torch from files — install_torch_from_file","text":"","code":"install_torch_from_file(   version = \"1.10.2\",   type = install_type(version = version),   libtorch,   liblantern,   ... )"},{"path":"https://torch.mlverse.org/docs/reference/install_torch_from_file.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Install Torch from files — install_torch_from_file","text":"version Torch version install. type installation type Torch. Valid values \"cpu\" 'CUDA' version. libtorch installation archive file use Torch. Shall \"file://\" URL scheme. liblantern installation archive file use Lantern. Shall \"file://\" URL scheme. ... parameters passed \"install_torch()\"","code":""},{"path":"https://torch.mlverse.org/docs/reference/install_torch_from_file.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Install Torch from files — install_torch_from_file","text":"\"install_torch()\" initiated download possible, installation archive files present local filesystem, \"install_torch_from_file()\" can used workaround installation issue. \"libtorch\" archive containing torch modules, \"liblantern\" C interface libtorch used R package. highly dependent, checked \"get_install_libs_url()\"","code":""},{"path":"https://torch.mlverse.org/docs/reference/is_dataloader.html","id":null,"dir":"Reference","previous_headings":"","what":"Checks if the object is a dataloader — is_dataloader","title":"Checks if the object is a dataloader — is_dataloader","text":"Checks object dataloader","code":""},{"path":"https://torch.mlverse.org/docs/reference/is_dataloader.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Checks if the object is a dataloader — is_dataloader","text":"","code":"is_dataloader(x)"},{"path":"https://torch.mlverse.org/docs/reference/is_dataloader.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Checks if the object is a dataloader — is_dataloader","text":"x object check","code":""},{"path":"https://torch.mlverse.org/docs/reference/is_nn_buffer.html","id":null,"dir":"Reference","previous_headings":"","what":"Checks if the object is a nn_buffer — is_nn_buffer","title":"Checks if the object is a nn_buffer — is_nn_buffer","text":"Checks object nn_buffer","code":""},{"path":"https://torch.mlverse.org/docs/reference/is_nn_buffer.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Checks if the object is a nn_buffer — is_nn_buffer","text":"","code":"is_nn_buffer(x)"},{"path":"https://torch.mlverse.org/docs/reference/is_nn_buffer.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Checks if the object is a nn_buffer — is_nn_buffer","text":"x object check","code":""},{"path":"https://torch.mlverse.org/docs/reference/is_nn_module.html","id":null,"dir":"Reference","previous_headings":"","what":"Checks if the object is an nn_module — is_nn_module","title":"Checks if the object is an nn_module — is_nn_module","text":"Checks object nn_module","code":""},{"path":"https://torch.mlverse.org/docs/reference/is_nn_module.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Checks if the object is an nn_module — is_nn_module","text":"","code":"is_nn_module(x)"},{"path":"https://torch.mlverse.org/docs/reference/is_nn_module.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Checks if the object is an nn_module — is_nn_module","text":"x object check","code":""},{"path":"https://torch.mlverse.org/docs/reference/is_nn_parameter.html","id":null,"dir":"Reference","previous_headings":"","what":"Checks if an object is a nn_parameter — is_nn_parameter","title":"Checks if an object is a nn_parameter — is_nn_parameter","text":"Checks object nn_parameter","code":""},{"path":"https://torch.mlverse.org/docs/reference/is_nn_parameter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Checks if an object is a nn_parameter — is_nn_parameter","text":"","code":"is_nn_parameter(x)"},{"path":"https://torch.mlverse.org/docs/reference/is_nn_parameter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Checks if an object is a nn_parameter — is_nn_parameter","text":"x object check","code":""},{"path":"https://torch.mlverse.org/docs/reference/is_optimizer.html","id":null,"dir":"Reference","previous_headings":"","what":"Checks if the object is a torch optimizer — is_optimizer","title":"Checks if the object is a torch optimizer — is_optimizer","text":"Checks object torch optimizer","code":""},{"path":"https://torch.mlverse.org/docs/reference/is_optimizer.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Checks if the object is a torch optimizer — is_optimizer","text":"","code":"is_optimizer(x)"},{"path":"https://torch.mlverse.org/docs/reference/is_optimizer.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Checks if the object is a torch optimizer — is_optimizer","text":"x object check","code":""},{"path":"https://torch.mlverse.org/docs/reference/is_torch_device.html","id":null,"dir":"Reference","previous_headings":"","what":"Checks if object is a device — is_torch_device","title":"Checks if object is a device — is_torch_device","text":"Checks object device","code":""},{"path":"https://torch.mlverse.org/docs/reference/is_torch_device.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Checks if object is a device — is_torch_device","text":"","code":"is_torch_device(x)"},{"path":"https://torch.mlverse.org/docs/reference/is_torch_device.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Checks if object is a device — is_torch_device","text":"x object check","code":""},{"path":"https://torch.mlverse.org/docs/reference/is_torch_dtype.html","id":null,"dir":"Reference","previous_headings":"","what":"Check if object is a torch data type — is_torch_dtype","title":"Check if object is a torch data type — is_torch_dtype","text":"Check object torch data type","code":""},{"path":"https://torch.mlverse.org/docs/reference/is_torch_dtype.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check if object is a torch data type — is_torch_dtype","text":"","code":"is_torch_dtype(x)"},{"path":"https://torch.mlverse.org/docs/reference/is_torch_dtype.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check if object is a torch data type — is_torch_dtype","text":"x object check.","code":""},{"path":"https://torch.mlverse.org/docs/reference/is_torch_layout.html","id":null,"dir":"Reference","previous_headings":"","what":"Check if an object is a torch layout. — is_torch_layout","title":"Check if an object is a torch layout. — is_torch_layout","text":"Check object torch layout.","code":""},{"path":"https://torch.mlverse.org/docs/reference/is_torch_layout.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check if an object is a torch layout. — is_torch_layout","text":"","code":"is_torch_layout(x)"},{"path":"https://torch.mlverse.org/docs/reference/is_torch_layout.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check if an object is a torch layout. — is_torch_layout","text":"x object check","code":""},{"path":"https://torch.mlverse.org/docs/reference/is_torch_memory_format.html","id":null,"dir":"Reference","previous_headings":"","what":"Check if an object is a memory format — is_torch_memory_format","title":"Check if an object is a memory format — is_torch_memory_format","text":"Check object memory format","code":""},{"path":"https://torch.mlverse.org/docs/reference/is_torch_memory_format.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check if an object is a memory format — is_torch_memory_format","text":"","code":"is_torch_memory_format(x)"},{"path":"https://torch.mlverse.org/docs/reference/is_torch_memory_format.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check if an object is a memory format — is_torch_memory_format","text":"x object check","code":""},{"path":"https://torch.mlverse.org/docs/reference/is_torch_qscheme.html","id":null,"dir":"Reference","previous_headings":"","what":"Checks if an object is a QScheme — is_torch_qscheme","title":"Checks if an object is a QScheme — is_torch_qscheme","text":"Checks object QScheme","code":""},{"path":"https://torch.mlverse.org/docs/reference/is_torch_qscheme.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Checks if an object is a QScheme — is_torch_qscheme","text":"","code":"is_torch_qscheme(x)"},{"path":"https://torch.mlverse.org/docs/reference/is_torch_qscheme.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Checks if an object is a QScheme — is_torch_qscheme","text":"x object check","code":""},{"path":"https://torch.mlverse.org/docs/reference/is_undefined_tensor.html","id":null,"dir":"Reference","previous_headings":"","what":"Checks if a tensor is undefined — is_undefined_tensor","title":"Checks if a tensor is undefined — is_undefined_tensor","text":"Checks tensor undefined","code":""},{"path":"https://torch.mlverse.org/docs/reference/is_undefined_tensor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Checks if a tensor is undefined — is_undefined_tensor","text":"","code":"is_undefined_tensor(x)"},{"path":"https://torch.mlverse.org/docs/reference/is_undefined_tensor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Checks if a tensor is undefined — is_undefined_tensor","text":"x tensor check","code":""},{"path":"https://torch.mlverse.org/docs/reference/jit_compile.html","id":null,"dir":"Reference","previous_headings":"","what":"Compile TorchScript code into a graph — jit_compile","title":"Compile TorchScript code into a graph — jit_compile","text":"See TorchScript language reference documentation write TorchScript code.","code":""},{"path":"https://torch.mlverse.org/docs/reference/jit_compile.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compile TorchScript code into a graph — jit_compile","text":"","code":"jit_compile(source)"},{"path":"https://torch.mlverse.org/docs/reference/jit_compile.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compile TorchScript code into a graph — jit_compile","text":"source valid TorchScript source code.","code":""},{"path":"https://torch.mlverse.org/docs/reference/jit_compile.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compile TorchScript code into a graph — jit_compile","text":"","code":"if (torch_is_installed()) { comp <- jit_compile(\" def fn (x):   return torch.abs(x)  def foo (x):   return torch.sum(x)  \")  comp$fn(torch_tensor(-1)) comp$foo(torch_randn(10)) } #> torch_tensor #> -0.467084 #> [ CPUFloatType{} ]"},{"path":"https://torch.mlverse.org/docs/reference/jit_load.html","id":null,"dir":"Reference","previous_headings":"","what":"Loads a script_function or script_module previously saved with jit_save — jit_load","title":"Loads a script_function or script_module previously saved with jit_save — jit_load","text":"Loads script_function script_module previously saved jit_save","code":""},{"path":"https://torch.mlverse.org/docs/reference/jit_load.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Loads a script_function or script_module previously saved with jit_save — jit_load","text":"","code":"jit_load(path, ...)"},{"path":"https://torch.mlverse.org/docs/reference/jit_load.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Loads a script_function or script_module previously saved with jit_save — jit_load","text":"path path script_function script_module serialized jit_save(). ... currently unused.","code":""},{"path":"https://torch.mlverse.org/docs/reference/jit_save.html","id":null,"dir":"Reference","previous_headings":"","what":"Saves a script_function to a path — jit_save","title":"Saves a script_function to a path — jit_save","text":"Saves script_function path","code":""},{"path":"https://torch.mlverse.org/docs/reference/jit_save.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Saves a script_function to a path — jit_save","text":"","code":"jit_save(obj, path, ...)"},{"path":"https://torch.mlverse.org/docs/reference/jit_save.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Saves a script_function to a path — jit_save","text":"obj script_function save path path save serialized function. ... currently unused","code":""},{"path":"https://torch.mlverse.org/docs/reference/jit_save.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Saves a script_function to a path — jit_save","text":"","code":"if (torch_is_installed()) { fn <- function(x) {   torch_relu(x) }  input <- torch_tensor(c(-1, 0, 1)) tr_fn <- jit_trace(fn, input)  tmp <- tempfile(\"tst\", fileext = \"pt\") jit_save(tr_fn, tmp) }"},{"path":"https://torch.mlverse.org/docs/reference/jit_save_for_mobile.html","id":null,"dir":"Reference","previous_headings":"","what":"Saves a script_function or script_module in bytecode form,\nto be loaded on a mobile device — jit_save_for_mobile","title":"Saves a script_function or script_module in bytecode form,\nto be loaded on a mobile device — jit_save_for_mobile","text":"Saves script_function script_module bytecode form, loaded mobile device","code":""},{"path":"https://torch.mlverse.org/docs/reference/jit_save_for_mobile.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Saves a script_function or script_module in bytecode form,\nto be loaded on a mobile device — jit_save_for_mobile","text":"","code":"jit_save_for_mobile(obj, path, ...)"},{"path":"https://torch.mlverse.org/docs/reference/jit_save_for_mobile.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Saves a script_function or script_module in bytecode form,\nto be loaded on a mobile device — jit_save_for_mobile","text":"obj script_function script_module save path path save serialized function. ... currently unused","code":""},{"path":"https://torch.mlverse.org/docs/reference/jit_save_for_mobile.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Saves a script_function or script_module in bytecode form,\nto be loaded on a mobile device — jit_save_for_mobile","text":"","code":"if (torch_is_installed()) { fn <- function(x) {   torch_relu(x) }  input <- torch_tensor(c(-1, 0, 1)) tr_fn <- jit_trace(fn, input)  tmp <- tempfile(\"tst\", fileext = \"pt\") jit_save_for_mobile(tr_fn, tmp) }"},{"path":"https://torch.mlverse.org/docs/reference/jit_scalar.html","id":null,"dir":"Reference","previous_headings":"","what":"Adds the 'jit_scalar' class to the input — jit_scalar","title":"Adds the 'jit_scalar' class to the input — jit_scalar","text":"Allows disambiguating length 1 vectors scalars passing jit.","code":""},{"path":"https://torch.mlverse.org/docs/reference/jit_scalar.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adds the 'jit_scalar' class to the input — jit_scalar","text":"","code":"jit_scalar(x)"},{"path":"https://torch.mlverse.org/docs/reference/jit_scalar.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adds the 'jit_scalar' class to the input — jit_scalar","text":"x length 1 R vector.","code":""},{"path":"https://torch.mlverse.org/docs/reference/jit_trace.html","id":null,"dir":"Reference","previous_headings":"","what":"Trace a function and return an executable script_function. — jit_trace","title":"Trace a function and return an executable script_function. — jit_trace","text":"Using jit_trace, can turn existing R function TorchScript script_function. must provide example inputs, run function, recording operations performed tensors.","code":""},{"path":"https://torch.mlverse.org/docs/reference/jit_trace.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Trace a function and return an executable script_function. — jit_trace","text":"","code":"jit_trace(func, ..., strict = TRUE)"},{"path":"https://torch.mlverse.org/docs/reference/jit_trace.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Trace a function and return an executable script_function. — jit_trace","text":"func R function run example_inputs. func arguments return values must tensors (possibly nested) lists contain tensors. Can also nn_module(), case jit_trace_module() used trace module. ... example inputs passed function tracing. resulting trace can run inputs different types shapes assuming traced operations support types shapes. example_inputs may also single Tensor case automatically wrapped list. Note ... can named, order respected. strict run tracer strict mode (default: TRUE). turn want tracer record mutable container types (currently list/dict) sure container using problem constant structure get used control flow (, ) conditions.","code":""},{"path":"https://torch.mlverse.org/docs/reference/jit_trace.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Trace a function and return an executable script_function. — jit_trace","text":"script_function func function script_module func nn_module().","code":""},{"path":"https://torch.mlverse.org/docs/reference/jit_trace.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Trace a function and return an executable script_function. — jit_trace","text":"resulting recording standalone function produces script_function. future also support tracing nn_modules.","code":""},{"path":"https://torch.mlverse.org/docs/reference/jit_trace.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Trace a function and return an executable script_function. — jit_trace","text":"Scripting yet supported R.","code":""},{"path":"https://torch.mlverse.org/docs/reference/jit_trace.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Trace a function and return an executable script_function. — jit_trace","text":"Tracing correctly records functions modules data dependent (e.g., conditionals data tensors) untracked external dependencies (e.g., perform input/output access global variables). Tracing records operations done given function run given tensors. Therefore, returned script_function always run traced graph input. important implications module expected run different sets operations, depending input /module state. example, Tracing record control-flow like -statements loops. control-flow constant across module, fine often inlines control-flow decisions. sometimes control-flow actually part model . instance, recurrent network loop (possibly dynamic) length input sequence. returned script_function, operations different behaviors training eval modes always behave mode tracing, matter mode script_function . cases like , tracing appropriate scripting better choice. trace models, may silently get incorrect results subsequent invocations model. tracer try emit warnings something may cause incorrect trace produced.","code":""},{"path":"https://torch.mlverse.org/docs/reference/jit_trace.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Trace a function and return an executable script_function. — jit_trace","text":"","code":"if (torch_is_installed()) { fn <- function(x) {   torch_relu(x) } input <- torch_tensor(c(-1, 0, 1)) tr_fn <- jit_trace(fn, input) tr_fn(input) } #> torch_tensor #>  0 #>  0 #>  1 #> [ CPUFloatType{3} ]"},{"path":"https://torch.mlverse.org/docs/reference/jit_trace_module.html","id":null,"dir":"Reference","previous_headings":"","what":"Trace a module — jit_trace_module","title":"Trace a module — jit_trace_module","text":"Trace module return executable ScriptModule optimized using just--time compilation. module passed jit_trace(), forward method run traced. jit_trace_module(), can specify named list method names example inputs trace (see inputs) argument .","code":""},{"path":"https://torch.mlverse.org/docs/reference/jit_trace_module.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Trace a module — jit_trace_module","text":"","code":"jit_trace_module(mod, ..., strict = TRUE)"},{"path":"https://torch.mlverse.org/docs/reference/jit_trace_module.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Trace a module — jit_trace_module","text":"mod torch nn_module()  containing methods whose names specified inputs. given methods compiled part single ScriptModule. ... named list containing sample inputs indexed method names mod. inputs passed methods whose names correspond inputs keys tracing. list('forward'=example_forward_input, 'method2'=example_method2_input). strict run tracer strict mode (default: TRUE). turn want tracer record mutable container types (currently list/dict) sure container using problem constant structure get used control flow (, ) conditions.","code":""},{"path":"https://torch.mlverse.org/docs/reference/jit_trace_module.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Trace a module — jit_trace_module","text":"See jit_trace information tracing.","code":""},{"path":"https://torch.mlverse.org/docs/reference/jit_trace_module.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Trace a module — jit_trace_module","text":"","code":"if (torch_is_installed()) { linear <- nn_linear(10, 1) tr_linear <- jit_trace_module(linear, forward = list(torch_randn(10, 10)))  x <- torch_randn(10, 10) torch_allclose(linear(x), tr_linear(x)) } #> [1] TRUE"},{"path":"https://torch.mlverse.org/docs/reference/jit_tuple.html","id":null,"dir":"Reference","previous_headings":"","what":"Adds the 'jit_tuple' class to the input — jit_tuple","title":"Adds the 'jit_tuple' class to the input — jit_tuple","text":"Allows specifying output input must considered jit tuple instead list dictionary tracing.","code":""},{"path":"https://torch.mlverse.org/docs/reference/jit_tuple.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adds the 'jit_tuple' class to the input — jit_tuple","text":"","code":"jit_tuple(x)"},{"path":"https://torch.mlverse.org/docs/reference/jit_tuple.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adds the 'jit_tuple' class to the input — jit_tuple","text":"x list object converted tuple.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_cholesky.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the Cholesky decomposition of a complex Hermitian or real symmetric positive-definite matrix. — linalg_cholesky","title":"Computes the Cholesky decomposition of a complex Hermitian or real symmetric positive-definite matrix. — linalg_cholesky","text":"Letting   , Cholesky decomposition complex Hermitian real symmetric positive-definite matrix  defined ","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_cholesky.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the Cholesky decomposition of a complex Hermitian or real symmetric positive-definite matrix. — linalg_cholesky","text":"","code":"linalg_cholesky(A)"},{"path":"https://torch.mlverse.org/docs/reference/linalg_cholesky.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the Cholesky decomposition of a complex Hermitian or real symmetric positive-definite matrix. — linalg_cholesky","text":"(Tensor): tensor shape (*, n, n) * zero batch dimensions consisting symmetric Hermitian positive-definite matrices.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_cholesky.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Computes the Cholesky decomposition of a complex Hermitian or real symmetric positive-definite matrix. — linalg_cholesky","text":"=LLHL∈Kn×n = LL^{H}\\mathrlap{\\qquad L \\\\mathbb{K}^{n \\times n}} =LLHL∈Kn×n  lower triangular matrix  conjugate transpose  complex, transpose  real-valued. Supports input float, double, cfloat cdouble dtypes. Also supports batches matrices, batch matrices output batch dimensions.","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/linalg_cholesky.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the Cholesky decomposition of a complex Hermitian or real symmetric positive-definite matrix. — linalg_cholesky","text":"","code":"if (torch_is_installed()) { a <- torch_eye(10) linalg_cholesky(a) } #> torch_tensor #>  1  0  0  0  0  0  0  0  0  0 #>  0  1  0  0  0  0  0  0  0  0 #>  0  0  1  0  0  0  0  0  0  0 #>  0  0  0  1  0  0  0  0  0  0 #>  0  0  0  0  1  0  0  0  0  0 #>  0  0  0  0  0  1  0  0  0  0 #>  0  0  0  0  0  0  1  0  0  0 #>  0  0  0  0  0  0  0  1  0  0 #>  0  0  0  0  0  0  0  0  1  0 #>  0  0  0  0  0  0  0  0  0  1 #> [ CPUFloatType{10,10} ]"},{"path":"https://torch.mlverse.org/docs/reference/linalg_cholesky_ex.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the Cholesky decomposition of a complex Hermitian or real\nsymmetric positive-definite matrix. — linalg_cholesky_ex","title":"Computes the Cholesky decomposition of a complex Hermitian or real\nsymmetric positive-definite matrix. — linalg_cholesky_ex","text":"function skips (slow) error checking error message construction linalg_cholesky(), instead directly returning LAPACK error codes part named tuple (L, info). makes function faster way check matrix positive-definite, provides opportunity handle decomposition errors gracefully performantly linalg_cholesky() . Supports input float, double, cfloat cdouble dtypes. Also supports batches matrices, batch matrices output batch dimensions. Hermitian positive-definite matrix, batch matrices one Hermitian positive-definite matrix, info stores positive integer corresponding matrix. positive integer indicates order leading minor positive-definite, decomposition completed. info filled zeros indicates decomposition successful. check_errors=TRUE info contains positive integers, RuntimeError thrown.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_cholesky_ex.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the Cholesky decomposition of a complex Hermitian or real\nsymmetric positive-definite matrix. — linalg_cholesky_ex","text":"","code":"linalg_cholesky_ex(A, check_errors = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/linalg_cholesky_ex.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the Cholesky decomposition of a complex Hermitian or real\nsymmetric positive-definite matrix. — linalg_cholesky_ex","text":"(Tensor): Hermitian n \\times n matrix batch matrices size (*, n, n) * one batch dimensions. check_errors (bool, optional): controls whether check content infos. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_cholesky_ex.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Computes the Cholesky decomposition of a complex Hermitian or real\nsymmetric positive-definite matrix. — linalg_cholesky_ex","text":"CUDA device, function may synchronize device CPU. function \"experimental\" may change future PyTorch release.","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/linalg_cholesky_ex.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the Cholesky decomposition of a complex Hermitian or real\nsymmetric positive-definite matrix. — linalg_cholesky_ex","text":"","code":"if (torch_is_installed()) { A <- torch_randn(2, 2) out <- linalg_cholesky_ex(A) out } #> $L #> torch_tensor #> -1.0269  0.0000 #>  0.2623 -0.0618 #> [ CPUFloatType{2,2} ] #>  #> $info #> torch_tensor #> 1 #> [ CPUIntType{} ] #>"},{"path":"https://torch.mlverse.org/docs/reference/linalg_cond.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the condition number of a matrix with respect to a matrix norm. — linalg_cond","title":"Computes the condition number of a matrix with respect to a matrix norm. — linalg_cond","text":"Letting   , condition number  matrix  defined ","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_cond.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the condition number of a matrix with respect to a matrix norm. — linalg_cond","text":"","code":"linalg_cond(A, p = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/linalg_cond.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the condition number of a matrix with respect to a matrix norm. — linalg_cond","text":"(Tensor): tensor shape (*, m, n) * zero batch dimensions p (2, -2), shape (*, n, n) every matrix invertible p ('fro', 'nuc', inf, -inf, 1, -1). p (int, inf, -inf, 'fro', 'nuc', optional): type matrix norm use computations (see ). Default: NULL","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_cond.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the condition number of a matrix with respect to a matrix norm. — linalg_cond","text":"real-valued tensor, even complex.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_cond.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Computes the condition number of a matrix with respect to a matrix norm. — linalg_cond","text":"κ()=∥∥p∥−1∥p\\kappa() = \\|\\|_p\\|^{-1}\\|_pκ()=∥∥p​∥−1∥p​ condition number measures numerical stability linear system AX = B respect matrix norm. Supports input float, double, cfloat cdouble dtypes. Also supports batches matrices, batch matrices output batch dimensions. p defines matrix norm computed. See table 'Details' find supported norms. p one ('fro', 'nuc', inf, -inf, 1, -1), function uses linalg_norm() linalg_inv(). , case, matrix (every matrix batch) square invertible. p (2, -2), function can computed terms singular values κ2()=σ1σnκ−2()=σnσ1\\kappa_2() = \\frac{\\sigma_1}{\\sigma_n}\\qquad \\kappa_{-2}() = \\frac{\\sigma_n}{\\sigma_1}κ2​()=σn​σ1​​κ−2​()=σ1​σn​​ cases, computed using linalg_svd(). norms, matrix (every matrix batch) may shape.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_cond.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Computes the condition number of a matrix with respect to a matrix norm. — linalg_cond","text":"inputs CUDA device, function synchronizes device CPU p one ('fro', 'nuc', inf, -inf, 1, -1).","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_cond.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the condition number of a matrix with respect to a matrix norm. — linalg_cond","text":"","code":"if (torch_is_installed()) { a <- torch_tensor(rbind(c(1., 0, -1), c(0, 1, 0), c(1, 0, 1))) linalg_cond(a) linalg_cond(a, \"fro\") } #> torch_tensor #> 3.16228 #> [ CPUFloatType{} ]"},{"path":"https://torch.mlverse.org/docs/reference/linalg_det.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the determinant of a square matrix. — linalg_det","title":"Computes the determinant of a square matrix. — linalg_det","text":"Supports input float, double, cfloat cdouble dtypes. Also supports batches matrices, batch matrices output batch dimensions.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_det.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the determinant of a square matrix. — linalg_det","text":"","code":"linalg_det(A)"},{"path":"https://torch.mlverse.org/docs/reference/linalg_det.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the determinant of a square matrix. — linalg_det","text":"(Tensor): tensor shape (*, n, n) * zero batch dimensions.","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/linalg_det.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the determinant of a square matrix. — linalg_det","text":"","code":"if (torch_is_installed()) { a <- torch_randn(3, 3) linalg_det(a)  a <- torch_randn(3, 3, 3) linalg_det(a) } #> torch_tensor #>  0.3038 #> -0.0061 #>  1.4470 #> [ CPUFloatType{3} ]"},{"path":"https://torch.mlverse.org/docs/reference/linalg_eig.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the eigenvalue decomposition of a square matrix if it exists. — linalg_eig","title":"Computes the eigenvalue decomposition of a square matrix if it exists. — linalg_eig","text":"Letting   , eigenvalue decomposition square matrix  (exists) defined ","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_eig.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the eigenvalue decomposition of a square matrix if it exists. — linalg_eig","text":"","code":"linalg_eig(A)"},{"path":"https://torch.mlverse.org/docs/reference/linalg_eig.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the eigenvalue decomposition of a square matrix if it exists. — linalg_eig","text":"(Tensor): tensor shape (*, n, n) * zero batch dimensions consisting diagonalizable matrices.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_eig.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the eigenvalue decomposition of a square matrix if it exists. — linalg_eig","text":"list (eigenvalues, eigenvectors) corresponds   . eigenvalues eigenvectors always complex-valued, even real. eigenvectors given columns eigenvectors.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_eig.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Computes the eigenvalue decomposition of a square matrix if it exists. — linalg_eig","text":"=Vdiag⁡(Λ)V−1V∈Cn×n,Λ∈Cn = V \\operatorname{diag}(\\Lambda) V^{-1}\\mathrlap{\\qquad V \\\\mathbb{C}^{n \\times n}, \\Lambda \\\\mathbb{C}^n} =Vdiag(Λ)V−1V∈Cn×n,Λ∈Cn decomposition exists  diagonalizable_. case eigenvalues different. Supports input float, double, cfloat cdouble dtypes. Also supports batches matrices, batch matrices output batch dimensions.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_eig.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Computes the eigenvalue decomposition of a square matrix if it exists. — linalg_eig","text":"eigenvalues eigenvectors real matrix may complex.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_eig.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Computes the eigenvalue decomposition of a square matrix if it exists. — linalg_eig","text":"function assumes diagonalizable_ (example, eigenvalues different). diagonalizable, returned eigenvalues correct . eigenvectors matrix unique, continuous respect . Due lack uniqueness, different hardware software may compute different eigenvectors. non-uniqueness caused fact multiplying eigenvector non-zero number produces another set valid eigenvectors matrix. implmentation, returned eigenvectors normalized norm 1 largest real component. Gradients computed using V finite repeated eigenvalues. Furthermore, distance two eigenvalues close zero, gradient numerically unstable, depends eigenvalues  computation .","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/linalg_eig.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the eigenvalue decomposition of a square matrix if it exists. — linalg_eig","text":"","code":"if (torch_is_installed()) { a <- torch_randn(2, 2) wv <- linalg_eig(a) }"},{"path":"https://torch.mlverse.org/docs/reference/linalg_eigh.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the eigenvalue decomposition of a complex Hermitian or real symmetric matrix. — linalg_eigh","title":"Computes the eigenvalue decomposition of a complex Hermitian or real symmetric matrix. — linalg_eigh","text":"Letting   , eigenvalue decomposition complex Hermitian real symmetric matrix  defined ","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_eigh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the eigenvalue decomposition of a complex Hermitian or real symmetric matrix. — linalg_eigh","text":"","code":"linalg_eigh(A, UPLO = \"L\")"},{"path":"https://torch.mlverse.org/docs/reference/linalg_eigh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the eigenvalue decomposition of a complex Hermitian or real symmetric matrix. — linalg_eigh","text":"(Tensor): tensor shape (*, n, n) * zero batch dimensions consisting symmetric Hermitian matrices. UPLO ('L', 'U', optional): controls whether use upper lower triangular part computations. Default: 'L'.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_eigh.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the eigenvalue decomposition of a complex Hermitian or real symmetric matrix. — linalg_eigh","text":"list (eigenvalues, eigenvectors) corresponds   . eigenvalues always real-valued, even complex. also ordered ascending order. eigenvectors dtype contain eigenvectors columns.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_eigh.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Computes the eigenvalue decomposition of a complex Hermitian or real symmetric matrix. — linalg_eigh","text":"=Qdiag⁡(Λ)QHQ∈Kn×n,Λ∈Rn = Q \\operatorname{diag}(\\Lambda) Q^{H}\\mathrlap{\\qquad Q \\\\mathbb{K}^{n \\times n}, \\Lambda \\\\mathbb{R}^n} =Qdiag(Λ)QHQ∈Kn×n,Λ∈Rn  conjugate transpose  complex, transpose  real-valued.  orthogonal real case unitary complex case. Supports input float, double, cfloat cdouble dtypes. Also supports batches matrices, batch matrices output batch dimensions. assumed Hermitian (resp. symmetric), checked internally, instead: UPLO\\ = 'L' (default), lower triangular part matrix used computation. UPLO\\ = 'U', upper triangular part matrix used. eigenvalues returned ascending order.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_eigh.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Computes the eigenvalue decomposition of a complex Hermitian or real symmetric matrix. — linalg_eigh","text":"eigenvalues real symmetric complex Hermitian matrices always real.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_eigh.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Computes the eigenvalue decomposition of a complex Hermitian or real symmetric matrix. — linalg_eigh","text":"eigenvectors symmetric matrix unique, continuous respect . Due lack uniqueness, different hardware software may compute different eigenvectors. non-uniqueness caused fact multiplying eigenvector -1 real case  complex case produces another set valid eigenvectors matrix. non-uniqueness problem even worse matrix repeated eigenvalues. case, one may multiply associated eigenvectors spanning subspace rotation matrix resulting eigenvectors valid eigenvectors. Gradients computed using eigenvectors tensor finite unique eigenvalues. Furthermore, distance two eigvalues close zero, gradient numerically unstable, depends eigenvalues  computation .","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/linalg_eigh.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the eigenvalue decomposition of a complex Hermitian or real symmetric matrix. — linalg_eigh","text":"","code":"if (torch_is_installed()) { a <- torch_randn(2, 2) linalg_eigh(a) } #> [[1]] #> torch_tensor #> -1.0987 #>  1.0196 #> [ CPUFloatType{2} ] #>  #> [[2]] #> torch_tensor #>  0.6044 -0.7967 #>  0.7967  0.6044 #> [ CPUFloatType{2,2} ] #>"},{"path":"https://torch.mlverse.org/docs/reference/linalg_eigvals.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the eigenvalues of a square matrix. — linalg_eigvals","title":"Computes the eigenvalues of a square matrix. — linalg_eigvals","text":"Letting   , eigenvalues square matrix  defined roots (counted multiplicity) polynomial p degree n given ","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_eigvals.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the eigenvalues of a square matrix. — linalg_eigvals","text":"","code":"linalg_eigvals(A)"},{"path":"https://torch.mlverse.org/docs/reference/linalg_eigvals.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the eigenvalues of a square matrix. — linalg_eigvals","text":"(Tensor): tensor shape (*, n, n) * zero batch dimensions.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_eigvals.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Computes the eigenvalues of a square matrix. — linalg_eigvals","text":"p(λ)=det⁡(−λIn)λ∈C p(\\lambda) = \\operatorname{det}(- \\lambda \\mathrm{}_n)\\mathrlap{\\qquad \\lambda \\\\mathbb{C}} p(λ)=det(−λIn​)λ∈C  n-dimensional identity matrix. Supports input float, double, cfloat cdouble dtypes. Also supports batches matrices, batch matrices output batch dimensions.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_eigvals.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Computes the eigenvalues of a square matrix. — linalg_eigvals","text":"eigenvalues real matrix may complex, roots real polynomial may complex. eigenvalues matrix always well-defined, even matrix diagonalizable.","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/linalg_eigvals.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the eigenvalues of a square matrix. — linalg_eigvals","text":"","code":"if (torch_is_installed()) { a <- torch_randn(2, 2) w <- linalg_eigvals(a) }"},{"path":"https://torch.mlverse.org/docs/reference/linalg_eigvalsh.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the eigenvalues of a complex Hermitian or real symmetric matrix. — linalg_eigvalsh","title":"Computes the eigenvalues of a complex Hermitian or real symmetric matrix. — linalg_eigvalsh","text":"Letting   , eigenvalues complex Hermitian real symmetric  matrix  defined roots (counted multiplicity) polynomial p degree n given ","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_eigvalsh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the eigenvalues of a complex Hermitian or real symmetric matrix. — linalg_eigvalsh","text":"","code":"linalg_eigvalsh(A, UPLO = \"L\")"},{"path":"https://torch.mlverse.org/docs/reference/linalg_eigvalsh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the eigenvalues of a complex Hermitian or real symmetric matrix. — linalg_eigvalsh","text":"(Tensor): tensor shape (*, n, n) * zero batch dimensions consisting symmetric Hermitian matrices. UPLO ('L', 'U', optional): controls whether use upper lower triangular part computations. Default: 'L'.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_eigvalsh.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the eigenvalues of a complex Hermitian or real symmetric matrix. — linalg_eigvalsh","text":"real-valued tensor cointaining eigenvalues even complex. eigenvalues returned ascending order.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_eigvalsh.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Computes the eigenvalues of a complex Hermitian or real symmetric matrix. — linalg_eigvalsh","text":"p(λ)=det⁡(−λIn)λ∈R p(\\lambda) = \\operatorname{det}(- \\lambda \\mathrm{}_n)\\mathrlap{\\qquad \\lambda \\\\mathbb{R}} p(λ)=det(−λIn​)λ∈R  n-dimensional identity matrix. eigenvalues real symmetric complex Hermitian matrix always real. Supports input float, double, cfloat cdouble dtypes. Also supports batches matrices, batch matrices output batch dimensions. eigenvalues returned ascending order. assumed Hermitian (resp. symmetric), checked internally, instead: UPLO\\ = 'L' (default), lower triangular part matrix used computation. UPLO\\ = 'U', upper triangular part matrix used.","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/linalg_eigvalsh.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the eigenvalues of a complex Hermitian or real symmetric matrix. — linalg_eigvalsh","text":"","code":"if (torch_is_installed()) { a <- torch_randn(2, 2) linalg_eigvalsh(a) } #> torch_tensor #> -1.0867 #>  2.0278 #> [ CPUFloatType{2} ]"},{"path":"https://torch.mlverse.org/docs/reference/linalg_householder_product.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the first n columns of a product of Householder matrices. — linalg_householder_product","title":"Computes the first n columns of a product of Householder matrices. — linalg_householder_product","text":"Letting   , matrix  columns   vector  , function computes first  columns matrix","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_householder_product.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the first n columns of a product of Householder matrices. — linalg_householder_product","text":"","code":"linalg_householder_product(A, tau)"},{"path":"https://torch.mlverse.org/docs/reference/linalg_householder_product.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the first n columns of a product of Householder matrices. — linalg_householder_product","text":"(Tensor): tensor shape (*, m, n) * zero batch dimensions. tau (Tensor): tensor shape (*, k) * zero batch dimensions.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_householder_product.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Computes the first n columns of a product of Householder matrices. — linalg_householder_product","text":"H1H2...HkwithHi=Im−τiviviH H_1H_2 ... H_k \\qquad \\qquad H_i = \\mathrm{}_m - \\tau_i v_i v_i^{H} H1​H2​...Hk​withHi​=Im​−τi​vi​viH​  m-dimensional identity matrix  conjugate transpose  complex, transpose  real-valued. See Representation Orthogonal Unitary Matrices details. Supports inputs float, double, cfloat cdouble dtypes. Also supports batches matrices, inputs batches matrices output batch dimensions.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_householder_product.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Computes the first n columns of a product of Householder matrices. — linalg_householder_product","text":"function uses values strictly main diagonal . values ignored.","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/linalg_householder_product.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the first n columns of a product of Householder matrices. — linalg_householder_product","text":"","code":"if (torch_is_installed()) { A <- torch_randn(2, 2) h_tau <- torch_geqrf(A) Q <- linalg_householder_product(h_tau[[1]], h_tau[[2]]) torch_allclose(Q, linalg_qr(A)[[1]]) } #> [1] TRUE"},{"path":"https://torch.mlverse.org/docs/reference/linalg_inv.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the inverse of a square matrix if it exists. — linalg_inv","title":"Computes the inverse of a square matrix if it exists. — linalg_inv","text":"Throws runtime_error matrix invertible.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_inv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the inverse of a square matrix if it exists. — linalg_inv","text":"","code":"linalg_inv(A)"},{"path":"https://torch.mlverse.org/docs/reference/linalg_inv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the inverse of a square matrix if it exists. — linalg_inv","text":"(Tensor): tensor shape (*, n, n) * zero batch dimensions consisting invertible matrices.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_inv.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Computes the inverse of a square matrix if it exists. — linalg_inv","text":"Letting   , matrix , inverse matrix  (exists) defined −1A=AA−1=^{-1}= AA^{-1} = \\mathrm{}_n −1A=AA−1=​  n-dimensional identity matrix. inverse matrix exists  invertible. case, inverse unique. Supports input float, double, cfloat cdouble dtypes. Also supports batches matrices, batch matrices output batch dimensions. Consider using linalg_solve() possible multiplying matrix left inverse, linalg_solve(, B) == $inv() %*% B always prefered use linalg_solve() possible, faster numerically stable computing inverse explicitly.","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/linalg_inv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the inverse of a square matrix if it exists. — linalg_inv","text":"","code":"if (torch_is_installed()) { A <- torch_randn(4, 4) linalg_inv(A) } #> torch_tensor #> -0.3617  0.3020  0.0682 -1.6960 #> -2.0115 -0.5945  0.3367 -4.5855 #> -1.4055  0.3159 -0.2382 -2.5192 #>  1.4039 -0.4235 -0.6591  2.8453 #> [ CPUFloatType{4,4} ]"},{"path":"https://torch.mlverse.org/docs/reference/linalg_inv_ex.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the inverse of a square matrix if it is invertible. — linalg_inv_ex","title":"Computes the inverse of a square matrix if it is invertible. — linalg_inv_ex","text":"Returns namedtuple (inverse, info). inverse contains result inverting info stores LAPACK error codes. invertible matrix, batch matrices one invertible matrix, info stores positive integer corresponding matrix. positive integer indicates diagonal element LU decomposition input matrix exactly zero. info filled zeros indicates inversion successful. check_errors=TRUE info contains positive integers, RuntimeError thrown. Supports input float, double, cfloat cdouble dtypes. Also supports batches matrices, batch matrices output batch dimensions.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_inv_ex.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the inverse of a square matrix if it is invertible. — linalg_inv_ex","text":"","code":"linalg_inv_ex(A, check_errors = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/linalg_inv_ex.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the inverse of a square matrix if it is invertible. — linalg_inv_ex","text":"(Tensor): tensor shape (*, n, n) * zero batch dimensions consisting square matrices. check_errors (bool, optional): controls whether check content info. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_inv_ex.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Computes the inverse of a square matrix if it is invertible. — linalg_inv_ex","text":"CUDA device function may synchronize device CPU. function \"experimental\" may change future PyTorch release.","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/linalg_inv_ex.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the inverse of a square matrix if it is invertible. — linalg_inv_ex","text":"","code":"if (torch_is_installed()) { A <- torch_randn(3, 3) out <- linalg_inv_ex(A) }"},{"path":"https://torch.mlverse.org/docs/reference/linalg_lstsq.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes a solution to the least squares problem of a system of linear equations. — linalg_lstsq","title":"Computes a solution to the least squares problem of a system of linear equations. — linalg_lstsq","text":"Letting   , least squares problem linear system   defined ","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_lstsq.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes a solution to the least squares problem of a system of linear equations. — linalg_lstsq","text":"","code":"linalg_lstsq(A, B, rcond = NULL, ..., driver = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/linalg_lstsq.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes a solution to the least squares problem of a system of linear equations. — linalg_lstsq","text":"(Tensor): lhs tensor shape (*, m, n) * zero batch dimensions. B (Tensor): rhs tensor shape (*, m, k) * zero batch dimensions. rcond (float, optional): used determine effective rank . rcond = NULL, rcond set machine precision dtype times max(m, n). Default: NULL. ... currently unused. driver (str, optional): name LAPACK/MAGMA method used. NULL, 'gelsy' used CPU inputs 'gels' CUDA inputs. Default: NULL.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_lstsq.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes a solution to the least squares problem of a system of linear equations. — linalg_lstsq","text":"list (solution, residuals, rank, singular_values).","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_lstsq.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Computes a solution to the least squares problem of a system of linear equations. — linalg_lstsq","text":"min⁡X∈Kn×k∥AX−B∥F \\min_{X \\\\mathbb{K}^{n \\times k}} \\|AX - B\\|_F X∈Kn×kmin​∥AX−B∥F​  denotes Frobenius norm. Supports inputs float, double, cfloat cdouble dtypes. Also supports batches matrices, inputs batches matrices output batch dimensions. driver chooses LAPACK/MAGMA function used. CPU inputs valid values 'gels', 'gelsy', 'gelsd, 'gelss'. CUDA input, valid driver 'gels', assumes full-rank. choose best driver CPU consider: well-conditioned (condition number large), mind precision loss. general matrix: 'gelsy' (QR pivoting) (default) full-rank: 'gels' (QR) well-conditioned. 'gelsd' (tridiagonal reduction SVD) run memory issues: 'gelss' (full SVD). See also full description drivers rcond used determine effective rank matrices driver one ('gelsy', 'gelsd', 'gelss'). case,  singular values decreasing order,  rounded zero . rcond = NULL (default), rcond set machine precision dtype . function returns solution problem extra information list four tensors (solution, residuals, rank, singular_values). inputs , B shape (*, m, n), (*, m, k) respectively, cointains solution: least squares solution. shape (*, n, k). residuals: squared residuals solutions, , . shape equal batch dimensions . computed m > n every matrix full-rank, otherwise, empty tensor. batch matrices matrix batch full rank, empty tensor returned. behavior may change future PyTorch release. rank: tensor ranks matrices . shape equal batch dimensions . computed driver one ('gelsy', 'gelsd', 'gelss'), otherwise empty tensor. singular_values: tensor singular values matrices . shape (*, min(m, n)). computed driver one ('gelsd', 'gelss'), otherwise empty tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_lstsq.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Computes a solution to the least squares problem of a system of linear equations. — linalg_lstsq","text":"function computes X = $pinverse() %*% B faster numerically stable way performing computations separately.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_lstsq.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Computes a solution to the least squares problem of a system of linear equations. — linalg_lstsq","text":"default value rcond may change future PyTorch release. therefore recommended use fixed value avoid potential breaking changes.","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/linalg_lstsq.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes a solution to the least squares problem of a system of linear equations. — linalg_lstsq","text":"","code":"if (torch_is_installed()) { A <- torch_tensor(rbind(c(10, 2, 3), c(3, 10, 5), c(5, 6, 12)))$unsqueeze(1) # shape (1, 3, 3) B <- torch_stack(list(   rbind(c(2, 5, 1), c(3, 2, 1), c(5, 1, 9)),   rbind(c(4, 2, 9), c(2, 0, 3), c(2, 5, 3)) ), dim = 1) # shape (2, 3, 3) X <- linalg_lstsq(A, B)$solution # A is broadcasted to shape (2, 3, 3) }"},{"path":"https://torch.mlverse.org/docs/reference/linalg_matrix_norm.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes a matrix norm. — linalg_matrix_norm","title":"Computes a matrix norm. — linalg_matrix_norm","text":"complex valued, computes norm $abs() Support input float, double, cfloat cdouble dtypes. Also supports batches matrices: norm computed dimensions specified 2-tuple dim dimensions treated batch dimensions. output batch dimensions.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_matrix_norm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes a matrix norm. — linalg_matrix_norm","text":"","code":"linalg_matrix_norm(   A,   ord = \"fro\",   dim = c(-2, -1),   keepdim = FALSE,   dtype = NULL )"},{"path":"https://torch.mlverse.org/docs/reference/linalg_matrix_norm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes a matrix norm. — linalg_matrix_norm","text":"(Tensor): tensor two dimensions. default shape interpreted (*, m, n) * zero batch dimensions, behavior can controlled using dim. ord (int, inf, -inf, 'fro', 'nuc', optional): order norm. Default: 'fro' dim (int, Tupleint, optional): dimensions compute vector matrix norm. See behavior dim=NULL. Default: NULL keepdim (bool, optional): set TRUE, reduced dimensions retained result dimensions size one. Default: FALSE dtype dtype (torch_dtype, optional): specified, input tensor cast dtype performing operation, returned tensor's type dtype. Default: NULL","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_matrix_norm.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Computes a matrix norm. — linalg_matrix_norm","text":"ord defines norm computed. following norms supported:","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/linalg_matrix_norm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes a matrix norm. — linalg_matrix_norm","text":"","code":"if (torch_is_installed()) { a <- torch_arange(0, 8, dtype = torch_float())$reshape(c(3, 3)) linalg_matrix_norm(a) linalg_matrix_norm(a, ord = -1) b <- a$expand(c(2, -1, -1)) linalg_matrix_norm(b) linalg_matrix_norm(b, dim = c(1, 3)) } #> torch_tensor #>   3.1623 #>  10.0000 #>  17.2627 #> [ CPUFloatType{3} ]"},{"path":"https://torch.mlverse.org/docs/reference/linalg_matrix_power.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the n-th power of a square matrix for an integer n. — linalg_matrix_power","title":"Computes the n-th power of a square matrix for an integer n. — linalg_matrix_power","text":"Supports input float, double, cfloat cdouble dtypes. Also supports batches matrices, batch matrices output batch dimensions.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_matrix_power.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the n-th power of a square matrix for an integer n. — linalg_matrix_power","text":"","code":"linalg_matrix_power(A, n)"},{"path":"https://torch.mlverse.org/docs/reference/linalg_matrix_power.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the n-th power of a square matrix for an integer n. — linalg_matrix_power","text":"(Tensor): tensor shape (*, m, m) * zero batch dimensions. n (int): exponent.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_matrix_power.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Computes the n-th power of a square matrix for an integer n. — linalg_matrix_power","text":"n=0, returns identity matrix (batch) shape . n negative, returns inverse matrix (invertible) raised power abs(n).","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/linalg_matrix_power.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the n-th power of a square matrix for an integer n. — linalg_matrix_power","text":"","code":"if (torch_is_installed()) { A <- torch_randn(3, 3) linalg_matrix_power(A, 0) } #> torch_tensor #>  1  0  0 #>  0  1  0 #>  0  0  1 #> [ CPUFloatType{3,3} ]"},{"path":"https://torch.mlverse.org/docs/reference/linalg_matrix_rank.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the numerical rank of a matrix. — linalg_matrix_rank","title":"Computes the numerical rank of a matrix. — linalg_matrix_rank","text":"matrix rank computed number singular values (eigenvalues absolute value hermitian = TRUE) greater specified tol threshold.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_matrix_rank.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the numerical rank of a matrix. — linalg_matrix_rank","text":"","code":"linalg_matrix_rank(A, tol = NULL, hermitian = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/linalg_matrix_rank.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the numerical rank of a matrix. — linalg_matrix_rank","text":"(Tensor): tensor shape (*, m, n) * zero batch dimensions. tol (float, Tensor, optional): tolerance value. See value takes NULL. Default: NULL. hermitian (bool, optional): indicates whether Hermitian complex symmetric real. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_matrix_rank.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Computes the numerical rank of a matrix. — linalg_matrix_rank","text":"Supports input float, double, cfloat cdouble dtypes. Also supports batches matrices, batch matrices output batch dimensions. hermitian = TRUE, assumed Hermitian complex symmetric real, checked internally. Instead, just lower triangular part matrix used computations. tol specified matrix dimensions (m, n), tolerance set tol=σ1max⁡(m,n)ε tol = \\sigma_1 \\max(m, n) \\varepsilon tol=σ1​max(m,n)ε  largest singular value (eigenvalue absolute value hermitian = TRUE),  epsilon value dtype (see torch_finfo()). batch matrices, tol computed way every element batch.","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/linalg_matrix_rank.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the numerical rank of a matrix. — linalg_matrix_rank","text":"","code":"if (torch_is_installed()) { a <- torch_eye(10) linalg_matrix_rank(a) } #> torch_tensor #> 10 #> [ CPULongType{} ]"},{"path":"https://torch.mlverse.org/docs/reference/linalg_multi_dot.html","id":null,"dir":"Reference","previous_headings":"","what":"Efficiently multiplies two or more matrices — linalg_multi_dot","title":"Efficiently multiplies two or more matrices — linalg_multi_dot","text":"Efficiently multiplies two matrices reordering multiplications fewest arithmetic operations performed.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_multi_dot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Efficiently multiplies two or more matrices — linalg_multi_dot","text":"","code":"linalg_multi_dot(tensors)"},{"path":"https://torch.mlverse.org/docs/reference/linalg_multi_dot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Efficiently multiplies two or more matrices — linalg_multi_dot","text":"tensors (Sequence[Tensor]): two tensors multiply. first last tensors may 1D 2D. Every tensor must 2D.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_multi_dot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Efficiently multiplies two or more matrices — linalg_multi_dot","text":"Supports inputs float, double, cfloat cdouble dtypes. function support batched inputs. Every tensor tensors must 2D, except first last may 1D. first tensor 1D vector shape (n,) treated row vector shape (1, n), similarly last tensor 1D vector shape (n,) treated column vector shape (n, 1). first last tensors matrices, output matrix. However, either 1D vector, output 1D vector.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_multi_dot.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Efficiently multiplies two or more matrices — linalg_multi_dot","text":"function implemented chaining torch_mm() calls computing optimal matrix multiplication order. cost multiplying two matrices shapes (, b) (b, c) * b * c. Given matrices , B, C shapes (10, 100), (100, 5), (5, 50) respectively, can calculate cost different multiplication orders follows: cost⁡((AB)C)=10×100×5+10×5×50=7500 cost⁡((BC))=10×100×50+100×5×50=75000 \\begin{align*} \\operatorname{cost}((AB)C) &= 10 \\times 100 \\times 5 + 10 \\times 5 \\times 50 = 7500 \\ \\operatorname{cost}((BC)) &= 10 \\times 100 \\times 50 + 100 \\times 5 \\times 50 = 75000 \\end{align*} cost((AB)C)​=10×100×5+10×5×50=7500 cost((BC))​=10×100×50+100×5×50=75000​ case, multiplying B first followed C 10 times faster.","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/linalg_multi_dot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Efficiently multiplies two or more matrices — linalg_multi_dot","text":"","code":"if (torch_is_installed()) {  linalg_multi_dot(list(torch_tensor(c(1, 2)), torch_tensor(c(2, 3)))) } #> torch_tensor #> 8 #> [ CPUFloatType{} ]"},{"path":"https://torch.mlverse.org/docs/reference/linalg_norm.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes a vector or matrix norm. — linalg_norm","title":"Computes a vector or matrix norm. — linalg_norm","text":"complex valued, computes norm $abs() Supports input float, double, cfloat cdouble dtypes. Whether function computes vector matrix norm determined follows:","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_norm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes a vector or matrix norm. — linalg_norm","text":"","code":"linalg_norm(A, ord = NULL, dim = NULL, keepdim = FALSE, dtype = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/linalg_norm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes a vector or matrix norm. — linalg_norm","text":"(Tensor): tensor shape (*, n) (*, m, n) * zero batch dimensions ord (int, float, inf, -inf, 'fro', 'nuc', optional): order norm. Default: NULL dim (int, Tupleint, optional): dimensions compute vector matrix norm. See behavior dim=NULL. Default: NULL keepdim (bool, optional): set TRUE, reduced dimensions retained result dimensions size one. Default: FALSE dtype dtype (torch_dtype, optional): specified, input tensor cast dtype performing operation, returned tensor's type dtype. Default: NULL","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_norm.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Computes a vector or matrix norm. — linalg_norm","text":"dim int, vector norm computed. dim 2-tuple, matrix norm computed. dim=NULL ord=NULL, flattened 1D 2-norm resulting vector computed. dim=NULL ord!=NULL, must 1D 2D. ord defines norm computed. following norms supported:","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/linalg_norm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes a vector or matrix norm. — linalg_norm","text":"","code":"if (torch_is_installed()) { a <- torch_arange(0, 8, dtype = torch_float()) - 4 a b <- a$reshape(c(3, 3)) b  linalg_norm(a) linalg_norm(b) } #> torch_tensor #> 7.74597 #> [ CPUFloatType{} ]"},{"path":"https://torch.mlverse.org/docs/reference/linalg_pinv.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the pseudoinverse (Moore-Penrose inverse) of a matrix. — linalg_pinv","title":"Computes the pseudoinverse (Moore-Penrose inverse) of a matrix. — linalg_pinv","text":"pseudoinverse may defined algebraically_ computationally convenient understand SVD_ Supports input float, double, cfloat cdouble dtypes. Also supports batches matrices, batch matrices output batch dimensions.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_pinv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the pseudoinverse (Moore-Penrose inverse) of a matrix. — linalg_pinv","text":"","code":"linalg_pinv(A, rcond = 1e-15, hermitian = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/linalg_pinv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the pseudoinverse (Moore-Penrose inverse) of a matrix. — linalg_pinv","text":"(Tensor): tensor shape (*, m, n) * zero batch dimensions. rcond (float Tensor, optional): tolerance value determine singular value zero torch_Tensor, shape must broadcastable singular values returned linalg_svd(). Default: 1e-15. hermitian (bool, optional): indicates whether Hermitian complex symmetric real. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_pinv.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Computes the pseudoinverse (Moore-Penrose inverse) of a matrix. — linalg_pinv","text":"hermitian= TRUE, assumed Hermitian complex symmetric real, checked internally. Instead, just lower triangular part matrix used computations. singular values (norm eigenvalues hermitian= TRUE) specified rcond threshold treated zero discarded computation.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_pinv.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Computes the pseudoinverse (Moore-Penrose inverse) of a matrix. — linalg_pinv","text":"function uses linalg_svd() hermitian= FALSE linalg_eigh() hermitian= TRUE. CUDA inputs, function synchronizes device CPU. Consider using linalg_lstsq() possible multiplying matrix left pseudoinverse, linalg_lstsq(, B)$solution == $pinv() %*% B always prefered use linalg_lstsq() possible, faster numerically stable computing pseudoinverse explicitly.","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/linalg_pinv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the pseudoinverse (Moore-Penrose inverse) of a matrix. — linalg_pinv","text":"","code":"if (torch_is_installed()) { A <- torch_randn(3, 5) linalg_pinv(A) } #> torch_tensor #> -0.0634  0.2726  0.5803 #> -0.1160  0.0856  0.0541 #>  0.5057  0.6576  0.1978 #>  0.1240  0.1646 -0.2429 #>  0.2866 -0.1429 -0.2102 #> [ CPUFloatType{5,3} ]"},{"path":"https://torch.mlverse.org/docs/reference/linalg_qr.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the QR decomposition of a matrix. — linalg_qr","title":"Computes the QR decomposition of a matrix. — linalg_qr","text":"Letting   , full QR decomposition matrix  defined ","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_qr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the QR decomposition of a matrix. — linalg_qr","text":"","code":"linalg_qr(A, mode = \"reduced\")"},{"path":"https://torch.mlverse.org/docs/reference/linalg_qr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the QR decomposition of a matrix. — linalg_qr","text":"(Tensor): tensor shape (*, m, n) * zero batch dimensions. mode (str, optional): one 'reduced', 'complete', 'r'. Controls shape returned tensors. Default: 'reduced'.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_qr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the QR decomposition of a matrix. — linalg_qr","text":"list (Q, R).","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_qr.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Computes the QR decomposition of a matrix. — linalg_qr","text":"=QRQ∈Km×m,R∈Km×n = QR\\mathrlap{\\qquad Q \\\\mathbb{K}^{m \\times m}, R \\\\mathbb{K}^{m \\times n}} =QRQ∈Km×m,R∈Km×n  orthogonal real case unitary complex case,  upper triangular. m > n (tall matrix), R upper triangular, last m - n rows zero. case, can drop last m - n columns Q form reduced QR decomposition: =QRQ∈Km×n,R∈Kn×n = QR\\mathrlap{\\qquad Q \\\\mathbb{K}^{m \\times n}, R \\\\mathbb{K}^{n \\times n}} =QRQ∈Km×n,R∈Kn×n reduced QR decomposition agrees full QR decomposition n >= m (wide matrix). Supports input float, double, cfloat cdouble dtypes. Also supports batches matrices, batch matrices output batch dimensions. parameter mode chooses full reduced QR decomposition. shape (*, m, n), denoting k = min(m, n) mode = 'reduced' (default): Returns (Q, R) shapes (*, m, k), (*, k, n) respectively. mode = 'complete': Returns (Q, R) shapes (*, m, m), (*, m, n) respectively. mode = 'r': Computes reduced R. Returns (Q, R) Q empty R shape (*, k, n).","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/linalg_qr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the QR decomposition of a matrix. — linalg_qr","text":"","code":"if (torch_is_installed()) { a <- torch_tensor(rbind(c(12., -51, 4), c(6, 167, -68), c(-4, 24, -41))) qr <- linalg_qr(a)  torch_mm(qr[[1]], qr[[2]])$round() torch_mm(qr[[1]]$t(), qr[[1]])$round() } #> torch_tensor #>  1  0  0 #>  0  1  0 #>  0  0  1 #> [ CPUFloatType{3,3} ]"},{"path":"https://torch.mlverse.org/docs/reference/linalg_slogdet.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the sign and natural logarithm of the absolute value of the determinant of a square matrix. — linalg_slogdet","title":"Computes the sign and natural logarithm of the absolute value of the determinant of a square matrix. — linalg_slogdet","text":"complex , returns angle natural logarithm modulus determinant, , logarithmic polar decomposition determinant. Supports input float, double, cfloat cdouble dtypes. Also supports batches matrices, batch matrices output batch dimensions.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_slogdet.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the sign and natural logarithm of the absolute value of the determinant of a square matrix. — linalg_slogdet","text":"","code":"linalg_slogdet(A)"},{"path":"https://torch.mlverse.org/docs/reference/linalg_slogdet.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the sign and natural logarithm of the absolute value of the determinant of a square matrix. — linalg_slogdet","text":"(Tensor): tensor shape (*, n, n) * zero batch dimensions.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_slogdet.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the sign and natural logarithm of the absolute value of the determinant of a square matrix. — linalg_slogdet","text":"list (sign, logabsdet). logabsdet always real-valued, even complex. sign dtype .","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_slogdet.html","id":"notes","dir":"Reference","previous_headings":"","what":"Notes","title":"Computes the sign and natural logarithm of the absolute value of the determinant of a square matrix. — linalg_slogdet","text":"determinant can recovered sign * exp(logabsdet). matrix determinant zero, returns (0, -Inf).","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/linalg_slogdet.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the sign and natural logarithm of the absolute value of the determinant of a square matrix. — linalg_slogdet","text":"","code":"if (torch_is_installed()) { a <- torch_randn(3, 3) linalg_slogdet(a) } #> [[1]] #> torch_tensor #> -1 #> [ CPUFloatType{} ] #>  #> [[2]] #> torch_tensor #> -1.90759 #> [ CPUFloatType{} ] #>"},{"path":"https://torch.mlverse.org/docs/reference/linalg_solve.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the solution of a square system of linear equations with a unique solution. — linalg_solve","title":"Computes the solution of a square system of linear equations with a unique solution. — linalg_solve","text":"Letting   , function computes solution  linear system associated , defined ","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_solve.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the solution of a square system of linear equations with a unique solution. — linalg_solve","text":"","code":"linalg_solve(A, B)"},{"path":"https://torch.mlverse.org/docs/reference/linalg_solve.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the solution of a square system of linear equations with a unique solution. — linalg_solve","text":"(Tensor): tensor shape (*, n, n) * zero batch dimensions. B (Tensor): right-hand side tensor shape (*, n)  (*, n, k) (n,) (n, k) according rules described ","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_solve.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Computes the solution of a square system of linear equations with a unique solution. — linalg_solve","text":"$$   AX = B $$ system linear equations one solution  invertible_. function assumes  invertible. Supports inputs float, double, cfloat cdouble dtypes. Also supports batches matrices, inputs batches matrices output batch dimensions. Letting * zero batch dimensions, shape (*, n, n) B shape (*, n) (batch vectors) shape (*, n, k) (batch matrices \"multiple right-hand sides\"), function returns X shape (*, n) (*, n, k) respectively. Otherwise, shape (*, n, n)  B shape (n,)  (n, k), B broadcasted shape (*, n) (*, n, k) respectively. function returns solution resulting batch systems linear equations.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_solve.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Computes the solution of a square system of linear equations with a unique solution. — linalg_solve","text":"function computes X = $inverse() @ B faster numerically stable way performing computations separately.","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/linalg_solve.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the solution of a square system of linear equations with a unique solution. — linalg_solve","text":"","code":"if (torch_is_installed()) { A <- torch_randn(3, 3) b <- torch_randn(3) x <- linalg_solve(A, b) torch_allclose(torch_matmul(A, x), b) } #> [1] TRUE"},{"path":"https://torch.mlverse.org/docs/reference/linalg_svd.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the singular value decomposition (SVD) of a matrix. — linalg_svd","title":"Computes the singular value decomposition (SVD) of a matrix. — linalg_svd","text":"Letting   , full SVD matrix , k = min(m,n), defined ","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_svd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the singular value decomposition (SVD) of a matrix. — linalg_svd","text":"","code":"linalg_svd(A, full_matrices = TRUE)"},{"path":"https://torch.mlverse.org/docs/reference/linalg_svd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the singular value decomposition (SVD) of a matrix. — linalg_svd","text":"(Tensor): tensor shape (*, m, n) * zero batch dimensions. full_matrices (bool, optional): controls whether compute full reduced SVD, consequently, shape returned tensors U V. Default: TRUE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_svd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the singular value decomposition (SVD) of a matrix. — linalg_svd","text":"list (U, S, V) corresponds , ,  . S always real-valued, even complex. also ordered descending order. U V dtype . left / right singular vectors given columns U rows V respectively.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_svd.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Computes the singular value decomposition (SVD) of a matrix. — linalg_svd","text":"=Udiag⁡(S)VHU∈Km×m,S∈Rk,V∈Kn×n = U \\operatorname{diag}(S) V^{H} \\mathrlap{\\qquad U \\\\mathbb{K}^{m \\times m}, S \\\\mathbb{R}^k, V \\\\mathbb{K}^{n \\times n}} =Udiag(S)VHU∈Km×m,S∈Rk,V∈Kn×n ,  conjugate transpose  complex, transpose  real-valued. matrices  ,  (thus ) orthogonal real case, unitary complex case. m > n (resp. m < n) can drop last m - n (resp. n - m) columns U (resp. V) form reduced SVD: =Udiag⁡(S)VHU∈Km×k,S∈Rk,V∈Kk×n = U \\operatorname{diag}(S) V^{H} \\mathrlap{\\qquad U \\\\mathbb{K}^{m \\times k}, S \\\\mathbb{R}^k, V \\\\mathbb{K}^{k \\times n}} =Udiag(S)VHU∈Km×k,S∈Rk,V∈Kk×n . case,   also orthonormal columns. Supports input float, double, cfloat cdouble dtypes. Also supports batches matrices, batch matrices output batch dimensions. returned decomposition named tuple (U, S, V) corresponds , ,  . singular values returned descending order. parameter full_matrices chooses full (default) reduced SVD.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_svd.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Computes the singular value decomposition (SVD) of a matrix. — linalg_svd","text":"full_matrices=TRUE, gradients respect U[..., :, min(m, n):] Vh[..., min(m, n):, :] ignored, vectors can arbitrary bases corresponding subspaces.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_svd.html","id":"warnings","dir":"Reference","previous_headings":"","what":"Warnings","title":"Computes the singular value decomposition (SVD) of a matrix. — linalg_svd","text":"returned tensors U V unique, continuous respect . Due lack uniqueness, different hardware software may compute different singular vectors. non-uniqueness caused fact multiplying pair singular vectors  -1 real case  complex case produces another two valid singular vectors matrix. non-uniqueness problem even worse matrix repeated singular values. case, one may multiply associated singular vectors U V spanning subspace rotation matrix resulting vectors span subspace. Gradients computed using U V finite zero singular value repeated singular values. Furthermore, distance two singular values close zero, gradient numerically unstable, depends singular values  computation . gradient also numerically unstable small singular values, also depends computaiton .","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/linalg_svd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the singular value decomposition (SVD) of a matrix. — linalg_svd","text":"","code":"if (torch_is_installed()) {  a <- torch_randn(5, 3) linalg_svd(a, full_matrices = FALSE) } #> [[1]] #> torch_tensor #> -0.5040  0.5134  0.0992 #> -0.6428  0.1399  0.3054 #>  0.2819  0.5506 -0.5038 #> -0.2576  0.2464 -0.5827 #> -0.4325 -0.5941 -0.5509 #> [ CPUFloatType{5,3} ] #>  #> [[2]] #> torch_tensor #>  2.2731 #>  1.4690 #>  0.5087 #> [ CPUFloatType{3} ] #>  #> [[3]] #> torch_tensor #>  0.6751 -0.7345 -0.0694 #>  0.5571  0.5692 -0.6047 #> -0.4836 -0.3695 -0.7935 #> [ CPUFloatType{3,3} ] #>"},{"path":"https://torch.mlverse.org/docs/reference/linalg_svdvals.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the singular values of a matrix. — linalg_svdvals","title":"Computes the singular values of a matrix. — linalg_svdvals","text":"Supports input float, double, cfloat cdouble dtypes. Also supports batches matrices, batch matrices output batch dimensions. singular values returned descending order.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_svdvals.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the singular values of a matrix. — linalg_svdvals","text":"","code":"linalg_svdvals(A)"},{"path":"https://torch.mlverse.org/docs/reference/linalg_svdvals.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the singular values of a matrix. — linalg_svdvals","text":"(Tensor): tensor shape (*, m, n) * zero batch dimensions.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_svdvals.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the singular values of a matrix. — linalg_svdvals","text":"real-valued tensor, even complex.","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/linalg_svdvals.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the singular values of a matrix. — linalg_svdvals","text":"","code":"if (torch_is_installed()) { A <- torch_randn(5, 3) S <- linalg_svdvals(A) S } #> torch_tensor #>  2.9854 #>  1.3161 #>  0.4644 #> [ CPUFloatType{3} ]"},{"path":"https://torch.mlverse.org/docs/reference/linalg_tensorinv.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the multiplicative inverse of torch_tensordot() — linalg_tensorinv","title":"Computes the multiplicative inverse of torch_tensordot() — linalg_tensorinv","text":"m product first ind dimensions n product rest dimensions, function expects m n equal. case, computes tensor X tensordot(, X, ind) identity matrix dimension m.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_tensorinv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the multiplicative inverse of torch_tensordot() — linalg_tensorinv","text":"","code":"linalg_tensorinv(A, ind = 3L)"},{"path":"https://torch.mlverse.org/docs/reference/linalg_tensorinv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the multiplicative inverse of torch_tensordot() — linalg_tensorinv","text":"(Tensor): tensor invert. ind (int): index compute inverse torch_tensordot(). Default: 3.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_tensorinv.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Computes the multiplicative inverse of torch_tensordot() — linalg_tensorinv","text":"Supports input float, double, cfloat cdouble dtypes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_tensorinv.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Computes the multiplicative inverse of torch_tensordot() — linalg_tensorinv","text":"Consider using linalg_tensorsolve() possible multiplying tensor left tensor inverse linalg_tensorsolve(, B) == torch_tensordot(linalg_tensorinv(), B)) always prefered use linalg_tensorsolve() possible, faster numerically stable computing pseudoinverse explicitly.","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/linalg_tensorinv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the multiplicative inverse of torch_tensordot() — linalg_tensorinv","text":"","code":"if (torch_is_installed()) { A <- torch_eye(4 * 6)$reshape(c(4, 6, 8, 3)) Ainv <- linalg_tensorinv(A, ind = 3) Ainv$shape B <- torch_randn(4, 6) torch_allclose(torch_tensordot(Ainv, B), linalg_tensorsolve(A, B))  A <- torch_randn(4, 4) Atensorinv <- linalg_tensorinv(A, 2) Ainv <- linalg_inv(A) torch_allclose(Atensorinv, Ainv) } #> [1] TRUE"},{"path":"https://torch.mlverse.org/docs/reference/linalg_tensorsolve.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the solution X to the system torch_tensordot(A, X) = B. — linalg_tensorsolve","title":"Computes the solution X to the system torch_tensordot(A, X) = B. — linalg_tensorsolve","text":"m product first B\\ .ndim  dimensions n product rest dimensions, function expects m n equal. returned tensor x satisfies tensordot(, x, dims=x$ndim) == B.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_tensorsolve.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the solution X to the system torch_tensordot(A, X) = B. — linalg_tensorsolve","text":"","code":"linalg_tensorsolve(A, B, dims = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/linalg_tensorsolve.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the solution X to the system torch_tensordot(A, X) = B. — linalg_tensorsolve","text":"(Tensor): tensor solve . B (Tensor): solution dims (Tupleint, optional): dimensions moved. NULL, dimensions moved. Default: NULL.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_tensorsolve.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Computes the solution X to the system torch_tensordot(A, X) = B. — linalg_tensorsolve","text":"dims specified, reshaped = movedim(, dims, seq(len(dims) - $ndim + 1, 0)) Supports inputs float, double, cfloat cdouble dtypes.","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/linalg_tensorsolve.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the solution X to the system torch_tensordot(A, X) = B. — linalg_tensorsolve","text":"","code":"if (torch_is_installed()) { A <- torch_eye(2 * 3 * 4)$reshape(c(2 * 3, 4, 2, 3, 4)) B <- torch_randn(2 * 3, 4) X <- linalg_tensorsolve(A, B) X$shape torch_allclose(torch_tensordot(A, X, dims = X$ndim), B)  A <- torch_randn(6, 4, 4, 3, 2) B <- torch_randn(4, 3, 2) X <- linalg_tensorsolve(A, B, dims = c(1, 3)) A <- A$permute(c(2, 4, 5, 1, 3)) torch_allclose(torch_tensordot(A, X, dims = X$ndim), B, atol = 1e-6) } #> [1] FALSE"},{"path":"https://torch.mlverse.org/docs/reference/linalg_vector_norm.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes a vector norm. — linalg_vector_norm","title":"Computes a vector norm. — linalg_vector_norm","text":"complex valued, computes norm $abs() Supports input float, double, cfloat cdouble dtypes. function necessarily treat multidimensonal batch vectors, instead:","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_vector_norm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes a vector norm. — linalg_vector_norm","text":"","code":"linalg_vector_norm(A, ord = 2, dim = NULL, keepdim = FALSE, dtype = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/linalg_vector_norm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes a vector norm. — linalg_vector_norm","text":"(Tensor): tensor, flattened default, behavior can controlled using dim. ord (int, float, inf, -inf, 'fro', 'nuc', optional): order norm. Default: 2 dim (int, Tupleint, optional): dimensions compute vector matrix norm. See behavior dim=NULL. Default: NULL keepdim (bool, optional): set TRUE, reduced dimensions retained result dimensions size one. Default: FALSE dtype dtype (torch_dtype, optional): specified, input tensor cast dtype performing operation, returned tensor's type dtype. Default: NULL","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_vector_norm.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Computes a vector norm. — linalg_vector_norm","text":"dim=NULL, flattened norm computed. dim int tuple, norm computed dimensions dimensions treated batch dimensions. behavior consistency linalg_norm(). ord defines norm computed. following norms supported:","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/linalg_vector_norm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes a vector norm. — linalg_vector_norm","text":"","code":"if (torch_is_installed()) { a <- torch_arange(0, 8, dtype = torch_float()) - 4 a b <- a$reshape(c(3, 3)) b  linalg_vector_norm(a, ord = 3.5) linalg_vector_norm(b, ord = 3.5) } #> torch_tensor #> 5.43449 #> [ CPUFloatType{} ]"},{"path":"https://torch.mlverse.org/docs/reference/load_state_dict.html","id":null,"dir":"Reference","previous_headings":"","what":"Load a state dict file — load_state_dict","title":"Load a state dict file — load_state_dict","text":"function used load models saved python. work correctly need use torch.save flag: _use_new_zipfile_serialization=True also remove nn.Parameter classes tensors dict.","code":""},{"path":"https://torch.mlverse.org/docs/reference/load_state_dict.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Load a state dict file — load_state_dict","text":"","code":"load_state_dict(path)"},{"path":"https://torch.mlverse.org/docs/reference/load_state_dict.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Load a state dict file — load_state_dict","text":"path state dict file","code":""},{"path":"https://torch.mlverse.org/docs/reference/load_state_dict.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Load a state dict file — load_state_dict","text":"named list tensors.","code":""},{"path":"https://torch.mlverse.org/docs/reference/load_state_dict.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Load a state dict file — load_state_dict","text":"might change development pytorch's C++ api.","code":""},{"path":"https://torch.mlverse.org/docs/reference/lr_lambda.html","id":null,"dir":"Reference","previous_headings":"","what":"Sets the learning rate of each parameter group to the initial lr\ntimes a given function. When last_epoch=-1, sets initial lr as lr. — lr_lambda","title":"Sets the learning rate of each parameter group to the initial lr\ntimes a given function. When last_epoch=-1, sets initial lr as lr. — lr_lambda","text":"Sets learning rate parameter group initial lr times given function. last_epoch=-1, sets initial lr lr.","code":""},{"path":"https://torch.mlverse.org/docs/reference/lr_lambda.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sets the learning rate of each parameter group to the initial lr\ntimes a given function. When last_epoch=-1, sets initial lr as lr. — lr_lambda","text":"","code":"lr_lambda(optimizer, lr_lambda, last_epoch = -1, verbose = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/lr_lambda.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sets the learning rate of each parameter group to the initial lr\ntimes a given function. When last_epoch=-1, sets initial lr as lr. — lr_lambda","text":"optimizer (Optimizer): Wrapped optimizer. lr_lambda (function list): function computes multiplicative factor given integer parameter epoch, list functions, one group optimizer.param_groups. last_epoch (int): index last epoch. Default: -1. verbose (bool): TRUE, prints message stdout update. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/lr_lambda.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sets the learning rate of each parameter group to the initial lr\ntimes a given function. When last_epoch=-1, sets initial lr as lr. — lr_lambda","text":"","code":"if (torch_is_installed()) { # Assuming optimizer has two groups. lambda1 <- function(epoch) epoch %/% 30 lambda2 <- function(epoch) 0.95^epoch if (FALSE) { scheduler <- lr_lambda(optimizer, lr_lambda = list(lambda1, lambda2)) for (epoch in 1:100) {   train(...)   validate(...)   scheduler$step() } }  }"},{"path":"https://torch.mlverse.org/docs/reference/lr_multiplicative.html","id":null,"dir":"Reference","previous_headings":"","what":"Multiply the learning rate of each parameter group by the factor given\nin the specified function. When last_epoch=-1, sets initial lr as lr. — lr_multiplicative","title":"Multiply the learning rate of each parameter group by the factor given\nin the specified function. When last_epoch=-1, sets initial lr as lr. — lr_multiplicative","text":"Multiply learning rate parameter group factor given specified function. last_epoch=-1, sets initial lr lr.","code":""},{"path":"https://torch.mlverse.org/docs/reference/lr_multiplicative.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multiply the learning rate of each parameter group by the factor given\nin the specified function. When last_epoch=-1, sets initial lr as lr. — lr_multiplicative","text":"","code":"lr_multiplicative(optimizer, lr_lambda, last_epoch = -1, verbose = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/lr_multiplicative.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Multiply the learning rate of each parameter group by the factor given\nin the specified function. When last_epoch=-1, sets initial lr as lr. — lr_multiplicative","text":"optimizer (Optimizer): Wrapped optimizer. lr_lambda (function list): function computes multiplicative factor given integer parameter epoch, list functions, one group optimizer.param_groups. last_epoch (int): index last epoch. Default: -1. verbose (bool): TRUE, prints message stdout update. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/lr_multiplicative.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multiply the learning rate of each parameter group by the factor given\nin the specified function. When last_epoch=-1, sets initial lr as lr. — lr_multiplicative","text":"","code":"if (torch_is_installed()) { if (FALSE) { lmbda <- function(epoch) 0.95 scheduler <- lr_multiplicative(optimizer, lr_lambda = lmbda) for (epoch in 1:100) {   train(...)   validate(...)   scheduler$step() } }  }"},{"path":"https://torch.mlverse.org/docs/reference/lr_one_cycle.html","id":null,"dir":"Reference","previous_headings":"","what":"Once cycle learning rate — lr_one_cycle","title":"Once cycle learning rate — lr_one_cycle","text":"Sets learning rate parameter group according 1cycle learning rate policy. 1cycle policy anneals learning rate initial learning rate maximum learning rate maximum learning rate minimum learning rate much lower initial learning rate.","code":""},{"path":"https://torch.mlverse.org/docs/reference/lr_one_cycle.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Once cycle learning rate — lr_one_cycle","text":"","code":"lr_one_cycle(   optimizer,   max_lr,   total_steps = NULL,   epochs = NULL,   steps_per_epoch = NULL,   pct_start = 0.3,   anneal_strategy = \"cos\",   cycle_momentum = TRUE,   base_momentum = 0.85,   max_momentum = 0.95,   div_factor = 25,   final_div_factor = 10000,   last_epoch = -1,   verbose = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/lr_one_cycle.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Once cycle learning rate — lr_one_cycle","text":"optimizer (Optimizer): Wrapped optimizer. max_lr (float list): Upper learning rate boundaries cycle parameter group. total_steps (int): total number steps cycle. Note value provided , must inferred providing value epochs steps_per_epoch. Default: NULL epochs (int): number epochs train . used along steps_per_epoch order infer total number steps cycle value total_steps provided. Default: NULL steps_per_epoch (int): number steps per epoch train . used along epochs order infer total number steps cycle value total_steps provided. Default: NULL pct_start (float): percentage cycle (number steps) spent increasing learning rate. Default: 0.3 anneal_strategy (str): 'cos', 'linear' Specifies annealing strategy: \"cos\" cosine annealing, \"linear\" linear annealing. Default: 'cos' cycle_momentum (bool): TRUE, momentum cycled inversely learning rate 'base_momentum' 'max_momentum'. Default: TRUE base_momentum (float list): Lower momentum boundaries cycle parameter group. Note momentum cycled inversely learning rate; peak cycle, momentum 'base_momentum' learning rate 'max_lr'. Default: 0.85 max_momentum (float list): Upper momentum boundaries cycle parameter group. Functionally, defines cycle amplitude (max_momentum - base_momentum). Note momentum cycled inversely learning rate; start cycle, momentum 'max_momentum' learning rate 'base_lr' Default: 0.95 div_factor (float): Determines initial learning rate via initial_lr = max_lr/div_factor Default: 25 final_div_factor (float): Determines minimum learning rate via min_lr = initial_lr/final_div_factor Default: 1e4 last_epoch (int): index last batch. parameter used resuming training job. Since step() invoked batch instead epoch, number represents total number batches computed, total number epochs computed. last_epoch=-1, schedule started beginning. Default: -1 verbose (bool): TRUE, prints message stdout update. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/lr_one_cycle.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Once cycle learning rate — lr_one_cycle","text":"policy initially described paper Super-Convergence: Fast Training Neural Networks Using Large Learning Rates. 1cycle learning rate policy changes learning rate every batch. step called batch used training. scheduler chainable. Note also total number steps cycle can determined one two ways (listed order precedence): value total_steps explicitly provided. number epochs (epochs) number steps per epoch (steps_per_epoch) provided. case, number total steps inferred total_steps = epochs * steps_per_epoch must either provide value total_steps provide value epochs steps_per_epoch.","code":""},{"path":"https://torch.mlverse.org/docs/reference/lr_one_cycle.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Once cycle learning rate — lr_one_cycle","text":"","code":"if (torch_is_installed()) { if (FALSE) { data_loader <- dataloader(...) optimizer <- optim_sgd(model$parameters, lr = 0.1, momentum = 0.9) scheduler <- lr_one_cycle(optimizer,   max_lr = 0.01, steps_per_epoch = length(data_loader),   epochs = 10 )  for (i in 1:epochs) {   coro::loop(for (batch in data_loader) {     train_batch(...)     scheduler$step()   }) } }  }"},{"path":"https://torch.mlverse.org/docs/reference/lr_scheduler.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates learning rate schedulers — lr_scheduler","title":"Creates learning rate schedulers — lr_scheduler","text":"Creates learning rate schedulers","code":""},{"path":"https://torch.mlverse.org/docs/reference/lr_scheduler.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates learning rate schedulers — lr_scheduler","text":"","code":"lr_scheduler(   classname = NULL,   inherit = LRScheduler,   ...,   parent_env = parent.frame() )"},{"path":"https://torch.mlverse.org/docs/reference/lr_scheduler.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates learning rate schedulers — lr_scheduler","text":"classname optional name learning rate scheduler inherit optional learning rate scheduler inherit ... named list methods. must implement get_lr() method take argument returns learning rates param_group optimizer. parent_env passed R6::R6Class().","code":""},{"path":"https://torch.mlverse.org/docs/reference/lr_step.html","id":null,"dir":"Reference","previous_headings":"","what":"Step learning rate decay — lr_step","title":"Step learning rate decay — lr_step","text":"Decays learning rate parameter group gamma every step_size epochs. Notice decay can happen simultaneously changes learning rate outside scheduler. last_epoch=-1, sets initial lr lr.","code":""},{"path":"https://torch.mlverse.org/docs/reference/lr_step.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Step learning rate decay — lr_step","text":"","code":"lr_step(optimizer, step_size, gamma = 0.1, last_epoch = -1)"},{"path":"https://torch.mlverse.org/docs/reference/lr_step.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Step learning rate decay — lr_step","text":"optimizer (Optimizer): Wrapped optimizer. step_size (int): Period learning rate decay. gamma (float): Multiplicative factor learning rate decay. Default: 0.1. last_epoch (int): index last epoch. Default: -1.","code":""},{"path":"https://torch.mlverse.org/docs/reference/lr_step.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Step learning rate decay — lr_step","text":"","code":"if (torch_is_installed()) { if (FALSE) { # Assuming optimizer uses lr = 0.05 for all groups # lr = 0.05     if epoch < 30 # lr = 0.005    if 30 <= epoch < 60 # lr = 0.0005   if 60 <= epoch < 90 # ... scheduler <- lr_step(optimizer, step_size = 30, gamma = 0.1) for (epoch in 1:100) {   train(...)   validate(...)   scheduler$step() } }  }"},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_avg_pool1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies a 1D adaptive average pooling over an input signal composed of several input planes. — nn_adaptive_avg_pool1d","title":"Applies a 1D adaptive average pooling over an input signal composed of several input planes. — nn_adaptive_avg_pool1d","text":"output size H, input size. number output features equal number input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_avg_pool1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies a 1D adaptive average pooling over an input signal composed of several input planes. — nn_adaptive_avg_pool1d","text":"","code":"nn_adaptive_avg_pool1d(output_size)"},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_avg_pool1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies a 1D adaptive average pooling over an input signal composed of several input planes. — nn_adaptive_avg_pool1d","text":"output_size target output size H","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_avg_pool1d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Applies a 1D adaptive average pooling over an input signal composed of several input planes. — nn_adaptive_avg_pool1d","text":"","code":"if (torch_is_installed()) { # target output size of 5 m <- nn_adaptive_avg_pool1d(5) input <- torch_randn(1, 64, 8) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_avg_pool2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies a 2D adaptive average pooling over an input signal composed of several input planes. — nn_adaptive_avg_pool2d","title":"Applies a 2D adaptive average pooling over an input signal composed of several input planes. — nn_adaptive_avg_pool2d","text":"output size H x W, input size. number output features equal number input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_avg_pool2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies a 2D adaptive average pooling over an input signal composed of several input planes. — nn_adaptive_avg_pool2d","text":"","code":"nn_adaptive_avg_pool2d(output_size)"},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_avg_pool2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies a 2D adaptive average pooling over an input signal composed of several input planes. — nn_adaptive_avg_pool2d","text":"output_size target output size image form H x W. Can tuple (H, W) single H square image H x H. H W can either int, NULL means size input.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_avg_pool2d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Applies a 2D adaptive average pooling over an input signal composed of several input planes. — nn_adaptive_avg_pool2d","text":"","code":"if (torch_is_installed()) { # target output size of 5x7 m <- nn_adaptive_avg_pool2d(c(5, 7)) input <- torch_randn(1, 64, 8, 9) output <- m(input) # target output size of 7x7 (square) m <- nn_adaptive_avg_pool2d(7) input <- torch_randn(1, 64, 10, 9) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_avg_pool3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies a 3D adaptive average pooling over an input signal composed of several input planes. — nn_adaptive_avg_pool3d","title":"Applies a 3D adaptive average pooling over an input signal composed of several input planes. — nn_adaptive_avg_pool3d","text":"output size D x H x W, input size. number output features equal number input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_avg_pool3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies a 3D adaptive average pooling over an input signal composed of several input planes. — nn_adaptive_avg_pool3d","text":"","code":"nn_adaptive_avg_pool3d(output_size)"},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_avg_pool3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies a 3D adaptive average pooling over an input signal composed of several input planes. — nn_adaptive_avg_pool3d","text":"output_size target output size form D x H x W. Can tuple (D, H, W) single number D cube D x D x D. D, H W can either int, None means size input.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_avg_pool3d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Applies a 3D adaptive average pooling over an input signal composed of several input planes. — nn_adaptive_avg_pool3d","text":"","code":"if (torch_is_installed()) { # target output size of 5x7x9 m <- nn_adaptive_avg_pool3d(c(5, 7, 9)) input <- torch_randn(1, 64, 8, 9, 10) output <- m(input) # target output size of 7x7x7 (cube) m <- nn_adaptive_avg_pool3d(7) input <- torch_randn(1, 64, 10, 9, 8) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_log_softmax_with_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"AdaptiveLogSoftmaxWithLoss module — nn_adaptive_log_softmax_with_loss","title":"AdaptiveLogSoftmaxWithLoss module — nn_adaptive_log_softmax_with_loss","text":"Efficient softmax approximation described Efficient softmax approximation GPUs Edouard Grave, Armand Joulin, Moustapha Cissé, David Grangier, Hervé Jégou","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_log_softmax_with_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"AdaptiveLogSoftmaxWithLoss module — nn_adaptive_log_softmax_with_loss","text":"","code":"nn_adaptive_log_softmax_with_loss(   in_features,   n_classes,   cutoffs,   div_value = 4,   head_bias = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_log_softmax_with_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"AdaptiveLogSoftmaxWithLoss module — nn_adaptive_log_softmax_with_loss","text":"in_features (int): Number features input tensor n_classes (int): Number classes dataset cutoffs (Sequence): Cutoffs used assign targets buckets div_value (float, optional): value used exponent compute sizes clusters. Default: 4.0 head_bias (bool, optional): True, adds bias term 'head' adaptive softmax. Default: False","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_log_softmax_with_loss.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"AdaptiveLogSoftmaxWithLoss module — nn_adaptive_log_softmax_with_loss","text":"NamedTuple output loss fields: output Tensor size N containing computed target log probabilities example loss Scalar representing computed negative log likelihood loss","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_log_softmax_with_loss.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"AdaptiveLogSoftmaxWithLoss module — nn_adaptive_log_softmax_with_loss","text":"Adaptive softmax approximate strategy training models large output spaces. effective label distribution highly imbalanced, example natural language modelling, word frequency distribution approximately follows Zipf's law. Adaptive softmax partitions labels several clusters, according frequency. clusters may contain different number targets . Additionally, clusters containing less frequent labels assign lower dimensional embeddings labels, speeds computation. minibatch, clusters least one target present evaluated. idea clusters accessed frequently (like first one, containing frequent labels), also cheap compute -- , contain small number assigned labels. highly recommend taking look original paper details. cutoffs ordered Sequence integers sorted increasing order. controls number clusters partitioning targets clusters. example setting cutoffs = c(10, 100, 1000) means first 10 targets assigned 'head' adaptive softmax, targets 11, 12, ..., 100 assigned first cluster, targets 101, 102, ..., 1000 assigned second cluster, targets 1001, 1002, ..., n_classes - 1 assigned last, third cluster. div_value used compute size additional cluster, given \\(\\left\\lfloor\\frac{\\mbox{\\_features}}{\\mbox{div\\_value}^{idx}}\\right\\rfloor\\), \\(idx\\) cluster index (clusters less frequent words larger indices, indices starting \\(1\\)). head_bias set True, adds bias term 'head' adaptive softmax. See paper details. Set False official implementation.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_log_softmax_with_loss.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"AdaptiveLogSoftmaxWithLoss module — nn_adaptive_log_softmax_with_loss","text":"module returns NamedTuple output loss fields. See documentation details. compute log-probabilities classes, log_prob method can used.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_log_softmax_with_loss.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"AdaptiveLogSoftmaxWithLoss module — nn_adaptive_log_softmax_with_loss","text":"Labels passed inputs module sorted according frequency. means frequent label represented index 0, least frequent label represented index n_classes - 1.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_log_softmax_with_loss.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"AdaptiveLogSoftmaxWithLoss module — nn_adaptive_log_softmax_with_loss","text":"input: \\((N, \\mbox{\\_features})\\) target: \\((N)\\) value satisfies \\(0 <= \\mbox{target[]} <= \\mbox{n\\_classes}\\) output1: \\((N)\\) output2: Scalar","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_max_pool1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies a 1D adaptive max pooling over an input signal composed of several input planes. — nn_adaptive_max_pool1d","title":"Applies a 1D adaptive max pooling over an input signal composed of several input planes. — nn_adaptive_max_pool1d","text":"output size H, input size. number output features equal number input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_max_pool1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies a 1D adaptive max pooling over an input signal composed of several input planes. — nn_adaptive_max_pool1d","text":"","code":"nn_adaptive_max_pool1d(output_size, return_indices = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_max_pool1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies a 1D adaptive max pooling over an input signal composed of several input planes. — nn_adaptive_max_pool1d","text":"output_size target output size H return_indices TRUE, return indices along outputs. Useful pass nn_max_unpool1d(). Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_max_pool1d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Applies a 1D adaptive max pooling over an input signal composed of several input planes. — nn_adaptive_max_pool1d","text":"","code":"if (torch_is_installed()) { # target output size of 5 m <- nn_adaptive_max_pool1d(5) input <- torch_randn(1, 64, 8) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_max_pool2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies a 2D adaptive max pooling over an input signal composed of several input planes. — nn_adaptive_max_pool2d","title":"Applies a 2D adaptive max pooling over an input signal composed of several input planes. — nn_adaptive_max_pool2d","text":"output size H x W, input size. number output features equal number input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_max_pool2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies a 2D adaptive max pooling over an input signal composed of several input planes. — nn_adaptive_max_pool2d","text":"","code":"nn_adaptive_max_pool2d(output_size, return_indices = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_max_pool2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies a 2D adaptive max pooling over an input signal composed of several input planes. — nn_adaptive_max_pool2d","text":"output_size target output size image form H x W. Can tuple (H, W) single H square image H x H. H W can either int, None means size input. return_indices TRUE, return indices along outputs. Useful pass nn_max_unpool2d(). Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_max_pool2d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Applies a 2D adaptive max pooling over an input signal composed of several input planes. — nn_adaptive_max_pool2d","text":"","code":"if (torch_is_installed()) { # target output size of 5x7 m <- nn_adaptive_max_pool2d(c(5, 7)) input <- torch_randn(1, 64, 8, 9) output <- m(input) # target output size of 7x7 (square) m <- nn_adaptive_max_pool2d(7) input <- torch_randn(1, 64, 10, 9) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_max_pool3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies a 3D adaptive max pooling over an input signal composed of several input planes. — nn_adaptive_max_pool3d","title":"Applies a 3D adaptive max pooling over an input signal composed of several input planes. — nn_adaptive_max_pool3d","text":"output size D x H x W, input size. number output features equal number input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_max_pool3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies a 3D adaptive max pooling over an input signal composed of several input planes. — nn_adaptive_max_pool3d","text":"","code":"nn_adaptive_max_pool3d(output_size, return_indices = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_max_pool3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies a 3D adaptive max pooling over an input signal composed of several input planes. — nn_adaptive_max_pool3d","text":"output_size target output size image form D x H x W. Can tuple (D, H, W) single D cube D x D x D. D, H W can either int, None means size input. return_indices TRUE, return indices along outputs. Useful pass nn_max_unpool3d(). Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_max_pool3d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Applies a 3D adaptive max pooling over an input signal composed of several input planes. — nn_adaptive_max_pool3d","text":"","code":"if (torch_is_installed()) { # target output size of 5x7x9 m <- nn_adaptive_max_pool3d(c(5, 7, 9)) input <- torch_randn(1, 64, 8, 9, 10) output <- m(input) # target output size of 7x7x7 (cube) m <- nn_adaptive_max_pool3d(7) input <- torch_randn(1, 64, 10, 9, 8) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_avg_pool1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies a 1D average pooling over an input signal composed of several\ninput planes. — nn_avg_pool1d","title":"Applies a 1D average pooling over an input signal composed of several\ninput planes. — nn_avg_pool1d","text":"simplest case, output value layer input size \\((N, C, L)\\), output \\((N, C, L_{})\\) kernel_size \\(k\\) can precisely described :","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_avg_pool1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies a 1D average pooling over an input signal composed of several\ninput planes. — nn_avg_pool1d","text":"","code":"nn_avg_pool1d(   kernel_size,   stride = NULL,   padding = 0,   ceil_mode = FALSE,   count_include_pad = TRUE )"},{"path":"https://torch.mlverse.org/docs/reference/nn_avg_pool1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies a 1D average pooling over an input signal composed of several\ninput planes. — nn_avg_pool1d","text":"kernel_size size window stride stride window. Default value kernel_size padding implicit zero padding added sides ceil_mode TRUE, use ceil instead floor compute output shape count_include_pad TRUE, include zero-padding averaging calculation","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_avg_pool1d.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Applies a 1D average pooling over an input signal composed of several\ninput planes. — nn_avg_pool1d","text":"$$   \\mbox{}(N_i, C_j, l) = \\frac{1}{k} \\sum_{m=0}^{k-1} \\mbox{input}(N_i, C_j, \\mbox{stride} \\times l + m) $$ padding non-zero, input implicitly zero-padded sides padding number points. parameters kernel_size, stride, padding can int one-element tuple.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_avg_pool1d.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Applies a 1D average pooling over an input signal composed of several\ninput planes. — nn_avg_pool1d","text":"Input: \\((N, C, L_{})\\) Output: \\((N, C, L_{})\\), $$   L_{} = \\left\\lfloor \\frac{L_{} +       2 \\times \\mbox{padding} - \\mbox{kernel\\_size}}{\\mbox{stride}} + 1\\right\\rfloor $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_avg_pool1d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Applies a 1D average pooling over an input signal composed of several\ninput planes. — nn_avg_pool1d","text":"","code":"if (torch_is_installed()) {  # pool with window of size=3, stride=2 m <- nn_avg_pool1d(3, stride = 2) m(torch_randn(1, 1, 8)) } #> torch_tensor #> (1,.,.) =  #>  -0.4666 -0.0392  0.3291 #> [ CPUFloatType{1,1,3} ]"},{"path":"https://torch.mlverse.org/docs/reference/nn_avg_pool2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies a 2D average pooling over an input signal composed of several input\nplanes. — nn_avg_pool2d","title":"Applies a 2D average pooling over an input signal composed of several input\nplanes. — nn_avg_pool2d","text":"simplest case, output value layer input size \\((N, C, H, W)\\), output \\((N, C, H_{}, W_{})\\) kernel_size \\((kH, kW)\\) can precisely described :","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_avg_pool2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies a 2D average pooling over an input signal composed of several input\nplanes. — nn_avg_pool2d","text":"","code":"nn_avg_pool2d(   kernel_size,   stride = NULL,   padding = 0,   ceil_mode = FALSE,   count_include_pad = TRUE,   divisor_override = NULL )"},{"path":"https://torch.mlverse.org/docs/reference/nn_avg_pool2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies a 2D average pooling over an input signal composed of several input\nplanes. — nn_avg_pool2d","text":"kernel_size size window stride stride window. Default value kernel_size padding implicit zero padding added sides ceil_mode TRUE, use ceil instead floor compute output shape count_include_pad TRUE, include zero-padding averaging calculation divisor_override specified, used divisor, otherwise kernel_size used","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_avg_pool2d.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Applies a 2D average pooling over an input signal composed of several input\nplanes. — nn_avg_pool2d","text":"$$   (N_i, C_j, h, w)  = \\frac{1}{kH * kW} \\sum_{m=0}^{kH-1} \\sum_{n=0}^{kW-1} input(N_i, C_j, stride[0] \\times h + m, stride[1] \\times w + n) $$ padding non-zero, input implicitly zero-padded sides padding number points. parameters kernel_size, stride, padding can either : single int -- case value used height width dimension tuple two ints -- case, first int used height dimension, second int width dimension","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_avg_pool2d.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Applies a 2D average pooling over an input signal composed of several input\nplanes. — nn_avg_pool2d","text":"Input: \\((N, C, H_{}, W_{})\\) Output: \\((N, C, H_{}, W_{})\\), $$   H_{} = \\left\\lfloor\\frac{H_{}  + 2 \\times \\mbox{padding}[0] -       \\mbox{kernel\\_size}[0]}{\\mbox{stride}[0]} + 1\\right\\rfloor $$ $$   W_{} = \\left\\lfloor\\frac{W_{}  + 2 \\times \\mbox{padding}[1] -       \\mbox{kernel\\_size}[1]}{\\mbox{stride}[1]} + 1\\right\\rfloor $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_avg_pool2d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Applies a 2D average pooling over an input signal composed of several input\nplanes. — nn_avg_pool2d","text":"","code":"if (torch_is_installed()) {  # pool of square window of size=3, stride=2 m <- nn_avg_pool2d(3, stride = 2) # pool of non-square window m <- nn_avg_pool2d(c(3, 2), stride = c(2, 1)) input <- torch_randn(20, 16, 50, 32) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_avg_pool3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies a 3D average pooling over an input signal composed of several input\nplanes. — nn_avg_pool3d","title":"Applies a 3D average pooling over an input signal composed of several input\nplanes. — nn_avg_pool3d","text":"simplest case, output value layer input size \\((N, C, D, H, W)\\), output \\((N, C, D_{}, H_{}, W_{})\\) kernel_size \\((kD, kH, kW)\\) can precisely described :","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_avg_pool3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies a 3D average pooling over an input signal composed of several input\nplanes. — nn_avg_pool3d","text":"","code":"nn_avg_pool3d(   kernel_size,   stride = NULL,   padding = 0,   ceil_mode = FALSE,   count_include_pad = TRUE,   divisor_override = NULL )"},{"path":"https://torch.mlverse.org/docs/reference/nn_avg_pool3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies a 3D average pooling over an input signal composed of several input\nplanes. — nn_avg_pool3d","text":"kernel_size size window stride stride window. Default value kernel_size padding implicit zero padding added three sides ceil_mode TRUE, use ceil instead floor compute output shape count_include_pad TRUE, include zero-padding averaging calculation divisor_override specified, used divisor, otherwise kernel_size used","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_avg_pool3d.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Applies a 3D average pooling over an input signal composed of several input\nplanes. — nn_avg_pool3d","text":"$$ \\begin{array}{ll} \\mbox{}(N_i, C_j, d, h, w) = & \\sum_{k=0}^{kD-1} \\sum_{m=0}^{kH-1} \\sum_{n=0}^{kW-1} \\\\ & \\frac{\\mbox{input}(N_i, C_j, \\mbox{stride}[0] \\times d + k, \\mbox{stride}[1] \\times h + m, \\mbox{stride}[2] \\times w + n)}{kD \\times kH \\times kW} \\end{array} $$ padding non-zero, input implicitly zero-padded three sides padding number points. parameters kernel_size, stride can either : single int -- case value used depth, height width dimension tuple three ints -- case, first int used depth dimension, second int height dimension third int width dimension","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_avg_pool3d.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Applies a 3D average pooling over an input signal composed of several input\nplanes. — nn_avg_pool3d","text":"Input: \\((N, C, D_{}, H_{}, W_{})\\) Output: \\((N, C, D_{}, H_{}, W_{})\\), $$   D_{} = \\left\\lfloor\\frac{D_{} + 2 \\times \\mbox{padding}[0] -       \\mbox{kernel\\_size}[0]}{\\mbox{stride}[0]} + 1\\right\\rfloor $$ $$   H_{} = \\left\\lfloor\\frac{H_{} + 2 \\times \\mbox{padding}[1] -       \\mbox{kernel\\_size}[1]}{\\mbox{stride}[1]} + 1\\right\\rfloor $$ $$   W_{} = \\left\\lfloor\\frac{W_{} + 2 \\times \\mbox{padding}[2] -       \\mbox{kernel\\_size}[2]}{\\mbox{stride}[2]} + 1\\right\\rfloor $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_avg_pool3d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Applies a 3D average pooling over an input signal composed of several input\nplanes. — nn_avg_pool3d","text":"","code":"if (torch_is_installed()) {  # pool of square window of size=3, stride=2 m <- nn_avg_pool3d(3, stride = 2) # pool of non-square window m <- nn_avg_pool3d(c(3, 2, 2), stride = c(2, 1, 2)) input <- torch_randn(20, 16, 50, 44, 31) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_batch_norm1d.html","id":null,"dir":"Reference","previous_headings":"","what":"BatchNorm1D module — nn_batch_norm1d","title":"BatchNorm1D module — nn_batch_norm1d","text":"Applies Batch Normalization 2D 3D input (mini-batch 1D inputs optional additional channel dimension) described paper Batch Normalization: Accelerating Deep Network Training Reducing Internal Covariate Shift","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_batch_norm1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"BatchNorm1D module — nn_batch_norm1d","text":"","code":"nn_batch_norm1d(   num_features,   eps = 1e-05,   momentum = 0.1,   affine = TRUE,   track_running_stats = TRUE )"},{"path":"https://torch.mlverse.org/docs/reference/nn_batch_norm1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"BatchNorm1D module — nn_batch_norm1d","text":"num_features \\(C\\) expected input size \\((N, C, L)\\) \\(L\\) input size \\((N, L)\\) eps value added denominator numerical stability. Default: 1e-5 momentum value used running_mean running_var computation. Can set NULL cumulative moving average (.e. simple average). Default: 0.1 affine boolean value set TRUE, module learnable affine parameters. Default: TRUE track_running_stats boolean value set TRUE, module tracks running mean variance, set FALSE, module track statistics always uses batch statistics training eval modes. Default: TRUE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_batch_norm1d.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"BatchNorm1D module — nn_batch_norm1d","text":"$$ y = \\frac{x - \\mathrm{E}[x]}{\\sqrt{\\mathrm{Var}[x] + \\epsilon}} * \\gamma + \\beta $$ mean standard-deviation calculated per-dimension mini-batches \\(\\gamma\\) \\(\\beta\\) learnable parameter vectors size C (C input size). default, elements \\(\\gamma\\) set 1 elements \\(\\beta\\) set 0. Also default, training layer keeps running estimates computed mean variance, used normalization evaluation. running estimates kept default :attr:momentum 0.1. track_running_stats set FALSE, layer keep running estimates, batch statistics instead used evaluation time well.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_batch_norm1d.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"BatchNorm1D module — nn_batch_norm1d","text":"momentum argument different one used optimizer classes conventional notion momentum. Mathematically, update rule running statistics \\(\\hat{x}_{\\mbox{new}} = (1 - \\mbox{momentum}) \\times \\hat{x} + \\mbox{momentum} \\times x_t\\), \\(\\hat{x}\\) estimated statistic \\(x_t\\) new observed value. Batch Normalization done C dimension, computing statistics (N, L) slices, common terminology call Temporal Batch Normalization.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_batch_norm1d.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"BatchNorm1D module — nn_batch_norm1d","text":"Input: \\((N, C)\\) \\((N, C, L)\\) Output: \\((N, C)\\) \\((N, C, L)\\) (shape input)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_batch_norm1d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"BatchNorm1D module — nn_batch_norm1d","text":"","code":"if (torch_is_installed()) { # With Learnable Parameters m <- nn_batch_norm1d(100) # Without Learnable Parameters m <- nn_batch_norm1d(100, affine = FALSE) input <- torch_randn(20, 100) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_batch_norm2d.html","id":null,"dir":"Reference","previous_headings":"","what":"BatchNorm2D — nn_batch_norm2d","title":"BatchNorm2D — nn_batch_norm2d","text":"Applies Batch Normalization 4D input (mini-batch 2D inputs additional channel dimension) described paper Batch Normalization: Accelerating Deep Network Training Reducing Internal Covariate Shift.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_batch_norm2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"BatchNorm2D — nn_batch_norm2d","text":"","code":"nn_batch_norm2d(   num_features,   eps = 1e-05,   momentum = 0.1,   affine = TRUE,   track_running_stats = TRUE )"},{"path":"https://torch.mlverse.org/docs/reference/nn_batch_norm2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"BatchNorm2D — nn_batch_norm2d","text":"num_features \\(C\\) expected input size \\((N, C, H, W)\\) eps value added denominator numerical stability. Default: 1e-5 momentum value used running_mean running_var computation. Can set None cumulative moving average (.e. simple average). Default: 0.1 affine boolean value set TRUE, module learnable affine parameters. Default: TRUE track_running_stats boolean value set TRUE, module tracks running mean variance, set FALSE, module track statistics uses batch statistics instead training eval modes running mean variance None. Default: TRUE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_batch_norm2d.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"BatchNorm2D — nn_batch_norm2d","text":"$$   y = \\frac{x - \\mathrm{E}[x]}{ \\sqrt{\\mathrm{Var}[x] + \\epsilon}} * \\gamma + \\beta $$ mean standard-deviation calculated per-dimension mini-batches \\(\\gamma\\) \\(\\beta\\) learnable parameter vectors size C (C input size). default, elements \\(\\gamma\\) set 1 elements \\(\\beta\\) set 0. standard-deviation calculated via biased estimator, equivalent torch_var(input, unbiased=FALSE). Also default, training layer keeps running estimates computed mean variance, used normalization evaluation. running estimates kept default momentum 0.1. track_running_stats set FALSE, layer keep running estimates, batch statistics instead used evaluation time well.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_batch_norm2d.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"BatchNorm2D — nn_batch_norm2d","text":"momentum argument different one used optimizer classes conventional notion momentum. Mathematically, update rule running statistics \\(\\hat{x}_{\\mbox{new}} = (1 - \\mbox{momentum}) \\times \\hat{x} + \\mbox{momentum} \\times x_t\\), \\(\\hat{x}\\) estimated statistic \\(x_t\\) new observed value. Batch Normalization done C dimension, computing statistics (N, H, W) slices, common terminology call Spatial Batch Normalization.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_batch_norm2d.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"BatchNorm2D — nn_batch_norm2d","text":"Input: \\((N, C, H, W)\\) Output: \\((N, C, H, W)\\) (shape input)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_batch_norm2d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"BatchNorm2D — nn_batch_norm2d","text":"","code":"if (torch_is_installed()) { # With Learnable Parameters m <- nn_batch_norm2d(100) # Without Learnable Parameters m <- nn_batch_norm2d(100, affine = FALSE) input <- torch_randn(20, 100, 35, 45) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_batch_norm3d.html","id":null,"dir":"Reference","previous_headings":"","what":"BatchNorm3D — nn_batch_norm3d","title":"BatchNorm3D — nn_batch_norm3d","text":"Applies Batch Normalization 5D input (mini-batch 3D inputs additional channel dimension) described paper Batch Normalization: Accelerating Deep Network Training Reducing Internal Covariate Shift.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_batch_norm3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"BatchNorm3D — nn_batch_norm3d","text":"","code":"nn_batch_norm3d(   num_features,   eps = 1e-05,   momentum = 0.1,   affine = TRUE,   track_running_stats = TRUE )"},{"path":"https://torch.mlverse.org/docs/reference/nn_batch_norm3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"BatchNorm3D — nn_batch_norm3d","text":"num_features \\(C\\) expected input size \\((N, C, D, H, W)\\) eps value added denominator numerical stability. Default: 1e-5 momentum value used running_mean running_var computation. Can set None cumulative moving average (.e. simple average). Default: 0.1 affine boolean value set TRUE, module learnable affine parameters. Default: TRUE track_running_stats boolean value set TRUE, module tracks running mean variance, set FALSE, module track statistics uses batch statistics instead training eval modes running mean variance None. Default: TRUE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_batch_norm3d.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"BatchNorm3D — nn_batch_norm3d","text":"$$   y = \\frac{x - \\mathrm{E}[x]}{ \\sqrt{\\mathrm{Var}[x] + \\epsilon}} * \\gamma + \\beta $$ mean standard-deviation calculated per-dimension mini-batches \\(\\gamma\\) \\(\\beta\\) learnable parameter vectors size C (C input size). default, elements \\(\\gamma\\) set 1 elements \\(\\beta\\) set 0. standard-deviation calculated via biased estimator, equivalent torch_var(input, unbiased = FALSE). Also default, training layer keeps running estimates computed mean variance, used normalization evaluation. running estimates kept default momentum 0.1. track_running_stats set FALSE, layer keep running estimates, batch statistics instead used evaluation time well.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_batch_norm3d.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"BatchNorm3D — nn_batch_norm3d","text":"momentum argument different one used optimizer classes conventional notion momentum. Mathematically, update rule running statistics : \\(\\hat{x}_{\\mbox{new}} = (1 - \\mbox{momentum}) \\times \\hat{x} + \\mbox{momentum} \\times x_t\\), \\(\\hat{x}\\) estimated statistic \\(x_t\\) new observed value. Batch Normalization done C dimension, computing statistics (N, D, H, W) slices, common terminology call Volumetric Batch Normalization Spatio-temporal Batch Normalization.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_batch_norm3d.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"BatchNorm3D — nn_batch_norm3d","text":"Input: \\((N, C, D, H, W)\\) Output: \\((N, C, D, H, W)\\) (shape input)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_batch_norm3d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"BatchNorm3D — nn_batch_norm3d","text":"","code":"if (torch_is_installed()) { # With Learnable Parameters m <- nn_batch_norm3d(100) # Without Learnable Parameters m <- nn_batch_norm3d(100, affine = FALSE) input <- torch_randn(20, 100, 35, 45, 55) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_bce_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Binary cross entropy loss — nn_bce_loss","title":"Binary cross entropy loss — nn_bce_loss","text":"Creates criterion measures Binary Cross Entropy target output:","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_bce_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Binary cross entropy loss — nn_bce_loss","text":"","code":"nn_bce_loss(weight = NULL, reduction = \"mean\")"},{"path":"https://torch.mlverse.org/docs/reference/nn_bce_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Binary cross entropy loss — nn_bce_loss","text":"weight (Tensor, optional): manual rescaling weight given loss batch element. given, Tensor size nbatch. reduction (string, optional): Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed. Note: size_average reduce process deprecated, meantime, specifying either two args override reduction. Default: 'mean'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_bce_loss.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Binary cross entropy loss — nn_bce_loss","text":"unreduced (.e. reduction set 'none') loss can described : $$   \\ell(x, y) = L = \\{l_1,\\dots,l_N\\}^\\top, \\quad l_n = - w_n \\left[ y_n \\cdot \\log x_n + (1 - y_n) \\cdot \\log (1 - x_n) \\right] $$ \\(N\\) batch size. reduction 'none' (default 'mean'), $$   \\ell(x, y) = \\left\\{ \\begin{array}{ll} \\mbox{mean}(L), & \\mbox{reduction} = \\mbox{'mean';}\\\\ \\mbox{sum}(L),  & \\mbox{reduction} = \\mbox{'sum'.} \\end{array} \\right. $$ used measuring error reconstruction example auto-encoder. Note targets \\(y\\) numbers 0 1. Notice \\(x_n\\) either 0 1, one log terms mathematically undefined loss equation. PyTorch chooses set \\(\\log (0) = -\\infty\\), since \\(\\lim_{x\\0} \\log (x) = -\\infty\\). However, infinite term loss equation desirable several reasons. one, either \\(y_n = 0\\) \\((1 - y_n) = 0\\), multiplying 0 infinity. Secondly, infinite loss value, also infinite term gradient, since \\(\\lim_{x\\0} \\frac{d}{dx} \\log (x) = \\infty\\). make BCELoss's backward method nonlinear respect \\(x_n\\), using things like linear regression straight-forward. solution BCELoss clamps log function outputs greater equal -100. way, can always finite loss value linear backward method.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_bce_loss.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Binary cross entropy loss — nn_bce_loss","text":"Input: \\((N, *)\\) \\(*\\) means, number additional dimensions Target: \\((N, *)\\), shape input Output: scalar. reduction 'none', \\((N, *)\\), shape input.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_bce_loss.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Binary cross entropy loss — nn_bce_loss","text":"","code":"if (torch_is_installed()) { m <- nn_sigmoid() loss <- nn_bce_loss() input <- torch_randn(3, requires_grad = TRUE) target <- torch_rand(3) output <- loss(m(input), target) output$backward() }"},{"path":"https://torch.mlverse.org/docs/reference/nn_bce_with_logits_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"BCE with logits loss — nn_bce_with_logits_loss","title":"BCE with logits loss — nn_bce_with_logits_loss","text":"loss combines Sigmoid layer BCELoss one single class. version numerically stable using plain Sigmoid followed BCELoss , combining operations one layer, take advantage log-sum-exp trick numerical stability.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_bce_with_logits_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"BCE with logits loss — nn_bce_with_logits_loss","text":"","code":"nn_bce_with_logits_loss(weight = NULL, reduction = \"mean\", pos_weight = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/nn_bce_with_logits_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"BCE with logits loss — nn_bce_with_logits_loss","text":"weight (Tensor, optional): manual rescaling weight given loss batch element. given, Tensor size nbatch. reduction (string, optional): Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed. Note: size_average reduce process deprecated, meantime, specifying either two args override reduction. Default: 'mean' pos_weight (Tensor, optional): weight positive examples. Must vector length equal number classes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_bce_with_logits_loss.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"BCE with logits loss — nn_bce_with_logits_loss","text":"unreduced (.e. reduction set 'none') loss can described : $$   \\ell(x, y) = L = \\{l_1,\\dots,l_N\\}^\\top, \\quad l_n = - w_n \\left[ y_n \\cdot \\log \\sigma(x_n)                    + (1 - y_n) \\cdot \\log (1 - \\sigma(x_n)) \\right], $$ \\(N\\) batch size. reduction 'none' (default 'mean'), $$   \\ell(x, y) = \\begin{array}{ll} \\mbox{mean}(L), & \\mbox{reduction} = \\mbox{'mean';}\\\\ \\mbox{sum}(L),  & \\mbox{reduction} = \\mbox{'sum'.} \\end{array} $$ used measuring error reconstruction example auto-encoder. Note targets t[] numbers 0 1. possible trade recall precision adding weights positive examples. case multi-label classification loss can described : $$ \\ell_c(x, y) = L_c = \\{l_{1,c},\\dots,l_{N,c}\\}^\\top, \\quad l_{n,c} = - w_{n,c} \\left[ p_c y_{n,c} \\cdot \\log \\sigma(x_{n,c}) + (1 - y_{n,c}) \\cdot \\log (1 - \\sigma(x_{n,c})) \\right], $$ \\(c\\) class number (\\(c > 1\\) multi-label binary classification, \\(c = 1\\) single-label binary classification), \\(n\\) number sample batch \\(p_c\\) weight positive answer class \\(c\\). \\(p_c > 1\\) increases recall, \\(p_c < 1\\) increases precision. example, dataset contains 100 positive 300 negative examples single class, pos_weight class equal \\(\\frac{300}{100}=3\\). loss act dataset contains \\(3\\times 100=300\\) positive examples.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_bce_with_logits_loss.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"BCE with logits loss — nn_bce_with_logits_loss","text":"Input: \\((N, *)\\) \\(*\\) means, number additional dimensions Target: \\((N, *)\\), shape input Output: scalar. reduction 'none', \\((N, *)\\), shape input.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_bce_with_logits_loss.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"BCE with logits loss — nn_bce_with_logits_loss","text":"","code":"if (torch_is_installed()) { loss <- nn_bce_with_logits_loss() input <- torch_randn(3, requires_grad = TRUE) target <- torch_empty(3)$random_(1, 2) output <- loss(input, target) output$backward()  target <- torch_ones(10, 64, dtype = torch_float32()) # 64 classes, batch size = 10 output <- torch_full(c(10, 64), 1.5) # A prediction (logit) pos_weight <- torch_ones(64) # All weights are equal to 1 criterion <- nn_bce_with_logits_loss(pos_weight = pos_weight) criterion(output, target) # -log(sigmoid(1.5)) } #> torch_tensor #> 0.201413 #> [ CPUFloatType{} ]"},{"path":"https://torch.mlverse.org/docs/reference/nn_bilinear.html","id":null,"dir":"Reference","previous_headings":"","what":"Bilinear module — nn_bilinear","title":"Bilinear module — nn_bilinear","text":"Applies bilinear transformation incoming data \\(y = x_1^T x_2 + b\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_bilinear.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bilinear module — nn_bilinear","text":"","code":"nn_bilinear(in1_features, in2_features, out_features, bias = TRUE)"},{"path":"https://torch.mlverse.org/docs/reference/nn_bilinear.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bilinear module — nn_bilinear","text":"in1_features size first input sample in2_features size second input sample out_features size output sample bias set FALSE, layer learn additive bias. Default: TRUE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_bilinear.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Bilinear module — nn_bilinear","text":"Input1: \\((N, *, H_{in1})\\) \\(H_{in1}=\\mbox{in1\\_features}\\) \\(*\\) means number additional dimensions. last dimension inputs . Input2: \\((N, *, H_{in2})\\) \\(H_{in2}=\\mbox{in2\\_features}\\). Output: \\((N, *, H_{})\\) \\(H_{}=\\mbox{\\_features}\\) last dimension shape input.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_bilinear.html","id":"attributes","dir":"Reference","previous_headings":"","what":"Attributes","title":"Bilinear module — nn_bilinear","text":"weight: learnable weights module shape \\((\\mbox{\\_features}, \\mbox{in1\\_features}, \\mbox{in2\\_features})\\). values initialized \\(\\mathcal{U}(-\\sqrt{k}, \\sqrt{k})\\), \\(k = \\frac{1}{\\mbox{in1\\_features}}\\) bias: learnable bias module shape \\((\\mbox{\\_features})\\). bias TRUE, values initialized \\(\\mathcal{U}(-\\sqrt{k}, \\sqrt{k})\\), \\(k = \\frac{1}{\\mbox{in1\\_features}}\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_bilinear.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Bilinear module — nn_bilinear","text":"","code":"if (torch_is_installed()) { m <- nn_bilinear(20, 30, 50) input1 <- torch_randn(128, 20) input2 <- torch_randn(128, 30) output <- m(input1, input2) print(output$size()) } #> [1] 128  50"},{"path":"https://torch.mlverse.org/docs/reference/nn_buffer.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates a nn_buffer — nn_buffer","title":"Creates a nn_buffer — nn_buffer","text":"Indicates tensor buffer nn_module","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_buffer.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates a nn_buffer — nn_buffer","text":"","code":"nn_buffer(x, persistent = TRUE)"},{"path":"https://torch.mlverse.org/docs/reference/nn_buffer.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates a nn_buffer — nn_buffer","text":"x tensor converted nn_buffer persistent whether buffer persistent .","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_celu.html","id":null,"dir":"Reference","previous_headings":"","what":"CELU module — nn_celu","title":"CELU module — nn_celu","text":"Applies element-wise function:","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_celu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CELU module — nn_celu","text":"","code":"nn_celu(alpha = 1, inplace = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nn_celu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"CELU module — nn_celu","text":"alpha \\(\\alpha\\) value CELU formulation. Default: 1.0 inplace can optionally operation -place. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_celu.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"CELU module — nn_celu","text":"$$   \\mbox{CELU}(x) = \\max(0,x) + \\min(0, \\alpha * (\\exp(x/\\alpha) - 1)) $$ details can found paper Continuously Differentiable Exponential Linear Units.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_celu.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"CELU module — nn_celu","text":"Input: \\((N, *)\\) * means, number additional dimensions Output: \\((N, *)\\), shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_celu.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"CELU module — nn_celu","text":"","code":"if (torch_is_installed()) { m <- nn_celu() input <- torch_randn(2) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_contrib_sparsemax.html","id":null,"dir":"Reference","previous_headings":"","what":"Sparsemax activation — nn_contrib_sparsemax","title":"Sparsemax activation — nn_contrib_sparsemax","text":"Sparsemax activation module.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_contrib_sparsemax.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sparsemax activation — nn_contrib_sparsemax","text":"","code":"nn_contrib_sparsemax(dim = -1)"},{"path":"https://torch.mlverse.org/docs/reference/nn_contrib_sparsemax.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sparsemax activation — nn_contrib_sparsemax","text":"dim dimension apply sparsemax function. (-1)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_contrib_sparsemax.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Sparsemax activation — nn_contrib_sparsemax","text":"SparseMax activation described 'Softmax Sparsemax: Sparse Model Attention Multi-Label Classification' implementation based aced125/sparsemax","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Conv1D module — nn_conv1d","title":"Conv1D module — nn_conv1d","text":"Applies 1D convolution input signal composed several input planes. simplest case, output value layer input size \\((N, C_{\\mbox{}}, L)\\) output \\((N, C_{\\mbox{}}, L_{\\mbox{}})\\) can precisely described :","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Conv1D module — nn_conv1d","text":"","code":"nn_conv1d(   in_channels,   out_channels,   kernel_size,   stride = 1,   padding = 0,   dilation = 1,   groups = 1,   bias = TRUE,   padding_mode = \"zeros\" )"},{"path":"https://torch.mlverse.org/docs/reference/nn_conv1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Conv1D module — nn_conv1d","text":"in_channels (int): Number channels input image out_channels (int): Number channels produced convolution kernel_size (int tuple): Size convolving kernel stride (int tuple, optional): Stride convolution. Default: 1 padding (int, tuple str, optional) – Padding added sides input. Default: 0 dilation (int tuple, optional): Spacing kernel elements. Default: 1 groups (int, optional): Number blocked connections input channels output channels. Default: 1 bias (bool, optional): TRUE, adds learnable bias output. Default: TRUE padding_mode (string, optional): 'zeros', 'reflect', 'replicate' 'circular'. Default: 'zeros'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv1d.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Conv1D module — nn_conv1d","text":"$$ \\mbox{}(N_i, C_{\\mbox{}_j}) = \\mbox{bias}(C_{\\mbox{}_j}) +   \\sum_{k = 0}^{C_{} - 1} \\mbox{weight}(C_{\\mbox{}_j}, k) \\star \\mbox{input}(N_i, k) $$ \\(\\star\\) valid cross-correlation operator, \\(N\\) batch size, \\(C\\) denotes number channels, \\(L\\) length signal sequence. stride controls stride cross-correlation, single number one-element tuple. padding controls amount implicit zero-paddings sides padding number points. dilation controls spacing kernel points; also known à trous algorithm. harder describe, link nice visualization dilation . groups controls connections inputs outputs. in_channels out_channels must divisible groups. example, groups=1, inputs convolved outputs. groups=2, operation becomes equivalent two conv layers side side, seeing half input channels, producing half output channels, subsequently concatenated. groups= in_channels, input channel convolved set filters, size \\(\\left\\lfloor\\frac{\\_channels}{\\_channels}\\right\\rfloor\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv1d.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Conv1D module — nn_conv1d","text":"Depending size kernel, several (last) columns input might lost, valid cross-correlation, full cross-correlation. user add proper padding. groups == in_channels out_channels == K * in_channels, K positive integer, operation also termed literature depthwise convolution. words, input size \\((N, C_{}, L_{})\\), depthwise convolution depthwise multiplier K, can constructed arguments \\((C_{\\mbox{}}=C_{}, C_{\\mbox{}}=C_{} \\times K, ..., \\mbox{groups}=C_{})\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv1d.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Conv1D module — nn_conv1d","text":"Input: \\((N, C_{}, L_{})\\) Output: \\((N, C_{}, L_{})\\) $$   L_{} = \\left\\lfloor\\frac{L_{} + 2 \\times \\mbox{padding} - \\mbox{dilation}     \\times (\\mbox{kernel\\_size} - 1) - 1}{\\mbox{stride}} + 1\\right\\rfloor $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv1d.html","id":"attributes","dir":"Reference","previous_headings":"","what":"Attributes","title":"Conv1D module — nn_conv1d","text":"weight (Tensor): learnable weights module shape \\((\\mbox{\\_channels}, \\frac{\\mbox{\\_channels}}{\\mbox{groups}}, \\mbox{kernel\\_size})\\). values weights sampled \\(\\mathcal{U}(-\\sqrt{k}, \\sqrt{k})\\) \\(k = \\frac{groups}{C_{\\mbox{}} * \\mbox{kernel\\_size}}\\) bias (Tensor): learnable bias module shape (out_channels). bias TRUE, values weights sampled \\(\\mathcal{U}(-\\sqrt{k}, \\sqrt{k})\\) \\(k = \\frac{groups}{C_{\\mbox{}} * \\mbox{kernel\\_size}}\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv1d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Conv1D module — nn_conv1d","text":"","code":"if (torch_is_installed()) { m <- nn_conv1d(16, 33, 3, stride = 2) input <- torch_randn(20, 16, 50) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_conv2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Conv2D module — nn_conv2d","title":"Conv2D module — nn_conv2d","text":"Applies 2D convolution input signal composed several input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Conv2D module — nn_conv2d","text":"","code":"nn_conv2d(   in_channels,   out_channels,   kernel_size,   stride = 1,   padding = 0,   dilation = 1,   groups = 1,   bias = TRUE,   padding_mode = \"zeros\" )"},{"path":"https://torch.mlverse.org/docs/reference/nn_conv2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Conv2D module — nn_conv2d","text":"in_channels (int): Number channels input image out_channels (int): Number channels produced convolution kernel_size (int tuple): Size convolving kernel stride (int tuple, optional): Stride convolution. Default: 1 padding (int tuple string, optional): Zero-padding added sides input. controls amount padding applied input. can either string 'valid', '' tuple ints giving amount implicit padding applied sides. Default: 0 dilation (int tuple, optional): Spacing kernel elements. Default: 1 groups (int, optional): Number blocked connections input channels output channels. Default: 1 bias (bool, optional): TRUE, adds learnable bias output. Default: TRUE padding_mode (string, optional): 'zeros', 'reflect', 'replicate' 'circular'. Default: 'zeros'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv2d.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Conv2D module — nn_conv2d","text":"simplest case, output value layer input size \\((N, C_{\\mbox{}}, H, W)\\) output \\((N, C_{\\mbox{}}, H_{\\mbox{}}, W_{\\mbox{}})\\) can precisely described : $$ \\mbox{}(N_i, C_{\\mbox{}_j}) = \\mbox{bias}(C_{\\mbox{}_j}) +   \\sum_{k = 0}^{C_{\\mbox{}} - 1} \\mbox{weight}(C_{\\mbox{}_j}, k) \\star \\mbox{input}(N_i, k) $$ \\(\\star\\) valid 2D cross-correlation operator, \\(N\\) batch size, \\(C\\) denotes number channels, \\(H\\) height input planes pixels, \\(W\\) width pixels. stride controls stride cross-correlation, single number tuple. padding controls amount implicit zero-paddings sides padding number points dimension. dilation controls spacing kernel points; also known à trous algorithm. harder describe, link_ nice visualization dilation . groups controls connections inputs outputs. in_channels out_channels must divisible groups. example, groups=1, inputs convolved outputs. groups=2, operation becomes equivalent two conv layers side side, seeing half input channels, producing half output channels, subsequently concatenated. groups= in_channels, input channel convolved set filters, size: \\(\\left\\lfloor\\frac{\\_channels}{\\_channels}\\right\\rfloor\\). parameters kernel_size, stride, padding, dilation can either : single int -- case value used height width dimension tuple two ints -- case, first int used height dimension, second int width dimension","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv2d.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Conv2D module — nn_conv2d","text":"Depending size kernel, several (last) columns input might lost, valid cross-correlation, full cross-correlation. user add proper padding. groups == in_channels out_channels == K * in_channels, K positive integer, operation also termed literature depthwise convolution. words, input size :math:(N, C_{}, H_{}, W_{}), depthwise convolution depthwise multiplier K, can constructed arguments \\((\\_channels=C_{}, \\_channels=C_{} \\times K, ..., groups=C_{})\\). circumstances using CUDA backend CuDNN, operator may select nondeterministic algorithm increase performance. undesirable, can try make operation deterministic (potentially performance cost) setting backends_cudnn_deterministic = TRUE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv2d.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Conv2D module — nn_conv2d","text":"Input: \\((N, C_{}, H_{}, W_{})\\) Output: \\((N, C_{}, H_{}, W_{})\\) $$   H_{} = \\left\\lfloor\\frac{H_{}  + 2 \\times \\mbox{padding}[0] - \\mbox{dilation}[0]     \\times (\\mbox{kernel\\_size}[0] - 1) - 1}{\\mbox{stride}[0]} + 1\\right\\rfloor $$ $$   W_{} = \\left\\lfloor\\frac{W_{}  + 2 \\times \\mbox{padding}[1] - \\mbox{dilation}[1]     \\times (\\mbox{kernel\\_size}[1] - 1) - 1}{\\mbox{stride}[1]} + 1\\right\\rfloor $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv2d.html","id":"attributes","dir":"Reference","previous_headings":"","what":"Attributes","title":"Conv2D module — nn_conv2d","text":"weight (Tensor): learnable weights module shape \\((\\mbox{\\_channels}, \\frac{\\mbox{\\_channels}}{\\mbox{groups}}\\), \\(\\mbox{kernel\\_size[0]}, \\mbox{kernel\\_size[1]})\\). values weights sampled \\(\\mathcal{U}(-\\sqrt{k}, \\sqrt{k})\\) \\(k = \\frac{groups}{C_{\\mbox{}} * \\prod_{=0}^{1}\\mbox{kernel\\_size}[]}\\) bias (Tensor): learnable bias module shape (out_channels). bias TRUE, values weights sampled \\(\\mathcal{U}(-\\sqrt{k}, \\sqrt{k})\\) \\(k = \\frac{groups}{C_{\\mbox{}} * \\prod_{=0}^{1}\\mbox{kernel\\_size}[]}\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv2d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Conv2D module — nn_conv2d","text":"","code":"if (torch_is_installed()) {  # With square kernels and equal stride m <- nn_conv2d(16, 33, 3, stride = 2) # non-square kernels and unequal stride and with padding m <- nn_conv2d(16, 33, c(3, 5), stride = c(2, 1), padding = c(4, 2)) # non-square kernels and unequal stride and with padding and dilation m <- nn_conv2d(16, 33, c(3, 5), stride = c(2, 1), padding = c(4, 2), dilation = c(3, 1)) input <- torch_randn(20, 16, 50, 100) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_conv3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Conv3D module — nn_conv3d","title":"Conv3D module — nn_conv3d","text":"Applies 3D convolution input signal composed several input planes. simplest case, output value layer input size \\((N, C_{}, D, H, W)\\) output \\((N, C_{}, D_{}, H_{}, W_{})\\) can precisely described :","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Conv3D module — nn_conv3d","text":"","code":"nn_conv3d(   in_channels,   out_channels,   kernel_size,   stride = 1,   padding = 0,   dilation = 1,   groups = 1,   bias = TRUE,   padding_mode = \"zeros\" )"},{"path":"https://torch.mlverse.org/docs/reference/nn_conv3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Conv3D module — nn_conv3d","text":"in_channels (int): Number channels input image out_channels (int): Number channels produced convolution kernel_size (int tuple): Size convolving kernel stride (int tuple, optional): Stride convolution. Default: 1 padding (int, tuple str, optional): padding added six sides input. Default: 0 dilation (int tuple, optional): Spacing kernel elements. Default: 1 groups (int, optional): Number blocked connections input channels output channels. Default: 1 bias (bool, optional): TRUE, adds learnable bias output. Default: TRUE padding_mode (string, optional): 'zeros', 'reflect', 'replicate' 'circular'. Default: 'zeros'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv3d.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Conv3D module — nn_conv3d","text":"$$   (N_i, C_{out_j}) = bias(C_{out_j}) +   \\sum_{k = 0}^{C_{} - 1} weight(C_{out_j}, k) \\star input(N_i, k) $$ \\(\\star\\) valid 3D cross-correlation operator stride controls stride cross-correlation. padding controls amount implicit zero-paddings sides padding number points dimension. dilation controls spacing kernel points; also known à trous algorithm. harder describe, link_ nice visualization dilation . groups controls connections inputs outputs. in_channels out_channels must divisible groups. example, groups=1, inputs convolved outputs. groups=2, operation becomes equivalent two conv layers side side, seeing half input channels, producing half output channels, subsequently concatenated. groups= in_channels, input channel convolved set filters, size \\(\\left\\lfloor\\frac{\\_channels}{\\_channels}\\right\\rfloor\\). parameters kernel_size, stride, padding, dilation can either : single int -- case value used depth, height width dimension tuple three ints -- case, first int used depth dimension, second int height dimension third int width dimension","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv3d.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Conv3D module — nn_conv3d","text":"Depending size kernel, several (last) columns input might lost, valid cross-correlation, full cross-correlation. user add proper padding. groups == in_channels out_channels == K * in_channels, K positive integer, operation also termed literature depthwise convolution. words, input size \\((N, C_{}, D_{}, H_{}, W_{})\\), depthwise convolution depthwise multiplier K, can constructed arguments \\((\\_channels=C_{}, \\_channels=C_{} \\times K, ..., groups=C_{})\\). circumstances using CUDA backend CuDNN, operator may select nondeterministic algorithm increase performance. undesirable, can try make operation deterministic (potentially performance cost) setting torch.backends.cudnn.deterministic = TRUE. Please see notes :doc:/notes/randomness background.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv3d.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Conv3D module — nn_conv3d","text":"Input: \\((N, C_{}, D_{}, H_{}, W_{})\\) Output: \\((N, C_{}, D_{}, H_{}, W_{})\\) $$   D_{} = \\left\\lfloor\\frac{D_{} + 2 \\times \\mbox{padding}[0] - \\mbox{dilation}[0]     \\times (\\mbox{kernel\\_size}[0] - 1) - 1}{\\mbox{stride}[0]} + 1\\right\\rfloor  $$ $$   H_{} = \\left\\lfloor\\frac{H_{} + 2 \\times \\mbox{padding}[1] - \\mbox{dilation}[1]     \\times (\\mbox{kernel\\_size}[1] - 1) - 1}{\\mbox{stride}[1]} + 1\\right\\rfloor  $$ $$   W_{} = \\left\\lfloor\\frac{W_{} + 2 \\times \\mbox{padding}[2] - \\mbox{dilation}[2]     \\times (\\mbox{kernel\\_size}[2] - 1) - 1}{\\mbox{stride}[2]} + 1\\right\\rfloor  $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv3d.html","id":"attributes","dir":"Reference","previous_headings":"","what":"Attributes","title":"Conv3D module — nn_conv3d","text":"weight (Tensor): learnable weights module shape \\((\\mbox{\\_channels}, \\frac{\\mbox{\\_channels}}{\\mbox{groups}},\\) \\(\\mbox{kernel\\_size[0]}, \\mbox{kernel\\_size[1]}, \\mbox{kernel\\_size[2]})\\). values weights sampled \\(\\mathcal{U}(-\\sqrt{k}, \\sqrt{k})\\) \\(k = \\frac{groups}{C_{\\mbox{}} * \\prod_{=0}^{2}\\mbox{kernel\\_size}[]}\\) bias (Tensor):   learnable bias module shape (out_channels). bias True, values weights sampled \\(\\mathcal{U}(-\\sqrt{k}, \\sqrt{k})\\) \\(k = \\frac{groups}{C_{\\mbox{}} * \\prod_{=0}^{2}\\mbox{kernel\\_size}[]}\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv3d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Conv3D module — nn_conv3d","text":"","code":"if (torch_is_installed()) { # With square kernels and equal stride m <- nn_conv3d(16, 33, 3, stride = 2) # non-square kernels and unequal stride and with padding m <- nn_conv3d(16, 33, c(3, 5, 2), stride = c(2, 1, 1), padding = c(4, 2, 0)) input <- torch_randn(20, 16, 10, 50, 100) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_conv_transpose1d.html","id":null,"dir":"Reference","previous_headings":"","what":"ConvTranspose1D — nn_conv_transpose1d","title":"ConvTranspose1D — nn_conv_transpose1d","text":"Applies 1D transposed convolution operator input image composed several input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv_transpose1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"ConvTranspose1D — nn_conv_transpose1d","text":"","code":"nn_conv_transpose1d(   in_channels,   out_channels,   kernel_size,   stride = 1,   padding = 0,   output_padding = 0,   groups = 1,   bias = TRUE,   dilation = 1,   padding_mode = \"zeros\" )"},{"path":"https://torch.mlverse.org/docs/reference/nn_conv_transpose1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"ConvTranspose1D — nn_conv_transpose1d","text":"in_channels (int): Number channels input image out_channels (int): Number channels produced convolution kernel_size (int tuple): Size convolving kernel stride (int tuple, optional): Stride convolution. Default: 1 padding (int tuple, optional): dilation * (kernel_size - 1) - padding zero-padding added sides input. Default: 0 output_padding (int tuple, optional): Additional size added one side output shape. Default: 0 groups (int, optional): Number blocked connections input channels output channels. Default: 1 bias (bool, optional): True, adds learnable bias output. Default: TRUE dilation (int tuple, optional): Spacing kernel elements. Default: 1 padding_mode (string, optional): 'zeros', 'reflect', 'replicate' 'circular'. Default: 'zeros'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv_transpose1d.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"ConvTranspose1D — nn_conv_transpose1d","text":"module can seen gradient Conv1d respect input. also known fractionally-strided convolution deconvolution (although actual deconvolution operation). stride controls stride cross-correlation. padding controls amount implicit zero-paddings sides dilation * (kernel_size - 1) - padding number points. See note details. output_padding controls additional size added one side output shape. See note details. dilation controls spacing kernel points; also known à trous algorithm. harder describe, link nice visualization dilation . groups controls connections inputs outputs. in_channels out_channels must divisible groups. example, groups=1, inputs convolved outputs. groups=2, operation becomes equivalent two conv layers side side, seeing half input channels, producing half output channels, subsequently concatenated. groups= in_channels, input channel convolved set filters (size \\(\\left\\lfloor\\frac{\\_channels}{\\_channels}\\right\\rfloor\\)).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv_transpose1d.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"ConvTranspose1D — nn_conv_transpose1d","text":"Depending size kernel, several (last) columns input might lost, valid cross-correlation, full cross-correlation. user add proper padding. padding argument effectively adds dilation * (kernel_size - 1) - padding amount zero padding sizes input. set ~torch.nn.Conv1d ~torch.nn.ConvTranspose1d initialized parameters, inverses regard input output shapes. However, stride > 1, ~torch.nn.Conv1d maps multiple input shapes output shape. output_padding provided resolve ambiguity effectively increasing calculated output shape one side. Note output_padding used find output shape, actually add zero-padding output. circumstances using CUDA backend CuDNN, operator may select nondeterministic algorithm increase performance. undesirable, can try make operation deterministic (potentially performance cost) setting torch.backends.cudnn.deterministic = TRUE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv_transpose1d.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"ConvTranspose1D — nn_conv_transpose1d","text":"Input: \\((N, C_{}, L_{})\\) Output: \\((N, C_{}, L_{})\\) $$   L_{} = (L_{} - 1) \\times \\mbox{stride} - 2 \\times \\mbox{padding} + \\mbox{dilation} \\times (\\mbox{kernel\\_size} - 1) + \\mbox{output\\_padding} + 1 $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv_transpose1d.html","id":"attributes","dir":"Reference","previous_headings":"","what":"Attributes","title":"ConvTranspose1D — nn_conv_transpose1d","text":"weight (Tensor): learnable weights module shape \\((\\mbox{\\_channels}, \\frac{\\mbox{\\_channels}}{\\mbox{groups}},\\) \\(\\mbox{kernel\\_size})\\). values weights sampled \\(\\mathcal{U}(-\\sqrt{k}, \\sqrt{k})\\) \\(k = \\frac{groups}{C_{\\mbox{}} * \\mbox{kernel\\_size}}\\) bias (Tensor):   learnable bias module shape (out_channels). bias TRUE, values weights sampled \\(\\mathcal{U}(-\\sqrt{k}, \\sqrt{k})\\) \\(k = \\frac{groups}{C_{\\mbox{}} * \\mbox{kernel\\_size}}\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv_transpose1d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"ConvTranspose1D — nn_conv_transpose1d","text":"","code":"if (torch_is_installed()) { m <- nn_conv_transpose1d(32, 16, 2) input <- torch_randn(10, 32, 2) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_conv_transpose2d.html","id":null,"dir":"Reference","previous_headings":"","what":"ConvTranpose2D module — nn_conv_transpose2d","title":"ConvTranpose2D module — nn_conv_transpose2d","text":"Applies 2D transposed convolution operator input image composed several input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv_transpose2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"ConvTranpose2D module — nn_conv_transpose2d","text":"","code":"nn_conv_transpose2d(   in_channels,   out_channels,   kernel_size,   stride = 1,   padding = 0,   output_padding = 0,   groups = 1,   bias = TRUE,   dilation = 1,   padding_mode = \"zeros\" )"},{"path":"https://torch.mlverse.org/docs/reference/nn_conv_transpose2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"ConvTranpose2D module — nn_conv_transpose2d","text":"in_channels (int): Number channels input image out_channels (int): Number channels produced convolution kernel_size (int tuple): Size convolving kernel stride (int tuple, optional): Stride convolution. Default: 1 padding (int tuple, optional): dilation * (kernel_size - 1) - padding zero-padding added sides dimension input. Default: 0 output_padding (int tuple, optional): Additional size added one side dimension output shape. Default: 0 groups (int, optional): Number blocked connections input channels output channels. Default: 1 bias (bool, optional): True, adds learnable bias output. Default: True dilation (int tuple, optional): Spacing kernel elements. Default: 1 padding_mode (string, optional): 'zeros', 'reflect', 'replicate' 'circular'. Default: 'zeros'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv_transpose2d.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"ConvTranpose2D module — nn_conv_transpose2d","text":"module can seen gradient Conv2d respect input. also known fractionally-strided convolution deconvolution (although actual deconvolution operation). stride controls stride cross-correlation. padding controls amount implicit zero-paddings sides dilation * (kernel_size - 1) - padding number points. See note details. output_padding controls additional size added one side output shape. See note details. dilation controls spacing kernel points; also known à trous algorithm. harder describe, link_ nice visualization dilation . groups controls connections inputs outputs. in_channels out_channels must divisible groups. example, groups=1, inputs convolved outputs. groups=2, operation becomes equivalent two conv layers side side, seeing half input channels, producing half output channels, subsequently concatenated. groups= in_channels, input channel convolved set filters (size \\(\\left\\lfloor\\frac{\\_channels}{\\_channels}\\right\\rfloor\\)). parameters kernel_size, stride, padding, output_padding can either : single int -- case value used height width dimensions tuple two ints -- case, first int used height dimension, second int width dimension","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv_transpose2d.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"ConvTranpose2D module — nn_conv_transpose2d","text":"Depending size kernel, several (last) columns input might lost, valid cross-correlation_, full cross-correlation. user add proper padding. padding argument effectively adds dilation * (kernel_size - 1) - padding amount zero padding sizes input. set nn_conv2d nn_conv_transpose2d initialized parameters, inverses regard input output shapes. However, stride > 1, nn_conv2d maps multiple input shapes output shape. output_padding provided resolve ambiguity effectively increasing calculated output shape one side. Note output_padding used find output shape, actually add zero-padding output. circumstances using CUDA backend CuDNN, operator may select nondeterministic algorithm increase performance. undesirable, can try make operation deterministic (potentially performance cost) setting torch.backends.cudnn.deterministic = TRUE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv_transpose2d.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"ConvTranpose2D module — nn_conv_transpose2d","text":"Input: \\((N, C_{}, H_{}, W_{})\\) Output: \\((N, C_{}, H_{}, W_{})\\) $$   H_{} = (H_{} - 1) \\times \\mbox{stride}[0] - 2 \\times \\mbox{padding}[0] + \\mbox{dilation}[0] \\times (\\mbox{kernel\\_size}[0] - 1) + \\mbox{output\\_padding}[0] + 1 $$ $$   W_{} = (W_{} - 1) \\times \\mbox{stride}[1] - 2 \\times \\mbox{padding}[1] + \\mbox{dilation}[1] \\times (\\mbox{kernel\\_size}[1] - 1) + \\mbox{output\\_padding}[1] + 1 $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv_transpose2d.html","id":"attributes","dir":"Reference","previous_headings":"","what":"Attributes","title":"ConvTranpose2D module — nn_conv_transpose2d","text":"weight (Tensor): learnable weights module shape \\((\\mbox{\\_channels}, \\frac{\\mbox{\\_channels}}{\\mbox{groups}},\\) \\(\\mbox{kernel\\_size[0]}, \\mbox{kernel\\_size[1]})\\). values weights sampled \\(\\mathcal{U}(-\\sqrt{k}, \\sqrt{k})\\) \\(k = \\frac{groups}{C_{\\mbox{}} * \\prod_{=0}^{1}\\mbox{kernel\\_size}[]}\\) bias (Tensor):   learnable bias module shape (out_channels) bias True, values weights sampled \\(\\mathcal{U}(-\\sqrt{k}, \\sqrt{k})\\) \\(k = \\frac{groups}{C_{\\mbox{}} * \\prod_{=0}^{1}\\mbox{kernel\\_size}[]}\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv_transpose2d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"ConvTranpose2D module — nn_conv_transpose2d","text":"","code":"if (torch_is_installed()) { # With square kernels and equal stride m <- nn_conv_transpose2d(16, 33, 3, stride = 2) # non-square kernels and unequal stride and with padding m <- nn_conv_transpose2d(16, 33, c(3, 5), stride = c(2, 1), padding = c(4, 2)) input <- torch_randn(20, 16, 50, 100) output <- m(input) # exact output size can be also specified as an argument input <- torch_randn(1, 16, 12, 12) downsample <- nn_conv2d(16, 16, 3, stride = 2, padding = 1) upsample <- nn_conv_transpose2d(16, 16, 3, stride = 2, padding = 1) h <- downsample(input) h$size() output <- upsample(h, output_size = input$size()) output$size() } #> [1]  1 16 12 12"},{"path":"https://torch.mlverse.org/docs/reference/nn_conv_transpose3d.html","id":null,"dir":"Reference","previous_headings":"","what":"ConvTranpose3D module — nn_conv_transpose3d","title":"ConvTranpose3D module — nn_conv_transpose3d","text":"Applies 3D transposed convolution operator input image composed several input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv_transpose3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"ConvTranpose3D module — nn_conv_transpose3d","text":"","code":"nn_conv_transpose3d(   in_channels,   out_channels,   kernel_size,   stride = 1,   padding = 0,   output_padding = 0,   groups = 1,   bias = TRUE,   dilation = 1,   padding_mode = \"zeros\" )"},{"path":"https://torch.mlverse.org/docs/reference/nn_conv_transpose3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"ConvTranpose3D module — nn_conv_transpose3d","text":"in_channels (int): Number channels input image out_channels (int): Number channels produced convolution kernel_size (int tuple): Size convolving kernel stride (int tuple, optional): Stride convolution. Default: 1 padding (int tuple, optional): dilation * (kernel_size - 1) - padding zero-padding added sides dimension input. Default: 0 output_padding (int tuple, optional): Additional size added one side dimension output shape. Default: 0 output_padding (int tuple, optional): Additional size added one side dimension output shape. Default: 0 groups (int, optional): Number blocked connections input channels output channels. Default: 1 bias (bool, optional): True, adds learnable bias output. Default: True dilation (int tuple, optional): Spacing kernel elements. Default: 1 padding_mode (string, optional): 'zeros', 'reflect', 'replicate' 'circular'. Default: 'zeros'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv_transpose3d.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"ConvTranpose3D module — nn_conv_transpose3d","text":"transposed convolution operator multiplies input value element-wise learnable kernel, sums outputs input feature planes. module can seen gradient Conv3d respect input. also known fractionally-strided convolution deconvolution (although actual deconvolution operation). stride controls stride cross-correlation. padding controls amount implicit zero-paddings sides dilation * (kernel_size - 1) - padding number points. See note details. output_padding controls additional size added one side output shape. See note details. dilation controls spacing kernel points; also known à trous algorithm. harder describe, link_ nice visualization dilation . groups controls connections inputs outputs. in_channels out_channels must divisible groups. example, groups=1, inputs convolved outputs. groups=2, operation becomes equivalent two conv layers side side, seeing half input channels, producing half output channels, subsequently concatenated. groups= in_channels, input channel convolved set filters (size \\(\\left\\lfloor\\frac{\\_channels}{\\_channels}\\right\\rfloor\\)). parameters kernel_size, stride, padding, output_padding can either : single int -- case value used depth, height width dimensions tuple three ints -- case, first int used depth dimension, second int height dimension third int width dimension","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv_transpose3d.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"ConvTranpose3D module — nn_conv_transpose3d","text":"Depending size kernel, several (last) columns input might lost, valid cross-correlation, full cross-correlation. user add proper padding. padding argument effectively adds dilation * (kernel_size - 1) - padding amount zero padding sizes input. set ~torch.nn.Conv3d ~torch.nn.ConvTranspose3d initialized parameters, inverses regard input output shapes. However, stride > 1, ~torch.nn.Conv3d maps multiple input shapes output shape. output_padding provided resolve ambiguity effectively increasing calculated output shape one side. Note output_padding used find output shape, actually add zero-padding output. circumstances using CUDA backend CuDNN, operator may select nondeterministic algorithm increase performance. undesirable, can try make operation deterministic (potentially performance cost) setting torch.backends.cudnn.deterministic = TRUE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv_transpose3d.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"ConvTranpose3D module — nn_conv_transpose3d","text":"Input: \\((N, C_{}, D_{}, H_{}, W_{})\\) Output: \\((N, C_{}, D_{}, H_{}, W_{})\\) $$   D_{} = (D_{} - 1) \\times \\mbox{stride}[0] - 2 \\times \\mbox{padding}[0] + \\mbox{dilation}[0] \\times (\\mbox{kernel\\_size}[0] - 1) + \\mbox{output\\_padding}[0] + 1 $$ $$   H_{} = (H_{} - 1) \\times \\mbox{stride}[1] - 2 \\times \\mbox{padding}[1] + \\mbox{dilation}[1] \\times (\\mbox{kernel\\_size}[1] - 1) + \\mbox{output\\_padding}[1] + 1 $$ $$   W_{} = (W_{} - 1) \\times \\mbox{stride}[2] - 2 \\times \\mbox{padding}[2] + \\mbox{dilation}[2] \\times (\\mbox{kernel\\_size}[2] - 1) + \\mbox{output\\_padding}[2] + 1 $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv_transpose3d.html","id":"attributes","dir":"Reference","previous_headings":"","what":"Attributes","title":"ConvTranpose3D module — nn_conv_transpose3d","text":"weight (Tensor): learnable weights module shape \\((\\mbox{\\_channels}, \\frac{\\mbox{\\_channels}}{\\mbox{groups}},\\) \\(\\mbox{kernel\\_size[0]}, \\mbox{kernel\\_size[1]}, \\mbox{kernel\\_size[2]})\\). values weights sampled \\(\\mathcal{U}(-\\sqrt{k}, \\sqrt{k})\\) \\(k = \\frac{groups}{C_{\\mbox{}} * \\prod_{=0}^{2}\\mbox{kernel\\_size}[]}\\) bias (Tensor):   learnable bias module shape (out_channels) bias True, values weights sampled \\(\\mathcal{U}(-\\sqrt{k}, \\sqrt{k})\\) \\(k = \\frac{groups}{C_{\\mbox{}} * \\prod_{=0}^{2}\\mbox{kernel\\_size}[]}\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv_transpose3d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"ConvTranpose3D module — nn_conv_transpose3d","text":"","code":"if (torch_is_installed()) { if (FALSE) { # With square kernels and equal stride m <- nn_conv_transpose3d(16, 33, 3, stride = 2) # non-square kernels and unequal stride and with padding m <- nn_conv_transpose3d(16, 33, c(3, 5, 2), stride = c(2, 1, 1), padding = c(0, 4, 2)) input <- torch_randn(20, 16, 10, 50, 100) output <- m(input) } }"},{"path":"https://torch.mlverse.org/docs/reference/nn_cosine_embedding_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Cosine embedding loss — nn_cosine_embedding_loss","title":"Cosine embedding loss — nn_cosine_embedding_loss","text":"Creates criterion measures loss given input tensors \\(x_1\\), \\(x_2\\) Tensor label \\(y\\) values 1 -1. used measuring whether two inputs similar dissimilar, using cosine distance, typically used learning nonlinear embeddings semi-supervised learning. loss function sample :","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_cosine_embedding_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cosine embedding loss — nn_cosine_embedding_loss","text":"","code":"nn_cosine_embedding_loss(margin = 0, reduction = \"mean\")"},{"path":"https://torch.mlverse.org/docs/reference/nn_cosine_embedding_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cosine embedding loss — nn_cosine_embedding_loss","text":"margin (float, optional): number \\(-1\\) \\(1\\), \\(0\\) \\(0.5\\) suggested. margin missing, default value \\(0\\). reduction (string, optional): Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed. Note: size_average reduce process deprecated, meantime, specifying either two args override reduction. Default: 'mean'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_cosine_embedding_loss.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Cosine embedding loss — nn_cosine_embedding_loss","text":"$$   \\mbox{loss}(x, y) =   \\begin{array}{ll} 1 - \\cos(x_1, x_2), & \\mbox{} y = 1 \\\\ \\max(0, \\cos(x_1, x_2) - \\mbox{margin}), & \\mbox{} y = -1 \\end{array} $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_cross_entropy_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"CrossEntropyLoss module — nn_cross_entropy_loss","title":"CrossEntropyLoss module — nn_cross_entropy_loss","text":"criterion combines nn_log_softmax() nn_nll_loss() one single class. useful training classification problem C classes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_cross_entropy_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CrossEntropyLoss module — nn_cross_entropy_loss","text":"","code":"nn_cross_entropy_loss(weight = NULL, ignore_index = -100, reduction = \"mean\")"},{"path":"https://torch.mlverse.org/docs/reference/nn_cross_entropy_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"CrossEntropyLoss module — nn_cross_entropy_loss","text":"weight (Tensor, optional): manual rescaling weight given class. given, Tensor size C ignore_index (int, optional): Specifies target value ignored contribute input gradient. size_average TRUE, loss averaged non-ignored targets. reduction (string, optional): Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed. Note: size_average reduce process deprecated, meantime, specifying either two args override reduction. Default: 'mean'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_cross_entropy_loss.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"CrossEntropyLoss module — nn_cross_entropy_loss","text":"provided, optional argument weight 1D Tensor assigning weight classes. particularly useful unbalanced training set. input expected contain raw, unnormalized scores class. input Tensor size either \\((minibatch, C)\\) \\((minibatch, C, d_1, d_2, ..., d_K)\\) \\(K \\geq 1\\) K-dimensional case (described later). criterion expects class index range \\([0, C-1]\\) target value 1D tensor size minibatch; ignore_index specified, criterion also accepts class index (index may necessarily class range). loss can described : $$   \\mbox{loss}(x, class) = -\\log\\left(\\frac{\\exp(x[class])}{\\sum_j \\exp(x[j])}\\right) = -x[class] + \\log\\left(\\sum_j \\exp(x[j])\\right) $$ case weight argument specified: $$   \\mbox{loss}(x, class) = weight[class] \\left(-x[class] + \\log\\left(\\sum_j \\exp(x[j])\\right)\\right) $$ losses averaged across observations minibatch. Can also used higher dimension inputs, 2D images, providing input size \\((minibatch, C, d_1, d_2, ..., d_K)\\) \\(K \\geq 1\\), \\(K\\) number dimensions, target appropriate shape (see ).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_cross_entropy_loss.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"CrossEntropyLoss module — nn_cross_entropy_loss","text":"Input: \\((N, C)\\) C = number classes, \\((N, C, d_1, d_2, ..., d_K)\\) \\(K \\geq 1\\) case K-dimensional loss. Target: \\((N)\\) value \\(0 \\leq \\mbox{targets}[] \\leq C-1\\), \\((N, d_1, d_2, ..., d_K)\\) \\(K \\geq 1\\) case K-dimensional loss. Output: scalar. reduction 'none', size target: \\((N)\\), \\((N, d_1, d_2, ..., d_K)\\) \\(K \\geq 1\\) case K-dimensional loss.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_cross_entropy_loss.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"CrossEntropyLoss module — nn_cross_entropy_loss","text":"","code":"if (torch_is_installed()) { loss <- nn_cross_entropy_loss() input <- torch_randn(3, 5, requires_grad = TRUE) target <- torch_randint(low = 1, high = 5, size = 3, dtype = torch_long()) output <- loss(input, target) output$backward() }"},{"path":"https://torch.mlverse.org/docs/reference/nn_ctc_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"The Connectionist Temporal Classification loss. — nn_ctc_loss","title":"The Connectionist Temporal Classification loss. — nn_ctc_loss","text":"Calculates loss continuous (unsegmented) time series target sequence. CTCLoss sums probability possible alignments input target, producing loss value differentiable respect input node. alignment input target assumed \"many--one\", limits length target sequence must \\(\\leq\\) input length.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_ctc_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"The Connectionist Temporal Classification loss. — nn_ctc_loss","text":"","code":"nn_ctc_loss(blank = 0, reduction = \"mean\", zero_infinity = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nn_ctc_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"The Connectionist Temporal Classification loss. — nn_ctc_loss","text":"blank (int, optional): blank label. Default \\(0\\). reduction (string, optional): Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': output losses divided target lengths mean batch taken. Default: 'mean' zero_infinity (bool, optional): Whether zero infinite losses associated gradients. Default: FALSE Infinite losses mainly occur inputs short aligned targets.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_ctc_loss.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"The Connectionist Temporal Classification loss. — nn_ctc_loss","text":"order use CuDNN, following must satisfied: targets must concatenated format, input_lengths must T.  \\(blank=0\\), target_lengths \\(\\leq 256\\), integer arguments must regular implementation uses (common PyTorch) torch_long dtype. dtype torch_int32. circumstances using CUDA backend CuDNN, operator may select nondeterministic algorithm increase performance. undesirable, can try make operation deterministic (potentially performance cost) setting torch.backends.cudnn.deterministic = TRUE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_ctc_loss.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"The Connectionist Temporal Classification loss. — nn_ctc_loss","text":"Log_probs: Tensor size \\((T, N, C)\\), \\(T = \\mbox{input length}\\), \\(N = \\mbox{batch size}\\), \\(C = \\mbox{number classes (including blank)}\\). logarithmized probabilities outputs (e.g. obtained [nnf)log_softmax()]). Targets: Tensor size \\((N, S)\\) \\((\\mbox{sum}(\\mbox{target\\_lengths}))\\), \\(N = \\mbox{batch size}\\) \\(S = \\mbox{max target length, shape } (N, S)\\). represent target sequences. element target sequence class index. target index blank (default=0). \\((N, S)\\) form, targets padded length longest sequence, stacked. \\((\\mbox{sum}(\\mbox{target\\_lengths}))\\) form, targets assumed un-padded concatenated within 1 dimension. Input_lengths: Tuple tensor size \\((N)\\), \\(N = \\mbox{batch size}\\). represent lengths inputs (must \\(\\leq T\\)). lengths specified sequence achieve masking assumption sequences padded equal lengths. Target_lengths: Tuple tensor size \\((N)\\), \\(N = \\mbox{batch size}\\). represent lengths targets. Lengths specified sequence achieve masking assumption sequences padded equal lengths. target shape \\((N,S)\\), target_lengths effectively stop index \\(s_n\\) target sequence, target_n = targets[n,0:s_n] target batch. Lengths must \\(\\leq S\\) targets given 1d tensor concatenation individual targets, target_lengths must add total length tensor. Output: scalar. reduction 'none', \\((N)\\), \\(N = \\mbox{batch size}\\). [nnf)log_softmax()]: R:nnf)log_softmax() [n,0:s_n]: R:n,0:s_n","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_ctc_loss.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"The Connectionist Temporal Classification loss. — nn_ctc_loss","text":". Graves et al.: Connectionist Temporal Classification: Labelling Unsegmented Sequence Data Recurrent Neural Networks: https://www.cs.toronto.edu/~graves/icml_2006.pdf","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_ctc_loss.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"The Connectionist Temporal Classification loss. — nn_ctc_loss","text":"","code":"if (torch_is_installed()) { # Target are to be padded T <- 50 # Input sequence length C <- 20 # Number of classes (including blank) N <- 16 # Batch size S <- 30 # Target sequence length of longest target in batch (padding length) S_min <- 10 # Minimum target length, for demonstration purposes  # Initialize random batch of input vectors, for *size = (T,N,C) input <- torch_randn(T, N, C)$log_softmax(2)$detach()$requires_grad_()  # Initialize random batch of targets (0 = blank, 1:C = classes) target <- torch_randint(low = 1, high = C, size = c(N, S), dtype = torch_long())  input_lengths <- torch_full(size = c(N), fill_value = TRUE, dtype = torch_long()) target_lengths <- torch_randint(low = S_min, high = S, size = c(N), dtype = torch_long()) ctc_loss <- nn_ctc_loss() loss <- ctc_loss(input, target, input_lengths, target_lengths) loss$backward()   # Target are to be un-padded T <- 50 # Input sequence length C <- 20 # Number of classes (including blank) N <- 16 # Batch size  # Initialize random batch of input vectors, for *size = (T,N,C) input <- torch_randn(T, N, C)$log_softmax(2)$detach()$requires_grad_() input_lengths <- torch_full(size = c(N), fill_value = TRUE, dtype = torch_long())  # Initialize random batch of targets (0 = blank, 1:C = classes) target_lengths <- torch_randint(low = 1, high = T, size = c(N), dtype = torch_long()) target <- torch_randint(   low = 1, high = C, size = as.integer(sum(target_lengths)),   dtype = torch_long() ) ctc_loss <- nn_ctc_loss() loss <- ctc_loss(input, target, input_lengths, target_lengths) loss$backward() }"},{"path":"https://torch.mlverse.org/docs/reference/nn_dropout.html","id":null,"dir":"Reference","previous_headings":"","what":"Dropout module — nn_dropout","title":"Dropout module — nn_dropout","text":"training, randomly zeroes elements input tensor probability p using samples Bernoulli distribution. channel zeroed independently every forward call.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_dropout.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Dropout module — nn_dropout","text":"","code":"nn_dropout(p = 0.5, inplace = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nn_dropout.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Dropout module — nn_dropout","text":"p probability element zeroed. Default: 0.5 inplace set TRUE, operation -place. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_dropout.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Dropout module — nn_dropout","text":"proven effective technique regularization preventing co-adaptation neurons described paper Improving neural networks preventing co-adaptation feature detectors. Furthermore, outputs scaled factor :math:\\frac{1}{1-p} training. means evaluation module simply computes identity function.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_dropout.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Dropout module — nn_dropout","text":"Input: \\((*)\\). Input can shape Output: \\((*)\\). Output shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_dropout.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Dropout module — nn_dropout","text":"","code":"if (torch_is_installed()) { m <- nn_dropout(p = 0.2) input <- torch_randn(20, 16) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_dropout2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Dropout2D module — nn_dropout2d","title":"Dropout2D module — nn_dropout2d","text":"Randomly zero entire channels (channel 2D feature map, e.g., \\(j\\)-th channel \\(\\)-th sample batched input 2D tensor \\(\\mbox{input}[, j]\\)).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_dropout2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Dropout2D module — nn_dropout2d","text":"","code":"nn_dropout2d(p = 0.5, inplace = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nn_dropout2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Dropout2D module — nn_dropout2d","text":"p (float, optional): probability element zero-ed. inplace (bool, optional): set TRUE, operation -place","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_dropout2d.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Dropout2D module — nn_dropout2d","text":"channel zeroed independently every forward call probability p using samples Bernoulli distribution. Usually input comes nn_conv2d modules. described paper Efficient Object Localization Using Convolutional Networks , adjacent pixels within feature maps strongly correlated (normally case early convolution layers) ..d. dropout regularize activations otherwise just result effective learning rate decrease. case, nn_dropout2d help promote independence feature maps used instead.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_dropout2d.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Dropout2D module — nn_dropout2d","text":"Input: \\((N, C, H, W)\\) Output: \\((N, C, H, W)\\) (shape input)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_dropout2d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Dropout2D module — nn_dropout2d","text":"","code":"if (torch_is_installed()) { m <- nn_dropout2d(p = 0.2) input <- torch_randn(20, 16, 32, 32) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_dropout3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Dropout3D module — nn_dropout3d","title":"Dropout3D module — nn_dropout3d","text":"Randomly zero entire channels (channel 3D feature map, e.g., \\(j\\)-th channel \\(\\)-th sample batched input 3D tensor \\(\\mbox{input}[, j]\\)).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_dropout3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Dropout3D module — nn_dropout3d","text":"","code":"nn_dropout3d(p = 0.5, inplace = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nn_dropout3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Dropout3D module — nn_dropout3d","text":"p (float, optional): probability element zeroed. inplace (bool, optional): set TRUE, operation -place","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_dropout3d.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Dropout3D module — nn_dropout3d","text":"channel zeroed independently every forward call probability p using samples Bernoulli distribution. Usually input comes nn_conv2d modules. described paper Efficient Object Localization Using Convolutional Networks , adjacent pixels within feature maps strongly correlated (normally case early convolution layers) ..d. dropout regularize activations otherwise just result effective learning rate decrease. case, nn_dropout3d help promote independence feature maps used instead.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_dropout3d.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Dropout3D module — nn_dropout3d","text":"Input: \\((N, C, D, H, W)\\) Output: \\((N, C, D, H, W)\\) (shape input)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_dropout3d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Dropout3D module — nn_dropout3d","text":"","code":"if (torch_is_installed()) { m <- nn_dropout3d(p = 0.2) input <- torch_randn(20, 16, 4, 32, 32) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_elu.html","id":null,"dir":"Reference","previous_headings":"","what":"ELU module — nn_elu","title":"ELU module — nn_elu","text":"Applies element-wise function:","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_elu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"ELU module — nn_elu","text":"","code":"nn_elu(alpha = 1, inplace = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nn_elu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"ELU module — nn_elu","text":"alpha \\(\\alpha\\) value ELU formulation. Default: 1.0 inplace can optionally operation -place. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_elu.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"ELU module — nn_elu","text":"$$   \\mbox{ELU}(x) = \\max(0,x) + \\min(0, \\alpha * (\\exp(x) - 1)) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_elu.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"ELU module — nn_elu","text":"Input: \\((N, *)\\) * means, number additional dimensions Output: \\((N, *)\\), shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_elu.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"ELU module — nn_elu","text":"","code":"if (torch_is_installed()) { m <- nn_elu() input <- torch_randn(2) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_embedding.html","id":null,"dir":"Reference","previous_headings":"","what":"Embedding module — nn_embedding","title":"Embedding module — nn_embedding","text":"simple lookup table stores embeddings fixed dictionary size. module often used store word embeddings retrieve using indices. input module list indices, output corresponding word embeddings.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_embedding.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Embedding module — nn_embedding","text":"","code":"nn_embedding(   num_embeddings,   embedding_dim,   padding_idx = NULL,   max_norm = NULL,   norm_type = 2,   scale_grad_by_freq = FALSE,   sparse = FALSE,   .weight = NULL )"},{"path":"https://torch.mlverse.org/docs/reference/nn_embedding.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Embedding module — nn_embedding","text":"num_embeddings (int): size dictionary embeddings embedding_dim (int): size embedding vector padding_idx (int, optional): given, pads output embedding vector padding_idx (initialized zeros) whenever encounters index. max_norm (float, optional): given, embedding vector norm larger max_norm renormalized norm max_norm. norm_type (float, optional): p p-norm compute max_norm option. Default 2. scale_grad_by_freq (boolean, optional): given, scale gradients inverse frequency words mini-batch. Default False. sparse (bool, optional): True, gradient w.r.t. weight matrix sparse tensor. .weight (Tensor) embeddings weights (case want set manually) See Notes details regarding sparse gradients.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_embedding.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Embedding module — nn_embedding","text":"Keep mind limited number optimizers support sparse gradients: currently optim.SGD (CUDA CPU), optim.SparseAdam (CUDA CPU) optim.Adagrad (CPU) padding_idx set, embedding vector padding_idx initialized zeros. However, note vector can modified afterwards, e.g., using customized initialization method, thus changing vector used pad output. gradient vector nn_embedding always zero.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_embedding.html","id":"attributes","dir":"Reference","previous_headings":"","what":"Attributes","title":"Embedding module — nn_embedding","text":"weight (Tensor): learnable weights module shape (num_embeddings, embedding_dim) initialized \\(\\mathcal{N}(0, 1)\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_embedding.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Embedding module — nn_embedding","text":"Input: \\((*)\\), LongTensor arbitrary shape containing indices extract Output: \\((*, H)\\), * input shape \\(H=\\mbox{embedding\\_dim}\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_embedding.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Embedding module — nn_embedding","text":"","code":"if (torch_is_installed()) { # an Embedding module containing 10 tensors of size 3 embedding <- nn_embedding(10, 3) # a batch of 2 samples of 4 indices each input <- torch_tensor(rbind(c(1, 2, 4, 5), c(4, 3, 2, 9)), dtype = torch_long()) embedding(input) # example with padding_idx embedding <- nn_embedding(10, 3, padding_idx = 1) input <- torch_tensor(matrix(c(1, 3, 1, 6), nrow = 1), dtype = torch_long()) embedding(input) } #> torch_tensor #> (1,.,.) =  #>   0.0000  0.0000  0.0000 #>  -0.3464  1.2327 -2.9766 #>   0.0000  0.0000  0.0000 #>  -0.2314  0.4479 -0.6888 #> [ CPUFloatType{1,4,3} ][ grad_fn = <EmbeddingBackward0> ]"},{"path":"https://torch.mlverse.org/docs/reference/nn_flatten.html","id":null,"dir":"Reference","previous_headings":"","what":"Flattens a contiguous range of dims into a tensor. — nn_flatten","title":"Flattens a contiguous range of dims into a tensor. — nn_flatten","text":"use nn_sequential.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_flatten.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Flattens a contiguous range of dims into a tensor. — nn_flatten","text":"","code":"nn_flatten(start_dim = 2, end_dim = -1)"},{"path":"https://torch.mlverse.org/docs/reference/nn_flatten.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Flattens a contiguous range of dims into a tensor. — nn_flatten","text":"start_dim first dim flatten (default = 2). end_dim last dim flatten (default = -1).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_flatten.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Flattens a contiguous range of dims into a tensor. — nn_flatten","text":"Input: (*, S_start,..., S_i, ..., S_end, *), S_i size dimension * means number dimensions including none. Output: (*, S_start*...*S_i*...S_end, *).","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/nn_flatten.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Flattens a contiguous range of dims into a tensor. — nn_flatten","text":"","code":"if (torch_is_installed()) { input <- torch_randn(32, 1, 5, 5) m <- nn_flatten() m(input)  } #> torch_tensor #> Columns 1 to 10-0.0009  0.0037  0.5204 -0.3667 -2.0531  0.2671 -0.5804 -0.2076 -0.6655  1.1677 #> -1.6947 -2.3617 -0.0151 -0.9661 -0.5683 -2.4593  1.0112 -0.6164 -1.1062  0.1686 #>  3.0387  0.7551 -2.3594  1.6931 -0.4003 -0.9425 -0.1783  1.5772 -0.4279  0.7089 #>  0.7899  1.6087 -0.0747 -1.7557  0.0622  0.3763  0.3831  0.5895  0.3669  0.0486 #> -1.3491 -0.1465 -0.0981 -0.1463  0.7424  0.5160 -0.3618 -0.5935 -0.2595 -0.6434 #> -0.7859 -1.3648 -0.0414  0.2196 -0.8855  1.3220  0.0348  0.9984  1.1103 -0.2425 #>  0.8881  1.0576 -0.4742 -0.4879 -1.1058 -0.6111  0.4100 -0.4836 -2.2143 -0.5398 #>  0.5408 -2.1837 -0.2428  1.4708  0.2433  1.0932  0.7066 -0.0637  0.9693 -0.9876 #> -1.5370 -0.3757 -0.9456 -0.5917 -1.4087  0.9152 -0.2538 -0.1560 -1.5692  0.3750 #> -0.1467 -0.0512 -0.3315 -0.2021  1.3404  0.7150  0.6986  1.8484  0.1063 -1.9778 #> -0.4032 -0.7702 -1.5078  0.2106 -1.3181  1.4029 -0.2071  0.6988  0.5814 -1.2226 #> -0.2139  0.2826 -0.6051  1.4271 -0.5023  0.1380  0.7487 -0.6512 -1.0667  0.6083 #>  1.1542 -1.1072 -0.0416 -0.7018 -0.2876 -0.5697  1.3263  1.3296 -0.7422  1.0843 #>  0.3823  0.3361  0.9064 -0.6069  1.0543 -1.0274 -1.0400 -1.9305 -0.0312  0.1556 #>  1.1169 -0.3283  0.1506  0.5543  0.8330 -0.6179  0.0503 -2.2701  0.7123  0.7061 #>  0.1040  1.7993 -0.8401 -0.0205 -0.0334 -0.9733 -1.3252 -0.4537 -0.3771 -0.5454 #> -0.6753 -0.3866  0.1658 -0.5853  0.8411 -0.6265  1.0690  0.8731 -0.9840 -0.0668 #> -1.3578  1.6471 -1.1438  0.1968 -0.1045 -0.2291  0.1634 -1.6033 -0.4936  0.3136 #>  0.3749  0.8226 -1.4063 -2.0339  0.8500 -0.2165  0.4049  0.3529  0.2333 -1.1750 #>  0.1571 -0.0908  0.3734  0.4290 -0.8236 -0.4525  0.6144  0.3749  0.0308 -0.1780 #>  0.1249 -0.0379  0.8394  0.0880  1.3442 -0.3450 -0.8468 -0.4069  1.3823 -1.0155 #> -1.7637  1.3288 -1.7400 -1.2977  2.1511 -0.6051  0.4648  0.7022  1.5900  1.9261 #> -1.2073  1.6272  0.8857  1.5837  0.8221 -0.0738 -0.8853  1.6199  0.0518  0.1564 #> -0.6355  0.8778  1.1055  0.6252  0.8287 -1.6925 -0.7142 -0.0719 -0.5059 -0.9019 #>  0.3946  0.4433  0.7126  0.2315  0.0714 -1.3102  0.5767  0.2772 -0.2313 -1.4692 #>  0.3674 -0.8891  0.0545 -1.2286 -0.3329 -0.8300 -0.0932  1.3368  0.1294  1.9112 #>  1.8811  1.1714  0.9356  0.0622 -0.1098  1.6281  2.4690  0.8353 -0.6021  1.1895 #> -0.9665  0.7614 -0.7413 -0.0969  1.6492 -0.8877  1.1068  1.9219  1.6268 -0.3664 #> -1.3160  1.0413  0.0369 -1.9015 -0.9639 -1.8736 -0.5516  0.0897 -1.4888  1.9580 #>  0.9566  0.2562  0.9344  0.5393  0.0169  0.8452 -0.8670 -0.6659 -1.0230 -0.2476 #> ... [the output was truncated (use n=-1 to disable)] #> [ CPUFloatType{32,25} ]"},{"path":"https://torch.mlverse.org/docs/reference/nn_fractional_max_pool2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies a 2D fractional max pooling over an input signal composed of several input planes. — nn_fractional_max_pool2d","title":"Applies a 2D fractional max pooling over an input signal composed of several input planes. — nn_fractional_max_pool2d","text":"Fractional MaxPooling described detail paper Fractional MaxPooling Ben Graham","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_fractional_max_pool2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies a 2D fractional max pooling over an input signal composed of several input planes. — nn_fractional_max_pool2d","text":"","code":"nn_fractional_max_pool2d(   kernel_size,   output_size = NULL,   output_ratio = NULL,   return_indices = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/nn_fractional_max_pool2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies a 2D fractional max pooling over an input signal composed of several input planes. — nn_fractional_max_pool2d","text":"kernel_size size window take max . Can single number k (square kernel k x k) tuple (kh, kw) output_size target output size image form oH x oW. Can tuple (oH, oW) single number oH square image oH x oH output_ratio one wants output size ratio input size, option can given. number tuple range (0, 1) return_indices TRUE, return indices along outputs. Useful pass nn_max_unpool2d(). Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_fractional_max_pool2d.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Applies a 2D fractional max pooling over an input signal composed of several input planes. — nn_fractional_max_pool2d","text":"max-pooling operation applied \\(kH \\times kW\\) regions stochastic step size determined target output size. number output features equal number input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_fractional_max_pool2d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Applies a 2D fractional max pooling over an input signal composed of several input planes. — nn_fractional_max_pool2d","text":"","code":"if (torch_is_installed()) { # pool of square window of size=3, and target output size 13x12 m <- nn_fractional_max_pool2d(3, output_size = c(13, 12)) # pool of square window and target output size being half of input image size m <- nn_fractional_max_pool2d(3, output_ratio = c(0.5, 0.5)) input <- torch_randn(20, 16, 50, 32) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_fractional_max_pool3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies a 3D fractional max pooling over an input signal composed of several input planes. — nn_fractional_max_pool3d","title":"Applies a 3D fractional max pooling over an input signal composed of several input planes. — nn_fractional_max_pool3d","text":"Fractional MaxPooling described detail paper Fractional MaxPooling Ben Graham","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_fractional_max_pool3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies a 3D fractional max pooling over an input signal composed of several input planes. — nn_fractional_max_pool3d","text":"","code":"nn_fractional_max_pool3d(   kernel_size,   output_size = NULL,   output_ratio = NULL,   return_indices = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/nn_fractional_max_pool3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies a 3D fractional max pooling over an input signal composed of several input planes. — nn_fractional_max_pool3d","text":"kernel_size size window take max . Can single number k (square kernel k x k x k) tuple (kt x kh x kw) output_size target output size image form oT x oH x oW. Can tuple (oT, oH, oW) single number oH square image oH x oH x oH output_ratio one wants output size ratio input size, option can given. number tuple range (0, 1) return_indices TRUE, return indices along outputs. Useful pass nn_max_unpool3d(). Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_fractional_max_pool3d.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Applies a 3D fractional max pooling over an input signal composed of several input planes. — nn_fractional_max_pool3d","text":"max-pooling operation applied \\(kTxkHxkW\\) regions stochastic step size determined target output size. number output features equal number input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_fractional_max_pool3d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Applies a 3D fractional max pooling over an input signal composed of several input planes. — nn_fractional_max_pool3d","text":"","code":"if (torch_is_installed()) { # pool of cubic window of size=3, and target output size 13x12x11 m <- nn_fractional_max_pool3d(3, output_size = c(13, 12, 11)) # pool of cubic window and target output size being half of input size m <- nn_fractional_max_pool3d(3, output_ratio = c(0.5, 0.5, 0.5)) input <- torch_randn(20, 16, 50, 32, 16) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_gelu.html","id":null,"dir":"Reference","previous_headings":"","what":"GELU module — nn_gelu","title":"GELU module — nn_gelu","text":"Applies Gaussian Error Linear Units function: $$\\mbox{GELU}(x) = x * \\Phi(x)$$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_gelu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"GELU module — nn_gelu","text":"","code":"nn_gelu()"},{"path":"https://torch.mlverse.org/docs/reference/nn_gelu.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"GELU module — nn_gelu","text":"\\(\\Phi(x)\\) Cumulative Distribution Function Gaussian Distribution.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_gelu.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"GELU module — nn_gelu","text":"Input: \\((N, *)\\) * means, number additional dimensions Output: \\((N, *)\\), shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_gelu.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"GELU module — nn_gelu","text":"","code":"if (torch_is_installed()) { m <- nn_gelu() input <- torch_randn(2) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_glu.html","id":null,"dir":"Reference","previous_headings":"","what":"GLU module — nn_glu","title":"GLU module — nn_glu","text":"Applies gated linear unit function \\({GLU}(, b)= \\otimes \\sigma(b)\\) \\(\\) first half input matrices \\(b\\) second half.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_glu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"GLU module — nn_glu","text":"","code":"nn_glu(dim = -1)"},{"path":"https://torch.mlverse.org/docs/reference/nn_glu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"GLU module — nn_glu","text":"dim (int): dimension split input. Default: -1","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_glu.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"GLU module — nn_glu","text":"Input: \\((\\ast_1, N, \\ast_2)\\) * means, number additional dimensions Output: \\((\\ast_1, M, \\ast_2)\\) \\(M=N/2\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_glu.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"GLU module — nn_glu","text":"","code":"if (torch_is_installed()) { m <- nn_glu() input <- torch_randn(4, 2) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_group_norm.html","id":null,"dir":"Reference","previous_headings":"","what":"Group normalization — nn_group_norm","title":"Group normalization — nn_group_norm","text":"Applies Group Normalization mini-batch inputs described paper Group Normalization.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_group_norm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Group normalization — nn_group_norm","text":"","code":"nn_group_norm(num_groups, num_channels, eps = 1e-05, affine = TRUE)"},{"path":"https://torch.mlverse.org/docs/reference/nn_group_norm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Group normalization — nn_group_norm","text":"num_groups (int): number groups separate channels num_channels (int): number channels expected input eps value added denominator numerical stability. Default: 1e-5 affine boolean value set TRUE, module learnable per-channel affine parameters initialized ones (weights) zeros (biases). Default: TRUE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_group_norm.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Group normalization — nn_group_norm","text":"$$   y = \\frac{x - \\mathrm{E}[x]}{ \\sqrt{\\mathrm{Var}[x] + \\epsilon}} * \\gamma + \\beta $$ input channels separated num_groups groups, containing num_channels / num_groups channels. mean standard-deviation calculated separately group. \\(\\gamma\\) \\(\\beta\\) learnable per-channel affine transform parameter vectors size num_channels affine TRUE. standard-deviation calculated via biased estimator, equivalent torch_var(input, unbiased=FALSE).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_group_norm.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Group normalization — nn_group_norm","text":"layer uses statistics computed input data training evaluation modes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_group_norm.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Group normalization — nn_group_norm","text":"Input: \\((N, C, *)\\) \\(C=\\mbox{num\\_channels}\\) Output: \\((N, C, *)\\)` (shape input)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_group_norm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Group normalization — nn_group_norm","text":"","code":"if (torch_is_installed()) {  input <- torch_randn(20, 6, 10, 10) # Separate 6 channels into 3 groups m <- nn_group_norm(3, 6) # Separate 6 channels into 6 groups (equivalent with [nn_instance_morm]) m <- nn_group_norm(6, 6) # Put all 6 channels into a single group (equivalent with [nn_layer_norm]) m <- nn_group_norm(1, 6) # Activating the module output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_gru.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies a multi-layer gated recurrent unit (GRU) RNN to an input sequence. — nn_gru","title":"Applies a multi-layer gated recurrent unit (GRU) RNN to an input sequence. — nn_gru","text":"element input sequence, layer computes following function:","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_gru.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies a multi-layer gated recurrent unit (GRU) RNN to an input sequence. — nn_gru","text":"","code":"nn_gru(   input_size,   hidden_size,   num_layers = 1,   bias = TRUE,   batch_first = FALSE,   dropout = 0,   bidirectional = FALSE,   ... )"},{"path":"https://torch.mlverse.org/docs/reference/nn_gru.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies a multi-layer gated recurrent unit (GRU) RNN to an input sequence. — nn_gru","text":"input_size number expected features input x hidden_size number features hidden state h num_layers Number recurrent layers. E.g., setting num_layers=2 mean stacking two GRUs together form stacked GRU, second GRU taking outputs first GRU computing final results. Default: 1 bias FALSE, layer use bias weights b_ih b_hh. Default: TRUE batch_first TRUE, input output tensors provided (batch, seq, feature). Default: FALSE dropout non-zero, introduces Dropout layer outputs GRU layer except last layer, dropout probability equal dropout. Default: 0 bidirectional TRUE, becomes bidirectional GRU. Default: FALSE ... currently unused.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_gru.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Applies a multi-layer gated recurrent unit (GRU) RNN to an input sequence. — nn_gru","text":"$$ \\begin{array}{ll} r_t = \\sigma(W_{ir} x_t + b_{ir} + W_{hr} h_{(t-1)} + b_{hr}) \\\\ z_t = \\sigma(W_{iz} x_t + b_{iz} + W_{hz} h_{(t-1)} + b_{hz}) \\\\ n_t = \\tanh(W_{} x_t + b_{} + r_t (W_{hn} h_{(t-1)}+ b_{hn})) \\\\ h_t = (1 - z_t) n_t + z_t h_{(t-1)} \\end{array} $$ \\(h_t\\) hidden state time t, \\(x_t\\) input time t, \\(h_{(t-1)}\\) hidden state previous layer time t-1 initial hidden state time 0, \\(r_t\\), \\(z_t\\), \\(n_t\\) reset, update, new gates, respectively. \\(\\sigma\\) sigmoid function.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_gru.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Applies a multi-layer gated recurrent unit (GRU) RNN to an input sequence. — nn_gru","text":"weights biases initialized \\(\\mathcal{U}(-\\sqrt{k}, \\sqrt{k})\\) \\(k = \\frac{1}{\\mbox{hidden\\_size}}\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_gru.html","id":"inputs","dir":"Reference","previous_headings":"","what":"Inputs","title":"Applies a multi-layer gated recurrent unit (GRU) RNN to an input sequence. — nn_gru","text":"Inputs: input, h_0 input shape (seq_len, batch, input_size): tensor containing features input sequence. input can also packed variable length sequence. See nn_utils_rnn_pack_padded_sequence() details. h_0 shape (num_layers * num_directions, batch, hidden_size): tensor containing initial hidden state element batch. Defaults zero provided.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_gru.html","id":"outputs","dir":"Reference","previous_headings":"","what":"Outputs","title":"Applies a multi-layer gated recurrent unit (GRU) RNN to an input sequence. — nn_gru","text":"Outputs: output, h_n output shape (seq_len, batch, num_directions * hidden_size): tensor containing output features h_t last layer GRU, t. PackedSequence given input, output also packed sequence. unpacked case, directions can separated using output$view(c(seq_len, batch, num_directions, hidden_size)), forward backward direction 0 1 respectively. Similarly, directions can separated packed case. h_n shape (num_layers * num_directions, batch, hidden_size): tensor containing hidden state t = seq_len Like output, layers can separated using h_n$view(num_layers, num_directions, batch, hidden_size).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_gru.html","id":"attributes","dir":"Reference","previous_headings":"","what":"Attributes","title":"Applies a multi-layer gated recurrent unit (GRU) RNN to an input sequence. — nn_gru","text":"weight_ih_l[k] : learnable input-hidden weights \\(\\mbox{k}^{th}\\) layer (W_ir|W_iz|W_in), shape (3*hidden_size x input_size) weight_hh_l[k] : learnable hidden-hidden weights \\(\\mbox{k}^{th}\\) layer (W_hr|W_hz|W_hn), shape (3*hidden_size x hidden_size) bias_ih_l[k] : learnable input-hidden bias \\(\\mbox{k}^{th}\\) layer (b_ir|b_iz|b_in), shape (3*hidden_size) bias_hh_l[k] : learnable hidden-hidden bias \\(\\mbox{k}^{th}\\) layer (b_hr|b_hz|b_hn), shape (3*hidden_size)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_gru.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Applies a multi-layer gated recurrent unit (GRU) RNN to an input sequence. — nn_gru","text":"","code":"if (torch_is_installed()) {  rnn <- nn_gru(10, 20, 2) input <- torch_randn(5, 3, 10) h0 <- torch_randn(2, 3, 20) output <- rnn(input, h0) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_hardshrink.html","id":null,"dir":"Reference","previous_headings":"","what":"Hardshwink module — nn_hardshrink","title":"Hardshwink module — nn_hardshrink","text":"Applies hard shrinkage function element-wise:","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_hardshrink.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hardshwink module — nn_hardshrink","text":"","code":"nn_hardshrink(lambd = 0.5)"},{"path":"https://torch.mlverse.org/docs/reference/nn_hardshrink.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hardshwink module — nn_hardshrink","text":"lambd \\(\\lambda\\) value Hardshrink formulation. Default: 0.5","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_hardshrink.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Hardshwink module — nn_hardshrink","text":"$$   \\mbox{HardShrink}(x) =   \\left\\{ \\begin{array}{ll} x, & \\mbox{ } x > \\lambda \\\\ x, & \\mbox{ } x < -\\lambda \\\\ 0, & \\mbox{ otherwise } \\end{array} \\right. $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_hardshrink.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Hardshwink module — nn_hardshrink","text":"Input: \\((N, *)\\) * means, number additional dimensions Output: \\((N, *)\\), shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_hardshrink.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Hardshwink module — nn_hardshrink","text":"","code":"if (torch_is_installed()) { m <- nn_hardshrink() input <- torch_randn(2) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_hardsigmoid.html","id":null,"dir":"Reference","previous_headings":"","what":"Hardsigmoid module — nn_hardsigmoid","title":"Hardsigmoid module — nn_hardsigmoid","text":"Applies element-wise function:","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_hardsigmoid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hardsigmoid module — nn_hardsigmoid","text":"","code":"nn_hardsigmoid()"},{"path":"https://torch.mlverse.org/docs/reference/nn_hardsigmoid.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Hardsigmoid module — nn_hardsigmoid","text":"$$ \\mbox{Hardsigmoid}(x) = \\left\\{ \\begin{array}{ll}   0 & \\mbox{~} x \\le -3, \\\\   1 & \\mbox{~} x \\ge +3, \\\\   x / 6 + 1 / 2 & \\mbox{otherwise} \\end{array} \\right. $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_hardsigmoid.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Hardsigmoid module — nn_hardsigmoid","text":"Input: \\((N, *)\\) * means, number additional dimensions Output: \\((N, *)\\), shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_hardsigmoid.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Hardsigmoid module — nn_hardsigmoid","text":"","code":"if (torch_is_installed()) { m <- nn_hardsigmoid() input <- torch_randn(2) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_hardswish.html","id":null,"dir":"Reference","previous_headings":"","what":"Hardswish module — nn_hardswish","title":"Hardswish module — nn_hardswish","text":"Applies hardswish function, element-wise, described paper: Searching MobileNetV3","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_hardswish.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hardswish module — nn_hardswish","text":"","code":"nn_hardswish()"},{"path":"https://torch.mlverse.org/docs/reference/nn_hardswish.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Hardswish module — nn_hardswish","text":"$$ \\mbox{Hardswish}(x) = \\left\\{   \\begin{array}{ll}   0 & \\mbox{} x \\le -3, \\\\   x & \\mbox{} x \\ge +3, \\\\   x \\cdot (x + 3)/6 & \\mbox{otherwise}   \\end{array}   \\right. $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_hardswish.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Hardswish module — nn_hardswish","text":"Input: \\((N, *)\\) * means, number additional dimensions Output: \\((N, *)\\), shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_hardswish.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Hardswish module — nn_hardswish","text":"","code":"if (torch_is_installed()) { if (FALSE) { m <- nn_hardswish() input <- torch_randn(2) output <- m(input) }  }"},{"path":"https://torch.mlverse.org/docs/reference/nn_hardtanh.html","id":null,"dir":"Reference","previous_headings":"","what":"Hardtanh module — nn_hardtanh","title":"Hardtanh module — nn_hardtanh","text":"Applies HardTanh function element-wise HardTanh defined :","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_hardtanh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hardtanh module — nn_hardtanh","text":"","code":"nn_hardtanh(min_val = -1, max_val = 1, inplace = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nn_hardtanh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hardtanh module — nn_hardtanh","text":"min_val minimum value linear region range. Default: -1 max_val maximum value linear region range. Default: 1 inplace can optionally operation -place. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_hardtanh.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Hardtanh module — nn_hardtanh","text":"$$ \\mbox{HardTanh}(x) = \\left\\{ \\begin{array}{ll}   1 & \\mbox{ } x > 1 \\\\   -1 & \\mbox{ } x < -1 \\\\   x & \\mbox{ otherwise } \\\\ \\end{array} \\right. $$ range linear region :math:[-1, 1] can adjusted using min_val max_val.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_hardtanh.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Hardtanh module — nn_hardtanh","text":"Input: \\((N, *)\\) * means, number additional dimensions Output: \\((N, *)\\), shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_hardtanh.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Hardtanh module — nn_hardtanh","text":"","code":"if (torch_is_installed()) { m <- nn_hardtanh(-2, 2) input <- torch_randn(2) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_hinge_embedding_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Hinge embedding loss — nn_hinge_embedding_loss","title":"Hinge embedding loss — nn_hinge_embedding_loss","text":"Measures loss given input tensor \\(x\\) labels tensor \\(y\\) (containing 1 -1).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_hinge_embedding_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hinge embedding loss — nn_hinge_embedding_loss","text":"","code":"nn_hinge_embedding_loss(margin = 1, reduction = \"mean\")"},{"path":"https://torch.mlverse.org/docs/reference/nn_hinge_embedding_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hinge embedding loss — nn_hinge_embedding_loss","text":"margin (float, optional): default value 1. reduction (string, optional): Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed. Note: size_average reduce process deprecated, meantime, specifying either two args override reduction. Default: 'mean'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_hinge_embedding_loss.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Hinge embedding loss — nn_hinge_embedding_loss","text":"usually used measuring whether two inputs similar dissimilar, e.g. using L1 pairwise distance \\(x\\), typically used learning nonlinear embeddings semi-supervised learning. loss function \\(n\\)-th sample mini-batch $$   l_n = \\begin{array}{ll} x_n, & \\mbox{}\\; y_n = 1,\\\\ \\max \\{0, \\Delta - x_n\\}, & \\mbox{}\\; y_n = -1, \\end{array} $$ total loss functions $$   \\ell(x, y) = \\begin{array}{ll} \\mbox{mean}(L), & \\mbox{reduction} = \\mbox{'mean';}\\\\ \\mbox{sum}(L),  & \\mbox{reduction} = \\mbox{'sum'.} \\end{array} $$ \\(L = \\{l_1,\\dots,l_N\\}^\\top\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_hinge_embedding_loss.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Hinge embedding loss — nn_hinge_embedding_loss","text":"Input: \\((*)\\) \\(*\\) means, number dimensions. sum operation operates elements. Target: \\((*)\\), shape input Output: scalar. reduction 'none', shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_identity.html","id":null,"dir":"Reference","previous_headings":"","what":"Identity module — nn_identity","title":"Identity module — nn_identity","text":"placeholder identity operator argument-insensitive.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_identity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Identity module — nn_identity","text":"","code":"nn_identity(...)"},{"path":"https://torch.mlverse.org/docs/reference/nn_identity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Identity module — nn_identity","text":"... arguments (unused)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_identity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Identity module — nn_identity","text":"","code":"if (torch_is_installed()) { m <- nn_identity(54, unused_argument1 = 0.1, unused_argument2 = FALSE) input <- torch_randn(128, 20) output <- m(input) print(output$size()) } #> [1] 128  20"},{"path":"https://torch.mlverse.org/docs/reference/nn_init_calculate_gain.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate gain — nn_init_calculate_gain","title":"Calculate gain — nn_init_calculate_gain","text":"Return recommended gain value given nonlinearity function.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_calculate_gain.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate gain — nn_init_calculate_gain","text":"","code":"nn_init_calculate_gain(nonlinearity, param = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/nn_init_calculate_gain.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate gain — nn_init_calculate_gain","text":"nonlinearity non-linear function param optional parameter non-linear function","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_constant_.html","id":null,"dir":"Reference","previous_headings":"","what":"Constant initialization — nn_init_constant_","title":"Constant initialization — nn_init_constant_","text":"Fills input Tensor value val.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_constant_.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Constant initialization — nn_init_constant_","text":"","code":"nn_init_constant_(tensor, val)"},{"path":"https://torch.mlverse.org/docs/reference/nn_init_constant_.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Constant initialization — nn_init_constant_","text":"tensor n-dimensional Tensor val value fill tensor ","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_constant_.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Constant initialization — nn_init_constant_","text":"","code":"if (torch_is_installed()) { w <- torch_empty(3, 5) nn_init_constant_(w, 0.3) } #> torch_tensor #>  0.3000  0.3000  0.3000  0.3000  0.3000 #>  0.3000  0.3000  0.3000  0.3000  0.3000 #>  0.3000  0.3000  0.3000  0.3000  0.3000 #> [ CPUFloatType{3,5} ]"},{"path":"https://torch.mlverse.org/docs/reference/nn_init_dirac_.html","id":null,"dir":"Reference","previous_headings":"","what":"Dirac initialization — nn_init_dirac_","title":"Dirac initialization — nn_init_dirac_","text":"Fills 3, 4, 5-dimensional input Tensor Dirac delta function. Preserves identity inputs Convolutional layers, many input channels preserved possible. case groups>1, group channels preserves identity.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_dirac_.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Dirac initialization — nn_init_dirac_","text":"","code":"nn_init_dirac_(tensor, groups = 1)"},{"path":"https://torch.mlverse.org/docs/reference/nn_init_dirac_.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Dirac initialization — nn_init_dirac_","text":"tensor 3, 4, 5-dimensional torch.Tensor groups (optional) number groups conv layer (default: 1)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_dirac_.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Dirac initialization — nn_init_dirac_","text":"","code":"if (torch_is_installed()) { if (FALSE) { w <- torch_empty(3, 16, 5, 5) nn_init_dirac_(w) }  }"},{"path":"https://torch.mlverse.org/docs/reference/nn_init_eye_.html","id":null,"dir":"Reference","previous_headings":"","what":"Eye initialization — nn_init_eye_","title":"Eye initialization — nn_init_eye_","text":"Fills 2-dimensional input Tensor identity matrix. Preserves identity inputs Linear layers, many inputs preserved possible.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_eye_.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Eye initialization — nn_init_eye_","text":"","code":"nn_init_eye_(tensor)"},{"path":"https://torch.mlverse.org/docs/reference/nn_init_eye_.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Eye initialization — nn_init_eye_","text":"tensor 2-dimensional torch tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_eye_.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Eye initialization — nn_init_eye_","text":"","code":"if (torch_is_installed()) { w <- torch_empty(3, 5) nn_init_eye_(w) } #> torch_tensor #>  1  0  0  0  0 #>  0  1  0  0  0 #>  0  0  1  0  0 #> [ CPUFloatType{3,5} ]"},{"path":"https://torch.mlverse.org/docs/reference/nn_init_kaiming_normal_.html","id":null,"dir":"Reference","previous_headings":"","what":"Kaiming normal initialization — nn_init_kaiming_normal_","title":"Kaiming normal initialization — nn_init_kaiming_normal_","text":"Fills input Tensor values according method described Delving deep rectifiers: Surpassing human-level performance ImageNet classification - , K. et al. (2015), using normal distribution.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_kaiming_normal_.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Kaiming normal initialization — nn_init_kaiming_normal_","text":"","code":"nn_init_kaiming_normal_(   tensor,   a = 0,   mode = \"fan_in\",   nonlinearity = \"leaky_relu\" )"},{"path":"https://torch.mlverse.org/docs/reference/nn_init_kaiming_normal_.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Kaiming normal initialization — nn_init_kaiming_normal_","text":"tensor n-dimensional torch.Tensor negative slope rectifier used layer (used 'leaky_relu') mode either 'fan_in' (default) 'fan_out'. Choosing 'fan_in' preserves magnitude variance weights forward pass. Choosing 'fan_out' preserves magnitudes backwards pass. nonlinearity non-linear function. recommended use 'relu' 'leaky_relu' (default).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_kaiming_normal_.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Kaiming normal initialization — nn_init_kaiming_normal_","text":"","code":"if (torch_is_installed()) { w <- torch_empty(3, 5) nn_init_kaiming_normal_(w, mode = \"fan_in\", nonlinearity = \"leaky_relu\") } #> torch_tensor #> -0.0119  0.6924 -0.9304  1.5996 -1.0900 #> -1.4419  0.8208 -0.2098 -0.2067  0.6960 #>  0.8102  0.5133  0.9255 -0.6329 -0.7233 #> [ CPUFloatType{3,5} ]"},{"path":"https://torch.mlverse.org/docs/reference/nn_init_kaiming_uniform_.html","id":null,"dir":"Reference","previous_headings":"","what":"Kaiming uniform initialization — nn_init_kaiming_uniform_","title":"Kaiming uniform initialization — nn_init_kaiming_uniform_","text":"Fills input Tensor values according method described Delving deep rectifiers: Surpassing human-level performance ImageNet classification - , K. et al. (2015), using uniform distribution.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_kaiming_uniform_.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Kaiming uniform initialization — nn_init_kaiming_uniform_","text":"","code":"nn_init_kaiming_uniform_(   tensor,   a = 0,   mode = \"fan_in\",   nonlinearity = \"leaky_relu\" )"},{"path":"https://torch.mlverse.org/docs/reference/nn_init_kaiming_uniform_.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Kaiming uniform initialization — nn_init_kaiming_uniform_","text":"tensor n-dimensional torch.Tensor negative slope rectifier used layer (used 'leaky_relu') mode either 'fan_in' (default) 'fan_out'. Choosing 'fan_in' preserves magnitude variance weights forward pass. Choosing 'fan_out' preserves magnitudes backwards pass. nonlinearity non-linear function. recommended use 'relu' 'leaky_relu' (default).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_kaiming_uniform_.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Kaiming uniform initialization — nn_init_kaiming_uniform_","text":"","code":"if (torch_is_installed()) { w <- torch_empty(3, 5) nn_init_kaiming_uniform_(w, mode = \"fan_in\", nonlinearity = \"leaky_relu\") } #> torch_tensor #> -1.0127  0.0117  0.1522 -0.2595  0.4061 #> -0.6062  0.1464 -0.8295  0.0521 -0.7205 #>  0.7301 -0.4684 -0.6242 -0.4436  0.1059 #> [ CPUFloatType{3,5} ]"},{"path":"https://torch.mlverse.org/docs/reference/nn_init_normal_.html","id":null,"dir":"Reference","previous_headings":"","what":"Normal initialization — nn_init_normal_","title":"Normal initialization — nn_init_normal_","text":"Fills input Tensor values drawn normal distribution","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_normal_.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Normal initialization — nn_init_normal_","text":"","code":"nn_init_normal_(tensor, mean = 0, std = 1)"},{"path":"https://torch.mlverse.org/docs/reference/nn_init_normal_.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Normal initialization — nn_init_normal_","text":"tensor n-dimensional Tensor mean mean normal distribution std standard deviation normal distribution","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_normal_.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Normal initialization — nn_init_normal_","text":"","code":"if (torch_is_installed()) { w <- torch_empty(3, 5) nn_init_normal_(w) } #> torch_tensor #>  1.1488  0.9012 -0.5716 -0.1876 -0.3635 #>  0.5749 -0.3356 -0.2284 -1.4978 -0.2064 #>  0.6891 -0.6885 -0.0284 -0.2636  0.5177 #> [ CPUFloatType{3,5} ]"},{"path":"https://torch.mlverse.org/docs/reference/nn_init_ones_.html","id":null,"dir":"Reference","previous_headings":"","what":"Ones initialization — nn_init_ones_","title":"Ones initialization — nn_init_ones_","text":"Fills input Tensor scalar value 1","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_ones_.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Ones initialization — nn_init_ones_","text":"","code":"nn_init_ones_(tensor)"},{"path":"https://torch.mlverse.org/docs/reference/nn_init_ones_.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Ones initialization — nn_init_ones_","text":"tensor n-dimensional Tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_ones_.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Ones initialization — nn_init_ones_","text":"","code":"if (torch_is_installed()) { w <- torch_empty(3, 5) nn_init_ones_(w) } #> torch_tensor #>  1  1  1  1  1 #>  1  1  1  1  1 #>  1  1  1  1  1 #> [ CPUFloatType{3,5} ]"},{"path":"https://torch.mlverse.org/docs/reference/nn_init_orthogonal_.html","id":null,"dir":"Reference","previous_headings":"","what":"Orthogonal initialization — nn_init_orthogonal_","title":"Orthogonal initialization — nn_init_orthogonal_","text":"Fills input Tensor (semi) orthogonal matrix, described Exact solutions nonlinear dynamics learning deep linear neural networks - Saxe, . et al. (2013). input tensor must least 2 dimensions, tensors 2 dimensions trailing dimensions flattened.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_orthogonal_.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Orthogonal initialization — nn_init_orthogonal_","text":"","code":"nn_init_orthogonal_(tensor, gain = 1)"},{"path":"https://torch.mlverse.org/docs/reference/nn_init_orthogonal_.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Orthogonal initialization — nn_init_orthogonal_","text":"tensor n-dimensional Tensor gain optional scaling factor","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_orthogonal_.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Orthogonal initialization — nn_init_orthogonal_","text":"","code":"if (torch_is_installed()) { w <- torch_empty(3, 5) nn_init_orthogonal_(w) } #> torch_tensor #> -0.1985 -0.1051 -0.7007  0.1139 -0.6675 #>  0.5548  0.4203 -0.2981  0.6254  0.1885 #> -0.1771  0.5799 -0.4552 -0.5535  0.3447 #> [ CPUFloatType{3,5} ]"},{"path":"https://torch.mlverse.org/docs/reference/nn_init_sparse_.html","id":null,"dir":"Reference","previous_headings":"","what":"Sparse initialization — nn_init_sparse_","title":"Sparse initialization — nn_init_sparse_","text":"Fills 2D input Tensor sparse matrix, non-zero elements drawn normal distribution described Deep learning via Hessian-free optimization - Martens, J. (2010).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_sparse_.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sparse initialization — nn_init_sparse_","text":"","code":"nn_init_sparse_(tensor, sparsity, std = 0.01)"},{"path":"https://torch.mlverse.org/docs/reference/nn_init_sparse_.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sparse initialization — nn_init_sparse_","text":"tensor n-dimensional Tensor sparsity fraction elements column set zero std standard deviation normal distribution used generate non-zero values","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_sparse_.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sparse initialization — nn_init_sparse_","text":"","code":"if (torch_is_installed()) { if (FALSE) { w <- torch_empty(3, 5) nn_init_sparse_(w, sparsity = 0.1) } }"},{"path":"https://torch.mlverse.org/docs/reference/nn_init_trunc_normal_.html","id":null,"dir":"Reference","previous_headings":"","what":"Truncated normal initialization — nn_init_trunc_normal_","title":"Truncated normal initialization — nn_init_trunc_normal_","text":"Fills input Tensor values drawn truncated normal distribution.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_trunc_normal_.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Truncated normal initialization — nn_init_trunc_normal_","text":"","code":"nn_init_trunc_normal_(tensor, mean = 0, std = 1, a = -2, b = 2)"},{"path":"https://torch.mlverse.org/docs/reference/nn_init_trunc_normal_.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Truncated normal initialization — nn_init_trunc_normal_","text":"tensor n-dimensional Tensor mean mean normal distribution std standard deviation normal distribution minimum cutoff value b maximum cutoff value","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_trunc_normal_.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Truncated normal initialization — nn_init_trunc_normal_","text":"","code":"if (torch_is_installed()) { w <- torch_empty(3, 5) nn_init_trunc_normal_(w) } #> torch_tensor #>  0.3541  0.0827  0.5821  1.2080 -1.4691 #>  0.0323  0.3596 -1.3777  0.2376  1.0621 #>  0.6410 -0.5628  0.3718 -0.3494 -0.9663 #> [ CPUFloatType{3,5} ]"},{"path":"https://torch.mlverse.org/docs/reference/nn_init_uniform_.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniform initialization — nn_init_uniform_","title":"Uniform initialization — nn_init_uniform_","text":"Fills input Tensor values drawn uniform distribution","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_uniform_.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniform initialization — nn_init_uniform_","text":"","code":"nn_init_uniform_(tensor, a = 0, b = 1)"},{"path":"https://torch.mlverse.org/docs/reference/nn_init_uniform_.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Uniform initialization — nn_init_uniform_","text":"tensor n-dimensional Tensor lower bound uniform distribution b upper bound uniform distribution","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_uniform_.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniform initialization — nn_init_uniform_","text":"","code":"if (torch_is_installed()) { w <- torch_empty(3, 5) nn_init_uniform_(w) } #> torch_tensor #>  0.6015  0.1084  0.7769  0.5726  0.9784 #>  0.3043  0.3190  0.9327  0.7583  0.7635 #>  0.9027  0.4158  0.7853  0.1498  0.0483 #> [ CPUFloatType{3,5} ]"},{"path":"https://torch.mlverse.org/docs/reference/nn_init_xavier_normal_.html","id":null,"dir":"Reference","previous_headings":"","what":"Xavier normal initialization — nn_init_xavier_normal_","title":"Xavier normal initialization — nn_init_xavier_normal_","text":"Fills input Tensor values according method described Understanding difficulty training deep feedforward neural networks - Glorot, X. & Bengio, Y. (2010), using normal distribution.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_xavier_normal_.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Xavier normal initialization — nn_init_xavier_normal_","text":"","code":"nn_init_xavier_normal_(tensor, gain = 1)"},{"path":"https://torch.mlverse.org/docs/reference/nn_init_xavier_normal_.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Xavier normal initialization — nn_init_xavier_normal_","text":"tensor n-dimensional Tensor gain optional scaling factor","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_xavier_normal_.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Xavier normal initialization — nn_init_xavier_normal_","text":"","code":"if (torch_is_installed()) { w <- torch_empty(3, 5) nn_init_xavier_normal_(w) } #> torch_tensor #> -1.0378  1.2928 -0.5099 -0.1658  0.1370 #>  0.9178  0.4987 -0.3369  0.2306 -0.1576 #>  0.2634 -0.3560  0.4081  0.5059 -0.5513 #> [ CPUFloatType{3,5} ]"},{"path":"https://torch.mlverse.org/docs/reference/nn_init_xavier_uniform_.html","id":null,"dir":"Reference","previous_headings":"","what":"Xavier uniform initialization — nn_init_xavier_uniform_","title":"Xavier uniform initialization — nn_init_xavier_uniform_","text":"Fills input Tensor values according method described Understanding difficulty training deep feedforward neural networks - Glorot, X. & Bengio, Y. (2010), using uniform distribution.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_xavier_uniform_.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Xavier uniform initialization — nn_init_xavier_uniform_","text":"","code":"nn_init_xavier_uniform_(tensor, gain = 1)"},{"path":"https://torch.mlverse.org/docs/reference/nn_init_xavier_uniform_.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Xavier uniform initialization — nn_init_xavier_uniform_","text":"tensor n-dimensional Tensor gain optional scaling factor","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_xavier_uniform_.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Xavier uniform initialization — nn_init_xavier_uniform_","text":"","code":"if (torch_is_installed()) { w <- torch_empty(3, 5) nn_init_xavier_uniform_(w) } #> torch_tensor #> -0.3992 -0.5062 -0.1031 -0.4119 -0.3039 #> -0.5257 -0.5671 -0.0562 -0.4832 -0.4938 #>  0.0203 -0.4390  0.5743 -0.2024 -0.8208 #> [ CPUFloatType{3,5} ]"},{"path":"https://torch.mlverse.org/docs/reference/nn_init_zeros_.html","id":null,"dir":"Reference","previous_headings":"","what":"Zeros initialization — nn_init_zeros_","title":"Zeros initialization — nn_init_zeros_","text":"Fills input Tensor scalar value 0","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_zeros_.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Zeros initialization — nn_init_zeros_","text":"","code":"nn_init_zeros_(tensor)"},{"path":"https://torch.mlverse.org/docs/reference/nn_init_zeros_.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Zeros initialization — nn_init_zeros_","text":"tensor n-dimensional tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_zeros_.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Zeros initialization — nn_init_zeros_","text":"","code":"if (torch_is_installed()) { w <- torch_empty(3, 5) nn_init_zeros_(w) } #> torch_tensor #>  0  0  0  0  0 #>  0  0  0  0  0 #>  0  0  0  0  0 #> [ CPUFloatType{3,5} ]"},{"path":"https://torch.mlverse.org/docs/reference/nn_kl_div_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Kullback-Leibler divergence loss — nn_kl_div_loss","title":"Kullback-Leibler divergence loss — nn_kl_div_loss","text":"Kullback-Leibler divergence loss measure Kullback-Leibler divergence useful distance measure continuous distributions often useful performing direct regression space (discretely sampled) continuous output distributions.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_kl_div_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Kullback-Leibler divergence loss — nn_kl_div_loss","text":"","code":"nn_kl_div_loss(reduction = \"mean\")"},{"path":"https://torch.mlverse.org/docs/reference/nn_kl_div_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Kullback-Leibler divergence loss — nn_kl_div_loss","text":"reduction (string, optional): Specifies reduction apply output: 'none' | 'batchmean' | 'sum' | 'mean'. 'none': reduction applied. 'batchmean': sum output divided batchsize. 'sum': output summed. 'mean': output divided number elements output. Default: 'mean'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_kl_div_loss.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Kullback-Leibler divergence loss — nn_kl_div_loss","text":"nn_nll_loss(), input given expected contain log-probabilities restricted 2D Tensor. targets interpreted probabilities default, considered log-probabilities log_target set TRUE. criterion expects target Tensor size input Tensor. unreduced (.e. reduction set 'none') loss can described : $$   l(x,y) = L = \\{ l_1,\\dots,l_N \\}, \\quad l_n = y_n \\cdot \\left( \\log y_n - x_n \\right) $$ index \\(N\\) spans dimensions input \\(L\\) shape input. reduction 'none' (default 'mean'), : $$   \\ell(x, y) = \\begin{array}{ll} \\mbox{mean}(L), & \\mbox{reduction} = \\mbox{'mean';} \\\\ \\mbox{sum}(L),  & \\mbox{reduction} = \\mbox{'sum'.} \\end{array} $$ default reduction mode 'mean', losses averaged minibatch observations well dimensions. 'batchmean' mode gives correct KL divergence losses averaged batch dimension . 'mean' mode's behavior changed 'batchmean' next major release.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_kl_div_loss.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Kullback-Leibler divergence loss — nn_kl_div_loss","text":"reduction = 'mean' return true kl divergence value, please use reduction = 'batchmean' aligns KL math definition. next major release, 'mean' changed 'batchmean'.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_kl_div_loss.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Kullback-Leibler divergence loss — nn_kl_div_loss","text":"Input: \\((N, *)\\) \\(*\\) means, number additional dimensions Target: \\((N, *)\\), shape input Output: scalar default. reduction 'none', \\((N, *)\\), shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_l1_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"L1 loss — nn_l1_loss","title":"L1 loss — nn_l1_loss","text":"Creates criterion measures mean absolute error (MAE) element input \\(x\\) target \\(y\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_l1_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"L1 loss — nn_l1_loss","text":"","code":"nn_l1_loss(reduction = \"mean\")"},{"path":"https://torch.mlverse.org/docs/reference/nn_l1_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"L1 loss — nn_l1_loss","text":"reduction (string, optional): Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed. Note: size_average reduce process deprecated, meantime, specifying either two args override reduction. Default: 'mean'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_l1_loss.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"L1 loss — nn_l1_loss","text":"unreduced (.e. reduction set 'none') loss can described : $$ \\ell(x, y) = L = \\{l_1,\\dots,l_N\\}^\\top, \\quad l_n = \\left| x_n - y_n \\right|, $$ \\(N\\) batch size. reduction 'none' (default 'mean'), : $$ \\ell(x, y) = \\begin{array}{ll} \\mbox{mean}(L), & \\mbox{reduction} = \\mbox{'mean';}\\\\ \\mbox{sum}(L),  & \\mbox{reduction} = \\mbox{'sum'.} \\end{array} $$ \\(x\\) \\(y\\) tensors arbitrary shapes total \\(n\\) elements . sum operation still operates elements, divides \\(n\\). division \\(n\\) can avoided one sets reduction = 'sum'.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_l1_loss.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"L1 loss — nn_l1_loss","text":"Input: \\((N, *)\\) \\(*\\) means, number additional dimensions Target: \\((N, *)\\), shape input Output: scalar. reduction 'none', \\((N, *)\\), shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_l1_loss.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"L1 loss — nn_l1_loss","text":"","code":"if (torch_is_installed()) { loss <- nn_l1_loss() input <- torch_randn(3, 5, requires_grad = TRUE) target <- torch_randn(3, 5) output <- loss(input, target) output$backward() }"},{"path":"https://torch.mlverse.org/docs/reference/nn_layer_norm.html","id":null,"dir":"Reference","previous_headings":"","what":"Layer normalization — nn_layer_norm","title":"Layer normalization — nn_layer_norm","text":"Applies Layer Normalization mini-batch inputs described paper Layer Normalization","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_layer_norm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Layer normalization — nn_layer_norm","text":"","code":"nn_layer_norm(normalized_shape, eps = 1e-05, elementwise_affine = TRUE)"},{"path":"https://torch.mlverse.org/docs/reference/nn_layer_norm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Layer normalization — nn_layer_norm","text":"normalized_shape (int list): input shape expected input size \\([* \\times \\mbox{normalized\\_shape}[0] \\times \\mbox{normalized\\_shape}[1] \\times \\ldots \\times \\mbox{normalized\\_shape}[-1]]\\) single integer used, treated singleton list, module normalize last dimension expected specific size. eps value added denominator numerical stability. Default: 1e-5 elementwise_affine boolean value set TRUE, module learnable per-element affine parameters initialized ones (weights) zeros (biases). Default: TRUE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_layer_norm.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Layer normalization — nn_layer_norm","text":"$$   y = \\frac{x - \\mathrm{E}[x]}{ \\sqrt{\\mathrm{Var}[x] + \\epsilon}} * \\gamma + \\beta $$ mean standard-deviation calculated separately last certain number dimensions shape specified normalized_shape. \\(\\gamma\\) \\(\\beta\\) learnable affine transform parameters normalized_shape elementwise_affine TRUE. standard-deviation calculated via biased estimator, equivalent torch_var(input, unbiased=FALSE).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_layer_norm.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Layer normalization — nn_layer_norm","text":"Unlike Batch Normalization Instance Normalization, applies scalar scale bias entire channel/plane affine option, Layer Normalization applies per-element scale bias elementwise_affine. layer uses statistics computed input data training evaluation modes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_layer_norm.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Layer normalization — nn_layer_norm","text":"Input: \\((N, *)\\) Output: \\((N, *)\\) (shape input)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_layer_norm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Layer normalization — nn_layer_norm","text":"","code":"if (torch_is_installed()) {  input <- torch_randn(20, 5, 10, 10) # With Learnable Parameters m <- nn_layer_norm(input$size()[-1]) # Without Learnable Parameters m <- nn_layer_norm(input$size()[-1], elementwise_affine = FALSE) # Normalize over last two dimensions m <- nn_layer_norm(c(10, 10)) # Normalize over last dimension of size 10 m <- nn_layer_norm(10) # Activating the module output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_leaky_relu.html","id":null,"dir":"Reference","previous_headings":"","what":"LeakyReLU module — nn_leaky_relu","title":"LeakyReLU module — nn_leaky_relu","text":"Applies element-wise function:","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_leaky_relu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"LeakyReLU module — nn_leaky_relu","text":"","code":"nn_leaky_relu(negative_slope = 0.01, inplace = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nn_leaky_relu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"LeakyReLU module — nn_leaky_relu","text":"negative_slope Controls angle negative slope. Default: 1e-2 inplace can optionally operation -place. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_leaky_relu.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"LeakyReLU module — nn_leaky_relu","text":"$$   \\mbox{LeakyReLU}(x) = \\max(0, x) + \\mbox{negative\\_slope} * \\min(0, x) $$ $$   \\mbox{LeakyRELU}(x) =   \\left\\{ \\begin{array}{ll} x, & \\mbox{ } x \\geq 0 \\\\ \\mbox{negative\\_slope} \\times x, & \\mbox{ otherwise } \\end{array} \\right. $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_leaky_relu.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"LeakyReLU module — nn_leaky_relu","text":"Input: \\((N, *)\\) * means, number additional dimensions Output: \\((N, *)\\), shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_leaky_relu.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"LeakyReLU module — nn_leaky_relu","text":"","code":"if (torch_is_installed()) { m <- nn_leaky_relu(0.1) input <- torch_randn(2) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_linear.html","id":null,"dir":"Reference","previous_headings":"","what":"Linear module — nn_linear","title":"Linear module — nn_linear","text":"Applies linear transformation incoming data: y = xA^T + b","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_linear.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Linear module — nn_linear","text":"","code":"nn_linear(in_features, out_features, bias = TRUE)"},{"path":"https://torch.mlverse.org/docs/reference/nn_linear.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Linear module — nn_linear","text":"in_features size input sample out_features size output sample bias set FALSE, layer learn additive bias. Default: TRUE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_linear.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Linear module — nn_linear","text":"Input: (N, *, H_in) * means number additional dimensions H_in = in_features. Output: (N, *, H_out) last dimension shape input :math:H_out = out_features.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_linear.html","id":"attributes","dir":"Reference","previous_headings":"","what":"Attributes","title":"Linear module — nn_linear","text":"weight: learnable weights module shape (out_features, in_features). values initialized \\(U(-\\sqrt{k}, \\sqrt{k})\\)s, \\(k = \\frac{1}{\\mbox{\\_features}}\\) bias: learnable bias module shape \\((\\mbox{\\_features})\\). bias TRUE, values initialized \\(\\mathcal{U}(-\\sqrt{k}, \\sqrt{k})\\) \\(k = \\frac{1}{\\mbox{\\_features}}\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_linear.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Linear module — nn_linear","text":"","code":"if (torch_is_installed()) { m <- nn_linear(20, 30) input <- torch_randn(128, 20) output <- m(input) print(output$size()) } #> [1] 128  30"},{"path":"https://torch.mlverse.org/docs/reference/nn_log_sigmoid.html","id":null,"dir":"Reference","previous_headings":"","what":"LogSigmoid module — nn_log_sigmoid","title":"LogSigmoid module — nn_log_sigmoid","text":"Applies element-wise function: $$   \\mbox{LogSigmoid}(x) = \\log\\left(\\frac{ 1 }{ 1 + \\exp(-x)}\\right)  $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_log_sigmoid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"LogSigmoid module — nn_log_sigmoid","text":"","code":"nn_log_sigmoid()"},{"path":"https://torch.mlverse.org/docs/reference/nn_log_sigmoid.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"LogSigmoid module — nn_log_sigmoid","text":"Input: \\((N, *)\\) * means, number additional dimensions Output: \\((N, *)\\), shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_log_sigmoid.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"LogSigmoid module — nn_log_sigmoid","text":"","code":"if (torch_is_installed()) { m <- nn_log_sigmoid() input <- torch_randn(2) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_log_softmax.html","id":null,"dir":"Reference","previous_headings":"","what":"LogSoftmax module — nn_log_softmax","title":"LogSoftmax module — nn_log_softmax","text":"Applies \\(\\log(\\mbox{Softmax}(x))\\) function n-dimensional input Tensor. LogSoftmax formulation can simplified :","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_log_softmax.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"LogSoftmax module — nn_log_softmax","text":"","code":"nn_log_softmax(dim)"},{"path":"https://torch.mlverse.org/docs/reference/nn_log_softmax.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"LogSoftmax module — nn_log_softmax","text":"dim (int): dimension along LogSoftmax computed.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_log_softmax.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"LogSoftmax module — nn_log_softmax","text":"Tensor dimension shape input values range [-inf, 0)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_log_softmax.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"LogSoftmax module — nn_log_softmax","text":"$$   \\mbox{LogSoftmax}(x_{}) = \\log\\left(\\frac{\\exp(x_i) }{ \\sum_j \\exp(x_j)} \\right) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_log_softmax.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"LogSoftmax module — nn_log_softmax","text":"Input: \\((*)\\) * means, number additional dimensions Output: \\((*)\\), shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_log_softmax.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"LogSoftmax module — nn_log_softmax","text":"","code":"if (torch_is_installed()) { m <- nn_log_softmax(1) input <- torch_randn(2, 3) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_lp_pool1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies a 1D power-average pooling over an input signal composed of several input\nplanes. — nn_lp_pool1d","title":"Applies a 1D power-average pooling over an input signal composed of several input\nplanes. — nn_lp_pool1d","text":"window, function computed :","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_lp_pool1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies a 1D power-average pooling over an input signal composed of several input\nplanes. — nn_lp_pool1d","text":"","code":"nn_lp_pool1d(norm_type, kernel_size, stride = NULL, ceil_mode = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nn_lp_pool1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies a 1D power-average pooling over an input signal composed of several input\nplanes. — nn_lp_pool1d","text":"norm_type inf one gets max pooling 0 get sum pooling ( proportional avg pooling) kernel_size single int, size window stride single int, stride window. Default value kernel_size ceil_mode TRUE, use ceil instead floor compute output shape","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_lp_pool1d.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Applies a 1D power-average pooling over an input signal composed of several input\nplanes. — nn_lp_pool1d","text":"$$   f(X) = \\sqrt[p]{\\sum_{x \\X} x^{p}} $$ p = \\(\\infty\\), one gets Max Pooling p = 1, one gets Sum Pooling (proportional Average Pooling)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_lp_pool1d.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Applies a 1D power-average pooling over an input signal composed of several input\nplanes. — nn_lp_pool1d","text":"sum power p zero, gradient function defined. implementation set gradient zero case.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_lp_pool1d.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Applies a 1D power-average pooling over an input signal composed of several input\nplanes. — nn_lp_pool1d","text":"Input: \\((N, C, L_{})\\) Output: \\((N, C, L_{})\\), $$   L_{} = \\left\\lfloor\\frac{L_{} - \\mbox{kernel\\_size}}{\\mbox{stride}} + 1\\right\\rfloor $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_lp_pool1d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Applies a 1D power-average pooling over an input signal composed of several input\nplanes. — nn_lp_pool1d","text":"","code":"if (torch_is_installed()) { # power-2 pool of window of length 3, with stride 2. m <- nn_lp_pool1d(2, 3, stride = 2) input <- torch_randn(20, 16, 50) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_lp_pool2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies a 2D power-average pooling over an input signal composed of several input\nplanes. — nn_lp_pool2d","title":"Applies a 2D power-average pooling over an input signal composed of several input\nplanes. — nn_lp_pool2d","text":"window, function computed :","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_lp_pool2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies a 2D power-average pooling over an input signal composed of several input\nplanes. — nn_lp_pool2d","text":"","code":"nn_lp_pool2d(norm_type, kernel_size, stride = NULL, ceil_mode = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nn_lp_pool2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies a 2D power-average pooling over an input signal composed of several input\nplanes. — nn_lp_pool2d","text":"norm_type inf one gets max pooling 0 get sum pooling ( proportional avg pooling) kernel_size size window stride stride window. Default value kernel_size ceil_mode TRUE, use ceil instead floor compute output shape","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_lp_pool2d.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Applies a 2D power-average pooling over an input signal composed of several input\nplanes. — nn_lp_pool2d","text":"$$   f(X) = \\sqrt[p]{\\sum_{x \\X} x^{p}} $$ p = \\(\\infty\\), one gets Max Pooling p = 1, one gets Sum Pooling (proportional average pooling) parameters kernel_size, stride can either : single int -- case value used height width dimension tuple two ints -- case, first int used height dimension, second int width dimension","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_lp_pool2d.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Applies a 2D power-average pooling over an input signal composed of several input\nplanes. — nn_lp_pool2d","text":"sum power p zero, gradient function defined. implementation set gradient zero case.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_lp_pool2d.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Applies a 2D power-average pooling over an input signal composed of several input\nplanes. — nn_lp_pool2d","text":"Input: \\((N, C, H_{}, W_{})\\) Output: \\((N, C, H_{}, W_{})\\), $$   H_{} = \\left\\lfloor\\frac{H_{} - \\mbox{kernel\\_size}[0]}{\\mbox{stride}[0]} + 1\\right\\rfloor $$ $$   W_{} = \\left\\lfloor\\frac{W_{} - \\mbox{kernel\\_size}[1]}{\\mbox{stride}[1]} + 1\\right\\rfloor $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_lp_pool2d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Applies a 2D power-average pooling over an input signal composed of several input\nplanes. — nn_lp_pool2d","text":"","code":"if (torch_is_installed()) {  # power-2 pool of square window of size=3, stride=2 m <- nn_lp_pool2d(2, 3, stride = 2) # pool of non-square window of power 1.2 m <- nn_lp_pool2d(1.2, c(3, 2), stride = c(2, 1)) input <- torch_randn(20, 16, 50, 32) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_lstm.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies a multi-layer long short-term memory (LSTM) RNN to an input\nsequence. — nn_lstm","title":"Applies a multi-layer long short-term memory (LSTM) RNN to an input\nsequence. — nn_lstm","text":"element input sequence, layer computes following function:","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_lstm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies a multi-layer long short-term memory (LSTM) RNN to an input\nsequence. — nn_lstm","text":"","code":"nn_lstm(   input_size,   hidden_size,   num_layers = 1,   bias = TRUE,   batch_first = FALSE,   dropout = 0,   bidirectional = FALSE,   ... )"},{"path":"https://torch.mlverse.org/docs/reference/nn_lstm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies a multi-layer long short-term memory (LSTM) RNN to an input\nsequence. — nn_lstm","text":"input_size number expected features input x hidden_size number features hidden state h num_layers Number recurrent layers. E.g., setting num_layers=2 mean stacking two LSTMs together form stacked LSTM, second LSTM taking outputs first LSTM computing final results. Default: 1 bias FALSE, layer use bias weights b_ih b_hh. Default: TRUE batch_first TRUE, input output tensors provided (batch, seq, feature). Default: FALSE dropout non-zero, introduces Dropout layer outputs LSTM layer except last layer, dropout probability equal dropout. Default: 0 bidirectional TRUE, becomes bidirectional LSTM. Default: FALSE ... currently unused.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_lstm.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Applies a multi-layer long short-term memory (LSTM) RNN to an input\nsequence. — nn_lstm","text":"$$ \\begin{array}{ll} \\\\ i_t = \\sigma(W_{ii} x_t + b_{ii} + W_{hi} h_{(t-1)} + b_{hi}) \\\\ f_t = \\sigma(W_{} x_t + b_{} + W_{hf} h_{(t-1)} + b_{hf}) \\\\ g_t = \\tanh(W_{ig} x_t + b_{ig} + W_{hg} h_{(t-1)} + b_{hg}) \\\\ o_t = \\sigma(W_{io} x_t + b_{io} + W_{ho} h_{(t-1)} + b_{ho}) \\\\ c_t = f_t c_{(t-1)} + i_t g_t \\\\ h_t = o_t \\tanh(c_t) \\\\ \\end{array} $$ \\(h_t\\) hidden state time t, \\(c_t\\) cell state time t, \\(x_t\\) input time t, \\(h_{(t-1)}\\) hidden state previous layer time t-1 initial hidden state time 0, \\(i_t\\), \\(f_t\\), \\(g_t\\), \\(o_t\\) input, forget, cell, output gates, respectively. \\(\\sigma\\) sigmoid function.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_lstm.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Applies a multi-layer long short-term memory (LSTM) RNN to an input\nsequence. — nn_lstm","text":"weights biases initialized \\(\\mathcal{U}(-\\sqrt{k}, \\sqrt{k})\\) \\(k = \\frac{1}{\\mbox{hidden\\_size}}\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_lstm.html","id":"inputs","dir":"Reference","previous_headings":"","what":"Inputs","title":"Applies a multi-layer long short-term memory (LSTM) RNN to an input\nsequence. — nn_lstm","text":"Inputs: input, (h_0, c_0) input shape (seq_len, batch, input_size): tensor containing features input sequence. input can also packed variable length sequence. See nn_utils_rnn_pack_padded_sequence() nn_utils_rnn_pack_sequence() details. h_0 shape (num_layers * num_directions, batch, hidden_size): tensor containing initial hidden state element batch. c_0 shape (num_layers * num_directions, batch, hidden_size): tensor containing initial cell state element batch. (h_0, c_0) provided, h_0 c_0 default zero.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_lstm.html","id":"outputs","dir":"Reference","previous_headings":"","what":"Outputs","title":"Applies a multi-layer long short-term memory (LSTM) RNN to an input\nsequence. — nn_lstm","text":"Outputs: output, (h_n, c_n) output shape (seq_len, batch, num_directions * hidden_size): tensor containing output features (h_t) last layer LSTM, t. torch_nn.utils.rnn.PackedSequence given input, output also packed sequence. unpacked case, directions can separated using output$view(c(seq_len, batch, num_directions, hidden_size)), forward backward direction 0 1 respectively. Similarly, directions can separated packed case. h_n shape (num_layers * num_directions, batch, hidden_size): tensor containing hidden state t = seq_len. Like output, layers can separated using h_n$view(c(num_layers, num_directions, batch, hidden_size)) similarly c_n. c_n (num_layers * num_directions, batch, hidden_size): tensor containing cell state t = seq_len","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_lstm.html","id":"attributes","dir":"Reference","previous_headings":"","what":"Attributes","title":"Applies a multi-layer long short-term memory (LSTM) RNN to an input\nsequence. — nn_lstm","text":"weight_ih_l[k] : learnable input-hidden weights \\(\\mbox{k}^{th}\\) layer (W_ii|W_if|W_ig|W_io), shape (4*hidden_size x input_size) weight_hh_l[k] : learnable hidden-hidden weights \\(\\mbox{k}^{th}\\) layer (W_hi|W_hf|W_hg|W_ho), shape (4*hidden_size x hidden_size) bias_ih_l[k] : learnable input-hidden bias \\(\\mbox{k}^{th}\\) layer (b_ii|b_if|b_ig|b_io), shape (4*hidden_size) bias_hh_l[k] : learnable hidden-hidden bias \\(\\mbox{k}^{th}\\) layer (b_hi|b_hf|b_hg|b_ho), shape (4*hidden_size)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_lstm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Applies a multi-layer long short-term memory (LSTM) RNN to an input\nsequence. — nn_lstm","text":"","code":"if (torch_is_installed()) { rnn <- nn_lstm(10, 20, 2) input <- torch_randn(5, 3, 10) h0 <- torch_randn(2, 3, 20) c0 <- torch_randn(2, 3, 20) output <- rnn(input, list(h0, c0)) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_margin_ranking_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Margin ranking loss — nn_margin_ranking_loss","title":"Margin ranking loss — nn_margin_ranking_loss","text":"Creates criterion measures loss given inputs \\(x1\\), \\(x2\\), two 1D mini-batch Tensors, label 1D mini-batch tensor \\(y\\) (containing 1 -1). \\(y = 1\\) assumed first input ranked higher (larger value) second input, vice-versa \\(y = -1\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_margin_ranking_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Margin ranking loss — nn_margin_ranking_loss","text":"","code":"nn_margin_ranking_loss(margin = 0, reduction = \"mean\")"},{"path":"https://torch.mlverse.org/docs/reference/nn_margin_ranking_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Margin ranking loss — nn_margin_ranking_loss","text":"margin (float, optional): default value \\(0\\). reduction (string, optional): Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed. Note: size_average reduce process deprecated, meantime, specifying either two args override reduction. Default: 'mean'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_margin_ranking_loss.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Margin ranking loss — nn_margin_ranking_loss","text":"loss function pair samples mini-batch : $$   \\mbox{loss}(x1, x2, y) = \\max(0, -y * (x1 - x2) + \\mbox{margin}) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_margin_ranking_loss.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Margin ranking loss — nn_margin_ranking_loss","text":"Input1: \\((N)\\) N batch size. Input2: \\((N)\\), shape Input1. Target: \\((N)\\), shape inputs. Output: scalar. reduction 'none', \\((N)\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_margin_ranking_loss.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Margin ranking loss — nn_margin_ranking_loss","text":"","code":"if (torch_is_installed()) { loss <- nn_margin_ranking_loss() input1 <- torch_randn(3, requires_grad = TRUE) input2 <- torch_randn(3, requires_grad = TRUE) target <- torch_randn(3)$sign() output <- loss(input1, input2, target) output$backward() }"},{"path":"https://torch.mlverse.org/docs/reference/nn_max_pool1d.html","id":null,"dir":"Reference","previous_headings":"","what":"MaxPool1D module — nn_max_pool1d","title":"MaxPool1D module — nn_max_pool1d","text":"Applies 1D max pooling input signal composed several input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_max_pool1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"MaxPool1D module — nn_max_pool1d","text":"","code":"nn_max_pool1d(   kernel_size,   stride = NULL,   padding = 0,   dilation = 1,   return_indices = FALSE,   ceil_mode = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/nn_max_pool1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"MaxPool1D module — nn_max_pool1d","text":"kernel_size size window take max stride stride window. Default value kernel_size padding implicit zero padding added sides dilation parameter controls stride elements window return_indices TRUE, return max indices along outputs. Useful  nn_max_unpool1d() later. ceil_mode TRUE, use ceil instead floor compute output shape","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_max_pool1d.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"MaxPool1D module — nn_max_pool1d","text":"simplest case, output value layer input size \\((N, C, L)\\) output \\((N, C, L_{})\\) can precisely described : $$   (N_i, C_j, k) = \\max_{m=0, \\ldots, \\mbox{kernel\\_size} - 1} input(N_i, C_j, stride \\times k + m) $$ padding non-zero, input implicitly zero-padded sides padding number points. dilation controls spacing kernel points. harder describe, link nice visualization dilation .","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_max_pool1d.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"MaxPool1D module — nn_max_pool1d","text":"Input: \\((N, C, L_{})\\) Output: \\((N, C, L_{})\\), $$   L_{} = \\left\\lfloor \\frac{L_{} + 2 \\times \\mbox{padding} - \\mbox{dilation}     \\times (\\mbox{kernel\\_size} - 1) - 1}{\\mbox{stride}} + 1\\right\\rfloor $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_max_pool1d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"MaxPool1D module — nn_max_pool1d","text":"","code":"if (torch_is_installed()) { # pool of size=3, stride=2 m <- nn_max_pool1d(3, stride = 2) input <- torch_randn(20, 16, 50) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_max_pool2d.html","id":null,"dir":"Reference","previous_headings":"","what":"MaxPool2D module — nn_max_pool2d","title":"MaxPool2D module — nn_max_pool2d","text":"Applies 2D max pooling input signal composed several input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_max_pool2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"MaxPool2D module — nn_max_pool2d","text":"","code":"nn_max_pool2d(   kernel_size,   stride = NULL,   padding = 0,   dilation = 1,   return_indices = FALSE,   ceil_mode = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/nn_max_pool2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"MaxPool2D module — nn_max_pool2d","text":"kernel_size size window take max stride stride window. Default value kernel_size padding implicit zero padding added sides dilation parameter controls stride elements window return_indices TRUE, return max indices along outputs. Useful nn_max_unpool2d() later. ceil_mode TRUE, use ceil instead floor compute output shape","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_max_pool2d.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"MaxPool2D module — nn_max_pool2d","text":"simplest case, output value layer input size \\((N, C, H, W)\\), output \\((N, C, H_{}, W_{})\\) kernel_size \\((kH, kW)\\) can precisely described : $$ \\begin{array}{ll} (N_i, C_j, h, w) ={} & \\max_{m=0, \\ldots, kH-1} \\max_{n=0, \\ldots, kW-1} \\\\ & \\mbox{input}(N_i, C_j, \\mbox{stride[0]} \\times h + m,                \\mbox{stride[1]} \\times w + n) \\end{array} $$ padding non-zero, input implicitly zero-padded sides padding number points. dilation controls spacing kernel points. harder describe, link nice visualization dilation . parameters kernel_size, stride, padding, dilation can either : single int -- case value used height width dimension tuple two ints -- case, first int used height dimension, second int width dimension","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_max_pool2d.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"MaxPool2D module — nn_max_pool2d","text":"Input: \\((N, C, H_{}, W_{})\\) Output: \\((N, C, H_{}, W_{})\\), $$   H_{} = \\left\\lfloor\\frac{H_{} + 2 * \\mbox{padding[0]} - \\mbox{dilation[0]}     \\times (\\mbox{kernel\\_size[0]} - 1) - 1}{\\mbox{stride[0]}} + 1\\right\\rfloor $$ $$   W_{} = \\left\\lfloor\\frac{W_{} + 2 * \\mbox{padding[1]} - \\mbox{dilation[1]}     \\times (\\mbox{kernel\\_size[1]} - 1) - 1}{\\mbox{stride[1]}} + 1\\right\\rfloor $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_max_pool2d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"MaxPool2D module — nn_max_pool2d","text":"","code":"if (torch_is_installed()) { # pool of square window of size=3, stride=2 m <- nn_max_pool2d(3, stride = 2) # pool of non-square window m <- nn_max_pool2d(c(3, 2), stride = c(2, 1)) input <- torch_randn(20, 16, 50, 32) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_max_pool3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies a 3D max pooling over an input signal composed of several input\nplanes. — nn_max_pool3d","title":"Applies a 3D max pooling over an input signal composed of several input\nplanes. — nn_max_pool3d","text":"simplest case, output value layer input size \\((N, C, D, H, W)\\), output \\((N, C, D_{}, H_{}, W_{})\\) kernel_size \\((kD, kH, kW)\\) can precisely described :","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_max_pool3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies a 3D max pooling over an input signal composed of several input\nplanes. — nn_max_pool3d","text":"","code":"nn_max_pool3d(   kernel_size,   stride = NULL,   padding = 0,   dilation = 1,   return_indices = FALSE,   ceil_mode = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/nn_max_pool3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies a 3D max pooling over an input signal composed of several input\nplanes. — nn_max_pool3d","text":"kernel_size size window take max stride stride window. Default value kernel_size padding implicit zero padding added three sides dilation parameter controls stride elements window return_indices TRUE, return max indices along outputs. Useful torch_nn.MaxUnpool3d later ceil_mode TRUE, use ceil instead floor compute output shape","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_max_pool3d.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Applies a 3D max pooling over an input signal composed of several input\nplanes. — nn_max_pool3d","text":"$$ \\begin{array}{ll} \\mbox{}(N_i, C_j, d, h, w) = & \\max_{k=0, \\ldots, kD-1} \\max_{m=0, \\ldots, kH-1} \\max_{n=0, \\ldots, kW-1} \\\\  & \\mbox{input}(N_i, C_j, \\mbox{stride[0]} \\times d + k, \\mbox{stride[1]} \\times h + m, \\mbox{stride[2]} \\times w + n) \\end{array} $$ padding non-zero, input implicitly zero-padded sides padding number points. dilation controls spacing kernel points. harder describe, link_ nice visualization dilation . parameters kernel_size, stride, padding, dilation can either : single int -- case value used depth, height width dimension tuple three ints -- case, first int used depth dimension, second int height dimension third int width dimension","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_max_pool3d.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Applies a 3D max pooling over an input signal composed of several input\nplanes. — nn_max_pool3d","text":"Input: \\((N, C, D_{}, H_{}, W_{})\\) Output: \\((N, C, D_{}, H_{}, W_{})\\), $$   D_{} = \\left\\lfloor\\frac{D_{} + 2 \\times \\mbox{padding}[0] - \\mbox{dilation}[0] \\times     (\\mbox{kernel\\_size}[0] - 1) - 1}{\\mbox{stride}[0]} + 1\\right\\rfloor $$ $$   H_{} = \\left\\lfloor\\frac{H_{} + 2 \\times \\mbox{padding}[1] - \\mbox{dilation}[1] \\times     (\\mbox{kernel\\_size}[1] - 1) - 1}{\\mbox{stride}[1]} + 1\\right\\rfloor $$ $$   W_{} = \\left\\lfloor\\frac{W_{} + 2 \\times \\mbox{padding}[2] - \\mbox{dilation}[2] \\times     (\\mbox{kernel\\_size}[2] - 1) - 1}{\\mbox{stride}[2]} + 1\\right\\rfloor $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_max_pool3d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Applies a 3D max pooling over an input signal composed of several input\nplanes. — nn_max_pool3d","text":"","code":"if (torch_is_installed()) { # pool of square window of size=3, stride=2 m <- nn_max_pool3d(3, stride = 2) # pool of non-square window m <- nn_max_pool3d(c(3, 2, 2), stride = c(2, 1, 2)) input <- torch_randn(20, 16, 50, 44, 31) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_max_unpool1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes a partial inverse of MaxPool1d. — nn_max_unpool1d","title":"Computes a partial inverse of MaxPool1d. — nn_max_unpool1d","text":"MaxPool1d fully invertible, since non-maximal values lost. MaxUnpool1d takes input output MaxPool1d including indices maximal values computes partial inverse non-maximal values set zero.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_max_unpool1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes a partial inverse of MaxPool1d. — nn_max_unpool1d","text":"","code":"nn_max_unpool1d(kernel_size, stride = NULL, padding = 0)"},{"path":"https://torch.mlverse.org/docs/reference/nn_max_unpool1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes a partial inverse of MaxPool1d. — nn_max_unpool1d","text":"kernel_size (int tuple): Size max pooling window. stride (int tuple): Stride max pooling window. set kernel_size default. padding (int tuple): Padding added input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_max_unpool1d.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Computes a partial inverse of MaxPool1d. — nn_max_unpool1d","text":"MaxPool1d can map several input sizes output sizes. Hence, inversion process can get ambiguous. accommodate , can provide needed output size additional argument output_size forward call. See Inputs Example .","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_max_unpool1d.html","id":"inputs","dir":"Reference","previous_headings":"","what":"Inputs","title":"Computes a partial inverse of MaxPool1d. — nn_max_unpool1d","text":"input: input Tensor invert indices: indices given nn_max_pool1d() output_size (optional): targeted output size","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_max_unpool1d.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Computes a partial inverse of MaxPool1d. — nn_max_unpool1d","text":"Input: \\((N, C, H_{})\\) Output: \\((N, C, H_{})\\), $$   H_{} = (H_{} - 1) \\times \\mbox{stride}[0] - 2 \\times \\mbox{padding}[0] + \\mbox{kernel\\_size}[0] $$ given output_size call operator","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_max_unpool1d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes a partial inverse of MaxPool1d. — nn_max_unpool1d","text":"","code":"if (torch_is_installed()) { pool <- nn_max_pool1d(2, stride = 2, return_indices = TRUE) unpool <- nn_max_unpool1d(2, stride = 2)  input <- torch_tensor(array(1:8 / 1, dim = c(1, 1, 8))) out <- pool(input) unpool(out[[1]], out[[2]])  # Example showcasing the use of output_size input <- torch_tensor(array(1:8 / 1, dim = c(1, 1, 8))) out <- pool(input) unpool(out[[1]], out[[2]], output_size = input$size()) unpool(out[[1]], out[[2]]) } #> torch_tensor #> (1,1,.,.) =  #>   0 #>   2 #>   0 #>   4 #>   0 #>   6 #>   0 #>   8 #> [ CPUFloatType{1,1,8,1} ]"},{"path":"https://torch.mlverse.org/docs/reference/nn_max_unpool2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes a partial inverse of MaxPool2d. — nn_max_unpool2d","title":"Computes a partial inverse of MaxPool2d. — nn_max_unpool2d","text":"MaxPool2d fully invertible, since non-maximal values lost. MaxUnpool2d takes input output MaxPool2d including indices maximal values computes partial inverse non-maximal values set zero.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_max_unpool2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes a partial inverse of MaxPool2d. — nn_max_unpool2d","text":"","code":"nn_max_unpool2d(kernel_size, stride = NULL, padding = 0)"},{"path":"https://torch.mlverse.org/docs/reference/nn_max_unpool2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes a partial inverse of MaxPool2d. — nn_max_unpool2d","text":"kernel_size (int tuple): Size max pooling window. stride (int tuple): Stride max pooling window. set kernel_size default. padding (int tuple): Padding added input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_max_unpool2d.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Computes a partial inverse of MaxPool2d. — nn_max_unpool2d","text":"MaxPool2d can map several input sizes output sizes. Hence, inversion process can get ambiguous. accommodate , can provide needed output size additional argument output_size forward call. See Inputs Example .","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_max_unpool2d.html","id":"inputs","dir":"Reference","previous_headings":"","what":"Inputs","title":"Computes a partial inverse of MaxPool2d. — nn_max_unpool2d","text":"input: input Tensor invert indices: indices given nn_max_pool2d() output_size (optional): targeted output size","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_max_unpool2d.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Computes a partial inverse of MaxPool2d. — nn_max_unpool2d","text":"Input: \\((N, C, H_{}, W_{})\\) Output: \\((N, C, H_{}, W_{})\\), $$   H_{} = (H_{} - 1) \\times \\mbox{stride[0]} - 2 \\times \\mbox{padding[0]} + \\mbox{kernel\\_size[0]} $$ $$   W_{} = (W_{} - 1) \\times \\mbox{stride[1]} - 2 \\times \\mbox{padding[1]} + \\mbox{kernel\\_size[1]} $$ given output_size call operator","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_max_unpool2d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes a partial inverse of MaxPool2d. — nn_max_unpool2d","text":"","code":"if (torch_is_installed()) {  pool <- nn_max_pool2d(2, stride = 2, return_indices = TRUE) unpool <- nn_max_unpool2d(2, stride = 2) input <- torch_randn(1, 1, 4, 4) out <- pool(input) unpool(out[[1]], out[[2]])  # specify a different output size than input size unpool(out[[1]], out[[2]], output_size = c(1, 1, 5, 5)) } #> torch_tensor #> (1,1,.,.) =  #>   0.0000  1.4613  1.4102  0.0000  0.0000 #>   0.0000  0.0000  0.0000  0.9929  0.0000 #>   0.0000  0.0000  0.0000  0.0000  0.0000 #>   1.3693  0.0000  0.0000  0.0000  0.0000 #>   0.0000  0.0000  0.0000  0.0000  0.0000 #> [ CPUFloatType{1,1,5,5} ]"},{"path":"https://torch.mlverse.org/docs/reference/nn_max_unpool3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes a partial inverse of MaxPool3d. — nn_max_unpool3d","title":"Computes a partial inverse of MaxPool3d. — nn_max_unpool3d","text":"MaxPool3d fully invertible, since non-maximal values lost. MaxUnpool3d takes input output MaxPool3d including indices maximal values computes partial inverse non-maximal values set zero.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_max_unpool3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes a partial inverse of MaxPool3d. — nn_max_unpool3d","text":"","code":"nn_max_unpool3d(kernel_size, stride = NULL, padding = 0)"},{"path":"https://torch.mlverse.org/docs/reference/nn_max_unpool3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes a partial inverse of MaxPool3d. — nn_max_unpool3d","text":"kernel_size (int tuple): Size max pooling window. stride (int tuple): Stride max pooling window. set kernel_size default. padding (int tuple): Padding added input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_max_unpool3d.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Computes a partial inverse of MaxPool3d. — nn_max_unpool3d","text":"MaxPool3d can map several input sizes output sizes. Hence, inversion process can get ambiguous. accommodate , can provide needed output size additional argument output_size forward call. See Inputs section .","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_max_unpool3d.html","id":"inputs","dir":"Reference","previous_headings":"","what":"Inputs","title":"Computes a partial inverse of MaxPool3d. — nn_max_unpool3d","text":"input: input Tensor invert indices: indices given nn_max_pool3d() output_size (optional): targeted output size","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_max_unpool3d.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Computes a partial inverse of MaxPool3d. — nn_max_unpool3d","text":"Input: \\((N, C, D_{}, H_{}, W_{})\\) Output: \\((N, C, D_{}, H_{}, W_{})\\), $$   D_{} = (D_{} - 1) \\times \\mbox{stride[0]} - 2 \\times \\mbox{padding[0]} + \\mbox{kernel\\_size[0]} $$ $$   H_{} = (H_{} - 1) \\times \\mbox{stride[1]} - 2 \\times \\mbox{padding[1]} + \\mbox{kernel\\_size[1]} $$ $$   W_{} = (W_{} - 1) \\times \\mbox{stride[2]} - 2 \\times \\mbox{padding[2]} + \\mbox{kernel\\_size[2]} $$ given output_size call operator","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_max_unpool3d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes a partial inverse of MaxPool3d. — nn_max_unpool3d","text":"","code":"if (torch_is_installed()) {  # pool of square window of size=3, stride=2 pool <- nn_max_pool3d(3, stride = 2, return_indices = TRUE) unpool <- nn_max_unpool3d(3, stride = 2) out <- pool(torch_randn(20, 16, 51, 33, 15)) unpooled_output <- unpool(out[[1]], out[[2]]) unpooled_output$size() } #> [1] 20 16 51 33 15"},{"path":"https://torch.mlverse.org/docs/reference/nn_module.html","id":null,"dir":"Reference","previous_headings":"","what":"Base class for all neural network modules. — nn_module","title":"Base class for all neural network modules. — nn_module","text":"models also subclass class.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_module.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Base class for all neural network modules. — nn_module","text":"","code":"nn_module(   classname = NULL,   inherit = nn_Module,   ...,   private = NULL,   active = NULL,   parent_env = parent.frame() )"},{"path":"https://torch.mlverse.org/docs/reference/nn_module.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Base class for all neural network modules. — nn_module","text":"classname optional name module inherit optional module inherit ... methods implementation private passed R6::R6Class(). active passed R6::R6Class(). parent_env passed R6::R6Class().","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_module.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Base class for all neural network modules. — nn_module","text":"Modules can also contain Modules, allowing nest tree structure. can assign submodules regular attributes. expected implement initialize forward create new nn_module.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_module.html","id":"initialize","dir":"Reference","previous_headings":"","what":"Initialize","title":"Base class for all neural network modules. — nn_module","text":"initialize function called whenever new instance nn_module created. use initialize functions define submodules parameters module. example: initialize function can number parameters. objects assigned self$ available methods implement. Tensors wrapped nn_parameter() nn_buffer() submodules automatically tracked assigned self$. initialize function optional module defining weights, submodules buffers.","code":"initialize = function(input_size, output_size) {    self$conv1 <- nn_conv2d(input_size, output_size, 5)    self$conv2 <- nn_conv2d(output_size, output_size, 5) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_module.html","id":"forward","dir":"Reference","previous_headings":"","what":"Forward","title":"Base class for all neural network modules. — nn_module","text":"forward method called whenever instance nn_module called. usually used implement computation module weights ad submodules defined initialize function. example: forward function can use self$training attribute make different computations depending wether model training , example implementing dropout module.","code":"forward = function(input) {    input <- self$conv1(input)    input <- nnf_relu(input)    input <- self$conv2(input)    input <- nnf_relu(input)    input  }"},{"path":"https://torch.mlverse.org/docs/reference/nn_module.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Base class for all neural network modules. — nn_module","text":"","code":"if (torch_is_installed()) { model <- nn_module(   initialize = function() {     self$conv1 <- nn_conv2d(1, 20, 5)     self$conv2 <- nn_conv2d(20, 20, 5)   },   forward = function(input) {     input <- self$conv1(input)     input <- nnf_relu(input)     input <- self$conv2(input)     input <- nnf_relu(input)     input   } ) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_module_list.html","id":null,"dir":"Reference","previous_headings":"","what":"Holds submodules in a list. — nn_module_list","title":"Holds submodules in a list. — nn_module_list","text":"nn_module_list can indexed like regular R list, modules contains properly registered, visible nn_module methods.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_module_list.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Holds submodules in a list. — nn_module_list","text":"","code":"nn_module_list(modules = list())"},{"path":"https://torch.mlverse.org/docs/reference/nn_module_list.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Holds submodules in a list. — nn_module_list","text":"modules list modules add","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_module_list.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Holds submodules in a list. — nn_module_list","text":"","code":"if (torch_is_installed()) {  my_module <- nn_module(   initialize = function() {     self$linears <- nn_module_list(lapply(1:10, function(x) nn_linear(10, 10)))   },   forward = function(x) {     for (i in 1:length(self$linears)) {       x <- self$linears[[i]](x)     }     x   } ) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_mse_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"MSE loss — nn_mse_loss","title":"MSE loss — nn_mse_loss","text":"Creates criterion measures mean squared error (squared L2 norm) element input \\(x\\) target \\(y\\). unreduced (.e. reduction set 'none') loss can described :","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_mse_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"MSE loss — nn_mse_loss","text":"","code":"nn_mse_loss(reduction = \"mean\")"},{"path":"https://torch.mlverse.org/docs/reference/nn_mse_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"MSE loss — nn_mse_loss","text":"reduction (string, optional): Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed. Note: size_average reduce process deprecated, meantime, specifying either two args override reduction. Default: 'mean'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_mse_loss.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"MSE loss — nn_mse_loss","text":"$$   \\ell(x, y) = L = \\{l_1,\\dots,l_N\\}^\\top, \\quad l_n = \\left( x_n - y_n \\right)^2, $$ \\(N\\) batch size. reduction 'none' (default 'mean'), : $$   \\ell(x, y) =   \\begin{array}{ll} \\mbox{mean}(L), &  \\mbox{reduction} = \\mbox{'mean';}\\\\ \\mbox{sum}(L),  &  \\mbox{reduction} = \\mbox{'sum'.} \\end{array} $$ \\(x\\) \\(y\\) tensors arbitrary shapes total \\(n\\) elements . mean operation still operates elements, divides \\(n\\). division \\(n\\) can avoided one sets reduction = 'sum'.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_mse_loss.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"MSE loss — nn_mse_loss","text":"Input: \\((N, *)\\) \\(*\\) means, number additional dimensions Target: \\((N, *)\\), shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_mse_loss.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"MSE loss — nn_mse_loss","text":"","code":"if (torch_is_installed()) { loss <- nn_mse_loss() input <- torch_randn(3, 5, requires_grad = TRUE) target <- torch_randn(3, 5) output <- loss(input, target) output$backward() }"},{"path":"https://torch.mlverse.org/docs/reference/nn_multi_margin_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi margin loss — nn_multi_margin_loss","title":"Multi margin loss — nn_multi_margin_loss","text":"Creates criterion optimizes multi-class classification hinge loss (margin-based loss) input \\(x\\) (2D mini-batch Tensor) output \\(y\\) (1D tensor target class indices, \\(0 \\leq y \\leq \\mbox{x.size}(1)-1\\)):","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_multi_margin_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi margin loss — nn_multi_margin_loss","text":"","code":"nn_multi_margin_loss(p = 1, margin = 1, weight = NULL, reduction = \"mean\")"},{"path":"https://torch.mlverse.org/docs/reference/nn_multi_margin_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Multi margin loss — nn_multi_margin_loss","text":"p (int, optional): default value \\(1\\). \\(1\\) \\(2\\) supported values. margin (float, optional): default value \\(1\\). weight (Tensor, optional): manual rescaling weight given class. given, Tensor size C. Otherwise, treated ones. reduction (string, optional): Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed. Note: size_average reduce process deprecated, meantime, specifying either two args override reduction. Default: 'mean'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_multi_margin_loss.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi margin loss — nn_multi_margin_loss","text":"mini-batch sample, loss terms 1D input \\(x\\) scalar output \\(y\\) : $$   \\mbox{loss}(x, y) = \\frac{\\sum_i \\max(0, \\mbox{margin} - x[y] + x[]))^p}{\\mbox{x.size}(0)} $$ \\(x \\\\left\\{0, \\; \\cdots , \\; \\mbox{x.size}(0) - 1\\right\\}\\) \\(\\neq y\\). Optionally, can give non-equal weighting classes passing 1D weight tensor constructor. loss function becomes: $$   \\mbox{loss}(x, y) = \\frac{\\sum_i \\max(0, w[y] * (\\mbox{margin} - x[y] + x[]))^p)}{\\mbox{x.size}(0)} $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_multihead_attention.html","id":null,"dir":"Reference","previous_headings":"","what":"MultiHead attention — nn_multihead_attention","title":"MultiHead attention — nn_multihead_attention","text":"Allows model jointly attend information different representation subspaces. See reference: Attention Need","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_multihead_attention.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"MultiHead attention — nn_multihead_attention","text":"","code":"nn_multihead_attention(   embed_dim,   num_heads,   dropout = 0,   bias = TRUE,   add_bias_kv = FALSE,   add_zero_attn = FALSE,   kdim = NULL,   vdim = NULL )"},{"path":"https://torch.mlverse.org/docs/reference/nn_multihead_attention.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"MultiHead attention — nn_multihead_attention","text":"embed_dim total dimension model. num_heads parallel attention heads. dropout Dropout layer attn_output_weights. Default: 0.0. bias add bias module parameter. Default: True. add_bias_kv add bias key value sequences dim=0. add_zero_attn add new batch zeros key value sequences dim=1. kdim total number features key. Default: NULL vdim total number features value. Default: NULL. Note: kdim vdim NULL, set embed_dim query, key, value number features.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_multihead_attention.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"MultiHead attention — nn_multihead_attention","text":"$$   \\mbox{MultiHead}(Q, K, V) = \\mbox{Concat}(head_1,\\dots,head_h)W^O \\mbox{} head_i = \\mbox{Attention}(QW_i^Q, KW_i^K, VW_i^V) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_multihead_attention.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"MultiHead attention — nn_multihead_attention","text":"Inputs: query: \\((L, N, E)\\) L target sequence length, N batch size, E embedding dimension. key: \\((S, N, E)\\), S source sequence length, N batch size, E embedding dimension. value: \\((S, N, E)\\) S source sequence length, N batch size, E embedding dimension. key_padding_mask: \\((N, S)\\) N batch size, S source sequence length. ByteTensor provided, non-zero positions ignored position zero positions unchanged. BoolTensor provided, positions value True ignored position value False unchanged. attn_mask: 2D mask \\((L, S)\\) L target sequence length, S source sequence length. 3D mask \\((N*num_heads, L, S)\\) N batch size, L target sequence length, S source sequence length. attn_mask ensure position allowed attend unmasked positions. ByteTensor provided, non-zero positions allowed attend zero positions unchanged. BoolTensor provided, positions True allowed attend False values unchanged. FloatTensor provided, added attention weight. Outputs: attn_output: \\((L, N, E)\\) L target sequence length, N batch size, E embedding dimension. attn_output_weights: avg_weights TRUE (default), output attention weights averaged attention heads, giving tensor shape \\((N, L, S)\\) N batch size, L target sequence length, S source sequence length. avg_weights FALSE, attention weight tensor output -, shape \\((N, H, L, S)\\), H number attention heads.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_multihead_attention.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"MultiHead attention — nn_multihead_attention","text":"","code":"if (torch_is_installed()) { if (FALSE) { multihead_attn <- nn_multihead_attention(embed_dim, num_heads) out <- multihead_attn(query, key, value) attn_output <- out[[1]] attn_output_weights <- out[[2]] }  }"},{"path":"https://torch.mlverse.org/docs/reference/nn_multilabel_margin_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Multilabel margin loss — nn_multilabel_margin_loss","title":"Multilabel margin loss — nn_multilabel_margin_loss","text":"Creates criterion optimizes multi-class multi-classification hinge loss (margin-based loss) input \\(x\\) (2D mini-batch Tensor) output \\(y\\) (2D Tensor target class indices). sample mini-batch:","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_multilabel_margin_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multilabel margin loss — nn_multilabel_margin_loss","text":"","code":"nn_multilabel_margin_loss(reduction = \"mean\")"},{"path":"https://torch.mlverse.org/docs/reference/nn_multilabel_margin_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Multilabel margin loss — nn_multilabel_margin_loss","text":"reduction (string, optional): Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed. Note: size_average reduce process deprecated, meantime, specifying either two args override reduction. Default: 'mean'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_multilabel_margin_loss.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multilabel margin loss — nn_multilabel_margin_loss","text":"$$   \\mbox{loss}(x, y) = \\sum_{ij}\\frac{\\max(0, 1 - (x[y[j]] - x[]))}{\\mbox{x.size}(0)} $$ \\(x \\\\left\\{0, \\; \\cdots , \\; \\mbox{x.size}(0) - 1\\right\\}\\), \\ \\(y \\\\left\\{0, \\; \\cdots , \\; \\mbox{y.size}(0) - 1\\right\\}\\), \\ \\(0 \\leq y[j] \\leq \\mbox{x.size}(0)-1\\), \\ \\(\\neq y[j]\\) \\(\\) \\(j\\). \\(y\\) \\(x\\) must size. criterion considers contiguous block non-negative targets starts front. allows different samples variable amounts target classes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_multilabel_margin_loss.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Multilabel margin loss — nn_multilabel_margin_loss","text":"Input: \\((C)\\) \\((N, C)\\) N batch size C number classes. Target: \\((C)\\) \\((N, C)\\), label targets padded -1 ensuring shape input. Output: scalar. reduction 'none', \\((N)\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_multilabel_margin_loss.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multilabel margin loss — nn_multilabel_margin_loss","text":"","code":"if (torch_is_installed()) { loss <- nn_multilabel_margin_loss() x <- torch_tensor(c(0.1, 0.2, 0.4, 0.8))$view(c(1, 4)) # for target y, only consider labels 4 and 1, not after label -1 y <- torch_tensor(c(4, 1, -1, 2), dtype = torch_long())$view(c(1, 4)) loss(x, y) } #> torch_tensor #> 0.85 #> [ CPUFloatType{} ]"},{"path":"https://torch.mlverse.org/docs/reference/nn_multilabel_soft_margin_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi label soft margin loss — nn_multilabel_soft_margin_loss","title":"Multi label soft margin loss — nn_multilabel_soft_margin_loss","text":"Creates criterion optimizes multi-label one-versus-loss based max-entropy, input \\(x\\) target \\(y\\) size \\((N, C)\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_multilabel_soft_margin_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi label soft margin loss — nn_multilabel_soft_margin_loss","text":"","code":"nn_multilabel_soft_margin_loss(weight = NULL, reduction = \"mean\")"},{"path":"https://torch.mlverse.org/docs/reference/nn_multilabel_soft_margin_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Multi label soft margin loss — nn_multilabel_soft_margin_loss","text":"weight (Tensor, optional): manual rescaling weight given class. given, Tensor size C. Otherwise, treated ones. reduction (string, optional): Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed. Note: size_average reduce process deprecated, meantime, specifying either two args override reduction. Default: 'mean'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_multilabel_soft_margin_loss.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi label soft margin loss — nn_multilabel_soft_margin_loss","text":"sample minibatch: $$   loss(x, y) = - \\frac{1}{C} * \\sum_i y[] * \\log((1 + \\exp(-x[]))^{-1}) + (1-y[]) * \\log\\left(\\frac{\\exp(-x[])}{(1 + \\exp(-x[]))}\\right) $$ \\(\\\\left\\{0, \\; \\cdots , \\; \\mbox{x.nElement}() - 1\\right\\}\\), \\(y[] \\\\left\\{0, \\; 1\\right\\}\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_multilabel_soft_margin_loss.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Multi label soft margin loss — nn_multilabel_soft_margin_loss","text":"Input: \\((N, C)\\) N batch size C number classes. Target: \\((N, C)\\), label targets padded -1 ensuring shape input. Output: scalar. reduction 'none', \\((N)\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_nll_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Nll loss — nn_nll_loss","title":"Nll loss — nn_nll_loss","text":"negative log likelihood loss. useful train classification problem C classes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_nll_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Nll loss — nn_nll_loss","text":"","code":"nn_nll_loss(weight = NULL, ignore_index = -100, reduction = \"mean\")"},{"path":"https://torch.mlverse.org/docs/reference/nn_nll_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Nll loss — nn_nll_loss","text":"weight (Tensor, optional): manual rescaling weight given class. given, Tensor size C. Otherwise, treated ones. ignore_index (int, optional): Specifies target value ignored contribute input gradient. reduction (string, optional): Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': weighted mean output taken, 'sum': output summed. Note: size_average reduce process deprecated, meantime, specifying either two args override reduction. Default: 'mean'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_nll_loss.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Nll loss — nn_nll_loss","text":"provided, optional argument weight 1D Tensor assigning weight classes. particularly useful unbalanced training set. input given forward call expected contain log-probabilities class. input Tensor size either \\((minibatch, C)\\) \\((minibatch, C, d_1, d_2, ..., d_K)\\) \\(K \\geq 1\\) K-dimensional case (described later). Obtaining log-probabilities neural network easily achieved adding  LogSoftmax  layer last layer network. may use CrossEntropyLoss instead, prefer add extra layer. target loss expects class index range \\([0, C-1]\\) C = number classes; ignore_index specified, loss also accepts class index (index may necessarily class range). unreduced (.e. reduction set 'none') loss can described : $$ \\ell(x, y) = L = \\{l_1,\\dots,l_N\\}^\\top, \\quad l_n = - w_{y_n} x_{n,y_n}, \\quad w_{c} = \\mbox{weight}[c] \\cdot \\mbox{1}\\{c \\= \\mbox{ignore\\_index}\\}, $$ \\(x\\) input, \\(y\\) target, \\(w\\) weight, \\(N\\) batch size. reduction 'none' (default 'mean'), $$ \\ell(x, y) = \\begin{array}{ll} \\sum_{n=1}^N \\frac{1}{\\sum_{n=1}^N w_{y_n}} l_n, &   \\mbox{reduction} = \\mbox{'mean';}\\\\ \\sum_{n=1}^N l_n,  &   \\mbox{reduction} = \\mbox{'sum'.} \\end{array} $$ Can also used higher dimension inputs, 2D images, providing input size \\((minibatch, C, d_1, d_2, ..., d_K)\\) \\(K \\geq 1\\), \\(K\\) number dimensions, target appropriate shape (see ). case images, computes NLL loss per-pixel.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_nll_loss.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Nll loss — nn_nll_loss","text":"Input: \\((N, C)\\) C = number classes, \\((N, C, d_1, d_2, ..., d_K)\\) \\(K \\geq 1\\) case K-dimensional loss. Target: \\((N)\\) value \\(0 \\leq \\mbox{targets}[] \\leq C-1\\), \\((N, d_1, d_2, ..., d_K)\\) \\(K \\geq 1\\) case K-dimensional loss. Output: scalar. reduction 'none', size target: \\((N)\\), \\((N, d_1, d_2, ..., d_K)\\) \\(K \\geq 1\\) case K-dimensional loss.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_nll_loss.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Nll loss — nn_nll_loss","text":"","code":"if (torch_is_installed()) { m <- nn_log_softmax(dim = 2) loss <- nn_nll_loss() # input is of size N x C = 3 x 5 input <- torch_randn(3, 5, requires_grad = TRUE) # each element in target has to have 0 <= value < C target <- torch_tensor(c(2, 1, 5), dtype = torch_long()) output <- loss(m(input), target) output$backward()  # 2D loss example (used, for example, with image inputs) N <- 5 C <- 4 loss <- nn_nll_loss() # input is of size N x C x height x width data <- torch_randn(N, 16, 10, 10) conv <- nn_conv2d(16, C, c(3, 3)) m <- nn_log_softmax(dim = 1) # each element in target has to have 0 <= value < C target <- torch_empty(N, 8, 8, dtype = torch_long())$random_(1, C) output <- loss(m(conv(data)), target) output$backward() }"},{"path":"https://torch.mlverse.org/docs/reference/nn_pairwise_distance.html","id":null,"dir":"Reference","previous_headings":"","what":"Pairwise distance — nn_pairwise_distance","title":"Pairwise distance — nn_pairwise_distance","text":"Computes batchwise pairwise distance vectors \\(v_1\\), \\(v_2\\) using p-norm:","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_pairwise_distance.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pairwise distance — nn_pairwise_distance","text":"","code":"nn_pairwise_distance(p = 2, eps = 1e-06, keepdim = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nn_pairwise_distance.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pairwise distance — nn_pairwise_distance","text":"p (real): norm degree. Default: 2 eps (float, optional): Small value avoid division zero. Default: 1e-6 keepdim (bool, optional): Determines whether keep vector dimension. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_pairwise_distance.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Pairwise distance — nn_pairwise_distance","text":"$$  \\Vert x \\Vert _p = \\left( \\sum_{=1}^n  \\vert x_i \\vert ^ p \\right) ^ {1/p}. $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_pairwise_distance.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Pairwise distance — nn_pairwise_distance","text":"Input1: \\((N, D)\\) D = vector dimension Input2: \\((N, D)\\), shape Input1 Output: \\((N)\\). keepdim TRUE, \\((N, 1)\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_pairwise_distance.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pairwise distance — nn_pairwise_distance","text":"","code":"if (torch_is_installed()) { pdist <- nn_pairwise_distance(p = 2) input1 <- torch_randn(100, 128) input2 <- torch_randn(100, 128) output <- pdist(input1, input2) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_parameter.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates an nn_parameter — nn_parameter","title":"Creates an nn_parameter — nn_parameter","text":"Indicates nn_module x parameter","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_parameter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates an nn_parameter — nn_parameter","text":"","code":"nn_parameter(x, requires_grad = TRUE)"},{"path":"https://torch.mlverse.org/docs/reference/nn_parameter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates an nn_parameter — nn_parameter","text":"x tensor want indicate parameter requires_grad whether parameter  requires_grad = TRUE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_poisson_nll_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Poisson NLL loss — nn_poisson_nll_loss","title":"Poisson NLL loss — nn_poisson_nll_loss","text":"Negative log likelihood loss Poisson distribution target. loss can described :","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_poisson_nll_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Poisson NLL loss — nn_poisson_nll_loss","text":"","code":"nn_poisson_nll_loss(   log_input = TRUE,   full = FALSE,   eps = 1e-08,   reduction = \"mean\" )"},{"path":"https://torch.mlverse.org/docs/reference/nn_poisson_nll_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Poisson NLL loss — nn_poisson_nll_loss","text":"log_input (bool, optional): TRUE loss computed \\(\\exp(\\mbox{input}) - \\mbox{target}*\\mbox{input}\\), FALSE loss \\(\\mbox{input} - \\mbox{target}*\\log(\\mbox{input}+\\mbox{eps})\\). full (bool, optional): whether compute full loss, . e. add Stirling approximation term \\(\\mbox{target}*\\log(\\mbox{target}) - \\mbox{target} + 0.5 * \\log(2\\pi\\mbox{target})\\). eps (float, optional): Small value avoid evaluation \\(\\log(0)\\) log_input = FALSE. Default: 1e-8 reduction (string, optional): Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed. Note: size_average reduce process deprecated, meantime, specifying either two args override reduction. Default: 'mean'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_poisson_nll_loss.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Poisson NLL loss — nn_poisson_nll_loss","text":"$$ \\mbox{target} \\sim \\mathrm{Poisson}(\\mbox{input}) \\mbox{loss}(\\mbox{input}, \\mbox{target}) = \\mbox{input} - \\mbox{target} * \\log(\\mbox{input}) + \\log(\\mbox{target!}) $$ last term can omitted approximated Stirling formula. approximation used target values 1. targets less equal 1 zeros added loss.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_poisson_nll_loss.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Poisson NLL loss — nn_poisson_nll_loss","text":"Input: \\((N, *)\\) \\(*\\) means, number additional dimensions Target: \\((N, *)\\), shape input Output: scalar default. reduction 'none', \\((N, *)\\), shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_poisson_nll_loss.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Poisson NLL loss — nn_poisson_nll_loss","text":"","code":"if (torch_is_installed()) { loss <- nn_poisson_nll_loss() log_input <- torch_randn(5, 2, requires_grad = TRUE) target <- torch_randn(5, 2) output <- loss(log_input, target) output$backward() }"},{"path":"https://torch.mlverse.org/docs/reference/nn_prelu.html","id":null,"dir":"Reference","previous_headings":"","what":"PReLU module — nn_prelu","title":"PReLU module — nn_prelu","text":"Applies element-wise function: $$   \\mbox{PReLU}(x) = \\max(0,x) + * \\min(0,x) $$ $$   \\mbox{PReLU}(x) =   \\left\\{ \\begin{array}{ll} x, & \\mbox{ } x \\geq 0 \\\\ ax, & \\mbox{ otherwise } \\end{array} \\right. $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_prelu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PReLU module — nn_prelu","text":"","code":"nn_prelu(num_parameters = 1, init = 0.25)"},{"path":"https://torch.mlverse.org/docs/reference/nn_prelu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PReLU module — nn_prelu","text":"num_parameters (int): number \\(\\) learn. Although takes int input, two values legitimate: 1, number channels input. Default: 1 init (float): initial value \\(\\). Default: 0.25","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_prelu.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"PReLU module — nn_prelu","text":"\\(\\) learnable parameter. called without arguments, nn.prelu() uses single parameter \\(\\) across input channels. called nn_prelu(nChannels), separate \\(\\) used input channel.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_prelu.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"PReLU module — nn_prelu","text":"weight decay used learning \\(\\) good performance. Channel dim 2nd dim input. input dims < 2, channel dim number channels = 1.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_prelu.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"PReLU module — nn_prelu","text":"Input: \\((N, *)\\) * means, number additional dimensions Output: \\((N, *)\\), shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_prelu.html","id":"attributes","dir":"Reference","previous_headings":"","what":"Attributes","title":"PReLU module — nn_prelu","text":"weight (Tensor): learnable weights shape (num_parameters).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_prelu.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PReLU module — nn_prelu","text":"","code":"if (torch_is_installed()) { m <- nn_prelu() input <- torch_randn(2) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_relu.html","id":null,"dir":"Reference","previous_headings":"","what":"ReLU module — nn_relu","title":"ReLU module — nn_relu","text":"Applies rectified linear unit function element-wise $$\\mbox{ReLU}(x) = (x)^+ = \\max(0, x)$$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_relu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"ReLU module — nn_relu","text":"","code":"nn_relu(inplace = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nn_relu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"ReLU module — nn_relu","text":"inplace can optionally operation -place. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_relu.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"ReLU module — nn_relu","text":"Input: \\((N, *)\\) * means, number additional dimensions Output: \\((N, *)\\), shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_relu.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"ReLU module — nn_relu","text":"","code":"if (torch_is_installed()) { m <- nn_relu() input <- torch_randn(2) m(input) } #> torch_tensor #>  0.5247 #>  0.0000 #> [ CPUFloatType{2} ]"},{"path":"https://torch.mlverse.org/docs/reference/nn_relu6.html","id":null,"dir":"Reference","previous_headings":"","what":"ReLu6 module — nn_relu6","title":"ReLu6 module — nn_relu6","text":"Applies element-wise function:","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_relu6.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"ReLu6 module — nn_relu6","text":"","code":"nn_relu6(inplace = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nn_relu6.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"ReLu6 module — nn_relu6","text":"inplace can optionally operation -place. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_relu6.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"ReLu6 module — nn_relu6","text":"$$   \\mbox{ReLU6}(x) = \\min(\\max(0,x), 6) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_relu6.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"ReLu6 module — nn_relu6","text":"Input: \\((N, *)\\) * means, number additional dimensions Output: \\((N, *)\\), shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_relu6.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"ReLu6 module — nn_relu6","text":"","code":"if (torch_is_installed()) { m <- nn_relu6() input <- torch_randn(2) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_rnn.html","id":null,"dir":"Reference","previous_headings":"","what":"RNN module — nn_rnn","title":"RNN module — nn_rnn","text":"Applies multi-layer Elman RNN \\(\\tanh\\) \\(\\mbox{ReLU}\\) non-linearity input sequence.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_rnn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"RNN module — nn_rnn","text":"","code":"nn_rnn(   input_size,   hidden_size,   num_layers = 1,   nonlinearity = NULL,   bias = TRUE,   batch_first = FALSE,   dropout = 0,   bidirectional = FALSE,   ... )"},{"path":"https://torch.mlverse.org/docs/reference/nn_rnn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"RNN module — nn_rnn","text":"input_size number expected features input x hidden_size number features hidden state h num_layers Number recurrent layers. E.g., setting num_layers=2 mean stacking two RNNs together form stacked RNN, second RNN taking outputs first RNN computing final results. Default: 1 nonlinearity non-linearity use. Can either 'tanh' 'relu'. Default: 'tanh' bias FALSE, layer use bias weights b_ih b_hh. Default: TRUE batch_first TRUE, input output tensors provided (batch, seq, feature). Default: FALSE dropout non-zero, introduces Dropout layer outputs RNN layer except last layer, dropout probability equal dropout. Default: 0 bidirectional TRUE, becomes bidirectional RNN. Default: FALSE ... arguments can passed super class.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_rnn.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"RNN module — nn_rnn","text":"element input sequence, layer computes following function: $$ h_t = \\tanh(W_{ih} x_t + b_{ih} + W_{hh} h_{(t-1)} + b_{hh}) $$ \\(h_t\\) hidden state time t, \\(x_t\\) input time t, \\(h_{(t-1)}\\) hidden state previous layer time t-1 initial hidden state time 0. nonlinearity 'relu', \\(\\mbox{ReLU}\\) used instead \\(\\tanh\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_rnn.html","id":"inputs","dir":"Reference","previous_headings":"","what":"Inputs","title":"RNN module — nn_rnn","text":"input shape (seq_len, batch, input_size): tensor containing features input sequence. input can also packed variable length sequence. h_0 shape (num_layers * num_directions, batch, hidden_size): tensor containing initial hidden state element batch. Defaults zero provided. RNN bidirectional, num_directions 2, else 1.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_rnn.html","id":"outputs","dir":"Reference","previous_headings":"","what":"Outputs","title":"RNN module — nn_rnn","text":"output shape (seq_len, batch, num_directions * hidden_size): tensor containing output features (h_t) last layer RNN, t.  :class:nn_packed_sequence given input, output also packed sequence. unpacked case, directions can separated using output$view(seq_len, batch, num_directions, hidden_size), forward backward direction 0 1 respectively. Similarly, directions can separated packed case. h_n shape (num_layers * num_directions, batch, hidden_size): tensor containing hidden state t = seq_len. Like output, layers can separated using h_n$view(num_layers, num_directions, batch, hidden_size).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_rnn.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"RNN module — nn_rnn","text":"Input1: \\((L, N, H_{})\\) tensor containing input features \\(H_{}=\\mbox{input\\_size}\\) L represents sequence length. Input2: \\((S, N, H_{})\\) tensor containing initial hidden state element batch. \\(H_{}=\\mbox{hidden\\_size}\\) Defaults zero provided. \\(S=\\mbox{num\\_layers} * \\mbox{num\\_directions}\\) RNN bidirectional, num_directions 2, else 1. Output1: \\((L, N, H_{})\\) \\(H_{}=\\mbox{num\\_directions} * \\mbox{hidden\\_size}\\) Output2: \\((S, N, H_{})\\) tensor containing next hidden state element batch","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_rnn.html","id":"attributes","dir":"Reference","previous_headings":"","what":"Attributes","title":"RNN module — nn_rnn","text":"weight_ih_l[k]: learnable input-hidden weights k-th layer, shape (hidden_size, input_size) k = 0. Otherwise, shape (hidden_size, num_directions * hidden_size) weight_hh_l[k]: learnable hidden-hidden weights k-th layer, shape (hidden_size, hidden_size) bias_ih_l[k]: learnable input-hidden bias k-th layer, shape (hidden_size) bias_hh_l[k]: learnable hidden-hidden bias k-th layer, shape (hidden_size)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_rnn.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"RNN module — nn_rnn","text":"weights biases initialized \\(\\mathcal{U}(-\\sqrt{k}, \\sqrt{k})\\) \\(k = \\frac{1}{\\mbox{hidden\\_size}}\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_rnn.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"RNN module — nn_rnn","text":"","code":"if (torch_is_installed()) { rnn <- nn_rnn(10, 20, 2) input <- torch_randn(5, 3, 10) h0 <- torch_randn(2, 3, 20) rnn(input, h0) } #> [[1]] #> torch_tensor #> (1,.,.) =  #>  Columns 1 to 9  0.3345 -0.8092  0.8616 -0.3814 -0.6915  0.4852  0.7867 -0.5962  0.9033 #>   0.2259  0.3547  0.1961  0.2019 -0.2665  0.6190  0.4105  0.9266 -0.1081 #>   0.6620 -0.2637  0.4504  0.3002 -0.1003 -0.9604 -0.4974  0.3616  0.2804 #>  #> Columns 10 to 18 -0.2662  0.4342  0.0859  0.6060 -0.7298  0.4275 -0.5170 -0.6527  0.0447 #>  -0.1489 -0.1532 -0.3676  0.7770  0.3155 -0.4149  0.4342 -0.2582 -0.4781 #>  -0.5659 -0.4697 -0.7085 -0.8518  0.8653 -0.0720  0.1830 -0.4675 -0.8121 #>  #> Columns 19 to 20  0.8197 -0.8390 #>   0.7136  0.8726 #>  -0.6550  0.9490 #>  #> (2,.,.) =  #>  Columns 1 to 9  0.1877 -0.0359  0.3635  0.1780  0.2971 -0.9101  0.4571 -0.4257  0.6578 #>  -0.0443 -0.2512  0.7624 -0.0894 -0.7454 -0.1553  0.0990  0.1471  0.1659 #>  -0.1011 -0.2845  0.2408  0.2113 -0.3587  0.3907  0.6455 -0.4355 -0.0128 #>  #> Columns 10 to 18 -0.2258  0.1592  0.4052 -0.3029  0.6346 -0.3514 -0.3189 -0.3640 -0.4152 #>  -0.3894  0.3147 -0.3049  0.2987  0.7429 -0.3471  0.1917  0.2159  0.0276 #>  -0.1765  0.4714 -0.3878 -0.1281 -0.3013 -0.0446  0.2997  0.2569  0.1633 #>  #> Columns 19 to 20  0.1087 -0.4652 #>  -0.0069  0.2470 #>   0.8382 -0.4049 #>  #> (3,.,.) =  #>  Columns 1 to 9  0.3968 -0.1327  0.4892  0.3467 -0.0534 -0.4809  0.6823 -0.4104  0.2749 #>   0.1936  0.4276  0.5926 -0.2124  0.0078 -0.5926  0.6900 -0.1461  0.2141 #>   0.0320  0.1210  0.3006  0.0531 -0.0199 -0.3555  0.2782 -0.2561  0.6810 #> ... [the output was truncated (use n=-1 to disable)] #> [ CPUFloatType{5,3,20} ][ grad_fn = <StackBackward0> ] #>  #> [[2]] #> torch_tensor #> (1,.,.) =  #>  Columns 1 to 9  0.3201 -0.0094 -0.5913 -0.2042  0.2034 -0.2921 -0.2716  0.1504  0.0855 #>   0.0986  0.3801 -0.7451 -0.0820  0.5822 -0.5968 -0.1580 -0.0045  0.2787 #>   0.4021  0.3402 -0.5956  0.0071 -0.0387 -0.4006  0.1451 -0.3226  0.3752 #>  #> Columns 10 to 18 -0.1617 -0.0465  0.0560 -0.5249 -0.2707  0.7919 -0.4247  0.2552  0.1553 #>  -0.3183  0.0216 -0.2405 -0.4657 -0.6597  0.7781 -0.7501 -0.0955  0.5029 #>   0.1261 -0.3039 -0.4118 -0.5502 -0.2039  0.4801 -0.3519  0.0634  0.0357 #>  #> Columns 19 to 20  0.4430 -0.2941 #>  -0.5167 -0.5764 #>   0.5123 -0.5078 #>  #> (2,.,.) =  #>  Columns 1 to 9  0.2787 -0.1695  0.3868  0.0903 -0.3336 -0.6068  0.4856 -0.2817  0.2359 #>   0.2348 -0.1015  0.6116 -0.0421 -0.1798 -0.1931  0.4836 -0.4568  0.5772 #>   0.3405  0.1997  0.5214  0.1526 -0.0931 -0.1824  0.6392 -0.1871  0.3944 #>  #> Columns 10 to 18 -0.3941 -0.1537 -0.2288 -0.2495  0.2253  0.0120 -0.2736  0.1356 -0.3282 #>  -0.2815  0.0964 -0.5073  0.0956 -0.1618 -0.0885 -0.1228 -0.0379 -0.2024 #>  -0.5154 -0.1136 -0.4561 -0.2810 -0.0525 -0.0471 -0.0752  0.0108 -0.1525 #>  #> Columns 19 to 20  0.0616  0.0758 #>  -0.0229 -0.0540 #>  -0.0506 -0.0383 #> [ CPUFloatType{2,3,20} ][ grad_fn = <StackBackward0> ] #>"},{"path":"https://torch.mlverse.org/docs/reference/nn_rrelu.html","id":null,"dir":"Reference","previous_headings":"","what":"RReLU module — nn_rrelu","title":"RReLU module — nn_rrelu","text":"Applies randomized leaky rectified liner unit function, element-wise, described paper:","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_rrelu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"RReLU module — nn_rrelu","text":"","code":"nn_rrelu(lower = 1/8, upper = 1/3, inplace = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nn_rrelu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"RReLU module — nn_rrelu","text":"lower lower bound uniform distribution. Default: \\(\\frac{1}{8}\\) upper upper bound uniform distribution. Default: \\(\\frac{1}{3}\\) inplace can optionally operation -place. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_rrelu.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"RReLU module — nn_rrelu","text":"Empirical Evaluation Rectified Activations Convolutional Network. function defined : $$ \\mbox{RReLU}(x) = \\left\\{ \\begin{array}{ll} x & \\mbox{} x \\geq 0 \\\\ ax & \\mbox{ otherwise } \\end{array} \\right. $$ \\(\\) randomly sampled uniform distribution \\(\\mathcal{U}(\\mbox{lower}, \\mbox{upper})\\). See: https://arxiv.org/pdf/1505.00853.pdf","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_rrelu.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"RReLU module — nn_rrelu","text":"Input: \\((N, *)\\) * means, number additional dimensions Output: \\((N, *)\\), shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_rrelu.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"RReLU module — nn_rrelu","text":"","code":"if (torch_is_installed()) { m <- nn_rrelu(0.1, 0.3) input <- torch_randn(2) m(input) } #> torch_tensor #> -0.1557 #>  1.7992 #> [ CPUFloatType{2} ]"},{"path":"https://torch.mlverse.org/docs/reference/nn_selu.html","id":null,"dir":"Reference","previous_headings":"","what":"SELU module — nn_selu","title":"SELU module — nn_selu","text":"Applied element-wise, :","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_selu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"SELU module — nn_selu","text":"","code":"nn_selu(inplace = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nn_selu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"SELU module — nn_selu","text":"inplace (bool, optional): can optionally operation -place. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_selu.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"SELU module — nn_selu","text":"$$   \\mbox{SELU}(x) = \\mbox{scale} * (\\max(0,x) + \\min(0, \\alpha * (\\exp(x) - 1))) $$ \\(\\alpha = 1.6732632423543772848170429916717\\) \\(\\mbox{scale} = 1.0507009873554804934193349852946\\). details can found paper Self-Normalizing Neural Networks.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_selu.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"SELU module — nn_selu","text":"Input: \\((N, *)\\) * means, number additional dimensions Output: \\((N, *)\\), shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_selu.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"SELU module — nn_selu","text":"","code":"if (torch_is_installed()) { m <- nn_selu() input <- torch_randn(2) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_sequential.html","id":null,"dir":"Reference","previous_headings":"","what":"A sequential container — nn_sequential","title":"A sequential container — nn_sequential","text":"sequential container. Modules added order passed constructor. See examples.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_sequential.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A sequential container — nn_sequential","text":"","code":"nn_sequential(...)"},{"path":"https://torch.mlverse.org/docs/reference/nn_sequential.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A sequential container — nn_sequential","text":"... sequence modules added","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_sequential.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A sequential container — nn_sequential","text":"","code":"if (torch_is_installed()) {  model <- nn_sequential(   nn_conv2d(1, 20, 5),   nn_relu(),   nn_conv2d(20, 64, 5),   nn_relu() ) input <- torch_randn(32, 1, 28, 28) output <- model(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_sigmoid.html","id":null,"dir":"Reference","previous_headings":"","what":"Sigmoid module — nn_sigmoid","title":"Sigmoid module — nn_sigmoid","text":"Applies element-wise function:","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_sigmoid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sigmoid module — nn_sigmoid","text":"","code":"nn_sigmoid()"},{"path":"https://torch.mlverse.org/docs/reference/nn_sigmoid.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Sigmoid module — nn_sigmoid","text":"$$   \\mbox{Sigmoid}(x) = \\sigma(x) = \\frac{1}{1 + \\exp(-x)} $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_sigmoid.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Sigmoid module — nn_sigmoid","text":"Input: \\((N, *)\\) * means, number additional dimensions Output: \\((N, *)\\), shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_sigmoid.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sigmoid module — nn_sigmoid","text":"","code":"if (torch_is_installed()) { m <- nn_sigmoid() input <- torch_randn(2) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_smooth_l1_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Smooth L1 loss — nn_smooth_l1_loss","title":"Smooth L1 loss — nn_smooth_l1_loss","text":"Creates criterion uses squared term absolute element-wise error falls 1 L1 term otherwise. less sensitive outliers MSELoss cases prevents exploding gradients (e.g. see Fast R-CNN paper Ross Girshick). Also known Huber loss:","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_smooth_l1_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Smooth L1 loss — nn_smooth_l1_loss","text":"","code":"nn_smooth_l1_loss(reduction = \"mean\")"},{"path":"https://torch.mlverse.org/docs/reference/nn_smooth_l1_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Smooth L1 loss — nn_smooth_l1_loss","text":"reduction (string, optional): Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed. Note: size_average reduce process deprecated, meantime, specifying either two args override reduction. Default: 'mean'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_smooth_l1_loss.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Smooth L1 loss — nn_smooth_l1_loss","text":"$$   \\mbox{loss}(x, y) = \\frac{1}{n} \\sum_{} z_{} $$ \\(z_{}\\) given : $$   z_{} =   \\begin{array}{ll} 0.5 (x_i - y_i)^2, & \\mbox{} |x_i - y_i| < 1 \\\\ |x_i - y_i| - 0.5, & \\mbox{otherwise } \\end{array} $$ \\(x\\) \\(y\\) arbitrary shapes total \\(n\\) elements sum operation still operates elements, divides \\(n\\). division \\(n\\) can avoided sets reduction = 'sum'.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_smooth_l1_loss.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Smooth L1 loss — nn_smooth_l1_loss","text":"Input: \\((N, *)\\) \\(*\\) means, number additional dimensions Target: \\((N, *)\\), shape input Output: scalar. reduction 'none', \\((N, *)\\), shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_soft_margin_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Soft margin loss — nn_soft_margin_loss","title":"Soft margin loss — nn_soft_margin_loss","text":"Creates criterion optimizes two-class classification logistic loss input tensor \\(x\\) target tensor \\(y\\) (containing 1 -1).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_soft_margin_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Soft margin loss — nn_soft_margin_loss","text":"","code":"nn_soft_margin_loss(reduction = \"mean\")"},{"path":"https://torch.mlverse.org/docs/reference/nn_soft_margin_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Soft margin loss — nn_soft_margin_loss","text":"reduction (string, optional): Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed. Note: size_average reduce process deprecated, meantime, specifying either two args override reduction. Default: 'mean'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_soft_margin_loss.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Soft margin loss — nn_soft_margin_loss","text":"$$   \\mbox{loss}(x, y) = \\sum_i \\frac{\\log(1 + \\exp(-y[]*x[]))}{\\mbox{x.nelement}()} $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_soft_margin_loss.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Soft margin loss — nn_soft_margin_loss","text":"Input: \\((*)\\) \\(*\\) means, number additional dimensions Target: \\((*)\\), shape input Output: scalar. reduction 'none', shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_softmax.html","id":null,"dir":"Reference","previous_headings":"","what":"Softmax module — nn_softmax","title":"Softmax module — nn_softmax","text":"Applies Softmax function n-dimensional input Tensor rescaling elements n-dimensional output Tensor lie range [0,1] sum 1. Softmax defined :","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_softmax.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Softmax module — nn_softmax","text":"","code":"nn_softmax(dim)"},{"path":"https://torch.mlverse.org/docs/reference/nn_softmax.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Softmax module — nn_softmax","text":"dim (int): dimension along Softmax computed (every slice along dim sum 1).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_softmax.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Softmax module — nn_softmax","text":": Tensor dimension shape input values range [0, 1]","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_softmax.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Softmax module — nn_softmax","text":"$$   \\mbox{Softmax}(x_{}) = \\frac{\\exp(x_i)}{\\sum_j \\exp(x_j)} $$ input Tensor sparse tensor unspecifed values treated -Inf.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_softmax.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Softmax module — nn_softmax","text":"module work directly NLLLoss, expects Log computed Softmax . Use LogSoftmax instead (faster better numerical properties).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_softmax.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Softmax module — nn_softmax","text":"Input: \\((*)\\) * means, number additional dimensions Output: \\((*)\\), shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_softmax.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Softmax module — nn_softmax","text":"","code":"if (torch_is_installed()) { m <- nn_softmax(1) input <- torch_randn(2, 3) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_softmax2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Softmax2d module — nn_softmax2d","title":"Softmax2d module — nn_softmax2d","text":"Applies SoftMax features spatial location. given image Channels x Height x Width, apply Softmax location \\((Channels, h_i, w_j)\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_softmax2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Softmax2d module — nn_softmax2d","text":"","code":"nn_softmax2d()"},{"path":"https://torch.mlverse.org/docs/reference/nn_softmax2d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Softmax2d module — nn_softmax2d","text":"Tensor dimension shape input values range [0, 1]","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_softmax2d.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Softmax2d module — nn_softmax2d","text":"Input: \\((N, C, H, W)\\) Output: \\((N, C, H, W)\\) (shape input)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_softmax2d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Softmax2d module — nn_softmax2d","text":"","code":"if (torch_is_installed()) { m <- nn_softmax2d() input <- torch_randn(2, 3, 12, 13) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_softmin.html","id":null,"dir":"Reference","previous_headings":"","what":"Softmin — nn_softmin","title":"Softmin — nn_softmin","text":"Applies Softmin function n-dimensional input Tensor rescaling elements n-dimensional output Tensor lie range [0, 1] sum 1. Softmin defined :","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_softmin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Softmin — nn_softmin","text":"","code":"nn_softmin(dim)"},{"path":"https://torch.mlverse.org/docs/reference/nn_softmin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Softmin — nn_softmin","text":"dim (int): dimension along Softmin computed (every slice along dim sum 1).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_softmin.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Softmin — nn_softmin","text":"Tensor dimension shape input, values range [0, 1].","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_softmin.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Softmin — nn_softmin","text":"$$   \\mbox{Softmin}(x_{}) = \\frac{\\exp(-x_i)}{\\sum_j \\exp(-x_j)} $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_softmin.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Softmin — nn_softmin","text":"Input: \\((*)\\) * means, number additional dimensions Output: \\((*)\\), shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_softmin.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Softmin — nn_softmin","text":"","code":"if (torch_is_installed()) { m <- nn_softmin(dim = 1) input <- torch_randn(2, 2) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_softplus.html","id":null,"dir":"Reference","previous_headings":"","what":"Softplus module — nn_softplus","title":"Softplus module — nn_softplus","text":"Applies element-wise function: $$   \\mbox{Softplus}(x) = \\frac{1}{\\beta} * \\log(1 + \\exp(\\beta * x)) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_softplus.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Softplus module — nn_softplus","text":"","code":"nn_softplus(beta = 1, threshold = 20)"},{"path":"https://torch.mlverse.org/docs/reference/nn_softplus.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Softplus module — nn_softplus","text":"beta \\(\\beta\\) value Softplus formulation. Default: 1 threshold values revert linear function. Default: 20","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_softplus.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Softplus module — nn_softplus","text":"SoftPlus smooth approximation ReLU function can used constrain output machine always positive. numerical stability implementation reverts linear function \\(input \\times \\beta > threshold\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_softplus.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Softplus module — nn_softplus","text":"Input: \\((N, *)\\) * means, number additional dimensions Output: \\((N, *)\\), shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_softplus.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Softplus module — nn_softplus","text":"","code":"if (torch_is_installed()) { m <- nn_softplus() input <- torch_randn(2) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_softshrink.html","id":null,"dir":"Reference","previous_headings":"","what":"Softshrink module — nn_softshrink","title":"Softshrink module — nn_softshrink","text":"Applies soft shrinkage function elementwise:","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_softshrink.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Softshrink module — nn_softshrink","text":"","code":"nn_softshrink(lambd = 0.5)"},{"path":"https://torch.mlverse.org/docs/reference/nn_softshrink.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Softshrink module — nn_softshrink","text":"lambd \\(\\lambda\\) (must less zero) value Softshrink formulation. Default: 0.5","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_softshrink.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Softshrink module — nn_softshrink","text":"$$   \\mbox{SoftShrinkage}(x) =   \\left\\{ \\begin{array}{ll} x - \\lambda, & \\mbox{ } x > \\lambda \\\\ x + \\lambda, & \\mbox{ } x < -\\lambda \\\\ 0, & \\mbox{ otherwise } \\end{array} \\right. $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_softshrink.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Softshrink module — nn_softshrink","text":"Input: \\((N, *)\\) * means, number additional dimensions Output: \\((N, *)\\), shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_softshrink.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Softshrink module — nn_softshrink","text":"","code":"if (torch_is_installed()) { m <- nn_softshrink() input <- torch_randn(2) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_softsign.html","id":null,"dir":"Reference","previous_headings":"","what":"Softsign module — nn_softsign","title":"Softsign module — nn_softsign","text":"Applies element-wise function: $$   \\mbox{SoftSign}(x) = \\frac{x}{ 1 + |x|} $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_softsign.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Softsign module — nn_softsign","text":"","code":"nn_softsign()"},{"path":"https://torch.mlverse.org/docs/reference/nn_softsign.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Softsign module — nn_softsign","text":"Input: \\((N, *)\\) * means, number additional dimensions Output: \\((N, *)\\), shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_softsign.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Softsign module — nn_softsign","text":"","code":"if (torch_is_installed()) { m <- nn_softsign() input <- torch_randn(2) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_tanh.html","id":null,"dir":"Reference","previous_headings":"","what":"Tanh module — nn_tanh","title":"Tanh module — nn_tanh","text":"Applies element-wise function:","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_tanh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tanh module — nn_tanh","text":"","code":"nn_tanh()"},{"path":"https://torch.mlverse.org/docs/reference/nn_tanh.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tanh module — nn_tanh","text":"$$   \\mbox{Tanh}(x) = \\tanh(x) = \\frac{\\exp(x) - \\exp(-x)} {\\exp(x) + \\exp(-x)} $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_tanh.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Tanh module — nn_tanh","text":"Input: \\((N, *)\\) * means, number additional dimensions Output: \\((N, *)\\), shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_tanh.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tanh module — nn_tanh","text":"","code":"if (torch_is_installed()) { m <- nn_tanh() input <- torch_randn(2) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_tanhshrink.html","id":null,"dir":"Reference","previous_headings":"","what":"Tanhshrink module — nn_tanhshrink","title":"Tanhshrink module — nn_tanhshrink","text":"Applies element-wise function:","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_tanhshrink.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tanhshrink module — nn_tanhshrink","text":"","code":"nn_tanhshrink()"},{"path":"https://torch.mlverse.org/docs/reference/nn_tanhshrink.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tanhshrink module — nn_tanhshrink","text":"$$   \\mbox{Tanhshrink}(x) = x - \\tanh(x) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_tanhshrink.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Tanhshrink module — nn_tanhshrink","text":"Input: \\((N, *)\\) * means, number additional dimensions Output: \\((N, *)\\), shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_tanhshrink.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tanhshrink module — nn_tanhshrink","text":"","code":"if (torch_is_installed()) { m <- nn_tanhshrink() input <- torch_randn(2) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_threshold.html","id":null,"dir":"Reference","previous_headings":"","what":"Threshoold module — nn_threshold","title":"Threshoold module — nn_threshold","text":"Thresholds element input Tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_threshold.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Threshoold module — nn_threshold","text":"","code":"nn_threshold(threshold, value, inplace = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nn_threshold.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Threshoold module — nn_threshold","text":"threshold value threshold value value replace inplace can optionally operation -place. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_threshold.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Threshoold module — nn_threshold","text":"Threshold defined : $$   y =   \\left\\{ \\begin{array}{ll}   x, &\\mbox{ } x > \\mbox{threshold} \\\\   \\mbox{value}, &\\mbox{ otherwise }   \\end{array}   \\right. $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_threshold.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Threshoold module — nn_threshold","text":"Input: \\((N, *)\\) * means, number additional dimensions Output: \\((N, *)\\), shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_threshold.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Threshoold module — nn_threshold","text":"","code":"if (torch_is_installed()) { m <- nn_threshold(0.1, 20) input <- torch_randn(2) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_triplet_margin_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Triplet margin loss — nn_triplet_margin_loss","title":"Triplet margin loss — nn_triplet_margin_loss","text":"Creates criterion measures triplet loss given input tensors \\(x1\\), \\(x2\\), \\(x3\\) margin value greater \\(0\\). used measuring relative similarity samples. triplet composed , p n (.e., anchor, positive examples negative examples respectively). shapes input tensors \\((N, D)\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_triplet_margin_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Triplet margin loss — nn_triplet_margin_loss","text":"","code":"nn_triplet_margin_loss(   margin = 1,   p = 2,   eps = 1e-06,   swap = FALSE,   reduction = \"mean\" )"},{"path":"https://torch.mlverse.org/docs/reference/nn_triplet_margin_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Triplet margin loss — nn_triplet_margin_loss","text":"margin (float, optional): Default: \\(1\\). p (int, optional): norm degree pairwise distance. Default: \\(2\\). eps constant avoid NaN's swap (bool, optional): distance swap described detail paper Learning shallow convolutional feature descriptors triplet losses V. Balntas, E. Riba et al. Default: FALSE. reduction (string, optional): Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed. Note: size_average reduce process deprecated, meantime, specifying either two args override reduction. Default: 'mean'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_triplet_margin_loss.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Triplet margin loss — nn_triplet_margin_loss","text":"distance swap described detail paper Learning shallow convolutional feature descriptors triplet losses V. Balntas, E. Riba et al. loss function sample mini-batch : $$   L(, p, n) = \\max \\{d(a_i, p_i) - d(a_i, n_i) + {\\rm margin}, 0\\} $$ $$   d(x_i, y_i) = | {\\bf x}_i - {\\bf y}_i |_p $$ See also nn_triplet_margin_with_distance_loss(), computes triplet margin loss input tensors using custom distance function.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_triplet_margin_loss.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Triplet margin loss — nn_triplet_margin_loss","text":"Input: \\((N, D)\\) \\(D\\) vector dimension. Output: Tensor shape \\((N)\\) reduction 'none', scalar otherwise.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_triplet_margin_loss.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Triplet margin loss — nn_triplet_margin_loss","text":"","code":"if (torch_is_installed()) { triplet_loss <- nn_triplet_margin_loss(margin = 1, p = 2) anchor <- torch_randn(100, 128, requires_grad = TRUE) positive <- torch_randn(100, 128, requires_grad = TRUE) negative <- torch_randn(100, 128, requires_grad = TRUE) output <- triplet_loss(anchor, positive, negative) output$backward() }"},{"path":"https://torch.mlverse.org/docs/reference/nn_triplet_margin_with_distance_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Triplet margin with distance loss — nn_triplet_margin_with_distance_loss","title":"Triplet margin with distance loss — nn_triplet_margin_with_distance_loss","text":"Creates criterion measures triplet loss given input tensors \\(\\), \\(p\\), \\(n\\) (representing anchor, positive, negative examples, respectively), nonnegative, real-valued function (\"distance function\") used compute relationship anchor positive example (\"positive distance\") anchor negative example (\"negative distance\").","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_triplet_margin_with_distance_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Triplet margin with distance loss — nn_triplet_margin_with_distance_loss","text":"","code":"nn_triplet_margin_with_distance_loss(   distance_function = NULL,   margin = 1,   swap = FALSE,   reduction = \"mean\" )"},{"path":"https://torch.mlverse.org/docs/reference/nn_triplet_margin_with_distance_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Triplet margin with distance loss — nn_triplet_margin_with_distance_loss","text":"distance_function (callable, optional): nonnegative, real-valued function quantifies closeness two tensors. specified, nn_pairwise_distance() used.  Default: None margin (float, optional): non-negative margin representing minimum difference positive negative distances required loss 0. Larger margins penalize cases negative examples distant enough anchors, relative positives. Default: \\(1\\). swap (bool, optional): Whether use distance swap described paper Learning shallow convolutional feature descriptors triplet losses V. Balntas, E. Riba et al. TRUE, positive example closer negative example anchor , swaps positive example anchor loss computation. Default: FALSE. reduction (string, optional): Specifies (optional) reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed. Default: 'mean'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_triplet_margin_with_distance_loss.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Triplet margin with distance loss — nn_triplet_margin_with_distance_loss","text":"unreduced loss (.e., reduction set 'none') can described : $$   \\ell(, p, n) = L = \\{l_1,\\dots,l_N\\}^\\top, \\quad l_i = \\max \\{d(a_i, p_i) - d(a_i, n_i) + {\\rm margin}, 0\\} $$ \\(N\\) batch size; \\(d\\) nonnegative, real-valued function quantifying closeness two tensors, referred distance_function; \\(margin\\) non-negative margin representing minimum difference positive negative distances required loss 0.  input tensors \\(N\\) elements can shape distance function can handle. reduction 'none' (default 'mean'), : $$ \\ell(x, y) = \\begin{array}{ll} \\mbox{mean}(L), &  \\mbox{reduction} = \\mbox{`mean';}\\\\             \\mbox{sum}(L),  &  \\mbox{reduction} = \\mbox{`sum'.} \\end{array} $$ See also nn_triplet_margin_loss(), computes triplet loss input tensors using \\(l_p\\) distance distance function.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_triplet_margin_with_distance_loss.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Triplet margin with distance loss — nn_triplet_margin_with_distance_loss","text":"Input: \\((N, *)\\) \\(*\\) represents number additional dimensions supported distance function. Output: Tensor shape \\((N)\\) reduction 'none', scalar otherwise.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_triplet_margin_with_distance_loss.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Triplet margin with distance loss — nn_triplet_margin_with_distance_loss","text":"","code":"if (torch_is_installed()) { # Initialize embeddings embedding <- nn_embedding(1000, 128) anchor_ids <- torch_randint(1, 1000, 1, dtype = torch_long()) positive_ids <- torch_randint(1, 1000, 1, dtype = torch_long()) negative_ids <- torch_randint(1, 1000, 1, dtype = torch_long()) anchor <- embedding(anchor_ids) positive <- embedding(positive_ids) negative <- embedding(negative_ids)  # Built-in Distance Function triplet_loss <- nn_triplet_margin_with_distance_loss(   distance_function = nn_pairwise_distance() ) output <- triplet_loss(anchor, positive, negative)  # Custom Distance Function l_infinity <- function(x1, x2) {   torch_max(torch_abs(x1 - x2), dim = 1)[[1]] }  triplet_loss <- nn_triplet_margin_with_distance_loss(   distance_function = l_infinity, margin = 1.5 ) output <- triplet_loss(anchor, positive, negative)  # Custom Distance Function (Lambda) triplet_loss <- nn_triplet_margin_with_distance_loss(   distance_function = function(x, y) {     1 - nnf_cosine_similarity(x, y)   } )  output <- triplet_loss(anchor, positive, negative) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_unflatten.html","id":null,"dir":"Reference","previous_headings":"","what":"Unflattens a tensor dim expanding it to a desired shape.\nFor use with [nn_sequential. — nn_unflatten","title":"Unflattens a tensor dim expanding it to a desired shape.\nFor use with [nn_sequential. — nn_unflatten","text":"Unflattens tensor dim expanding desired shape. use [nn_sequential.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_unflatten.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Unflattens a tensor dim expanding it to a desired shape.\nFor use with [nn_sequential. — nn_unflatten","text":"","code":"nn_unflatten(dim, unflattened_size)"},{"path":"https://torch.mlverse.org/docs/reference/nn_unflatten.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Unflattens a tensor dim expanding it to a desired shape.\nFor use with [nn_sequential. — nn_unflatten","text":"dim Dimension unflattened unflattened_size New shape unflattened dimension","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_unflatten.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Unflattens a tensor dim expanding it to a desired shape.\nFor use with [nn_sequential. — nn_unflatten","text":"","code":"if (torch_is_installed()) { input <- torch_randn(2, 50)  m <- nn_sequential(   nn_linear(50, 50),   nn_unflatten(2, c(2, 5, 5)) ) output <- m(input) output$size()  } #> [1] 2 2 5 5"},{"path":"https://torch.mlverse.org/docs/reference/nn_upsample.html","id":null,"dir":"Reference","previous_headings":"","what":"Upsample module — nn_upsample","title":"Upsample module — nn_upsample","text":"Upsamples given multi-channel 1D (temporal), 2D (spatial) 3D (volumetric) data. input data assumed form minibatch x channels x optional depth x optional height] x width. Hence, spatial inputs, expect 4D Tensor volumetric inputs, expect 5D Tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_upsample.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Upsample module — nn_upsample","text":"","code":"nn_upsample(   size = NULL,   scale_factor = NULL,   mode = \"nearest\",   align_corners = NULL )"},{"path":"https://torch.mlverse.org/docs/reference/nn_upsample.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Upsample module — nn_upsample","text":"size (int Tuple[int] Tuple[int, int] Tuple[int, int, int], optional): output spatial sizes scale_factor (float Tuple[float] Tuple[float, float] Tuple[float, float, float], optional): multiplier spatial size. match input size tuple. mode (str, optional): upsampling algorithm: one 'nearest', 'linear', 'bilinear', 'bicubic' 'trilinear'. Default: 'nearest' align_corners (bool, optional): TRUE, corner pixels input output tensors aligned, thus preserving values pixels. effect mode 'linear', 'bilinear', 'trilinear'. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_upsample.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Upsample module — nn_upsample","text":"algorithms available upsampling nearest neighbor linear, bilinear, bicubic trilinear 3D, 4D 5D input Tensor, respectively. One can either give scale_factor target output size calculate output size. (give , ambiguous)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_upsample.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Upsample module — nn_upsample","text":"","code":"if (torch_is_installed()) { input <- torch_arange(start = 1, end = 4, dtype = torch_float())$view(c(1, 1, 2, 2)) nn_upsample(scale_factor = c(2), mode = \"nearest\")(input) nn_upsample(scale_factor = c(2, 2), mode = \"nearest\")(input) } #> torch_tensor #> (1,1,.,.) =  #>   1  1  2  2 #>   1  1  2  2 #>   3  3  4  4 #>   3  3  4  4 #> [ CPUFloatType{1,1,4,4} ]"},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_clip_grad_norm_.html","id":null,"dir":"Reference","previous_headings":"","what":"Clips gradient norm of an iterable of parameters. — nn_utils_clip_grad_norm_","title":"Clips gradient norm of an iterable of parameters. — nn_utils_clip_grad_norm_","text":"norm computed gradients together, concatenated single vector. Gradients modified -place.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_clip_grad_norm_.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Clips gradient norm of an iterable of parameters. — nn_utils_clip_grad_norm_","text":"","code":"nn_utils_clip_grad_norm_(parameters, max_norm, norm_type = 2)"},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_clip_grad_norm_.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Clips gradient norm of an iterable of parameters. — nn_utils_clip_grad_norm_","text":"parameters (IterableTensor Tensor): iterable Tensors single Tensor gradients normalized max_norm (float int): max norm gradients norm_type (float int): type used p-norm. Can Inf infinity norm.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_clip_grad_norm_.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Clips gradient norm of an iterable of parameters. — nn_utils_clip_grad_norm_","text":"Total norm parameters (viewed single vector).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_clip_grad_value_.html","id":null,"dir":"Reference","previous_headings":"","what":"Clips gradient of an iterable of parameters at specified value. — nn_utils_clip_grad_value_","title":"Clips gradient of an iterable of parameters at specified value. — nn_utils_clip_grad_value_","text":"Gradients modified -place.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_clip_grad_value_.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Clips gradient of an iterable of parameters at specified value. — nn_utils_clip_grad_value_","text":"","code":"nn_utils_clip_grad_value_(parameters, clip_value)"},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_clip_grad_value_.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Clips gradient of an iterable of parameters at specified value. — nn_utils_clip_grad_value_","text":"parameters (Iterable(Tensor) Tensor): iterable Tensors single Tensor gradients normalized clip_value (float int): maximum allowed value gradients.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_clip_grad_value_.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Clips gradient of an iterable of parameters at specified value. — nn_utils_clip_grad_value_","text":"gradients clipped range \\(\\left[\\mbox{-clip\\_value}, \\mbox{clip\\_value}\\right]\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_rnn_pack_padded_sequence.html","id":null,"dir":"Reference","previous_headings":"","what":"Packs a Tensor containing padded sequences of variable length. — nn_utils_rnn_pack_padded_sequence","title":"Packs a Tensor containing padded sequences of variable length. — nn_utils_rnn_pack_padded_sequence","text":"input can size T x B x * T length longest sequence (equal lengths[1]), B batch size, * number dimensions (including 0). batch_first TRUE, B x T x * input expected.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_rnn_pack_padded_sequence.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Packs a Tensor containing padded sequences of variable length. — nn_utils_rnn_pack_padded_sequence","text":"","code":"nn_utils_rnn_pack_padded_sequence(   input,   lengths,   batch_first = FALSE,   enforce_sorted = TRUE )"},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_rnn_pack_padded_sequence.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Packs a Tensor containing padded sequences of variable length. — nn_utils_rnn_pack_padded_sequence","text":"input (Tensor): padded batch variable length sequences. lengths (Tensor): list sequences lengths batch element. batch_first (bool, optional): TRUE, input expected B x T x * format. enforce_sorted (bool, optional): TRUE, input expected contain sequences sorted length decreasing order. FALSE, input get sorted unconditionally. Default: TRUE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_rnn_pack_padded_sequence.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Packs a Tensor containing padded sequences of variable length. — nn_utils_rnn_pack_padded_sequence","text":"PackedSequence object","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_rnn_pack_padded_sequence.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Packs a Tensor containing padded sequences of variable length. — nn_utils_rnn_pack_padded_sequence","text":"unsorted sequences, use enforce_sorted = FALSE. enforce_sorted TRUE, sequences sorted length decreasing order, .e. input[,1] longest sequence, input[,B] shortest one. enforce_sorted = TRUE necessary ONNX export.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_rnn_pack_padded_sequence.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Packs a Tensor containing padded sequences of variable length. — nn_utils_rnn_pack_padded_sequence","text":"function accepts input least two dimensions. can apply pack labels, use output RNN compute loss directly. Tensor can retrieved PackedSequence object accessing .data attribute.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_rnn_pack_sequence.html","id":null,"dir":"Reference","previous_headings":"","what":"Packs a list of variable length Tensors — nn_utils_rnn_pack_sequence","title":"Packs a list of variable length Tensors — nn_utils_rnn_pack_sequence","text":"sequences list Tensors size L x *, L length sequence * number trailing dimensions, including zero.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_rnn_pack_sequence.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Packs a list of variable length Tensors — nn_utils_rnn_pack_sequence","text":"","code":"nn_utils_rnn_pack_sequence(sequences, enforce_sorted = TRUE)"},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_rnn_pack_sequence.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Packs a list of variable length Tensors — nn_utils_rnn_pack_sequence","text":"sequences (list[Tensor]): list sequences decreasing length. enforce_sorted (bool, optional): TRUE, checks input contains sequences sorted length decreasing order. FALSE, condition checked. Default: TRUE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_rnn_pack_sequence.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Packs a list of variable length Tensors — nn_utils_rnn_pack_sequence","text":"PackedSequence object","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_rnn_pack_sequence.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Packs a list of variable length Tensors — nn_utils_rnn_pack_sequence","text":"unsorted sequences, use enforce_sorted = FALSE. enforce_sorted TRUE, sequences sorted order decreasing length. enforce_sorted = TRUE necessary ONNX export.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_rnn_pack_sequence.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Packs a list of variable length Tensors — nn_utils_rnn_pack_sequence","text":"","code":"if (torch_is_installed()) { x <- torch_tensor(c(1, 2, 3), dtype = torch_long()) y <- torch_tensor(c(4, 5), dtype = torch_long()) z <- torch_tensor(c(6), dtype = torch_long())  p <- nn_utils_rnn_pack_sequence(list(x, y, z)) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_rnn_pad_packed_sequence.html","id":null,"dir":"Reference","previous_headings":"","what":"Pads a packed batch of variable length sequences. — nn_utils_rnn_pad_packed_sequence","title":"Pads a packed batch of variable length sequences. — nn_utils_rnn_pad_packed_sequence","text":"inverse operation nn_utils_rnn_pack_padded_sequence().","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_rnn_pad_packed_sequence.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pads a packed batch of variable length sequences. — nn_utils_rnn_pad_packed_sequence","text":"","code":"nn_utils_rnn_pad_packed_sequence(   sequence,   batch_first = FALSE,   padding_value = 0,   total_length = NULL )"},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_rnn_pad_packed_sequence.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pads a packed batch of variable length sequences. — nn_utils_rnn_pad_packed_sequence","text":"sequence (PackedSequence): batch pad batch_first (bool, optional): True, output ``B x T x *` format. padding_value (float, optional): values padded elements. total_length (int, optional): NULL, output padded length total_length. method throw ValueError total_length less max sequence length sequence.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_rnn_pad_packed_sequence.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Pads a packed batch of variable length sequences. — nn_utils_rnn_pad_packed_sequence","text":"Tuple Tensor containing padded sequence, Tensor containing list lengths sequence batch. Batch elements re-ordered ordered originally batch passed nn_utils_rnn_pack_padded_sequence() nn_utils_rnn_pack_sequence().","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_rnn_pad_packed_sequence.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Pads a packed batch of variable length sequences. — nn_utils_rnn_pad_packed_sequence","text":"returned Tensor's data size T x B x *, T length longest sequence B batch size. batch_first TRUE, data transposed B x T x * format.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_rnn_pad_packed_sequence.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Pads a packed batch of variable length sequences. — nn_utils_rnn_pad_packed_sequence","text":"total_length useful implement pack sequence -> recurrent network -> unpack sequence pattern nn_module wrapped ~torch.nn.DataParallel.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_rnn_pad_packed_sequence.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pads a packed batch of variable length sequences. — nn_utils_rnn_pad_packed_sequence","text":"","code":"if (torch_is_installed()) { seq <- torch_tensor(rbind(c(1, 2, 0), c(3, 0, 0), c(4, 5, 6))) lens <- c(2, 1, 3) packed <- nn_utils_rnn_pack_padded_sequence(seq, lens,   batch_first = TRUE,   enforce_sorted = FALSE ) packed nn_utils_rnn_pad_packed_sequence(packed, batch_first = TRUE) } #> [[1]] #> torch_tensor #>  1  2  0 #>  3  0  0 #>  4  5  6 #> [ CPUFloatType{3,3} ] #>  #> [[2]] #> torch_tensor #>  2 #>  1 #>  3 #> [ CPULongType{3} ] #>"},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_rnn_pad_sequence.html","id":null,"dir":"Reference","previous_headings":"","what":"Pad a list of variable length Tensors with padding_value — nn_utils_rnn_pad_sequence","title":"Pad a list of variable length Tensors with padding_value — nn_utils_rnn_pad_sequence","text":"pad_sequence stacks list Tensors along new dimension, pads equal length. example, input list sequences size L x * batch_first False, T x B x * otherwise.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_rnn_pad_sequence.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pad a list of variable length Tensors with padding_value — nn_utils_rnn_pad_sequence","text":"","code":"nn_utils_rnn_pad_sequence(sequences, batch_first = FALSE, padding_value = 0)"},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_rnn_pad_sequence.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pad a list of variable length Tensors with padding_value — nn_utils_rnn_pad_sequence","text":"sequences (list[Tensor]): list variable length sequences. batch_first (bool, optional): output B x T x * TRUE, T x B x * otherwise padding_value (float, optional): value padded elements. Default: 0.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_rnn_pad_sequence.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Pad a list of variable length Tensors with padding_value — nn_utils_rnn_pad_sequence","text":"Tensor size T x B x * batch_first FALSE. Tensor size B x T x * otherwise","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_rnn_pad_sequence.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Pad a list of variable length Tensors with padding_value — nn_utils_rnn_pad_sequence","text":"B batch size. equal number elements sequences. T length longest sequence. L length sequence. * number trailing dimensions, including none.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_rnn_pad_sequence.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Pad a list of variable length Tensors with padding_value — nn_utils_rnn_pad_sequence","text":"function returns Tensor size T x B x * B x T x * T length longest sequence. function assumes trailing dimensions type Tensors sequences .","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_rnn_pad_sequence.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pad a list of variable length Tensors with padding_value — nn_utils_rnn_pad_sequence","text":"","code":"if (torch_is_installed()) { a <- torch_ones(25, 300) b <- torch_ones(22, 300) c <- torch_ones(15, 300) nn_utils_rnn_pad_sequence(list(a, b, c))$size() } #> [1]  25   3 300"},{"path":"https://torch.mlverse.org/docs/reference/nnf_adaptive_avg_pool1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Adaptive_avg_pool1d — nnf_adaptive_avg_pool1d","title":"Adaptive_avg_pool1d — nnf_adaptive_avg_pool1d","text":"Applies 1D adaptive average pooling input signal composed several input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_adaptive_avg_pool1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adaptive_avg_pool1d — nnf_adaptive_avg_pool1d","text":"","code":"nnf_adaptive_avg_pool1d(input, output_size)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_adaptive_avg_pool1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adaptive_avg_pool1d — nnf_adaptive_avg_pool1d","text":"input input tensor shape (minibatch , in_channels , iW) output_size target output size (single integer)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_adaptive_avg_pool2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Adaptive_avg_pool2d — nnf_adaptive_avg_pool2d","title":"Adaptive_avg_pool2d — nnf_adaptive_avg_pool2d","text":"Applies 2D adaptive average pooling input signal composed several input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_adaptive_avg_pool2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adaptive_avg_pool2d — nnf_adaptive_avg_pool2d","text":"","code":"nnf_adaptive_avg_pool2d(input, output_size)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_adaptive_avg_pool2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adaptive_avg_pool2d — nnf_adaptive_avg_pool2d","text":"input input tensor (minibatch, in_channels , iH , iW) output_size target output size (single integer double-integer tuple)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_adaptive_avg_pool3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Adaptive_avg_pool3d — nnf_adaptive_avg_pool3d","title":"Adaptive_avg_pool3d — nnf_adaptive_avg_pool3d","text":"Applies 3D adaptive average pooling input signal composed several input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_adaptive_avg_pool3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adaptive_avg_pool3d — nnf_adaptive_avg_pool3d","text":"","code":"nnf_adaptive_avg_pool3d(input, output_size)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_adaptive_avg_pool3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adaptive_avg_pool3d — nnf_adaptive_avg_pool3d","text":"input input tensor (minibatch, in_channels , * iH , iW) output_size target output size (single integer triple-integer tuple)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_adaptive_max_pool1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Adaptive_max_pool1d — nnf_adaptive_max_pool1d","title":"Adaptive_max_pool1d — nnf_adaptive_max_pool1d","text":"Applies 1D adaptive max pooling input signal composed several input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_adaptive_max_pool1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adaptive_max_pool1d — nnf_adaptive_max_pool1d","text":"","code":"nnf_adaptive_max_pool1d(input, output_size, return_indices = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_adaptive_max_pool1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adaptive_max_pool1d — nnf_adaptive_max_pool1d","text":"input input tensor shape (minibatch , in_channels , iW) output_size target output size (single integer) return_indices whether return pooling indices. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_adaptive_max_pool2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Adaptive_max_pool2d — nnf_adaptive_max_pool2d","title":"Adaptive_max_pool2d — nnf_adaptive_max_pool2d","text":"Applies 2D adaptive max pooling input signal composed several input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_adaptive_max_pool2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adaptive_max_pool2d — nnf_adaptive_max_pool2d","text":"","code":"nnf_adaptive_max_pool2d(input, output_size, return_indices = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_adaptive_max_pool2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adaptive_max_pool2d — nnf_adaptive_max_pool2d","text":"input input tensor (minibatch, in_channels , iH , iW) output_size target output size (single integer double-integer tuple) return_indices whether return pooling indices. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_adaptive_max_pool3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Adaptive_max_pool3d — nnf_adaptive_max_pool3d","title":"Adaptive_max_pool3d — nnf_adaptive_max_pool3d","text":"Applies 3D adaptive max pooling input signal composed several input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_adaptive_max_pool3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adaptive_max_pool3d — nnf_adaptive_max_pool3d","text":"","code":"nnf_adaptive_max_pool3d(input, output_size, return_indices = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_adaptive_max_pool3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adaptive_max_pool3d — nnf_adaptive_max_pool3d","text":"input input tensor (minibatch, in_channels , * iH , iW) output_size target output size (single integer triple-integer tuple) return_indices whether return pooling indices. Default:FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_affine_grid.html","id":null,"dir":"Reference","previous_headings":"","what":"Affine_grid — nnf_affine_grid","title":"Affine_grid — nnf_affine_grid","text":"Generates 2D 3D flow field (sampling grid), given batch affine matrices theta.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_affine_grid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Affine_grid — nnf_affine_grid","text":"","code":"nnf_affine_grid(theta, size, align_corners = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_affine_grid.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Affine_grid — nnf_affine_grid","text":"theta (Tensor) input batch affine matrices shape (\\(N \\times 2 \\times 3\\)) 2D  (\\(N \\times 3 \\times 4\\)) 3D size (torch.Size) target output image size. (\\(N \\times C \\times H \\times W\\) 2D \\(N \\times C \\times D \\times H \\times W\\) 3D) Example: torch.Size((32, 3, 24, 24)) align_corners (bool, optional) True, consider -1 1 refer centers corner pixels rather image corners. Refer nnf_grid_sample() complete description. grid generated nnf_affine_grid() passed nnf_grid_sample()  setting option. Default: False","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_affine_grid.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Affine_grid — nnf_affine_grid","text":"function often used conjunction nnf_grid_sample() build Spatial Transformer Networks_ .","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_alpha_dropout.html","id":null,"dir":"Reference","previous_headings":"","what":"Alpha_dropout — nnf_alpha_dropout","title":"Alpha_dropout — nnf_alpha_dropout","text":"Applies alpha dropout input.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_alpha_dropout.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Alpha_dropout — nnf_alpha_dropout","text":"","code":"nnf_alpha_dropout(input, p = 0.5, training = FALSE, inplace = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_alpha_dropout.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Alpha_dropout — nnf_alpha_dropout","text":"input input tensor p probability element zeroed. Default: 0.5 training apply dropout TRUE. Default: TRUE inplace set TRUE, operation -place. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_avg_pool1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Avg_pool1d — nnf_avg_pool1d","title":"Avg_pool1d — nnf_avg_pool1d","text":"Applies 1D average pooling input signal composed several input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_avg_pool1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Avg_pool1d — nnf_avg_pool1d","text":"","code":"nnf_avg_pool1d(   input,   kernel_size,   stride = NULL,   padding = 0,   ceil_mode = FALSE,   count_include_pad = TRUE )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_avg_pool1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Avg_pool1d — nnf_avg_pool1d","text":"input input tensor shape (minibatch , in_channels , iW) kernel_size size window. Can single number tuple (kW,). stride stride window. Can single number tuple (sW,). Default: kernel_size padding implicit zero paddings sides input. Can single number tuple (padW,). Default: 0 ceil_mode True, use ceil instead floor compute output shape. Default: FALSE count_include_pad True, include zero-padding averaging calculation. Default: TRUE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_avg_pool2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Avg_pool2d — nnf_avg_pool2d","title":"Avg_pool2d — nnf_avg_pool2d","text":"Applies 2D average-pooling operation \\(kH * kW\\) regions step size \\(sH * sW\\) steps. number output features equal number input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_avg_pool2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Avg_pool2d — nnf_avg_pool2d","text":"","code":"nnf_avg_pool2d(   input,   kernel_size,   stride = NULL,   padding = 0,   ceil_mode = FALSE,   count_include_pad = TRUE,   divisor_override = NULL )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_avg_pool2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Avg_pool2d — nnf_avg_pool2d","text":"input input tensor (minibatch, in_channels , iH , iW) kernel_size size pooling region. Can single number tuple (kH, kW) stride stride pooling operation. Can single number tuple (sH, sW). Default: kernel_size padding implicit zero paddings sides input. Can single number tuple (padH, padW). Default: 0 ceil_mode True, use ceil instead floor formula compute output shape. Default: FALSE count_include_pad True, include zero-padding averaging calculation. Default: TRUE divisor_override specified, used divisor, otherwise size pooling region used. Default: NULL","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_avg_pool3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Avg_pool3d — nnf_avg_pool3d","title":"Avg_pool3d — nnf_avg_pool3d","text":"Applies 3D average-pooling operation \\(kT * kH * kW\\) regions step size \\(sT * sH * sW\\) steps. number output features equal \\(\\lfloor \\frac{ \\mbox{input planes} }{sT} \\rfloor\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_avg_pool3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Avg_pool3d — nnf_avg_pool3d","text":"","code":"nnf_avg_pool3d(   input,   kernel_size,   stride = NULL,   padding = 0,   ceil_mode = FALSE,   count_include_pad = TRUE,   divisor_override = NULL )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_avg_pool3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Avg_pool3d — nnf_avg_pool3d","text":"input input tensor (minibatch, in_channels , * iH , iW) kernel_size size pooling region. Can single number tuple (kT, kH, kW) stride stride pooling operation. Can single number tuple (sT, sH, sW). Default: kernel_size padding implicit zero paddings sides input. Can single number tuple (padT, padH, padW), Default: 0 ceil_mode True, use ceil instead floor formula compute output shape count_include_pad True, include zero-padding averaging calculation divisor_override NA specified, used divisor, otherwise size pooling region used. Default: NULL","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_batch_norm.html","id":null,"dir":"Reference","previous_headings":"","what":"Batch_norm — nnf_batch_norm","title":"Batch_norm — nnf_batch_norm","text":"Applies Batch Normalization channel across batch data.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_batch_norm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Batch_norm — nnf_batch_norm","text":"","code":"nnf_batch_norm(   input,   running_mean,   running_var,   weight = NULL,   bias = NULL,   training = FALSE,   momentum = 0.1,   eps = 1e-05 )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_batch_norm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Batch_norm — nnf_batch_norm","text":"input input tensor running_mean running_mean tensor running_var running_var tensor weight weight tensor bias bias tensor training bool wether training. Default: FALSE momentum value used running_mean running_var computation. Can set None cumulative moving average (.e. simple average). Default: 0.1 eps value added denominator numerical stability. Default: 1e-5","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_bilinear.html","id":null,"dir":"Reference","previous_headings":"","what":"Bilinear — nnf_bilinear","title":"Bilinear — nnf_bilinear","text":"Applies bilinear transformation incoming data: \\(y = x_1 x_2 + b\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_bilinear.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bilinear — nnf_bilinear","text":"","code":"nnf_bilinear(input1, input2, weight, bias = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_bilinear.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bilinear — nnf_bilinear","text":"input1 \\((N, *, H_{in1})\\) \\(H_{in1}=\\mbox{in1\\_features}\\) \\(*\\) means number additional dimensions. last dimension inputs . input2 \\((N, *, H_{in2})\\) \\(H_{in2}=\\mbox{in2\\_features}\\) weight \\((\\mbox{\\_features}, \\mbox{in1\\_features}, \\mbox{in2\\_features})\\) bias \\((\\mbox{\\_features})\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_bilinear.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Bilinear — nnf_bilinear","text":"output \\((N, *, H_{})\\) \\(H_{}=\\mbox{\\_features}\\)last dimension shape input.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_binary_cross_entropy.html","id":null,"dir":"Reference","previous_headings":"","what":"Binary_cross_entropy — nnf_binary_cross_entropy","title":"Binary_cross_entropy — nnf_binary_cross_entropy","text":"Function measures Binary Cross Entropy target output.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_binary_cross_entropy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Binary_cross_entropy — nnf_binary_cross_entropy","text":"","code":"nnf_binary_cross_entropy(   input,   target,   weight = NULL,   reduction = c(\"mean\", \"sum\", \"none\") )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_binary_cross_entropy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Binary_cross_entropy — nnf_binary_cross_entropy","text":"input tensor (N,*) ** means, number additional dimensions target tensor (N,*) , shape input weight (tensor) weight value. reduction (string, optional) – Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed. Default: 'mean'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_binary_cross_entropy_with_logits.html","id":null,"dir":"Reference","previous_headings":"","what":"Binary_cross_entropy_with_logits — nnf_binary_cross_entropy_with_logits","title":"Binary_cross_entropy_with_logits — nnf_binary_cross_entropy_with_logits","text":"Function measures Binary Cross Entropy target output logits.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_binary_cross_entropy_with_logits.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Binary_cross_entropy_with_logits — nnf_binary_cross_entropy_with_logits","text":"","code":"nnf_binary_cross_entropy_with_logits(   input,   target,   weight = NULL,   reduction = c(\"mean\", \"sum\", \"none\"),   pos_weight = NULL )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_binary_cross_entropy_with_logits.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Binary_cross_entropy_with_logits — nnf_binary_cross_entropy_with_logits","text":"input Tensor arbitrary shape target Tensor shape input weight (Tensor, optional) manual rescaling weight provided repeated match input tensor shape. reduction (string, optional) – Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed. Default: 'mean' pos_weight (Tensor, optional) weight positive examples. Must vector length equal number classes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_celu.html","id":null,"dir":"Reference","previous_headings":"","what":"Celu — nnf_celu","title":"Celu — nnf_celu","text":"Applies element-wise, \\(CELU(x) = max(0,x) + min(0, \\alpha * (exp(x \\alpha) - 1))\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_celu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Celu — nnf_celu","text":"","code":"nnf_celu(input, alpha = 1, inplace = FALSE)  nnf_celu_(input, alpha = 1)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_celu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Celu — nnf_celu","text":"input (N,*) tensor, * means, number additional dimensions alpha alpha value CELU formulation. Default: 1.0 inplace can optionally operation -place. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_contrib_sparsemax.html","id":null,"dir":"Reference","previous_headings":"","what":"Sparsemax — nnf_contrib_sparsemax","title":"Sparsemax — nnf_contrib_sparsemax","text":"Applies SparseMax activation.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_contrib_sparsemax.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sparsemax — nnf_contrib_sparsemax","text":"","code":"nnf_contrib_sparsemax(input, dim = -1)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_contrib_sparsemax.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sparsemax — nnf_contrib_sparsemax","text":"input input tensor dim dimension apply sparsemax function. (-1)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_contrib_sparsemax.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Sparsemax — nnf_contrib_sparsemax","text":"SparseMax activation described 'Softmax Sparsemax: Sparse Model Attention Multi-Label Classification' implementation based aced125/sparsemax","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_conv1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Conv1d — nnf_conv1d","title":"Conv1d — nnf_conv1d","text":"Applies 1D convolution input signal composed several input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_conv1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Conv1d — nnf_conv1d","text":"","code":"nnf_conv1d(   input,   weight,   bias = NULL,   stride = 1,   padding = 0,   dilation = 1,   groups = 1 )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_conv1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Conv1d — nnf_conv1d","text":"input input tensor shape (minibatch, in_channels , iW) weight filters shape (out_channels, in_channels/groups , kW) bias optional bias shape (out_channels). Default: NULL stride stride convolving kernel. Can single number one-element tuple (sW,). Default: 1 padding implicit paddings sides input. Can single number one-element tuple (padW,). Default: 0 dilation spacing kernel elements. Can single number one-element tuple (dW,). Default: 1 groups split input groups, in_channels divisible number groups. Default: 1","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_conv2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Conv2d — nnf_conv2d","title":"Conv2d — nnf_conv2d","text":"Applies 2D convolution input image composed several input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_conv2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Conv2d — nnf_conv2d","text":"","code":"nnf_conv2d(   input,   weight,   bias = NULL,   stride = 1,   padding = 0,   dilation = 1,   groups = 1 )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_conv2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Conv2d — nnf_conv2d","text":"input input tensor shape (minibatch, in_channels, iH , iW) weight filters shape (out_channels , in_channels/groups, kH , kW) bias optional bias tensor shape (out_channels). Default: NULL stride stride convolving kernel. Can single number tuple (sH, sW). Default: 1 padding implicit paddings sides input. Can single number tuple (padH, padW). Default: 0 dilation spacing kernel elements. Can single number tuple (dH, dW). Default: 1 groups split input groups, in_channels divisible number groups. Default: 1","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_conv3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Conv3d — nnf_conv3d","title":"Conv3d — nnf_conv3d","text":"Applies 3D convolution input image composed several input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_conv3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Conv3d — nnf_conv3d","text":"","code":"nnf_conv3d(   input,   weight,   bias = NULL,   stride = 1,   padding = 0,   dilation = 1,   groups = 1 )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_conv3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Conv3d — nnf_conv3d","text":"input input tensor shape (minibatch, in_channels , , iH , iW) weight filters shape (out_channels , in_channels/groups, kT , kH , kW) bias optional bias tensor shape (out_channels). Default: NULL stride stride convolving kernel. Can single number tuple (sT, sH, sW). Default: 1 padding implicit paddings sides input. Can single number tuple (padT, padH, padW). Default: 0 dilation spacing kernel elements. Can single number tuple (dT, dH, dW). Default: 1 groups split input groups, in_channels divisible number groups. Default: 1","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_conv_tbc.html","id":null,"dir":"Reference","previous_headings":"","what":"Conv_tbc — nnf_conv_tbc","title":"Conv_tbc — nnf_conv_tbc","text":"Applies 1-dimensional sequence convolution input sequence. Input output dimensions (Time, Batch, Channels) - hence TBC.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_conv_tbc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Conv_tbc — nnf_conv_tbc","text":"","code":"nnf_conv_tbc(input, weight, bias, pad = 0)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_conv_tbc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Conv_tbc — nnf_conv_tbc","text":"input input tensor shape \\((\\mbox{sequence length} \\times batch \\times \\mbox{\\_channels})\\) weight filter shape (\\(\\mbox{kernel width} \\times \\mbox{\\_channels} \\times \\mbox{\\_channels}\\)) bias bias shape (\\(\\mbox{\\_channels}\\)) pad number timesteps pad. Default: 0","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_conv_transpose1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Conv_transpose1d — nnf_conv_transpose1d","title":"Conv_transpose1d — nnf_conv_transpose1d","text":"Applies 1D transposed convolution operator input signal composed several input planes, sometimes also called \"deconvolution\".","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_conv_transpose1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Conv_transpose1d — nnf_conv_transpose1d","text":"","code":"nnf_conv_transpose1d(   input,   weight,   bias = NULL,   stride = 1,   padding = 0,   output_padding = 0,   groups = 1,   dilation = 1 )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_conv_transpose1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Conv_transpose1d — nnf_conv_transpose1d","text":"input input tensor shape (minibatch, in_channels , iW) weight filters shape (out_channels, in_channels/groups , kW) bias optional bias shape (out_channels). Default: NULL stride stride convolving kernel. Can single number one-element tuple (sW,). Default: 1 padding implicit paddings sides input. Can single number one-element tuple (padW,). Default: 0 output_padding padding applied output groups split input groups, in_channels divisible number groups. Default: 1 dilation spacing kernel elements. Can single number one-element tuple (dW,). Default: 1","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_conv_transpose2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Conv_transpose2d — nnf_conv_transpose2d","title":"Conv_transpose2d — nnf_conv_transpose2d","text":"Applies 2D transposed convolution operator input image composed several input planes, sometimes also called \"deconvolution\".","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_conv_transpose2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Conv_transpose2d — nnf_conv_transpose2d","text":"","code":"nnf_conv_transpose2d(   input,   weight,   bias = NULL,   stride = 1,   padding = 0,   output_padding = 0,   groups = 1,   dilation = 1 )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_conv_transpose2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Conv_transpose2d — nnf_conv_transpose2d","text":"input input tensor shape (minibatch, in_channels, iH , iW) weight filters shape (out_channels , in_channels/groups, kH , kW) bias optional bias tensor shape (out_channels). Default: NULL stride stride convolving kernel. Can single number tuple (sH, sW). Default: 1 padding implicit paddings sides input. Can single number tuple (padH, padW). Default: 0 output_padding padding applied output groups split input groups, in_channels divisible number groups. Default: 1 dilation spacing kernel elements. Can single number tuple (dH, dW). Default: 1","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_conv_transpose3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Conv_transpose3d — nnf_conv_transpose3d","title":"Conv_transpose3d — nnf_conv_transpose3d","text":"Applies 3D transposed convolution operator input image composed several input planes, sometimes also called \"deconvolution\"","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_conv_transpose3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Conv_transpose3d — nnf_conv_transpose3d","text":"","code":"nnf_conv_transpose3d(   input,   weight,   bias = NULL,   stride = 1,   padding = 0,   output_padding = 0,   groups = 1,   dilation = 1 )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_conv_transpose3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Conv_transpose3d — nnf_conv_transpose3d","text":"input input tensor shape (minibatch, in_channels , , iH , iW) weight filters shape (out_channels , in_channels/groups, kT , kH , kW) bias optional bias tensor shape (out_channels). Default: NULL stride stride convolving kernel. Can single number tuple (sT, sH, sW). Default: 1 padding implicit paddings sides input. Can single number tuple (padT, padH, padW). Default: 0 output_padding padding applied output groups split input groups, in_channels divisible number groups. Default: 1 dilation spacing kernel elements. Can single number tuple (dT, dH, dW). Default: 1","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_cosine_embedding_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Cosine_embedding_loss — nnf_cosine_embedding_loss","title":"Cosine_embedding_loss — nnf_cosine_embedding_loss","text":"Creates criterion measures loss given input tensors x_1, x_2 Tensor label y values 1 -1. used measuring whether two inputs similar dissimilar, using cosine distance, typically used learning nonlinear embeddings semi-supervised learning.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_cosine_embedding_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cosine_embedding_loss — nnf_cosine_embedding_loss","text":"","code":"nnf_cosine_embedding_loss(   input1,   input2,   target,   margin = 0,   reduction = c(\"mean\", \"sum\", \"none\") )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_cosine_embedding_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cosine_embedding_loss — nnf_cosine_embedding_loss","text":"input1 input x_1 tensor input2 input x_2 tensor target target tensor margin number -1 1 , 0 0.5 suggested. margin missing, default value 0. reduction (string, optional) – Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed. Default: 'mean'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_cosine_similarity.html","id":null,"dir":"Reference","previous_headings":"","what":"Cosine_similarity — nnf_cosine_similarity","title":"Cosine_similarity — nnf_cosine_similarity","text":"Returns cosine similarity x1 x2, computed along dim.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_cosine_similarity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cosine_similarity — nnf_cosine_similarity","text":"","code":"nnf_cosine_similarity(x1, x2, dim = 2, eps = 1e-08)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_cosine_similarity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cosine_similarity — nnf_cosine_similarity","text":"x1 (Tensor) First input. x2 (Tensor) Second input (size matching x1). dim (int, optional) Dimension vectors. Default: 2 eps (float, optional) Small value avoid division zero. Default: 1e-8","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_cosine_similarity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Cosine_similarity — nnf_cosine_similarity","text":"$$     \\mbox{similarity} = \\frac{x_1 \\cdot x_2}{\\max(\\Vert x_1 \\Vert _2 \\cdot \\Vert x_2 \\Vert _2, \\epsilon)} $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_cross_entropy.html","id":null,"dir":"Reference","previous_headings":"","what":"Cross_entropy — nnf_cross_entropy","title":"Cross_entropy — nnf_cross_entropy","text":"criterion combines log_softmax nll_loss single function.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_cross_entropy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cross_entropy — nnf_cross_entropy","text":"","code":"nnf_cross_entropy(   input,   target,   weight = NULL,   ignore_index = -100,   reduction = c(\"mean\", \"sum\", \"none\") )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_cross_entropy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cross_entropy — nnf_cross_entropy","text":"input (Tensor) \\((N, C)\\) C = number classes \\((N, C, H, W)\\) case 2D Loss, \\((N, C, d_1, d_2, ..., d_K)\\) \\(K \\geq 1\\) case K-dimensional loss. target (Tensor) \\((N)\\) value \\(0 \\leq \\mbox{targets}[] \\leq C-1\\), \\((N, d_1, d_2, ..., d_K)\\) \\(K \\geq 1\\) K-dimensional loss. weight (Tensor, optional) manual rescaling weight given class. given, Tensor size C ignore_index (int, optional) Specifies target value ignored contribute input gradient. reduction (string, optional) – Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed. Default: 'mean'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_ctc_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Ctc_loss — nnf_ctc_loss","title":"Ctc_loss — nnf_ctc_loss","text":"Connectionist Temporal Classification loss.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_ctc_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Ctc_loss — nnf_ctc_loss","text":"","code":"nnf_ctc_loss(   log_probs,   targets,   input_lengths,   target_lengths,   blank = 0,   reduction = c(\"mean\", \"sum\", \"none\"),   zero_infinity = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_ctc_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Ctc_loss — nnf_ctc_loss","text":"log_probs \\((T, N, C)\\) C = number characters alphabet including blank, T = input length, N = batch size. logarithmized probabilities outputs (e.g. obtained nnf_log_softmax). targets \\((N, S)\\) (sum(target_lengths)). Targets blank. second form, targets assumed concatenated. input_lengths \\((N)\\). Lengths inputs (must \\(\\leq T\\)) target_lengths \\((N)\\). Lengths targets blank (int, optional) Blank label. Default \\(0\\). reduction (string, optional) – Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed. Default: 'mean' zero_infinity (bool, optional) Whether zero infinite losses associated gradients. Default: FALSE Infinite losses mainly occur inputs short aligned targets.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_dropout.html","id":null,"dir":"Reference","previous_headings":"","what":"Dropout — nnf_dropout","title":"Dropout — nnf_dropout","text":"training, randomly zeroes elements input tensor probability p using samples Bernoulli distribution.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_dropout.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Dropout — nnf_dropout","text":"","code":"nnf_dropout(input, p = 0.5, training = TRUE, inplace = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_dropout.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Dropout — nnf_dropout","text":"input input tensor p probability element zeroed. Default: 0.5 training apply dropout TRUE. Default: TRUE inplace set TRUE, operation -place. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_dropout2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Dropout2d — nnf_dropout2d","title":"Dropout2d — nnf_dropout2d","text":"Randomly zero entire channels (channel 2D feature map, e.g., \\(j\\)-th channel \\(\\)-th sample batched input 2D tensor \\(input[, j]\\)) input tensor). channel zeroed independently every forward call probability p using samples Bernoulli distribution.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_dropout2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Dropout2d — nnf_dropout2d","text":"","code":"nnf_dropout2d(input, p = 0.5, training = TRUE, inplace = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_dropout2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Dropout2d — nnf_dropout2d","text":"input input tensor p probability channel zeroed. Default: 0.5 training apply dropout TRUE. Default: TRUE. inplace set TRUE, operation -place. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_dropout3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Dropout3d — nnf_dropout3d","title":"Dropout3d — nnf_dropout3d","text":"Randomly zero entire channels (channel 3D feature map, e.g., \\(j\\)-th channel \\(\\)-th sample batched input 3D tensor \\(input[, j]\\)) input tensor). channel zeroed independently every forward call probability p using samples Bernoulli distribution.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_dropout3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Dropout3d — nnf_dropout3d","text":"","code":"nnf_dropout3d(input, p = 0.5, training = TRUE, inplace = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_dropout3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Dropout3d — nnf_dropout3d","text":"input input tensor p probability channel zeroed. Default: 0.5 training apply dropout TRUE. Default: TRUE. inplace set TRUE, operation -place. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_elu.html","id":null,"dir":"Reference","previous_headings":"","what":"Elu — nnf_elu","title":"Elu — nnf_elu","text":"Applies element-wise, $$ELU(x) = max(0,x) + min(0, \\alpha * (exp(x) - 1))$$.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_elu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Elu — nnf_elu","text":"","code":"nnf_elu(input, alpha = 1, inplace = FALSE)  nnf_elu_(input, alpha = 1)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_elu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Elu — nnf_elu","text":"input (N,*) tensor, * means, number additional dimensions alpha alpha value ELU formulation. Default: 1.0 inplace can optionally operation -place. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_elu.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Elu — nnf_elu","text":"","code":"if (torch_is_installed()) { x <- torch_randn(2, 2) y <- nnf_elu(x, alpha = 1) nnf_elu_(x, alpha = 1) torch_equal(x, y) } #> [1] TRUE"},{"path":"https://torch.mlverse.org/docs/reference/nnf_embedding.html","id":null,"dir":"Reference","previous_headings":"","what":"Embedding — nnf_embedding","title":"Embedding — nnf_embedding","text":"simple lookup table looks embeddings fixed dictionary size.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_embedding.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Embedding — nnf_embedding","text":"","code":"nnf_embedding(   input,   weight,   padding_idx = NULL,   max_norm = NULL,   norm_type = 2,   scale_grad_by_freq = FALSE,   sparse = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_embedding.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Embedding — nnf_embedding","text":"input (LongTensor) Tensor containing indices embedding matrix weight (Tensor) embedding matrix number rows equal maximum possible index + 1, number columns equal embedding size padding_idx (int, optional) given, pads output embedding vector padding_idx (initialized zeros) whenever encounters index. max_norm (float, optional) given, embedding vector norm larger max_norm renormalized norm max_norm. Note: modify weight -place. norm_type (float, optional) p p-norm compute max_norm option. Default 2. scale_grad_by_freq (boolean, optional) given, scale gradients inverse frequency words mini-batch. Default FALSE. sparse (bool, optional) TRUE, gradient w.r.t. weight sparse tensor. See Notes nn_embedding details regarding sparse gradients.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_embedding.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Embedding — nnf_embedding","text":"module often used retrieve word embeddings using indices. input module list indices, embedding matrix, output corresponding word embeddings.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_embedding_bag.html","id":null,"dir":"Reference","previous_headings":"","what":"Embedding_bag — nnf_embedding_bag","title":"Embedding_bag — nnf_embedding_bag","text":"Computes sums, means maxes bags embeddings, without instantiating intermediate embeddings.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_embedding_bag.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Embedding_bag — nnf_embedding_bag","text":"","code":"nnf_embedding_bag(   input,   weight,   offsets = NULL,   max_norm = NULL,   norm_type = 2,   scale_grad_by_freq = FALSE,   mode = \"mean\",   sparse = FALSE,   per_sample_weights = NULL,   include_last_offset = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_embedding_bag.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Embedding_bag — nnf_embedding_bag","text":"input (LongTensor) Tensor containing bags indices embedding matrix weight (Tensor) embedding matrix number rows equal maximum possible index + 1, number columns equal embedding size offsets (LongTensor, optional) used input 1D. offsets determines starting index position bag (sequence) input. max_norm (float, optional) given, embedding vector norm larger max_norm renormalized norm max_norm. Note: modify weight -place. norm_type (float, optional) p p-norm compute max_norm option. Default 2. scale_grad_by_freq (boolean, optional) given, scale gradients inverse frequency words mini-batch. Default FALSE.                                            Note: option supported mode=\"max\". mode (string, optional) \"sum\", \"mean\" \"max\". Specifies way reduce bag. Default: 'mean' sparse (bool, optional) TRUE, gradient w.r.t. weight sparse tensor. See Notes nn_embedding details regarding sparse gradients. Note: option supported mode=\"max\". per_sample_weights (Tensor, optional) tensor float / double weights, NULL indicate weights taken 1. specified, per_sample_weights must exactly shape input treated offsets, NULL. include_last_offset (bool, optional) TRUE, size offsets equal number bags + 1.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_fold.html","id":null,"dir":"Reference","previous_headings":"","what":"Fold — nnf_fold","title":"Fold — nnf_fold","text":"Combines array sliding local blocks large containing tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_fold.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fold — nnf_fold","text":"","code":"nnf_fold(   input,   output_size,   kernel_size,   dilation = 1,   padding = 0,   stride = 1 )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_fold.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fold — nnf_fold","text":"input input tensor output_size shape spatial dimensions output (.e., output$sizes()[-c(1,2)]) kernel_size size sliding blocks dilation parameter controls stride elements within neighborhood. Default: 1 padding implicit zero padding added sides input. Default: 0 stride stride sliding blocks input spatial dimensions. Default: 1","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_fold.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Fold — nnf_fold","text":"Currently, 4-D output tensors (batched image-like tensors) supported.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_fractional_max_pool2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Fractional_max_pool2d — nnf_fractional_max_pool2d","title":"Fractional_max_pool2d — nnf_fractional_max_pool2d","text":"Applies 2D fractional max pooling input signal composed several input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_fractional_max_pool2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fractional_max_pool2d — nnf_fractional_max_pool2d","text":"","code":"nnf_fractional_max_pool2d(   input,   kernel_size,   output_size = NULL,   output_ratio = NULL,   return_indices = FALSE,   random_samples = NULL )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_fractional_max_pool2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fractional_max_pool2d — nnf_fractional_max_pool2d","text":"input input tensor kernel_size size window take max . Can single number \\(k\\) (square kernel \\(k * k\\)) tuple (kH, kW) output_size target output size image form \\(oH * oW\\). Can tuple (oH, oW) single number \\(oH\\) square image \\(oH * oH\\) output_ratio one wants output size ratio input size, option can given. number tuple range (0, 1) return_indices True, return indices along outputs. random_samples optional random samples.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_fractional_max_pool2d.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Fractional_max_pool2d — nnf_fractional_max_pool2d","text":"Fractional MaxPooling described detail paper Fractional MaxPooling_ Ben Graham max-pooling operation applied \\(kH * kW\\) regions stochastic step size determined target output size. number output features equal number input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_fractional_max_pool3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Fractional_max_pool3d — nnf_fractional_max_pool3d","title":"Fractional_max_pool3d — nnf_fractional_max_pool3d","text":"Applies 3D fractional max pooling input signal composed several input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_fractional_max_pool3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fractional_max_pool3d — nnf_fractional_max_pool3d","text":"","code":"nnf_fractional_max_pool3d(   input,   kernel_size,   output_size = NULL,   output_ratio = NULL,   return_indices = FALSE,   random_samples = NULL )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_fractional_max_pool3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fractional_max_pool3d — nnf_fractional_max_pool3d","text":"input input tensor kernel_size size window take max . Can single number \\(k\\) (square kernel \\(k * k * k\\)) tuple (kT, kH, kW) output_size target output size form \\(oT * oH * oW\\). Can tuple (oT, oH, oW) single number \\(oH\\) cubic output \\(oH * oH * oH\\) output_ratio one wants output size ratio input size, option can given. number tuple range (0, 1) return_indices True, return indices along outputs. random_samples undocumented argument.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_fractional_max_pool3d.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Fractional_max_pool3d — nnf_fractional_max_pool3d","text":"Fractional MaxPooling described detail paper Fractional MaxPooling_ Ben Graham max-pooling operation applied \\(kT * kH * kW\\) regions stochastic step size determined target output size. number output features equal number input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_gelu.html","id":null,"dir":"Reference","previous_headings":"","what":"Gelu — nnf_gelu","title":"Gelu — nnf_gelu","text":"Gelu","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_gelu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Gelu — nnf_gelu","text":"","code":"nnf_gelu(input)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_gelu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Gelu — nnf_gelu","text":"input (N,*) tensor, * means, number additional dimensions","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_gelu.html","id":"gelu-input-gt-tensor-","dir":"Reference","previous_headings":"","what":"gelu(input) -> Tensor","title":"Gelu — nnf_gelu","text":"Applies element-wise function \\(GELU(x) = x * \\Phi(x)\\) \\(\\Phi(x)\\) Cumulative Distribution Function Gaussian Distribution. See Gaussian Error Linear Units (GELUs).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_glu.html","id":null,"dir":"Reference","previous_headings":"","what":"Glu — nnf_glu","title":"Glu — nnf_glu","text":"gated linear unit. Computes:","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_glu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Glu — nnf_glu","text":"","code":"nnf_glu(input, dim = -1)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_glu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Glu — nnf_glu","text":"input (Tensor) input tensor dim (int) dimension split input. Default: -1","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_glu.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Glu — nnf_glu","text":"$$GLU(, b) = \\otimes \\sigma(b)$$ input split half along dim form b, \\(\\sigma\\) sigmoid function \\(\\otimes\\) element-wise product matrices. See Language Modeling Gated Convolutional Networks.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_grid_sample.html","id":null,"dir":"Reference","previous_headings":"","what":"Grid_sample — nnf_grid_sample","title":"Grid_sample — nnf_grid_sample","text":"Given input flow-field grid, computes output using input values pixel locations grid.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_grid_sample.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Grid_sample — nnf_grid_sample","text":"","code":"nnf_grid_sample(   input,   grid,   mode = c(\"bilinear\", \"nearest\"),   padding_mode = c(\"zeros\", \"border\", \"reflection\"),   align_corners = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_grid_sample.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Grid_sample — nnf_grid_sample","text":"input (Tensor) input shape \\((N, C, H_{\\mbox{}}, W_{\\mbox{}})\\) (4-D case)                    \\((N, C, D_{\\mbox{}}, H_{\\mbox{}}, W_{\\mbox{}})\\) (5-D case) grid (Tensor) flow-field shape \\((N, H_{\\mbox{}}, W_{\\mbox{}}, 2)\\) (4-D case)                   \\((N, D_{\\mbox{}}, H_{\\mbox{}}, W_{\\mbox{}}, 3)\\) (5-D case) mode (str) interpolation mode calculate output values 'bilinear' | 'nearest'. Default: 'bilinear' padding_mode (str) padding mode outside grid values 'zeros' | 'border' | 'reflection'. Default: 'zeros' align_corners (bool, optional) Geometrically, consider pixels input  squares rather points. set True, extrema (-1 1) considered referring center points input's corner pixels. set False, instead considered referring corner points input's corner pixels, making sampling resolution agnostic. option parallels align_corners option  nnf_interpolate(), whichever option used also used resize input image grid sampling. Default: False","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_grid_sample.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Grid_sample — nnf_grid_sample","text":"Currently, spatial (4-D) volumetric (5-D) input supported. spatial (4-D) case, input shape \\((N, C, H_{\\mbox{}}, W_{\\mbox{}})\\) grid shape \\((N, H_{\\mbox{}}, W_{\\mbox{}}, 2)\\), output shape \\((N, C, H_{\\mbox{}}, W_{\\mbox{}})\\). output location output[n, :, h, w], size-2 vector grid[n, h, w] specifies input pixel locations x y, used interpolate output value output[n, :, h, w]. case 5D inputs, grid[n, d, h, w] specifies x, y, z pixel locations interpolating output[n, :, d, h, w]. mode argument specifies nearest bilinear interpolation method sample input pixels. grid specifies sampling pixel locations normalized input spatial dimensions. Therefore, values range [-1, 1]. example, values x = -1, y = -1 left-top pixel input, values  x = 1, y = 1 right-bottom pixel input. grid values outside range [-1, 1], corresponding outputs handled defined padding_mode. Options padding_mode=\"zeros\": use 0 --bound grid locations, padding_mode=\"border\": use border values --bound grid locations, padding_mode=\"reflection\": use values locations reflected border --bound grid locations. location far away border, keep reflected becoming bound, e.g., (normalized) pixel location x = -3.5 reflects border -1 becomes x' = 1.5, reflects border 1 becomes x'' = -0.5.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_grid_sample.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Grid_sample — nnf_grid_sample","text":"function often used conjunction nnf_affine_grid() build Spatial Transformer Networks_ .","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_group_norm.html","id":null,"dir":"Reference","previous_headings":"","what":"Group_norm — nnf_group_norm","title":"Group_norm — nnf_group_norm","text":"Applies Group Normalization last certain number dimensions.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_group_norm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Group_norm — nnf_group_norm","text":"","code":"nnf_group_norm(input, num_groups, weight = NULL, bias = NULL, eps = 1e-05)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_group_norm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Group_norm — nnf_group_norm","text":"input input tensor num_groups number groups separate channels weight weight tensor bias bias tensor eps value added denominator numerical stability. Default: 1e-5","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_gumbel_softmax.html","id":null,"dir":"Reference","previous_headings":"","what":"Gumbel_softmax — nnf_gumbel_softmax","title":"Gumbel_softmax — nnf_gumbel_softmax","text":"Samples Gumbel-Softmax distribution optionally discretizes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_gumbel_softmax.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Gumbel_softmax — nnf_gumbel_softmax","text":"","code":"nnf_gumbel_softmax(logits, tau = 1, hard = FALSE, dim = -1)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_gumbel_softmax.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Gumbel_softmax — nnf_gumbel_softmax","text":"logits [..., num_features] unnormalized log probabilities tau non-negative scalar temperature hard True, returned samples discretized one-hot vectors,        differentiated soft sample autograd dim (int) dimension along softmax computed. Default: -1.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_hardshrink.html","id":null,"dir":"Reference","previous_headings":"","what":"Hardshrink — nnf_hardshrink","title":"Hardshrink — nnf_hardshrink","text":"Applies hard shrinkage function element-wise","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_hardshrink.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hardshrink — nnf_hardshrink","text":"","code":"nnf_hardshrink(input, lambd = 0.5)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_hardshrink.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hardshrink — nnf_hardshrink","text":"input (N,*) tensor, * means, number additional dimensions lambd lambda value Hardshrink formulation. Default: 0.5","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_hardsigmoid.html","id":null,"dir":"Reference","previous_headings":"","what":"Hardsigmoid — nnf_hardsigmoid","title":"Hardsigmoid — nnf_hardsigmoid","text":"Applies element-wise function \\(\\mbox{Hardsigmoid}(x) = \\frac{ReLU6(x + 3)}{6}\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_hardsigmoid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hardsigmoid — nnf_hardsigmoid","text":"","code":"nnf_hardsigmoid(input, inplace = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_hardsigmoid.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hardsigmoid — nnf_hardsigmoid","text":"input (N,*) tensor, * means, number additional dimensions inplace NA set True, operation -place. Default: False","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_hardswish.html","id":null,"dir":"Reference","previous_headings":"","what":"Hardswish — nnf_hardswish","title":"Hardswish — nnf_hardswish","text":"Applies hardswish function, element-wise, described paper: Searching MobileNetV3.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_hardswish.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hardswish — nnf_hardswish","text":"","code":"nnf_hardswish(input, inplace = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_hardswish.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hardswish — nnf_hardswish","text":"input (N,*) tensor, * means, number additional dimensions inplace can optionally operation -place. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_hardswish.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Hardswish — nnf_hardswish","text":"$$ \\mbox{Hardswish}(x) = \\left\\{   \\begin{array}{ll}   0 & \\mbox{} x \\le -3, \\\\   x & \\mbox{} x \\ge +3, \\\\   x \\cdot (x + 3)/6 & \\mbox{otherwise}   \\end{array}   \\right. $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_hardtanh.html","id":null,"dir":"Reference","previous_headings":"","what":"Hardtanh — nnf_hardtanh","title":"Hardtanh — nnf_hardtanh","text":"Applies HardTanh function element-wise.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_hardtanh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hardtanh — nnf_hardtanh","text":"","code":"nnf_hardtanh(input, min_val = -1, max_val = 1, inplace = FALSE)  nnf_hardtanh_(input, min_val = -1, max_val = 1)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_hardtanh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hardtanh — nnf_hardtanh","text":"input (N,*) tensor, * means, number additional dimensions min_val minimum value linear region range. Default: -1 max_val maximum value linear region range. Default: 1 inplace can optionally operation -place. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_hinge_embedding_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Hinge_embedding_loss — nnf_hinge_embedding_loss","title":"Hinge_embedding_loss — nnf_hinge_embedding_loss","text":"Measures loss given input tensor xx labels tensor yy (containing 1 -1). usually used measuring whether two inputs similar dissimilar, e.g. using L1 pairwise distance xx , typically used learning nonlinear embeddings semi-supervised learning.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_hinge_embedding_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hinge_embedding_loss — nnf_hinge_embedding_loss","text":"","code":"nnf_hinge_embedding_loss(input, target, margin = 1, reduction = \"mean\")"},{"path":"https://torch.mlverse.org/docs/reference/nnf_hinge_embedding_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hinge_embedding_loss — nnf_hinge_embedding_loss","text":"input tensor (N,*) ** means, number additional dimensions target tensor (N,*) , shape input margin default value 1. reduction (string, optional) – Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed. Default: 'mean'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_instance_norm.html","id":null,"dir":"Reference","previous_headings":"","what":"Instance_norm — nnf_instance_norm","title":"Instance_norm — nnf_instance_norm","text":"Applies Instance Normalization channel data sample batch.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_instance_norm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instance_norm — nnf_instance_norm","text":"","code":"nnf_instance_norm(   input,   running_mean = NULL,   running_var = NULL,   weight = NULL,   bias = NULL,   use_input_stats = TRUE,   momentum = 0.1,   eps = 1e-05 )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_instance_norm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instance_norm — nnf_instance_norm","text":"input input tensor running_mean running_mean tensor running_var running var tensor weight weight tensor bias bias tensor use_input_stats whether use input stats momentum double momentum eps eps double numerical stability","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_interpolate.html","id":null,"dir":"Reference","previous_headings":"","what":"Interpolate — nnf_interpolate","title":"Interpolate — nnf_interpolate","text":"/samples input either given size given scale_factor","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_interpolate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Interpolate — nnf_interpolate","text":"","code":"nnf_interpolate(   input,   size = NULL,   scale_factor = NULL,   mode = \"nearest\",   align_corners = FALSE,   recompute_scale_factor = NULL )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_interpolate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Interpolate — nnf_interpolate","text":"input (Tensor) input tensor size (int Tuple[int] Tuple[int, int] Tuple[int, int, int]) output spatial size. scale_factor (float Tuple[float]) multiplier spatial size. match input size tuple. mode (str) algorithm used upsampling: 'nearest' | 'linear' | 'bilinear' | 'bicubic' | 'trilinear' | 'area' Default: 'nearest' align_corners (bool, optional) Geometrically, consider pixels input output squares rather points. set TRUE, input output tensors aligned center points corner pixels, preserving values corner pixels. set False, input output tensors aligned corner points corner pixels, interpolation uses edge value padding --boundary values, making operation independent input size scale_factor kept . effect mode  'linear', 'bilinear', 'bicubic' 'trilinear'.  Default: False recompute_scale_factor (bool, optional) recompute scale_factor use interpolation calculation.  scale_factor passed parameter, used compute output_size.  recompute_scale_factor ```True`` specified, new scale_factor computed based output input sizes use interpolation computation (.e. computation identical computed `output_size` passed-explicitly).  Otherwise, passed-`scale_factor` used interpolation computation.  Note `scale_factor` floating-point, recomputed scale_factor may differ one passed due rounding precision issues.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_interpolate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Interpolate — nnf_interpolate","text":"algorithm used interpolation determined mode. Currently temporal, spatial volumetric sampling supported, .e. expected inputs 3-D, 4-D 5-D shape. input dimensions interpreted form: mini-batch x channels x [optional depth] x [optional height] x width. modes available resizing : nearest, linear (3D-), bilinear, bicubic (4D-), trilinear (5D-), area","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_kl_div.html","id":null,"dir":"Reference","previous_headings":"","what":"Kl_div — nnf_kl_div","title":"Kl_div — nnf_kl_div","text":"Kullback-Leibler divergence Loss.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_kl_div.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Kl_div — nnf_kl_div","text":"","code":"nnf_kl_div(input, target, reduction = \"mean\")"},{"path":"https://torch.mlverse.org/docs/reference/nnf_kl_div.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Kl_div — nnf_kl_div","text":"input tensor (N,*) ** means, number additional dimensions target tensor (N,*) , shape input reduction (string, optional) – Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed. Default: 'mean'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_l1_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"L1_loss — nnf_l1_loss","title":"L1_loss — nnf_l1_loss","text":"Function takes mean element-wise absolute value difference.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_l1_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"L1_loss — nnf_l1_loss","text":"","code":"nnf_l1_loss(input, target, reduction = \"mean\")"},{"path":"https://torch.mlverse.org/docs/reference/nnf_l1_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"L1_loss — nnf_l1_loss","text":"input tensor (N,*) ** means, number additional dimensions target tensor (N,*) , shape input reduction (string, optional) – Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed. Default: 'mean'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_layer_norm.html","id":null,"dir":"Reference","previous_headings":"","what":"Layer_norm — nnf_layer_norm","title":"Layer_norm — nnf_layer_norm","text":"Applies Layer Normalization last certain number dimensions.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_layer_norm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Layer_norm — nnf_layer_norm","text":"","code":"nnf_layer_norm(   input,   normalized_shape,   weight = NULL,   bias = NULL,   eps = 1e-05 )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_layer_norm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Layer_norm — nnf_layer_norm","text":"input input tensor normalized_shape input shape expected input size. single integer used, treated singleton list, module normalize last dimension expected specific size. weight weight tensor bias bias tensor eps value added denominator numerical stability. Default: 1e-5","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_leaky_relu.html","id":null,"dir":"Reference","previous_headings":"","what":"Leaky_relu — nnf_leaky_relu","title":"Leaky_relu — nnf_leaky_relu","text":"Applies element-wise, \\(LeakyReLU(x) = max(0, x) + negative_slope * min(0, x)\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_leaky_relu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Leaky_relu — nnf_leaky_relu","text":"","code":"nnf_leaky_relu(input, negative_slope = 0.01, inplace = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_leaky_relu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Leaky_relu — nnf_leaky_relu","text":"input (N,*) tensor, * means, number additional dimensions negative_slope Controls angle negative slope. Default: 1e-2 inplace can optionally operation -place. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_linear.html","id":null,"dir":"Reference","previous_headings":"","what":"Linear — nnf_linear","title":"Linear — nnf_linear","text":"Applies linear transformation incoming data: \\(y = xA^T + b\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_linear.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Linear — nnf_linear","text":"","code":"nnf_linear(input, weight, bias = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_linear.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Linear — nnf_linear","text":"input \\((N, *, \\_features)\\) * means number additional dimensions weight \\((\\_features, \\_features)\\) weights tensor. bias optional tensor \\((\\_features)\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_local_response_norm.html","id":null,"dir":"Reference","previous_headings":"","what":"Local_response_norm — nnf_local_response_norm","title":"Local_response_norm — nnf_local_response_norm","text":"Applies local response normalization input signal composed several input planes, channels occupy second dimension. Applies normalization across channels.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_local_response_norm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Local_response_norm — nnf_local_response_norm","text":"","code":"nnf_local_response_norm(input, size, alpha = 1e-04, beta = 0.75, k = 1)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_local_response_norm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Local_response_norm — nnf_local_response_norm","text":"input input tensor size amount neighbouring channels used normalization alpha multiplicative factor. Default: 0.0001 beta exponent. Default: 0.75 k additive factor. Default: 1","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_log_softmax.html","id":null,"dir":"Reference","previous_headings":"","what":"Log_softmax — nnf_log_softmax","title":"Log_softmax — nnf_log_softmax","text":"Applies softmax followed logarithm.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_log_softmax.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Log_softmax — nnf_log_softmax","text":"","code":"nnf_log_softmax(input, dim = NULL, dtype = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_log_softmax.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Log_softmax — nnf_log_softmax","text":"input (Tensor) input dim (int) dimension along log_softmax computed. dtype (torch.dtype, optional) desired data type returned tensor. specified, input tensor casted dtype operation performed. useful preventing data type overflows. Default: NULL.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_log_softmax.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Log_softmax — nnf_log_softmax","text":"mathematically equivalent log(softmax(x)), two operations separately slower, numerically unstable. function uses alternative formulation compute output gradient correctly.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_logsigmoid.html","id":null,"dir":"Reference","previous_headings":"","what":"Logsigmoid — nnf_logsigmoid","title":"Logsigmoid — nnf_logsigmoid","text":"Applies element-wise \\(LogSigmoid(x_i) = log(\\frac{1}{1 + exp(-x_i)})\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_logsigmoid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Logsigmoid — nnf_logsigmoid","text":"","code":"nnf_logsigmoid(input)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_logsigmoid.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Logsigmoid — nnf_logsigmoid","text":"input (N,*) tensor, * means, number additional dimensions","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_lp_pool1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Lp_pool1d — nnf_lp_pool1d","title":"Lp_pool1d — nnf_lp_pool1d","text":"Applies 1D power-average pooling input signal composed several input planes. sum inputs power p zero, gradient set zero well.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_lp_pool1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Lp_pool1d — nnf_lp_pool1d","text":"","code":"nnf_lp_pool1d(input, norm_type, kernel_size, stride = NULL, ceil_mode = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_lp_pool1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Lp_pool1d — nnf_lp_pool1d","text":"input input tensor norm_type inf one gets max pooling 0 get sum pooling ( proportional avg pooling) kernel_size single int, size window stride single int, stride window. Default value kernel_size ceil_mode True, use ceil instead floor compute output shape","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_lp_pool2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Lp_pool2d — nnf_lp_pool2d","title":"Lp_pool2d — nnf_lp_pool2d","text":"Applies 2D power-average pooling input signal composed several input planes. sum inputs power p zero, gradient set zero well.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_lp_pool2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Lp_pool2d — nnf_lp_pool2d","text":"","code":"nnf_lp_pool2d(input, norm_type, kernel_size, stride = NULL, ceil_mode = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_lp_pool2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Lp_pool2d — nnf_lp_pool2d","text":"input input tensor norm_type inf one gets max pooling 0 get sum pooling ( proportional avg pooling) kernel_size single int, size window stride single int, stride window. Default value kernel_size ceil_mode True, use ceil instead floor compute output shape","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_margin_ranking_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Margin_ranking_loss — nnf_margin_ranking_loss","title":"Margin_ranking_loss — nnf_margin_ranking_loss","text":"Creates criterion measures loss given inputs x1 , x2 , two 1D mini-batch Tensors, label 1D mini-batch tensor y (containing 1 -1).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_margin_ranking_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Margin_ranking_loss — nnf_margin_ranking_loss","text":"","code":"nnf_margin_ranking_loss(input1, input2, target, margin = 0, reduction = \"mean\")"},{"path":"https://torch.mlverse.org/docs/reference/nnf_margin_ranking_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Margin_ranking_loss — nnf_margin_ranking_loss","text":"input1 first tensor input2 second input tensor target target tensor margin default value 00 . reduction (string, optional) – Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed. Default: 'mean'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_max_pool1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Max_pool1d — nnf_max_pool1d","title":"Max_pool1d — nnf_max_pool1d","text":"Applies 1D max pooling input signal composed several input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_max_pool1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Max_pool1d — nnf_max_pool1d","text":"","code":"nnf_max_pool1d(   input,   kernel_size,   stride = NULL,   padding = 0,   dilation = 1,   ceil_mode = FALSE,   return_indices = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_max_pool1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Max_pool1d — nnf_max_pool1d","text":"input input tensor shape (minibatch , in_channels , iW) kernel_size size window. Can single number tuple (kW,). stride stride window. Can single number tuple (sW,). Default: kernel_size padding implicit zero paddings sides input. Can single number tuple (padW,). Default: 0 dilation controls spacing kernel points; also known à trous algorithm. ceil_mode True, use ceil instead floor compute output shape. Default: FALSE return_indices whether return indices max occurs.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_max_pool2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Max_pool2d — nnf_max_pool2d","title":"Max_pool2d — nnf_max_pool2d","text":"Applies 2D max pooling input signal composed several input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_max_pool2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Max_pool2d — nnf_max_pool2d","text":"","code":"nnf_max_pool2d(   input,   kernel_size,   stride = kernel_size,   padding = 0,   dilation = 1,   ceil_mode = FALSE,   return_indices = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_max_pool2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Max_pool2d — nnf_max_pool2d","text":"input input tensor (minibatch, in_channels , iH , iW) kernel_size size pooling region. Can single number tuple (kH, kW) stride stride pooling operation. Can single number tuple (sH, sW). Default: kernel_size padding implicit zero paddings sides input. Can single number tuple (padH, padW). Default: 0 dilation controls spacing kernel points; also known à trous algorithm. ceil_mode True, use ceil instead floor formula compute output shape. Default: FALSE return_indices whether return indices max occurs.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_max_pool3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Max_pool3d — nnf_max_pool3d","title":"Max_pool3d — nnf_max_pool3d","text":"Applies 3D max pooling input signal composed several input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_max_pool3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Max_pool3d — nnf_max_pool3d","text":"","code":"nnf_max_pool3d(   input,   kernel_size,   stride = NULL,   padding = 0,   dilation = 1,   ceil_mode = FALSE,   return_indices = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_max_pool3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Max_pool3d — nnf_max_pool3d","text":"input input tensor (minibatch, in_channels , * iH , iW) kernel_size size pooling region. Can single number tuple (kT, kH, kW) stride stride pooling operation. Can single number tuple (sT, sH, sW). Default: kernel_size padding implicit zero paddings sides input. Can single number tuple (padT, padH, padW), Default: 0 dilation controls spacing kernel points; also known à trous algorithm. ceil_mode True, use ceil instead floor formula compute output shape return_indices whether return indices max occurs.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_max_unpool1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Max_unpool1d — nnf_max_unpool1d","title":"Max_unpool1d — nnf_max_unpool1d","text":"Computes partial inverse MaxPool1d.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_max_unpool1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Max_unpool1d — nnf_max_unpool1d","text":"","code":"nnf_max_unpool1d(   input,   indices,   kernel_size,   stride = NULL,   padding = 0,   output_size = NULL )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_max_unpool1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Max_unpool1d — nnf_max_unpool1d","text":"input input Tensor invert indices indices given max pool kernel_size Size max pooling window. stride Stride max pooling window. set kernel_size default. padding Padding added input output_size targeted output size","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_max_unpool2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Max_unpool2d — nnf_max_unpool2d","title":"Max_unpool2d — nnf_max_unpool2d","text":"Computes partial inverse MaxPool2d.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_max_unpool2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Max_unpool2d — nnf_max_unpool2d","text":"","code":"nnf_max_unpool2d(   input,   indices,   kernel_size,   stride = NULL,   padding = 0,   output_size = NULL )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_max_unpool2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Max_unpool2d — nnf_max_unpool2d","text":"input input Tensor invert indices indices given max pool kernel_size Size max pooling window. stride Stride max pooling window. set kernel_size default. padding Padding added input output_size targeted output size","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_max_unpool3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Max_unpool3d — nnf_max_unpool3d","title":"Max_unpool3d — nnf_max_unpool3d","text":"Computes partial inverse MaxPool3d.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_max_unpool3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Max_unpool3d — nnf_max_unpool3d","text":"","code":"nnf_max_unpool3d(   input,   indices,   kernel_size,   stride = NULL,   padding = 0,   output_size = NULL )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_max_unpool3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Max_unpool3d — nnf_max_unpool3d","text":"input input Tensor invert indices indices given max pool kernel_size Size max pooling window. stride Stride max pooling window. set kernel_size default. padding Padding added input output_size targeted output size","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_mse_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Mse_loss — nnf_mse_loss","title":"Mse_loss — nnf_mse_loss","text":"Measures element-wise mean squared error.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_mse_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mse_loss — nnf_mse_loss","text":"","code":"nnf_mse_loss(input, target, reduction = \"mean\")"},{"path":"https://torch.mlverse.org/docs/reference/nnf_mse_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mse_loss — nnf_mse_loss","text":"input tensor (N,*) ** means, number additional dimensions target tensor (N,*) , shape input reduction (string, optional) – Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed. Default: 'mean'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_multi_head_attention_forward.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi head attention forward — nnf_multi_head_attention_forward","title":"Multi head attention forward — nnf_multi_head_attention_forward","text":"Allows model jointly attend information different representation subspaces. See reference: Attention Need","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_multi_head_attention_forward.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi head attention forward — nnf_multi_head_attention_forward","text":"","code":"nnf_multi_head_attention_forward(   query,   key,   value,   embed_dim_to_check,   num_heads,   in_proj_weight,   in_proj_bias,   bias_k,   bias_v,   add_zero_attn,   dropout_p,   out_proj_weight,   out_proj_bias,   training = TRUE,   key_padding_mask = NULL,   need_weights = TRUE,   attn_mask = NULL,   avg_weights = TRUE,   use_separate_proj_weight = FALSE,   q_proj_weight = NULL,   k_proj_weight = NULL,   v_proj_weight = NULL,   static_k = NULL,   static_v = NULL )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_multi_head_attention_forward.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Multi head attention forward — nnf_multi_head_attention_forward","text":"query \\((L, N, E)\\) L target sequence length, N batch size, E embedding dimension. key \\((S, N, E)\\), S source sequence length, N batch size, E embedding dimension. value \\((S, N, E)\\) S source sequence length, N batch size, E embedding dimension. embed_dim_to_check total dimension model. num_heads parallel attention heads. in_proj_weight input projection weight bias. in_proj_bias currently undocumented. bias_k bias key value sequences added dim=0. bias_v currently undocumented. add_zero_attn add new batch zeros key value sequences dim=1. dropout_p probability element zeroed. out_proj_weight output projection weight bias. out_proj_bias currently undocumented. training apply dropout TRUE. key_padding_mask \\((N, S)\\) N batch size, S source sequence length. ByteTensor provided, non-zero positions ignored position zero positions unchanged. BoolTensor provided, positions value True ignored position value False unchanged. need_weights output attn_output_weights. attn_mask 2D mask \\((L, S)\\) L target sequence length, S source sequence length. 3D mask \\((N*num_heads, L, S)\\) N batch size, L target sequence length, S source sequence length. attn_mask ensure position allowed attend unmasked positions. ByteTensor provided, non-zero positions allowed attend zero positions unchanged. BoolTensor provided, positions True allowed attend False values unchanged. FloatTensor provided, added attention weight. avg_weights Logical; whether average attn_output_weights attention heads outputting . change returned value attn_output; affects returned attention weight matrix. use_separate_proj_weight function accept proj. weights query, key, value different forms. false, in_proj_weight used, combination q_proj_weight, k_proj_weight, v_proj_weight. q_proj_weight input projection weight bias. k_proj_weight currently undocumented. v_proj_weight currently undocumented. static_k static key value used attention operators. static_v currently undocumented.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_multi_margin_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi_margin_loss — nnf_multi_margin_loss","title":"Multi_margin_loss — nnf_multi_margin_loss","text":"Creates criterion optimizes multi-class classification hinge loss (margin-based loss) input x (2D mini-batch Tensor) output y (1D tensor target class indices, 0 <= y <= x$size(2) - 1 ).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_multi_margin_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi_margin_loss — nnf_multi_margin_loss","text":"","code":"nnf_multi_margin_loss(   input,   target,   p = 1,   margin = 1,   weight = NULL,   reduction = \"mean\" )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_multi_margin_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Multi_margin_loss — nnf_multi_margin_loss","text":"input tensor (N,*) ** means, number additional dimensions target tensor (N,*) , shape input p default value 1. 1 2 supported values. margin default value 1. weight manual rescaling weight given class. given, Tensor size C. Otherwise, treated ones. reduction (string, optional) – Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed. Default: 'mean'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_multilabel_margin_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Multilabel_margin_loss — nnf_multilabel_margin_loss","title":"Multilabel_margin_loss — nnf_multilabel_margin_loss","text":"Creates criterion optimizes multi-class multi-classification hinge loss (margin-based loss) input x (2D mini-batch Tensor) output y (2D Tensor target class indices).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_multilabel_margin_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multilabel_margin_loss — nnf_multilabel_margin_loss","text":"","code":"nnf_multilabel_margin_loss(input, target, reduction = \"mean\")"},{"path":"https://torch.mlverse.org/docs/reference/nnf_multilabel_margin_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Multilabel_margin_loss — nnf_multilabel_margin_loss","text":"input tensor (N,*) ** means, number additional dimensions target tensor (N,*) , shape input reduction (string, optional) – Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed. Default: 'mean'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_multilabel_soft_margin_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Multilabel_soft_margin_loss — nnf_multilabel_soft_margin_loss","title":"Multilabel_soft_margin_loss — nnf_multilabel_soft_margin_loss","text":"Creates criterion optimizes multi-label one-versus-loss based max-entropy, input x target y size (N, C).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_multilabel_soft_margin_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multilabel_soft_margin_loss — nnf_multilabel_soft_margin_loss","text":"","code":"nnf_multilabel_soft_margin_loss(   input,   target,   weight = NULL,   reduction = \"mean\" )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_multilabel_soft_margin_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Multilabel_soft_margin_loss — nnf_multilabel_soft_margin_loss","text":"input tensor (N,*) ** means, number additional dimensions target tensor (N,*) , shape input weight weight tensor apply loss. reduction (string, optional) – Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed. Default: 'mean'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_nll_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Nll_loss — nnf_nll_loss","title":"Nll_loss — nnf_nll_loss","text":"negative log likelihood loss.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_nll_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Nll_loss — nnf_nll_loss","text":"","code":"nnf_nll_loss(   input,   target,   weight = NULL,   ignore_index = -100,   reduction = \"mean\" )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_nll_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Nll_loss — nnf_nll_loss","text":"input \\((N, C)\\) C = number classes \\((N, C, H, W)\\) case 2D Loss, \\((N, C, d_1, d_2, ..., d_K)\\) \\(K \\geq 1\\) case K-dimensional loss. target \\((N)\\) value \\(0 \\leq \\mbox{targets}[] \\leq C-1\\), \\((N, d_1, d_2, ..., d_K)\\) \\(K \\geq 1\\) K-dimensional loss. weight (Tensor, optional) manual rescaling weight given class. given, Tensor size C ignore_index (int, optional) Specifies target value ignored contribute input gradient. reduction (string, optional) – Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed. Default: 'mean'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_normalize.html","id":null,"dir":"Reference","previous_headings":"","what":"Normalize — nnf_normalize","title":"Normalize — nnf_normalize","text":"Performs \\(L_p\\) normalization inputs specified dimension.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_normalize.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Normalize — nnf_normalize","text":"","code":"nnf_normalize(input, p = 2, dim = 2, eps = 1e-12, out = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_normalize.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Normalize — nnf_normalize","text":"input input tensor shape p (float) exponent value norm formulation. Default: 2 dim (int) dimension reduce. Default: 1 eps (float) small value avoid division zero. Default: 1e-12 (Tensor, optional) output tensor. used,                            operation differentiable.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_normalize.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Normalize — nnf_normalize","text":"tensor input sizes \\((n_0, ..., n_{dim}, ..., n_k)\\), \\(n_{dim}\\) -element vector \\(v\\) along dimension dim transformed $$         v = \\frac{v}{\\max(\\Vert v \\Vert_p, \\epsilon)}. $$ default arguments uses Euclidean norm vectors along dimension \\(1\\) normalization.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_one_hot.html","id":null,"dir":"Reference","previous_headings":"","what":"One_hot — nnf_one_hot","title":"One_hot — nnf_one_hot","text":"Takes LongTensor index values shape (*) returns tensor shape (*, num_classes) zeros everywhere except index last dimension matches corresponding value input tensor, case 1.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_one_hot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"One_hot — nnf_one_hot","text":"","code":"nnf_one_hot(tensor, num_classes = -1)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_one_hot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"One_hot — nnf_one_hot","text":"tensor (LongTensor) class values shape. num_classes (int) Total number classes. set -1, number classes inferred one greater largest class value input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_one_hot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"One_hot — nnf_one_hot","text":"One-hot Wikipedia: https://en.wikipedia.org/wiki/One-hot","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_pad.html","id":null,"dir":"Reference","previous_headings":"","what":"Pad — nnf_pad","title":"Pad — nnf_pad","text":"Pads tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_pad.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pad — nnf_pad","text":"","code":"nnf_pad(input, pad, mode = \"constant\", value = 0)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_pad.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pad — nnf_pad","text":"input (Tensor) N-dimensional tensor pad (tuple) m-elements tuple, \\(\\frac{m}{2} \\leq\\) input dimensions \\(m\\) even. mode 'constant', 'reflect', 'replicate' 'circular'. Default: 'constant' value fill value 'constant' padding. Default: 0.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_pad.html","id":"padding-size","dir":"Reference","previous_headings":"","what":"Padding size","title":"Pad — nnf_pad","text":"padding size pad dimensions input described starting last dimension moving forward. \\(\\left\\lfloor\\frac{\\mbox{len(pad)}}{2}\\right\\rfloor\\) dimensions input padded. example, pad last dimension input tensor, pad form \\((\\mbox{padding\\_left}, \\mbox{padding\\_right})\\); pad last 2 dimensions input tensor, use \\((\\mbox{padding\\_left}, \\mbox{padding\\_right},\\) \\(\\mbox{padding\\_top}, \\mbox{padding\\_bottom})\\); pad last 3 dimensions, use \\((\\mbox{padding\\_left}, \\mbox{padding\\_right},\\) \\(\\mbox{padding\\_top}, \\mbox{padding\\_bottom}\\) \\(\\mbox{padding\\_front}, \\mbox{padding\\_back})\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_pad.html","id":"padding-mode","dir":"Reference","previous_headings":"","what":"Padding mode","title":"Pad — nnf_pad","text":"See nn_constant_pad_2d, nn_reflection_pad_2d, nn_replication_pad_2d concrete examples padding modes works. Constant padding implemented arbitrary dimensions. tensor, last 2 dimensions 4D input tensor, last dimension 3D input tensor. Reflect padding implemented padding last 2 dimensions 4D input tensor, last dimension 3D input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_pairwise_distance.html","id":null,"dir":"Reference","previous_headings":"","what":"Pairwise_distance — nnf_pairwise_distance","title":"Pairwise_distance — nnf_pairwise_distance","text":"Computes batchwise pairwise distance vectors using p-norm.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_pairwise_distance.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pairwise_distance — nnf_pairwise_distance","text":"","code":"nnf_pairwise_distance(x1, x2, p = 2, eps = 1e-06, keepdim = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_pairwise_distance.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pairwise_distance — nnf_pairwise_distance","text":"x1 (Tensor) First input. x2 (Tensor) Second input (size matching x1). p norm degree. Default: 2 eps (float, optional) Small value avoid division zero. Default: 1e-8 keepdim Determines whether keep vector dimension. Default: False","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_pdist.html","id":null,"dir":"Reference","previous_headings":"","what":"Pdist — nnf_pdist","title":"Pdist — nnf_pdist","text":"Computes p-norm distance every pair row vectors input. identical upper triangular portion, excluding diagonal, torch_norm(input[:, None] - input, dim=2, p=p). function faster rows contiguous.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_pdist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pdist — nnf_pdist","text":"","code":"nnf_pdist(input, p = 2)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_pdist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pdist — nnf_pdist","text":"input input tensor shape \\(N \\times M\\). p p value p-norm distance calculate vector pair \\(\\[0, \\infty]\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_pdist.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Pdist — nnf_pdist","text":"input shape \\(N \\times M\\) output shape \\(\\frac{1}{2} N (N - 1)\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_pixel_shuffle.html","id":null,"dir":"Reference","previous_headings":"","what":"Pixel_shuffle — nnf_pixel_shuffle","title":"Pixel_shuffle — nnf_pixel_shuffle","text":"Rearranges elements tensor shape \\((*, C \\times r^2, H, W)\\) tensor shape \\((*, C, H \\times r, W \\times r)\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_pixel_shuffle.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pixel_shuffle — nnf_pixel_shuffle","text":"","code":"nnf_pixel_shuffle(input, upscale_factor)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_pixel_shuffle.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pixel_shuffle — nnf_pixel_shuffle","text":"input (Tensor) input tensor upscale_factor (int) factor increase spatial resolution ","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_poisson_nll_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Poisson_nll_loss — nnf_poisson_nll_loss","title":"Poisson_nll_loss — nnf_poisson_nll_loss","text":"Poisson negative log likelihood loss.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_poisson_nll_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Poisson_nll_loss — nnf_poisson_nll_loss","text":"","code":"nnf_poisson_nll_loss(   input,   target,   log_input = TRUE,   full = FALSE,   eps = 1e-08,   reduction = \"mean\" )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_poisson_nll_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Poisson_nll_loss — nnf_poisson_nll_loss","text":"input tensor (N,*) ** means, number additional dimensions target tensor (N,*) , shape input log_input TRUE loss computed \\(\\exp(\\mbox{input}) - \\mbox{target} * \\mbox{input}\\), FALSE loss \\(\\mbox{input} - \\mbox{target} * \\log(\\mbox{input}+\\mbox{eps})\\). Default: TRUE. full whether compute full loss, . e. add Stirling approximation term. Default: FALSE. eps (float, optional) Small value avoid evaluation \\(\\log(0)\\) log_input=FALSE. Default: 1e-8 reduction (string, optional) – Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed. Default: 'mean'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_prelu.html","id":null,"dir":"Reference","previous_headings":"","what":"Prelu — nnf_prelu","title":"Prelu — nnf_prelu","text":"Applies element-wise function \\(PReLU(x) = max(0,x) + weight * min(0,x)\\) weight learnable parameter.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_prelu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prelu — nnf_prelu","text":"","code":"nnf_prelu(input, weight)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_prelu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prelu — nnf_prelu","text":"input (N,*) tensor, * means, number additional dimensions weight (Tensor) learnable weights","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_relu.html","id":null,"dir":"Reference","previous_headings":"","what":"Relu — nnf_relu","title":"Relu — nnf_relu","text":"Applies rectified linear unit function element-wise.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_relu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Relu — nnf_relu","text":"","code":"nnf_relu(input, inplace = FALSE)  nnf_relu_(input)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_relu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Relu — nnf_relu","text":"input (N,*) tensor, * means, number additional dimensions inplace can optionally operation -place. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_relu6.html","id":null,"dir":"Reference","previous_headings":"","what":"Relu6 — nnf_relu6","title":"Relu6 — nnf_relu6","text":"Applies element-wise function \\(ReLU6(x) = min(max(0,x), 6)\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_relu6.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Relu6 — nnf_relu6","text":"","code":"nnf_relu6(input, inplace = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_relu6.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Relu6 — nnf_relu6","text":"input (N,*) tensor, * means, number additional dimensions inplace can optionally operation -place. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_rrelu.html","id":null,"dir":"Reference","previous_headings":"","what":"Rrelu — nnf_rrelu","title":"Rrelu — nnf_rrelu","text":"Randomized leaky ReLU.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_rrelu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rrelu — nnf_rrelu","text":"","code":"nnf_rrelu(input, lower = 1/8, upper = 1/3, training = FALSE, inplace = FALSE)  nnf_rrelu_(input, lower = 1/8, upper = 1/3, training = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_rrelu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rrelu — nnf_rrelu","text":"input (N,*) tensor, * means, number additional dimensions lower lower bound uniform distribution. Default: 1/8 upper upper bound uniform distribution. Default: 1/3 training bool wether training pass. DEfault: FALSE inplace can optionally operation -place. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_selu.html","id":null,"dir":"Reference","previous_headings":"","what":"Selu — nnf_selu","title":"Selu — nnf_selu","text":"Applies element-wise, $$SELU(x) = scale * (max(0,x) + min(0, \\alpha * (exp(x) - 1)))$$, \\(\\alpha=1.6732632423543772848170429916717\\) \\(scale=1.0507009873554804934193349852946\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_selu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Selu — nnf_selu","text":"","code":"nnf_selu(input, inplace = FALSE)  nnf_selu_(input)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_selu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Selu — nnf_selu","text":"input (N,*) tensor, * means, number additional dimensions inplace can optionally operation -place. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_selu.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Selu — nnf_selu","text":"","code":"if (torch_is_installed()) { x <- torch_randn(2, 2) y <- nnf_selu(x) nnf_selu_(x) torch_equal(x, y) } #> [1] TRUE"},{"path":"https://torch.mlverse.org/docs/reference/nnf_sigmoid.html","id":null,"dir":"Reference","previous_headings":"","what":"Sigmoid — nnf_sigmoid","title":"Sigmoid — nnf_sigmoid","text":"Applies element-wise \\(Sigmoid(x_i) = \\frac{1}{1 + exp(-x_i)}\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_sigmoid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sigmoid — nnf_sigmoid","text":"","code":"nnf_sigmoid(input)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_sigmoid.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sigmoid — nnf_sigmoid","text":"input (N,*) tensor, * means, number additional dimensions","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_smooth_l1_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Smooth_l1_loss — nnf_smooth_l1_loss","title":"Smooth_l1_loss — nnf_smooth_l1_loss","text":"Function uses squared term absolute element-wise error falls 1 L1 term otherwise.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_smooth_l1_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Smooth_l1_loss — nnf_smooth_l1_loss","text":"","code":"nnf_smooth_l1_loss(input, target, reduction = \"mean\")"},{"path":"https://torch.mlverse.org/docs/reference/nnf_smooth_l1_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Smooth_l1_loss — nnf_smooth_l1_loss","text":"input tensor (N,*) ** means, number additional dimensions target tensor (N,*) , shape input reduction (string, optional) – Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed. Default: 'mean'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_soft_margin_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Soft_margin_loss — nnf_soft_margin_loss","title":"Soft_margin_loss — nnf_soft_margin_loss","text":"Creates criterion optimizes two-class classification logistic loss input tensor x target tensor y (containing 1 -1).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_soft_margin_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Soft_margin_loss — nnf_soft_margin_loss","text":"","code":"nnf_soft_margin_loss(input, target, reduction = \"mean\")"},{"path":"https://torch.mlverse.org/docs/reference/nnf_soft_margin_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Soft_margin_loss — nnf_soft_margin_loss","text":"input tensor (N,*) ** means, number additional dimensions target tensor (N,*) , shape input reduction (string, optional) – Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed. Default: 'mean'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_softmax.html","id":null,"dir":"Reference","previous_headings":"","what":"Softmax — nnf_softmax","title":"Softmax — nnf_softmax","text":"Applies softmax function.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_softmax.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Softmax — nnf_softmax","text":"","code":"nnf_softmax(input, dim, dtype = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_softmax.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Softmax — nnf_softmax","text":"input (Tensor) input dim (int) dimension along softmax computed. dtype (torch.dtype, optional) desired data type returned tensor.      specified, input tensor casted dtype operation      performed. useful preventing data type overflows. Default: NULL.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_softmax.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Softmax — nnf_softmax","text":"Softmax defined : $$Softmax(x_{}) = exp(x_i)/\\sum_j exp(x_j)$$ applied slices along dim, re-scale elements lie range [0, 1] sum 1.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_softmin.html","id":null,"dir":"Reference","previous_headings":"","what":"Softmin — nnf_softmin","title":"Softmin — nnf_softmin","text":"Applies softmin function.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_softmin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Softmin — nnf_softmin","text":"","code":"nnf_softmin(input, dim, dtype = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_softmin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Softmin — nnf_softmin","text":"input (Tensor) input dim (int) dimension along softmin computed (every slice        along dim sum 1). dtype (torch.dtype, optional) desired data type returned tensor.      specified, input tensor casted dtype operation      performed. useful preventing data type overflows. Default: NULL.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_softmin.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Softmin — nnf_softmin","text":"Note $$Softmin(x) = Softmax(-x)$$. See nnf_softmax definition mathematical formula.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_softplus.html","id":null,"dir":"Reference","previous_headings":"","what":"Softplus — nnf_softplus","title":"Softplus — nnf_softplus","text":"Applies element-wise, function \\(Softplus(x) = 1/\\beta * log(1 + exp(\\beta * x))\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_softplus.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Softplus — nnf_softplus","text":"","code":"nnf_softplus(input, beta = 1, threshold = 20)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_softplus.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Softplus — nnf_softplus","text":"input (N,*) tensor, * means, number additional dimensions beta beta value Softplus formulation. Default: 1 threshold values revert linear function. Default: 20","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_softplus.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Softplus — nnf_softplus","text":"numerical stability implementation reverts linear function \\(input * \\beta > threshold\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_softshrink.html","id":null,"dir":"Reference","previous_headings":"","what":"Softshrink — nnf_softshrink","title":"Softshrink — nnf_softshrink","text":"Applies soft shrinkage function elementwise","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_softshrink.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Softshrink — nnf_softshrink","text":"","code":"nnf_softshrink(input, lambd = 0.5)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_softshrink.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Softshrink — nnf_softshrink","text":"input (N,*) tensor, * means, number additional dimensions lambd lambda (must less zero) value Softshrink formulation. Default: 0.5","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_softsign.html","id":null,"dir":"Reference","previous_headings":"","what":"Softsign — nnf_softsign","title":"Softsign — nnf_softsign","text":"Applies element-wise, function \\(SoftSign(x) = x/(1 + |x|\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_softsign.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Softsign — nnf_softsign","text":"","code":"nnf_softsign(input)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_softsign.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Softsign — nnf_softsign","text":"input (N,*) tensor, * means, number additional dimensions","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_tanhshrink.html","id":null,"dir":"Reference","previous_headings":"","what":"Tanhshrink — nnf_tanhshrink","title":"Tanhshrink — nnf_tanhshrink","text":"Applies element-wise, \\(Tanhshrink(x) = x - Tanh(x)\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_tanhshrink.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tanhshrink — nnf_tanhshrink","text":"","code":"nnf_tanhshrink(input)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_tanhshrink.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tanhshrink — nnf_tanhshrink","text":"input (N,*) tensor, * means, number additional dimensions","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_threshold.html","id":null,"dir":"Reference","previous_headings":"","what":"Threshold — nnf_threshold","title":"Threshold — nnf_threshold","text":"Thresholds element input Tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_threshold.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Threshold — nnf_threshold","text":"","code":"nnf_threshold(input, threshold, value, inplace = FALSE)  nnf_threshold_(input, threshold, value)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_threshold.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Threshold — nnf_threshold","text":"input (N,*) tensor, * means, number additional dimensions threshold value threshold value value replace inplace can optionally operation -place. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_triplet_margin_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Triplet_margin_loss — nnf_triplet_margin_loss","title":"Triplet_margin_loss — nnf_triplet_margin_loss","text":"Creates criterion measures triplet loss given input tensors x1 , x2 , x3 margin value greater 0 . used measuring relative similarity samples. triplet composed , p n (.e., anchor, positive examples negative examples respectively). shapes input tensors (N, D).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_triplet_margin_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Triplet_margin_loss — nnf_triplet_margin_loss","text":"","code":"nnf_triplet_margin_loss(   anchor,   positive,   negative,   margin = 1,   p = 2,   eps = 1e-06,   swap = FALSE,   reduction = \"mean\" )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_triplet_margin_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Triplet_margin_loss — nnf_triplet_margin_loss","text":"anchor anchor input tensor positive positive input tensor negative negative input tensor margin Default: 1. p norm degree pairwise distance. Default: 2. eps (float, optional) Small value avoid division zero. swap distance swap described detail paper Learning shallow convolutional feature descriptors triplet losses V. Balntas, E. Riba et al. Default: FALSE. reduction (string, optional) – Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed. Default: 'mean'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_triplet_margin_with_distance_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Triplet margin with distance loss — nnf_triplet_margin_with_distance_loss","title":"Triplet margin with distance loss — nnf_triplet_margin_with_distance_loss","text":"See nn_triplet_margin_with_distance_loss()","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_triplet_margin_with_distance_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Triplet margin with distance loss — nnf_triplet_margin_with_distance_loss","text":"","code":"nnf_triplet_margin_with_distance_loss(   anchor,   positive,   negative,   distance_function = NULL,   margin = 1,   swap = FALSE,   reduction = \"mean\" )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_triplet_margin_with_distance_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Triplet margin with distance loss — nnf_triplet_margin_with_distance_loss","text":"anchor anchor input tensor positive positive input tensor negative negative input tensor distance_function (callable, optional): nonnegative, real-valued function quantifies closeness two tensors. specified, nn_pairwise_distance() used.  Default: None margin Default: 1. swap distance swap described detail paper Learning shallow convolutional feature descriptors triplet losses V. Balntas, E. Riba et al. Default: FALSE. reduction (string, optional) – Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed. Default: 'mean'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_unfold.html","id":null,"dir":"Reference","previous_headings":"","what":"Unfold — nnf_unfold","title":"Unfold — nnf_unfold","text":"Extracts sliding local blocks batched input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_unfold.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Unfold — nnf_unfold","text":"","code":"nnf_unfold(input, kernel_size, dilation = 1, padding = 0, stride = 1)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_unfold.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Unfold — nnf_unfold","text":"input input tensor kernel_size size sliding blocks dilation parameter controls stride elements within neighborhood. Default: 1 padding implicit zero padding added sides input. Default: 0 stride stride sliding blocks input spatial dimensions. Default: 1","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_unfold.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Unfold — nnf_unfold","text":"Currently, 4-D input tensors (batched image-like tensors) supported. one element unfolded tensor may refer single memory location. result, -place operations (especially ones vectorized) may result incorrect behavior. need write tensor, please clone first.","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_adadelta.html","id":null,"dir":"Reference","previous_headings":"","what":"Adadelta optimizer — optim_adadelta","title":"Adadelta optimizer — optim_adadelta","text":"proposed ADADELTA: Adaptive Learning Rate Method","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_adadelta.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adadelta optimizer — optim_adadelta","text":"","code":"optim_adadelta(params, lr = 1, rho = 0.9, eps = 1e-06, weight_decay = 0)"},{"path":"https://torch.mlverse.org/docs/reference/optim_adadelta.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adadelta optimizer — optim_adadelta","text":"params (iterable): list parameters optimize list defining parameter groups lr (float, optional): learning rate (default: 1e-3) rho (float, optional): coefficient used computing running average squared gradients (default: 0.9) eps (float, optional): term added denominator improve numerical stability (default: 1e-6) weight_decay (float, optional): weight decay (L2 penalty) (default: 0)","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_adadelta.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Adadelta optimizer — optim_adadelta","text":"According original paper, decaying average squared gradients computed follows: $$ E[g^2]_{t} = \\rho E[g^2]_{t- 1} + (1 - \\rho){g_{t}}^2 $$ RMS previous squared gradients time t: $$ RMS[g_{t}] = \\sqrt{E[g^2]_{t} + \\epsilon } $$ Adadelta update rule: $$  \\begin{array}{ll}  \\Delta \\theta_{t} = - \\frac{RMS [\\Delta \\theta]_{t - 1} }{RMS[g]_{t}}  \\theta_{t+1} = \\theta_{t} + \\Delta \\theta_{t} \\end{array} $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_adadelta.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Adadelta optimizer — optim_adadelta","text":"need move model GPU via $cuda(), please constructing optimizers . Parameters model $cuda() different objects call. general, make sure objects pointed model parameters subject optimization remain whole lifecycle optimizer creation usage.","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_adadelta.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Adadelta optimizer — optim_adadelta","text":"","code":"if (torch_is_installed()) { if (FALSE) { optimizer <- optim_adadelta(model$parameters, lr = 0.1) optimizer$zero_grad() loss_fn(model(input), target)$backward() optimizer$step() } }"},{"path":"https://torch.mlverse.org/docs/reference/optim_adagrad.html","id":null,"dir":"Reference","previous_headings":"","what":"Adagrad optimizer — optim_adagrad","title":"Adagrad optimizer — optim_adagrad","text":"Proposed Adaptive Subgradient Methods Online Learning Stochastic Optimization","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_adagrad.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adagrad optimizer — optim_adagrad","text":"","code":"optim_adagrad(   params,   lr = 0.01,   lr_decay = 0,   weight_decay = 0,   initial_accumulator_value = 0,   eps = 1e-10 )"},{"path":"https://torch.mlverse.org/docs/reference/optim_adagrad.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adagrad optimizer — optim_adagrad","text":"params (iterable): list parameters optimize list parameter groups lr (float, optional): learning rate (default: 1e-2) lr_decay (float, optional): learning rate decay (default: 0) weight_decay (float, optional): weight decay (L2 penalty) (default: 0) initial_accumulator_value initial value accumulator. (default: 0) Adagrad especially good optimizer sparse data. individually modifies learning rate every single parameter, dividing original learning rate value sum squares gradients. causes rarely occurring features get greater learning rates. main downside method fact learning rate may getting small fast, point model learn anymore. eps (float, optional): term added denominator improve numerical stability (default: 1e-10)","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_adagrad.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Adagrad optimizer — optim_adagrad","text":"Update rule: $$ \\theta_{t+1} = \\theta_{t} - \\frac{\\eta }{\\sqrt{G_{t} + \\epsilon}} \\odot g_{t} $$ equation remarks quoted overview gradient descent optimization algorithms Sebastian Ruder.","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_adagrad.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Adagrad optimizer — optim_adagrad","text":"need move model GPU via $cuda(), please constructing optimizers . Parameters model $cuda() different objects call. general, make sure objects pointed model parameters subject optimization remain whole lifecycle optimizer creation usage.","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_adam.html","id":null,"dir":"Reference","previous_headings":"","what":"Implements Adam algorithm. — optim_adam","title":"Implements Adam algorithm. — optim_adam","text":"proposed Adam: Method Stochastic Optimization.","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_adam.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Implements Adam algorithm. — optim_adam","text":"","code":"optim_adam(   params,   lr = 0.001,   betas = c(0.9, 0.999),   eps = 1e-08,   weight_decay = 0,   amsgrad = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/optim_adam.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Implements Adam algorithm. — optim_adam","text":"params (iterable): iterable parameters optimize dicts defining parameter groups lr (float, optional): learning rate (default: 1e-3) betas (Tuple[float, float], optional): coefficients used computing running averages gradient square (default: (0.9, 0.999)) eps (float, optional): term added denominator improve numerical stability (default: 1e-8) weight_decay (float, optional): weight decay (L2 penalty) (default: 0) amsgrad (boolean, optional): whether use AMSGrad variant algorithm paper Convergence Adam Beyond (default: FALSE)","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_adam.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Implements Adam algorithm. — optim_adam","text":"need move model GPU via $cuda(), please constructing optimizers . Parameters model $cuda() different objects call. general, make sure objects pointed model parameters subject optimization remain whole lifecycle optimizer creation usage.","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_adam.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Implements Adam algorithm. — optim_adam","text":"","code":"if (torch_is_installed()) { if (FALSE) { optimizer <- optim_adam(model$parameters(), lr = 0.1) optimizer$zero_grad() loss_fn(model(input), target)$backward() optimizer$step() }  }"},{"path":"https://torch.mlverse.org/docs/reference/optim_asgd.html","id":null,"dir":"Reference","previous_headings":"","what":"Averaged Stochastic Gradient Descent optimizer — optim_asgd","title":"Averaged Stochastic Gradient Descent optimizer — optim_asgd","text":"Proposed Acceleration stochastic approximation averaging","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_asgd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Averaged Stochastic Gradient Descent optimizer — optim_asgd","text":"","code":"optim_asgd(   params,   lr = 0.01,   lambda = 1e-04,   alpha = 0.75,   t0 = 1e+06,   weight_decay = 0 )"},{"path":"https://torch.mlverse.org/docs/reference/optim_asgd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Averaged Stochastic Gradient Descent optimizer — optim_asgd","text":"params (iterable): iterable parameters optimize lists defining parameter groups lr (float): learning rate lambda (float, optional): decay term (default: 1e-4) alpha (float, optional): power eta update (default: 0.75) t0 (float, optional): point start averaging (default: 1e6) weight_decay (float, optional): weight decay (L2 penalty) (default: 0)","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_asgd.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Averaged Stochastic Gradient Descent optimizer — optim_asgd","text":"need move model GPU via $cuda(), please constructing optimizers . Parameters model $cuda() different objects call. general, make sure objects pointed model parameters subject optimization remain whole lifecycle optimizer creation usage.","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_asgd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Averaged Stochastic Gradient Descent optimizer — optim_asgd","text":"","code":"if (torch_is_installed()) { if (FALSE) { optimizer <- optim_asgd(model$parameters(), lr = 0.1) optimizer$zero_grad() loss_fn(model(input), target)$backward() optimizer$step() }  }"},{"path":"https://torch.mlverse.org/docs/reference/optim_lbfgs.html","id":null,"dir":"Reference","previous_headings":"","what":"LBFGS optimizer — optim_lbfgs","title":"LBFGS optimizer — optim_lbfgs","text":"Implements L-BFGS algorithm, heavily inspired minFunc","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_lbfgs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"LBFGS optimizer — optim_lbfgs","text":"","code":"optim_lbfgs(   params,   lr = 1,   max_iter = 20,   max_eval = NULL,   tolerance_grad = 1e-07,   tolerance_change = 1e-09,   history_size = 100,   line_search_fn = NULL )"},{"path":"https://torch.mlverse.org/docs/reference/optim_lbfgs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"LBFGS optimizer — optim_lbfgs","text":"params (iterable): iterable parameters optimize dicts defining parameter groups lr (float): learning rate (default: 1) max_iter (int): maximal number iterations per optimization step (default: 20) max_eval (int): maximal number function evaluations per optimization step (default: max_iter * 1.25). tolerance_grad (float): termination tolerance first order optimality (default: 1e-5). tolerance_change (float): termination tolerance function value/parameter changes (default: 1e-9). history_size (int): update history size (default: 100). line_search_fn (str): either 'strong_wolfe' None (default: None).","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_lbfgs.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"LBFGS optimizer — optim_lbfgs","text":"memory intensive optimizer (requires additional param_bytes * (history_size + 1) bytes). fit memory try reducing history size, use different algorithm.","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_lbfgs.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"LBFGS optimizer — optim_lbfgs","text":"optimizer support per-parameter options parameter groups (can one). Right now parameters single device. improved future. need move model GPU via $cuda(), please constructing optimizers . Parameters model $cuda() different objects call. general, make sure objects pointed model parameters subject optimization remain whole lifecycle optimizer creation usage.","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_required.html","id":null,"dir":"Reference","previous_headings":"","what":"Dummy value indicating a required value. — optim_required","title":"Dummy value indicating a required value. — optim_required","text":"export","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_required.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Dummy value indicating a required value. — optim_required","text":"","code":"optim_required()"},{"path":"https://torch.mlverse.org/docs/reference/optim_rmsprop.html","id":null,"dir":"Reference","previous_headings":"","what":"RMSprop optimizer — optim_rmsprop","title":"RMSprop optimizer — optim_rmsprop","text":"Proposed G. Hinton course.","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_rmsprop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"RMSprop optimizer — optim_rmsprop","text":"","code":"optim_rmsprop(   params,   lr = 0.01,   alpha = 0.99,   eps = 1e-08,   weight_decay = 0,   momentum = 0,   centered = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/optim_rmsprop.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"RMSprop optimizer — optim_rmsprop","text":"params (iterable): iterable parameters optimize list defining parameter groups lr (float, optional): learning rate (default: 1e-2) alpha (float, optional): smoothing constant (default: 0.99) eps (float, optional): term added denominator improve numerical stability (default: 1e-8) weight_decay optional weight decay penalty. (default: 0) momentum (float, optional): momentum factor (default: 0) centered (bool, optional) : TRUE, compute centered RMSProp, gradient normalized estimation variance weight_decay (float, optional): weight decay (L2 penalty) (default: 0)","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_rmsprop.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"RMSprop optimizer — optim_rmsprop","text":"centered version first appears Generating Sequences Recurrent Neural Networks. implementation takes square root gradient average adding epsilon (note TensorFlow interchanges two operations). effective learning rate thus \\(\\alpha/(\\sqrt{v} + \\epsilon)\\) \\(\\alpha\\) scheduled learning rate \\(v\\) weighted moving average squared gradient. Update rule: $$ \\theta_{t+1} = \\theta_{t} - \\frac{\\eta }{\\sqrt{{E[g^2]}_{t} + \\epsilon}} * g_{t} $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_rmsprop.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"RMSprop optimizer — optim_rmsprop","text":"need move model GPU via $cuda(), please constructing optimizers . Parameters model $cuda() different objects call. general, make sure objects pointed model parameters subject optimization remain whole lifecycle optimizer creation usage.","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_rprop.html","id":null,"dir":"Reference","previous_headings":"","what":"Implements the resilient backpropagation algorithm. — optim_rprop","title":"Implements the resilient backpropagation algorithm. — optim_rprop","text":"Proposed first RPROP - Fast Adaptive Learning Algorithm","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_rprop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Implements the resilient backpropagation algorithm. — optim_rprop","text":"","code":"optim_rprop(params, lr = 0.01, etas = c(0.5, 1.2), step_sizes = c(1e-06, 50))"},{"path":"https://torch.mlverse.org/docs/reference/optim_rprop.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Implements the resilient backpropagation algorithm. — optim_rprop","text":"params (iterable): iterable parameters optimize lists defining parameter groups lr (float, optional): learning rate (default: 1e-2) etas (Tuple(float, float), optional): pair (etaminus, etaplis), multiplicative increase decrease factors (default: (0.5, 1.2)) step_sizes (vector(float, float), optional): pair minimal maximal allowed step sizes (default: (1e-6, 50))","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_rprop.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Implements the resilient backpropagation algorithm. — optim_rprop","text":"need move model GPU via $cuda(), please constructing optimizers . Parameters model $cuda() different objects call. general, make sure objects pointed model parameters subject optimization remain whole lifecycle optimizer creation usage.","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_rprop.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Implements the resilient backpropagation algorithm. — optim_rprop","text":"","code":"if (torch_is_installed()) { if (FALSE) { optimizer <- optim_rprop(model$parameters(), lr = 0.1) optimizer$zero_grad() loss_fn(model(input), target)$backward() optimizer$step() } }"},{"path":"https://torch.mlverse.org/docs/reference/optim_sgd.html","id":null,"dir":"Reference","previous_headings":"","what":"SGD optimizer — optim_sgd","title":"SGD optimizer — optim_sgd","text":"Implements stochastic gradient descent (optionally momentum). Nesterov momentum based formula importance initialization momentum deep learning.","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_sgd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"SGD optimizer — optim_sgd","text":"","code":"optim_sgd(   params,   lr = optim_required(),   momentum = 0,   dampening = 0,   weight_decay = 0,   nesterov = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/optim_sgd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"SGD optimizer — optim_sgd","text":"params (iterable): iterable parameters optimize dicts defining parameter groups lr (float): learning rate momentum (float, optional): momentum factor (default: 0) dampening (float, optional): dampening momentum (default: 0) weight_decay (float, optional): weight decay (L2 penalty) (default: 0) nesterov (bool, optional): enables Nesterov momentum (default: FALSE)","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_sgd.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"SGD optimizer — optim_sgd","text":"implementation SGD Momentum-Nesterov subtly differs Sutskever et. al. implementations frameworks. Considering specific case Momentum, update can written $$   \\begin{array}{ll} v_{t+1} & = \\mu * v_{t} + g_{t+1}, \\\\ p_{t+1} & = p_{t} - \\mbox{lr} * v_{t+1}, \\end{array} $$ \\(p\\), \\(g\\), \\(v\\) \\(\\mu\\) denote parameters, gradient, velocity, momentum respectively. contrast Sutskever et. al. frameworks employ update form $$   \\begin{array}{ll} v_{t+1} & = \\mu * v_{t} + \\mbox{lr} * g_{t+1}, \\\\ p_{t+1} & = p_{t} - v_{t+1}. \\end{array} $$ Nesterov version analogously modified.","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_sgd.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"SGD optimizer — optim_sgd","text":"need move model GPU via $cuda(), please constructing optimizers . Parameters model $cuda() different objects call. general, make sure objects pointed model parameters subject optimization remain whole lifecycle optimizer creation usage.","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_sgd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"SGD optimizer — optim_sgd","text":"","code":"if (torch_is_installed()) { if (FALSE) { optimizer <- optim_sgd(model$parameters(), lr = 0.1, momentum = 0.9) optimizer$zero_grad() loss_fn(model(input), target)$backward() optimizer$step() }  }"},{"path":"https://torch.mlverse.org/docs/reference/optimizer.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates a custom optimizer — optimizer","title":"Creates a custom optimizer — optimizer","text":"implementing custom optimizers usually need implement initialize step methods. See example section full example.","code":""},{"path":"https://torch.mlverse.org/docs/reference/optimizer.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates a custom optimizer — optimizer","text":"","code":"optimizer(   name = NULL,   inherit = Optimizer,   ...,   private = NULL,   active = NULL,   parent_env = parent.frame() )"},{"path":"https://torch.mlverse.org/docs/reference/optimizer.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates a custom optimizer — optimizer","text":"name (optional) name optimizer inherit (optional) can inherit optimizers re-use methods. ... Pass number fields methods. least define initialize step methods. See examples section. private (optional) list private methods optimizer. active (optional) list active methods optimizer. parent_env used capture right environment define class. default fine situations.","code":""},{"path":"https://torch.mlverse.org/docs/reference/optimizer.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Creates a custom optimizer — optimizer","text":"need move model GPU via $cuda(), please constructing optimizers . Parameters model $cuda() different objects call. general, make sure objects pointed model parameters subject optimization remain whole lifecycle optimizer creation usage.","code":""},{"path":"https://torch.mlverse.org/docs/reference/optimizer.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Creates a custom optimizer — optimizer","text":"","code":"if (torch_is_installed()) {  # In this example we will create a custom optimizer # that's just a simplified version of the `optim_sgd` function.  optim_sgd2 <- optimizer(   initialize = function(params, learning_rate) {     defaults <- list(       learning_rate = learning_rate     )     super$initialize(params, defaults)   },   step = function() {     with_no_grad({       for (g in seq_along(self$param_groups)) {         group <- self$param_groups[[g]]         for (p in seq_along(group$params)) {           param <- group$params[[p]]            if (is.null(param$grad) || is_undefined_tensor(param$grad)) {             next           }            param$add_(param$grad, alpha = -group$learning_rate)         }       }     })   } )  x <- torch_randn(1, requires_grad = TRUE) opt <- optim_sgd2(x, learning_rate = 0.1) for (i in 1:100) {   opt$zero_grad()   y <- x^2   y$backward()   opt$step() } all.equal(x$item(), 0, tolerance = 1e-9) } #> [1] TRUE"},{"path":"https://torch.mlverse.org/docs/reference/pipe.html","id":null,"dir":"Reference","previous_headings":"","what":"Pipe operator — %>%","title":"Pipe operator — %>%","text":"See magrittr::%>% details.","code":""},{"path":"https://torch.mlverse.org/docs/reference/pipe.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pipe operator — %>%","text":"","code":"lhs %>% rhs"},{"path":"https://torch.mlverse.org/docs/reference/reexports.html","id":null,"dir":"Reference","previous_headings":"","what":"Re-exporting the as_iterator function. — reexports","title":"Re-exporting the as_iterator function. — reexports","text":"objects imported packages. Follow links see documentation. coro as_iterator, loop, yield","code":""},{"path":"https://torch.mlverse.org/docs/reference/slc.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates a slice — slc","title":"Creates a slice — slc","text":"Creates slice object can used indexing torch tensors.","code":""},{"path":"https://torch.mlverse.org/docs/reference/slc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates a slice — slc","text":"","code":"slc(start, end, step = 1)"},{"path":"https://torch.mlverse.org/docs/reference/slc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates a slice — slc","text":"start (integer) starting index. end (integer) last selected index. step (integer) step indexes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/slc.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Creates a slice — slc","text":"","code":"if (torch_is_installed()) { x <- torch_randn(10) x[slc(start = 1, end = 5, step = 2)] } #> torch_tensor #>  0.8545 #>  0.1855 #>  0.6493 #> [ CPUFloatType{3} ]"},{"path":"https://torch.mlverse.org/docs/reference/tensor_dataset.html","id":null,"dir":"Reference","previous_headings":"","what":"Dataset wrapping tensors. — tensor_dataset","title":"Dataset wrapping tensors. — tensor_dataset","text":"sample retrieved indexing tensors along first dimension.","code":""},{"path":"https://torch.mlverse.org/docs/reference/tensor_dataset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Dataset wrapping tensors. — tensor_dataset","text":"","code":"tensor_dataset(...)"},{"path":"https://torch.mlverse.org/docs/reference/tensor_dataset.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Dataset wrapping tensors. — tensor_dataset","text":"... tensors size first dimension.","code":""},{"path":"https://torch.mlverse.org/docs/reference/threads.html","id":null,"dir":"Reference","previous_headings":"","what":"Number of threads — threads","title":"Number of threads — threads","text":"Get set numbers used torch computations.","code":""},{"path":"https://torch.mlverse.org/docs/reference/threads.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Number of threads — threads","text":"","code":"torch_set_num_threads(num_threads)  torch_set_num_interop_threads(num_threads)  torch_get_num_interop_threads()  torch_get_num_threads()"},{"path":"https://torch.mlverse.org/docs/reference/threads.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Number of threads — threads","text":"num_threads number threads set.","code":""},{"path":"https://torch.mlverse.org/docs/reference/threads.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Number of threads — threads","text":"details see CPU threading article PyTorch documentation.","code":""},{"path":"https://torch.mlverse.org/docs/reference/threads.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Number of threads — threads","text":"torch_set_threads work macOS system must 1.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_abs.html","id":null,"dir":"Reference","previous_headings":"","what":"Abs — torch_abs","title":"Abs — torch_abs","text":"Abs","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_abs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Abs — torch_abs","text":"","code":"torch_abs(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_abs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Abs — torch_abs","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_abs.html","id":"abs-input-gt-tensor-","dir":"Reference","previous_headings":"","what":"abs(input) -> Tensor","title":"Abs — torch_abs","text":"Computes element-wise absolute value given input tensor. $$     \\mbox{}_{} = |\\mbox{input}_{}| $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_abs.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Abs — torch_abs","text":"","code":"if (torch_is_installed()) {  torch_abs(torch_tensor(c(-1, -2, 3))) } #> torch_tensor #>  1 #>  2 #>  3 #> [ CPUFloatType{3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_absolute.html","id":null,"dir":"Reference","previous_headings":"","what":"Absolute — torch_absolute","title":"Absolute — torch_absolute","text":"Absolute","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_absolute.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Absolute — torch_absolute","text":"","code":"torch_absolute(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_absolute.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Absolute — torch_absolute","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_absolute.html","id":"absolute-input-out-none-gt-tensor","dir":"Reference","previous_headings":"","what":"absolute(input, *, out=None) -> Tensor","title":"Absolute — torch_absolute","text":"Alias torch_abs()","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_acos.html","id":null,"dir":"Reference","previous_headings":"","what":"Acos — torch_acos","title":"Acos — torch_acos","text":"Acos","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_acos.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Acos — torch_acos","text":"","code":"torch_acos(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_acos.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Acos — torch_acos","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_acos.html","id":"acos-input-gt-tensor-","dir":"Reference","previous_headings":"","what":"acos(input) -> Tensor","title":"Acos — torch_acos","text":"Returns new tensor arccosine  elements input. $$     \\mbox{}_{} = \\cos^{-1}(\\mbox{input}_{}) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_acos.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Acos — torch_acos","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(4)) a torch_acos(a) } #> torch_tensor #>  1.5933 #>  1.7693 #>  1.5475 #>  1.6346 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_acosh.html","id":null,"dir":"Reference","previous_headings":"","what":"Acosh — torch_acosh","title":"Acosh — torch_acosh","text":"Acosh","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_acosh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Acosh — torch_acosh","text":"","code":"torch_acosh(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_acosh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Acosh — torch_acosh","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_acosh.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Acosh — torch_acosh","text":"domain inverse hyperbolic cosine [1, inf) values outside range mapped NaN, except + INF output mapped + INF. $$     \\mbox{}_{} = \\cosh^{-1}(\\mbox{input}_{}) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_acosh.html","id":"acosh-input-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"acosh(input, *, out=None) -> Tensor","title":"Acosh — torch_acosh","text":"Returns new tensor inverse hyperbolic cosine elements input.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_acosh.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Acosh — torch_acosh","text":"","code":"if (torch_is_installed()) {  a <- torch_randn(c(4))$uniform_(1, 2) a torch_acosh(a) } #> torch_tensor #>  1.0354 #>  1.3027 #>  1.2424 #>  0.9036 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_adaptive_avg_pool1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Adaptive_avg_pool1d — torch_adaptive_avg_pool1d","title":"Adaptive_avg_pool1d — torch_adaptive_avg_pool1d","text":"Adaptive_avg_pool1d","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_adaptive_avg_pool1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adaptive_avg_pool1d — torch_adaptive_avg_pool1d","text":"","code":"torch_adaptive_avg_pool1d(self, output_size)"},{"path":"https://torch.mlverse.org/docs/reference/torch_adaptive_avg_pool1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adaptive_avg_pool1d — torch_adaptive_avg_pool1d","text":"self input tensor output_size target output size (single integer)","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_adaptive_avg_pool1d.html","id":"adaptive-avg-pool-d-input-output-size-gt-tensor-","dir":"Reference","previous_headings":"","what":"adaptive_avg_pool1d(input, output_size) -> Tensor","title":"Adaptive_avg_pool1d — torch_adaptive_avg_pool1d","text":"Applies 1D adaptive average pooling input signal composed several input planes. See nn_adaptive_avg_pool1d() details output shape.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_add.html","id":null,"dir":"Reference","previous_headings":"","what":"Add — torch_add","title":"Add — torch_add","text":"Add","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_add.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add — torch_add","text":"","code":"torch_add(self, other, alpha = 1L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_add.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add — torch_add","text":"self (Tensor) input tensor. (Tensor/Number) second input tensor/number. alpha (Number) scalar multiplier ","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_add.html","id":"add-input-other-out-null-","dir":"Reference","previous_headings":"","what":"add(input, other, out=NULL)","title":"Add — torch_add","text":"Adds scalar element input input returns new resulting tensor. $$     \\mbox{} = \\mbox{input} + \\mbox{} $$ input type FloatTensor DoubleTensor, must real number, otherwise integer.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_add.html","id":"add-input-other-alpha-out-null-","dir":"Reference","previous_headings":"","what":"add(input, other, *, alpha=1, out=NULL)","title":"Add — torch_add","text":"element tensor multiplied scalar alpha added element tensor input. resulting tensor returned. shapes input must broadcastable . $$     \\mbox{} = \\mbox{input} + \\mbox{alpha} \\times \\mbox{} $$ type FloatTensor DoubleTensor, alpha must real number, otherwise integer.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_add.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add — torch_add","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(4)) a torch_add(a, 20)   a = torch_randn(c(4)) a b = torch_randn(c(4, 1)) b torch_add(a, b) } #> torch_tensor #> -0.6325  0.4658 -1.3500  0.4279 #> -0.1713  0.9270 -0.8887  0.8892 #> -0.6622  0.4360 -1.3797  0.3982 #> -0.0610  1.0372 -0.7785  0.9994 #> [ CPUFloatType{4,4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_addbmm.html","id":null,"dir":"Reference","previous_headings":"","what":"Addbmm — torch_addbmm","title":"Addbmm — torch_addbmm","text":"Addbmm","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_addbmm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Addbmm — torch_addbmm","text":"","code":"torch_addbmm(self, batch1, batch2, beta = 1L, alpha = 1L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_addbmm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Addbmm — torch_addbmm","text":"self (Tensor) matrix added batch1 (Tensor) first batch matrices multiplied batch2 (Tensor) second batch matrices multiplied beta (Number, optional) multiplier input (\\(\\beta\\)) alpha (Number, optional) multiplier batch1 @ batch2 (\\(\\alpha\\))","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_addbmm.html","id":"addbmm-input-batch-batch-beta-alpha-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"addbmm(input, batch1, batch2, *, beta=1, alpha=1, out=NULL) -> Tensor","title":"Addbmm — torch_addbmm","text":"Performs batch matrix-matrix product matrices stored batch1 batch2, reduced add step (matrix multiplications get accumulated along first dimension). input added final result. batch1 batch2 must 3-D tensors containing number matrices. batch1 \\((b \\times n \\times m)\\) tensor, batch2 \\((b \\times m \\times p)\\) tensor, input must broadcastable  \\((n \\times p)\\) tensor \\((n \\times p)\\) tensor. $$     = \\beta\\ \\mbox{input} + \\alpha\\ (\\sum_{=0}^{b-1} \\mbox{batch1}_i \\mathbin{@} \\mbox{batch2}_i) $$ inputs type FloatTensor DoubleTensor, arguments beta alpha must real numbers, otherwise integers.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_addbmm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Addbmm — torch_addbmm","text":"","code":"if (torch_is_installed()) {  M = torch_randn(c(3, 5)) batch1 = torch_randn(c(10, 3, 4)) batch2 = torch_randn(c(10, 4, 5)) torch_addbmm(M, batch1, batch2) } #> torch_tensor #>  12.0062  -8.3688   5.4071  -0.5481   5.2430 #>   1.1898   4.0082  -0.8169   2.2780   4.0116 #>   5.7964 -10.4914  -1.0635   3.0282  -9.6365 #> [ CPUFloatType{3,5} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_addcdiv.html","id":null,"dir":"Reference","previous_headings":"","what":"Addcdiv — torch_addcdiv","title":"Addcdiv — torch_addcdiv","text":"Addcdiv","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_addcdiv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Addcdiv — torch_addcdiv","text":"","code":"torch_addcdiv(self, tensor1, tensor2, value = 1L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_addcdiv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Addcdiv — torch_addcdiv","text":"self (Tensor) tensor added tensor1 (Tensor) numerator tensor tensor2 (Tensor) denominator tensor value (Number, optional) multiplier \\(\\mbox{tensor1} / \\mbox{tensor2}\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_addcdiv.html","id":"addcdiv-input-tensor-tensor-value-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"addcdiv(input, tensor1, tensor2, *, value=1, out=NULL) -> Tensor","title":"Addcdiv — torch_addcdiv","text":"Performs element-wise division tensor1 tensor2, multiply result scalar value add input.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_addcdiv.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Addcdiv — torch_addcdiv","text":"Integer division addcdiv deprecated, future release addcdiv perform true division tensor1 tensor2. current addcdiv behavior can replicated using torch_floor_divide() integral inputs (input + value * tensor1 // tensor2) torch_div() float inputs (input + value * tensor1 / tensor2). new addcdiv behavior can implemented torch_true_divide() (input + value * torch.true_divide(tensor1, tensor2). $$     \\mbox{}_i = \\mbox{input}_i + \\mbox{value} \\times \\frac{\\mbox{tensor1}_i}{\\mbox{tensor2}_i} $$ shapes input, tensor1, tensor2 must broadcastable . inputs type FloatTensor DoubleTensor, value must real number, otherwise integer.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_addcdiv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Addcdiv — torch_addcdiv","text":"","code":"if (torch_is_installed()) {  t = torch_randn(c(1, 3)) t1 = torch_randn(c(3, 1)) t2 = torch_randn(c(1, 3)) torch_addcdiv(t, t1, t2, 0.1) } #> torch_tensor #>  -1.5574   0.1888  -0.2277 #>  11.4811  -0.1016  -0.1035 #>  -3.1919   0.2252  -0.2433 #> [ CPUFloatType{3,3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_addcmul.html","id":null,"dir":"Reference","previous_headings":"","what":"Addcmul — torch_addcmul","title":"Addcmul — torch_addcmul","text":"Addcmul","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_addcmul.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Addcmul — torch_addcmul","text":"","code":"torch_addcmul(self, tensor1, tensor2, value = 1L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_addcmul.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Addcmul — torch_addcmul","text":"self (Tensor) tensor added tensor1 (Tensor) tensor multiplied tensor2 (Tensor) tensor multiplied value (Number, optional) multiplier \\(tensor1 .* tensor2\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_addcmul.html","id":"addcmul-input-tensor-tensor-value-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"addcmul(input, tensor1, tensor2, *, value=1, out=NULL) -> Tensor","title":"Addcmul — torch_addcmul","text":"Performs element-wise multiplication tensor1 tensor2, multiply result scalar value add input. $$     \\mbox{}_i = \\mbox{input}_i + \\mbox{value} \\times \\mbox{tensor1}_i \\times \\mbox{tensor2}_i $$ shapes tensor, tensor1, tensor2 must broadcastable . inputs type FloatTensor DoubleTensor, value must real number, otherwise integer.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_addcmul.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Addcmul — torch_addcmul","text":"","code":"if (torch_is_installed()) {  t = torch_randn(c(1, 3)) t1 = torch_randn(c(3, 1)) t2 = torch_randn(c(1, 3)) torch_addcmul(t, t1, t2, 0.1) } #> torch_tensor #>  1.1672  0.4947  2.2721 #>  0.9772  0.4381  2.1526 #>  1.0474  0.4590  2.1968 #> [ CPUFloatType{3,3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_addmm.html","id":null,"dir":"Reference","previous_headings":"","what":"Addmm — torch_addmm","title":"Addmm — torch_addmm","text":"Addmm","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_addmm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Addmm — torch_addmm","text":"","code":"torch_addmm(self, mat1, mat2, beta = 1L, alpha = 1L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_addmm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Addmm — torch_addmm","text":"self (Tensor) matrix added mat1 (Tensor) first matrix multiplied mat2 (Tensor) second matrix multiplied beta (Number, optional) multiplier input (\\(\\beta\\)) alpha (Number, optional) multiplier \\(mat1 @ mat2\\) (\\(\\alpha\\))","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_addmm.html","id":"addmm-input-mat-mat-beta-alpha-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"addmm(input, mat1, mat2, *, beta=1, alpha=1, out=NULL) -> Tensor","title":"Addmm — torch_addmm","text":"Performs matrix multiplication matrices mat1 mat2. matrix input added final result. mat1 \\((n \\times m)\\) tensor, mat2 \\((m \\times p)\\) tensor, input must broadcastable  \\((n \\times p)\\) tensor \\((n \\times p)\\) tensor. alpha beta scaling factors matrix-vector product mat1 mat2 added matrix input respectively. $$     \\mbox{} = \\beta\\ \\mbox{input} + \\alpha\\ (\\mbox{mat1}_i \\mathbin{@} \\mbox{mat2}_i) $$ inputs type FloatTensor DoubleTensor, arguments beta alpha must real numbers, otherwise integers.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_addmm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Addmm — torch_addmm","text":"","code":"if (torch_is_installed()) {  M = torch_randn(c(2, 3)) mat1 = torch_randn(c(2, 3)) mat2 = torch_randn(c(3, 3)) torch_addmm(M, mat1, mat2) } #> torch_tensor #> -2.4386 -1.5421  1.4678 #>  2.5538  1.2239  0.7606 #> [ CPUFloatType{2,3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_addmv.html","id":null,"dir":"Reference","previous_headings":"","what":"Addmv — torch_addmv","title":"Addmv — torch_addmv","text":"Addmv","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_addmv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Addmv — torch_addmv","text":"","code":"torch_addmv(self, mat, vec, beta = 1L, alpha = 1L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_addmv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Addmv — torch_addmv","text":"self (Tensor) vector added mat (Tensor) matrix multiplied vec (Tensor) vector multiplied beta (Number, optional) multiplier input (\\(\\beta\\)) alpha (Number, optional) multiplier \\(mat @ vec\\) (\\(\\alpha\\))","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_addmv.html","id":"addmv-input-mat-vec-beta-alpha-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"addmv(input, mat, vec, *, beta=1, alpha=1, out=NULL) -> Tensor","title":"Addmv — torch_addmv","text":"Performs matrix-vector product matrix mat vector vec. vector input added final result. mat \\((n \\times m)\\) tensor, vec 1-D tensor size m, input must broadcastable  1-D tensor size n 1-D tensor size n. alpha beta scaling factors matrix-vector product mat vec added tensor input respectively. $$     \\mbox{} = \\beta\\ \\mbox{input} + \\alpha\\ (\\mbox{mat} \\mathbin{@} \\mbox{vec}) $$ inputs type FloatTensor DoubleTensor, arguments beta alpha must real numbers, otherwise integers","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_addmv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Addmv — torch_addmv","text":"","code":"if (torch_is_installed()) {  M = torch_randn(c(2)) mat = torch_randn(c(2, 3)) vec = torch_randn(c(3)) torch_addmv(M, mat, vec) } #> torch_tensor #> -4.2610 #> -3.2303 #> [ CPUFloatType{2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_addr.html","id":null,"dir":"Reference","previous_headings":"","what":"Addr — torch_addr","title":"Addr — torch_addr","text":"Addr","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_addr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Addr — torch_addr","text":"","code":"torch_addr(self, vec1, vec2, beta = 1L, alpha = 1L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_addr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Addr — torch_addr","text":"self (Tensor) matrix added vec1 (Tensor) first vector outer product vec2 (Tensor) second vector outer product beta (Number, optional) multiplier input (\\(\\beta\\)) alpha (Number, optional) multiplier \\(\\mbox{vec1} \\otimes \\mbox{vec2}\\) (\\(\\alpha\\))","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_addr.html","id":"addr-input-vec-vec-beta-alpha-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"addr(input, vec1, vec2, *, beta=1, alpha=1, out=NULL) -> Tensor","title":"Addr — torch_addr","text":"Performs outer-product vectors vec1 vec2 adds matrix input. Optional values beta alpha scaling factors outer product vec1 vec2 added matrix input respectively. $$     \\mbox{} = \\beta\\ \\mbox{input} + \\alpha\\ (\\mbox{vec1} \\otimes \\mbox{vec2}) $$ vec1 vector size n vec2 vector size m, input must broadcastable  matrix size \\((n \\times m)\\) matrix size \\((n \\times m)\\). inputs type FloatTensor DoubleTensor, arguments beta alpha must real numbers, otherwise integers","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_addr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Addr — torch_addr","text":"","code":"if (torch_is_installed()) {  vec1 = torch_arange(1, 3) vec2 = torch_arange(1, 2) M = torch_zeros(c(3, 2)) torch_addr(M, vec1, vec2) } #> torch_tensor #>  1  2 #>  2  4 #>  3  6 #> [ CPUFloatType{3,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_allclose.html","id":null,"dir":"Reference","previous_headings":"","what":"Allclose — torch_allclose","title":"Allclose — torch_allclose","text":"Allclose","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_allclose.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Allclose — torch_allclose","text":"","code":"torch_allclose(self, other, rtol = 1e-05, atol = 1e-08, equal_nan = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_allclose.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Allclose — torch_allclose","text":"self (Tensor) first tensor compare (Tensor) second tensor compare rtol (float, optional) relative tolerance. Default: 1e-05 atol (float, optional) absolute tolerance. Default: 1e-08 equal_nan (bool, optional) TRUE, two NaN s compared equal. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_allclose.html","id":"allclose-input-other-rtol-e-atol-e-equal-nan-false-gt-bool-","dir":"Reference","previous_headings":"","what":"allclose(input, other, rtol=1e-05, atol=1e-08, equal_nan=False) -> bool","title":"Allclose — torch_allclose","text":"function checks input satisfy condition: $$     \\vert \\mbox{input} - \\mbox{} \\vert \\leq \\mbox{atol} + \\mbox{rtol} \\times \\vert \\mbox{} \\vert $$ elementwise, elements input . behaviour function analogous numpy.allclose <https://docs.scipy.org/doc/numpy/reference/generated/numpy.allclose.html>_","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_allclose.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Allclose — torch_allclose","text":"","code":"if (torch_is_installed()) {  torch_allclose(torch_tensor(c(10000., 1e-07)), torch_tensor(c(10000.1, 1e-08))) torch_allclose(torch_tensor(c(10000., 1e-08)), torch_tensor(c(10000.1, 1e-09))) torch_allclose(torch_tensor(c(1.0, NaN)), torch_tensor(c(1.0, NaN))) torch_allclose(torch_tensor(c(1.0, NaN)), torch_tensor(c(1.0, NaN)), equal_nan=TRUE) } #> [1] TRUE"},{"path":"https://torch.mlverse.org/docs/reference/torch_amax.html","id":null,"dir":"Reference","previous_headings":"","what":"Amax — torch_amax","title":"Amax — torch_amax","text":"Amax","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_amax.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Amax — torch_amax","text":"","code":"torch_amax(self, dim = list(), keepdim = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_amax.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Amax — torch_amax","text":"self (Tensor) input tensor. dim (int tuple ints) dimension dimensions reduce. keepdim (bool) whether output tensor dim retained .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_amax.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Amax — torch_amax","text":"difference max/min amax/amin : amax/amin supports reducing multiple dimensions, amax/amin return indices, amax/amin evenly distributes gradient equal values, max(dim)/min(dim) propagates gradient single index source tensor. keepdim TRUE, output tensors size inputexcept dimension(s)dimwhere size 1. Otherwise,dims squeezed (see [torch_squeeze()]), resulting output tensors fewer dimension input`.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_amax.html","id":"amax-input-dim-keepdim-false-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"amax(input, dim, keepdim=FALSE, *, out=None) -> Tensor","title":"Amax — torch_amax","text":"Returns maximum value slice input tensor given dimension(s) dim.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_amax.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Amax — torch_amax","text":"","code":"if (torch_is_installed()) {  a <- torch_randn(c(4, 4)) a torch_amax(a, 1) } #> torch_tensor #>  1.3746 #>  1.4029 #>  0.7899 #>  1.4408 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_amin.html","id":null,"dir":"Reference","previous_headings":"","what":"Amin — torch_amin","title":"Amin — torch_amin","text":"Amin","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_amin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Amin — torch_amin","text":"","code":"torch_amin(self, dim = list(), keepdim = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_amin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Amin — torch_amin","text":"self (Tensor) input tensor. dim (int tuple ints) dimension dimensions reduce. keepdim (bool) whether output tensor dim retained .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_amin.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Amin — torch_amin","text":"difference max/min amax/amin : amax/amin supports reducing multiple dimensions, amax/amin return indices, amax/amin evenly distributes gradient equal values, max(dim)/min(dim) propagates gradient single index source tensor. keepdim TRUE, output tensors size input except dimension(s) dim size 1. Otherwise, dims squeezed (see torch_squeeze()), resulting output tensors fewer dimensions input.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_amin.html","id":"amin-input-dim-keepdim-false-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"amin(input, dim, keepdim=FALSE, *, out=None) -> Tensor","title":"Amin — torch_amin","text":"Returns minimum value slice input tensor given dimension(s) dim.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_amin.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Amin — torch_amin","text":"","code":"if (torch_is_installed()) {  a <- torch_randn(c(4, 4)) a torch_amin(a, 1) } #> torch_tensor #> -0.9522 #> -1.7592 #> -0.1854 #> -2.5131 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_angle.html","id":null,"dir":"Reference","previous_headings":"","what":"Angle — torch_angle","title":"Angle — torch_angle","text":"Angle","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_angle.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Angle — torch_angle","text":"","code":"torch_angle(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_angle.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Angle — torch_angle","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_angle.html","id":"angle-input-gt-tensor-","dir":"Reference","previous_headings":"","what":"angle(input) -> Tensor","title":"Angle — torch_angle","text":"Computes element-wise angle (radians) given input tensor. $$     \\mbox{}_{} = angle(\\mbox{input}_{}) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_angle.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Angle — torch_angle","text":"","code":"if (torch_is_installed()) { if (FALSE) { torch_angle(torch_tensor(c(-1 + 1i, -2 + 2i, 3 - 3i)))*180/3.14159 }  }"},{"path":"https://torch.mlverse.org/docs/reference/torch_arange.html","id":null,"dir":"Reference","previous_headings":"","what":"Arange — torch_arange","title":"Arange — torch_arange","text":"Arange","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_arange.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Arange — torch_arange","text":"","code":"torch_arange(   start,   end,   step = 1,   dtype = NULL,   layout = torch_strided(),   device = NULL,   requires_grad = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/torch_arange.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Arange — torch_arange","text":"start (Number) starting value set points. Default: 0. end (Number) ending value set points step (Number) gap pair adjacent points. Default: 1. dtype (torch.dtype, optional) desired data type returned tensor.        Default: NULL, uses global default (see torch_set_default_tensor_type). dtype given, infer data type input        arguments. start, end, stop floating-point,        dtype inferred default dtype, see        ~torch.get_default_dtype. Otherwise, dtype inferred        torch.int64. layout (torch.layout, optional) desired layout returned Tensor.        Default: torch_strided. device (torch.device, optional) desired device returned tensor.        Default: NULL, uses current device default tensor type        (see torch_set_default_tensor_type). device CPU        CPU tensor types current CUDA device CUDA tensor types. requires_grad (bool, optional) autograd record operations        returned tensor. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_arange.html","id":"arange-start-end-step-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-","dir":"Reference","previous_headings":"","what":"arange(start=0, end, step=1, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor","title":"Arange — torch_arange","text":"Returns 1-D tensor size \\(\\left\\lceil \\frac{\\mbox{end} - \\mbox{start}}{\\mbox{step}} \\right\\rceil\\) values interval [start, end) taken common difference step beginning start. Note non-integer step subject floating point rounding errors comparing end; avoid inconsistency, advise adding small epsilon end cases. $$     \\mbox{}_{{+1}} = \\mbox{}_{} + \\mbox{step} $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_arange.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Arange — torch_arange","text":"","code":"if (torch_is_installed()) {  torch_arange(start = 0, end = 5) torch_arange(1, 4) torch_arange(1, 2.5, 0.5) } #> torch_tensor #>  1.0000 #>  1.5000 #>  2.0000 #>  2.5000 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_arccos.html","id":null,"dir":"Reference","previous_headings":"","what":"Arccos — torch_arccos","title":"Arccos — torch_arccos","text":"Arccos","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_arccos.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Arccos — torch_arccos","text":"","code":"torch_arccos(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_arccos.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Arccos — torch_arccos","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_arccos.html","id":"arccos-input-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"arccos(input, *, out=None) -> Tensor","title":"Arccos — torch_arccos","text":"Alias torch_acos().","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_arccosh.html","id":null,"dir":"Reference","previous_headings":"","what":"Arccosh — torch_arccosh","title":"Arccosh — torch_arccosh","text":"Arccosh","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_arccosh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Arccosh — torch_arccosh","text":"","code":"torch_arccosh(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_arccosh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Arccosh — torch_arccosh","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_arccosh.html","id":"arccosh-input-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"arccosh(input, *, out=None) -> Tensor","title":"Arccosh — torch_arccosh","text":"Alias torch_acosh().","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_arcsin.html","id":null,"dir":"Reference","previous_headings":"","what":"Arcsin — torch_arcsin","title":"Arcsin — torch_arcsin","text":"Arcsin","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_arcsin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Arcsin — torch_arcsin","text":"","code":"torch_arcsin(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_arcsin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Arcsin — torch_arcsin","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_arcsin.html","id":"arcsin-input-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"arcsin(input, *, out=None) -> Tensor","title":"Arcsin — torch_arcsin","text":"Alias torch_asin().","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_arcsinh.html","id":null,"dir":"Reference","previous_headings":"","what":"Arcsinh — torch_arcsinh","title":"Arcsinh — torch_arcsinh","text":"Arcsinh","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_arcsinh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Arcsinh — torch_arcsinh","text":"","code":"torch_arcsinh(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_arcsinh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Arcsinh — torch_arcsinh","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_arcsinh.html","id":"arcsinh-input-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"arcsinh(input, *, out=None) -> Tensor","title":"Arcsinh — torch_arcsinh","text":"Alias torch_asinh().","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_arctan.html","id":null,"dir":"Reference","previous_headings":"","what":"Arctan — torch_arctan","title":"Arctan — torch_arctan","text":"Arctan","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_arctan.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Arctan — torch_arctan","text":"","code":"torch_arctan(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_arctan.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Arctan — torch_arctan","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_arctan.html","id":"arctan-input-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"arctan(input, *, out=None) -> Tensor","title":"Arctan — torch_arctan","text":"Alias torch_atan().","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_arctanh.html","id":null,"dir":"Reference","previous_headings":"","what":"Arctanh — torch_arctanh","title":"Arctanh — torch_arctanh","text":"Arctanh","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_arctanh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Arctanh — torch_arctanh","text":"","code":"torch_arctanh(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_arctanh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Arctanh — torch_arctanh","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_arctanh.html","id":"arctanh-input-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"arctanh(input, *, out=None) -> Tensor","title":"Arctanh — torch_arctanh","text":"Alias torch_atanh().","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_argmax.html","id":null,"dir":"Reference","previous_headings":"","what":"Argmax — torch_argmax","title":"Argmax — torch_argmax","text":"Argmax","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_argmax.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Argmax — torch_argmax","text":"self (Tensor) input tensor. dim (int) dimension reduce. NULL, argmax flattened input returned. keepdim (bool) whether output tensor dim retained . Ignored dim=NULL.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_argmax.html","id":"argmax-input-gt-longtensor-","dir":"Reference","previous_headings":"","what":"argmax(input) -> LongTensor","title":"Argmax — torch_argmax","text":"Returns indices maximum value elements input tensor. second value returned torch_max. See documentation exact semantics method.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_argmax.html","id":"argmax-input-dim-keepdim-false-gt-longtensor-","dir":"Reference","previous_headings":"","what":"argmax(input, dim, keepdim=False) -> LongTensor","title":"Argmax — torch_argmax","text":"Returns indices maximum values tensor across dimension. second value returned torch_max. See documentation exact semantics method.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_argmax.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Argmax — torch_argmax","text":"","code":"if (torch_is_installed()) {  if (FALSE) { a = torch_randn(c(4, 4)) a torch_argmax(a) }   a = torch_randn(c(4, 4)) a torch_argmax(a, dim=1) } #> torch_tensor #>  2 #>  4 #>  3 #>  1 #> [ CPULongType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_argmin.html","id":null,"dir":"Reference","previous_headings":"","what":"Argmin — torch_argmin","title":"Argmin — torch_argmin","text":"Argmin","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_argmin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Argmin — torch_argmin","text":"self (Tensor) input tensor. dim (int) dimension reduce. NULL, argmin flattened input returned. keepdim (bool) whether output tensor dim retained . Ignored dim=NULL.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_argmin.html","id":"argmin-input-gt-longtensor-","dir":"Reference","previous_headings":"","what":"argmin(input) -> LongTensor","title":"Argmin — torch_argmin","text":"Returns indices minimum value elements input tensor. second value returned torch_min. See documentation exact semantics method.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_argmin.html","id":"argmin-input-dim-keepdim-false-out-null-gt-longtensor-","dir":"Reference","previous_headings":"","what":"argmin(input, dim, keepdim=False, out=NULL) -> LongTensor","title":"Argmin — torch_argmin","text":"Returns indices minimum values tensor across dimension. second value returned torch_min. See documentation exact semantics method.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_argmin.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Argmin — torch_argmin","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(4, 4)) a torch_argmin(a)   a = torch_randn(c(4, 4)) a torch_argmin(a, dim=1) } #> torch_tensor #>  1 #>  2 #>  1 #>  2 #> [ CPULongType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_argsort.html","id":null,"dir":"Reference","previous_headings":"","what":"Argsort — torch_argsort","title":"Argsort — torch_argsort","text":"Argsort","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_argsort.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Argsort — torch_argsort","text":"","code":"torch_argsort(self, dim = -1L, descending = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_argsort.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Argsort — torch_argsort","text":"self (Tensor) input tensor. dim (int, optional) dimension sort along descending (bool, optional) controls sorting order (ascending descending)","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_argsort.html","id":"argsort-input-dim-descending-false-gt-longtensor-","dir":"Reference","previous_headings":"","what":"argsort(input, dim=-1, descending=False) -> LongTensor","title":"Argsort — torch_argsort","text":"Returns indices sort tensor along given dimension ascending order value. second value returned torch_sort.  See documentation exact semantics method.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_argsort.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Argsort — torch_argsort","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(4, 4)) a torch_argsort(a, dim=1) } #> torch_tensor #>  2  3  3  1 #>  1  2  4  2 #>  4  1  1  4 #>  3  4  2  3 #> [ CPULongType{4,4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_as_strided.html","id":null,"dir":"Reference","previous_headings":"","what":"As_strided — torch_as_strided","title":"As_strided — torch_as_strided","text":"As_strided","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_as_strided.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"As_strided — torch_as_strided","text":"","code":"torch_as_strided(self, size, stride, storage_offset = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/torch_as_strided.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"As_strided — torch_as_strided","text":"self (Tensor) input tensor. size (tuple ints) shape output tensor stride (tuple ints) stride output tensor storage_offset (int, optional) offset underlying storage output tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_as_strided.html","id":"as-strided-input-size-stride-storage-offset-gt-tensor-","dir":"Reference","previous_headings":"","what":"as_strided(input, size, stride, storage_offset=0) -> Tensor","title":"As_strided — torch_as_strided","text":"Create view existing torch_Tensor input specified size, stride storage_offset.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_as_strided.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"As_strided — torch_as_strided","text":"one element created tensor may refer single memory location. result, -place operations (especially ones vectorized) may result incorrect behavior. need write tensors, please clone first.","code":"Many PyTorch functions, which return a view of a tensor, are internally implemented with this function. Those functions, like `torch_Tensor.expand`, are easier to read and are therefore more advisable to use."},{"path":"https://torch.mlverse.org/docs/reference/torch_as_strided.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"As_strided — torch_as_strided","text":"","code":"if (torch_is_installed()) {  x = torch_randn(c(3, 3)) x t = torch_as_strided(x, list(2, 2), list(1, 2)) t t = torch_as_strided(x, list(2, 2), list(1, 2), 1) t } #> torch_tensor #>  0.0357  0.2241 #>  0.1163  0.0760 #> [ CPUFloatType{2,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_asin.html","id":null,"dir":"Reference","previous_headings":"","what":"Asin — torch_asin","title":"Asin — torch_asin","text":"Asin","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_asin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Asin — torch_asin","text":"","code":"torch_asin(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_asin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Asin — torch_asin","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_asin.html","id":"asin-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"asin(input, out=NULL) -> Tensor","title":"Asin — torch_asin","text":"Returns new tensor arcsine  elements input. $$     \\mbox{}_{} = \\sin^{-1}(\\mbox{input}_{}) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_asin.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Asin — torch_asin","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(4)) a torch_asin(a) } #> torch_tensor #>     nan #>     nan #> -0.4348 #>     nan #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_asinh.html","id":null,"dir":"Reference","previous_headings":"","what":"Asinh — torch_asinh","title":"Asinh — torch_asinh","text":"Asinh","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_asinh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Asinh — torch_asinh","text":"","code":"torch_asinh(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_asinh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Asinh — torch_asinh","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_asinh.html","id":"asinh-input-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"asinh(input, *, out=None) -> Tensor","title":"Asinh — torch_asinh","text":"Returns new tensor inverse hyperbolic sine elements input. $$     \\mbox{}_{} = \\sinh^{-1}(\\mbox{input}_{}) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_asinh.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Asinh — torch_asinh","text":"","code":"if (torch_is_installed()) {  a <- torch_randn(c(4)) a torch_asinh(a) } #> torch_tensor #> -0.9800 #> -0.7436 #> -1.3847 #> -0.8686 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_atan.html","id":null,"dir":"Reference","previous_headings":"","what":"Atan — torch_atan","title":"Atan — torch_atan","text":"Atan","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_atan.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Atan — torch_atan","text":"","code":"torch_atan(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_atan.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Atan — torch_atan","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_atan.html","id":"atan-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"atan(input, out=NULL) -> Tensor","title":"Atan — torch_atan","text":"Returns new tensor arctangent  elements input. $$     \\mbox{}_{} = \\tan^{-1}(\\mbox{input}_{}) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_atan.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Atan — torch_atan","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(4)) a torch_atan(a) } #> torch_tensor #>  0.5585 #>  0.3162 #> -0.5697 #>  0.7496 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_atan2.html","id":null,"dir":"Reference","previous_headings":"","what":"Atan2 — torch_atan2","title":"Atan2 — torch_atan2","text":"Atan2","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_atan2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Atan2 — torch_atan2","text":"","code":"torch_atan2(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_atan2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Atan2 — torch_atan2","text":"self (Tensor) first input tensor (Tensor) second input tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_atan2.html","id":"atan-input-other-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"atan2(input, other, out=NULL) -> Tensor","title":"Atan2 — torch_atan2","text":"Element-wise arctangent \\(\\mbox{input}_{} / \\mbox{}_{}\\) consideration quadrant. Returns new tensor signed angles radians vector \\((\\mbox{}_{}, \\mbox{input}_{})\\) vector \\((1, 0)\\). (Note \\(\\mbox{}_{}\\), second parameter, x-coordinate, \\(\\mbox{input}_{}\\), first parameter, y-coordinate.) shapes input must broadcastable .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_atan2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Atan2 — torch_atan2","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(4)) a torch_atan2(a, torch_randn(c(4))) } #> torch_tensor #>  0.6994 #> -2.0529 #> -2.1063 #>  1.4267 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_atanh.html","id":null,"dir":"Reference","previous_headings":"","what":"Atanh — torch_atanh","title":"Atanh — torch_atanh","text":"Atanh","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_atanh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Atanh — torch_atanh","text":"","code":"torch_atanh(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_atanh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Atanh — torch_atanh","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_atanh.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Atanh — torch_atanh","text":"domain inverse hyperbolic tangent (-1, 1) values outside range mapped NaN, except values 1 -1 output mapped +/-INF respectively. $$     \\mbox{}_{} = \\tanh^{-1}(\\mbox{input}_{}) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_atanh.html","id":"atanh-input-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"atanh(input, *, out=None) -> Tensor","title":"Atanh — torch_atanh","text":"Returns new tensor inverse hyperbolic tangent elements input.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_atanh.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Atanh — torch_atanh","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(4))$uniform_(-1, 1) a torch_atanh(a) } #> torch_tensor #> -0.1883 #> -0.3554 #> -0.1854 #>  0.7062 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_atleast_1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Atleast_1d — torch_atleast_1d","title":"Atleast_1d — torch_atleast_1d","text":"Returns 1-dimensional view input tensor zero dimensions. Input tensors one dimensions returned -.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_atleast_1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Atleast_1d — torch_atleast_1d","text":"","code":"torch_atleast_1d(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_atleast_1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Atleast_1d — torch_atleast_1d","text":"self (Tensor list Tensors)","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_atleast_1d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Atleast_1d — torch_atleast_1d","text":"","code":"if (torch_is_installed()) {  x <- torch_randn(c(2)) x torch_atleast_1d(x) x <- torch_tensor(1.) x torch_atleast_1d(x) x <- torch_tensor(0.5) y <- torch_tensor(1.) torch_atleast_1d(list(x,y)) } #> [[1]] #> torch_tensor #>  0.5000 #> [ CPUFloatType{1} ] #>  #> [[2]] #> torch_tensor #>  1 #> [ CPUFloatType{1} ] #>"},{"path":"https://torch.mlverse.org/docs/reference/torch_atleast_2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Atleast_2d — torch_atleast_2d","title":"Atleast_2d — torch_atleast_2d","text":"Returns 2-dimensional view input tensor zero dimensions. Input tensors two dimensions returned -.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_atleast_2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Atleast_2d — torch_atleast_2d","text":"","code":"torch_atleast_2d(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_atleast_2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Atleast_2d — torch_atleast_2d","text":"self (Tensor list Tensors)","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_atleast_2d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Atleast_2d — torch_atleast_2d","text":"","code":"if (torch_is_installed()) {  x <- torch_tensor(1.) x torch_atleast_2d(x) x <- torch_randn(c(2,2)) x torch_atleast_2d(x) x <- torch_tensor(0.5) y <- torch_tensor(1.) torch_atleast_2d(list(x,y)) } #> [[1]] #> torch_tensor #>  0.5000 #> [ CPUFloatType{1,1} ] #>  #> [[2]] #> torch_tensor #>  1 #> [ CPUFloatType{1,1} ] #>"},{"path":"https://torch.mlverse.org/docs/reference/torch_atleast_3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Atleast_3d — torch_atleast_3d","title":"Atleast_3d — torch_atleast_3d","text":"Returns 3-dimensional view input tensor zero dimensions. Input tensors three dimensions returned -.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_atleast_3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Atleast_3d — torch_atleast_3d","text":"","code":"torch_atleast_3d(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_atleast_3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Atleast_3d — torch_atleast_3d","text":"self (Tensor list Tensors)","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_avg_pool1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Avg_pool1d — torch_avg_pool1d","title":"Avg_pool1d — torch_avg_pool1d","text":"Avg_pool1d","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_avg_pool1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Avg_pool1d — torch_avg_pool1d","text":"","code":"torch_avg_pool1d(   self,   kernel_size,   stride = list(),   padding = 0L,   ceil_mode = FALSE,   count_include_pad = TRUE )"},{"path":"https://torch.mlverse.org/docs/reference/torch_avg_pool1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Avg_pool1d — torch_avg_pool1d","text":"self input tensor shape \\((\\mbox{minibatch} , \\mbox{\\_channels} , iW)\\) kernel_size size window. Can single number tuple (kW,) stride stride window. Can single number tuple (sW,). Default: kernel_size padding implicit zero paddings sides input. Can single number tuple (padW,). Default: 0 ceil_mode TRUE, use ceil instead floor compute output shape. Default: FALSE count_include_pad TRUE, include zero-padding averaging calculation. Default: TRUE","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_avg_pool1d.html","id":"avg-pool-d-input-kernel-size-stride-null-padding-ceil-mode-false-count-include-pad-true-gt-tensor-","dir":"Reference","previous_headings":"","what":"avg_pool1d(input, kernel_size, stride=NULL, padding=0, ceil_mode=FALSE, count_include_pad=TRUE) -> Tensor","title":"Avg_pool1d — torch_avg_pool1d","text":"Applies 1D average pooling input signal composed several input planes. See nn_avg_pool1d() details output shape.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_baddbmm.html","id":null,"dir":"Reference","previous_headings":"","what":"Baddbmm — torch_baddbmm","title":"Baddbmm — torch_baddbmm","text":"Baddbmm","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_baddbmm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Baddbmm — torch_baddbmm","text":"","code":"torch_baddbmm(self, batch1, batch2, beta = 1L, alpha = 1L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_baddbmm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Baddbmm — torch_baddbmm","text":"self (Tensor) tensor added batch1 (Tensor) first batch matrices multiplied batch2 (Tensor) second batch matrices multiplied beta (Number, optional) multiplier input (\\(\\beta\\)) alpha (Number, optional) multiplier \\(\\mbox{batch1} \\mathbin{@} \\mbox{batch2}\\) (\\(\\alpha\\))","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_baddbmm.html","id":"baddbmm-input-batch-batch-beta-alpha-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"baddbmm(input, batch1, batch2, *, beta=1, alpha=1, out=NULL) -> Tensor","title":"Baddbmm — torch_baddbmm","text":"Performs batch matrix-matrix product matrices batch1 batch2. input added final result. batch1 batch2 must 3-D tensors containing number matrices. batch1 \\((b \\times n \\times m)\\) tensor, batch2 \\((b \\times m \\times p)\\) tensor, input must broadcastable  \\((b \\times n \\times p)\\) tensor \\((b \\times n \\times p)\\) tensor. alpha beta mean scaling factors used torch_addbmm. $$     \\mbox{}_i = \\beta\\ \\mbox{input}_i + \\alpha\\ (\\mbox{batch1}_i \\mathbin{@} \\mbox{batch2}_i) $$ inputs type FloatTensor DoubleTensor, arguments beta alpha must real numbers, otherwise integers.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_baddbmm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Baddbmm — torch_baddbmm","text":"","code":"if (torch_is_installed()) {  M = torch_randn(c(10, 3, 5)) batch1 = torch_randn(c(10, 3, 4)) batch2 = torch_randn(c(10, 4, 5)) torch_baddbmm(M, batch1, batch2) } #> torch_tensor #> (1,.,.) =  #>   1.5314  2.1523  0.2472  0.4278 -3.3137 #>  -0.8508  1.5365 -1.0732  2.6646 -3.1006 #>  -0.1376  2.0982 -0.2566 -0.3295  2.0059 #>  #> (2,.,.) =  #>  -1.1111 -1.7371  2.3423 -0.7417 -2.2889 #>  -0.6591 -1.2342  0.2033 -3.7738  1.1918 #>  -0.9252 -1.3643 -0.1002 -1.4271  1.9439 #>  #> (3,.,.) =  #>  -0.7869  0.6415 -3.3768 -0.1575  0.1333 #>   0.4960 -2.1156  2.8423 -1.0345  0.3262 #>  -2.2556 -2.6786 -1.3520 -2.4898 -4.4178 #>  #> (4,.,.) =  #>  -3.0603 -2.5317  0.4861 -0.5448 -0.5457 #>  -1.7122 -3.5291 -0.6682  1.4289 -2.2926 #>  -3.4081 -1.2381 -0.2655 -0.2416 -1.3346 #>  #> (5,.,.) =  #>   2.3543 -1.4084 -5.9649 -1.7255  2.7500 #>  -2.4835  0.1321  5.2300 -2.0340 -0.5638 #>  -3.0828  0.0535  5.6805 -0.4303 -1.4146 #>  #> (6,.,.) =  #>  -1.2848 -0.2140  0.2244 -0.9828 -1.6569 #>  -0.5352  0.3073  0.0673  2.4922  1.9925 #>   2.7745  1.9500  1.8242  3.1233  3.6875 #>  #> ... [the output was truncated (use n=-1 to disable)] #> [ CPUFloatType{10,3,5} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_bartlett_window.html","id":null,"dir":"Reference","previous_headings":"","what":"Bartlett_window — torch_bartlett_window","title":"Bartlett_window — torch_bartlett_window","text":"Bartlett_window","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_bartlett_window.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bartlett_window — torch_bartlett_window","text":"","code":"torch_bartlett_window(   window_length,   periodic = TRUE,   dtype = NULL,   layout = torch_strided(),   device = NULL,   requires_grad = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/torch_bartlett_window.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bartlett_window — torch_bartlett_window","text":"window_length (int) size returned window periodic (bool, optional) TRUE, returns window used periodic        function. False, return symmetric window. dtype (torch.dtype, optional) desired data type returned tensor.        Default: NULL, uses global default (see torch_set_default_tensor_type). floating point types supported. layout (torch.layout, optional) desired layout returned window tensor.          torch_strided (dense layout) supported. device (torch.device, optional) desired device returned tensor.        Default: NULL, uses current device default tensor type        (see torch_set_default_tensor_type). device CPU        CPU tensor types current CUDA device CUDA tensor types. requires_grad (bool, optional) autograd record operations        returned tensor. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_bartlett_window.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Bartlett_window — torch_bartlett_window","text":"","code":"If `window_length` \\eqn{=1}, the returned window contains a single value 1."},{"path":"https://torch.mlverse.org/docs/reference/torch_bartlett_window.html","id":"bartlett-window-window-length-periodic-true-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-","dir":"Reference","previous_headings":"","what":"bartlett_window(window_length, periodic=TRUE, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor","title":"Bartlett_window — torch_bartlett_window","text":"Bartlett window function. $$     w[n] = 1 - \\left| \\frac{2n}{N-1} - 1 \\right| = \\left\\{ \\begin{array}{ll}         \\frac{2n}{N - 1} & \\mbox{} 0 \\leq n \\leq \\frac{N - 1}{2} \\\\         2 - \\frac{2n}{N - 1} & \\mbox{} \\frac{N - 1}{2} < n < N \\\\     \\end{array}     \\right. , $$ \\(N\\) full window size. input window_length positive integer controlling returned window size. periodic flag determines whether returned window trims last duplicate value symmetric window ready used periodic window functions like torch_stft. Therefore, periodic true, \\(N\\) formula fact \\(\\mbox{window\\_length} + 1\\). Also, always torch_bartlett_window(L, periodic=TRUE) equal torch_bartlett_window(L + 1, periodic=False)[:-1]).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_bernoulli.html","id":null,"dir":"Reference","previous_headings":"","what":"Bernoulli — torch_bernoulli","title":"Bernoulli — torch_bernoulli","text":"Bernoulli","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_bernoulli.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bernoulli — torch_bernoulli","text":"","code":"torch_bernoulli(self, p, generator = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/torch_bernoulli.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bernoulli — torch_bernoulli","text":"self (Tensor) input tensor probability values Bernoulli distribution p (Number) probability value. p passed used instead values self tensor. generator (torch.Generator, optional) pseudorandom number generator sampling","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_bernoulli.html","id":"bernoulli-input-generator-null-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"bernoulli(input, *, generator=NULL, out=NULL) -> Tensor","title":"Bernoulli — torch_bernoulli","text":"Draws binary random numbers (0 1) Bernoulli distribution. input tensor tensor containing probabilities used drawing binary random number. Hence, values input range: \\(0 \\leq \\mbox{input}_i \\leq 1\\). \\(\\mbox{}^{th}\\) element output tensor draw value \\(1\\) according \\(\\mbox{}^{th}\\) probability value given input. $$     \\mbox{}_{} \\sim \\mathrm{Bernoulli}(p = \\mbox{input}_{}) $$ returned tensor values 0 1 shape input. can integral dtype, input must floating point dtype.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_bernoulli.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Bernoulli — torch_bernoulli","text":"","code":"if (torch_is_installed()) {  a = torch_empty(c(3, 3))$uniform_(0, 1)  # generate a uniform random matrix with range c(0, 1) a torch_bernoulli(a) a = torch_ones(c(3, 3)) # probability of drawing \"1\" is 1 torch_bernoulli(a) a = torch_zeros(c(3, 3)) # probability of drawing \"1\" is 0 torch_bernoulli(a) } #> torch_tensor #>  0  0  0 #>  0  0  0 #>  0  0  0 #> [ CPUFloatType{3,3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_bincount.html","id":null,"dir":"Reference","previous_headings":"","what":"Bincount — torch_bincount","title":"Bincount — torch_bincount","text":"Bincount","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_bincount.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bincount — torch_bincount","text":"","code":"torch_bincount(self, weights = list(), minlength = 0L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_bincount.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bincount — torch_bincount","text":"self (Tensor) 1-d int tensor weights (Tensor) optional, weight value input tensor.        size input tensor. minlength (int) optional, minimum number bins. non-negative.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_bincount.html","id":"bincount-input-weights-null-minlength-gt-tensor-","dir":"Reference","previous_headings":"","what":"bincount(input, weights=NULL, minlength=0) -> Tensor","title":"Bincount — torch_bincount","text":"Count frequency value array non-negative ints. number bins (size 1) one larger largest value input unless input empty, case result tensor size 0. minlength specified, number bins least minlength input empty, result tensor size minlength filled zeros. n value position , [n] += weights[] weights specified else [n] += 1. .. include:: cuda_deterministic.rst","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_bincount.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Bincount — torch_bincount","text":"","code":"if (torch_is_installed()) {  input = torch_randint(0, 8, list(5), dtype=torch_int64()) weights = torch_linspace(0, 1, steps=5) input weights torch_bincount(input, weights) input$bincount(weights) } #> torch_tensor #>  0.7500 #>  1.0000 #>  0.0000 #>  0.5000 #>  0.0000 #>  0.0000 #>  0.0000 #>  0.2500 #> [ CPUFloatType{8} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_bitwise_and.html","id":null,"dir":"Reference","previous_headings":"","what":"Bitwise_and — torch_bitwise_and","title":"Bitwise_and — torch_bitwise_and","text":"Bitwise_and","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_bitwise_and.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bitwise_and — torch_bitwise_and","text":"","code":"torch_bitwise_and(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_bitwise_and.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bitwise_and — torch_bitwise_and","text":"self NA first input tensor NA second input tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_bitwise_and.html","id":"bitwise-and-input-other-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"bitwise_and(input, other, out=NULL) -> Tensor","title":"Bitwise_and — torch_bitwise_and","text":"Computes bitwise input . input tensor must integral Boolean types. bool tensors, computes logical .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_bitwise_not.html","id":null,"dir":"Reference","previous_headings":"","what":"Bitwise_not — torch_bitwise_not","title":"Bitwise_not — torch_bitwise_not","text":"Bitwise_not","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_bitwise_not.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bitwise_not — torch_bitwise_not","text":"","code":"torch_bitwise_not(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_bitwise_not.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bitwise_not — torch_bitwise_not","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_bitwise_not.html","id":"bitwise-not-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"bitwise_not(input, out=NULL) -> Tensor","title":"Bitwise_not — torch_bitwise_not","text":"Computes bitwise given input tensor. input tensor must integral Boolean types. bool tensors, computes logical .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_bitwise_or.html","id":null,"dir":"Reference","previous_headings":"","what":"Bitwise_or — torch_bitwise_or","title":"Bitwise_or — torch_bitwise_or","text":"Bitwise_or","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_bitwise_or.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bitwise_or — torch_bitwise_or","text":"","code":"torch_bitwise_or(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_bitwise_or.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bitwise_or — torch_bitwise_or","text":"self NA first input tensor NA second input tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_bitwise_or.html","id":"bitwise-or-input-other-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"bitwise_or(input, other, out=NULL) -> Tensor","title":"Bitwise_or — torch_bitwise_or","text":"Computes bitwise input . input tensor must integral Boolean types. bool tensors, computes logical .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_bitwise_xor.html","id":null,"dir":"Reference","previous_headings":"","what":"Bitwise_xor — torch_bitwise_xor","title":"Bitwise_xor — torch_bitwise_xor","text":"Bitwise_xor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_bitwise_xor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bitwise_xor — torch_bitwise_xor","text":"","code":"torch_bitwise_xor(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_bitwise_xor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bitwise_xor — torch_bitwise_xor","text":"self NA first input tensor NA second input tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_bitwise_xor.html","id":"bitwise-xor-input-other-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"bitwise_xor(input, other, out=NULL) -> Tensor","title":"Bitwise_xor — torch_bitwise_xor","text":"Computes bitwise XOR input . input tensor must integral Boolean types. bool tensors, computes logical XOR.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_blackman_window.html","id":null,"dir":"Reference","previous_headings":"","what":"Blackman_window — torch_blackman_window","title":"Blackman_window — torch_blackman_window","text":"Blackman_window","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_blackman_window.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Blackman_window — torch_blackman_window","text":"","code":"torch_blackman_window(   window_length,   periodic = TRUE,   dtype = NULL,   layout = torch_strided(),   device = NULL,   requires_grad = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/torch_blackman_window.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Blackman_window — torch_blackman_window","text":"window_length (int) size returned window periodic (bool, optional) TRUE, returns window used periodic        function. False, return symmetric window. dtype (torch.dtype, optional) desired data type returned tensor.        Default: NULL, uses global default (see torch_set_default_tensor_type). floating point types supported. layout (torch.layout, optional) desired layout returned window tensor.          torch_strided (dense layout) supported. device (torch.device, optional) desired device returned tensor.        Default: NULL, uses current device default tensor type        (see torch_set_default_tensor_type). device CPU        CPU tensor types current CUDA device CUDA tensor types. requires_grad (bool, optional) autograd record operations        returned tensor. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_blackman_window.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Blackman_window — torch_blackman_window","text":"","code":"If `window_length` \\eqn{=1}, the returned window contains a single value 1."},{"path":"https://torch.mlverse.org/docs/reference/torch_blackman_window.html","id":"blackman-window-window-length-periodic-true-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-","dir":"Reference","previous_headings":"","what":"blackman_window(window_length, periodic=TRUE, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor","title":"Blackman_window — torch_blackman_window","text":"Blackman window function. $$     w[n] = 0.42 - 0.5 \\cos \\left( \\frac{2 \\pi n}{N - 1} \\right) + 0.08 \\cos \\left( \\frac{4 \\pi n}{N - 1} \\right) $$ \\(N\\) full window size. input window_length positive integer controlling returned window size. periodic flag determines whether returned window trims last duplicate value symmetric window ready used periodic window functions like torch_stft. Therefore, periodic true, \\(N\\) formula fact \\(\\mbox{window\\_length} + 1\\). Also, always torch_blackman_window(L, periodic=TRUE) equal torch_blackman_window(L + 1, periodic=False)[:-1]).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_block_diag.html","id":null,"dir":"Reference","previous_headings":"","what":"Block_diag — torch_block_diag","title":"Block_diag — torch_block_diag","text":"Create block diagonal matrix provided tensors.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_block_diag.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Block_diag — torch_block_diag","text":"","code":"torch_block_diag(tensors)"},{"path":"https://torch.mlverse.org/docs/reference/torch_block_diag.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Block_diag — torch_block_diag","text":"tensors (list tensors) One tensors 0, 1, 2 dimensions.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_block_diag.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Block_diag — torch_block_diag","text":"","code":"if (torch_is_installed()) {  A <- torch_tensor(rbind(c(0, 1), c(1, 0))) B <- torch_tensor(rbind(c(3, 4, 5), c(6, 7, 8))) C <- torch_tensor(7) D <- torch_tensor(c(1, 2, 3)) E <- torch_tensor(rbind(4, 5, 6)) torch_block_diag(list(A, B, C, D, E)) } #> torch_tensor #>  0  1  0  0  0  0  0  0  0  0 #>  1  0  0  0  0  0  0  0  0  0 #>  0  0  3  4  5  0  0  0  0  0 #>  0  0  6  7  8  0  0  0  0  0 #>  0  0  0  0  0  7  0  0  0  0 #>  0  0  0  0  0  0  1  2  3  0 #>  0  0  0  0  0  0  0  0  0  4 #>  0  0  0  0  0  0  0  0  0  5 #>  0  0  0  0  0  0  0  0  0  6 #> [ CPUFloatType{9,10} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_bmm.html","id":null,"dir":"Reference","previous_headings":"","what":"Bmm — torch_bmm","title":"Bmm — torch_bmm","text":"Bmm","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_bmm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bmm — torch_bmm","text":"","code":"torch_bmm(self, mat2)"},{"path":"https://torch.mlverse.org/docs/reference/torch_bmm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bmm — torch_bmm","text":"self (Tensor) first batch matrices multiplied mat2 (Tensor) second batch matrices multiplied","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_bmm.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Bmm — torch_bmm","text":"function broadcast . broadcasting matrix products, see torch_matmul.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_bmm.html","id":"bmm-input-mat-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"bmm(input, mat2, out=NULL) -> Tensor","title":"Bmm — torch_bmm","text":"Performs batch matrix-matrix product matrices stored input mat2. input mat2 must 3-D tensors containing number matrices. input \\((b \\times n \\times m)\\) tensor, mat2 \\((b \\times m \\times p)\\) tensor, \\((b \\times n \\times p)\\) tensor. $$     \\mbox{}_i = \\mbox{input}_i \\mathbin{@} \\mbox{mat2}_i $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_bmm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Bmm — torch_bmm","text":"","code":"if (torch_is_installed()) {  input = torch_randn(c(10, 3, 4)) mat2 = torch_randn(c(10, 4, 5)) res = torch_bmm(input, mat2) res } #> torch_tensor #> (1,.,.) =  #>   1.1496 -1.3264 -0.0155  1.6350 -0.3408 #>  -3.4424 -2.9396 -1.0153  3.8839  0.2192 #>  -0.2311  1.1350 -1.5783 -0.9564 -1.1007 #>  #> (2,.,.) =  #>   1.0680 -0.0246 -3.8276 -0.6074  0.8695 #>   1.8761 -0.8042  0.2076  2.3332 -0.0249 #>  -1.2795  2.4611  1.3843 -0.7258  1.1189 #>  #> (3,.,.) =  #>   2.0340 -3.7801  0.4020 -2.4461  0.6206 #>   4.2814 -5.9884 -1.0261 -4.5182 -1.1445 #>   2.2079 -0.4313  0.0599 -2.2269 -2.2408 #>  #> (4,.,.) =  #>   0.0438  1.1780  2.4251 -0.9283  0.1883 #>   0.3184  1.2769 -1.6914  0.1830 -0.3450 #>  -0.4319  1.6848  1.9182  1.5409 -1.5786 #>  #> (5,.,.) =  #>   1.8508 -1.7860 -1.6277  3.3500 -3.9996 #>   1.1662 -0.6075 -0.8746 -0.5356 -1.8053 #>   0.5967 -1.2575  1.4572  2.6120  0.0918 #>  #> (6,.,.) =  #>  -2.3077 -0.1633 -0.7897  1.3703 -2.5176 #>   0.4733  2.0210 -5.1606 -1.5100 -8.7580 #>  -1.8399  2.0785 -0.8735 -2.3808  0.0939 #>  #> ... [the output was truncated (use n=-1 to disable)] #> [ CPUFloatType{10,3,5} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_broadcast_tensors.html","id":null,"dir":"Reference","previous_headings":"","what":"Broadcast_tensors — torch_broadcast_tensors","title":"Broadcast_tensors — torch_broadcast_tensors","text":"Broadcast_tensors","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_broadcast_tensors.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Broadcast_tensors — torch_broadcast_tensors","text":"","code":"torch_broadcast_tensors(tensors)"},{"path":"https://torch.mlverse.org/docs/reference/torch_broadcast_tensors.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Broadcast_tensors — torch_broadcast_tensors","text":"tensors list containing number tensors type","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_broadcast_tensors.html","id":"broadcast-tensors-tensors-gt-list-of-tensors-","dir":"Reference","previous_headings":"","what":"broadcast_tensors(tensors) -> List of Tensors","title":"Broadcast_tensors — torch_broadcast_tensors","text":"Broadcasts given tensors according broadcasting-semantics.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_broadcast_tensors.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Broadcast_tensors — torch_broadcast_tensors","text":"","code":"if (torch_is_installed()) {  x = torch_arange(0, 3)$view(c(1, 4)) y = torch_arange(0, 2)$view(c(3, 1)) out = torch_broadcast_tensors(list(x, y)) out[[1]] } #> torch_tensor #>  0  1  2  3 #>  0  1  2  3 #>  0  1  2  3 #> [ CPUFloatType{3,4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_bucketize.html","id":null,"dir":"Reference","previous_headings":"","what":"Bucketize — torch_bucketize","title":"Bucketize — torch_bucketize","text":"Bucketize","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_bucketize.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bucketize — torch_bucketize","text":"","code":"torch_bucketize(self, boundaries, out_int32 = FALSE, right = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_bucketize.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bucketize — torch_bucketize","text":"self (Tensor Scalar) N-D tensor Scalar containing search value(s). boundaries (Tensor) 1-D tensor, must contain monotonically increasing sequence. out_int32 (bool, optional) – indicate output data type. torch_int32() True, torch_int64() otherwise. Default value FALSE, .e. default output data type torch_int64(). right (bool, optional) – False, return first suitable location found. True, return last index. suitable index found, return 0 non-numerical value (eg. nan, inf) size boundaries (one pass last index). words, False, gets lower bound index value input boundaries. True, gets upper bound index instead. Default value False.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_bucketize.html","id":"bucketize-input-boundaries-out-int-false-right-false-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"bucketize(input, boundaries, *, out_int32=FALSE, right=FALSE, out=None) -> Tensor","title":"Bucketize — torch_bucketize","text":"Returns indices buckets value input belongs, boundaries buckets set boundaries. Return new tensor size input. right FALSE (default), left boundary closed.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_bucketize.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Bucketize — torch_bucketize","text":"","code":"if (torch_is_installed()) {  boundaries <- torch_tensor(c(1, 3, 5, 7, 9)) boundaries v <- torch_tensor(rbind(c(3, 6, 9), c(3, 6, 9))) v torch_bucketize(v, boundaries) torch_bucketize(v, boundaries, right=TRUE) } #> torch_tensor #>  2  3  5 #>  2  3  5 #> [ CPULongType{2,3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_can_cast.html","id":null,"dir":"Reference","previous_headings":"","what":"Can_cast — torch_can_cast","title":"Can_cast — torch_can_cast","text":"Can_cast","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_can_cast.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Can_cast — torch_can_cast","text":"","code":"torch_can_cast(from, to)"},{"path":"https://torch.mlverse.org/docs/reference/torch_can_cast.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Can_cast — torch_can_cast","text":"(dtype) original torch_dtype. (dtype) target torch_dtype.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_can_cast.html","id":"can-cast-from-to-gt-bool-","dir":"Reference","previous_headings":"","what":"can_cast(from, to) -> bool","title":"Can_cast — torch_can_cast","text":"Determines type conversion allowed PyTorch casting rules described type promotion documentation .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_can_cast.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Can_cast — torch_can_cast","text":"","code":"if (torch_is_installed()) {  torch_can_cast(torch_double(), torch_float()) torch_can_cast(torch_float(), torch_int()) } #> [1] FALSE"},{"path":"https://torch.mlverse.org/docs/reference/torch_cartesian_prod.html","id":null,"dir":"Reference","previous_headings":"","what":"Cartesian_prod — torch_cartesian_prod","title":"Cartesian_prod — torch_cartesian_prod","text":"cartesian product given sequence tensors.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cartesian_prod.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cartesian_prod — torch_cartesian_prod","text":"","code":"torch_cartesian_prod(tensors)"},{"path":"https://torch.mlverse.org/docs/reference/torch_cartesian_prod.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cartesian_prod — torch_cartesian_prod","text":"tensors list containing number 1 dimensional tensors.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cartesian_prod.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cartesian_prod — torch_cartesian_prod","text":"","code":"if (torch_is_installed()) {  a = c(1, 2, 3) b = c(4, 5) tensor_a = torch_tensor(a) tensor_b = torch_tensor(b) torch_cartesian_prod(list(tensor_a, tensor_b)) } #> torch_tensor #>  1  4 #>  1  5 #>  2  4 #>  2  5 #>  3  4 #>  3  5 #> [ CPUFloatType{6,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_cat.html","id":null,"dir":"Reference","previous_headings":"","what":"Cat — torch_cat","title":"Cat — torch_cat","text":"Cat","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cat.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cat — torch_cat","text":"","code":"torch_cat(tensors, dim = 1L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_cat.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cat — torch_cat","text":"tensors (sequence Tensors) python sequence tensors type.        Non-empty tensors provided must shape, except        cat dimension. dim (int, optional) dimension tensors concatenated","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cat.html","id":"cat-tensors-dim-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"cat(tensors, dim=0, out=NULL) -> Tensor","title":"Cat — torch_cat","text":"Concatenates given sequence seq tensors given dimension. tensors must either shape (except concatenating dimension) empty. torch_cat can seen inverse operation torch_split() torch_chunk. torch_cat can best understood via examples.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cat.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cat — torch_cat","text":"","code":"if (torch_is_installed()) {  x = torch_randn(c(2, 3)) x torch_cat(list(x, x, x), 1) torch_cat(list(x, x, x), 2) } #> torch_tensor #>  0.8862  0.3090 -0.9575  0.8862  0.3090 -0.9575  0.8862  0.3090 -0.9575 #> -0.2062  0.1593  1.2549 -0.2062  0.1593  1.2549 -0.2062  0.1593  1.2549 #> [ CPUFloatType{2,9} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_cdist.html","id":null,"dir":"Reference","previous_headings":"","what":"Cdist — torch_cdist","title":"Cdist — torch_cdist","text":"Cdist","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cdist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cdist — torch_cdist","text":"","code":"torch_cdist(x1, x2, p = 2L, compute_mode = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/torch_cdist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cdist — torch_cdist","text":"x1 (Tensor) input tensor shape \\(B \\times P \\times M\\). x2 (Tensor) input tensor shape \\(B \\times R \\times M\\). p NA p value p-norm distance calculate vector pair        \\(\\[0, \\infty]\\). compute_mode NA 'use_mm_for_euclid_dist_if_necessary' - use matrix multiplication approach calculate        euclidean distance (p = 2) P > 25 R > 25        'use_mm_for_euclid_dist' - always use matrix multiplication approach calculate        euclidean distance (p = 2)        'donot_use_mm_for_euclid_dist' - never use matrix multiplication approach calculate        euclidean distance (p = 2)        Default: use_mm_for_euclid_dist_if_necessary.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cdist.html","id":"test-","dir":"Reference","previous_headings":"","what":"TEST","title":"Cdist — torch_cdist","text":"Computes batched p-norm distance pair two collections row vectors.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_ceil.html","id":null,"dir":"Reference","previous_headings":"","what":"Ceil — torch_ceil","title":"Ceil — torch_ceil","text":"Ceil","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_ceil.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Ceil — torch_ceil","text":"","code":"torch_ceil(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_ceil.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Ceil — torch_ceil","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_ceil.html","id":"ceil-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"ceil(input, out=NULL) -> Tensor","title":"Ceil — torch_ceil","text":"Returns new tensor ceil elements input, smallest integer greater equal element. $$     \\mbox{}_{} = \\left\\lceil \\mbox{input}_{} \\right\\rceil = \\left\\lfloor \\mbox{input}_{} \\right\\rfloor + 1 $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_ceil.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Ceil — torch_ceil","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(4)) a torch_ceil(a) } #> torch_tensor #> -1 #>  1 #> -0 #> -1 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_celu.html","id":null,"dir":"Reference","previous_headings":"","what":"Celu — torch_celu","title":"Celu — torch_celu","text":"Celu","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_celu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Celu — torch_celu","text":"","code":"torch_celu(self, alpha = 1L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_celu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Celu — torch_celu","text":"self input tensor alpha alpha value CELU formulation. Default: 1.0","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_celu.html","id":"celu-input-alpha-gt-tensor-","dir":"Reference","previous_headings":"","what":"celu(input, alpha=1.) -> Tensor","title":"Celu — torch_celu","text":"See nnf_celu() info.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_celu_.html","id":null,"dir":"Reference","previous_headings":"","what":"Celu_ — torch_celu_","title":"Celu_ — torch_celu_","text":"Celu_","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_celu_.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Celu_ — torch_celu_","text":"","code":"torch_celu_(self, alpha = 1L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_celu_.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Celu_ — torch_celu_","text":"self input tensor alpha alpha value CELU formulation. Default: 1.0","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_celu_.html","id":"celu-input-alpha-gt-tensor-","dir":"Reference","previous_headings":"","what":"celu_(input, alpha=1.) -> Tensor","title":"Celu_ — torch_celu_","text":"-place version torch_celu().","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_chain_matmul.html","id":null,"dir":"Reference","previous_headings":"","what":"Chain_matmul — torch_chain_matmul","title":"Chain_matmul — torch_chain_matmul","text":"Chain_matmul","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_chain_matmul.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Chain_matmul — torch_chain_matmul","text":"","code":"torch_chain_matmul(matrices)"},{"path":"https://torch.mlverse.org/docs/reference/torch_chain_matmul.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Chain_matmul — torch_chain_matmul","text":"matrices (Tensors...) sequence 2 2-D tensors whose product determined.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_chain_matmul.html","id":"test-","dir":"Reference","previous_headings":"","what":"TEST","title":"Chain_matmul — torch_chain_matmul","text":"Returns matrix product \\(N\\) 2-D tensors. product efficiently computed using matrix chain order algorithm selects order incurs lowest cost terms arithmetic operations ([CLRS]_). Note since function compute product, \\(N\\) needs greater equal 2; equal 2 trivial matrix-matrix product returned. \\(N\\) 1, -op - original matrix returned .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_chain_matmul.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Chain_matmul — torch_chain_matmul","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(3, 4)) b = torch_randn(c(4, 5)) c = torch_randn(c(5, 6)) d = torch_randn(c(6, 7)) torch_chain_matmul(list(a, b, c, d)) } #> torch_tensor #>  -1.5076  -0.1371  -1.8188   0.3816   2.4127  -4.2103  -0.8165 #>   3.0752 -22.0085   5.1744 -14.5629 -20.3681  11.4027  -7.0557 #>  -1.5996  -2.4373  -1.0525  -1.2670   2.1774  -5.1985   0.4090 #> [ CPUFloatType{3,7} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_channel_shuffle.html","id":null,"dir":"Reference","previous_headings":"","what":"Channel_shuffle — torch_channel_shuffle","title":"Channel_shuffle — torch_channel_shuffle","text":"Channel_shuffle","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_channel_shuffle.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Channel_shuffle — torch_channel_shuffle","text":"","code":"torch_channel_shuffle(self, groups)"},{"path":"https://torch.mlverse.org/docs/reference/torch_channel_shuffle.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Channel_shuffle — torch_channel_shuffle","text":"self (Tensor) input tensor groups (int) number groups divide channels rearrange.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_channel_shuffle.html","id":"divide-the-channels-in-a-tensor-of-shape-","dir":"Reference","previous_headings":"","what":"Divide the channels in a tensor of shape","title":"Channel_shuffle — torch_channel_shuffle","text":"math:(*, C , H, W) : Divide channels tensor shape \\((*, C , H, W)\\) g groups rearrange \\((*, C \\frac g, g, H, W)\\), keeping original tensor shape.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_channel_shuffle.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Channel_shuffle — torch_channel_shuffle","text":"","code":"if (torch_is_installed()) {  input <- torch_randn(c(1, 4, 2, 2)) print(input) output <- torch_channel_shuffle(input, 2) print(output) } #> torch_tensor #> (1,1,.,.) =  #>  -0.0450 -1.6163 #>   1.6970 -2.3007 #>  #> (1,2,.,.) =  #>  -0.0475 -1.6849 #>   0.1281  0.1671 #>  #> (1,3,.,.) =  #>   0.4841 -0.6150 #>  -0.5135  0.4203 #>  #> (1,4,.,.) =  #>  -0.2222 -0.4254 #>   2.1632  0.2921 #> [ CPUFloatType{1,4,2,2} ] #> torch_tensor #> (1,1,.,.) =  #>  -0.0450 -1.6163 #>   1.6970 -2.3007 #>  #> (1,2,.,.) =  #>   0.4841 -0.6150 #>  -0.5135  0.4203 #>  #> (1,3,.,.) =  #>  -0.0475 -1.6849 #>   0.1281  0.1671 #>  #> (1,4,.,.) =  #>  -0.2222 -0.4254 #>   2.1632  0.2921 #> [ CPUFloatType{1,4,2,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_cholesky.html","id":null,"dir":"Reference","previous_headings":"","what":"Cholesky — torch_cholesky","title":"Cholesky — torch_cholesky","text":"Cholesky","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cholesky.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cholesky — torch_cholesky","text":"","code":"torch_cholesky(self, upper = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_cholesky.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cholesky — torch_cholesky","text":"self (Tensor) input tensor \\(\\) size \\((*, n, n)\\) * zero batch dimensions consisting symmetric positive-definite matrices. upper (bool, optional) flag indicates whether return upper lower triangular matrix. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cholesky.html","id":"cholesky-input-upper-false-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"cholesky(input, upper=False, out=NULL) -> Tensor","title":"Cholesky — torch_cholesky","text":"Computes Cholesky decomposition symmetric positive-definite matrix \\(\\) batches symmetric positive-definite matrices. upper TRUE, returned matrix U upper-triangular, decomposition form: $$   = U^TU $$ upper FALSE, returned matrix L lower-triangular, decomposition form: $$     = LL^T $$ upper TRUE, \\(\\) batch symmetric positive-definite matrices, returned tensor composed upper-triangular Cholesky factors individual matrices. Similarly, upper FALSE, returned tensor composed lower-triangular Cholesky factors individual matrices.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cholesky.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cholesky — torch_cholesky","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(3, 3)) a = torch_mm(a, a$t()) # make symmetric positive-definite l = torch_cholesky(a) a l torch_mm(l, l$t()) a = torch_randn(c(3, 2, 2)) if (FALSE) { a = torch_matmul(a, a$transpose(-1, -2)) + 1e-03 # make symmetric positive-definite l = torch_cholesky(a) z = torch_matmul(l, l$transpose(-1, -2)) torch_max(torch_abs(z - a)) # Max non-zero } }"},{"path":"https://torch.mlverse.org/docs/reference/torch_cholesky_inverse.html","id":null,"dir":"Reference","previous_headings":"","what":"Cholesky_inverse — torch_cholesky_inverse","title":"Cholesky_inverse — torch_cholesky_inverse","text":"Cholesky_inverse","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cholesky_inverse.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cholesky_inverse — torch_cholesky_inverse","text":"","code":"torch_cholesky_inverse(self, upper = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_cholesky_inverse.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cholesky_inverse — torch_cholesky_inverse","text":"self (Tensor) input 2-D tensor \\(u\\), upper lower triangular           Cholesky factor upper (bool, optional) whether return lower (default) upper triangular matrix","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cholesky_inverse.html","id":"cholesky-inverse-input-upper-false-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"cholesky_inverse(input, upper=False, out=NULL) -> Tensor","title":"Cholesky_inverse — torch_cholesky_inverse","text":"Computes inverse symmetric positive-definite matrix \\(\\) using Cholesky factor \\(u\\): returns matrix inv. inverse computed using LAPACK routines dpotri spotri (corresponding MAGMA routines). upper FALSE, \\(u\\) lower triangular returned tensor $$     inv = (uu^{{T}})^{{-1}} $$ upper TRUE provided, \\(u\\) upper triangular returned tensor $$     inv = (u^T u)^{{-1}} $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cholesky_inverse.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cholesky_inverse — torch_cholesky_inverse","text":"","code":"if (torch_is_installed()) {  if (FALSE) { a = torch_randn(c(3, 3)) a = torch_mm(a, a$t()) + 1e-05 * torch_eye(3) # make symmetric positive definite u = torch_cholesky(a) a torch_cholesky_inverse(u) a$inverse() } }"},{"path":"https://torch.mlverse.org/docs/reference/torch_cholesky_solve.html","id":null,"dir":"Reference","previous_headings":"","what":"Cholesky_solve — torch_cholesky_solve","title":"Cholesky_solve — torch_cholesky_solve","text":"Cholesky_solve","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cholesky_solve.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cholesky_solve — torch_cholesky_solve","text":"","code":"torch_cholesky_solve(self, input2, upper = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_cholesky_solve.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cholesky_solve — torch_cholesky_solve","text":"self (Tensor) input matrix \\(b\\) size \\((*, m, k)\\),                \\(*\\) zero batch dimensions input2 (Tensor) input matrix \\(u\\) size \\((*, m, m)\\),                \\(*\\) zero batch dimensions composed                upper lower triangular Cholesky factor upper (bool, optional) whether consider Cholesky factor                            lower upper triangular matrix. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cholesky_solve.html","id":"cholesky-solve-input-input-upper-false-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"cholesky_solve(input, input2, upper=False, out=NULL) -> Tensor","title":"Cholesky_solve — torch_cholesky_solve","text":"Solves linear system equations positive semidefinite matrix inverted given Cholesky factor matrix \\(u\\). upper FALSE, \\(u\\) lower triangular c returned : $$     c = (u u^T)^{{-1}} b $$ upper TRUE provided, \\(u\\) upper triangular c returned : $$     c = (u^T u)^{{-1}} b $$ torch_cholesky_solve(b, u) can take 2D inputs b, u inputs batches 2D matrices. inputs batches, returns batched outputs c","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cholesky_solve.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cholesky_solve — torch_cholesky_solve","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(3, 3)) a = torch_mm(a, a$t()) # make symmetric positive definite u = torch_cholesky(a) a b = torch_randn(c(3, 2)) b torch_cholesky_solve(b, u) torch_mm(a$inverse(), b) } #> torch_tensor #>  1.1763 -0.5688 #> -0.0367 -0.5543 #> -1.7456  1.3668 #> [ CPUFloatType{3,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_chunk.html","id":null,"dir":"Reference","previous_headings":"","what":"Chunk — torch_chunk","title":"Chunk — torch_chunk","text":"Chunk","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_chunk.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Chunk — torch_chunk","text":"","code":"torch_chunk(self, chunks, dim = 1L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_chunk.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Chunk — torch_chunk","text":"self (Tensor) tensor split chunks (int) number chunks return dim (int) dimension along split tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_chunk.html","id":"chunk-input-chunks-dim-gt-list-of-tensors-","dir":"Reference","previous_headings":"","what":"chunk(input, chunks, dim=0) -> List of Tensors","title":"Chunk — torch_chunk","text":"Splits tensor specific number chunks. chunk view input tensor. Last chunk smaller tensor size along given dimension dim divisible chunks.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_clamp.html","id":null,"dir":"Reference","previous_headings":"","what":"Clamp — torch_clamp","title":"Clamp — torch_clamp","text":"Clamp","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_clamp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Clamp — torch_clamp","text":"","code":"torch_clamp(self, min = NULL, max = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/torch_clamp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Clamp — torch_clamp","text":"self (Tensor) input tensor. min (Number) lower-bound range clamped max (Number) upper-bound range clamped ","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_clamp.html","id":"clamp-input-min-max-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"clamp(input, min, max, out=NULL) -> Tensor","title":"Clamp — torch_clamp","text":"Clamp elements input range [ min, max ] return resulting tensor: $$     y_i = \\left\\{ \\begin{array}{ll}         \\mbox{min} & \\mbox{} x_i < \\mbox{min} \\\\         x_i & \\mbox{} \\mbox{min} \\leq x_i \\leq \\mbox{max} \\\\         \\mbox{max} & \\mbox{} x_i > \\mbox{max}     \\end{array}     \\right. $$ input type FloatTensor DoubleTensor, args min max must real numbers, otherwise integers.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_clamp.html","id":"clamp-input-min-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"clamp(input, *, min, out=NULL) -> Tensor","title":"Clamp — torch_clamp","text":"Clamps elements input larger equal min. input type FloatTensor DoubleTensor, value real number, otherwise integer.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_clamp.html","id":"clamp-input-max-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"clamp(input, *, max, out=NULL) -> Tensor","title":"Clamp — torch_clamp","text":"Clamps elements input smaller equal max. input type FloatTensor DoubleTensor, value real number, otherwise integer.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_clamp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Clamp — torch_clamp","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(4)) a torch_clamp(a, min=-0.5, max=0.5)   a = torch_randn(c(4)) a torch_clamp(a, min=0.5)   a = torch_randn(c(4)) a torch_clamp(a, max=0.5) } #> torch_tensor #>  0.5000 #>  0.5000 #>  0.0811 #> -0.7677 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_clip.html","id":null,"dir":"Reference","previous_headings":"","what":"Clip — torch_clip","title":"Clip — torch_clip","text":"Clip","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_clip.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Clip — torch_clip","text":"","code":"torch_clip(self, min = NULL, max = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/torch_clip.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Clip — torch_clip","text":"self (Tensor) input tensor. min (Number) lower-bound range clamped max (Number) upper-bound range clamped ","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_clip.html","id":"clip-input-min-max-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"clip(input, min, max, *, out=None) -> Tensor","title":"Clip — torch_clip","text":"Alias torch_clamp().","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_clone.html","id":null,"dir":"Reference","previous_headings":"","what":"Clone — torch_clone","title":"Clone — torch_clone","text":"Clone","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_clone.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Clone — torch_clone","text":"","code":"torch_clone(self, memory_format = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/torch_clone.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Clone — torch_clone","text":"self (Tensor) input tensor. memory_format torch memory format. see torch_preserve_format().","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_clone.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Clone — torch_clone","text":"function differentiable, gradients flow back result operation input. create tensor without autograd relationship input see Tensor$detach.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_clone.html","id":"clone-input-memory-format-torch-preserve-format-gt-tensor-","dir":"Reference","previous_headings":"","what":"clone(input, *, memory_format=torch.preserve_format) -> Tensor","title":"Clone — torch_clone","text":"Returns copy input.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_combinations.html","id":null,"dir":"Reference","previous_headings":"","what":"Combinations — torch_combinations","title":"Combinations — torch_combinations","text":"Combinations","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_combinations.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Combinations — torch_combinations","text":"","code":"torch_combinations(self, r = 2L, with_replacement = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_combinations.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Combinations — torch_combinations","text":"self (Tensor) 1D vector. r (int, optional) number elements combine with_replacement (boolean, optional) whether allow duplication combination","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_combinations.html","id":"combinations-input-r-with-replacement-false-gt-seq-","dir":"Reference","previous_headings":"","what":"combinations(input, r=2, with_replacement=False) -> seq","title":"Combinations — torch_combinations","text":"Compute combinations length \\(r\\) given tensor. behavior similar python's itertools.combinations with_replacement set False, itertools.combinations_with_replacement with_replacement set TRUE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_combinations.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Combinations — torch_combinations","text":"","code":"if (torch_is_installed()) {  a = c(1, 2, 3) tensor_a = torch_tensor(a) torch_combinations(tensor_a) torch_combinations(tensor_a, r=3) torch_combinations(tensor_a, with_replacement=TRUE) } #> torch_tensor #>  1  1 #>  1  2 #>  1  3 #>  2  2 #>  2  3 #>  3  3 #> [ CPUFloatType{6,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_complex.html","id":null,"dir":"Reference","previous_headings":"","what":"Complex — torch_complex","title":"Complex — torch_complex","text":"Complex","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_complex.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Complex — torch_complex","text":"","code":"torch_complex(real, imag)"},{"path":"https://torch.mlverse.org/docs/reference/torch_complex.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Complex — torch_complex","text":"real (Tensor) real part complex tensor. Must float double. imag (Tensor) imaginary part complex tensor. Must dtype real.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_complex.html","id":"complex-real-imag-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"complex(real, imag, *, out=None) -> Tensor","title":"Complex — torch_complex","text":"Constructs complex tensor real part equal real imaginary part equal imag.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_complex.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Complex — torch_complex","text":"","code":"if (torch_is_installed()) {  real <- torch_tensor(c(1, 2), dtype=torch_float32()) imag <- torch_tensor(c(3, 4), dtype=torch_float32()) z <- torch_complex(real, imag) z z$dtype } #> torch_ComplexFloat"},{"path":"https://torch.mlverse.org/docs/reference/torch_conj.html","id":null,"dir":"Reference","previous_headings":"","what":"Conj — torch_conj","title":"Conj — torch_conj","text":"Conj","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_conj.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Conj — torch_conj","text":"","code":"torch_conj(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_conj.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Conj — torch_conj","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_conj.html","id":"conj-input-gt-tensor-","dir":"Reference","previous_headings":"","what":"conj(input) -> Tensor","title":"Conj — torch_conj","text":"Computes element-wise conjugate given input tensor. $$     \\mbox{}_{} = conj(\\mbox{input}_{}) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_conj.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Conj — torch_conj","text":"","code":"if (torch_is_installed()) { if (FALSE) { torch_conj(torch_tensor(c(-1 + 1i, -2 + 2i, 3 - 3i))) } }"},{"path":"https://torch.mlverse.org/docs/reference/torch_conv1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Conv1d — torch_conv1d","title":"Conv1d — torch_conv1d","text":"Conv1d","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_conv1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Conv1d — torch_conv1d","text":"","code":"torch_conv1d(   input,   weight,   bias = list(),   stride = 1L,   padding = 0L,   dilation = 1L,   groups = 1L )"},{"path":"https://torch.mlverse.org/docs/reference/torch_conv1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Conv1d — torch_conv1d","text":"input input tensor shape \\((\\mbox{minibatch} , \\mbox{\\_channels} , iW)\\) weight filters shape \\((\\mbox{\\_channels} , \\frac{\\mbox{\\_channels}}{\\mbox{groups}} , kW)\\) bias optional bias shape \\((\\mbox{\\_channels})\\). Default: NULL stride stride convolving kernel. Can single number      one-element tuple (sW,). Default: 1 padding implicit paddings sides input. Can      single number one-element tuple (padW,). Default: 0 dilation spacing kernel elements. Can single number      one-element tuple (dW,). Default: 1 groups split input groups, \\(\\mbox{\\_channels}\\) divisible      number groups. Default: 1","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_conv1d.html","id":"conv-d-input-weight-bias-null-stride-padding-dilation-groups-gt-tensor-","dir":"Reference","previous_headings":"","what":"conv1d(input, weight, bias=NULL, stride=1, padding=0, dilation=1, groups=1) -> Tensor","title":"Conv1d — torch_conv1d","text":"Applies 1D convolution input signal composed several input planes. See nn_conv1d() details output shape.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_conv1d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Conv1d — torch_conv1d","text":"","code":"if (torch_is_installed()) {  filters = torch_randn(c(33, 16, 3)) inputs = torch_randn(c(20, 16, 50)) nnf_conv1d(inputs, filters) } #> torch_tensor #> (1,.,.) =  #>  Columns 1 to 8  -0.1542   3.4135  -0.1581  -2.8605   6.4160  -5.6069 -10.3032 -15.2803 #>    5.1342  -3.5374  -8.9578   4.5869   2.9852   0.4431  -1.0060   8.9873 #>   -1.7416  -9.0486  -7.7904  -3.7909  -3.5183  -5.9111   7.8429  -6.3230 #>   -0.3658  15.0050   3.7242   7.0546   2.3607  -8.8656  -7.7292   8.3001 #>   -4.1100   3.6249   6.6928  -3.1840   8.1599   0.4516  -2.8514  -8.3353 #>    3.2858 -10.2937   5.4372   3.2013  -2.0425   4.1454   2.8838   6.7277 #>   -3.4854  -1.6171   4.3029  -4.9802  -2.6822   9.4102 -13.1095  -1.1281 #>    8.3718  14.9510   6.1654  19.0315   8.4695   3.6365   7.3711   3.6864 #>    2.9024   7.7248   5.4770  -5.8791   1.1387  -7.6156  19.6924 -13.1195 #>   14.4638   0.0415  11.7669 -12.8064   6.8673   3.1586   8.9562  -1.1277 #>   -5.5094 -12.7778   6.0125  -6.0579   5.1150  -2.0295  -1.1427  -0.6692 #>    5.0350   1.7307   9.4901   2.6822   1.6487   0.5533  -4.8333  -6.2269 #>    6.8528  -6.1475   0.4119   2.2705  -4.6832  -0.9580  -4.3037   9.4792 #>   -1.8328  11.3740   1.9746   5.3325  13.3612  -2.0364   5.8420   6.0938 #>   10.3431   0.0416   5.3293   3.3731  -0.0542  11.0566  -4.9786   4.9954 #>   -1.5569  -2.0948  -7.7495   3.5368  -5.3445   7.1250   0.9995  -1.8311 #>    0.6794   5.7923 -19.8360  11.4253   5.7549  -4.2408   4.7486   0.1175 #>    6.4457   1.8916   5.6098  -0.6581  -8.6552   3.2149   5.9491  -4.2275 #>    6.3539   0.8091   1.6356   6.8544   4.3692   3.3312   4.6573  -5.9681 #>   -5.1080   2.7822  -9.0617 -11.4169   6.9733  -2.7671  -2.9646  -3.8370 #>   -3.6828  -7.6977  -3.7632  -2.5626   1.2108  -0.1260  -3.3887   8.4665 #>    1.8564  -3.6885  10.4462   8.8728  -3.8608  -3.4630   1.5713  -6.6485 #>    7.7897  -9.7382  13.4032  -2.9148  -8.3491   1.2266  -7.9193  14.4212 #>   -8.0326   3.4138  -4.4798   2.3124   5.3823 -15.7694  -0.4647  -4.7632 #>   -1.9282  10.2627  -2.2343   4.9496   2.1573   1.1019  -6.4203   3.6094 #>    5.8772   8.6403   6.4355  14.2855   1.5232   2.6452   1.3816   8.0950 #>   12.2064  -9.2259   5.5244  13.7715   1.4958   1.4696   0.7451  -3.1958 #>    6.2437  -8.0909   0.9359  -0.9759  -1.2340   4.0282  -8.8889   3.7367 #>   -6.4115   3.2042   2.0177 -10.9509  -2.6127  13.2810  -1.2975   3.1901 #> ... [the output was truncated (use n=-1 to disable)] #> [ CPUFloatType{20,33,48} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_conv2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Conv2d — torch_conv2d","title":"Conv2d — torch_conv2d","text":"Conv2d","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_conv2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Conv2d — torch_conv2d","text":"","code":"torch_conv2d(   input,   weight,   bias = list(),   stride = 1L,   padding = 0L,   dilation = 1L,   groups = 1L )"},{"path":"https://torch.mlverse.org/docs/reference/torch_conv2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Conv2d — torch_conv2d","text":"input input tensor shape \\((\\mbox{minibatch} , \\mbox{\\_channels} , iH , iW)\\) weight filters shape \\((\\mbox{\\_channels} , \\frac{\\mbox{\\_channels}}{\\mbox{groups}} , kH , kW)\\) bias optional bias tensor shape \\((\\mbox{\\_channels})\\). Default: NULL stride stride convolving kernel. Can single number      tuple (sH, sW). Default: 1 padding implicit paddings sides input. Can      single number tuple (padH, padW). Default: 0 dilation spacing kernel elements. Can single number      tuple (dH, dW). Default: 1 groups split input groups, \\(\\mbox{\\_channels}\\) divisible      number groups. Default: 1","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_conv2d.html","id":"conv-d-input-weight-bias-null-stride-padding-dilation-groups-gt-tensor-","dir":"Reference","previous_headings":"","what":"conv2d(input, weight, bias=NULL, stride=1, padding=0, dilation=1, groups=1) -> Tensor","title":"Conv2d — torch_conv2d","text":"Applies 2D convolution input image composed several input planes. See nn_conv2d() details output shape.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_conv2d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Conv2d — torch_conv2d","text":"","code":"if (torch_is_installed()) {  # With square kernels and equal stride filters = torch_randn(c(8,4,3,3)) inputs = torch_randn(c(1,4,5,5)) nnf_conv2d(inputs, filters, padding=1) } #> torch_tensor #> (1,1,.,.) =  #>  -4.5604  3.6075 -7.7548 -2.0152 -2.6639 #>  -3.8808 -4.6239 -1.7576  3.6281  1.2977 #>  -2.3843  7.0486  2.8052 -22.6599  0.6155 #>   2.7980 -0.2546  8.9002  8.9416 -9.0826 #>  -0.4175 -1.0085 -2.9638  0.4125  5.4529 #>  #> (1,2,.,.) =  #>  -0.2811 -0.0460  6.2378  2.9127 -2.7631 #>   0.9616  6.6324 -1.6217 -1.7990 -2.7289 #>   4.2375 -6.1837 -6.6807 -21.2378 -1.5258 #>  -1.0897 -2.9327 -4.3952  6.2345 -0.3323 #>  -0.5916  3.3897 -4.8208  6.4621  2.0097 #>  #> (1,3,.,.) =  #>    7.5336  -5.0560  -8.2436   1.7727  -7.0711 #>    6.8953   7.2543   8.8907  -4.3056  -8.6904 #>    0.2697  -0.6551  -1.4192   0.1952   5.8929 #>   -6.4188   8.0741  -4.7706   2.8552   8.6150 #>   -1.8651   3.0169   1.3711  12.9714  -1.8199 #>  #> (1,4,.,.) =  #>  -9.2736 -3.3896 -3.9684 -6.7150 -0.5721 #>   6.2935 -2.8171 -11.0965 -5.5832  8.9105 #>   1.0285 -2.5675  0.5406 -5.0206 -12.5906 #>  -0.5946  5.7171  4.3061  5.9100 -3.6071 #>   4.6603  4.4622  5.2592 -6.1512 -12.0855 #>  #> (1,5,.,.) =  #>   1.9455 -2.0833  6.6330 -8.8840  5.9554 #> ... [the output was truncated (use n=-1 to disable)] #> [ CPUFloatType{1,8,5,5} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_conv3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Conv3d — torch_conv3d","title":"Conv3d — torch_conv3d","text":"Conv3d","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_conv3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Conv3d — torch_conv3d","text":"","code":"torch_conv3d(   input,   weight,   bias = list(),   stride = 1L,   padding = 0L,   dilation = 1L,   groups = 1L )"},{"path":"https://torch.mlverse.org/docs/reference/torch_conv3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Conv3d — torch_conv3d","text":"input input tensor shape \\((\\mbox{minibatch} , \\mbox{\\_channels} , , iH , iW)\\) weight filters shape \\((\\mbox{\\_channels} , \\frac{\\mbox{\\_channels}}{\\mbox{groups}} , kT , kH , kW)\\) bias optional bias tensor shape \\((\\mbox{\\_channels})\\). Default: NULL stride stride convolving kernel. Can single number      tuple (sT, sH, sW). Default: 1 padding implicit paddings sides input. Can      single number tuple (padT, padH, padW). Default: 0 dilation spacing kernel elements. Can single number      tuple (dT, dH, dW). Default: 1 groups split input groups, \\(\\mbox{\\_channels}\\) divisible      number groups. Default: 1","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_conv3d.html","id":"conv-d-input-weight-bias-null-stride-padding-dilation-groups-gt-tensor-","dir":"Reference","previous_headings":"","what":"conv3d(input, weight, bias=NULL, stride=1, padding=0, dilation=1, groups=1) -> Tensor","title":"Conv3d — torch_conv3d","text":"Applies 3D convolution input image composed several input planes. See nn_conv3d() details output shape.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_conv3d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Conv3d — torch_conv3d","text":"","code":"if (torch_is_installed()) {  # filters = torch_randn(c(33, 16, 3, 3, 3)) # inputs = torch_randn(c(20, 16, 50, 10, 20)) # nnf_conv3d(inputs, filters) } #> NULL"},{"path":"https://torch.mlverse.org/docs/reference/torch_conv_tbc.html","id":null,"dir":"Reference","previous_headings":"","what":"Conv_tbc — torch_conv_tbc","title":"Conv_tbc — torch_conv_tbc","text":"Conv_tbc","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_conv_tbc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Conv_tbc — torch_conv_tbc","text":"","code":"torch_conv_tbc(self, weight, bias, pad = 0L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_conv_tbc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Conv_tbc — torch_conv_tbc","text":"self NA input tensor shape \\((\\mbox{sequence length} \\times batch \\times \\mbox{\\_channels})\\) weight NA filter shape (\\(\\mbox{kernel width} \\times \\mbox{\\_channels} \\times \\mbox{\\_channels}\\)) bias NA bias shape (\\(\\mbox{\\_channels}\\)) pad NA number timesteps pad. Default: 0","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_conv_tbc.html","id":"test-","dir":"Reference","previous_headings":"","what":"TEST","title":"Conv_tbc — torch_conv_tbc","text":"Applies 1-dimensional sequence convolution input sequence. Input output dimensions (Time, Batch, Channels) - hence TBC.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_conv_transpose1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Conv_transpose1d — torch_conv_transpose1d","title":"Conv_transpose1d — torch_conv_transpose1d","text":"Conv_transpose1d","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_conv_transpose1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Conv_transpose1d — torch_conv_transpose1d","text":"","code":"torch_conv_transpose1d(   input,   weight,   bias = list(),   stride = 1L,   padding = 0L,   output_padding = 0L,   groups = 1L,   dilation = 1L )"},{"path":"https://torch.mlverse.org/docs/reference/torch_conv_transpose1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Conv_transpose1d — torch_conv_transpose1d","text":"input input tensor shape \\((\\mbox{minibatch} , \\mbox{\\_channels} , iW)\\) weight filters shape \\((\\mbox{\\_channels} , \\frac{\\mbox{\\_channels}}{\\mbox{groups}} , kW)\\) bias optional bias shape \\((\\mbox{\\_channels})\\). Default: NULL stride stride convolving kernel. Can single number      tuple (sW,). Default: 1 padding dilation * (kernel_size - 1) - padding zero-padding added      sides dimension input. Can single number tuple      (padW,). Default: 0 output_padding additional size added one side dimension      output shape. Can single number tuple (out_padW). Default: 0 groups split input groups, \\(\\mbox{\\_channels}\\) divisible      number groups. Default: 1 dilation spacing kernel elements. Can single number      tuple (dW,). Default: 1","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_conv_transpose1d.html","id":"conv-transpose-d-input-weight-bias-null-stride-padding-output-padding-groups-dilation-gt-tensor-","dir":"Reference","previous_headings":"","what":"conv_transpose1d(input, weight, bias=NULL, stride=1, padding=0, output_padding=0, groups=1, dilation=1) -> Tensor","title":"Conv_transpose1d — torch_conv_transpose1d","text":"Applies 1D transposed convolution operator input signal composed several input planes, sometimes also called \"deconvolution\". See nn_conv_transpose1d()  details output shape.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_conv_transpose1d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Conv_transpose1d — torch_conv_transpose1d","text":"","code":"if (torch_is_installed()) {  inputs = torch_randn(c(20, 16, 50)) weights = torch_randn(c(16, 33, 5)) nnf_conv_transpose1d(inputs, weights) } #> torch_tensor #> (1,.,.) =  #>  Columns 1 to 6  7.2623e+00 -1.7055e+01 -8.1537e+00 -6.3030e+00  2.2526e+01 -9.2313e+00 #>   1.2945e+00 -4.6094e+00 -6.6874e-01 -6.2709e+00 -8.0662e+00 -9.8570e+00 #>   3.6372e-03  2.7183e-01  1.4382e+01  8.9109e+00 -5.4207e+00  5.8586e+00 #>  -3.1104e+00  7.4930e+00  9.1830e+00 -6.7250e+00 -9.7572e+00 -2.6488e-01 #>   1.2463e+00  7.5699e+00  1.5752e+01 -1.7233e+01 -1.0988e+01  6.5096e+00 #>  -6.0034e+00 -1.1895e+01  1.5994e+01 -2.5725e-01  2.0101e+01 -2.0946e+00 #>   9.4055e-02  9.3410e+00 -8.1753e+00 -4.3880e+00 -1.1199e+01 -1.1815e+00 #>   8.0030e-01 -2.1163e+00 -1.0413e+00 -1.0789e-01 -1.4620e+00  3.5262e+00 #>   8.5993e-01  8.8738e+00  4.6152e+00  5.8162e-01  8.1022e+00  2.9554e+00 #>  -5.5466e+00 -9.2410e+00 -2.7887e+00  4.0144e-01  1.3152e+01  8.9877e+00 #>   8.7762e+00  5.3933e+00  1.8271e+00  4.8730e+00  5.4257e+00 -9.7746e+00 #>   6.0982e-01  2.3967e-02  5.3979e+00 -5.8992e+00  1.8067e+01  1.5937e+00 #>   5.2943e+00 -2.6622e+00  6.0488e+00 -1.1476e+01 -2.6782e+01 -2.1627e+01 #>  -5.6996e+00 -1.5072e+00 -2.8376e+00  1.2578e+01  2.6264e+00 -2.2501e+00 #>   2.2144e+00 -8.0781e-02 -1.3545e+00  5.7635e+00 -1.4623e+01 -1.9362e+01 #>   7.1008e-02  1.3292e+00 -3.0182e+00  1.1991e+00 -4.4152e+00 -3.6085e+00 #>  -4.2953e+00  6.8780e-01 -9.2720e+00  5.8672e+00  1.0984e+01  7.7386e+00 #>   3.4999e+00  9.5388e-01 -5.5494e+00  5.7161e+00 -1.1978e-01 -3.3832e+00 #>  -6.9111e-01 -5.1461e+00  1.0320e+01  2.8589e+00 -2.2605e+00  1.1588e+01 #>   4.4698e+00  5.3238e+00  4.4958e-01 -9.7556e+00 -9.0811e+00  4.6425e+00 #>   2.9048e+00 -1.1800e+01  6.5441e+00 -4.0105e-01 -7.2522e+00  7.0799e+00 #>  -9.5608e+00  5.6030e+00  2.9562e+00 -3.4275e+00 -1.5356e+00  4.3073e+00 #>  -2.7037e+00 -1.7313e-02 -1.6407e+00 -7.2330e+00  1.5195e+01  2.5670e+00 #>   2.7652e+00 -4.4173e+00 -8.3351e+00  2.8537e+00 -4.7832e+00 -5.4605e+00 #>   3.8609e+00  8.5058e+00 -3.4572e-01 -8.6110e+00  1.4051e+01 -7.3177e+00 #>  -5.0703e+00 -6.0179e+00 -2.7001e+00 -9.1383e+00  1.1483e+01  1.2736e+01 #>   3.2076e+00 -5.3511e+00 -6.4300e+00  5.7287e+00 -3.9354e+00 -2.9230e+01 #>   1.6252e+00 -1.1506e+01 -1.3173e+00 -1.4891e+01  1.8824e+00  1.1005e+01 #>   3.2842e+00 -2.5989e+00  6.7036e+00 -2.3197e+00  1.5627e+00 -1.6391e+00 #> ... [the output was truncated (use n=-1 to disable)] #> [ CPUFloatType{20,33,54} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_conv_transpose2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Conv_transpose2d — torch_conv_transpose2d","title":"Conv_transpose2d — torch_conv_transpose2d","text":"Conv_transpose2d","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_conv_transpose2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Conv_transpose2d — torch_conv_transpose2d","text":"","code":"torch_conv_transpose2d(   input,   weight,   bias = list(),   stride = 1L,   padding = 0L,   output_padding = 0L,   groups = 1L,   dilation = 1L )"},{"path":"https://torch.mlverse.org/docs/reference/torch_conv_transpose2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Conv_transpose2d — torch_conv_transpose2d","text":"input input tensor shape \\((\\mbox{minibatch} , \\mbox{\\_channels} , iH , iW)\\) weight filters shape \\((\\mbox{\\_channels} , \\frac{\\mbox{\\_channels}}{\\mbox{groups}} , kH , kW)\\) bias optional bias shape \\((\\mbox{\\_channels})\\). Default: NULL stride stride convolving kernel. Can single number      tuple (sH, sW). Default: 1 padding dilation * (kernel_size - 1) - padding zero-padding added      sides dimension input. Can single number tuple      (padH, padW). Default: 0 output_padding additional size added one side dimension      output shape. Can single number tuple (out_padH, out_padW).      Default: 0 groups split input groups, \\(\\mbox{\\_channels}\\) divisible      number groups. Default: 1 dilation spacing kernel elements. Can single number      tuple (dH, dW). Default: 1","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_conv_transpose2d.html","id":"conv-transpose-d-input-weight-bias-null-stride-padding-output-padding-groups-dilation-gt-tensor-","dir":"Reference","previous_headings":"","what":"conv_transpose2d(input, weight, bias=NULL, stride=1, padding=0, output_padding=0, groups=1, dilation=1) -> Tensor","title":"Conv_transpose2d — torch_conv_transpose2d","text":"Applies 2D transposed convolution operator input image composed several input planes, sometimes also called \"deconvolution\". See nn_conv_transpose2d() details output shape.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_conv_transpose2d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Conv_transpose2d — torch_conv_transpose2d","text":"","code":"if (torch_is_installed()) {  # With square kernels and equal stride inputs = torch_randn(c(1, 4, 5, 5)) weights = torch_randn(c(4, 8, 3, 3)) nnf_conv_transpose2d(inputs, weights, padding=1) } #> torch_tensor #> (1,1,.,.) =  #>   0.1660 -1.5897  1.0874  0.4090  8.1254 #>   1.1556 -4.4604  3.7456  3.4538 -1.9520 #>   5.8230  3.2628  2.2321  9.0939 -8.2842 #>   2.9295 -3.2340 -0.6061  4.9616  5.0762 #>   2.3821 -5.6495  3.3409 -6.2373 -4.7248 #>  #> (1,2,.,.) =  #>  -1.1295  0.1410  1.3278 -1.9805 -1.5600 #>  -2.7530  3.6852  0.9425 -4.3010  3.9575 #>  -4.7844 -0.5516  4.1571  4.7782 -0.4255 #>  -4.3439  5.6680  1.3968 -2.5844  6.6445 #>  -1.4198  5.4590  2.0474 -4.4219 -2.4769 #>  #> (1,3,.,.) =  #>  -1.7355 -2.8450 -3.0814  0.6761 -0.2276 #>  -0.0030 -10.6445  0.2677  2.6822 -3.7247 #>   3.2591 -8.1509  6.7926  0.1654 -5.7573 #>   0.5255  6.7771  4.9500  3.1723  0.2542 #>   1.3332 -4.4439 -2.0032  0.7206  2.7938 #>  #> (1,4,.,.) =  #>   -6.3917  -0.5038  -1.3253   0.6057  -0.0418 #>   -4.2435   2.4131   4.4904 -11.1527  -4.3966 #>   -5.9192   1.7862 -12.0118  -1.0356   6.1951 #>    6.7473  10.4891  -5.6258   5.9848  -1.0510 #>    3.0320  -0.3424   0.1542  10.4825   3.5601 #>  #> (1,5,.,.) =  #>   4.1955 -1.1414 -0.9134  5.7342  2.5826 #> ... [the output was truncated (use n=-1 to disable)] #> [ CPUFloatType{1,8,5,5} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_conv_transpose3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Conv_transpose3d — torch_conv_transpose3d","title":"Conv_transpose3d — torch_conv_transpose3d","text":"Conv_transpose3d","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_conv_transpose3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Conv_transpose3d — torch_conv_transpose3d","text":"","code":"torch_conv_transpose3d(   input,   weight,   bias = list(),   stride = 1L,   padding = 0L,   output_padding = 0L,   groups = 1L,   dilation = 1L )"},{"path":"https://torch.mlverse.org/docs/reference/torch_conv_transpose3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Conv_transpose3d — torch_conv_transpose3d","text":"input input tensor shape \\((\\mbox{minibatch} , \\mbox{\\_channels} , , iH , iW)\\) weight filters shape \\((\\mbox{\\_channels} , \\frac{\\mbox{\\_channels}}{\\mbox{groups}} , kT , kH , kW)\\) bias optional bias shape \\((\\mbox{\\_channels})\\). Default: NULL stride stride convolving kernel. Can single number      tuple (sT, sH, sW). Default: 1 padding dilation * (kernel_size - 1) - padding zero-padding added      sides dimension input. Can single number tuple      (padT, padH, padW). Default: 0 output_padding additional size added one side dimension      output shape. Can single number tuple      (out_padT, out_padH, out_padW). Default: 0 groups split input groups, \\(\\mbox{\\_channels}\\) divisible      number groups. Default: 1 dilation spacing kernel elements. Can single number      tuple (dT, dH, dW). Default: 1","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_conv_transpose3d.html","id":"conv-transpose-d-input-weight-bias-null-stride-padding-output-padding-groups-dilation-gt-tensor-","dir":"Reference","previous_headings":"","what":"conv_transpose3d(input, weight, bias=NULL, stride=1, padding=0, output_padding=0, groups=1, dilation=1) -> Tensor","title":"Conv_transpose3d — torch_conv_transpose3d","text":"Applies 3D transposed convolution operator input image composed several input planes, sometimes also called \"deconvolution\" See nn_conv_transpose3d() details output shape.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_conv_transpose3d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Conv_transpose3d — torch_conv_transpose3d","text":"","code":"if (torch_is_installed()) { if (FALSE) { inputs = torch_randn(c(20, 16, 50, 10, 20)) weights = torch_randn(c(16, 33, 3, 3, 3)) nnf_conv_transpose3d(inputs, weights) } }"},{"path":"https://torch.mlverse.org/docs/reference/torch_cos.html","id":null,"dir":"Reference","previous_headings":"","what":"Cos — torch_cos","title":"Cos — torch_cos","text":"Cos","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cos.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cos — torch_cos","text":"","code":"torch_cos(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_cos.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cos — torch_cos","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cos.html","id":"cos-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"cos(input, out=NULL) -> Tensor","title":"Cos — torch_cos","text":"Returns new tensor cosine  elements input. $$     \\mbox{}_{} = \\cos(\\mbox{input}_{}) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cos.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cos — torch_cos","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(4)) a torch_cos(a) } #> torch_tensor #>  0.9998 #>  0.5379 #>  0.8324 #>  0.9991 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_cosh.html","id":null,"dir":"Reference","previous_headings":"","what":"Cosh — torch_cosh","title":"Cosh — torch_cosh","text":"Cosh","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cosh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cosh — torch_cosh","text":"","code":"torch_cosh(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_cosh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cosh — torch_cosh","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cosh.html","id":"cosh-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"cosh(input, out=NULL) -> Tensor","title":"Cosh — torch_cosh","text":"Returns new tensor hyperbolic cosine  elements input. $$     \\mbox{}_{} = \\cosh(\\mbox{input}_{}) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cosh.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cosh — torch_cosh","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(4)) a torch_cosh(a) } #> torch_tensor #>  1.0087 #>  2.8242 #>  1.1153 #>  1.0097 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_cosine_similarity.html","id":null,"dir":"Reference","previous_headings":"","what":"Cosine_similarity — torch_cosine_similarity","title":"Cosine_similarity — torch_cosine_similarity","text":"Cosine_similarity","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cosine_similarity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cosine_similarity — torch_cosine_similarity","text":"","code":"torch_cosine_similarity(x1, x2, dim = 2L, eps = 1e-08)"},{"path":"https://torch.mlverse.org/docs/reference/torch_cosine_similarity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cosine_similarity — torch_cosine_similarity","text":"x1 (Tensor) First input. x2 (Tensor) Second input (size matching x1). dim (int, optional) Dimension vectors. Default: 1 eps (float, optional) Small value avoid division zero.        Default: 1e-8","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cosine_similarity.html","id":"cosine-similarity-x-x-dim-eps-e-gt-tensor-","dir":"Reference","previous_headings":"","what":"cosine_similarity(x1, x2, dim=1, eps=1e-8) -> Tensor","title":"Cosine_similarity — torch_cosine_similarity","text":"Returns cosine similarity x1 x2, computed along dim. $$     \\mbox{similarity} = \\frac{x_1 \\cdot x_2}{\\max(\\Vert x_1 \\Vert _2 \\cdot \\Vert x_2 \\Vert _2, \\epsilon)} $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cosine_similarity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cosine_similarity — torch_cosine_similarity","text":"","code":"if (torch_is_installed()) {  input1 = torch_randn(c(100, 128)) input2 = torch_randn(c(100, 128)) output = torch_cosine_similarity(input1, input2) output } #> torch_tensor #> -0.0444 #>  0.1909 #>  0.0705 #>  0.0031 #>  0.1083 #>  0.1506 #>  0.0003 #>  0.0612 #>  0.0125 #>  0.1003 #>  0.1032 #> -0.0915 #> -0.0261 #>  0.0257 #> -0.0444 #> -0.1241 #> -0.1165 #> -0.0921 #>  0.0203 #> -0.0405 #> -0.0685 #>  0.0604 #> -0.1574 #> -0.0031 #> -0.0103 #>  0.0160 #> -0.0217 #> -0.0528 #> -0.0807 #>  0.0173 #> ... [the output was truncated (use n=-1 to disable)] #> [ CPUFloatType{100} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_count_nonzero.html","id":null,"dir":"Reference","previous_headings":"","what":"Count_nonzero — torch_count_nonzero","title":"Count_nonzero — torch_count_nonzero","text":"Count_nonzero","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_count_nonzero.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Count_nonzero — torch_count_nonzero","text":"","code":"torch_count_nonzero(self, dim = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/torch_count_nonzero.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Count_nonzero — torch_count_nonzero","text":"self (Tensor) input tensor. dim (int tuple ints, optional) Dim tuple dims along count non-zeros.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_count_nonzero.html","id":"count-nonzero-input-dim-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"count_nonzero(input, dim=None) -> Tensor","title":"Count_nonzero — torch_count_nonzero","text":"Counts number non-zero values tensor input along given dim. dim specified non-zeros tensor counted.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_count_nonzero.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Count_nonzero — torch_count_nonzero","text":"","code":"if (torch_is_installed()) {  x <- torch_zeros(3,3) x[torch_randn(3,3) > 0.5] = 1 x torch_count_nonzero(x) torch_count_nonzero(x, dim=1) } #> torch_tensor #>  2 #>  0 #>  2 #> [ CPULongType{3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_cross.html","id":null,"dir":"Reference","previous_headings":"","what":"Cross — torch_cross","title":"Cross — torch_cross","text":"Cross","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cross.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cross — torch_cross","text":"","code":"torch_cross(self, other, dim = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/torch_cross.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cross — torch_cross","text":"self (Tensor) input tensor. (Tensor) second input tensor dim (int, optional) dimension take cross-product .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cross.html","id":"cross-input-other-dim-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"cross(input, other, dim=-1, out=NULL) -> Tensor","title":"Cross — torch_cross","text":"Returns cross product vectors dimension dim input . input must size, size dim dimension 3. dim given, defaults first dimension found size 3.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cross.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cross — torch_cross","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(4, 3)) a b = torch_randn(c(4, 3)) b torch_cross(a, b, dim=2) torch_cross(a, b) } #> torch_tensor #> -0.0845  0.2132  0.4054 #> -0.0907 -2.3545  1.1211 #> -0.2083 -1.6346 -1.6994 #> -0.4848 -0.9600  0.1598 #> [ CPUFloatType{4,3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_cummax.html","id":null,"dir":"Reference","previous_headings":"","what":"Cummax — torch_cummax","title":"Cummax — torch_cummax","text":"Cummax","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cummax.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cummax — torch_cummax","text":"","code":"torch_cummax(self, dim)"},{"path":"https://torch.mlverse.org/docs/reference/torch_cummax.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cummax — torch_cummax","text":"self (Tensor) input tensor. dim (int) dimension operation ","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cummax.html","id":"cummax-input-dim-gt-tensor-longtensor-","dir":"Reference","previous_headings":"","what":"cummax(input, dim) -> (Tensor, LongTensor)","title":"Cummax — torch_cummax","text":"Returns namedtuple (values, indices) values cumulative maximum elements input dimension dim. indices index location maximum value found dimension dim. $$     y_i = max(x_1, x_2, x_3, \\dots, x_i) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cummax.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cummax — torch_cummax","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(10)) a torch_cummax(a, dim=1) } #> [[1]] #> torch_tensor #>  1.6653 #>  1.6653 #>  1.6653 #>  1.6653 #>  1.6653 #>  1.6653 #>  1.6653 #>  1.6653 #>  1.6653 #>  1.6653 #> [ CPUFloatType{10} ] #>  #> [[2]] #> torch_tensor #>  0 #>  0 #>  0 #>  0 #>  0 #>  0 #>  0 #>  0 #>  0 #>  0 #> [ CPULongType{10} ] #>"},{"path":"https://torch.mlverse.org/docs/reference/torch_cummin.html","id":null,"dir":"Reference","previous_headings":"","what":"Cummin — torch_cummin","title":"Cummin — torch_cummin","text":"Cummin","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cummin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cummin — torch_cummin","text":"","code":"torch_cummin(self, dim)"},{"path":"https://torch.mlverse.org/docs/reference/torch_cummin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cummin — torch_cummin","text":"self (Tensor) input tensor. dim (int) dimension operation ","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cummin.html","id":"cummin-input-dim-gt-tensor-longtensor-","dir":"Reference","previous_headings":"","what":"cummin(input, dim) -> (Tensor, LongTensor)","title":"Cummin — torch_cummin","text":"Returns namedtuple (values, indices) values cumulative minimum elements input dimension dim. indices index location maximum value found dimension dim. $$     y_i = min(x_1, x_2, x_3, \\dots, x_i) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cummin.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cummin — torch_cummin","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(10)) a torch_cummin(a, dim=1) } #> [[1]] #> torch_tensor #>  0.6368 #> -0.9793 #> -0.9793 #> -1.2178 #> -1.2178 #> -1.2178 #> -1.2178 #> -1.2178 #> -1.2178 #> -1.2178 #> [ CPUFloatType{10} ] #>  #> [[2]] #> torch_tensor #>  0 #>  1 #>  1 #>  3 #>  3 #>  3 #>  3 #>  3 #>  3 #>  3 #> [ CPULongType{10} ] #>"},{"path":"https://torch.mlverse.org/docs/reference/torch_cumprod.html","id":null,"dir":"Reference","previous_headings":"","what":"Cumprod — torch_cumprod","title":"Cumprod — torch_cumprod","text":"Cumprod","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cumprod.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cumprod — torch_cumprod","text":"","code":"torch_cumprod(self, dim, dtype = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/torch_cumprod.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cumprod — torch_cumprod","text":"self (Tensor) input tensor. dim (int) dimension operation dtype (torch.dtype, optional) desired data type returned tensor.        specified, input tensor casted dtype operation        performed. useful preventing data type overflows. Default: NULL.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cumprod.html","id":"cumprod-input-dim-out-null-dtype-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"cumprod(input, dim, out=NULL, dtype=NULL) -> Tensor","title":"Cumprod — torch_cumprod","text":"Returns cumulative product elements input dimension dim. example, input vector size N, result also vector size N, elements. $$     y_i = x_1 \\times x_2\\times x_3\\times \\dots \\times x_i $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cumprod.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cumprod — torch_cumprod","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(10)) a torch_cumprod(a, dim=1) } #> torch_tensor #> 0.01 * #> -3.8929 #>  1.3768 #> -1.6106 #> -0.4555 #>  0.3654 #> -0.0245 #> -0.0300 #>  0.0007 #> -0.0008 #>  0.0012 #> [ CPUFloatType{10} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_cumsum.html","id":null,"dir":"Reference","previous_headings":"","what":"Cumsum — torch_cumsum","title":"Cumsum — torch_cumsum","text":"Cumsum","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cumsum.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cumsum — torch_cumsum","text":"","code":"torch_cumsum(self, dim, dtype = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/torch_cumsum.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cumsum — torch_cumsum","text":"self (Tensor) input tensor. dim (int) dimension operation dtype (torch.dtype, optional) desired data type returned tensor.        specified, input tensor casted dtype operation        performed. useful preventing data type overflows. Default: NULL.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cumsum.html","id":"cumsum-input-dim-out-null-dtype-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"cumsum(input, dim, out=NULL, dtype=NULL) -> Tensor","title":"Cumsum — torch_cumsum","text":"Returns cumulative sum elements input dimension dim. example, input vector size N, result also vector size N, elements. $$     y_i = x_1 + x_2 + x_3 + \\dots + x_i $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cumsum.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cumsum — torch_cumsum","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(10)) a torch_cumsum(a, dim=1) } #> torch_tensor #>  0.6890 #>  1.2324 #>  1.6022 #>  0.0321 #>  0.9204 #>  0.0819 #>  0.1066 #>  0.0006 #>  0.6855 #>  2.5382 #> [ CPUFloatType{10} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_deg2rad.html","id":null,"dir":"Reference","previous_headings":"","what":"Deg2rad — torch_deg2rad","title":"Deg2rad — torch_deg2rad","text":"Deg2rad","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_deg2rad.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Deg2rad — torch_deg2rad","text":"","code":"torch_deg2rad(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_deg2rad.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Deg2rad — torch_deg2rad","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_deg2rad.html","id":"deg-rad-input-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"deg2rad(input, *, out=None) -> Tensor","title":"Deg2rad — torch_deg2rad","text":"Returns new tensor elements input converted angles degrees radians.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_deg2rad.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Deg2rad — torch_deg2rad","text":"","code":"if (torch_is_installed()) {  a <- torch_tensor(rbind(c(180.0, -180.0), c(360.0, -360.0), c(90.0, -90.0))) torch_deg2rad(a) } #> torch_tensor #>  3.1416 -3.1416 #>  6.2832 -6.2832 #>  1.5708 -1.5708 #> [ CPUFloatType{3,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_dequantize.html","id":null,"dir":"Reference","previous_headings":"","what":"Dequantize — torch_dequantize","title":"Dequantize — torch_dequantize","text":"Dequantize","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_dequantize.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Dequantize — torch_dequantize","text":"","code":"torch_dequantize(tensor)"},{"path":"https://torch.mlverse.org/docs/reference/torch_dequantize.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Dequantize — torch_dequantize","text":"tensor (Tensor) quantized Tensor list oof quantized tensors","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_dequantize.html","id":"dequantize-tensor-gt-tensor-","dir":"Reference","previous_headings":"","what":"dequantize(tensor) -> Tensor","title":"Dequantize — torch_dequantize","text":"Returns fp32 Tensor dequantizing quantized Tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_dequantize.html","id":"dequantize-tensors-gt-sequence-of-tensors-","dir":"Reference","previous_headings":"","what":"dequantize(tensors) -> sequence of Tensors","title":"Dequantize — torch_dequantize","text":"Given list quantized Tensors, dequantize return list fp32 Tensors","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_det.html","id":null,"dir":"Reference","previous_headings":"","what":"Det — torch_det","title":"Det — torch_det","text":"Det","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_det.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Det — torch_det","text":"","code":"torch_det(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_det.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Det — torch_det","text":"self (Tensor) input tensor size (*, n, n) * zero                batch dimensions.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_det.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Det — torch_det","text":"","code":"Backward through `det` internally uses SVD results when `input` is not invertible. In this case, double backward through `det` will be unstable in when `input` doesn't have distinct singular values. See `~torch.svd` for details."},{"path":"https://torch.mlverse.org/docs/reference/torch_det.html","id":"det-input-gt-tensor-","dir":"Reference","previous_headings":"","what":"det(input) -> Tensor","title":"Det — torch_det","text":"Calculates determinant square matrix batches square matrices.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_det.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Det — torch_det","text":"","code":"if (torch_is_installed()) {  A = torch_randn(c(3, 3)) torch_det(A) A = torch_randn(c(3, 2, 2)) A A$det() } #> torch_tensor #>  2.6027 #>  1.4142 #> -0.3293 #> [ CPUFloatType{3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_device.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a Device object — torch_device","title":"Create a Device object — torch_device","text":"torch_device  object representing device torch_tensor allocated.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_device.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a Device object — torch_device","text":"","code":"torch_device(type, index = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/torch_device.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a Device object — torch_device","text":"type (character) device type \"cuda\" \"cpu\" index (integer) optional device ordinal device type.  device ordinal present, object always represent current device device type, even torch_cuda_set_device() called; e.g., torch_tensor constructed device 'cuda' equivalent 'cuda:X' X result torch_cuda_current_device(). torch_device can constructed via string via string device ordinal","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_device.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a Device object — torch_device","text":"","code":"if (torch_is_installed()) {  # Via string torch_device(\"cuda:1\") torch_device(\"cpu\") torch_device(\"cuda\") # current cuda device  # Via string and device ordinal torch_device(\"cuda\", 0) torch_device(\"cpu\", 0) } #> torch_device(type='cpu', index=0)"},{"path":"https://torch.mlverse.org/docs/reference/torch_diag.html","id":null,"dir":"Reference","previous_headings":"","what":"Diag — torch_diag","title":"Diag — torch_diag","text":"Diag","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_diag.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Diag — torch_diag","text":"","code":"torch_diag(self, diagonal = 0L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_diag.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Diag — torch_diag","text":"self (Tensor) input tensor. diagonal (int, optional) diagonal consider","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_diag.html","id":"diag-input-diagonal-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"diag(input, diagonal=0, out=NULL) -> Tensor","title":"Diag — torch_diag","text":"input vector (1-D tensor), returns 2-D square tensor elements input diagonal. input matrix (2-D tensor), returns 1-D tensor diagonal elements input. argument diagonal controls diagonal consider: diagonal = 0, main diagonal. diagonal > 0, main diagonal. diagonal < 0, main diagonal.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_diag_embed.html","id":null,"dir":"Reference","previous_headings":"","what":"Diag_embed — torch_diag_embed","title":"Diag_embed — torch_diag_embed","text":"Diag_embed","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_diag_embed.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Diag_embed — torch_diag_embed","text":"","code":"torch_diag_embed(self, offset = 0L, dim1 = -2L, dim2 = -1L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_diag_embed.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Diag_embed — torch_diag_embed","text":"self (Tensor) input tensor. Must least 1-dimensional. offset (int, optional) diagonal consider. Default: 0        (main diagonal). dim1 (int, optional) first dimension respect        take diagonal. Default: -2. dim2 (int, optional) second dimension respect        take diagonal. Default: -1.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_diag_embed.html","id":"diag-embed-input-offset-dim-dim-gt-tensor-","dir":"Reference","previous_headings":"","what":"diag_embed(input, offset=0, dim1=-2, dim2=-1) -> Tensor","title":"Diag_embed — torch_diag_embed","text":"Creates tensor whose diagonals certain 2D planes (specified dim1 dim2) filled input. facilitate creating batched diagonal matrices, 2D planes formed last two dimensions returned tensor chosen default. argument offset controls diagonal consider: offset = 0, main diagonal. offset > 0, main diagonal. offset < 0, main diagonal. size new matrix calculated make specified diagonal size last input dimension. Note offset \\(0\\), order dim1 dim2 matters. Exchanging equivalent changing sign offset. Applying torch_diagonal output function arguments yields matrix identical input. However, torch_diagonal different default dimensions, need explicitly specified.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_diag_embed.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Diag_embed — torch_diag_embed","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(2, 3)) torch_diag_embed(a) torch_diag_embed(a, offset=1, dim1=1, dim2=3) } #> torch_tensor #> (1,.,.) =  #>   0.0000  1.1072  0.0000  0.0000 #>   0.0000  1.4113  0.0000  0.0000 #>  #> (2,.,.) =  #>   0.0000  0.0000  1.0521  0.0000 #>   0.0000  0.0000  0.6029  0.0000 #>  #> (3,.,.) =  #>   0.0000  0.0000  0.0000 -0.7156 #>   0.0000  0.0000  0.0000 -0.2509 #>  #> (4,.,.) =  #>   0  0  0  0 #>   0  0  0  0 #> [ CPUFloatType{4,2,4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_diagflat.html","id":null,"dir":"Reference","previous_headings":"","what":"Diagflat — torch_diagflat","title":"Diagflat — torch_diagflat","text":"Diagflat","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_diagflat.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Diagflat — torch_diagflat","text":"","code":"torch_diagflat(self, offset = 0L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_diagflat.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Diagflat — torch_diagflat","text":"self (Tensor) input tensor. offset (int, optional) diagonal consider. Default: 0 (main        diagonal).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_diagflat.html","id":"diagflat-input-offset-gt-tensor-","dir":"Reference","previous_headings":"","what":"diagflat(input, offset=0) -> Tensor","title":"Diagflat — torch_diagflat","text":"input vector (1-D tensor), returns 2-D square tensor elements input diagonal. input tensor one dimension, returns 2-D tensor diagonal elements equal flattened input. argument offset controls diagonal consider: offset = 0, main diagonal. offset > 0, main diagonal. offset < 0, main diagonal.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_diagflat.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Diagflat — torch_diagflat","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(3)) a torch_diagflat(a) torch_diagflat(a, 1) a = torch_randn(c(2, 2)) a torch_diagflat(a) } #> torch_tensor #>  0.3021  0.0000  0.0000  0.0000 #>  0.0000  0.4805  0.0000  0.0000 #>  0.0000  0.0000  1.2035  0.0000 #>  0.0000  0.0000  0.0000 -0.8634 #> [ CPUFloatType{4,4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_diagonal.html","id":null,"dir":"Reference","previous_headings":"","what":"Diagonal — torch_diagonal","title":"Diagonal — torch_diagonal","text":"Diagonal","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_diagonal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Diagonal — torch_diagonal","text":"","code":"torch_diagonal(self, outdim, dim1 = 1L, dim2 = 2L, offset = 0L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_diagonal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Diagonal — torch_diagonal","text":"self (Tensor) input tensor. Must least 2-dimensional. outdim dimension name self named tensor. dim1 (int, optional) first dimension respect        take diagonal. Default: 0. dim2 (int, optional) second dimension respect        take diagonal. Default: 1. offset (int, optional) diagonal consider. Default: 0        (main diagonal).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_diagonal.html","id":"diagonal-input-offset-dim-dim-gt-tensor-","dir":"Reference","previous_headings":"","what":"diagonal(input, offset=0, dim1=0, dim2=1) -> Tensor","title":"Diagonal — torch_diagonal","text":"Returns partial view input diagonal elements respect dim1 dim2 appended dimension end shape. argument offset controls diagonal consider: offset = 0, main diagonal. offset > 0, main diagonal. offset < 0, main diagonal. Applying torch_diag_embed output function arguments yields diagonal matrix diagonal entries input. However, torch_diag_embed different default dimensions, need explicitly specified.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_diagonal.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Diagonal — torch_diagonal","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(3, 3)) a torch_diagonal(a, offset = 0) torch_diagonal(a, offset = 1) x = torch_randn(c(2, 5, 4, 2)) torch_diagonal(x, offset=-1, dim1=1, dim2=2) } #> torch_tensor #> (1,.,.) =  #>  -1.6822 #>  -0.7877 #>  #> (2,.,.) =  #>  -1.3171 #>   0.4693 #>  #> (3,.,.) =  #>   1.2076 #>   0.9640 #>  #> (4,.,.) =  #>  -0.8800 #>  -1.2734 #> [ CPUFloatType{4,2,1} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_diff.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the n-th forward difference along the given dimension. — torch_diff","title":"Computes the n-th forward difference along the given dimension. — torch_diff","text":"first-order differences given [] = input[+ 1] - input[]. Higher-order differences calculated using torch_diff() recursively.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_diff.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the n-th forward difference along the given dimension. — torch_diff","text":"","code":"torch_diff(self, n = 1L, dim = -1L, prepend = list(), append = list())"},{"path":"https://torch.mlverse.org/docs/reference/torch_diff.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the n-th forward difference along the given dimension. — torch_diff","text":"self tensor compute differences n number times recursively compute difference dim dimension compute difference along. Default last dimension. prepend values prepend input along dim computing difference. dimensions must equivalent input, shapes must match input’s shape except dim. append values append input along dim computing difference. dimensions must equivalent input, shapes must match input’s shape except dim.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_diff.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Computes the n-th forward difference along the given dimension. — torch_diff","text":"n = 1 currently supported","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_diff.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the n-th forward difference along the given dimension. — torch_diff","text":"","code":"if (torch_is_installed()) { a <- torch_tensor(c(1,2,3)) torch_diff(a)  b <- torch_tensor(c(4, 5)) torch_diff(a, append = b)  c <- torch_tensor(rbind(c(1,2,3), c(3,4,5))) torch_diff(c, dim = 1) torch_diff(c, dim = 2)   } #> torch_tensor #>  1  1 #>  1  1 #> [ CPUFloatType{2,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_digamma.html","id":null,"dir":"Reference","previous_headings":"","what":"Digamma — torch_digamma","title":"Digamma — torch_digamma","text":"Digamma","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_digamma.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Digamma — torch_digamma","text":"","code":"torch_digamma(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_digamma.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Digamma — torch_digamma","text":"self (Tensor) tensor compute digamma function ","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_digamma.html","id":"digamma-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"digamma(input, out=NULL) -> Tensor","title":"Digamma — torch_digamma","text":"Computes logarithmic derivative gamma function input. $$     \\psi(x) = \\frac{d}{dx} \\ln\\left(\\Gamma\\left(x\\right)\\right) = \\frac{\\Gamma'(x)}{\\Gamma(x)} $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_digamma.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Digamma — torch_digamma","text":"","code":"if (torch_is_installed()) {  a = torch_tensor(c(1, 0.5)) torch_digamma(a) } #> torch_tensor #> -0.5772 #> -1.9635 #> [ CPUFloatType{2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_dist.html","id":null,"dir":"Reference","previous_headings":"","what":"Dist — torch_dist","title":"Dist — torch_dist","text":"Dist","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_dist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Dist — torch_dist","text":"","code":"torch_dist(self, other, p = 2L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_dist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Dist — torch_dist","text":"self (Tensor) input tensor. (Tensor) Right-hand-side input tensor p (float, optional) norm computed","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_dist.html","id":"dist-input-other-p-gt-tensor-","dir":"Reference","previous_headings":"","what":"dist(input, other, p=2) -> Tensor","title":"Dist — torch_dist","text":"Returns p-norm (input - ) shapes input must broadcastable .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_dist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Dist — torch_dist","text":"","code":"if (torch_is_installed()) {  x = torch_randn(c(4)) x y = torch_randn(c(4)) y torch_dist(x, y, 3.5) torch_dist(x, y, 3) torch_dist(x, y, 0) torch_dist(x, y, 1) } #> torch_tensor #> 2.38916 #> [ CPUFloatType{} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_div.html","id":null,"dir":"Reference","previous_headings":"","what":"Div — torch_div","title":"Div — torch_div","text":"Div","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_div.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Div — torch_div","text":"","code":"torch_div(self, other, rounding_mode)"},{"path":"https://torch.mlverse.org/docs/reference/torch_div.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Div — torch_div","text":"self (Tensor) input tensor. (Number) number divided element input rounding_mode (str, optional) – Type rounding applied result: NULL - default behavior. Performs rounding , input integer types, promotes inputs default scalar type. Equivalent true division Python (/ operator) NumPy’s np.true_divide. \"trunc\" - rounds results division towards zero. Equivalent C-style integer division. \"floor\" - rounds results division . Equivalent floor division Python (// operator) NumPy’s np.floor_divide.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_div.html","id":"div-input-other-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"div(input, other, out=NULL) -> Tensor","title":"Div — torch_div","text":"Divides element input input scalar returns new resulting tensor. element tensor input divided element tensor . resulting tensor returned. $$     \\mbox{}_i = \\frac{\\mbox{input}_i}{\\mbox{}_i} $$ shapes input must broadcastable . torch_dtype input differ, torch_dtype result tensor determined following rules described type promotion documentation . specified, result must castable  torch_dtype specified output tensor. Integral division zero leads undefined behavior.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_div.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Div — torch_div","text":"Integer division using div deprecated, future release div perform true division like torch_true_divide(). Use torch_floor_divide() perform integer division, instead. $$     \\mbox{}_i = \\frac{\\mbox{input}_i}{\\mbox{}} $$ torch_dtype input differ, torch_dtype result tensor determined following rules described type promotion documentation . specified, result must castable torch_dtype specified output tensor. Integral division zero leads undefined behavior.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_div.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Div — torch_div","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(5)) a torch_div(a, 0.5)   a = torch_randn(c(4, 4)) a b = torch_randn(c(4)) b torch_div(a, b) } #> torch_tensor #>   1.7242   4.5418 -10.1465   0.8887 #>   0.6280  -4.1159  -2.1881   0.5456 #>  -1.3812  -1.8686  -6.5700  -0.7843 #>   0.8885  -7.5946  35.1556  -0.5610 #> [ CPUFloatType{4,4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_divide.html","id":null,"dir":"Reference","previous_headings":"","what":"Divide — torch_divide","title":"Divide — torch_divide","text":"Divide","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_divide.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Divide — torch_divide","text":"","code":"torch_divide(self, other, rounding_mode)"},{"path":"https://torch.mlverse.org/docs/reference/torch_divide.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Divide — torch_divide","text":"self (Tensor) input tensor. (Number) number divided element input rounding_mode (str, optional) – Type rounding applied result: NULL - default behavior. Performs rounding , input integer types, promotes inputs default scalar type. Equivalent true division Python (/ operator) NumPy’s np.true_divide. \"trunc\" - rounds results division towards zero. Equivalent C-style integer division. \"floor\" - rounds results division . Equivalent floor division Python (// operator) NumPy’s np.floor_divide.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_divide.html","id":"divide-input-other-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"divide(input, other, *, out=None) -> Tensor","title":"Divide — torch_divide","text":"Alias torch_div().","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_dot.html","id":null,"dir":"Reference","previous_headings":"","what":"Dot — torch_dot","title":"Dot — torch_dot","text":"Dot","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_dot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Dot — torch_dot","text":"","code":"torch_dot(self, tensor)"},{"path":"https://torch.mlverse.org/docs/reference/torch_dot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Dot — torch_dot","text":"self input tensor tensor input tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_dot.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Dot — torch_dot","text":"function broadcast .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_dot.html","id":"dot-input-tensor-gt-tensor-","dir":"Reference","previous_headings":"","what":"dot(input, tensor) -> Tensor","title":"Dot — torch_dot","text":"Computes dot product (inner product) two tensors.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_dot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Dot — torch_dot","text":"","code":"if (torch_is_installed()) {  torch_dot(torch_tensor(c(2, 3)), torch_tensor(c(2, 1))) } #> torch_tensor #> 7 #> [ CPUFloatType{} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_dstack.html","id":null,"dir":"Reference","previous_headings":"","what":"Dstack — torch_dstack","title":"Dstack — torch_dstack","text":"Dstack","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_dstack.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Dstack — torch_dstack","text":"","code":"torch_dstack(tensors)"},{"path":"https://torch.mlverse.org/docs/reference/torch_dstack.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Dstack — torch_dstack","text":"tensors (sequence Tensors) sequence tensors concatenate","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_dstack.html","id":"dstack-tensors-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"dstack(tensors, *, out=None) -> Tensor","title":"Dstack — torch_dstack","text":"Stack tensors sequence depthwise (along third axis). equivalent concatenation along third axis 1-D 2-D tensors reshaped torch_atleast_3d().","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_dstack.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Dstack — torch_dstack","text":"","code":"if (torch_is_installed()) {  a <- torch_tensor(c(1, 2, 3)) b <- torch_tensor(c(4, 5, 6)) torch_dstack(list(a,b)) a <- torch_tensor(rbind(1,2,3)) b <- torch_tensor(rbind(4,5,6)) torch_dstack(list(a,b)) } #> torch_tensor #> (1,.,.) =  #>   1  4 #>  #> (2,.,.) =  #>   2  5 #>  #> (3,.,.) =  #>   3  6 #> [ CPUFloatType{3,1,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_dtype.html","id":null,"dir":"Reference","previous_headings":"","what":"Torch data types — torch_dtype","title":"Torch data types — torch_dtype","text":"Returns correspondent data type.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_dtype.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Torch data types — torch_dtype","text":"","code":"torch_float32()  torch_float()  torch_float64()  torch_double()  torch_float16()  torch_half()  torch_uint8()  torch_int8()  torch_int16()  torch_short()  torch_int32()  torch_int()  torch_int64()  torch_long()  torch_bool()  torch_quint8()  torch_qint8()  torch_qint32()"},{"path":"https://torch.mlverse.org/docs/reference/torch_eig.html","id":null,"dir":"Reference","previous_headings":"","what":"Eig — torch_eig","title":"Eig — torch_eig","text":"Eig","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_eig.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Eig — torch_eig","text":"","code":"torch_eig(self, eigenvectors = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_eig.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Eig — torch_eig","text":"self (Tensor) square matrix shape \\((n \\times n)\\) eigenvalues eigenvectors        computed eigenvectors (bool) TRUE compute eigenvalues eigenvectors;        otherwise, eigenvalues computed","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_eig.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Eig — torch_eig","text":"","code":"Since eigenvalues and eigenvectors might be complex, backward pass is supported only for [`torch_symeig`]"},{"path":"https://torch.mlverse.org/docs/reference/torch_eig.html","id":"eig-input-eigenvectors-false-out-null-gt-tensor-tensor-","dir":"Reference","previous_headings":"","what":"eig(input, eigenvectors=False, out=NULL) -> (Tensor, Tensor)","title":"Eig — torch_eig","text":"Computes eigenvalues eigenvectors real square matrix.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_einsum.html","id":null,"dir":"Reference","previous_headings":"","what":"Einsum — torch_einsum","title":"Einsum — torch_einsum","text":"Einsum","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_einsum.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Einsum — torch_einsum","text":"","code":"torch_einsum(equation, tensors)"},{"path":"https://torch.mlverse.org/docs/reference/torch_einsum.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Einsum — torch_einsum","text":"equation (string) equation given terms lower case letters (indices) associated           dimension operands result. left hand side lists operands           dimensions, separated commas. one index letter per tensor dimension.           right hand side follows -> gives indices output.           -> right hand side omitted, implicitly defined alphabetically           sorted list indices appearing exactly left hand side.           indices apprearing output summed multiplying operands           entries.           index appears several times operand, diagonal taken.           Ellipses ... represent fixed number dimensions. right hand side inferred,           ellipsis dimensions beginning output. tensors (Tensor) operands compute Einstein sum .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_einsum.html","id":"einsum-equation-operands-gt-tensor-","dir":"Reference","previous_headings":"","what":"einsum(equation, *operands) -> Tensor","title":"Einsum — torch_einsum","text":"function provides way computing multilinear expressions (.e. sums products) using Einstein summation convention.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_einsum.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Einsum — torch_einsum","text":"","code":"if (torch_is_installed()) {  x = torch_randn(c(5)) y = torch_randn(c(4)) torch_einsum('i,j->ij', list(x, y))  # outer product A = torch_randn(c(3,5,4)) l = torch_randn(c(2,5)) r = torch_randn(c(2,4)) torch_einsum('bn,anm,bm->ba', list(l, A, r)) # compare torch_nn$functional$bilinear As = torch_randn(c(3,2,5)) Bs = torch_randn(c(3,5,4)) torch_einsum('bij,bjk->bik', list(As, Bs)) # batch matrix multiplication A = torch_randn(c(3, 3)) torch_einsum('ii->i', list(A)) # diagonal A = torch_randn(c(4, 3, 3)) torch_einsum('...ii->...i', list(A)) # batch diagonal A = torch_randn(c(2, 3, 4, 5)) torch_einsum('...ij->...ji', list(A))$shape # batch permute  } #> [1] 2 3 5 4"},{"path":"https://torch.mlverse.org/docs/reference/torch_empty.html","id":null,"dir":"Reference","previous_headings":"","what":"Empty — torch_empty","title":"Empty — torch_empty","text":"Empty","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_empty.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Empty — torch_empty","text":"","code":"torch_empty(   ...,   names = NULL,   dtype = NULL,   layout = torch_strided(),   device = NULL,   requires_grad = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/torch_empty.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Empty — torch_empty","text":"... sequence integers defining shape output tensor. names optional character vector naming dimension. dtype (torch.dtype, optional) desired data type returned tensor.        Default: NULL, uses global default (see torch_set_default_tensor_type). layout (torch.layout, optional) desired layout returned Tensor.        Default: torch_strided. device (torch.device, optional) desired device returned tensor.        Default: NULL, uses current device default tensor type        (see torch_set_default_tensor_type). device CPU        CPU tensor types current CUDA device CUDA tensor types. requires_grad (bool, optional) autograd record operations        returned tensor. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_empty.html","id":"empty-size-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-pin-memory-false-gt-tensor-","dir":"Reference","previous_headings":"","what":"empty(*size, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False, pin_memory=False) -> Tensor","title":"Empty — torch_empty","text":"Returns tensor filled uninitialized data. shape tensor defined variable argument size.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_empty.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Empty — torch_empty","text":"","code":"if (torch_is_installed()) {  torch_empty(c(2, 3)) } #> torch_tensor #>  4.7275e+27  9.1066e-12  1.7592e+22 #>  7.2143e+22  7.0308e+28  3.1986e+21 #> [ CPUFloatType{2,3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_empty_like.html","id":null,"dir":"Reference","previous_headings":"","what":"Empty_like — torch_empty_like","title":"Empty_like — torch_empty_like","text":"Empty_like","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_empty_like.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Empty_like — torch_empty_like","text":"","code":"torch_empty_like(   input,   dtype = NULL,   layout = torch_strided(),   device = NULL,   requires_grad = FALSE,   memory_format = torch_preserve_format() )"},{"path":"https://torch.mlverse.org/docs/reference/torch_empty_like.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Empty_like — torch_empty_like","text":"input (Tensor) size input determine size output tensor. dtype (torch.dtype, optional) desired data type returned Tensor.        Default: NULL, defaults dtype input. layout (torch.layout, optional) desired layout returned tensor.        Default: NULL, defaults layout input. device (torch.device, optional) desired device returned tensor.        Default: NULL, defaults device input. requires_grad (bool, optional) autograd record operations        returned tensor. Default: FALSE. memory_format (torch.memory_format, optional) desired memory format        returned Tensor. Default: torch_preserve_format.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_empty_like.html","id":"empty-like-input-dtype-null-layout-null-device-null-requires-grad-false-memory-format-torch-preserve-format-gt-tensor-","dir":"Reference","previous_headings":"","what":"empty_like(input, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, memory_format=torch.preserve_format) -> Tensor","title":"Empty_like — torch_empty_like","text":"Returns uninitialized tensor size input. torch_empty_like(input) equivalent torch_empty(input.size(), dtype=input.dtype, layout=input.layout, device=input.device).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_empty_like.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Empty_like — torch_empty_like","text":"","code":"if (torch_is_installed()) {  torch_empty(list(2,3), dtype = torch_int64()) } #> torch_tensor #>  8.5899e+09  1.4035e+14  1.4035e+14 #>  1.0000e+00  3.4360e+10  1.0000e+00 #> [ CPULongType{2,3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_empty_strided.html","id":null,"dir":"Reference","previous_headings":"","what":"Empty_strided — torch_empty_strided","title":"Empty_strided — torch_empty_strided","text":"Empty_strided","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_empty_strided.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Empty_strided — torch_empty_strided","text":"","code":"torch_empty_strided(   size,   stride,   dtype = NULL,   layout = torch_strided(),   device = NULL,   requires_grad = FALSE,   pin_memory = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/torch_empty_strided.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Empty_strided — torch_empty_strided","text":"size (tuple ints) shape output tensor stride (tuple ints) strides output tensor dtype (torch.dtype, optional) desired data type returned tensor.        Default: NULL, uses global default (see torch_set_default_tensor_type). layout (torch.layout, optional) desired layout returned Tensor.        Default: torch_strided. device (torch.device, optional) desired device returned tensor.        Default: NULL, uses current device default tensor type        (see torch_set_default_tensor_type). device CPU        CPU tensor types current CUDA device CUDA tensor types. requires_grad (bool, optional) autograd record operations        returned tensor. Default: FALSE. pin_memory (bool, optional) set, returned tensor allocated        pinned memory. Works CPU tensors. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_empty_strided.html","id":"empty-strided-size-stride-dtype-null-layout-null-device-null-requires-grad-false-pin-memory-false-gt-tensor-","dir":"Reference","previous_headings":"","what":"empty_strided(size, stride, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, pin_memory=False) -> Tensor","title":"Empty_strided — torch_empty_strided","text":"Returns tensor filled uninitialized data. shape strides tensor defined variable argument size stride respectively. torch_empty_strided(size, stride) equivalent torch_empty(size).as_strided(size, stride).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_empty_strided.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Empty_strided — torch_empty_strided","text":"one element created tensor may refer single memory location. result, -place operations (especially ones vectorized) may result incorrect behavior. need write tensors, please clone first.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_empty_strided.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Empty_strided — torch_empty_strided","text":"","code":"if (torch_is_installed()) {  a = torch_empty_strided(list(2, 3), list(1, 2)) a a$stride(1) a$size(1) } #> [1] 2"},{"path":"https://torch.mlverse.org/docs/reference/torch_eq.html","id":null,"dir":"Reference","previous_headings":"","what":"Eq — torch_eq","title":"Eq — torch_eq","text":"Eq","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_eq.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Eq — torch_eq","text":"","code":"torch_eq(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_eq.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Eq — torch_eq","text":"self (Tensor) tensor compare (Tensor float) tensor value compare Must ByteTensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_eq.html","id":"eq-input-other-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"eq(input, other, out=NULL) -> Tensor","title":"Eq — torch_eq","text":"Computes element-wise equality second argument can number tensor whose shape broadcastable  first argument.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_eq.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Eq — torch_eq","text":"","code":"if (torch_is_installed()) {  torch_eq(torch_tensor(c(1,2,3,4)), torch_tensor(c(1, 3, 2, 4))) } #> torch_tensor #>  1 #>  0 #>  0 #>  1 #> [ CPUBoolType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_equal.html","id":null,"dir":"Reference","previous_headings":"","what":"Equal — torch_equal","title":"Equal — torch_equal","text":"Equal","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_equal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Equal — torch_equal","text":"","code":"torch_equal(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_equal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Equal — torch_equal","text":"self input tensor input tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_equal.html","id":"equal-input-other-gt-bool-","dir":"Reference","previous_headings":"","what":"equal(input, other) -> bool","title":"Equal — torch_equal","text":"TRUE two tensors size elements, FALSE otherwise.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_equal.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Equal — torch_equal","text":"","code":"if (torch_is_installed()) {  torch_equal(torch_tensor(c(1, 2)), torch_tensor(c(1, 2))) } #> [1] TRUE"},{"path":"https://torch.mlverse.org/docs/reference/torch_erf.html","id":null,"dir":"Reference","previous_headings":"","what":"Erf — torch_erf","title":"Erf — torch_erf","text":"Erf","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_erf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Erf — torch_erf","text":"","code":"torch_erf(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_erf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Erf — torch_erf","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_erf.html","id":"erf-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"erf(input, out=NULL) -> Tensor","title":"Erf — torch_erf","text":"Computes error function element. error function defined follows: $$     \\mathrm{erf}(x) = \\frac{2}{\\sqrt{\\pi}} \\int_{0}^{x} e^{-t^2} dt $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_erf.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Erf — torch_erf","text":"","code":"if (torch_is_installed()) {  torch_erf(torch_tensor(c(0, -1., 10.))) } #> torch_tensor #>  0.0000 #> -0.8427 #>  1.0000 #> [ CPUFloatType{3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_erfc.html","id":null,"dir":"Reference","previous_headings":"","what":"Erfc — torch_erfc","title":"Erfc — torch_erfc","text":"Erfc","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_erfc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Erfc — torch_erfc","text":"","code":"torch_erfc(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_erfc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Erfc — torch_erfc","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_erfc.html","id":"erfc-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"erfc(input, out=NULL) -> Tensor","title":"Erfc — torch_erfc","text":"Computes complementary error function element input. complementary error function defined follows: $$     \\mathrm{erfc}(x) = 1 - \\frac{2}{\\sqrt{\\pi}} \\int_{0}^{x} e^{-t^2} dt $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_erfc.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Erfc — torch_erfc","text":"","code":"if (torch_is_installed()) {  torch_erfc(torch_tensor(c(0, -1., 10.))) } #> torch_tensor #>  1.0000e+00 #>  1.8427e+00 #>  1.4013e-45 #> [ CPUFloatType{3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_erfinv.html","id":null,"dir":"Reference","previous_headings":"","what":"Erfinv — torch_erfinv","title":"Erfinv — torch_erfinv","text":"Erfinv","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_erfinv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Erfinv — torch_erfinv","text":"","code":"torch_erfinv(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_erfinv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Erfinv — torch_erfinv","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_erfinv.html","id":"erfinv-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"erfinv(input, out=NULL) -> Tensor","title":"Erfinv — torch_erfinv","text":"Computes inverse error function element input. inverse error function defined range \\((-1, 1)\\) : $$     \\mathrm{erfinv}(\\mathrm{erf}(x)) = x $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_erfinv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Erfinv — torch_erfinv","text":"","code":"if (torch_is_installed()) {  torch_erfinv(torch_tensor(c(0, 0.5, -1.))) } #> torch_tensor #>  0.0000 #>  0.4769 #>    -inf #> [ CPUFloatType{3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_exp.html","id":null,"dir":"Reference","previous_headings":"","what":"Exp — torch_exp","title":"Exp — torch_exp","text":"Exp","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_exp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Exp — torch_exp","text":"","code":"torch_exp(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_exp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Exp — torch_exp","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_exp.html","id":"exp-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"exp(input, out=NULL) -> Tensor","title":"Exp — torch_exp","text":"Returns new tensor exponential elements input tensor input. $$     y_{} = e^{x_{}} $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_exp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Exp — torch_exp","text":"","code":"if (torch_is_installed()) {  torch_exp(torch_tensor(c(0, log(2)))) } #> torch_tensor #>  1 #>  2 #> [ CPUFloatType{2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_exp2.html","id":null,"dir":"Reference","previous_headings":"","what":"Exp2 — torch_exp2","title":"Exp2 — torch_exp2","text":"Exp2","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_exp2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Exp2 — torch_exp2","text":"","code":"torch_exp2(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_exp2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Exp2 — torch_exp2","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_exp2.html","id":"exp-input-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"exp2(input, *, out=None) -> Tensor","title":"Exp2 — torch_exp2","text":"Computes base two exponential function input. $$     y_{} = 2^{x_{}} $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_exp2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Exp2 — torch_exp2","text":"","code":"if (torch_is_installed()) {  torch_exp2(torch_tensor(c(0, log2(2.), 3, 4))) } #> torch_tensor #>   1 #>   2 #>   8 #>  16 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_expm1.html","id":null,"dir":"Reference","previous_headings":"","what":"Expm1 — torch_expm1","title":"Expm1 — torch_expm1","text":"Expm1","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_expm1.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Expm1 — torch_expm1","text":"","code":"torch_expm1(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_expm1.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Expm1 — torch_expm1","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_expm1.html","id":"expm-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"expm1(input, out=NULL) -> Tensor","title":"Expm1 — torch_expm1","text":"Returns new tensor exponential elements minus 1 input. $$     y_{} = e^{x_{}} - 1 $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_expm1.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Expm1 — torch_expm1","text":"","code":"if (torch_is_installed()) {  torch_expm1(torch_tensor(c(0, log(2)))) } #> torch_tensor #>  0 #>  1 #> [ CPUFloatType{2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_eye.html","id":null,"dir":"Reference","previous_headings":"","what":"Eye — torch_eye","title":"Eye — torch_eye","text":"Eye","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_eye.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Eye — torch_eye","text":"","code":"torch_eye(   n,   m = n,   dtype = NULL,   layout = torch_strided(),   device = NULL,   requires_grad = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/torch_eye.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Eye — torch_eye","text":"n (int) number rows m (int, optional) number columns default n dtype (torch.dtype, optional) desired data type returned tensor.        Default: NULL, uses global default (see torch_set_default_tensor_type). layout (torch.layout, optional) desired layout returned Tensor.        Default: torch_strided. device (torch.device, optional) desired device returned tensor.        Default: NULL, uses current device default tensor type        (see torch_set_default_tensor_type). device CPU        CPU tensor types current CUDA device CUDA tensor types. requires_grad (bool, optional) autograd record operations        returned tensor. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_eye.html","id":"eye-n-m-null-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-","dir":"Reference","previous_headings":"","what":"eye(n, m=NULL, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor","title":"Eye — torch_eye","text":"Returns 2-D tensor ones diagonal zeros elsewhere.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_eye.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Eye — torch_eye","text":"","code":"if (torch_is_installed()) {  torch_eye(3) } #> torch_tensor #>  1  0  0 #>  0  1  0 #>  0  0  1 #> [ CPUFloatType{3,3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_fft_fft.html","id":null,"dir":"Reference","previous_headings":"","what":"Fft — torch_fft_fft","title":"Fft — torch_fft_fft","text":"Computes one dimensional discrete Fourier transform input.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_fft_fft.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fft — torch_fft_fft","text":"","code":"torch_fft_fft(self, n = NULL, dim = -1L, norm = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/torch_fft_fft.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fft — torch_fft_fft","text":"self (Tensor) input tensor n (int) Signal length. given, input either zero-padded trimmed length computing FFT. dim (int, optional) dimension along take one dimensional FFT. norm (str, optional) Normalization mode. forward transform, correspond : \"forward\" - normalize 1/n \"backward\" - normalization \"ortho\" - normalize 1/sqrt(n) (making FFT orthonormal) Calling backward transform (ifft()) normalization mode apply overall normalization 1/n two transforms. required make IFFT exact inverse. Default \"backward\" (normalization).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_fft_fft.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Fft — torch_fft_fft","text":"Fourier domain representation real signal satisfies Hermitian property: X[] = conj(X[-]). function always returns positive negative frequency terms even though, real inputs, negative frequencies redundant. rfft() returns compact one-sided representation positive frequencies returned.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_fft_fft.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fft — torch_fft_fft","text":"","code":"if (torch_is_installed()) { t <- torch_arange(start = 0, end = 3) t torch_fft_fft(t, norm = \"backward\")  } #> torch_tensor #>  6 #> -2 #> -2 #> -2 #> [ CPUComplexFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_fft_ifft.html","id":null,"dir":"Reference","previous_headings":"","what":"Ifft — torch_fft_ifft","title":"Ifft — torch_fft_ifft","text":"Computes one dimensional inverse discrete Fourier transform input.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_fft_ifft.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Ifft — torch_fft_ifft","text":"","code":"torch_fft_ifft(self, n = NULL, dim = -1L, norm = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/torch_fft_ifft.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Ifft — torch_fft_ifft","text":"self (Tensor) input tensor n (int, optional) – Signal length. given, input either zero-padded trimmed length computing IFFT. dim (int, optional) – dimension along take one dimensional IFFT. norm (str, optional) – Normalization mode. backward transform, correspond : \"forward\" - normalization \"backward\" - normalize 1/n \"ortho\" - normalize 1/sqrt(n) (making IFFT orthonormal) Calling forward transform normalization mode apply overall normalization 1/n two transforms. required make ifft() exact inverse. Default \"backward\" (normalize 1/n).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_fft_ifft.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Ifft — torch_fft_ifft","text":"","code":"if (torch_is_installed()) { t <- torch_arange(start = 0, end = 3) t x <- torch_fft_fft(t, norm = \"backward\") torch_fft_ifft(x)   } #> torch_tensor #>  0 #>  1 #>  2 #>  3 #> [ CPUComplexFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_fft_irfft.html","id":null,"dir":"Reference","previous_headings":"","what":"Irfft — torch_fft_irfft","title":"Irfft — torch_fft_irfft","text":"Computes inverse torch_fft_rfft(). Input interpreted one-sided Hermitian signal Fourier domain, produced torch_fft_rfft(). Hermitian property, output real-valued.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_fft_irfft.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Irfft — torch_fft_irfft","text":"","code":"torch_fft_irfft(self, n = NULL, dim = -1L, norm = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/torch_fft_irfft.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Irfft — torch_fft_irfft","text":"self (Tensor) input tensor representing half-Hermitian signal n (int) Output signal length. determines length output signal. given, input either zero-padded trimmed length computing real IFFT. Defaults even output: n=2*(input.size(dim) - 1). dim (int, optional) – dimension along take one dimensional real IFFT. norm (str, optional) – Normalization mode. backward transform, correspond : \"forward\" - normalization \"backward\" - normalize 1/n \"ortho\" - normalize 1/sqrt(n) (making real IFFT orthonormal) Calling forward transform (torch_fft_rfft()) normalization mode apply overall normalization 1/n two transforms. required make irfft() exact inverse. Default \"backward\" (normalize 1/n).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_fft_irfft.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Irfft — torch_fft_irfft","text":"input frequencies must real-valued satisfy Hermitian property. cases imaginary component ignored. example, imaginary component zero-frequency term represented real output always ignored. correct interpretation Hermitian input depends length original data, given n. input shape correspond either odd even length signal. default, signal assumed even length odd signals round-trip properly. , recommended always pass signal length n.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_fft_irfft.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Irfft — torch_fft_irfft","text":"","code":"if (torch_is_installed()) { t <- torch_arange(start = 0, end = 4) x <- torch_fft_rfft(t) torch_fft_irfft(x) torch_fft_irfft(x, n = t$numel())  } #> torch_tensor #>  0 #>  1 #>  2 #>  3 #>  4 #> [ CPUFloatType{5} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_fft_rfft.html","id":null,"dir":"Reference","previous_headings":"","what":"Rfft — torch_fft_rfft","title":"Rfft — torch_fft_rfft","text":"Computes one dimensional Fourier transform real-valued input.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_fft_rfft.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rfft — torch_fft_rfft","text":"","code":"torch_fft_rfft(self, n = NULL, dim = -1L, norm = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/torch_fft_rfft.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rfft — torch_fft_rfft","text":"self (Tensor)  real input tensor n (int) Signal length. given, input either zero-padded trimmed length computing real FFT. dim (int, optional) – dimension along take one dimensional real FFT. norm norm (str, optional) – Normalization mode. forward transform, correspond : \"forward\" - normalize 1/n \"backward\" - normalization \"ortho\" - normalize 1/sqrt(n) (making FFT orthonormal) Calling backward transform (torch_fft_irfft()) normalization mode apply overall normalization 1/n two transforms. required make irfft() exact inverse. Default \"backward\" (normalization).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_fft_rfft.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Rfft — torch_fft_rfft","text":"FFT real signal Hermitian-symmetric, X[] = conj(X[-]) output contains positive frequencies Nyquist frequency. compute full output, use torch_fft_fft().","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_fft_rfft.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rfft — torch_fft_rfft","text":"","code":"if (torch_is_installed()) { t <- torch_arange(start = 0, end = 3) torch_fft_rfft(t)  } #> torch_tensor #>  6 #> -2 #> -2 #> [ CPUComplexFloatType{3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_finfo.html","id":null,"dir":"Reference","previous_headings":"","what":"Floating point type info — torch_finfo","title":"Floating point type info — torch_finfo","text":"list represents numerical properties floating point torch.dtype","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_finfo.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Floating point type info — torch_finfo","text":"","code":"torch_finfo(dtype)"},{"path":"https://torch.mlverse.org/docs/reference/torch_finfo.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Floating point type info — torch_finfo","text":"dtype dtype check information","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_fix.html","id":null,"dir":"Reference","previous_headings":"","what":"Fix — torch_fix","title":"Fix — torch_fix","text":"Fix","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_fix.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fix — torch_fix","text":"","code":"torch_fix(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_fix.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fix — torch_fix","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_fix.html","id":"fix-input-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"fix(input, *, out=None) -> Tensor","title":"Fix — torch_fix","text":"Alias torch_trunc()","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_flatten.html","id":null,"dir":"Reference","previous_headings":"","what":"Flatten — torch_flatten","title":"Flatten — torch_flatten","text":"Flatten","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_flatten.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Flatten — torch_flatten","text":"","code":"torch_flatten(self, dims, start_dim = 1L, end_dim = -1L, out_dim)"},{"path":"https://torch.mlverse.org/docs/reference/torch_flatten.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Flatten — torch_flatten","text":"self (Tensor) input tensor. dims tensor named can pass name dimensions flatten start_dim (int) first dim flatten end_dim (int) last dim flatten out_dim name resulting dimension named tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_flatten.html","id":"flatten-input-start-dim-end-dim-gt-tensor-","dir":"Reference","previous_headings":"","what":"flatten(input, start_dim=0, end_dim=-1) -> Tensor","title":"Flatten — torch_flatten","text":"Flattens contiguous range dims tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_flatten.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Flatten — torch_flatten","text":"","code":"if (torch_is_installed()) {  t = torch_tensor(matrix(c(1, 2), ncol = 2)) torch_flatten(t) torch_flatten(t, start_dim=2) } #> torch_tensor #>  1  2 #> [ CPUFloatType{1,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_flip.html","id":null,"dir":"Reference","previous_headings":"","what":"Flip — torch_flip","title":"Flip — torch_flip","text":"Flip","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_flip.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Flip — torch_flip","text":"","code":"torch_flip(self, dims)"},{"path":"https://torch.mlverse.org/docs/reference/torch_flip.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Flip — torch_flip","text":"self (Tensor) input tensor. dims (list tuple) axis flip ","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_flip.html","id":"flip-input-dims-gt-tensor-","dir":"Reference","previous_headings":"","what":"flip(input, dims) -> Tensor","title":"Flip — torch_flip","text":"Reverse order n-D tensor along given axis dims.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_flip.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Flip — torch_flip","text":"","code":"if (torch_is_installed()) {  x <- torch_arange(1, 8)$view(c(2, 2, 2)) x torch_flip(x, c(1, 2)) } #> torch_tensor #> (1,.,.) =  #>   7  8 #>   5  6 #>  #> (2,.,.) =  #>   3  4 #>   1  2 #> [ CPUFloatType{2,2,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_fliplr.html","id":null,"dir":"Reference","previous_headings":"","what":"Fliplr — torch_fliplr","title":"Fliplr — torch_fliplr","text":"Fliplr","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_fliplr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fliplr — torch_fliplr","text":"","code":"torch_fliplr(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_fliplr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fliplr — torch_fliplr","text":"self (Tensor) Must least 2-dimensional.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_fliplr.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Fliplr — torch_fliplr","text":"Equivalent input[,-1]. Requires array least 2-D.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_fliplr.html","id":"fliplr-input-gt-tensor-","dir":"Reference","previous_headings":"","what":"fliplr(input) -> Tensor","title":"Fliplr — torch_fliplr","text":"Flip array left/right direction, returning new tensor. Flip entries row left/right direction. Columns preserved, appear different order .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_fliplr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fliplr — torch_fliplr","text":"","code":"if (torch_is_installed()) {  x <- torch_arange(start = 1, end = 4)$view(c(2, 2)) x torch_fliplr(x) } #> torch_tensor #>  2  1 #>  4  3 #> [ CPUFloatType{2,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_flipud.html","id":null,"dir":"Reference","previous_headings":"","what":"Flipud — torch_flipud","title":"Flipud — torch_flipud","text":"Flipud","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_flipud.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Flipud — torch_flipud","text":"","code":"torch_flipud(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_flipud.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Flipud — torch_flipud","text":"self (Tensor) Must least 1-dimensional.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_flipud.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Flipud — torch_flipud","text":"Equivalent input[-1,]. Requires array least 1-D.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_flipud.html","id":"flipud-input-gt-tensor-","dir":"Reference","previous_headings":"","what":"flipud(input) -> Tensor","title":"Flipud — torch_flipud","text":"Flip array /direction, returning new tensor. Flip entries column /direction. Rows preserved, appear different order .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_flipud.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Flipud — torch_flipud","text":"","code":"if (torch_is_installed()) {  x <- torch_arange(start = 1, end = 4)$view(c(2, 2)) x torch_flipud(x) } #> torch_tensor #>  3  4 #>  1  2 #> [ CPUFloatType{2,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_floor.html","id":null,"dir":"Reference","previous_headings":"","what":"Floor — torch_floor","title":"Floor — torch_floor","text":"Floor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_floor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Floor — torch_floor","text":"","code":"torch_floor(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_floor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Floor — torch_floor","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_floor.html","id":"floor-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"floor(input, out=NULL) -> Tensor","title":"Floor — torch_floor","text":"Returns new tensor floor elements input, largest integer less equal element. $$     \\mbox{}_{} = \\left\\lfloor \\mbox{input}_{} \\right\\rfloor $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_floor.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Floor — torch_floor","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(4)) a torch_floor(a) } #> torch_tensor #>  1 #> -1 #> -1 #>  1 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_floor_divide.html","id":null,"dir":"Reference","previous_headings":"","what":"Floor_divide — torch_floor_divide","title":"Floor_divide — torch_floor_divide","text":"Floor_divide","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_floor_divide.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Floor_divide — torch_floor_divide","text":"","code":"torch_floor_divide(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_floor_divide.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Floor_divide — torch_floor_divide","text":"self (Tensor) numerator tensor (Tensor Scalar) denominator","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_floor_divide.html","id":"floor-divide-input-other-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"floor_divide(input, other, out=NULL) -> Tensor","title":"Floor_divide — torch_floor_divide","text":"Return division inputs rounded nearest integer. See torch_div type promotion broadcasting rules. $$     \\mbox{{}}_i = \\left\\lfloor \\frac{{\\mbox{{input}}_i}}{{\\mbox{{}}_i}} \\right\\rfloor $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_floor_divide.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Floor_divide — torch_floor_divide","text":"","code":"if (torch_is_installed()) {  a = torch_tensor(c(4.0, 3.0)) b = torch_tensor(c(2.0, 2.0)) torch_floor_divide(a, b) torch_floor_divide(a, 1.4) } #> torch_tensor #>  2 #>  2 #> [ CPUFloatType{2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_fmod.html","id":null,"dir":"Reference","previous_headings":"","what":"Fmod — torch_fmod","title":"Fmod — torch_fmod","text":"Fmod","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_fmod.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fmod — torch_fmod","text":"","code":"torch_fmod(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_fmod.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fmod — torch_fmod","text":"self (Tensor) dividend (Tensor float) divisor, may either number tensor shape dividend","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_fmod.html","id":"fmod-input-other-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"fmod(input, other, out=NULL) -> Tensor","title":"Fmod — torch_fmod","text":"Computes element-wise remainder division. dividend divisor may contain integer floating point numbers. remainder sign dividend input. tensor, shapes input must broadcastable .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_fmod.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fmod — torch_fmod","text":"","code":"if (torch_is_installed()) {  torch_fmod(torch_tensor(c(-3., -2, -1, 1, 2, 3)), 2) torch_fmod(torch_tensor(c(1., 2, 3, 4, 5)), 1.5) } #> torch_tensor #>  1.0000 #>  0.5000 #>  0.0000 #>  1.0000 #>  0.5000 #> [ CPUFloatType{5} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_frac.html","id":null,"dir":"Reference","previous_headings":"","what":"Frac — torch_frac","title":"Frac — torch_frac","text":"Frac","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_frac.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Frac — torch_frac","text":"","code":"torch_frac(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_frac.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Frac — torch_frac","text":"self input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_frac.html","id":"frac-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"frac(input, out=NULL) -> Tensor","title":"Frac — torch_frac","text":"Computes fractional portion element input. $$     \\mbox{}_{} = \\mbox{input}_{} - \\left\\lfloor |\\mbox{input}_{}| \\right\\rfloor * \\mbox{sgn}(\\mbox{input}_{}) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_frac.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Frac — torch_frac","text":"","code":"if (torch_is_installed()) {  torch_frac(torch_tensor(c(1, 2.5, -3.2))) } #> torch_tensor #>  0.0000 #>  0.5000 #> -0.2000 #> [ CPUFloatType{3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_full.html","id":null,"dir":"Reference","previous_headings":"","what":"Full — torch_full","title":"Full — torch_full","text":"Full","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_full.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Full — torch_full","text":"","code":"torch_full(   size,   fill_value,   names = NULL,   dtype = NULL,   layout = torch_strided(),   device = NULL,   requires_grad = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/torch_full.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Full — torch_full","text":"size (int...) list, tuple, torch_Size integers defining        shape output tensor. fill_value NA number fill output tensor . names optional names dimensions dtype (torch.dtype, optional) desired data type returned tensor.        Default: NULL, uses global default (see torch_set_default_tensor_type). layout (torch.layout, optional) desired layout returned Tensor.        Default: torch_strided. device (torch.device, optional) desired device returned tensor.        Default: NULL, uses current device default tensor type        (see torch_set_default_tensor_type). device CPU        CPU tensor types current CUDA device CUDA tensor types. requires_grad (bool, optional) autograd record operations        returned tensor. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_full.html","id":"full-size-fill-value-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-","dir":"Reference","previous_headings":"","what":"full(size, fill_value, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor","title":"Full — torch_full","text":"Returns tensor size size filled fill_value.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_full.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Full — torch_full","text":"PyTorch 1.5 bool integral fill_value produce warning dtype set. future PyTorch release, dtype set bool fill_value return tensor torch.bool dtype, integral fill_value return tensor torch.long dtype.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_full.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Full — torch_full","text":"","code":"if (torch_is_installed()) {  torch_full(list(2, 3), 3.141592) } #> torch_tensor #>  3.1416  3.1416  3.1416 #>  3.1416  3.1416  3.1416 #> [ CPUFloatType{2,3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_full_like.html","id":null,"dir":"Reference","previous_headings":"","what":"Full_like — torch_full_like","title":"Full_like — torch_full_like","text":"Full_like","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_full_like.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Full_like — torch_full_like","text":"","code":"torch_full_like(   input,   fill_value,   dtype = NULL,   layout = torch_strided(),   device = NULL,   requires_grad = FALSE,   memory_format = torch_preserve_format() )"},{"path":"https://torch.mlverse.org/docs/reference/torch_full_like.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Full_like — torch_full_like","text":"input (Tensor) size input determine size output tensor. fill_value number fill output tensor . dtype (torch.dtype, optional) desired data type returned Tensor.        Default: NULL, defaults dtype input. layout (torch.layout, optional) desired layout returned tensor.        Default: NULL, defaults layout input. device (torch.device, optional) desired device returned tensor.        Default: NULL, defaults device input. requires_grad (bool, optional) autograd record operations        returned tensor. Default: FALSE. memory_format (torch.memory_format, optional) desired memory format        returned Tensor. Default: torch_preserve_format.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_full_like.html","id":"full-like-input-fill-value-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-","dir":"Reference","previous_headings":"","what":"full_like(input, fill_value, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False,","title":"Full_like — torch_full_like","text":"memory_format=torch.preserve_format) -> Tensor Returns tensor size input filled fill_value. torch_full_like(input, fill_value) equivalent torch_full(input.size(), fill_value, dtype=input.dtype, layout=input.layout, device=input.device).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_gather.html","id":null,"dir":"Reference","previous_headings":"","what":"Gather — torch_gather","title":"Gather — torch_gather","text":"Gather","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_gather.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Gather — torch_gather","text":"","code":"torch_gather(self, dim, index, sparse_grad = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_gather.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Gather — torch_gather","text":"self (Tensor) source tensor dim (int) axis along index index (LongTensor) indices elements gather sparse_grad (bool,optional) TRUE, gradient w.r.t. input sparse tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_gather.html","id":"gather-input-dim-index-sparse-grad-false-gt-tensor-","dir":"Reference","previous_headings":"","what":"gather(input, dim, index, sparse_grad=FALSE) -> Tensor","title":"Gather — torch_gather","text":"Gathers values along axis specified dim. 3-D tensor output specified :: input n-dimensional tensor size \\((x_0, x_1..., x_{-1}, x_i, x_{+1}, ..., x_{n-1})\\) dim = , index must \\(n\\)-dimensional tensor size \\((x_0, x_1, ..., x_{-1}, y, x_{+1}, ..., x_{n-1})\\) \\(y \\geq 1\\) size index.","code":"out[i][j][k] = input[index[i][j][k]][j][k]  # if dim == 0 out[i][j][k] = input[i][index[i][j][k]][k]  # if dim == 1 out[i][j][k] = input[i][j][index[i][j][k]]  # if dim == 2"},{"path":"https://torch.mlverse.org/docs/reference/torch_gather.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Gather — torch_gather","text":"","code":"if (torch_is_installed()) {  t = torch_tensor(matrix(c(1,2,3,4), ncol = 2, byrow = TRUE)) torch_gather(t, 2, torch_tensor(matrix(c(1,1,2,1), ncol = 2, byrow=TRUE), dtype = torch_int64())) } #> torch_tensor #>  1  1 #>  4  3 #> [ CPUFloatType{2,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_gcd.html","id":null,"dir":"Reference","previous_headings":"","what":"Gcd — torch_gcd","title":"Gcd — torch_gcd","text":"Gcd","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_gcd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Gcd — torch_gcd","text":"","code":"torch_gcd(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_gcd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Gcd — torch_gcd","text":"self (Tensor) input tensor. (Tensor) second input tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_gcd.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Gcd — torch_gcd","text":"defines \\(gcd(0, 0) = 0\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_gcd.html","id":"gcd-input-other-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"gcd(input, other, *, out=None) -> Tensor","title":"Gcd — torch_gcd","text":"Computes element-wise greatest common divisor (GCD) input . input must integer types.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_gcd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Gcd — torch_gcd","text":"","code":"if (torch_is_installed()) {  if (torch::cuda_is_available()) { a <- torch_tensor(c(5, 10, 15), dtype = torch_long(), device = \"cuda\") b <- torch_tensor(c(3, 4, 5), dtype = torch_long(), device = \"cuda\") torch_gcd(a, b) c <- torch_tensor(c(3L), device = \"cuda\") torch_gcd(a, c) } }"},{"path":"https://torch.mlverse.org/docs/reference/torch_ge.html","id":null,"dir":"Reference","previous_headings":"","what":"Ge — torch_ge","title":"Ge — torch_ge","text":"Ge","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_ge.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Ge — torch_ge","text":"","code":"torch_ge(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_ge.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Ge — torch_ge","text":"self (Tensor) tensor compare (Tensor float) tensor value compare","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_ge.html","id":"ge-input-other-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"ge(input, other, out=NULL) -> Tensor","title":"Ge — torch_ge","text":"Computes \\(\\mbox{input} \\geq \\mbox{}\\) element-wise. second argument can number tensor whose shape broadcastable  first argument.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_ge.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Ge — torch_ge","text":"","code":"if (torch_is_installed()) {  torch_ge(torch_tensor(matrix(1:4, ncol = 2, byrow=TRUE)),           torch_tensor(matrix(c(1,1,4,4), ncol = 2, byrow=TRUE))) } #> torch_tensor #>  1  1 #>  0  1 #> [ CPUBoolType{2,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_generator.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a Generator object — torch_generator","title":"Create a Generator object — torch_generator","text":"torch_generator  object manages state algorithm produces pseudo random numbers. Used keyword argument many -place random sampling functions.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_generator.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a Generator object — torch_generator","text":"","code":"torch_generator()"},{"path":"https://torch.mlverse.org/docs/reference/torch_generator.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a Generator object — torch_generator","text":"","code":"if (torch_is_installed()) {  # Via string generator <- torch_generator() generator$current_seed() generator$set_current_seed(1234567L) generator$current_seed()  } #> integer64 #> [1] 1234567"},{"path":"https://torch.mlverse.org/docs/reference/torch_geqrf.html","id":null,"dir":"Reference","previous_headings":"","what":"Geqrf — torch_geqrf","title":"Geqrf — torch_geqrf","text":"Geqrf","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_geqrf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Geqrf — torch_geqrf","text":"","code":"torch_geqrf(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_geqrf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Geqrf — torch_geqrf","text":"self (Tensor) input matrix","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_geqrf.html","id":"geqrf-input-out-null-gt-tensor-tensor-","dir":"Reference","previous_headings":"","what":"geqrf(input, out=NULL) -> (Tensor, Tensor)","title":"Geqrf — torch_geqrf","text":"low-level function calling LAPACK directly. function returns namedtuple (, tau) defined LAPACK documentation geqrf_ . generally want use torch_qr instead. Computes QR decomposition input, without constructing \\(Q\\) \\(R\\) explicit separate matrices. Rather, directly calls underlying LAPACK function ?geqrf produces sequence 'elementary reflectors'. See LAPACK documentation geqrf_ details.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_ger.html","id":null,"dir":"Reference","previous_headings":"","what":"Ger — torch_ger","title":"Ger — torch_ger","text":"Ger","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_ger.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Ger — torch_ger","text":"","code":"torch_ger(self, vec2)"},{"path":"https://torch.mlverse.org/docs/reference/torch_ger.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Ger — torch_ger","text":"self (Tensor) 1-D input vector vec2 (Tensor) 1-D input vector","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_ger.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Ger — torch_ger","text":"function broadcast .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_ger.html","id":"ger-input-vec-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"ger(input, vec2, out=NULL) -> Tensor","title":"Ger — torch_ger","text":"Outer product input vec2. input vector size \\(n\\) vec2 vector size \\(m\\), must matrix size \\((n \\times m)\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_ger.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Ger — torch_ger","text":"","code":"if (torch_is_installed()) {  v1 = torch_arange(1., 5.) v2 = torch_arange(1., 4.) torch_ger(v1, v2) } #> torch_tensor #>   1   2   3   4 #>   2   4   6   8 #>   3   6   9  12 #>   4   8  12  16 #>   5  10  15  20 #> [ CPUFloatType{5,4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_greater.html","id":null,"dir":"Reference","previous_headings":"","what":"Greater — torch_greater","title":"Greater — torch_greater","text":"Greater","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_greater.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Greater — torch_greater","text":"","code":"torch_greater(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_greater.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Greater — torch_greater","text":"self (Tensor) tensor compare (Tensor float) tensor value compare","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_greater.html","id":"greater-input-other-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"greater(input, other, *, out=None) -> Tensor","title":"Greater — torch_greater","text":"Alias torch_gt().","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_greater_equal.html","id":null,"dir":"Reference","previous_headings":"","what":"Greater_equal — torch_greater_equal","title":"Greater_equal — torch_greater_equal","text":"Greater_equal","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_greater_equal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Greater_equal — torch_greater_equal","text":"","code":"torch_greater_equal(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_greater_equal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Greater_equal — torch_greater_equal","text":"self (Tensor) tensor compare (Tensor float) tensor value compare","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_greater_equal.html","id":"greater-equal-input-other-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"greater_equal(input, other, *, out=None) -> Tensor","title":"Greater_equal — torch_greater_equal","text":"Alias torch_ge().","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_gt.html","id":null,"dir":"Reference","previous_headings":"","what":"Gt — torch_gt","title":"Gt — torch_gt","text":"Gt","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_gt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Gt — torch_gt","text":"","code":"torch_gt(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_gt.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Gt — torch_gt","text":"self (Tensor) tensor compare (Tensor float) tensor value compare","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_gt.html","id":"gt-input-other-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"gt(input, other, out=NULL) -> Tensor","title":"Gt — torch_gt","text":"Computes \\(\\mbox{input} > \\mbox{}\\) element-wise. second argument can number tensor whose shape broadcastable  first argument.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_gt.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Gt — torch_gt","text":"","code":"if (torch_is_installed()) {  torch_gt(torch_tensor(matrix(1:4, ncol = 2, byrow=TRUE)),           torch_tensor(matrix(c(1,1,4,4), ncol = 2, byrow=TRUE))) } #> torch_tensor #>  0  1 #>  0  0 #> [ CPUBoolType{2,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_hamming_window.html","id":null,"dir":"Reference","previous_headings":"","what":"Hamming_window — torch_hamming_window","title":"Hamming_window — torch_hamming_window","text":"Hamming_window","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_hamming_window.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hamming_window — torch_hamming_window","text":"","code":"torch_hamming_window(   window_length,   periodic = TRUE,   alpha = 0.54,   beta = 0.46,   dtype = NULL,   layout = torch_strided(),   device = NULL,   requires_grad = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/torch_hamming_window.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hamming_window — torch_hamming_window","text":"window_length (int) size returned window periodic (bool, optional) TRUE, returns window used periodic        function. False, return symmetric window. alpha (float, optional) coefficient \\(\\alpha\\) equation beta (float, optional) coefficient \\(\\beta\\) equation dtype (torch.dtype, optional) desired data type returned tensor.        Default: NULL, uses global default (see torch_set_default_tensor_type). floating point types supported. layout (torch.layout, optional) desired layout returned window tensor.          torch_strided (dense layout) supported. device (torch.device, optional) desired device returned tensor.        Default: NULL, uses current device default tensor type        (see torch_set_default_tensor_type). device CPU        CPU tensor types current CUDA device CUDA tensor types. requires_grad (bool, optional) autograd record operations        returned tensor. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_hamming_window.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Hamming_window — torch_hamming_window","text":"","code":"If `window_length` \\eqn{=1}, the returned window contains a single value 1. This is a generalized version of `torch_hann_window`."},{"path":"https://torch.mlverse.org/docs/reference/torch_hamming_window.html","id":"hamming-window-window-length-periodic-true-alpha-beta-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-","dir":"Reference","previous_headings":"","what":"hamming_window(window_length, periodic=TRUE, alpha=0.54, beta=0.46, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor","title":"Hamming_window — torch_hamming_window","text":"Hamming window function. $$     w[n] = \\alpha - \\beta\\ \\cos \\left( \\frac{2 \\pi n}{N - 1} \\right), $$ \\(N\\) full window size. input window_length positive integer controlling returned window size. periodic flag determines whether returned window trims last duplicate value symmetric window ready used periodic window functions like torch_stft. Therefore, periodic true, \\(N\\) formula fact \\(\\mbox{window\\_length} + 1\\). Also, always torch_hamming_window(L, periodic=TRUE) equal torch_hamming_window(L + 1, periodic=False)[:-1]).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_hann_window.html","id":null,"dir":"Reference","previous_headings":"","what":"Hann_window — torch_hann_window","title":"Hann_window — torch_hann_window","text":"Hann_window","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_hann_window.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hann_window — torch_hann_window","text":"","code":"torch_hann_window(   window_length,   periodic = TRUE,   dtype = NULL,   layout = torch_strided(),   device = NULL,   requires_grad = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/torch_hann_window.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hann_window — torch_hann_window","text":"window_length (int) size returned window periodic (bool, optional) TRUE, returns window used periodic        function. False, return symmetric window. dtype (torch.dtype, optional) desired data type returned tensor.        Default: NULL, uses global default (see torch_set_default_tensor_type). floating point types supported. layout (torch.layout, optional) desired layout returned window tensor.          torch_strided (dense layout) supported. device (torch.device, optional) desired device returned tensor.        Default: NULL, uses current device default tensor type        (see torch_set_default_tensor_type). device CPU        CPU tensor types current CUDA device CUDA tensor types. requires_grad (bool, optional) autograd record operations        returned tensor. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_hann_window.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Hann_window — torch_hann_window","text":"","code":"If `window_length` \\eqn{=1}, the returned window contains a single value 1."},{"path":"https://torch.mlverse.org/docs/reference/torch_hann_window.html","id":"hann-window-window-length-periodic-true-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-","dir":"Reference","previous_headings":"","what":"hann_window(window_length, periodic=TRUE, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor","title":"Hann_window — torch_hann_window","text":"Hann window function. $$     w[n] = \\frac{1}{2}\\ \\left[1 - \\cos \\left( \\frac{2 \\pi n}{N - 1} \\right)\\right] =             \\sin^2 \\left( \\frac{\\pi n}{N - 1} \\right), $$ \\(N\\) full window size. input window_length positive integer controlling returned window size. periodic flag determines whether returned window trims last duplicate value symmetric window ready used periodic window functions like torch_stft. Therefore, periodic true, \\(N\\) formula fact \\(\\mbox{window\\_length} + 1\\). Also, always torch_hann_window(L, periodic=TRUE) equal torch_hann_window(L + 1, periodic=False)[:-1]).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_heaviside.html","id":null,"dir":"Reference","previous_headings":"","what":"Heaviside — torch_heaviside","title":"Heaviside — torch_heaviside","text":"Heaviside","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_heaviside.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Heaviside — torch_heaviside","text":"","code":"torch_heaviside(self, values)"},{"path":"https://torch.mlverse.org/docs/reference/torch_heaviside.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Heaviside — torch_heaviside","text":"self (Tensor) input tensor. values (Tensor) values use input zero.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_heaviside.html","id":"heaviside-input-values-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"heaviside(input, values, *, out=None) -> Tensor","title":"Heaviside — torch_heaviside","text":"Computes Heaviside step function element input. Heaviside step function defined : $$ \\mbox{{heaviside}}(input, values) = \\begin{array}{ll}  0, & \\mbox{input < 0}\\\\  values, & \\mbox{input == 0}\\\\  1, & \\mbox{input > 0}  \\end{array} $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_heaviside.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Heaviside — torch_heaviside","text":"","code":"if (torch_is_installed()) {  input <- torch_tensor(c(-1.5, 0, 2.0)) values <- torch_tensor(c(0.5)) torch_heaviside(input, values) values <- torch_tensor(c(1.2, -2.0, 3.5)) torch_heaviside(input, values) } #> torch_tensor #>  0 #> -2 #>  1 #> [ CPUFloatType{3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_histc.html","id":null,"dir":"Reference","previous_headings":"","what":"Histc — torch_histc","title":"Histc — torch_histc","text":"Histc","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_histc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Histc — torch_histc","text":"","code":"torch_histc(self, bins = 100L, min = 0L, max = 0L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_histc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Histc — torch_histc","text":"self (Tensor) input tensor. bins (int) number histogram bins min (int) lower end range (inclusive) max (int) upper end range (inclusive)","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_histc.html","id":"histc-input-bins-min-max-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"histc(input, bins=100, min=0, max=0, out=NULL) -> Tensor","title":"Histc — torch_histc","text":"Computes histogram tensor. elements sorted equal width bins min max. min max zero, minimum maximum values data used.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_histc.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Histc — torch_histc","text":"","code":"if (torch_is_installed()) {  torch_histc(torch_tensor(c(1., 2, 1)), bins=4, min=0, max=3) } #> torch_tensor #>  0 #>  2 #>  1 #>  0 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_hstack.html","id":null,"dir":"Reference","previous_headings":"","what":"Hstack — torch_hstack","title":"Hstack — torch_hstack","text":"Hstack","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_hstack.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hstack — torch_hstack","text":"","code":"torch_hstack(tensors)"},{"path":"https://torch.mlverse.org/docs/reference/torch_hstack.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hstack — torch_hstack","text":"tensors (sequence Tensors) sequence tensors concatenate","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_hstack.html","id":"hstack-tensors-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"hstack(tensors, *, out=None) -> Tensor","title":"Hstack — torch_hstack","text":"Stack tensors sequence horizontally (column wise). equivalent concatenation along first axis 1-D tensors, along second axis tensors.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_hstack.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Hstack — torch_hstack","text":"","code":"if (torch_is_installed()) {  a <- torch_tensor(c(1, 2, 3)) b <- torch_tensor(c(4, 5, 6)) torch_hstack(list(a,b)) a <- torch_tensor(rbind(1,2,3)) b <- torch_tensor(rbind(4,5,6)) torch_hstack(list(a,b)) } #> torch_tensor #>  1  4 #>  2  5 #>  3  6 #> [ CPUFloatType{3,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_hypot.html","id":null,"dir":"Reference","previous_headings":"","what":"Hypot — torch_hypot","title":"Hypot — torch_hypot","text":"Hypot","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_hypot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hypot — torch_hypot","text":"","code":"torch_hypot(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_hypot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hypot — torch_hypot","text":"self (Tensor) first input tensor (Tensor) second input tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_hypot.html","id":"hypot-input-other-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"hypot(input, other, *, out=None) -> Tensor","title":"Hypot — torch_hypot","text":"Given legs right triangle, return hypotenuse. $$ \\mbox{}_{} = \\sqrt{\\mbox{input}_{}^{2} + \\mbox{}_{}^{2}} $$ shapes input must broadcastable .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_hypot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Hypot — torch_hypot","text":"","code":"if (torch_is_installed()) {  torch_hypot(torch_tensor(c(4.0)), torch_tensor(c(3.0, 4.0, 5.0))) } #> torch_tensor #>  5.0000 #>  5.6569 #>  6.4031 #> [ CPUFloatType{3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_i0.html","id":null,"dir":"Reference","previous_headings":"","what":"I0 — torch_i0","title":"I0 — torch_i0","text":"I0","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_i0.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"I0 — torch_i0","text":"","code":"torch_i0(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_i0.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"I0 — torch_i0","text":"self (Tensor) input tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_i0.html","id":"i-input-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"i0(input, *, out=None) -> Tensor","title":"I0 — torch_i0","text":"Computes zeroth order modified Bessel function first kind element input. $$ \\mbox{}_{} = I_0(\\mbox{input}_{}) = \\sum_{k=0}^{\\infty} \\frac{(\\mbox{input}_{}^2/4)^k}{(k!)^2} $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_i0.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"I0 — torch_i0","text":"","code":"if (torch_is_installed()) {  torch_i0(torch_arange(start = 0, end = 5, dtype=torch_float32())) } #> torch_tensor #>   1.0000 #>   1.2661 #>   2.2796 #>   4.8808 #>  11.3019 #>  27.2399 #> [ CPUFloatType{6} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_iinfo.html","id":null,"dir":"Reference","previous_headings":"","what":"Integer type info — torch_iinfo","title":"Integer type info — torch_iinfo","text":"list represents numerical properties integer type.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_iinfo.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Integer type info — torch_iinfo","text":"","code":"torch_iinfo(dtype)"},{"path":"https://torch.mlverse.org/docs/reference/torch_iinfo.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Integer type info — torch_iinfo","text":"dtype dtype get information .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_imag.html","id":null,"dir":"Reference","previous_headings":"","what":"Imag — torch_imag","title":"Imag — torch_imag","text":"Imag","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_imag.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Imag — torch_imag","text":"","code":"torch_imag(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_imag.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Imag — torch_imag","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_imag.html","id":"imag-input-gt-tensor-","dir":"Reference","previous_headings":"","what":"imag(input) -> Tensor","title":"Imag — torch_imag","text":"Returns imaginary part input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_imag.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Imag — torch_imag","text":"yet implemented. $$     \\mbox{}_{} = imag(\\mbox{input}_{}) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_imag.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Imag — torch_imag","text":"","code":"if (torch_is_installed()) { if (FALSE) { torch_imag(torch_tensor(c(-1 + 1i, -2 + 2i, 3 - 3i))) } }"},{"path":"https://torch.mlverse.org/docs/reference/torch_index.html","id":null,"dir":"Reference","previous_headings":"","what":"Index torch tensors — torch_index","title":"Index torch tensors — torch_index","text":"Helper functions index tensors.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_index.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Index torch tensors — torch_index","text":"","code":"torch_index(self, indices)"},{"path":"https://torch.mlverse.org/docs/reference/torch_index.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Index torch tensors — torch_index","text":"self (Tensor) Tensor indexed. indices (List[Tensor]) List indices. Indices torch tensors torch_long() dtype.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_index_put.html","id":null,"dir":"Reference","previous_headings":"","what":"Modify values selected by indices. — torch_index_put","title":"Modify values selected by indices. — torch_index_put","text":"Modify values selected indices.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_index_put.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Modify values selected by indices. — torch_index_put","text":"","code":"torch_index_put(self, indices, values, accumulate = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_index_put.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Modify values selected by indices. — torch_index_put","text":"self (Tensor) Tensor indexed. indices (List[Tensor]) List indices. Indices torch tensors torch_long() dtype. values (Tensor) values replaced indexed location. Used torch_index_put torch_index_put_. accumulate (bool) Wether instead replacing current values values, want add .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_index_put_.html","id":null,"dir":"Reference","previous_headings":"","what":"In-place version of torch_index_put. — torch_index_put_","title":"In-place version of torch_index_put. — torch_index_put_","text":"-place version torch_index_put.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_index_put_.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"In-place version of torch_index_put. — torch_index_put_","text":"","code":"torch_index_put_(self, indices, values, accumulate = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_index_put_.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"In-place version of torch_index_put. — torch_index_put_","text":"self (Tensor) Tensor indexed. indices (List[Tensor]) List indices. Indices torch tensors torch_long() dtype. values (Tensor) values replaced indexed location. Used torch_index_put torch_index_put_. accumulate (bool) Wether instead replacing current values values, want add .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_index_select.html","id":null,"dir":"Reference","previous_headings":"","what":"Index_select — torch_index_select","title":"Index_select — torch_index_select","text":"Index_select","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_index_select.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Index_select — torch_index_select","text":"","code":"torch_index_select(self, dim, index)"},{"path":"https://torch.mlverse.org/docs/reference/torch_index_select.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Index_select — torch_index_select","text":"self (Tensor) input tensor. dim (int) dimension index index (LongTensor) 1-D tensor containing indices index","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_index_select.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Index_select — torch_index_select","text":"returned tensor use storage original tensor.  different shape expected, silently change correct shape, reallocating underlying storage necessary.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_index_select.html","id":"index-select-input-dim-index-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"index_select(input, dim, index, out=NULL) -> Tensor","title":"Index_select — torch_index_select","text":"Returns new tensor indexes input tensor along dimension dim using entries index LongTensor. returned tensor number dimensions original tensor (input).  dim\\ th dimension size length index; dimensions size original tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_index_select.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Index_select — torch_index_select","text":"","code":"if (torch_is_installed()) {  x = torch_randn(c(3, 4)) x indices = torch_tensor(c(1, 3), dtype = torch_int64()) torch_index_select(x, 1, indices) torch_index_select(x, 2, indices) } #> torch_tensor #> -1.3321 -1.1262 #>  0.2194 -0.5341 #>  0.9468  1.1817 #> [ CPUFloatType{3,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_install_path.html","id":null,"dir":"Reference","previous_headings":"","what":"A simple exported version of install_path\nReturns the torch installation path. — torch_install_path","title":"A simple exported version of install_path\nReturns the torch installation path. — torch_install_path","text":"simple exported version install_path Returns torch installation path.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_install_path.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A simple exported version of install_path\nReturns the torch installation path. — torch_install_path","text":"","code":"torch_install_path()"},{"path":"https://torch.mlverse.org/docs/reference/torch_inverse.html","id":null,"dir":"Reference","previous_headings":"","what":"Inverse — torch_inverse","title":"Inverse — torch_inverse","text":"Inverse","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_inverse.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Inverse — torch_inverse","text":"","code":"torch_inverse(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_inverse.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Inverse — torch_inverse","text":"self (Tensor) input tensor size \\((*, n, n)\\) * zero                    batch dimensions","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_inverse.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Inverse — torch_inverse","text":"","code":"Irrespective of the original strides, the returned tensors will be transposed, i.e. with strides like `input.contiguous().transpose(-2, -1).stride()`"},{"path":"https://torch.mlverse.org/docs/reference/torch_inverse.html","id":"inverse-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"inverse(input, out=NULL) -> Tensor","title":"Inverse — torch_inverse","text":"Takes inverse square matrix input. input can batches 2D square tensors, case function return tensor composed individual inverses.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_inverse.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Inverse — torch_inverse","text":"","code":"if (torch_is_installed()) { if (FALSE) { x = torch_rand(c(4, 4)) y = torch_inverse(x) z = torch_mm(x, y) z torch_max(torch_abs(z - torch_eye(4))) # Max non-zero # Batched inverse example x = torch_randn(c(2, 3, 4, 4)) y = torch_inverse(x) z = torch_matmul(x, y) torch_max(torch_abs(z - torch_eye(4)$expand_as(x))) # Max non-zero } }"},{"path":"https://torch.mlverse.org/docs/reference/torch_is_complex.html","id":null,"dir":"Reference","previous_headings":"","what":"Is_complex — torch_is_complex","title":"Is_complex — torch_is_complex","text":"Is_complex","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_is_complex.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Is_complex — torch_is_complex","text":"","code":"torch_is_complex(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_is_complex.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Is_complex — torch_is_complex","text":"self (Tensor) PyTorch tensor test","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_is_complex.html","id":"is-complex-input-gt-bool-","dir":"Reference","previous_headings":"","what":"is_complex(input) -> (bool)","title":"Is_complex — torch_is_complex","text":"Returns TRUE data type input complex data type .e., one torch_complex64, torch.complex128.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_is_floating_point.html","id":null,"dir":"Reference","previous_headings":"","what":"Is_floating_point — torch_is_floating_point","title":"Is_floating_point — torch_is_floating_point","text":"Is_floating_point","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_is_floating_point.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Is_floating_point — torch_is_floating_point","text":"","code":"torch_is_floating_point(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_is_floating_point.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Is_floating_point — torch_is_floating_point","text":"self (Tensor) PyTorch tensor test","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_is_floating_point.html","id":"is-floating-point-input-gt-bool-","dir":"Reference","previous_headings":"","what":"is_floating_point(input) -> (bool)","title":"Is_floating_point — torch_is_floating_point","text":"Returns TRUE data type input floating point data type .e., one torch_float64, torch.float32 torch.float16.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_is_installed.html","id":null,"dir":"Reference","previous_headings":"","what":"Verifies if torch is installed — torch_is_installed","title":"Verifies if torch is installed — torch_is_installed","text":"Verifies torch installed","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_is_installed.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Verifies if torch is installed — torch_is_installed","text":"","code":"torch_is_installed()"},{"path":"https://torch.mlverse.org/docs/reference/torch_is_nonzero.html","id":null,"dir":"Reference","previous_headings":"","what":"Is_nonzero — torch_is_nonzero","title":"Is_nonzero — torch_is_nonzero","text":"Is_nonzero","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_is_nonzero.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Is_nonzero — torch_is_nonzero","text":"","code":"torch_is_nonzero(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_is_nonzero.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Is_nonzero — torch_is_nonzero","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_is_nonzero.html","id":"is-nonzero-input-gt-bool-","dir":"Reference","previous_headings":"","what":"is_nonzero(input) -> (bool)","title":"Is_nonzero — torch_is_nonzero","text":"Returns TRUE input single element tensor equal zero type conversions. .e. equal torch_tensor(c(0)) torch_tensor(c(0)) torch_tensor(c(FALSE)). Throws RuntimeError torch_numel() != 1 (even case sparse tensors).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_is_nonzero.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Is_nonzero — torch_is_nonzero","text":"","code":"if (torch_is_installed()) {  torch_is_nonzero(torch_tensor(c(0.))) torch_is_nonzero(torch_tensor(c(1.5))) torch_is_nonzero(torch_tensor(c(FALSE))) torch_is_nonzero(torch_tensor(c(3))) if (FALSE) { torch_is_nonzero(torch_tensor(c(1, 3, 5))) torch_is_nonzero(torch_tensor(c())) } }"},{"path":"https://torch.mlverse.org/docs/reference/torch_isclose.html","id":null,"dir":"Reference","previous_headings":"","what":"Isclose — torch_isclose","title":"Isclose — torch_isclose","text":"Isclose","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_isclose.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Isclose — torch_isclose","text":"","code":"torch_isclose(self, other, rtol = 1e-05, atol = 1e-08, equal_nan = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_isclose.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Isclose — torch_isclose","text":"self (Tensor) first tensor compare (Tensor) second tensor compare rtol (float, optional) relative tolerance. Default: 1e-05 atol (float, optional) absolute tolerance. Default: 1e-08 equal_nan (bool, optional) TRUE, two NaN s considered equal. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_isclose.html","id":"isclose-input-other-rtol-e-atol-e-equal-nan-false-gt-tensor-","dir":"Reference","previous_headings":"","what":"isclose(input, other, rtol=1e-05, atol=1e-08, equal_nan=FALSE) -> Tensor","title":"Isclose — torch_isclose","text":"Returns new tensor boolean elements representing element input \"close\" corresponding element . Closeness defined : $$     \\vert \\mbox{input} - \\mbox{} \\vert \\leq \\mbox{atol} + \\mbox{rtol} \\times \\vert \\mbox{} \\vert $$ input finite. input /nonfinite close equal, NaNs considered equal equal_nan TRUE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_isclose.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Isclose — torch_isclose","text":"","code":"if (torch_is_installed()) {  torch_isclose(torch_tensor(c(1., 2, 3)), torch_tensor(c(1 + 1e-10, 3, 4))) torch_isclose(torch_tensor(c(Inf, 4)), torch_tensor(c(Inf, 6)), rtol=.5) } #> torch_tensor #>  1 #>  1 #> [ CPUBoolType{2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_isfinite.html","id":null,"dir":"Reference","previous_headings":"","what":"Isfinite — torch_isfinite","title":"Isfinite — torch_isfinite","text":"Isfinite","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_isfinite.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Isfinite — torch_isfinite","text":"","code":"torch_isfinite(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_isfinite.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Isfinite — torch_isfinite","text":"self (Tensor) tensor check","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_isfinite.html","id":"test-","dir":"Reference","previous_headings":"","what":"TEST","title":"Isfinite — torch_isfinite","text":"Returns new tensor boolean elements representing element Finite .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_isfinite.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Isfinite — torch_isfinite","text":"","code":"if (torch_is_installed()) {  torch_isfinite(torch_tensor(c(1, Inf, 2, -Inf, NaN))) } #> torch_tensor #>  1 #>  0 #>  1 #>  0 #>  0 #> [ CPUBoolType{5} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_isinf.html","id":null,"dir":"Reference","previous_headings":"","what":"Isinf — torch_isinf","title":"Isinf — torch_isinf","text":"Isinf","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_isinf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Isinf — torch_isinf","text":"","code":"torch_isinf(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_isinf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Isinf — torch_isinf","text":"self (Tensor) tensor check","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_isinf.html","id":"test-","dir":"Reference","previous_headings":"","what":"TEST","title":"Isinf — torch_isinf","text":"Returns new tensor boolean elements representing element +/-INF .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_isinf.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Isinf — torch_isinf","text":"","code":"if (torch_is_installed()) {  torch_isinf(torch_tensor(c(1, Inf, 2, -Inf, NaN))) } #> torch_tensor #>  0 #>  1 #>  0 #>  1 #>  0 #> [ CPUBoolType{5} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_isnan.html","id":null,"dir":"Reference","previous_headings":"","what":"Isnan — torch_isnan","title":"Isnan — torch_isnan","text":"Isnan","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_isnan.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Isnan — torch_isnan","text":"","code":"torch_isnan(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_isnan.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Isnan — torch_isnan","text":"self (Tensor) tensor check","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_isnan.html","id":"test-","dir":"Reference","previous_headings":"","what":"TEST","title":"Isnan — torch_isnan","text":"Returns new tensor boolean elements representing element NaN .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_isnan.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Isnan — torch_isnan","text":"","code":"if (torch_is_installed()) {  torch_isnan(torch_tensor(c(1, NaN, 2))) } #> torch_tensor #>  0 #>  1 #>  0 #> [ CPUBoolType{3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_isneginf.html","id":null,"dir":"Reference","previous_headings":"","what":"Isneginf — torch_isneginf","title":"Isneginf — torch_isneginf","text":"Isneginf","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_isneginf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Isneginf — torch_isneginf","text":"","code":"torch_isneginf(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_isneginf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Isneginf — torch_isneginf","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_isneginf.html","id":"isneginf-input-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"isneginf(input, *, out=None) -> Tensor","title":"Isneginf — torch_isneginf","text":"Tests element input negative infinity .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_isneginf.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Isneginf — torch_isneginf","text":"","code":"if (torch_is_installed()) {  a <- torch_tensor(c(-Inf, Inf, 1.2)) torch_isneginf(a) } #> torch_tensor #>  1 #>  0 #>  0 #> [ CPUBoolType{3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_isposinf.html","id":null,"dir":"Reference","previous_headings":"","what":"Isposinf — torch_isposinf","title":"Isposinf — torch_isposinf","text":"Isposinf","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_isposinf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Isposinf — torch_isposinf","text":"","code":"torch_isposinf(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_isposinf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Isposinf — torch_isposinf","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_isposinf.html","id":"isposinf-input-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"isposinf(input, *, out=None) -> Tensor","title":"Isposinf — torch_isposinf","text":"Tests element input positive infinity .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_isposinf.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Isposinf — torch_isposinf","text":"","code":"if (torch_is_installed()) {  a <- torch_tensor(c(-Inf, Inf, 1.2)) torch_isposinf(a) } #> torch_tensor #>  0 #>  1 #>  0 #> [ CPUBoolType{3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_isreal.html","id":null,"dir":"Reference","previous_headings":"","what":"Isreal — torch_isreal","title":"Isreal — torch_isreal","text":"Isreal","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_isreal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Isreal — torch_isreal","text":"","code":"torch_isreal(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_isreal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Isreal — torch_isreal","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_isreal.html","id":"isreal-input-gt-tensor-","dir":"Reference","previous_headings":"","what":"isreal(input) -> Tensor","title":"Isreal — torch_isreal","text":"Returns new tensor boolean elements representing element input real-valued . real-valued types considered real. Complex values considered real imaginary part 0.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_isreal.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Isreal — torch_isreal","text":"","code":"if (torch_is_installed()) { if (FALSE) { torch_isreal(torch_tensor(c(1, 1+1i, 2+0i))) } }"},{"path":"https://torch.mlverse.org/docs/reference/torch_istft.html","id":null,"dir":"Reference","previous_headings":"","what":"Istft — torch_istft","title":"Istft — torch_istft","text":"Inverse short time Fourier Transform. expected inverse torch_stft().","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_istft.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Istft — torch_istft","text":"","code":"torch_istft(   self,   n_fft,   hop_length = NULL,   win_length = NULL,   window = list(),   center = TRUE,   normalized = FALSE,   onesided = NULL,   length = NULL,   return_complex = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/torch_istft.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Istft — torch_istft","text":"self (Tensor) input tensor. Expected output torch_stft(), can either complex (channel, fft_size, n_frame), real (channel, fft_size, n_frame, 2) channel dimension optional. n_fft (int) Size Fourier transform hop_length (Optional[int]) distance neighboring sliding window frames. (Default: n_fft %% 4) win_length (Optional[int]) size window frame STFT filter. (Default: n_fft) window (Optional(torch.Tensor)) optional window function. (Default: torch_ones(win_length)) center (bool) Whether input padded sides \\(t\\)-th frame centered time \\(t \\times \\mbox{hop\\_length}\\). (Default: TRUE) normalized (bool) Whether STFT normalized. (Default: FALSE) onesided (Optional(bool)) Whether STFT onesided. (Default: TRUE n_fft != fft_size input size) length (Optional(int)]) amount trim signal (.e. original signal length). (Default: whole signal) return_complex (Optional(bool)) Whether output complex, input assumed derive real signal window. Note incompatible onesided=TRUE. (Default: FALSE)","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_istft.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Istft — torch_istft","text":"parameters (+ additional optional parameter length) return least squares estimation original signal. algorithm check using NOLA condition (nonzero overlap). Important consideration parameters window center envelop created summation windows never zero certain point time. Specifically, \\(\\sum_{t=-\\infty}^{\\infty} |w|^2(n-t\\times hop_length) \\neq 0\\). Since torch_stft() discards elements end signal fit frame, istft may return shorter signal original signal (can occur center FALSE since signal padded). center TRUE, padding e.g. 'constant', 'reflect', etc. Left padding can trimmed exactly can calculated right padding calculated without additional information. Example: Suppose last window : [c(17, 18, 0, 0, 0) vs c(18, 0, 0, 0, 0) n_fft, hop_length, win_length prevents calculation right padding. additional values zeros reflection signal providing length useful. length None padding aggressively removed (loss signal). D. W. Griffin J. S. Lim, \"Signal estimation modified short-time Fourier transform,\" IEEE Trans. ASSP, vol.32, .2, pp.236-243, Apr. 1984.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_kaiser_window.html","id":null,"dir":"Reference","previous_headings":"","what":"Kaiser_window — torch_kaiser_window","title":"Kaiser_window — torch_kaiser_window","text":"Kaiser_window","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_kaiser_window.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Kaiser_window — torch_kaiser_window","text":"","code":"torch_kaiser_window(   window_length,   periodic,   beta,   dtype = torch_float(),   layout = NULL,   device = NULL,   requires_grad = NULL )"},{"path":"https://torch.mlverse.org/docs/reference/torch_kaiser_window.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Kaiser_window — torch_kaiser_window","text":"window_length (int) length window. periodic (bool, optional) TRUE, returns periodic window suitable use spectral analysis.        FALSE, returns symmetric window suitable use filter design. beta (float, optional) shape parameter window. dtype (torch.dtype, optional) desired data type returned tensor.        Default: NULL, uses global default (see torch_set_default_tensor_type). dtype given, infer data type input        arguments. start, end, stop floating-point,        dtype inferred default dtype, see        ~torch.get_default_dtype. Otherwise, dtype inferred        torch.int64. layout (torch.layout, optional) desired layout returned Tensor.        Default: torch_strided. device (torch.device, optional) desired device returned tensor.        Default: NULL, uses current device default tensor type        (see torch_set_default_tensor_type). device CPU        CPU tensor types current CUDA device CUDA tensor types. requires_grad (bool, optional) autograd record operations        returned tensor. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_kaiser_window.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Kaiser_window — torch_kaiser_window","text":"window_length one, returned window single element tensor containing one.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_kaiser_window.html","id":"kaiser-window-window-length-periodic-true-beta-dtype-none-layout-torch-strided-device-none-requires-grad-false-gt-tensor-","dir":"Reference","previous_headings":"","what":"kaiser_window(window_length, periodic=TRUE, beta=12.0, *, dtype=None, layout=torch.strided, device=None, requires_grad=FALSE) -> Tensor","title":"Kaiser_window — torch_kaiser_window","text":"Computes Kaiser window window length window_length shape parameter beta. Let I_0 zeroth order modified Bessel function first kind (see torch_i0()) N = L - 1 periodic FALSE L periodic TRUE, L window_length. function computes: $$     out_i = I_0 \\left( \\beta \\sqrt{1 - \\left( {\\frac{- N/2}{N/2}} \\right) ^2 } \\right) / I_0( \\beta ) $$ Calling torch_kaiser_window(L, B, periodic=TRUE) equivalent calling torch_kaiser_window(L + 1, B, periodic=FALSE)[:-1]). periodic argument intended helpful shorthand produce periodic window input functions like torch_stft().","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_kthvalue.html","id":null,"dir":"Reference","previous_headings":"","what":"Kthvalue — torch_kthvalue","title":"Kthvalue — torch_kthvalue","text":"Kthvalue","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_kthvalue.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Kthvalue — torch_kthvalue","text":"","code":"torch_kthvalue(self, k, dim = -1L, keepdim = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_kthvalue.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Kthvalue — torch_kthvalue","text":"self (Tensor) input tensor. k (int) k k-th smallest element dim (int, optional) dimension find kth value along keepdim (bool) whether output tensor dim retained .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_kthvalue.html","id":"kthvalue-input-k-dim-null-keepdim-false-out-null-gt-tensor-longtensor-","dir":"Reference","previous_headings":"","what":"kthvalue(input, k, dim=NULL, keepdim=False, out=NULL) -> (Tensor, LongTensor)","title":"Kthvalue — torch_kthvalue","text":"Returns namedtuple (values, indices) values k th smallest element row input tensor given dimension dim. indices index location element found. dim given, last dimension input chosen. keepdim TRUE, values indices tensors size input, except dimension dim size 1. Otherwise, dim squeezed (see torch_squeeze), resulting values indices tensors 1 fewer dimension input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_kthvalue.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Kthvalue — torch_kthvalue","text":"","code":"if (torch_is_installed()) {  x <- torch_arange(1, 6) x torch_kthvalue(x, 4) x <- torch_arange(1,6)$resize_(c(2,3)) x torch_kthvalue(x, 2, 1, TRUE) } #> [[1]] #> torch_tensor #>  4  5  6 #> [ CPUFloatType{1,3} ] #>  #> [[2]] #> torch_tensor #>  1  1  1 #> [ CPULongType{1,3} ] #>"},{"path":"https://torch.mlverse.org/docs/reference/torch_layout.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates the corresponding layout — torch_layout","title":"Creates the corresponding layout — torch_layout","text":"Creates corresponding layout","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_layout.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates the corresponding layout — torch_layout","text":"","code":"torch_strided()  torch_sparse_coo()"},{"path":"https://torch.mlverse.org/docs/reference/torch_lcm.html","id":null,"dir":"Reference","previous_headings":"","what":"Lcm — torch_lcm","title":"Lcm — torch_lcm","text":"Lcm","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_lcm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Lcm — torch_lcm","text":"","code":"torch_lcm(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_lcm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Lcm — torch_lcm","text":"self (Tensor) input tensor. (Tensor) second input tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_lcm.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Lcm — torch_lcm","text":"defines \\(lcm(0, 0) = 0\\) \\(lcm(0, ) = 0\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_lcm.html","id":"lcm-input-other-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"lcm(input, other, *, out=None) -> Tensor","title":"Lcm — torch_lcm","text":"Computes element-wise least common multiple (LCM) input . input must integer types.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_lcm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Lcm — torch_lcm","text":"","code":"if (torch_is_installed()) {  if (torch::cuda_is_available()) { a <- torch_tensor(c(5, 10, 15), dtype = torch_long(), device = \"cuda\") b <- torch_tensor(c(3, 4, 5), dtype = torch_long(), device = \"cuda\") torch_lcm(a, b) c <- torch_tensor(c(3L), device = \"cuda\") torch_lcm(a, c) } }"},{"path":"https://torch.mlverse.org/docs/reference/torch_le.html","id":null,"dir":"Reference","previous_headings":"","what":"Le — torch_le","title":"Le — torch_le","text":"Le","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_le.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Le — torch_le","text":"","code":"torch_le(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_le.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Le — torch_le","text":"self (Tensor) tensor compare (Tensor float) tensor value compare","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_le.html","id":"le-input-other-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"le(input, other, out=NULL) -> Tensor","title":"Le — torch_le","text":"Computes \\(\\mbox{input} \\leq \\mbox{}\\) element-wise. second argument can number tensor whose shape broadcastable  first argument.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_le.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Le — torch_le","text":"","code":"if (torch_is_installed()) {  torch_le(torch_tensor(matrix(1:4, ncol = 2, byrow=TRUE)),           torch_tensor(matrix(c(1,1,4,4), ncol = 2, byrow=TRUE))) } #> torch_tensor #>  1  0 #>  1  1 #> [ CPUBoolType{2,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_lerp.html","id":null,"dir":"Reference","previous_headings":"","what":"Lerp — torch_lerp","title":"Lerp — torch_lerp","text":"Lerp","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_lerp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Lerp — torch_lerp","text":"","code":"torch_lerp(self, end, weight)"},{"path":"https://torch.mlverse.org/docs/reference/torch_lerp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Lerp — torch_lerp","text":"self (Tensor) tensor starting points end (Tensor) tensor ending points weight (float tensor) weight interpolation formula","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_lerp.html","id":"lerp-input-end-weight-out-null-","dir":"Reference","previous_headings":"","what":"lerp(input, end, weight, out=NULL)","title":"Lerp — torch_lerp","text":"linear interpolation two tensors start (given input) end based scalar tensor weight returns resulting tensor. $$     \\mbox{}_i = \\mbox{start}_i + \\mbox{weight}_i \\times (\\mbox{end}_i - \\mbox{start}_i) $$ shapes start end must broadcastable . weight tensor, shapes weight, start, end must broadcastable .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_lerp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Lerp — torch_lerp","text":"","code":"if (torch_is_installed()) {  start = torch_arange(1, 4) end = torch_empty(4)$fill_(10) start end torch_lerp(start, end, 0.5) torch_lerp(start, end, torch_full_like(start, 0.5)) } #> torch_tensor #>  5.5000 #>  6.0000 #>  6.5000 #>  7.0000 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_less.html","id":null,"dir":"Reference","previous_headings":"","what":"Less — torch_less","title":"Less — torch_less","text":"Less","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_less.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Less — torch_less","text":"","code":"torch_less(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_less.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Less — torch_less","text":"self (Tensor) tensor compare (Tensor float) tensor value compare","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_less.html","id":"less-input-other-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"less(input, other, *, out=None) -> Tensor","title":"Less — torch_less","text":"Alias torch_lt().","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_less_equal.html","id":null,"dir":"Reference","previous_headings":"","what":"Less_equal — torch_less_equal","title":"Less_equal — torch_less_equal","text":"Less_equal","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_less_equal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Less_equal — torch_less_equal","text":"","code":"torch_less_equal(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_less_equal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Less_equal — torch_less_equal","text":"self (Tensor) tensor compare (Tensor float) tensor value compare","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_less_equal.html","id":"less-equal-input-other-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"less_equal(input, other, *, out=None) -> Tensor","title":"Less_equal — torch_less_equal","text":"Alias torch_le().","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_lgamma.html","id":null,"dir":"Reference","previous_headings":"","what":"Lgamma — torch_lgamma","title":"Lgamma — torch_lgamma","text":"Lgamma","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_lgamma.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Lgamma — torch_lgamma","text":"","code":"torch_lgamma(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_lgamma.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Lgamma — torch_lgamma","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_lgamma.html","id":"lgamma-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"lgamma(input, out=NULL) -> Tensor","title":"Lgamma — torch_lgamma","text":"Computes logarithm gamma function input. $$     \\mbox{}_{} = \\log \\Gamma(\\mbox{input}_{}) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_lgamma.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Lgamma — torch_lgamma","text":"","code":"if (torch_is_installed()) {  a = torch_arange(0.5, 2, 0.5) torch_lgamma(a) } #> torch_tensor #>  0.5724 #>  0.0000 #> -0.1208 #>  0.0000 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_linspace.html","id":null,"dir":"Reference","previous_headings":"","what":"Linspace — torch_linspace","title":"Linspace — torch_linspace","text":"Linspace","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_linspace.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Linspace — torch_linspace","text":"","code":"torch_linspace(   start,   end,   steps = 100,   dtype = NULL,   layout = torch_strided(),   device = NULL,   requires_grad = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/torch_linspace.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Linspace — torch_linspace","text":"start (float) starting value set points end (float) ending value set points steps (int) number points sample start        end. Default: 100. dtype (torch.dtype, optional) desired data type returned tensor.        Default: NULL, uses global default (see torch_set_default_tensor_type). layout (torch.layout, optional) desired layout returned Tensor.        Default: torch_strided. device (torch.device, optional) desired device returned tensor.        Default: NULL, uses current device default tensor type        (see torch_set_default_tensor_type). device CPU        CPU tensor types current CUDA device CUDA tensor types. requires_grad (bool, optional) autograd record operations        returned tensor. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_linspace.html","id":"linspace-start-end-steps-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-","dir":"Reference","previous_headings":"","what":"linspace(start, end, steps=100, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor","title":"Linspace — torch_linspace","text":"Returns one-dimensional tensor steps equally spaced points start end. output tensor 1-D size steps.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_linspace.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Linspace — torch_linspace","text":"","code":"if (torch_is_installed()) {  torch_linspace(3, 10, steps=5) torch_linspace(-10, 10, steps=5) torch_linspace(start=-10, end=10, steps=5) torch_linspace(start=-10, end=10, steps=1) } #> torch_tensor #> -10 #> [ CPUFloatType{1} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_load.html","id":null,"dir":"Reference","previous_headings":"","what":"Loads a saved object — torch_load","title":"Loads a saved object — torch_load","text":"Loads saved object","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_load.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Loads a saved object — torch_load","text":"","code":"torch_load(path, device = \"cpu\")"},{"path":"https://torch.mlverse.org/docs/reference/torch_load.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Loads a saved object — torch_load","text":"path path saved object device device load tensors . default load cpu can also load cuda device. NULL device tensor saved reused.","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/torch_log.html","id":null,"dir":"Reference","previous_headings":"","what":"Log — torch_log","title":"Log — torch_log","text":"Log","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_log.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Log — torch_log","text":"","code":"torch_log(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_log.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Log — torch_log","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_log.html","id":"log-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"log(input, out=NULL) -> Tensor","title":"Log — torch_log","text":"Returns new tensor natural logarithm elements input. $$     y_{} = \\log_{e} (x_{}) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_log.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Log — torch_log","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(5)) a torch_log(a) } #> torch_tensor #>     nan #>     nan #>     nan #> -4.7006 #>     nan #> [ CPUFloatType{5} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_log10.html","id":null,"dir":"Reference","previous_headings":"","what":"Log10 — torch_log10","title":"Log10 — torch_log10","text":"Log10","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_log10.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Log10 — torch_log10","text":"","code":"torch_log10(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_log10.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Log10 — torch_log10","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_log10.html","id":"log-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"log10(input, out=NULL) -> Tensor","title":"Log10 — torch_log10","text":"Returns new tensor logarithm base 10 elements input. $$     y_{} = \\log_{10} (x_{}) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_log10.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Log10 — torch_log10","text":"","code":"if (torch_is_installed()) {  a = torch_rand(5) a torch_log10(a) } #> torch_tensor #> -0.1267 #> -0.5351 #> -0.1341 #> -0.6519 #> -0.5757 #> [ CPUFloatType{5} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_log1p.html","id":null,"dir":"Reference","previous_headings":"","what":"Log1p — torch_log1p","title":"Log1p — torch_log1p","text":"Log1p","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_log1p.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Log1p — torch_log1p","text":"","code":"torch_log1p(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_log1p.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Log1p — torch_log1p","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_log1p.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Log1p — torch_log1p","text":"function accurate torch_log small values input","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_log1p.html","id":"log-p-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"log1p(input, out=NULL) -> Tensor","title":"Log1p — torch_log1p","text":"Returns new tensor natural logarithm (1 + input). $$     y_i = \\log_{e} (x_i + 1) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_log1p.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Log1p — torch_log1p","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(5)) a torch_log1p(a) } #> torch_tensor #>  0.8064 #>  0.6569 #> -1.5694 #>  0.9993 #> -0.1102 #> [ CPUFloatType{5} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_log2.html","id":null,"dir":"Reference","previous_headings":"","what":"Log2 — torch_log2","title":"Log2 — torch_log2","text":"Log2","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_log2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Log2 — torch_log2","text":"","code":"torch_log2(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_log2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Log2 — torch_log2","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_log2.html","id":"log-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"log2(input, out=NULL) -> Tensor","title":"Log2 — torch_log2","text":"Returns new tensor logarithm base 2 elements input. $$     y_{} = \\log_{2} (x_{}) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_log2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Log2 — torch_log2","text":"","code":"if (torch_is_installed()) {  a = torch_rand(5) a torch_log2(a) } #> torch_tensor #> -0.2708 #> -0.2082 #> -3.5860 #> -3.7349 #> -3.9478 #> [ CPUFloatType{5} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_logaddexp.html","id":null,"dir":"Reference","previous_headings":"","what":"Logaddexp — torch_logaddexp","title":"Logaddexp — torch_logaddexp","text":"Logaddexp","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logaddexp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Logaddexp — torch_logaddexp","text":"","code":"torch_logaddexp(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_logaddexp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Logaddexp — torch_logaddexp","text":"self (Tensor) input tensor. (Tensor) second input tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logaddexp.html","id":"logaddexp-input-other-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"logaddexp(input, other, *, out=None) -> Tensor","title":"Logaddexp — torch_logaddexp","text":"Logarithm sum exponentiations inputs. Calculates pointwise \\(\\log\\left(e^x + e^y\\right)\\). function useful statistics calculated probabilities events may small exceed range normal floating point numbers. cases logarithm calculated probability stored. function allows adding probabilities stored fashion. op disambiguated torch_logsumexp() performs reduction single tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logaddexp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Logaddexp — torch_logaddexp","text":"","code":"if (torch_is_installed()) {  torch_logaddexp(torch_tensor(c(-1.0)), torch_tensor(c(-1.0, -2, -3))) torch_logaddexp(torch_tensor(c(-100.0, -200, -300)), torch_tensor(c(-1.0, -2, -3))) torch_logaddexp(torch_tensor(c(1.0, 2000, 30000)), torch_tensor(c(-1.0, -2, -3))) } #> torch_tensor #>      1.1269 #>   2000.0000 #>  30000.0000 #> [ CPUFloatType{3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_logaddexp2.html","id":null,"dir":"Reference","previous_headings":"","what":"Logaddexp2 — torch_logaddexp2","title":"Logaddexp2 — torch_logaddexp2","text":"Logaddexp2","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logaddexp2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Logaddexp2 — torch_logaddexp2","text":"","code":"torch_logaddexp2(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_logaddexp2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Logaddexp2 — torch_logaddexp2","text":"self (Tensor) input tensor. (Tensor) second input tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logaddexp2.html","id":"logaddexp-input-other-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"logaddexp2(input, other, *, out=None) -> Tensor","title":"Logaddexp2 — torch_logaddexp2","text":"Logarithm sum exponentiations inputs base-2. Calculates pointwise \\(\\log_2\\left(2^x + 2^y\\right)\\). See torch_logaddexp() details.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logcumsumexp.html","id":null,"dir":"Reference","previous_headings":"","what":"Logcumsumexp — torch_logcumsumexp","title":"Logcumsumexp — torch_logcumsumexp","text":"Logcumsumexp","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logcumsumexp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Logcumsumexp — torch_logcumsumexp","text":"","code":"torch_logcumsumexp(self, dim)"},{"path":"https://torch.mlverse.org/docs/reference/torch_logcumsumexp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Logcumsumexp — torch_logcumsumexp","text":"self (Tensor) input tensor. dim (int) dimension operation ","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logcumsumexp.html","id":"logcumsumexp-input-dim-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"logcumsumexp(input, dim, *, out=None) -> Tensor","title":"Logcumsumexp — torch_logcumsumexp","text":"Returns logarithm cumulative summation exponentiation elements input dimension dim. summation index \\(j\\) given dim indices \\(\\), result $$         \\mbox{logcumsumexp}(x)_{ij} = \\log \\sum\\limits_{j=0}^{} \\exp(x_{ij}) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logcumsumexp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Logcumsumexp — torch_logcumsumexp","text":"","code":"if (torch_is_installed()) {  a <- torch_randn(c(10)) torch_logcumsumexp(a, dim=1) } #> torch_tensor #> -0.9025 #>  0.7117 #>  1.0725 #>  1.1551 #>  1.7743 #>  1.7817 #>  1.8073 #>  1.9513 #>  1.9586 #>  2.0104 #> [ CPUFloatType{10} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_logdet.html","id":null,"dir":"Reference","previous_headings":"","what":"Logdet — torch_logdet","title":"Logdet — torch_logdet","text":"Logdet","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logdet.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Logdet — torch_logdet","text":"","code":"torch_logdet(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_logdet.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Logdet — torch_logdet","text":"self (Tensor) input tensor size (*, n, n) * zero                batch dimensions.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logdet.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Logdet — torch_logdet","text":"","code":"Result is `-inf` if `input` has zero log determinant, and is `NaN` if `input` has negative determinant. Backward through `logdet` internally uses SVD results when `input` is not invertible. In this case, double backward through `logdet` will be unstable in when `input` doesn't have distinct singular values. See `~torch.svd` for details."},{"path":"https://torch.mlverse.org/docs/reference/torch_logdet.html","id":"logdet-input-gt-tensor-","dir":"Reference","previous_headings":"","what":"logdet(input) -> Tensor","title":"Logdet — torch_logdet","text":"Calculates log determinant square matrix batches square matrices.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logdet.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Logdet — torch_logdet","text":"","code":"if (torch_is_installed()) {  A = torch_randn(c(3, 3)) torch_det(A) torch_logdet(A) A A$det() A$det()$log() } #> torch_tensor #> -0.336194 #> [ CPUFloatType{} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_logical_and.html","id":null,"dir":"Reference","previous_headings":"","what":"Logical_and — torch_logical_and","title":"Logical_and — torch_logical_and","text":"Logical_and","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logical_and.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Logical_and — torch_logical_and","text":"","code":"torch_logical_and(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_logical_and.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Logical_and — torch_logical_and","text":"self (Tensor) input tensor. (Tensor) tensor compute ","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logical_and.html","id":"logical-and-input-other-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"logical_and(input, other, out=NULL) -> Tensor","title":"Logical_and — torch_logical_and","text":"Computes element-wise logical given input tensors. Zeros treated FALSE nonzeros treated TRUE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logical_and.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Logical_and — torch_logical_and","text":"","code":"if (torch_is_installed()) {  torch_logical_and(torch_tensor(c(TRUE, FALSE, TRUE)), torch_tensor(c(TRUE, FALSE, FALSE))) a = torch_tensor(c(0, 1, 10, 0), dtype=torch_int8()) b = torch_tensor(c(4, 0, 1, 0), dtype=torch_int8()) torch_logical_and(a, b) if (FALSE) { torch_logical_and(a, b, out=torch_empty(4, dtype=torch_bool())) } }"},{"path":"https://torch.mlverse.org/docs/reference/torch_logical_not.html","id":null,"dir":"Reference","previous_headings":"","what":"Logical_not — torch_logical_not","title":"Logical_not — torch_logical_not","text":"Logical_not","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logical_not.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Logical_not — torch_logical_not","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logical_not.html","id":"logical-not-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"logical_not(input, out=NULL) -> Tensor","title":"Logical_not — torch_logical_not","text":"Computes element-wise logical given input tensor. specified, output tensor bool dtype. input tensor bool tensor, zeros treated FALSE non-zeros treated TRUE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logical_not.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Logical_not — torch_logical_not","text":"","code":"if (torch_is_installed()) {  torch_logical_not(torch_tensor(c(TRUE, FALSE))) torch_logical_not(torch_tensor(c(0, 1, -10), dtype=torch_int8())) torch_logical_not(torch_tensor(c(0., 1.5, -10.), dtype=torch_double())) } #> torch_tensor #>  1 #>  0 #>  0 #> [ CPUBoolType{3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_logical_or.html","id":null,"dir":"Reference","previous_headings":"","what":"Logical_or — torch_logical_or","title":"Logical_or — torch_logical_or","text":"Logical_or","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logical_or.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Logical_or — torch_logical_or","text":"","code":"torch_logical_or(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_logical_or.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Logical_or — torch_logical_or","text":"self (Tensor) input tensor. (Tensor) tensor compute ","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logical_or.html","id":"logical-or-input-other-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"logical_or(input, other, out=NULL) -> Tensor","title":"Logical_or — torch_logical_or","text":"Computes element-wise logical given input tensors. Zeros treated FALSE nonzeros treated TRUE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logical_or.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Logical_or — torch_logical_or","text":"","code":"if (torch_is_installed()) {  torch_logical_or(torch_tensor(c(TRUE, FALSE, TRUE)), torch_tensor(c(TRUE, FALSE, FALSE))) a = torch_tensor(c(0, 1, 10, 0), dtype=torch_int8()) b = torch_tensor(c(4, 0, 1, 0), dtype=torch_int8()) torch_logical_or(a, b) if (FALSE) { torch_logical_or(a$double(), b$double()) torch_logical_or(a$double(), b) torch_logical_or(a, b, out=torch_empty(4, dtype=torch_bool())) } }"},{"path":"https://torch.mlverse.org/docs/reference/torch_logical_xor.html","id":null,"dir":"Reference","previous_headings":"","what":"Logical_xor — torch_logical_xor","title":"Logical_xor — torch_logical_xor","text":"Logical_xor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logical_xor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Logical_xor — torch_logical_xor","text":"","code":"torch_logical_xor(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_logical_xor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Logical_xor — torch_logical_xor","text":"self (Tensor) input tensor. (Tensor) tensor compute XOR ","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logical_xor.html","id":"logical-xor-input-other-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"logical_xor(input, other, out=NULL) -> Tensor","title":"Logical_xor — torch_logical_xor","text":"Computes element-wise logical XOR given input tensors. Zeros treated FALSE nonzeros treated TRUE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logical_xor.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Logical_xor — torch_logical_xor","text":"","code":"if (torch_is_installed()) {  torch_logical_xor(torch_tensor(c(TRUE, FALSE, TRUE)), torch_tensor(c(TRUE, FALSE, FALSE))) a = torch_tensor(c(0, 1, 10, 0), dtype=torch_int8()) b = torch_tensor(c(4, 0, 1, 0), dtype=torch_int8()) torch_logical_xor(a, b) torch_logical_xor(a$to(dtype=torch_double()), b$to(dtype=torch_double())) torch_logical_xor(a$to(dtype=torch_double()), b) } #> torch_tensor #>  1 #>  1 #>  0 #>  0 #> [ CPUBoolType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_logit.html","id":null,"dir":"Reference","previous_headings":"","what":"Logit — torch_logit","title":"Logit — torch_logit","text":"Logit","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logit.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Logit — torch_logit","text":"","code":"torch_logit(self, eps = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/torch_logit.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Logit — torch_logit","text":"self (Tensor) input tensor. eps (float, optional) epsilon input clamp bound. Default: None","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logit.html","id":"logit-input-eps-none-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"logit(input, eps=None, *, out=None) -> Tensor","title":"Logit — torch_logit","text":"Returns new tensor logit elements input. input clamped [eps, 1 - eps] eps None. eps None input < 0 input > 1, function yields NaN. $$     y_{} = \\ln(\\frac{z_{}}{1 - z_{}}) \\\\     z_{} = \\begin{array}{ll}         x_{} & \\mbox{eps None} \\\\         \\mbox{eps} & \\mbox{} x_{} < \\mbox{eps} \\\\         x_{} & \\mbox{} \\mbox{eps} \\leq x_{} \\leq 1 - \\mbox{eps} \\\\         1 - \\mbox{eps} & \\mbox{} x_{} > 1 - \\mbox{eps}     \\end{array} $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logit.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Logit — torch_logit","text":"","code":"if (torch_is_installed()) {  a <- torch_rand(5) a torch_logit(a, eps=1e-6) } #> torch_tensor #> -1.8133 #>  0.6135 #> -2.5095 #>  1.1855 #>  0.1235 #> [ CPUFloatType{5} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_logspace.html","id":null,"dir":"Reference","previous_headings":"","what":"Logspace — torch_logspace","title":"Logspace — torch_logspace","text":"Logspace","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logspace.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Logspace — torch_logspace","text":"","code":"torch_logspace(   start,   end,   steps = 100,   base = 10,   dtype = NULL,   layout = torch_strided(),   device = NULL,   requires_grad = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/torch_logspace.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Logspace — torch_logspace","text":"start (float) starting value set points end (float) ending value set points steps (int) number points sample start        end. Default: 100. base (float) base logarithm function. Default: 10.0. dtype (torch.dtype, optional) desired data type returned tensor.        Default: NULL, uses global default (see torch_set_default_tensor_type). layout (torch.layout, optional) desired layout returned Tensor.        Default: torch_strided. device (torch.device, optional) desired device returned tensor.        Default: NULL, uses current device default tensor type        (see torch_set_default_tensor_type). device CPU        CPU tensor types current CUDA device CUDA tensor types. requires_grad (bool, optional) autograd record operations        returned tensor. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logspace.html","id":"logspace-start-end-steps-base-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-","dir":"Reference","previous_headings":"","what":"logspace(start, end, steps=100, base=10.0, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor","title":"Logspace — torch_logspace","text":"Returns one-dimensional tensor steps points logarithmically spaced base base \\({\\mbox{base}}^{\\mbox{start}}\\) \\({\\mbox{base}}^{\\mbox{end}}\\). output tensor 1-D size steps.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logspace.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Logspace — torch_logspace","text":"","code":"if (torch_is_installed()) {  torch_logspace(start=-10, end=10, steps=5) torch_logspace(start=0.1, end=1.0, steps=5) torch_logspace(start=0.1, end=1.0, steps=1) torch_logspace(start=2, end=2, steps=1, base=2) } #> torch_tensor #>  4 #> [ CPUFloatType{1} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_logsumexp.html","id":null,"dir":"Reference","previous_headings":"","what":"Logsumexp — torch_logsumexp","title":"Logsumexp — torch_logsumexp","text":"Logsumexp","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logsumexp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Logsumexp — torch_logsumexp","text":"","code":"torch_logsumexp(self, dim, keepdim = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_logsumexp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Logsumexp — torch_logsumexp","text":"self (Tensor) input tensor. dim (int tuple ints) dimension dimensions reduce. keepdim (bool) whether output tensor dim retained .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logsumexp.html","id":"logsumexp-input-dim-keepdim-false-out-null-","dir":"Reference","previous_headings":"","what":"logsumexp(input, dim, keepdim=False, out=NULL)","title":"Logsumexp — torch_logsumexp","text":"Returns log summed exponentials row input tensor given dimension dim. computation numerically stabilized. summation index \\(j\\) given dim indices \\(\\), result $$         \\mbox{logsumexp}(x)_{} = \\log \\sum_j \\exp(x_{ij}) $$ keepdim TRUE, output tensor size input except dimension(s) dim size 1. Otherwise, dim squeezed (see torch_squeeze), resulting output tensor 1 (len(dim)) fewer dimension(s).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logsumexp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Logsumexp — torch_logsumexp","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(3, 3)) torch_logsumexp(a, 1) } #> torch_tensor #>  1.7026 #>  1.1355 #>  1.7146 #> [ CPUFloatType{3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_lstsq.html","id":null,"dir":"Reference","previous_headings":"","what":"Lstsq — torch_lstsq","title":"Lstsq — torch_lstsq","text":"Lstsq","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_lstsq.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Lstsq — torch_lstsq","text":"","code":"torch_lstsq(self, A)"},{"path":"https://torch.mlverse.org/docs/reference/torch_lstsq.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Lstsq — torch_lstsq","text":"self (Tensor) matrix \\(B\\) (Tensor) \\(m\\) \\(n\\) matrix \\(\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_lstsq.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Lstsq — torch_lstsq","text":"","code":"The case when \\eqn{m < n} is not supported on the GPU."},{"path":"https://torch.mlverse.org/docs/reference/torch_lstsq.html","id":"lstsq-input-a-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"lstsq(input, A, out=NULL) -> Tensor","title":"Lstsq — torch_lstsq","text":"Computes solution least squares least norm problems full rank matrix \\(\\) size \\((m \\times n)\\) matrix \\(B\\) size \\((m \\times k)\\). \\(m \\geq n\\), torch_lstsq() solves least-squares problem: $$    \\begin{array}{ll}    \\min_X & \\|AX-B\\|_2.    \\end{array} $$ \\(m < n\\), torch_lstsq() solves least-norm problem: $$    \\begin{array}{llll}    \\min_X & \\|X\\|_2 & \\mbox{subject } & AX = B.    \\end{array} $$ Returned tensor \\(X\\) shape \\((\\mbox{max}(m, n) \\times k)\\). first \\(n\\) rows \\(X\\) contains solution. \\(m \\geq n\\), residual sum squares solution column given sum squares elements remaining \\(m - n\\) rows column.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_lstsq.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Lstsq — torch_lstsq","text":"","code":"if (torch_is_installed()) {  A = torch_tensor(rbind(  c(1,1,1),  c(2,3,4),  c(3,5,2),  c(4,2,5),  c(5,4,3) )) B = torch_tensor(rbind(  c(-10, -3),  c(12, 14),  c(14, 12),  c(16, 16),  c(18, 16) )) out = torch_lstsq(B, A) out[[1]] } #> torch_tensor #>   2.0000   1.0000 #>   1.0000   1.0000 #>   1.0000   2.0000 #>  10.9635   4.8501 #>   8.9332   5.2418 #> [ CPUFloatType{5,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_lt.html","id":null,"dir":"Reference","previous_headings":"","what":"Lt — torch_lt","title":"Lt — torch_lt","text":"Lt","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_lt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Lt — torch_lt","text":"","code":"torch_lt(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_lt.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Lt — torch_lt","text":"self (Tensor) tensor compare (Tensor float) tensor value compare","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_lt.html","id":"lt-input-other-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"lt(input, other, out=NULL) -> Tensor","title":"Lt — torch_lt","text":"Computes \\(\\mbox{input} < \\mbox{}\\) element-wise. second argument can number tensor whose shape broadcastable  first argument.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_lt.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Lt — torch_lt","text":"","code":"if (torch_is_installed()) {  torch_lt(torch_tensor(matrix(1:4, ncol = 2, byrow=TRUE)),           torch_tensor(matrix(c(1,1,4,4), ncol = 2, byrow=TRUE))) } #> torch_tensor #>  0  0 #>  1  0 #> [ CPUBoolType{2,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_lu.html","id":null,"dir":"Reference","previous_headings":"","what":"LU — torch_lu","title":"LU — torch_lu","text":"Computes LU factorization matrix batches matrices . Returns tuple containing LU factorization pivots . Pivoting done pivot set True.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_lu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"LU — torch_lu","text":"","code":"torch_lu(A, pivot = TRUE, get_infos = FALSE, out = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/torch_lu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"LU — torch_lu","text":"(Tensor) tensor factor size (, m, n)(,m,n) pivot (bool, optional) – controls whether pivoting done. Default: TRUE get_infos (bool, optional) – set True, returns info IntTensor. Default: FALSE (tuple, optional) – optional output tuple. get_infos True, elements tuple Tensor, IntTensor, IntTensor. get_infos False, elements tuple Tensor, IntTensor. Default: NULL","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_lu.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"LU — torch_lu","text":"","code":"if (torch_is_installed()) {  A <- torch_randn(c(2, 3, 3)) torch_lu(A) } #> [[1]] #> torch_tensor #> (1,.,.) =  #>   0.8885  0.4276  0.5209 #>  -0.5745  1.2627 -0.8762 #>   0.8760 -0.0408  0.0814 #>  #> (2,.,.) =  #>  -0.5752 -1.3390  1.0259 #>  -0.2306 -0.6162 -1.6195 #>   0.1260  0.0925  0.3801 #> [ CPUFloatType{2,3,3} ] #>  #> [[2]] #> torch_tensor #>  1  2  3 #>  2  3  3 #> [ CPUIntType{2,3} ] #>"},{"path":"https://torch.mlverse.org/docs/reference/torch_lu_solve.html","id":null,"dir":"Reference","previous_headings":"","what":"Lu_solve — torch_lu_solve","title":"Lu_solve — torch_lu_solve","text":"Lu_solve","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_lu_solve.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Lu_solve — torch_lu_solve","text":"","code":"torch_lu_solve(self, LU_data, LU_pivots)"},{"path":"https://torch.mlverse.org/docs/reference/torch_lu_solve.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Lu_solve — torch_lu_solve","text":"self (Tensor) RHS tensor size \\((*, m, k)\\), \\(*\\)                zero batch dimensions. LU_data (Tensor) pivoted LU factorization torch_lu size \\((*, m, m)\\),                       \\(*\\) zero batch dimensions. LU_pivots (IntTensor) pivots LU factorization torch_lu size \\((*, m)\\),                           \\(*\\) zero batch dimensions.                           batch dimensions LU_pivots must equal batch dimensions                           LU_data.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_lu_solve.html","id":"lu-solve-input-lu-data-lu-pivots-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"lu_solve(input, LU_data, LU_pivots, out=NULL) -> Tensor","title":"Lu_solve — torch_lu_solve","text":"Returns LU solve linear system \\(Ax = b\\) using partially pivoted LU factorization torch_lu.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_lu_solve.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Lu_solve — torch_lu_solve","text":"","code":"if (torch_is_installed()) { A = torch_randn(c(2, 3, 3)) b = torch_randn(c(2, 3, 1)) out = torch_lu(A) x = torch_lu_solve(b, out[[1]], out[[2]]) torch_norm(torch_bmm(A, x) - b) } #> torch_tensor #> 1.18011e-06 #> [ CPUFloatType{} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_manual_seed.html","id":null,"dir":"Reference","previous_headings":"","what":"Sets the seed for generating random numbers. — torch_manual_seed","title":"Sets the seed for generating random numbers. — torch_manual_seed","text":"Sets seed generating random numbers.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_manual_seed.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sets the seed for generating random numbers. — torch_manual_seed","text":"","code":"torch_manual_seed(seed)"},{"path":"https://torch.mlverse.org/docs/reference/torch_manual_seed.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sets the seed for generating random numbers. — torch_manual_seed","text":"seed integer seed.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_masked_select.html","id":null,"dir":"Reference","previous_headings":"","what":"Masked_select — torch_masked_select","title":"Masked_select — torch_masked_select","text":"Masked_select","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_masked_select.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Masked_select — torch_masked_select","text":"","code":"torch_masked_select(self, mask)"},{"path":"https://torch.mlverse.org/docs/reference/torch_masked_select.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Masked_select — torch_masked_select","text":"self (Tensor) input tensor. mask (BoolTensor) tensor containing binary mask index ","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_masked_select.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Masked_select — torch_masked_select","text":"returned tensor use storage original tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_masked_select.html","id":"masked-select-input-mask-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"masked_select(input, mask, out=NULL) -> Tensor","title":"Masked_select — torch_masked_select","text":"Returns new 1-D tensor indexes input tensor according boolean mask mask BoolTensor. shapes mask tensor input tensor need match, must broadcastable .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_masked_select.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Masked_select — torch_masked_select","text":"","code":"if (torch_is_installed()) {  x = torch_randn(c(3, 4)) x mask = x$ge(0.5) mask torch_masked_select(x, mask) } #> torch_tensor #>  1.0384 #>  1.5945 #>  2.0981 #>  0.5444 #>  0.5651 #> [ CPUFloatType{5} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_matmul.html","id":null,"dir":"Reference","previous_headings":"","what":"Matmul — torch_matmul","title":"Matmul — torch_matmul","text":"Matmul","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_matmul.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Matmul — torch_matmul","text":"","code":"torch_matmul(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_matmul.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Matmul — torch_matmul","text":"self (Tensor) first tensor multiplied (Tensor) second tensor multiplied","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_matmul.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Matmul — torch_matmul","text":"","code":"The 1-dimensional dot product version of this function does not support an `out` parameter."},{"path":"https://torch.mlverse.org/docs/reference/torch_matmul.html","id":"matmul-input-other-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"matmul(input, other, out=NULL) -> Tensor","title":"Matmul — torch_matmul","text":"Matrix product two tensors. behavior depends dimensionality tensors follows: tensors 1-dimensional, dot product (scalar) returned. arguments 2-dimensional, matrix-matrix product returned. first argument 1-dimensional second argument 2-dimensional, 1 prepended dimension purpose matrix multiply. matrix multiply, prepended dimension removed. first argument 2-dimensional second argument 1-dimensional, matrix-vector product returned. arguments least 1-dimensional least one argument N-dimensional (N > 2), batched matrix multiply returned.  first argument 1-dimensional, 1 prepended dimension purpose batched matrix multiply removed .  second argument 1-dimensional, 1 appended dimension purpose batched matrix multiple removed . non-matrix (.e. batch) dimensions broadcasted  (thus must broadcastable).  example, input \\((j \\times 1 \\times n \\times m)\\) tensor \\((k \\times m \\times p)\\) tensor, \\((j \\times k \\times n \\times p)\\) tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_matmul.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Matmul — torch_matmul","text":"","code":"if (torch_is_installed()) {  # vector x vector tensor1 = torch_randn(c(3)) tensor2 = torch_randn(c(3)) torch_matmul(tensor1, tensor2) # matrix x vector tensor1 = torch_randn(c(3, 4)) tensor2 = torch_randn(c(4)) torch_matmul(tensor1, tensor2) # batched matrix x broadcasted vector tensor1 = torch_randn(c(10, 3, 4)) tensor2 = torch_randn(c(4)) torch_matmul(tensor1, tensor2) # batched matrix x batched matrix tensor1 = torch_randn(c(10, 3, 4)) tensor2 = torch_randn(c(10, 4, 5)) torch_matmul(tensor1, tensor2) # batched matrix x broadcasted matrix tensor1 = torch_randn(c(10, 3, 4)) tensor2 = torch_randn(c(4, 5)) torch_matmul(tensor1, tensor2) } #> torch_tensor #> (1,.,.) =  #>   0.2467  0.0267  0.2256  0.3048 -0.2469 #>   0.5704 -0.0074 -3.7815 -0.8250  3.3963 #>  -0.9025  0.3102 -0.3818 -1.0451  0.1589 #>  #> (2,.,.) =  #>  -1.1851 -0.1799 -0.2034  4.6517  0.3584 #>  -3.3281  1.0199 -0.6615  3.3140 -0.0723 #>   0.5760 -0.2987  1.7212 -0.4701 -1.3388 #>  #> (3,.,.) =  #>  -2.2704  1.2708 -0.6654  1.7082 -0.3315 #>   1.0337  0.0243 -0.2888 -2.8101  0.2049 #>  -3.8316  1.2946  2.1583  4.3194 -2.8303 #>  #> (4,.,.) =  #>   0.8220  0.9216  1.8024 -2.3606 -2.4297 #>  -0.8171  0.5321 -0.5868  0.0176  0.1392 #>  -1.2666  1.4857 -0.5426  1.4303 -0.6876 #>  #> (5,.,.) =  #>  -2.3602  1.0924  0.5167  0.7857 -1.2332 #>   1.9469 -1.1037  1.3619 -2.1359 -0.4112 #>  -0.7107 -0.6890  0.3136  1.5808  0.3235 #>  #> (6,.,.) =  #>   0.6544 -1.2847 -0.1382  0.8716  1.1478 #>   4.1693 -2.7576  1.3553 -3.2945  0.8458 #>  -0.7586  0.1057  1.7404  1.0001 -1.6229 #>  #> ... [the output was truncated (use n=-1 to disable)] #> [ CPUFloatType{10,3,5} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_matrix_exp.html","id":null,"dir":"Reference","previous_headings":"","what":"Matrix_exp — torch_matrix_exp","title":"Matrix_exp — torch_matrix_exp","text":"Matrix_exp","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_matrix_exp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Matrix_exp — torch_matrix_exp","text":"","code":"torch_matrix_exp(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_matrix_exp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Matrix_exp — torch_matrix_exp","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_matrix_exp.html","id":"matrix-power-input-gt-tensor-","dir":"Reference","previous_headings":"","what":"matrix_power(input) -> Tensor","title":"Matrix_exp — torch_matrix_exp","text":"Returns matrix exponential. Supports batched input. matrix , matrix exponential defined $$     \\exp^= \\sum_{k=0}^\\infty ^k / k!. $$ implementation based : Bader, P.; Blanes, S.; Casas, F. Computing Matrix Exponential Optimized Taylor Polynomial Approximation. Mathematics 2019, 7, 1174.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_matrix_exp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Matrix_exp — torch_matrix_exp","text":"","code":"if (torch_is_installed()) {  a <- torch_randn(c(2, 2, 2)) a[1, , ] <- torch_eye(2, 2) a[2, , ] <- 2 * torch_eye(2, 2) a torch_matrix_exp(a)  x <- torch_tensor(rbind(c(0, pi/3), c(-pi/3, 0))) x$matrix_exp() # should be [[cos(pi/3), sin(pi/3)], [-sin(pi/3), cos(pi/3)]] } #> torch_tensor #>  0.5000  0.8660 #> -0.8660  0.5000 #> [ CPUFloatType{2,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_matrix_power.html","id":null,"dir":"Reference","previous_headings":"","what":"Matrix_power — torch_matrix_power","title":"Matrix_power — torch_matrix_power","text":"Matrix_power","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_matrix_power.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Matrix_power — torch_matrix_power","text":"","code":"torch_matrix_power(self, n)"},{"path":"https://torch.mlverse.org/docs/reference/torch_matrix_power.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Matrix_power — torch_matrix_power","text":"self (Tensor) input tensor. n (int) power raise matrix ","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_matrix_power.html","id":"matrix-power-input-n-gt-tensor-","dir":"Reference","previous_headings":"","what":"matrix_power(input, n) -> Tensor","title":"Matrix_power — torch_matrix_power","text":"Returns matrix raised power n square matrices. batch matrices, individual matrix raised power n. n negative, inverse matrix (invertible) raised power n.  batch matrices, batched inverse (invertible) raised power n. n 0, identity matrix returned.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_matrix_power.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Matrix_power — torch_matrix_power","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(2, 2, 2)) a torch_matrix_power(a, 3) } #> torch_tensor #> (1,.,.) =  #>  0.01 * #>  -8.2401 -109.0933 #>   -37.7152 -470.9431 #>  #> (2,.,.) =  #>   1.0132 -0.6814 #>   1.6588  0.0417 #> [ CPUFloatType{2,2,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_matrix_rank.html","id":null,"dir":"Reference","previous_headings":"","what":"Matrix_rank — torch_matrix_rank","title":"Matrix_rank — torch_matrix_rank","text":"Matrix_rank","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_matrix_rank.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Matrix_rank — torch_matrix_rank","text":"","code":"torch_matrix_rank(self, tol, symmetric = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_matrix_rank.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Matrix_rank — torch_matrix_rank","text":"self (Tensor) input 2-D tensor tol (float, optional) tolerance value. Default: NULL symmetric (bool, optional) indicates whether input symmetric.                               Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_matrix_rank.html","id":"matrix-rank-input-tol-null-symmetric-false-gt-tensor-","dir":"Reference","previous_headings":"","what":"matrix_rank(input, tol=NULL, symmetric=False) -> Tensor","title":"Matrix_rank — torch_matrix_rank","text":"Returns numerical rank 2-D tensor. method compute matrix rank done using SVD default. symmetric TRUE, input assumed symmetric, computation rank done obtaining eigenvalues. tol threshold singular values (eigenvalues symmetric TRUE) considered 0. tol specified, tol set S.max() * max(S.size()) * eps S singular values (eigenvalues symmetric TRUE), eps epsilon value datatype input.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_matrix_rank.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Matrix_rank — torch_matrix_rank","text":"","code":"if (torch_is_installed()) {  a = torch_eye(10) torch_matrix_rank(a) } #> torch_tensor #> 10 #> [ CPULongType{} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_max.html","id":null,"dir":"Reference","previous_headings":"","what":"Max — torch_max","title":"Max — torch_max","text":"Max","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_max.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Max — torch_max","text":"self (Tensor) input tensor. dim (int) dimension reduce. keepdim (bool) whether output tensor dim retained . Default: FALSE. (tuple, optional) result tuple two output tensors (max, max_indices) (Tensor) second input tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_max.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Max — torch_max","text":"shapes match, shape returned output tensor follows broadcasting rules .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_max.html","id":"max-input-gt-tensor-","dir":"Reference","previous_headings":"","what":"max(input) -> Tensor","title":"Max — torch_max","text":"Returns maximum value elements input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_max.html","id":"max-input-dim-keepdim-false-out-null-gt-tensor-longtensor-","dir":"Reference","previous_headings":"","what":"max(input, dim, keepdim=False, out=NULL) -> (Tensor, LongTensor)","title":"Max — torch_max","text":"Returns namedtuple (values, indices) values maximum value row input tensor given dimension dim. indices index location maximum value found (argmax).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_max.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Max — torch_max","text":"indices necessarily contain first occurrence maximal value found, unless unique. exact implementation details device-specific. expect result run CPU GPU general. keepdim TRUE, output tensors size input except dimension dim size 1. Otherwise, dim squeezed (see torch_squeeze), resulting output tensors 1 fewer dimension input.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_max.html","id":"max-input-other-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"max(input, other, out=NULL) -> Tensor","title":"Max — torch_max","text":"element tensor input compared corresponding element tensor element-wise maximum taken. shapes input need match, must broadcastable . $$     \\mbox{}_i = \\max(\\mbox{tensor}_i, \\mbox{}_i) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_max.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Max — torch_max","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(1, 3)) a torch_max(a)   a = torch_randn(c(4, 4)) a torch_max(a, dim = 1)   a = torch_randn(c(4)) a b = torch_randn(c(4)) b torch_max(a, other = b) } #> torch_tensor #> -0.0171 #>  0.4346 #>  1.5751 #> -0.1117 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_maximum.html","id":null,"dir":"Reference","previous_headings":"","what":"Maximum — torch_maximum","title":"Maximum — torch_maximum","text":"Maximum","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_maximum.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Maximum — torch_maximum","text":"","code":"torch_maximum(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_maximum.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Maximum — torch_maximum","text":"self (Tensor) input tensor. (Tensor) second input tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_maximum.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Maximum — torch_maximum","text":"one elements compared NaN, element returned. torch_maximum() supported tensors complex dtypes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_maximum.html","id":"maximum-input-other-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"maximum(input, other, *, out=None) -> Tensor","title":"Maximum — torch_maximum","text":"Computes element-wise maximum input .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_maximum.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Maximum — torch_maximum","text":"","code":"if (torch_is_installed()) {  a <- torch_tensor(c(1, 2, -1)) b <- torch_tensor(c(3, 0, 4)) torch_maximum(a, b) } #> torch_tensor #>  3 #>  2 #>  4 #> [ CPUFloatType{3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_mean.html","id":null,"dir":"Reference","previous_headings":"","what":"Mean — torch_mean","title":"Mean — torch_mean","text":"Mean","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_mean.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mean — torch_mean","text":"","code":"torch_mean(self, dim, keepdim = FALSE, dtype = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/torch_mean.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mean — torch_mean","text":"self (Tensor) input tensor. dim (int tuple ints) dimension dimensions reduce. keepdim (bool) whether output tensor dim retained . dtype resulting data type.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_mean.html","id":"mean-input-gt-tensor-","dir":"Reference","previous_headings":"","what":"mean(input) -> Tensor","title":"Mean — torch_mean","text":"Returns mean value elements input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_mean.html","id":"mean-input-dim-keepdim-false-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"mean(input, dim, keepdim=False, out=NULL) -> Tensor","title":"Mean — torch_mean","text":"Returns mean value row input tensor given dimension dim. dim list dimensions, reduce . keepdim TRUE, output tensor size input except dimension(s) dim size 1. Otherwise, dim squeezed (see torch_squeeze), resulting output tensor 1 (len(dim)) fewer dimension(s).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_mean.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Mean — torch_mean","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(1, 3)) a torch_mean(a)   a = torch_randn(c(4, 4)) a torch_mean(a, 1) torch_mean(a, 1, TRUE) } #> torch_tensor #>  0.3247 -0.1993 -0.1385 -0.1443 #> [ CPUFloatType{1,4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_median.html","id":null,"dir":"Reference","previous_headings":"","what":"Median — torch_median","title":"Median — torch_median","text":"Median","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_median.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Median — torch_median","text":"","code":"torch_median(self, dim, keepdim = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_median.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Median — torch_median","text":"self (Tensor) input tensor. dim (int) dimension reduce. keepdim (bool) whether output tensor dim retained .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_median.html","id":"median-input-gt-tensor-","dir":"Reference","previous_headings":"","what":"median(input) -> Tensor","title":"Median — torch_median","text":"Returns median value elements input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_median.html","id":"median-input-dim-keepdim-false-out-null-gt-tensor-longtensor-","dir":"Reference","previous_headings":"","what":"median(input, dim=-1, keepdim=False, out=NULL) -> (Tensor, LongTensor)","title":"Median — torch_median","text":"Returns namedtuple (values, indices) values median value row input tensor given dimension dim. indices index location median value found. default, dim last dimension input tensor. keepdim TRUE, output tensors size input except dimension dim size 1. Otherwise, dim squeezed (see torch_squeeze), resulting outputs tensor 1 fewer dimension input.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_median.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Median — torch_median","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(1, 3)) a torch_median(a)   a = torch_randn(c(4, 5)) a torch_median(a, 1) } #> [[1]] #> torch_tensor #>  0.1617 #>  0.4404 #> -0.3647 #>  0.2535 #> -0.2494 #> [ CPUFloatType{5} ] #>  #> [[2]] #> torch_tensor #>  0 #>  1 #>  3 #>  2 #>  2 #> [ CPULongType{5} ] #>"},{"path":"https://torch.mlverse.org/docs/reference/torch_memory_format.html","id":null,"dir":"Reference","previous_headings":"","what":"Memory format — torch_memory_format","title":"Memory format — torch_memory_format","text":"Returns correspondent memory format.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_memory_format.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Memory format — torch_memory_format","text":"","code":"torch_contiguous_format()  torch_preserve_format()  torch_channels_last_format()"},{"path":"https://torch.mlverse.org/docs/reference/torch_meshgrid.html","id":null,"dir":"Reference","previous_headings":"","what":"Meshgrid — torch_meshgrid","title":"Meshgrid — torch_meshgrid","text":"Take \\(N\\) tensors, can either scalar 1-dimensional vector, create \\(N\\) N-dimensional grids, \\(\\) th grid defined expanding \\(\\) th input dimensions defined inputs.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_meshgrid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Meshgrid — torch_meshgrid","text":"","code":"torch_meshgrid(tensors, indexing)"},{"path":"https://torch.mlverse.org/docs/reference/torch_meshgrid.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Meshgrid — torch_meshgrid","text":"tensors (list Tensor) list scalars 1 dimensional tensors. Scalars treated (1,). indexing (str, optional): indexing mode, either “xy” “ij”, defaults “ij”. See warning future changes. “xy” selected, first dimension corresponds cardinality second input second dimension corresponds cardinality first input. “ij” selected, dimensions order cardinality inputs.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_meshgrid.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Meshgrid — torch_meshgrid","text":"future torch_meshgrid transition indexing=’xy’ default. issue tracks issue goal migrating NumPy’s behavior.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_meshgrid.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Meshgrid — torch_meshgrid","text":"","code":"if (torch_is_installed()) {  x = torch_tensor(c(1, 2, 3)) y = torch_tensor(c(4, 5, 6)) out = torch_meshgrid(list(x, y)) out } #> [[1]] #> torch_tensor #>  1  1  1 #>  2  2  2 #>  3  3  3 #> [ CPUFloatType{3,3} ] #>  #> [[2]] #> torch_tensor #>  4  5  6 #>  4  5  6 #>  4  5  6 #> [ CPUFloatType{3,3} ] #>"},{"path":"https://torch.mlverse.org/docs/reference/torch_min.html","id":null,"dir":"Reference","previous_headings":"","what":"Min — torch_min","title":"Min — torch_min","text":"Min","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_min.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Min — torch_min","text":"self (Tensor) input tensor. dim (int) dimension reduce. keepdim (bool) whether output tensor dim retained . (tuple, optional) tuple two output tensors (min, min_indices) (Tensor) second input tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_min.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Min — torch_min","text":"shapes match, shape returned output tensor follows broadcasting rules .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_min.html","id":"min-input-gt-tensor-","dir":"Reference","previous_headings":"","what":"min(input) -> Tensor","title":"Min — torch_min","text":"Returns minimum value elements input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_min.html","id":"min-input-dim-keepdim-false-out-null-gt-tensor-longtensor-","dir":"Reference","previous_headings":"","what":"min(input, dim, keepdim=False, out=NULL) -> (Tensor, LongTensor)","title":"Min — torch_min","text":"Returns namedtuple (values, indices) values minimum value row input tensor given dimension dim. indices index location minimum value found (argmin).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_min.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Min — torch_min","text":"indices necessarily contain first occurrence minimal value found, unless unique. exact implementation details device-specific. expect result run CPU GPU general. keepdim TRUE, output tensors size input except dimension dim size 1. Otherwise, dim squeezed (see torch_squeeze), resulting output tensors 1 fewer dimension input.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_min.html","id":"min-input-other-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"min(input, other, out=NULL) -> Tensor","title":"Min — torch_min","text":"element tensor input compared corresponding element tensor element-wise minimum taken. resulting tensor returned. shapes input need match, must broadcastable . $$     \\mbox{}_i = \\min(\\mbox{tensor}_i, \\mbox{}_i) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_min.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Min — torch_min","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(1, 3)) a torch_min(a)   a = torch_randn(c(4, 4)) a torch_min(a, dim = 1)   a = torch_randn(c(4)) a b = torch_randn(c(4)) b torch_min(a, other = b) } #> torch_tensor #> -1.4927 #> -0.2869 #> -1.2572 #> -0.4288 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_minimum.html","id":null,"dir":"Reference","previous_headings":"","what":"Minimum — torch_minimum","title":"Minimum — torch_minimum","text":"Minimum","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_minimum.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Minimum — torch_minimum","text":"","code":"torch_minimum(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_minimum.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Minimum — torch_minimum","text":"self (Tensor) input tensor. (Tensor) second input tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_minimum.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Minimum — torch_minimum","text":"one elements compared NaN, element returned. torch_minimum() supported tensors complex dtypes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_minimum.html","id":"minimum-input-other-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"minimum(input, other, *, out=None) -> Tensor","title":"Minimum — torch_minimum","text":"Computes element-wise minimum input .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_minimum.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Minimum — torch_minimum","text":"","code":"if (torch_is_installed()) {  a <- torch_tensor(c(1, 2, -1)) b <- torch_tensor(c(3, 0, 4)) torch_minimum(a, b) } #> torch_tensor #>  1 #>  0 #> -1 #> [ CPUFloatType{3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_mm.html","id":null,"dir":"Reference","previous_headings":"","what":"Mm — torch_mm","title":"Mm — torch_mm","text":"Mm","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_mm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mm — torch_mm","text":"","code":"torch_mm(self, mat2)"},{"path":"https://torch.mlverse.org/docs/reference/torch_mm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mm — torch_mm","text":"self (Tensor) first matrix multiplied mat2 (Tensor) second matrix multiplied","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_mm.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Mm — torch_mm","text":"function broadcast . broadcasting matrix products, see torch_matmul.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_mm.html","id":"mm-input-mat-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"mm(input, mat2, out=NULL) -> Tensor","title":"Mm — torch_mm","text":"Performs matrix multiplication matrices input mat2. input \\((n \\times m)\\) tensor, mat2 \\((m \\times p)\\) tensor, \\((n \\times p)\\) tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_mm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Mm — torch_mm","text":"","code":"if (torch_is_installed()) {  mat1 = torch_randn(c(2, 3)) mat2 = torch_randn(c(3, 3)) torch_mm(mat1, mat2) } #> torch_tensor #> -0.2582  1.1655 -0.0292 #> -1.4012  0.7270  0.9580 #> [ CPUFloatType{2,3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_mode.html","id":null,"dir":"Reference","previous_headings":"","what":"Mode — torch_mode","title":"Mode — torch_mode","text":"Mode","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_mode.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mode — torch_mode","text":"","code":"torch_mode(self, dim = -1L, keepdim = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_mode.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mode — torch_mode","text":"self (Tensor) input tensor. dim (int) dimension reduce. keepdim (bool) whether output tensor dim retained .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_mode.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Mode — torch_mode","text":"function defined torch_cuda.Tensor yet.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_mode.html","id":"mode-input-dim-keepdim-false-out-null-gt-tensor-longtensor-","dir":"Reference","previous_headings":"","what":"mode(input, dim=-1, keepdim=False, out=NULL) -> (Tensor, LongTensor)","title":"Mode — torch_mode","text":"Returns namedtuple (values, indices) values mode value row input tensor given dimension dim, .e. value appears often row, indices index location mode value found. default, dim last dimension input tensor. keepdim TRUE, output tensors size input except dimension dim size 1. Otherwise, dim squeezed (see torch_squeeze), resulting output tensors 1 fewer dimension input.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_mode.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Mode — torch_mode","text":"","code":"if (torch_is_installed()) {  a = torch_randint(0, 50, size = list(5)) a torch_mode(a, 1) } #> [[1]] #> torch_tensor #> 20 #> [ CPUFloatType{} ] #>  #> [[2]] #> torch_tensor #> 3 #> [ CPULongType{} ] #>"},{"path":"https://torch.mlverse.org/docs/reference/torch_movedim.html","id":null,"dir":"Reference","previous_headings":"","what":"Movedim — torch_movedim","title":"Movedim — torch_movedim","text":"Movedim","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_movedim.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Movedim — torch_movedim","text":"","code":"torch_movedim(self, source, destination)"},{"path":"https://torch.mlverse.org/docs/reference/torch_movedim.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Movedim — torch_movedim","text":"self (Tensor) input tensor. source (int tuple ints) Original positions dims move. must unique. destination (int tuple ints) Destination positions original dims. must also unique.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_movedim.html","id":"movedim-input-source-destination-gt-tensor-","dir":"Reference","previous_headings":"","what":"movedim(input, source, destination) -> Tensor","title":"Movedim — torch_movedim","text":"Moves dimension(s) input position(s) source position(s) destination. dimensions input explicitly moved remain original order appear positions specified destination.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_movedim.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Movedim — torch_movedim","text":"","code":"if (torch_is_installed()) {  t <- torch_randn(c(3,2,1)) t torch_movedim(t, 2, 1)$shape torch_movedim(t, 2, 1) torch_movedim(t, c(2, 3), c(1, 2))$shape torch_movedim(t, c(2, 3), c(1, 2)) } #> torch_tensor #> (1,.,.) =  #>  0.01 * #>   4.1230 -105.8317 -20.6793 #>  #> (2,.,.) =  #>   1.3951  1.1874 -0.3547 #> [ CPUFloatType{2,1,3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_mul.html","id":null,"dir":"Reference","previous_headings":"","what":"Mul — torch_mul","title":"Mul — torch_mul","text":"Mul","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_mul.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mul — torch_mul","text":"","code":"torch_mul(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_mul.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mul — torch_mul","text":"self (Tensor) first multiplicand tensor (Tensor) second multiplicand tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_mul.html","id":"mul-input-other-out-null-","dir":"Reference","previous_headings":"","what":"mul(input, other, out=NULL)","title":"Mul — torch_mul","text":"Multiplies element input input scalar returns new resulting tensor. $$     \\mbox{}_i = \\mbox{} \\times \\mbox{input}_i $$ input type FloatTensor DoubleTensor, real number, otherwise integer element tensor input multiplied corresponding element Tensor . resulting tensor returned. shapes input must broadcastable . $$     \\mbox{}_i = \\mbox{input}_i \\times \\mbox{}_i $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_mul.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Mul — torch_mul","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(3)) a torch_mul(a, 100)   a = torch_randn(c(4, 1)) a b = torch_randn(c(1, 4)) b torch_mul(a, b) } #> torch_tensor #> -0.7258 -1.1372  0.7316  1.6862 #>  0.4254  0.6666 -0.4288 -0.9884 #> -0.1046 -0.1638  0.1054  0.2429 #> -0.8322 -1.3040  0.8389  1.9336 #> [ CPUFloatType{4,4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_multinomial.html","id":null,"dir":"Reference","previous_headings":"","what":"Multinomial — torch_multinomial","title":"Multinomial — torch_multinomial","text":"Multinomial","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_multinomial.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multinomial — torch_multinomial","text":"","code":"torch_multinomial(self, num_samples, replacement = FALSE, generator = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/torch_multinomial.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Multinomial — torch_multinomial","text":"self (Tensor) input tensor containing probabilities num_samples (int) number samples draw replacement (bool, optional) whether draw replacement generator (torch.Generator, optional) pseudorandom number generator sampling","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_multinomial.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Multinomial — torch_multinomial","text":"Indices ordered left right according sampled (first samples placed first column). input vector, vector size num_samples. input matrix m rows, matrix shape \\((m \\times \\mbox{num\\_samples})\\). replacement TRUE, samples drawn replacement. , drawn without replacement, means sample index drawn row, drawn row.","code":"The rows of `input` do not need to sum to one (in which case we use the values as weights), but must be non-negative, finite and have a non-zero sum. When drawn without replacement, `num_samples` must be lower than number of non-zero elements in `input` (or the min number of non-zero elements in each row of `input` if it is a matrix)."},{"path":"https://torch.mlverse.org/docs/reference/torch_multinomial.html","id":"multinomial-input-num-samples-replacement-false-generator-null-out-null-gt-longtensor-","dir":"Reference","previous_headings":"","what":"multinomial(input, num_samples, replacement=False, *, generator=NULL, out=NULL) -> LongTensor","title":"Multinomial — torch_multinomial","text":"Returns tensor row contains num_samples indices sampled multinomial probability distribution located corresponding row tensor input.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_multinomial.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multinomial — torch_multinomial","text":"","code":"if (torch_is_installed()) {  weights = torch_tensor(c(0, 10, 3, 0), dtype=torch_float()) # create a tensor of weights torch_multinomial(weights, 2) torch_multinomial(weights, 4, replacement=TRUE) } #> torch_tensor #>  2 #>  2 #>  2 #>  2 #> [ CPULongType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_multiply.html","id":null,"dir":"Reference","previous_headings":"","what":"Multiply — torch_multiply","title":"Multiply — torch_multiply","text":"Multiply","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_multiply.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multiply — torch_multiply","text":"","code":"torch_multiply(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_multiply.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Multiply — torch_multiply","text":"self (Tensor) first multiplicand tensor (Tensor) second multiplicand tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_multiply.html","id":"multiply-input-other-out-none-","dir":"Reference","previous_headings":"","what":"multiply(input, other, *, out=None)","title":"Multiply — torch_multiply","text":"Alias torch_mul().","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_mv.html","id":null,"dir":"Reference","previous_headings":"","what":"Mv — torch_mv","title":"Mv — torch_mv","text":"Mv","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_mv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mv — torch_mv","text":"","code":"torch_mv(self, vec)"},{"path":"https://torch.mlverse.org/docs/reference/torch_mv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mv — torch_mv","text":"self (Tensor) matrix multiplied vec (Tensor) vector multiplied","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_mv.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Mv — torch_mv","text":"function broadcast .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_mv.html","id":"mv-input-vec-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"mv(input, vec, out=NULL) -> Tensor","title":"Mv — torch_mv","text":"Performs matrix-vector product matrix input vector vec. input \\((n \\times m)\\) tensor, vec 1-D tensor size \\(m\\), 1-D size \\(n\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_mv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Mv — torch_mv","text":"","code":"if (torch_is_installed()) {  mat = torch_randn(c(2, 3)) vec = torch_randn(c(3)) torch_mv(mat, vec) } #> torch_tensor #>  1.5577 #>  1.0739 #> [ CPUFloatType{2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_mvlgamma.html","id":null,"dir":"Reference","previous_headings":"","what":"Mvlgamma — torch_mvlgamma","title":"Mvlgamma — torch_mvlgamma","text":"Mvlgamma","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_mvlgamma.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mvlgamma — torch_mvlgamma","text":"","code":"torch_mvlgamma(self, p)"},{"path":"https://torch.mlverse.org/docs/reference/torch_mvlgamma.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mvlgamma — torch_mvlgamma","text":"self (Tensor) tensor compute multivariate log-gamma function p (int) number dimensions","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_mvlgamma.html","id":"mvlgamma-input-p-gt-tensor-","dir":"Reference","previous_headings":"","what":"mvlgamma(input, p) -> Tensor","title":"Mvlgamma — torch_mvlgamma","text":"Computes multivariate log-gamma function <https://en.wikipedia.org/wiki/Multivariate_gamma_function>_) dimension \\(p\\) element-wise, given $$     \\log(\\Gamma_{p}()) = C + \\displaystyle \\sum_{=1}^{p} \\log\\left(\\Gamma\\left(- \\frac{- 1}{2}\\right)\\right) $$ \\(C = \\log(\\pi) \\times \\frac{p (p - 1)}{4}\\) \\(\\Gamma(\\cdot)\\) Gamma function. elements must greater \\(\\frac{p - 1}{2}\\), otherwise error thrown.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_mvlgamma.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Mvlgamma — torch_mvlgamma","text":"","code":"if (torch_is_installed()) {  a = torch_empty(c(2, 3))$uniform_(1, 2) a torch_mvlgamma(a, 2) } #> torch_tensor #>  0.5447  0.5908  0.4083 #>  0.3935  0.3996  0.4849 #> [ CPUFloatType{2,3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_nanquantile.html","id":null,"dir":"Reference","previous_headings":"","what":"Nanquantile — torch_nanquantile","title":"Nanquantile — torch_nanquantile","text":"Nanquantile","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_nanquantile.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Nanquantile — torch_nanquantile","text":"","code":"torch_nanquantile(self, q, dim = NULL, keepdim = FALSE, interpolation)"},{"path":"https://torch.mlverse.org/docs/reference/torch_nanquantile.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Nanquantile — torch_nanquantile","text":"self (Tensor) input tensor. q (float Tensor) scalar 1D tensor quantile values range [0, 1] dim (int) dimension reduce. keepdim (bool) whether output tensor dim retained . interpolation interpolation method.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_nanquantile.html","id":"nanquantile-input-q-dim-none-keepdim-false-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"nanquantile(input, q, dim=None, keepdim=FALSE, *, out=None) -> Tensor","title":"Nanquantile — torch_nanquantile","text":"variant torch_quantile() \"ignores\" NaN values, computing quantiles q NaN values input exist. values reduced row NaN quantiles reduction NaN. See documentation torch_quantile().","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_nanquantile.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Nanquantile — torch_nanquantile","text":"","code":"if (torch_is_installed()) {  t <- torch_tensor(c(NaN, 1, 2)) t$quantile(0.5) t$nanquantile(0.5) t <- torch_tensor(rbind(c(NaN, NaN), c(1, 2))) t t$nanquantile(0.5, dim=1) t$nanquantile(0.5, dim=2) torch_nanquantile(t, 0.5, dim = 1) torch_nanquantile(t, 0.5, dim = 2) } #> torch_tensor #>     nan  1.5000 #> [ CPUFloatType{1,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_nansum.html","id":null,"dir":"Reference","previous_headings":"","what":"Nansum — torch_nansum","title":"Nansum — torch_nansum","text":"Nansum","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_nansum.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Nansum — torch_nansum","text":"","code":"torch_nansum(self, dim, keepdim = FALSE, dtype = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/torch_nansum.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Nansum — torch_nansum","text":"self (Tensor) input tensor. dim (int tuple ints) dimension dimensions reduce. keepdim (bool) whether output tensor dim retained . dtype desired data type returned tensor. specified, input tensor casted dtype operation performed. useful preventing data type overflows. Default: NULL.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_nansum.html","id":"nansum-input-dtype-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"nansum(input, *, dtype=None) -> Tensor","title":"Nansum — torch_nansum","text":"Returns sum elements, treating Numbers (NaNs) zero.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_nansum.html","id":"nansum-input-dim-keepdim-false-dtype-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"nansum(input, dim, keepdim=FALSE, *, dtype=None) -> Tensor","title":"Nansum — torch_nansum","text":"Returns sum row input tensor given dimension dim, treating Numbers (NaNs) zero. dim list dimensions, reduce . keepdim TRUE, output tensor size input except dimension(s) dim size 1. Otherwise, dim squeezed (see torch_squeeze), resulting output tensor 1 (len(dim)) fewer dimension(s).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_nansum.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Nansum — torch_nansum","text":"","code":"if (torch_is_installed()) {  a <- torch_tensor(c(1., 2., NaN, 4.)) torch_nansum(a)   torch_nansum(torch_tensor(c(1., NaN))) a <- torch_tensor(rbind(c(1, 2), c(3., NaN))) torch_nansum(a) torch_nansum(a, dim=1) torch_nansum(a, dim=2) } #> torch_tensor #>  3 #>  3 #> [ CPUFloatType{2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_narrow.html","id":null,"dir":"Reference","previous_headings":"","what":"Narrow — torch_narrow","title":"Narrow — torch_narrow","text":"Narrow","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_narrow.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Narrow — torch_narrow","text":"","code":"torch_narrow(self, dim, start, length)"},{"path":"https://torch.mlverse.org/docs/reference/torch_narrow.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Narrow — torch_narrow","text":"self (Tensor) tensor narrow dim (int) dimension along narrow start (int) starting dimension length (int) distance ending dimension","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_narrow.html","id":"narrow-input-dim-start-length-gt-tensor-","dir":"Reference","previous_headings":"","what":"narrow(input, dim, start, length) -> Tensor","title":"Narrow — torch_narrow","text":"Returns new tensor narrowed version input tensor. dimension dim input start start + length. returned tensor input tensor share underlying storage.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_narrow.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Narrow — torch_narrow","text":"","code":"if (torch_is_installed()) {  x = torch_tensor(matrix(c(1:9), ncol = 3, byrow= TRUE)) torch_narrow(x, 1, 1, 2) torch_narrow(x, 2, 2, 2) } #> torch_tensor #>  2  3 #>  5  6 #>  8  9 #> [ CPULongType{3,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_ne.html","id":null,"dir":"Reference","previous_headings":"","what":"Ne — torch_ne","title":"Ne — torch_ne","text":"Ne","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_ne.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Ne — torch_ne","text":"","code":"torch_ne(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_ne.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Ne — torch_ne","text":"self (Tensor) tensor compare (Tensor float) tensor value compare","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_ne.html","id":"ne-input-other-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"ne(input, other, out=NULL) -> Tensor","title":"Ne — torch_ne","text":"Computes \\(input \\neq \\) element-wise. second argument can number tensor whose shape broadcastable  first argument.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_ne.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Ne — torch_ne","text":"","code":"if (torch_is_installed()) {  torch_ne(torch_tensor(matrix(1:4, ncol = 2, byrow=TRUE)),           torch_tensor(matrix(rep(c(1,4), each = 2), ncol = 2, byrow=TRUE))) } #> torch_tensor #>  0  1 #>  1  0 #> [ CPUBoolType{2,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_neg.html","id":null,"dir":"Reference","previous_headings":"","what":"Neg — torch_neg","title":"Neg — torch_neg","text":"Neg","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_neg.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Neg — torch_neg","text":"","code":"torch_neg(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_neg.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Neg — torch_neg","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_neg.html","id":"neg-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"neg(input, out=NULL) -> Tensor","title":"Neg — torch_neg","text":"Returns new tensor negative elements input. $$     \\mbox{} = -1 \\times \\mbox{input} $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_neg.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Neg — torch_neg","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(5)) a torch_neg(a) } #> torch_tensor #> -1.6512 #> -0.5301 #>  1.3428 #>  0.3650 #>  0.7776 #> [ CPUFloatType{5} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_negative.html","id":null,"dir":"Reference","previous_headings":"","what":"Negative — torch_negative","title":"Negative — torch_negative","text":"Negative","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_negative.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Negative — torch_negative","text":"","code":"torch_negative(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_negative.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Negative — torch_negative","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_negative.html","id":"negative-input-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"negative(input, *, out=None) -> Tensor","title":"Negative — torch_negative","text":"Alias torch_neg()","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_nextafter.html","id":null,"dir":"Reference","previous_headings":"","what":"Nextafter — torch_nextafter","title":"Nextafter — torch_nextafter","text":"Nextafter","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_nextafter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Nextafter — torch_nextafter","text":"","code":"torch_nextafter(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_nextafter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Nextafter — torch_nextafter","text":"self (Tensor) first input tensor (Tensor) second input tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_nextafter.html","id":"nextafter-input-other-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"nextafter(input, other, *, out=None) -> Tensor","title":"Nextafter — torch_nextafter","text":"Return next floating-point value input towards , elementwise. shapes input must broadcastable .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_nextafter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Nextafter — torch_nextafter","text":"","code":"if (torch_is_installed()) {  eps <- torch_finfo(torch_float32())$eps torch_nextafter(torch_tensor(c(1, 2)), torch_tensor(c(2, 1))) == torch_tensor(c(eps + 1, 2 - eps)) } #> torch_tensor #>  1 #>  1 #> [ CPUBoolType{2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_nonzero.html","id":null,"dir":"Reference","previous_headings":"","what":"Nonzero — torch_nonzero","title":"Nonzero — torch_nonzero","text":"Nonzero elements tensors.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_nonzero.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Nonzero — torch_nonzero","text":"","code":"torch_nonzero(self, as_list = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_nonzero.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Nonzero — torch_nonzero","text":"self (Tensor) input tensor. as_list FALSE, output tensor containing indices. TRUE, one 1-D tensor dimension, containing indices nonzero element along dimension. as_list FALSE (default): Returns tensor containing indices non-zero elements input.  row result contains indices non-zero element input. result sorted lexicographically, last index changing fastest (C-style). input \\(n\\) dimensions, resulting indices tensor size \\((z \\times n)\\), \\(z\\) total number non-zero elements input tensor. as_list TRUE: Returns tuple 1-D tensors, one dimension input, containing indices (dimension) non-zero elements input . input \\(n\\) dimensions, resulting tuple contains \\(n\\) tensors size \\(z\\), \\(z\\) total number non-zero elements input tensor. special case, input zero dimensions nonzero scalar value, treated one-dimensional tensor one element.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_nonzero.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Nonzero — torch_nonzero","text":"","code":"if (torch_is_installed()) {  torch_nonzero(torch_tensor(c(1, 1, 1, 0, 1))) } #> torch_tensor #>  1 #>  2 #>  3 #>  5 #> [ CPULongType{4,1} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_norm.html","id":null,"dir":"Reference","previous_headings":"","what":"Norm — torch_norm","title":"Norm — torch_norm","text":"Norm","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_norm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Norm — torch_norm","text":"","code":"torch_norm(self, p = 2L, dim, keepdim = FALSE, dtype)"},{"path":"https://torch.mlverse.org/docs/reference/torch_norm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Norm — torch_norm","text":"self (Tensor) input tensor p (int, float, inf, -inf, 'fro', 'nuc', optional) order norm. Default: 'fro'        following norms can calculated:        =====  ============================  ==========================        ord    matrix norm                   vector norm        =====  ============================  ==========================        NULL   Frobenius norm                2-norm        'fro'  Frobenius norm                --        'nuc'  nuclear norm                  --         vec norm dim NULL  sum(abs(x)ord)(1./ord)        =====  ============================  ========================== dim (int, 2-tuple ints, 2-list ints, optional) int,        vector norm calculated, 2-tuple ints, matrix norm        calculated. value NULL, matrix norm calculated        input tensor two dimensions, vector norm        calculated input tensor one dimension. input        tensor two dimensions, vector norm applied        last dimension. keepdim (bool, optional) whether output tensors dim        retained . Ignored dim = NULL        = NULL. Default: FALSE Ignored        dim = NULL = NULL. dtype (torch.dtype, optional) desired data type        returned tensor. specified, input tensor casted        'dtype' performing operation. Default: NULL.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_norm.html","id":"test-","dir":"Reference","previous_headings":"","what":"TEST","title":"Norm — torch_norm","text":"Returns matrix norm vector norm given tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_norm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Norm — torch_norm","text":"","code":"if (torch_is_installed()) {  a <- torch_arange(1, 9, dtype = torch_float()) b <- a$reshape(list(3, 3)) torch_norm(a) torch_norm(b) torch_norm(a, Inf) torch_norm(b, Inf)  } #> torch_tensor #> 9 #> [ CPUFloatType{} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_normal.html","id":null,"dir":"Reference","previous_headings":"","what":"Normal — torch_normal","title":"Normal — torch_normal","text":"Normal Normal distributed","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_normal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Normal — torch_normal","text":"","code":"torch_normal(mean, std, size = NULL, generator = NULL, ...)"},{"path":"https://torch.mlverse.org/docs/reference/torch_normal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Normal — torch_normal","text":"mean (tensor scalar double) Mean normal distribution. torch_tensor() output dim mean represents per-element mean. scalar value, reused elements. std (tensor scalar double) standard deviation normal distribution. torch_tensor() output size std represents per-element standard deviation. scalar value, reused elements. size (integers, optional) used mean std scalars. generator random number generator created torch_generator(). NULL default generator used. ... Tensor option parameters like dtype, layout, device. Can used mean std scalar numerics.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_normal.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Normal — torch_normal","text":"shapes match, shape mean used shape returned output tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_normal.html","id":"normal-mean-std-gt-tensor-","dir":"Reference","previous_headings":"","what":"normal(mean, std, *) -> Tensor","title":"Normal — torch_normal","text":"Returns tensor random numbers drawn separate normal distributions whose mean standard deviation given. mean tensor mean output element's normal distribution std tensor standard deviation output element's normal distribution shapes mean std need match, total number elements tensor need .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_normal.html","id":"normal-mean-std-gt-tensor--1","dir":"Reference","previous_headings":"","what":"normal(mean=0.0, std) -> Tensor","title":"Normal — torch_normal","text":"Similar function , means shared among drawn elements.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_normal.html","id":"normal-mean-std-gt-tensor--2","dir":"Reference","previous_headings":"","what":"normal(mean, std=1.0) -> Tensor","title":"Normal — torch_normal","text":"Similar function , standard-deviations shared among drawn elements.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_normal.html","id":"normal-mean-std-size-gt-tensor-","dir":"Reference","previous_headings":"","what":"normal(mean, std, size, *) -> Tensor","title":"Normal — torch_normal","text":"Similar function , means standard deviations shared among drawn elements. resulting tensor size given size.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_normal.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Normal — torch_normal","text":"","code":"if (torch_is_installed()) {  torch_normal(mean=0, std=torch_arange(1, 0, -0.1) + 1e-6) torch_normal(mean=0.5, std=torch_arange(1., 6.)) torch_normal(mean=torch_arange(1., 6.)) torch_normal(2, 3, size=c(1, 4))  } #> torch_tensor #> -0.5876  3.8757  4.9848 -3.6009 #> [ CPUFloatType{1,4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_not_equal.html","id":null,"dir":"Reference","previous_headings":"","what":"Not_equal — torch_not_equal","title":"Not_equal — torch_not_equal","text":"Not_equal","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_not_equal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Not_equal — torch_not_equal","text":"","code":"torch_not_equal(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_not_equal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Not_equal — torch_not_equal","text":"self (Tensor) tensor compare (Tensor float) tensor value compare","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_not_equal.html","id":"not-equal-input-other-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"not_equal(input, other, *, out=None) -> Tensor","title":"Not_equal — torch_not_equal","text":"Alias torch_ne().","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_ones.html","id":null,"dir":"Reference","previous_headings":"","what":"Ones — torch_ones","title":"Ones — torch_ones","text":"Ones","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_ones.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Ones — torch_ones","text":"","code":"torch_ones(   ...,   names = NULL,   dtype = NULL,   layout = torch_strided(),   device = NULL,   requires_grad = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/torch_ones.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Ones — torch_ones","text":"... (int...) sequence integers defining shape output tensor.        Can variable number arguments collection like list tuple. names optional names dimensions dtype (torch.dtype, optional) desired data type returned tensor.        Default: NULL, uses global default (see torch_set_default_tensor_type). layout (torch.layout, optional) desired layout returned Tensor.        Default: torch_strided. device (torch.device, optional) desired device returned tensor.        Default: NULL, uses current device default tensor type        (see torch_set_default_tensor_type). device CPU        CPU tensor types current CUDA device CUDA tensor types. requires_grad (bool, optional) autograd record operations        returned tensor. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_ones.html","id":"ones-size-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-","dir":"Reference","previous_headings":"","what":"ones(*size, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor","title":"Ones — torch_ones","text":"Returns tensor filled scalar value 1, shape defined variable argument size.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_ones.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Ones — torch_ones","text":"","code":"if (torch_is_installed()) {  torch_ones(c(2, 3)) torch_ones(c(5)) } #> torch_tensor #>  1 #>  1 #>  1 #>  1 #>  1 #> [ CPUFloatType{5} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_ones_like.html","id":null,"dir":"Reference","previous_headings":"","what":"Ones_like — torch_ones_like","title":"Ones_like — torch_ones_like","text":"Ones_like","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_ones_like.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Ones_like — torch_ones_like","text":"","code":"torch_ones_like(   input,   dtype = NULL,   layout = torch_strided(),   device = NULL,   requires_grad = FALSE,   memory_format = torch_preserve_format() )"},{"path":"https://torch.mlverse.org/docs/reference/torch_ones_like.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Ones_like — torch_ones_like","text":"input (Tensor) size input determine size output tensor. dtype (torch.dtype, optional) desired data type returned Tensor.        Default: NULL, defaults dtype input. layout (torch.layout, optional) desired layout returned tensor.        Default: NULL, defaults layout input. device (torch.device, optional) desired device returned tensor.        Default: NULL, defaults device input. requires_grad (bool, optional) autograd record operations        returned tensor. Default: FALSE. memory_format (torch.memory_format, optional) desired memory format        returned Tensor. Default: torch_preserve_format.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_ones_like.html","id":"ones-like-input-dtype-null-layout-null-device-null-requires-grad-false-memory-format-torch-preserve-format-gt-tensor-","dir":"Reference","previous_headings":"","what":"ones_like(input, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, memory_format=torch.preserve_format) -> Tensor","title":"Ones_like — torch_ones_like","text":"Returns tensor filled scalar value 1, size input. torch_ones_like(input) equivalent torch_ones(input.size(), dtype=input.dtype, layout=input.layout, device=input.device).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_ones_like.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Ones_like — torch_ones_like","text":"0.4, function support keyword. alternative, old torch_ones_like(input, =output) equivalent torch_ones(input.size(), =output).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_ones_like.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Ones_like — torch_ones_like","text":"","code":"if (torch_is_installed()) {  input = torch_empty(c(2, 3)) torch_ones_like(input) } #> torch_tensor #>  1  1  1 #>  1  1  1 #> [ CPUFloatType{2,3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_orgqr.html","id":null,"dir":"Reference","previous_headings":"","what":"Orgqr — torch_orgqr","title":"Orgqr — torch_orgqr","text":"Orgqr","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_orgqr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Orgqr — torch_orgqr","text":"","code":"torch_orgqr(self, input2)"},{"path":"https://torch.mlverse.org/docs/reference/torch_orgqr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Orgqr — torch_orgqr","text":"self (Tensor) torch_geqrf. input2 (Tensor) tau torch_geqrf.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_orgqr.html","id":"orgqr-input-input-gt-tensor-","dir":"Reference","previous_headings":"","what":"orgqr(input, input2) -> Tensor","title":"Orgqr — torch_orgqr","text":"Computes orthogonal matrix Q QR factorization, (input, input2) tuple returned torch_geqrf. directly calls underlying LAPACK function ?orgqr. See LAPACK documentation orgqr_ details.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_ormqr.html","id":null,"dir":"Reference","previous_headings":"","what":"Ormqr — torch_ormqr","title":"Ormqr — torch_ormqr","text":"Ormqr","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_ormqr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Ormqr — torch_ormqr","text":"","code":"torch_ormqr(self, input2, input3, left = TRUE, transpose = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_ormqr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Ormqr — torch_ormqr","text":"self (Tensor) torch_geqrf. input2 (Tensor) tau torch_geqrf. input3 (Tensor) matrix multiplied. left see LAPACK documentation transpose see LAPACK documentation","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_ormqr.html","id":"ormqr-input-input-input-left-true-transpose-false-gt-tensor-","dir":"Reference","previous_headings":"","what":"ormqr(input, input2, input3, left=TRUE, transpose=False) -> Tensor","title":"Ormqr — torch_ormqr","text":"Multiplies mat (given input3) orthogonal Q matrix QR factorization formed torch_geqrf() represented (, tau) (given (input, input2)). directly calls underlying LAPACK function ?ormqr. See LAPACK documentation ormqr details.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_outer.html","id":null,"dir":"Reference","previous_headings":"","what":"Outer — torch_outer","title":"Outer — torch_outer","text":"Outer","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_outer.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Outer — torch_outer","text":"","code":"torch_outer(self, vec2)"},{"path":"https://torch.mlverse.org/docs/reference/torch_outer.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Outer — torch_outer","text":"self (Tensor) 1-D input vector vec2 (Tensor) 1-D input vector","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_outer.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Outer — torch_outer","text":"function broadcast.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_outer.html","id":"outer-input-vec-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"outer(input, vec2, *, out=None) -> Tensor","title":"Outer — torch_outer","text":"Outer product input vec2. input vector size \\(n\\) vec2 vector size \\(m\\), must matrix size \\((n \\times m)\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_outer.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Outer — torch_outer","text":"","code":"if (torch_is_installed()) {  v1 <- torch_arange(1., 5.) v2 <- torch_arange(1., 4.) torch_outer(v1, v2) } #> torch_tensor #>   1   2   3   4 #>   2   4   6   8 #>   3   6   9  12 #>   4   8  12  16 #>   5  10  15  20 #> [ CPUFloatType{5,4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_pdist.html","id":null,"dir":"Reference","previous_headings":"","what":"Pdist — torch_pdist","title":"Pdist — torch_pdist","text":"Pdist","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_pdist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pdist — torch_pdist","text":"","code":"torch_pdist(self, p = 2L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_pdist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pdist — torch_pdist","text":"self NA input tensor shape \\(N \\times M\\). p NA p value p-norm distance calculate vector pair        \\(\\[0, \\infty]\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_pdist.html","id":"pdist-input-p-gt-tensor-","dir":"Reference","previous_headings":"","what":"pdist(input, p=2) -> Tensor","title":"Pdist — torch_pdist","text":"Computes p-norm distance every pair row vectors input. identical upper triangular portion, excluding diagonal, torch_norm(input[:, NULL] - input, dim=2, p=p). function faster rows contiguous. input shape \\(N \\times M\\) output shape \\(\\frac{1}{2} N (N - 1)\\). function equivalent scipy.spatial.distance.pdist(input, 'minkowski', p=p) \\(p \\(0, \\infty)\\). \\(p = 0\\) equivalent scipy.spatial.distance.pdist(input, 'hamming') * M. \\(p = \\infty\\), closest scipy function scipy.spatial.distance.pdist(xn, lambda x, y: np.abs(x - y).max()).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_pinverse.html","id":null,"dir":"Reference","previous_headings":"","what":"Pinverse — torch_pinverse","title":"Pinverse — torch_pinverse","text":"Pinverse","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_pinverse.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pinverse — torch_pinverse","text":"","code":"torch_pinverse(self, rcond = 1e-15)"},{"path":"https://torch.mlverse.org/docs/reference/torch_pinverse.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pinverse — torch_pinverse","text":"self (Tensor) input tensor size \\((*, m, n)\\) \\(*\\) zero batch dimensions rcond (float) floating point value determine cutoff small singular values.                   Default: 1e-15","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_pinverse.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Pinverse — torch_pinverse","text":"","code":"This method is implemented using the Singular Value Decomposition. The pseudo-inverse is not necessarily a continuous function in the elements of the matrix `[1]`_. Therefore, derivatives are not always existent, and exist for a constant rank only `[2]`_. However, this method is backprop-able due to the implementation by using SVD results, and could be unstable. Double-backward will also be unstable due to the usage of SVD internally. See `~torch.svd` for more details."},{"path":"https://torch.mlverse.org/docs/reference/torch_pinverse.html","id":"pinverse-input-rcond-e-gt-tensor-","dir":"Reference","previous_headings":"","what":"pinverse(input, rcond=1e-15) -> Tensor","title":"Pinverse — torch_pinverse","text":"Calculates pseudo-inverse (also known Moore-Penrose inverse) 2D tensor. Please look Moore-Penrose inverse_ details","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_pinverse.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pinverse — torch_pinverse","text":"","code":"if (torch_is_installed()) {  input = torch_randn(c(3, 5)) input torch_pinverse(input) # Batched pinverse example a = torch_randn(c(2,6,3)) b = torch_pinverse(a) torch_matmul(b, a) } #> torch_tensor #> (1,.,.) =  #>   1.0000 -0.0000  0.0000 #>  -0.0000  1.0000  0.0000 #>   0.0000  0.0000  1.0000 #>  #> (2,.,.) =  #>   1.0000e+00  1.1921e-07  1.3411e-07 #>   1.5274e-07  1.0000e+00  6.7987e-08 #>   1.4529e-07  1.0431e-07  1.0000e+00 #> [ CPUFloatType{2,3,3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_pixel_shuffle.html","id":null,"dir":"Reference","previous_headings":"","what":"Pixel_shuffle — torch_pixel_shuffle","title":"Pixel_shuffle — torch_pixel_shuffle","text":"Pixel_shuffle","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_pixel_shuffle.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pixel_shuffle — torch_pixel_shuffle","text":"","code":"torch_pixel_shuffle(self, upscale_factor)"},{"path":"https://torch.mlverse.org/docs/reference/torch_pixel_shuffle.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pixel_shuffle — torch_pixel_shuffle","text":"self (Tensor) input tensor upscale_factor (int) factor increase spatial resolution ","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_pixel_shuffle.html","id":"rearranges-elements-in-a-tensor-of-shape-","dir":"Reference","previous_headings":"","what":"Rearranges elements in a tensor of shape","title":"Pixel_shuffle — torch_pixel_shuffle","text":"math:(*, C \\times r^2, H, W) : Rearranges elements tensor shape \\((*, C \\times r^2, H, W)\\) tensor shape \\((*, C, H \\times r, W \\times r)\\). See ~torch.nn.PixelShuffle details.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_pixel_shuffle.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pixel_shuffle — torch_pixel_shuffle","text":"","code":"if (torch_is_installed()) {  input = torch_randn(c(1, 9, 4, 4)) output = nnf_pixel_shuffle(input, 3) print(output$size()) } #> [1]  1  1 12 12"},{"path":"https://torch.mlverse.org/docs/reference/torch_poisson.html","id":null,"dir":"Reference","previous_headings":"","what":"Poisson — torch_poisson","title":"Poisson — torch_poisson","text":"Poisson","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_poisson.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Poisson — torch_poisson","text":"","code":"torch_poisson(self, generator = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/torch_poisson.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Poisson — torch_poisson","text":"self (Tensor) input tensor containing rates Poisson distribution generator (torch.Generator, optional) pseudorandom number generator sampling","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_poisson.html","id":"poisson-input-generator-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"poisson(input *, generator=NULL) -> Tensor","title":"Poisson — torch_poisson","text":"Returns tensor size input element sampled Poisson distribution rate parameter given corresponding element input .e., $$     \\mbox{}_i \\sim \\mbox{Poisson}(\\mbox{input}_i) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_poisson.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Poisson — torch_poisson","text":"","code":"if (torch_is_installed()) {  rates = torch_rand(c(4, 4)) * 5  # rate parameter between 0 and 5 torch_poisson(rates) } #> torch_tensor #>  1  1  2  1 #>  1  6  0  6 #>  4  4  4  1 #>  0  4  5  0 #> [ CPUFloatType{4,4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_polar.html","id":null,"dir":"Reference","previous_headings":"","what":"Polar — torch_polar","title":"Polar — torch_polar","text":"Polar","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_polar.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Polar — torch_polar","text":"","code":"torch_polar(abs, angle)"},{"path":"https://torch.mlverse.org/docs/reference/torch_polar.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Polar — torch_polar","text":"abs (Tensor) absolute value complex tensor. Must float double. angle (Tensor) angle complex tensor. Must dtype abs.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_polar.html","id":"polar-abs-angle-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"polar(abs, angle, *, out=None) -> Tensor","title":"Polar — torch_polar","text":"Constructs complex tensor whose elements Cartesian coordinates corresponding polar coordinates absolute value abs angle angle. $$     \\mbox{} = \\mbox{abs} \\cdot \\cos(\\mbox{angle}) + \\mbox{abs} \\cdot \\sin(\\mbox{angle}) \\cdot j $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_polar.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Polar — torch_polar","text":"","code":"if (torch_is_installed()) {  abs <- torch_tensor(c(1, 2), dtype=torch_float64()) angle <- torch_tensor(c(pi / 2, 5 * pi / 4), dtype=torch_float64()) z <- torch_polar(abs, angle) z } #> torch_tensor #> 1e-17 * #>  6.1232 #> -141421356237309520.0000 #> [ CPUComplexDoubleType{2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_polygamma.html","id":null,"dir":"Reference","previous_headings":"","what":"Polygamma — torch_polygamma","title":"Polygamma — torch_polygamma","text":"Polygamma","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_polygamma.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Polygamma — torch_polygamma","text":"","code":"torch_polygamma(n, input)"},{"path":"https://torch.mlverse.org/docs/reference/torch_polygamma.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Polygamma — torch_polygamma","text":"n (int) order polygamma function input (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_polygamma.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Polygamma — torch_polygamma","text":"","code":"This function is not implemented for \\eqn{n \\geq 2}."},{"path":"https://torch.mlverse.org/docs/reference/torch_polygamma.html","id":"polygamma-n-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"polygamma(n, input, out=NULL) -> Tensor","title":"Polygamma — torch_polygamma","text":"Computes \\(n^{th}\\) derivative digamma function input. \\(n \\geq 0\\) called order polygamma function. $$     \\psi^{(n)}(x) = \\frac{d^{(n)}}{dx^{(n)}} \\psi(x) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_polygamma.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Polygamma — torch_polygamma","text":"","code":"if (torch_is_installed()) { if (FALSE) { a = torch_tensor(c(1, 0.5)) torch_polygamma(1, a) } }"},{"path":"https://torch.mlverse.org/docs/reference/torch_pow.html","id":null,"dir":"Reference","previous_headings":"","what":"Pow — torch_pow","title":"Pow — torch_pow","text":"Pow","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_pow.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pow — torch_pow","text":"","code":"torch_pow(self, exponent)"},{"path":"https://torch.mlverse.org/docs/reference/torch_pow.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pow — torch_pow","text":"self (float) scalar base value power operation exponent (float tensor) exponent value","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_pow.html","id":"pow-input-exponent-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"pow(input, exponent, out=NULL) -> Tensor","title":"Pow — torch_pow","text":"Takes power element input exponent returns tensor result. exponent can either single float number Tensor number elements input. exponent scalar value, operation applied : $$     \\mbox{}_i = x_i^{\\mbox{exponent}} $$ exponent tensor, operation applied : $$     \\mbox{}_i = x_i^{\\mbox{exponent}_i} $$ exponent tensor, shapes input exponent must broadcastable .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_pow.html","id":"pow-self-exponent-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"pow(self, exponent, out=NULL) -> Tensor","title":"Pow — torch_pow","text":"self scalar float value, exponent tensor. returned tensor shape exponent operation applied : $$     \\mbox{}_i = \\mbox{self} ^ {\\mbox{exponent}_i} $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_pow.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pow — torch_pow","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(4)) a torch_pow(a, 2) exp <- torch_arange(1, 5) a <- torch_arange(1, 5) a exp torch_pow(a, exp)   exp <- torch_arange(1, 5) base <- 2 torch_pow(base, exp) } #> torch_tensor #>   2 #>   4 #>   8 #>  16 #>  32 #> [ CPUFloatType{5} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_prod.html","id":null,"dir":"Reference","previous_headings":"","what":"Prod — torch_prod","title":"Prod — torch_prod","text":"Prod","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_prod.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prod — torch_prod","text":"","code":"torch_prod(self, dim, keepdim = FALSE, dtype = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/torch_prod.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prod — torch_prod","text":"self (Tensor) input tensor. dim (int) dimension reduce. keepdim (bool) whether output tensor dim retained . dtype (torch.dtype, optional) desired data type returned tensor.        specified, input tensor casted dtype operation        performed. useful preventing data type overflows. Default: NULL.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_prod.html","id":"prod-input-dtype-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"prod(input, dtype=NULL) -> Tensor","title":"Prod — torch_prod","text":"Returns product elements input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_prod.html","id":"prod-input-dim-keepdim-false-dtype-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"prod(input, dim, keepdim=False, dtype=NULL) -> Tensor","title":"Prod — torch_prod","text":"Returns product row input tensor given dimension dim. keepdim TRUE, output tensor size input except dimension dim size 1. Otherwise, dim squeezed (see torch_squeeze), resulting output tensor 1 fewer dimension input.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_prod.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Prod — torch_prod","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(1, 3)) a torch_prod(a)   a = torch_randn(c(4, 2)) a torch_prod(a, 1) } #> torch_tensor #>  0.4314 #> -0.0025 #> [ CPUFloatType{2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_promote_types.html","id":null,"dir":"Reference","previous_headings":"","what":"Promote_types — torch_promote_types","title":"Promote_types — torch_promote_types","text":"Promote_types","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_promote_types.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Promote_types — torch_promote_types","text":"","code":"torch_promote_types(type1, type2)"},{"path":"https://torch.mlverse.org/docs/reference/torch_promote_types.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Promote_types — torch_promote_types","text":"type1 (torch.dtype) type2 (torch.dtype)","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_promote_types.html","id":"promote-types-type-type-gt-dtype-","dir":"Reference","previous_headings":"","what":"promote_types(type1, type2) -> dtype","title":"Promote_types — torch_promote_types","text":"Returns torch_dtype smallest size scalar kind smaller lower kind either type1 type2. See type promotion documentation  information type promotion logic.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_promote_types.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Promote_types — torch_promote_types","text":"","code":"if (torch_is_installed()) {  torch_promote_types(torch_int32(), torch_float32()) torch_promote_types(torch_uint8(), torch_long()) } #> torch_Long"},{"path":"https://torch.mlverse.org/docs/reference/torch_qr.html","id":null,"dir":"Reference","previous_headings":"","what":"Qr — torch_qr","title":"Qr — torch_qr","text":"Qr","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_qr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Qr — torch_qr","text":"","code":"torch_qr(self, some = TRUE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_qr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Qr — torch_qr","text":"self (Tensor) input tensor size \\((*, m, n)\\) * zero                batch dimensions consisting matrices dimension \\(m \\times n\\). (bool, optional) Set TRUE reduced QR decomposition FALSE                complete QR decomposition.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_qr.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Qr — torch_qr","text":"precision may lost magnitudes elements input large always give valid decomposition, may give one across platforms - depend LAPACK implementation.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_qr.html","id":"qr-input-some-true-out-null-gt-tensor-tensor-","dir":"Reference","previous_headings":"","what":"qr(input, some=TRUE, out=NULL) -> (Tensor, Tensor)","title":"Qr — torch_qr","text":"Computes QR decomposition matrix batch matrices input, returns namedtuple (Q, R) tensors \\(\\mbox{input} = Q R\\) \\(Q\\) orthogonal matrix batch orthogonal matrices \\(R\\) upper triangular matrix batch upper triangular matrices. TRUE, function returns thin (reduced) QR factorization. Otherwise, FALSE, function returns complete QR factorization.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_qr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Qr — torch_qr","text":"","code":"if (torch_is_installed()) {  a = torch_tensor(matrix(c(12., -51, 4, 6, 167, -68, -4, 24, -41), ncol = 3, byrow = TRUE)) out = torch_qr(a) q = out[[1]] r = out[[2]] torch_mm(q, r)$round() torch_mm(q$t(), q)$round() } #> torch_tensor #>  1  0  0 #>  0  1  0 #>  0  0  1 #> [ CPUFloatType{3,3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_qscheme.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates the corresponding Scheme object — torch_qscheme","title":"Creates the corresponding Scheme object — torch_qscheme","text":"Creates corresponding Scheme object","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_qscheme.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates the corresponding Scheme object — torch_qscheme","text":"","code":"torch_per_channel_affine()  torch_per_tensor_affine()  torch_per_channel_symmetric()  torch_per_tensor_symmetric()"},{"path":"https://torch.mlverse.org/docs/reference/torch_quantile.html","id":null,"dir":"Reference","previous_headings":"","what":"Quantile — torch_quantile","title":"Quantile — torch_quantile","text":"Quantile","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_quantile.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Quantile — torch_quantile","text":"","code":"torch_quantile(self, q, dim = NULL, keepdim = FALSE, interpolation)"},{"path":"https://torch.mlverse.org/docs/reference/torch_quantile.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Quantile — torch_quantile","text":"self (Tensor) input tensor. q (float Tensor) scalar 1D tensor quantile values range [0, 1] dim (int) dimension reduce. keepdim (bool) whether output tensor dim retained . interpolation interpolation method.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_quantile.html","id":"quantile-input-q-gt-tensor-","dir":"Reference","previous_headings":"","what":"quantile(input, q) -> Tensor","title":"Quantile — torch_quantile","text":"Returns q-th quantiles elements input tensor, linear interpolation q-th quantile lies two data points.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_quantile.html","id":"quantile-input-q-dim-none-keepdim-false-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"quantile(input, q, dim=None, keepdim=FALSE, *, out=None) -> Tensor","title":"Quantile — torch_quantile","text":"Returns q-th quantiles row input tensor along dimension dim, linear interpolation q-th quantile lies two data points. default, dim None resulting input tensor flattened computation. keepdim TRUE, output dimensions size input except dimensions reduced (dim dim NULL) size 1. Otherwise, dimensions reduced squeezed (see torch_squeeze). q 1D tensor, extra dimension prepended output tensor size q represents quantiles.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_quantile.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Quantile — torch_quantile","text":"","code":"if (torch_is_installed()) {  a <- torch_randn(c(1, 3)) a q <- torch_tensor(c(0, 0.5, 1)) torch_quantile(a, q)   a <- torch_randn(c(2, 3)) a q <- torch_tensor(c(0.25, 0.5, 0.75)) torch_quantile(a, q, dim=1, keepdim=TRUE) torch_quantile(a, q, dim=1, keepdim=TRUE)$shape } #> [1] 3 1 3"},{"path":"https://torch.mlverse.org/docs/reference/torch_quantize_per_channel.html","id":null,"dir":"Reference","previous_headings":"","what":"Quantize_per_channel — torch_quantize_per_channel","title":"Quantize_per_channel — torch_quantize_per_channel","text":"Quantize_per_channel","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_quantize_per_channel.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Quantize_per_channel — torch_quantize_per_channel","text":"","code":"torch_quantize_per_channel(self, scales, zero_points, axis, dtype)"},{"path":"https://torch.mlverse.org/docs/reference/torch_quantize_per_channel.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Quantize_per_channel — torch_quantize_per_channel","text":"self (Tensor) float tensor quantize scales (Tensor) float 1D tensor scales use, size match input.size(axis) zero_points (int) integer 1D tensor offset use, size match input.size(axis) axis (int) dimension apply per-channel quantization dtype (torch.dtype) desired data type returned tensor.        one quantized dtypes: torch_quint8, torch.qint8, torch.qint32","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_quantize_per_channel.html","id":"quantize-per-channel-input-scales-zero-points-axis-dtype-gt-tensor-","dir":"Reference","previous_headings":"","what":"quantize_per_channel(input, scales, zero_points, axis, dtype) -> Tensor","title":"Quantize_per_channel — torch_quantize_per_channel","text":"Converts float tensor per-channel quantized tensor given scales zero points.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_quantize_per_channel.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Quantize_per_channel — torch_quantize_per_channel","text":"","code":"if (torch_is_installed()) { x = torch_tensor(matrix(c(-1.0, 0.0, 1.0, 2.0), ncol = 2, byrow = TRUE)) torch_quantize_per_channel(x, torch_tensor(c(0.1, 0.01)),                             torch_tensor(c(10L, 0L)), 0, torch_quint8()) torch_quantize_per_channel(x, torch_tensor(c(0.1, 0.01)),                             torch_tensor(c(10L, 0L)), 0, torch_quint8())$int_repr() } #> torch_tensor #>    0   10 #>  100  200 #> [ CPUByteType{2,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_quantize_per_tensor.html","id":null,"dir":"Reference","previous_headings":"","what":"Quantize_per_tensor — torch_quantize_per_tensor","title":"Quantize_per_tensor — torch_quantize_per_tensor","text":"Quantize_per_tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_quantize_per_tensor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Quantize_per_tensor — torch_quantize_per_tensor","text":"","code":"torch_quantize_per_tensor(self, scale, zero_point, dtype)"},{"path":"https://torch.mlverse.org/docs/reference/torch_quantize_per_tensor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Quantize_per_tensor — torch_quantize_per_tensor","text":"self (Tensor) float tensor quantize scale (float) scale apply quantization formula zero_point (int) offset integer value maps float zero dtype (torch.dtype) desired data type returned tensor.        one quantized dtypes: torch_quint8, torch.qint8, torch.qint32","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_quantize_per_tensor.html","id":"quantize-per-tensor-input-scale-zero-point-dtype-gt-tensor-","dir":"Reference","previous_headings":"","what":"quantize_per_tensor(input, scale, zero_point, dtype) -> Tensor","title":"Quantize_per_tensor — torch_quantize_per_tensor","text":"Converts float tensor quantized tensor given scale zero point.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_quantize_per_tensor.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Quantize_per_tensor — torch_quantize_per_tensor","text":"","code":"if (torch_is_installed()) { torch_quantize_per_tensor(torch_tensor(c(-1.0, 0.0, 1.0, 2.0)), 0.1, 10, torch_quint8()) torch_quantize_per_tensor(torch_tensor(c(-1.0, 0.0, 1.0, 2.0)), 0.1, 10, torch_quint8())$int_repr() } #> torch_tensor #>   0 #>  10 #>  20 #>  30 #> [ CPUByteType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_rad2deg.html","id":null,"dir":"Reference","previous_headings":"","what":"Rad2deg — torch_rad2deg","title":"Rad2deg — torch_rad2deg","text":"Rad2deg","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_rad2deg.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rad2deg — torch_rad2deg","text":"","code":"torch_rad2deg(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_rad2deg.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rad2deg — torch_rad2deg","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_rad2deg.html","id":"rad-deg-input-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"rad2deg(input, *, out=None) -> Tensor","title":"Rad2deg — torch_rad2deg","text":"Returns new tensor elements input converted angles radians degrees.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_rad2deg.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rad2deg — torch_rad2deg","text":"","code":"if (torch_is_installed()) {  a <- torch_tensor(rbind(c(3.142, -3.142), c(6.283, -6.283), c(1.570, -1.570))) torch_rad2deg(a) } #> torch_tensor #>  180.0233 -180.0233 #>  359.9894 -359.9894 #>   89.9544  -89.9544 #> [ CPUFloatType{3,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_rand.html","id":null,"dir":"Reference","previous_headings":"","what":"Rand — torch_rand","title":"Rand — torch_rand","text":"Rand","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_rand.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rand — torch_rand","text":"","code":"torch_rand(   ...,   names = NULL,   dtype = NULL,   layout = torch_strided(),   device = NULL,   requires_grad = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/torch_rand.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rand — torch_rand","text":"... (int...) sequence integers defining shape output tensor.        Can variable number arguments collection like list tuple. names optional dimension names dtype (torch.dtype, optional) desired data type returned tensor.        Default: NULL, uses global default (see torch_set_default_tensor_type). layout (torch.layout, optional) desired layout returned Tensor.        Default: torch_strided. device (torch.device, optional) desired device returned tensor.        Default: NULL, uses current device default tensor type        (see torch_set_default_tensor_type). device CPU        CPU tensor types current CUDA device CUDA tensor types. requires_grad (bool, optional) autograd record operations        returned tensor. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_rand.html","id":"rand-size-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-","dir":"Reference","previous_headings":"","what":"rand(*size, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor","title":"Rand — torch_rand","text":"Returns tensor filled random numbers uniform distribution interval \\([0, 1)\\) shape tensor defined variable argument size.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_rand.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rand — torch_rand","text":"","code":"if (torch_is_installed()) {  torch_rand(4) torch_rand(c(2, 3)) } #> torch_tensor #>  0.7649  0.7496  0.7544 #>  0.8929  0.6352  0.6037 #> [ CPUFloatType{2,3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_rand_like.html","id":null,"dir":"Reference","previous_headings":"","what":"Rand_like — torch_rand_like","title":"Rand_like — torch_rand_like","text":"Rand_like","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_rand_like.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rand_like — torch_rand_like","text":"","code":"torch_rand_like(   input,   dtype = NULL,   layout = torch_strided(),   device = NULL,   requires_grad = FALSE,   memory_format = torch_preserve_format() )"},{"path":"https://torch.mlverse.org/docs/reference/torch_rand_like.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rand_like — torch_rand_like","text":"input (Tensor) size input determine size output tensor. dtype (torch.dtype, optional) desired data type returned Tensor.        Default: NULL, defaults dtype input. layout (torch.layout, optional) desired layout returned tensor.        Default: NULL, defaults layout input. device (torch.device, optional) desired device returned tensor.        Default: NULL, defaults device input. requires_grad (bool, optional) autograd record operations        returned tensor. Default: FALSE. memory_format (torch.memory_format, optional) desired memory format        returned Tensor. Default: torch_preserve_format.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_rand_like.html","id":"rand-like-input-dtype-null-layout-null-device-null-requires-grad-false-memory-format-torch-preserve-format-gt-tensor-","dir":"Reference","previous_headings":"","what":"rand_like(input, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, memory_format=torch.preserve_format) -> Tensor","title":"Rand_like — torch_rand_like","text":"Returns tensor size input filled random numbers uniform distribution interval \\([0, 1)\\). torch_rand_like(input) equivalent torch_rand(input.size(), dtype=input.dtype, layout=input.layout, device=input.device).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_randint.html","id":null,"dir":"Reference","previous_headings":"","what":"Randint — torch_randint","title":"Randint — torch_randint","text":"Randint","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_randint.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Randint — torch_randint","text":"","code":"torch_randint(   low,   high,   size,   generator = NULL,   dtype = NULL,   layout = torch_strided(),   device = NULL,   requires_grad = FALSE,   memory_format = torch_preserve_format() )"},{"path":"https://torch.mlverse.org/docs/reference/torch_randint.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Randint — torch_randint","text":"low (int, optional) Lowest integer drawn distribution. Default: 0. high (int) One highest integer drawn distribution. size (tuple) tuple defining shape output tensor. generator (torch.Generator, optional) pseudorandom number generator sampling dtype (torch.dtype, optional) desired data type returned tensor.        Default: NULL, uses global default (see torch_set_default_tensor_type). layout (torch.layout, optional) desired layout returned Tensor.        Default: torch_strided. device (torch.device, optional) desired device returned tensor.        Default: NULL, uses current device default tensor type        (see torch_set_default_tensor_type). device CPU        CPU tensor types current CUDA device CUDA tensor types. requires_grad (bool, optional) autograd record operations        returned tensor. Default: FALSE. memory_format memory format resulting tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_randint.html","id":"randint-low-high-size-generator-null-out-null-","dir":"Reference","previous_headings":"","what":"randint(low=0, high, size, *, generator=NULL, out=NULL, \\","title":"Randint — torch_randint","text":"dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor Returns tensor filled random integers generated uniformly low (inclusive) high (exclusive). shape tensor defined variable argument size. .. note: global dtype default (torch_float32), function returns tensor dtype torch_int64.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_randint.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Randint — torch_randint","text":"","code":"if (torch_is_installed()) {  torch_randint(3, 5, list(3)) torch_randint(0, 10, size = list(2, 2)) torch_randint(3, 10, list(2, 2)) } #> torch_tensor #>  9  4 #>  7  6 #> [ CPUFloatType{2,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_randint_like.html","id":null,"dir":"Reference","previous_headings":"","what":"Randint_like — torch_randint_like","title":"Randint_like — torch_randint_like","text":"Randint_like","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_randint_like.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Randint_like — torch_randint_like","text":"","code":"torch_randint_like(   input,   low,   high,   dtype = NULL,   layout = torch_strided(),   device = NULL,   requires_grad = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/torch_randint_like.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Randint_like — torch_randint_like","text":"input (Tensor) size input determine size output tensor. low (int, optional) Lowest integer drawn distribution. Default: 0. high (int) One highest integer drawn distribution. dtype (torch.dtype, optional) desired data type returned Tensor.        Default: NULL, defaults dtype input. layout (torch.layout, optional) desired layout returned tensor.        Default: NULL, defaults layout input. device (torch.device, optional) desired device returned tensor.        Default: NULL, defaults device input. requires_grad (bool, optional) autograd record operations        returned tensor. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_randint_like.html","id":"randint-like-input-low-high-dtype-null-layout-torch-strided-device-null-requires-grad-false-","dir":"Reference","previous_headings":"","what":"randint_like(input, low=0, high, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False,","title":"Randint_like — torch_randint_like","text":"memory_format=torch.preserve_format) -> Tensor Returns tensor shape Tensor input filled random integers generated uniformly low (inclusive) high (exclusive). .. note: global dtype default (torch_float32), function returns tensor dtype torch_int64.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_randn.html","id":null,"dir":"Reference","previous_headings":"","what":"Randn — torch_randn","title":"Randn — torch_randn","text":"Randn","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_randn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Randn — torch_randn","text":"","code":"torch_randn(   ...,   names = NULL,   dtype = NULL,   layout = torch_strided(),   device = NULL,   requires_grad = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/torch_randn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Randn — torch_randn","text":"... (int...) sequence integers defining shape output tensor.        Can variable number arguments collection like list tuple. names optional names dimensions dtype (torch.dtype, optional) desired data type returned tensor.        Default: NULL, uses global default (see torch_set_default_tensor_type). layout (torch.layout, optional) desired layout returned Tensor.        Default: torch_strided. device (torch.device, optional) desired device returned tensor.        Default: NULL, uses current device default tensor type        (see torch_set_default_tensor_type). device CPU        CPU tensor types current CUDA device CUDA tensor types. requires_grad (bool, optional) autograd record operations        returned tensor. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_randn.html","id":"randn-size-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-","dir":"Reference","previous_headings":"","what":"randn(*size, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor","title":"Randn — torch_randn","text":"Returns tensor filled random numbers normal distribution mean 0 variance 1 (also called standard normal distribution). $$     \\mbox{}_{} \\sim \\mathcal{N}(0, 1) $$ shape tensor defined variable argument size.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_randn.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Randn — torch_randn","text":"","code":"if (torch_is_installed()) {  torch_randn(c(4)) torch_randn(c(2, 3)) } #> torch_tensor #> -2.5092 -0.5033  0.2772 #>  0.2459 -0.3065 -1.1800 #> [ CPUFloatType{2,3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_randn_like.html","id":null,"dir":"Reference","previous_headings":"","what":"Randn_like — torch_randn_like","title":"Randn_like — torch_randn_like","text":"Randn_like","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_randn_like.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Randn_like — torch_randn_like","text":"","code":"torch_randn_like(   input,   dtype = NULL,   layout = torch_strided(),   device = NULL,   requires_grad = FALSE,   memory_format = torch_preserve_format() )"},{"path":"https://torch.mlverse.org/docs/reference/torch_randn_like.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Randn_like — torch_randn_like","text":"input (Tensor) size input determine size output tensor. dtype (torch.dtype, optional) desired data type returned Tensor.        Default: NULL, defaults dtype input. layout (torch.layout, optional) desired layout returned tensor.        Default: NULL, defaults layout input. device (torch.device, optional) desired device returned tensor.        Default: NULL, defaults device input. requires_grad (bool, optional) autograd record operations        returned tensor. Default: FALSE. memory_format (torch.memory_format, optional) desired memory format        returned Tensor. Default: torch_preserve_format.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_randn_like.html","id":"randn-like-input-dtype-null-layout-null-device-null-requires-grad-false-memory-format-torch-preserve-format-gt-tensor-","dir":"Reference","previous_headings":"","what":"randn_like(input, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, memory_format=torch.preserve_format) -> Tensor","title":"Randn_like — torch_randn_like","text":"Returns tensor size input filled random numbers normal distribution mean 0 variance 1. torch_randn_like(input) equivalent torch_randn(input.size(), dtype=input.dtype, layout=input.layout, device=input.device).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_randperm.html","id":null,"dir":"Reference","previous_headings":"","what":"Randperm — torch_randperm","title":"Randperm — torch_randperm","text":"Randperm","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_randperm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Randperm — torch_randperm","text":"","code":"torch_randperm(   n,   dtype = torch_int64(),   layout = torch_strided(),   device = NULL,   requires_grad = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/torch_randperm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Randperm — torch_randperm","text":"n (int) upper bound (exclusive) dtype (torch.dtype, optional) desired data type returned tensor.        Default: torch_int64. layout (torch.layout, optional) desired layout returned Tensor.        Default: torch_strided. device (torch.device, optional) desired device returned tensor.        Default: NULL, uses current device default tensor type        (see torch_set_default_tensor_type). device CPU        CPU tensor types current CUDA device CUDA tensor types. requires_grad (bool, optional) autograd record operations        returned tensor. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_randperm.html","id":"randperm-n-out-null-dtype-torch-int-layout-torch-strided-device-null-requires-grad-false-gt-longtensor-","dir":"Reference","previous_headings":"","what":"randperm(n, out=NULL, dtype=torch.int64, layout=torch.strided, device=NULL, requires_grad=False) -> LongTensor","title":"Randperm — torch_randperm","text":"Returns random permutation integers 0 n - 1.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_randperm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Randperm — torch_randperm","text":"","code":"if (torch_is_installed()) {  torch_randperm(4) } #> torch_tensor #>  3 #>  1 #>  2 #>  0 #> [ CPULongType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_range.html","id":null,"dir":"Reference","previous_headings":"","what":"Range — torch_range","title":"Range — torch_range","text":"Range","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_range.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Range — torch_range","text":"","code":"torch_range(   start,   end,   step = 1,   dtype = NULL,   layout = torch_strided(),   device = NULL,   requires_grad = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/torch_range.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Range — torch_range","text":"start (float) starting value set points. Default: 0. end (float) ending value set points step (float) gap pair adjacent points. Default: 1. dtype (torch.dtype, optional) desired data type returned tensor.        Default: NULL, uses global default (see torch_set_default_tensor_type). dtype given, infer data type input        arguments. start, end, stop floating-point,        dtype inferred default dtype, see        ~torch.get_default_dtype. Otherwise, dtype inferred        torch.int64. layout (torch.layout, optional) desired layout returned Tensor.        Default: torch_strided. device (torch.device, optional) desired device returned tensor.        Default: NULL, uses current device default tensor type        (see torch_set_default_tensor_type). device CPU        CPU tensor types current CUDA device CUDA tensor types. requires_grad (bool, optional) autograd record operations        returned tensor. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_range.html","id":"range-start-end-step-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-","dir":"Reference","previous_headings":"","what":"range(start=0, end, step=1, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor","title":"Range — torch_range","text":"Returns 1-D tensor size \\(\\left\\lfloor \\frac{\\mbox{end} - \\mbox{start}}{\\mbox{step}} \\right\\rfloor + 1\\) values start end step step. Step gap two values tensor. $$     \\mbox{}_{+1} = \\mbox{}_i + \\mbox{step}. $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_range.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Range — torch_range","text":"function deprecated favor torch_arange.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_range.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Range — torch_range","text":"","code":"if (torch_is_installed()) {  torch_range(1, 4) torch_range(1, 4, 0.5) } #> Warning: This function is deprecated in favor of torch_arange. #> Warning: This function is deprecated in favor of torch_arange. #> torch_tensor #>  1.0000 #>  1.5000 #>  2.0000 #>  2.5000 #>  3.0000 #>  3.5000 #>  4.0000 #> [ CPUFloatType{7} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_real.html","id":null,"dir":"Reference","previous_headings":"","what":"Real — torch_real","title":"Real — torch_real","text":"Real","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_real.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Real — torch_real","text":"","code":"torch_real(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_real.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Real — torch_real","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_real.html","id":"real-input-gt-tensor-","dir":"Reference","previous_headings":"","what":"real(input) -> Tensor","title":"Real — torch_real","text":"Returns real part input tensor. input real (non-complex) tensor, function just returns .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_real.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Real — torch_real","text":"yet implemented complex tensors. $$     \\mbox{}_{} = real(\\mbox{input}_{}) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_real.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Real — torch_real","text":"","code":"if (torch_is_installed()) { if (FALSE) { torch_real(torch_tensor(c(-1 + 1i, -2 + 2i, 3 - 3i))) } }"},{"path":"https://torch.mlverse.org/docs/reference/torch_reciprocal.html","id":null,"dir":"Reference","previous_headings":"","what":"Reciprocal — torch_reciprocal","title":"Reciprocal — torch_reciprocal","text":"Reciprocal","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_reciprocal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reciprocal — torch_reciprocal","text":"","code":"torch_reciprocal(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_reciprocal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reciprocal — torch_reciprocal","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_reciprocal.html","id":"reciprocal-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"reciprocal(input, out=NULL) -> Tensor","title":"Reciprocal — torch_reciprocal","text":"Returns new tensor reciprocal elements input $$     \\mbox{}_{} = \\frac{1}{\\mbox{input}_{}} $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_reciprocal.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Reciprocal — torch_reciprocal","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(4)) a torch_reciprocal(a) } #> torch_tensor #>  15.0493 #>   1.2671 #>   0.8328 #>   0.3938 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_reduction.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates the reduction objet — torch_reduction","title":"Creates the reduction objet — torch_reduction","text":"Creates reduction objet","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_reduction.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates the reduction objet — torch_reduction","text":"","code":"torch_reduction_sum()  torch_reduction_mean()  torch_reduction_none()"},{"path":"https://torch.mlverse.org/docs/reference/torch_relu.html","id":null,"dir":"Reference","previous_headings":"","what":"Relu — torch_relu","title":"Relu — torch_relu","text":"Relu","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_relu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Relu — torch_relu","text":"","code":"torch_relu(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_relu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Relu — torch_relu","text":"self input tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_relu.html","id":"relu-input-gt-tensor-","dir":"Reference","previous_headings":"","what":"relu(input) -> Tensor","title":"Relu — torch_relu","text":"Computes relu tranformation.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_relu_.html","id":null,"dir":"Reference","previous_headings":"","what":"Relu_ — torch_relu_","title":"Relu_ — torch_relu_","text":"Relu_","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_relu_.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Relu_ — torch_relu_","text":"","code":"torch_relu_(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_relu_.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Relu_ — torch_relu_","text":"self input tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_relu_.html","id":"relu-input-gt-tensor-","dir":"Reference","previous_headings":"","what":"relu_(input) -> Tensor","title":"Relu_ — torch_relu_","text":"-place version torch_relu().","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_remainder.html","id":null,"dir":"Reference","previous_headings":"","what":"Remainder — torch_remainder","title":"Remainder — torch_remainder","text":"Remainder","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_remainder.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Remainder — torch_remainder","text":"","code":"torch_remainder(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_remainder.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Remainder — torch_remainder","text":"self (Tensor) dividend (Tensor float) divisor may either number                               Tensor shape dividend","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_remainder.html","id":"remainder-input-other-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"remainder(input, other, out=NULL) -> Tensor","title":"Remainder — torch_remainder","text":"Computes element-wise remainder division. divisor dividend may contain integer floating point numbers. remainder sign divisor. tensor, shapes input must broadcastable .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_remainder.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Remainder — torch_remainder","text":"","code":"if (torch_is_installed()) {  torch_remainder(torch_tensor(c(-3., -2, -1, 1, 2, 3)), 2) torch_remainder(torch_tensor(c(1., 2, 3, 4, 5)), 1.5) } #> torch_tensor #>  1.0000 #>  0.5000 #>  0.0000 #>  1.0000 #>  0.5000 #> [ CPUFloatType{5} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_renorm.html","id":null,"dir":"Reference","previous_headings":"","what":"Renorm — torch_renorm","title":"Renorm — torch_renorm","text":"Renorm","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_renorm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Renorm — torch_renorm","text":"","code":"torch_renorm(self, p, dim, maxnorm)"},{"path":"https://torch.mlverse.org/docs/reference/torch_renorm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Renorm — torch_renorm","text":"self (Tensor) input tensor. p (float) power norm computation dim (int) dimension slice get sub-tensors maxnorm (float) maximum norm keep sub-tensor ","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_renorm.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Renorm — torch_renorm","text":"norm row lower maxnorm, row unchanged","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_renorm.html","id":"renorm-input-p-dim-maxnorm-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"renorm(input, p, dim, maxnorm, out=NULL) -> Tensor","title":"Renorm — torch_renorm","text":"Returns tensor sub-tensor input along dimension dim normalized p-norm sub-tensor lower value maxnorm","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_renorm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Renorm — torch_renorm","text":"","code":"if (torch_is_installed()) { x = torch_ones(c(3, 3)) x[2,]$fill_(2) x[3,]$fill_(3) x torch_renorm(x, 1, 1, 5) } #> torch_tensor #>  1.0000  1.0000  1.0000 #>  1.6667  1.6667  1.6667 #>  1.6667  1.6667  1.6667 #> [ CPUFloatType{3,3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_repeat_interleave.html","id":null,"dir":"Reference","previous_headings":"","what":"Repeat_interleave — torch_repeat_interleave","title":"Repeat_interleave — torch_repeat_interleave","text":"Repeat_interleave","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_repeat_interleave.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Repeat_interleave — torch_repeat_interleave","text":"","code":"torch_repeat_interleave(self, repeats, dim = NULL, output_size = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/torch_repeat_interleave.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Repeat_interleave — torch_repeat_interleave","text":"self (Tensor) input tensor. repeats (Tensor int) number repetitions element.        repeats broadcasted fit shape given axis. dim (int, optional) dimension along repeat values.        default, use flattened input array, return flat output        array. output_size (int, optional) – Total output size given axis ( e.g. sum repeats). given, avoid stream syncronization needed calculate output shape tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_repeat_interleave.html","id":"repeat-interleave-input-repeats-dim-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"repeat_interleave(input, repeats, dim=NULL) -> Tensor","title":"Repeat_interleave — torch_repeat_interleave","text":"Repeat elements tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_repeat_interleave.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Repeat_interleave — torch_repeat_interleave","text":"","code":"This is different from `torch_Tensor.repeat` but similar to `numpy.repeat`."},{"path":"https://torch.mlverse.org/docs/reference/torch_repeat_interleave.html","id":"repeat-interleave-repeats-gt-tensor-","dir":"Reference","previous_headings":"","what":"repeat_interleave(repeats) -> Tensor","title":"Repeat_interleave — torch_repeat_interleave","text":"repeats tensor([n1, n2, n3, ...]), output tensor([0, 0, ..., 1, 1, ..., 2, 2, ..., ...]) 0 appears n1 times, 1 appears n2 times, 2 appears n3 times, etc.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_repeat_interleave.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Repeat_interleave — torch_repeat_interleave","text":"","code":"if (torch_is_installed()) { if (FALSE) { x = torch_tensor(c(1, 2, 3)) x$repeat_interleave(2) y = torch_tensor(matrix(c(1, 2, 3, 4), ncol = 2, byrow=TRUE)) torch_repeat_interleave(y, 2) torch_repeat_interleave(y, 3, dim=1) torch_repeat_interleave(y, torch_tensor(c(1, 2)), dim=1) } }"},{"path":"https://torch.mlverse.org/docs/reference/torch_reshape.html","id":null,"dir":"Reference","previous_headings":"","what":"Reshape — torch_reshape","title":"Reshape — torch_reshape","text":"Reshape","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_reshape.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reshape — torch_reshape","text":"","code":"torch_reshape(self, shape)"},{"path":"https://torch.mlverse.org/docs/reference/torch_reshape.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reshape — torch_reshape","text":"self (Tensor) tensor reshaped shape (tuple ints) new shape","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_reshape.html","id":"reshape-input-shape-gt-tensor-","dir":"Reference","previous_headings":"","what":"reshape(input, shape) -> Tensor","title":"Reshape — torch_reshape","text":"Returns tensor data number elements input, specified shape. possible, returned tensor view input. Otherwise, copy. Contiguous inputs inputs compatible strides can reshaped without copying, depend copying vs. viewing behavior. See torch_Tensor.view possible return view. single dimension may -1, case inferred remaining dimensions number elements input.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_reshape.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Reshape — torch_reshape","text":"","code":"if (torch_is_installed()) {  a <- torch_arange(0, 3) torch_reshape(a, list(2, 2)) b <- torch_tensor(matrix(c(0, 1, 2, 3), ncol = 2, byrow=TRUE)) torch_reshape(b, list(-1)) } #> torch_tensor #>  0 #>  1 #>  2 #>  3 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_result_type.html","id":null,"dir":"Reference","previous_headings":"","what":"Result_type — torch_result_type","title":"Result_type — torch_result_type","text":"Result_type","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_result_type.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Result_type — torch_result_type","text":"","code":"torch_result_type(tensor1, tensor2)"},{"path":"https://torch.mlverse.org/docs/reference/torch_result_type.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Result_type — torch_result_type","text":"tensor1 (Tensor Number) input tensor number tensor2 (Tensor Number) input tensor number","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_result_type.html","id":"result-type-tensor-tensor-gt-dtype-","dir":"Reference","previous_headings":"","what":"result_type(tensor1, tensor2) -> dtype","title":"Result_type — torch_result_type","text":"Returns torch_dtype result performing arithmetic operation provided input tensors. See type promotion documentation information type promotion logic.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_result_type.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Result_type — torch_result_type","text":"","code":"if (torch_is_installed()) {  torch_result_type(tensor1 = torch_tensor(c(1, 2), dtype=torch_int()), tensor2 = 1) } #> torch_Float"},{"path":"https://torch.mlverse.org/docs/reference/torch_roll.html","id":null,"dir":"Reference","previous_headings":"","what":"Roll — torch_roll","title":"Roll — torch_roll","text":"Roll","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_roll.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Roll — torch_roll","text":"","code":"torch_roll(self, shifts, dims = list())"},{"path":"https://torch.mlverse.org/docs/reference/torch_roll.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Roll — torch_roll","text":"self (Tensor) input tensor. shifts (int tuple ints) number places elements        tensor shifted. shifts tuple, dims must tuple        size, dimension rolled corresponding        value dims (int tuple ints) Axis along roll","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_roll.html","id":"roll-input-shifts-dims-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"roll(input, shifts, dims=NULL) -> Tensor","title":"Roll — torch_roll","text":"Roll tensor along given dimension(s). Elements shifted beyond last position re-introduced first position. dimension specified, tensor flattened rolling restored original shape.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_roll.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Roll — torch_roll","text":"","code":"if (torch_is_installed()) {  x = torch_tensor(c(1, 2, 3, 4, 5, 6, 7, 8))$view(c(4, 2)) x torch_roll(x, 1, 1) torch_roll(x, -1, 1) torch_roll(x, shifts=list(2, 1), dims=list(1, 2)) } #> torch_tensor #>  6  5 #>  8  7 #>  2  1 #>  4  3 #> [ CPUFloatType{4,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_rot90.html","id":null,"dir":"Reference","previous_headings":"","what":"Rot90 — torch_rot90","title":"Rot90 — torch_rot90","text":"Rot90","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_rot90.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rot90 — torch_rot90","text":"","code":"torch_rot90(self, k = 1L, dims = c(0, 1))"},{"path":"https://torch.mlverse.org/docs/reference/torch_rot90.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rot90 — torch_rot90","text":"self (Tensor) input tensor. k (int) number times rotate dims (list tuple) axis rotate","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_rot90.html","id":"rot-input-k-dims-gt-tensor-","dir":"Reference","previous_headings":"","what":"rot90(input, k, dims) -> Tensor","title":"Rot90 — torch_rot90","text":"Rotate n-D tensor 90 degrees plane specified dims axis. Rotation direction first towards second axis k > 0, second towards first k < 0.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_rot90.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rot90 — torch_rot90","text":"","code":"if (torch_is_installed()) {  x <- torch_arange(1, 4)$view(c(2, 2)) x torch_rot90(x, 1, c(1, 2)) x <- torch_arange(1, 8)$view(c(2, 2, 2)) x torch_rot90(x, 1, c(1, 2)) } #> torch_tensor #> (1,.,.) =  #>   3  4 #>   7  8 #>  #> (2,.,.) =  #>   1  2 #>   5  6 #> [ CPUFloatType{2,2,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_round.html","id":null,"dir":"Reference","previous_headings":"","what":"Round — torch_round","title":"Round — torch_round","text":"Round","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_round.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Round — torch_round","text":"","code":"torch_round(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_round.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Round — torch_round","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_round.html","id":"round-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"round(input, out=NULL) -> Tensor","title":"Round — torch_round","text":"Returns new tensor elements input rounded closest integer.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_round.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Round — torch_round","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(4)) a torch_round(a) } #> torch_tensor #> -2 #>  1 #> -1 #> -1 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_rrelu_.html","id":null,"dir":"Reference","previous_headings":"","what":"Rrelu_ — torch_rrelu_","title":"Rrelu_ — torch_rrelu_","text":"Rrelu_","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_rrelu_.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rrelu_ — torch_rrelu_","text":"","code":"torch_rrelu_(   self,   lower = 0.125,   upper = 0.333333333333333,   training = FALSE,   generator = NULL )"},{"path":"https://torch.mlverse.org/docs/reference/torch_rrelu_.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rrelu_ — torch_rrelu_","text":"self input tensor lower lower bound uniform distribution. Default: 1/8 upper upper bound uniform distribution. Default: 1/3 training bool wether training pass. DEfault: FALSE generator random number generator","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_rrelu_.html","id":"rrelu-input-lower-upper-training-false-gt-tensor-","dir":"Reference","previous_headings":"","what":"rrelu_(input, lower=1./8, upper=1./3, training=False) -> Tensor","title":"Rrelu_ — torch_rrelu_","text":"-place version torch_rrelu.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_rsqrt.html","id":null,"dir":"Reference","previous_headings":"","what":"Rsqrt — torch_rsqrt","title":"Rsqrt — torch_rsqrt","text":"Rsqrt","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_rsqrt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rsqrt — torch_rsqrt","text":"","code":"torch_rsqrt(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_rsqrt.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rsqrt — torch_rsqrt","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_rsqrt.html","id":"rsqrt-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"rsqrt(input, out=NULL) -> Tensor","title":"Rsqrt — torch_rsqrt","text":"Returns new tensor reciprocal square-root elements input. $$     \\mbox{}_{} = \\frac{1}{\\sqrt{\\mbox{input}_{}}} $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_rsqrt.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rsqrt — torch_rsqrt","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(4)) a torch_rsqrt(a) } #> torch_tensor #>     nan #>  1.5397 #>  4.0054 #>  0.6926 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_save.html","id":null,"dir":"Reference","previous_headings":"","what":"Saves an object to a disk file. — torch_save","title":"Saves an object to a disk file. — torch_save","text":"function experimental, use long term storage.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_save.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Saves an object to a disk file. — torch_save","text":"","code":"torch_save(obj, path, ...)"},{"path":"https://torch.mlverse.org/docs/reference/torch_save.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Saves an object to a disk file. — torch_save","text":"obj saved object path connection name file save. ... currently used.","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/torch_scalar_tensor.html","id":null,"dir":"Reference","previous_headings":"","what":"Scalar tensor — torch_scalar_tensor","title":"Scalar tensor — torch_scalar_tensor","text":"Creates singleton dimension tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_scalar_tensor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Scalar tensor — torch_scalar_tensor","text":"","code":"torch_scalar_tensor(value, dtype = NULL, device = NULL, requires_grad = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_scalar_tensor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Scalar tensor — torch_scalar_tensor","text":"value value want use dtype (torch.dtype, optional) desired data type returned tensor.        Default: NULL, uses global default (see torch_set_default_tensor_type). device (torch.device, optional) desired device returned tensor.        Default: NULL, uses current device default tensor type        (see torch_set_default_tensor_type). device CPU        CPU tensor types current CUDA device CUDA tensor types. requires_grad (bool, optional) autograd record operations        returned tensor. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_searchsorted.html","id":null,"dir":"Reference","previous_headings":"","what":"Searchsorted — torch_searchsorted","title":"Searchsorted — torch_searchsorted","text":"Searchsorted","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_searchsorted.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Searchsorted — torch_searchsorted","text":"","code":"torch_searchsorted(sorted_sequence, self, out_int32 = FALSE, right = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_searchsorted.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Searchsorted — torch_searchsorted","text":"sorted_sequence (Tensor) N-D 1-D tensor, containing monotonically increasing sequence innermost dimension. self (Tensor Scalar) N-D tensor Scalar containing search value(s). out_int32 (bool, optional) – indicate output data type. torch_int32() True, torch_int64() otherwise. Default value FALSE, .e. default output data type torch_int64(). right (bool, optional) – False, return first suitable location found. True, return last index. suitable index found, return 0 non-numerical value (eg. nan, inf) size boundaries (one pass last index). words, False, gets lower bound index value input boundaries. True, gets upper bound index instead. Default value False.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_searchsorted.html","id":"searchsorted-sorted-sequence-values-out-int-false-right-false-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"searchsorted(sorted_sequence, values, *, out_int32=FALSE, right=FALSE, out=None) -> Tensor","title":"Searchsorted — torch_searchsorted","text":"Find indices innermost dimension sorted_sequence , corresponding values values inserted indices, order corresponding innermost dimension within sorted_sequence preserved. Return new tensor size values. right FALSE (default), left boundary sorted_sequence closed.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_searchsorted.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Searchsorted — torch_searchsorted","text":"","code":"if (torch_is_installed()) {  sorted_sequence <- torch_tensor(rbind(c(1, 3, 5, 7, 9), c(2, 4, 6, 8, 10))) sorted_sequence values <- torch_tensor(rbind(c(3, 6, 9), c(3, 6, 9))) values torch_searchsorted(sorted_sequence, values) torch_searchsorted(sorted_sequence, values, right=TRUE) sorted_sequence_1d <- torch_tensor(c(1, 3, 5, 7, 9)) sorted_sequence_1d torch_searchsorted(sorted_sequence_1d, values) } #> torch_tensor #>  1  3  4 #>  1  3  4 #> [ CPULongType{2,3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_selu.html","id":null,"dir":"Reference","previous_headings":"","what":"Selu — torch_selu","title":"Selu — torch_selu","text":"Selu","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_selu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Selu — torch_selu","text":"","code":"torch_selu(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_selu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Selu — torch_selu","text":"self input tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_selu.html","id":"selu-input-gt-tensor-","dir":"Reference","previous_headings":"","what":"selu(input) -> Tensor","title":"Selu — torch_selu","text":"Computes selu transformation.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_selu_.html","id":null,"dir":"Reference","previous_headings":"","what":"Selu_ — torch_selu_","title":"Selu_ — torch_selu_","text":"Selu_","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_selu_.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Selu_ — torch_selu_","text":"","code":"torch_selu_(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_selu_.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Selu_ — torch_selu_","text":"self input tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_selu_.html","id":"selu-input-gt-tensor-","dir":"Reference","previous_headings":"","what":"selu_(input) -> Tensor","title":"Selu_ — torch_selu_","text":"-place version torch_selu().","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sgn.html","id":null,"dir":"Reference","previous_headings":"","what":"Sgn — torch_sgn","title":"Sgn — torch_sgn","text":"Sgn","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sgn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sgn — torch_sgn","text":"","code":"torch_sgn(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_sgn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sgn — torch_sgn","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sgn.html","id":"sgn-input-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"sgn(input, *, out=None) -> Tensor","title":"Sgn — torch_sgn","text":"complex tensors, function returns new tensor whose elemants angle elements input absolute value 1. non-complex tensor, function returns signs elements input (see torch_sign). \\(\\mbox{}_{} = 0\\), \\(|{\\mbox{{input}}_i}| == 0\\) \\(\\mbox{}_{} = \\frac{{\\mbox{{input}}_i}}{|{\\mbox{{input}}_i}|}\\), otherwise","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sgn.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sgn — torch_sgn","text":"","code":"if (torch_is_installed()) { if (FALSE) { x <- torch_tensor(c(3+4i, 7-24i, 0, 1+2i)) x$sgn() torch_sgn(x) } }"},{"path":"https://torch.mlverse.org/docs/reference/torch_sigmoid.html","id":null,"dir":"Reference","previous_headings":"","what":"Sigmoid — torch_sigmoid","title":"Sigmoid — torch_sigmoid","text":"Sigmoid","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sigmoid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sigmoid — torch_sigmoid","text":"","code":"torch_sigmoid(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_sigmoid.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sigmoid — torch_sigmoid","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sigmoid.html","id":"sigmoid-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"sigmoid(input, out=NULL) -> Tensor","title":"Sigmoid — torch_sigmoid","text":"Returns new tensor sigmoid elements input. $$     \\mbox{}_{} = \\frac{1}{1 + e^{-\\mbox{input}_{}}} $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sigmoid.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sigmoid — torch_sigmoid","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(4)) a torch_sigmoid(a) } #> torch_tensor #>  0.5183 #>  0.7670 #>  0.5439 #>  0.1101 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_sign.html","id":null,"dir":"Reference","previous_headings":"","what":"Sign — torch_sign","title":"Sign — torch_sign","text":"Sign","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sign.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sign — torch_sign","text":"","code":"torch_sign(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_sign.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sign — torch_sign","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sign.html","id":"sign-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"sign(input, out=NULL) -> Tensor","title":"Sign — torch_sign","text":"Returns new tensor signs elements input. $$     \\mbox{}_{} = \\mbox{sgn}(\\mbox{input}_{}) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sign.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sign — torch_sign","text":"","code":"if (torch_is_installed()) {  a = torch_tensor(c(0.7, -1.2, 0., 2.3)) a torch_sign(a) } #> torch_tensor #>  1 #> -1 #>  0 #>  1 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_signbit.html","id":null,"dir":"Reference","previous_headings":"","what":"Signbit — torch_signbit","title":"Signbit — torch_signbit","text":"Signbit","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_signbit.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Signbit — torch_signbit","text":"","code":"torch_signbit(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_signbit.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Signbit — torch_signbit","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_signbit.html","id":"signbit-input-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"signbit(input, *, out=None) -> Tensor","title":"Signbit — torch_signbit","text":"Tests element input sign bit set (less zero) .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_signbit.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Signbit — torch_signbit","text":"","code":"if (torch_is_installed()) {  a <- torch_tensor(c(0.7, -1.2, 0., 2.3)) torch_signbit(a) } #> torch_tensor #>  0 #>  1 #>  0 #>  0 #> [ CPUBoolType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_sin.html","id":null,"dir":"Reference","previous_headings":"","what":"Sin — torch_sin","title":"Sin — torch_sin","text":"Sin","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sin — torch_sin","text":"","code":"torch_sin(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_sin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sin — torch_sin","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sin.html","id":"sin-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"sin(input, out=NULL) -> Tensor","title":"Sin — torch_sin","text":"Returns new tensor sine elements input. $$     \\mbox{}_{} = \\sin(\\mbox{input}_{}) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sin.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sin — torch_sin","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(4)) a torch_sin(a) } #> torch_tensor #>  0.3942 #>  0.9255 #> -0.2259 #>  0.1994 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_sinh.html","id":null,"dir":"Reference","previous_headings":"","what":"Sinh — torch_sinh","title":"Sinh — torch_sinh","text":"Sinh","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sinh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sinh — torch_sinh","text":"","code":"torch_sinh(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_sinh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sinh — torch_sinh","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sinh.html","id":"sinh-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"sinh(input, out=NULL) -> Tensor","title":"Sinh — torch_sinh","text":"Returns new tensor hyperbolic sine elements input. $$     \\mbox{}_{} = \\sinh(\\mbox{input}_{}) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sinh.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sinh — torch_sinh","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(4)) a torch_sinh(a) } #> torch_tensor #> -0.2181 #>  0.4530 #> -0.3427 #> -1.0866 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_slogdet.html","id":null,"dir":"Reference","previous_headings":"","what":"Slogdet — torch_slogdet","title":"Slogdet — torch_slogdet","text":"Slogdet","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_slogdet.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Slogdet — torch_slogdet","text":"","code":"torch_slogdet(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_slogdet.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Slogdet — torch_slogdet","text":"self (Tensor) input tensor size (*, n, n) * zero                batch dimensions.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_slogdet.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Slogdet — torch_slogdet","text":"","code":"If `input` has zero determinant, this returns `(0, -inf)`. Backward through `slogdet` internally uses SVD results when `input` is not invertible. In this case, double backward through `slogdet` will be unstable in when `input` doesn't have distinct singular values. See `~torch.svd` for details."},{"path":"https://torch.mlverse.org/docs/reference/torch_slogdet.html","id":"slogdet-input-gt-tensor-tensor-","dir":"Reference","previous_headings":"","what":"slogdet(input) -> (Tensor, Tensor)","title":"Slogdet — torch_slogdet","text":"Calculates sign log absolute value determinant(s) square matrix batches square matrices.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_slogdet.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Slogdet — torch_slogdet","text":"","code":"if (torch_is_installed()) {  A = torch_randn(c(3, 3)) A torch_det(A) torch_logdet(A) torch_slogdet(A) } #> [[1]] #> torch_tensor #> 1 #> [ CPUFloatType{} ] #>  #> [[2]] #> torch_tensor #> -5.06819 #> [ CPUFloatType{} ] #>"},{"path":"https://torch.mlverse.org/docs/reference/torch_solve.html","id":null,"dir":"Reference","previous_headings":"","what":"Solve — torch_solve","title":"Solve — torch_solve","text":"Solve","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_solve.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Solve — torch_solve","text":"","code":"torch_solve(self, A)"},{"path":"https://torch.mlverse.org/docs/reference/torch_solve.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Solve — torch_solve","text":"self (Tensor) input matrix \\(B\\) size \\((*, m, k)\\) , \\(*\\)                zero batch dimensions. (Tensor) input square matrix size \\((*, m, m)\\),                \\(*\\) zero batch dimensions.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_solve.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Solve — torch_solve","text":"","code":"Irrespective of the original strides, the returned matrices `solution` and `LU` will be transposed, i.e. with strides like `B$contiguous()$transpose(-1, -2)$stride()` and `A$contiguous()$transpose(-1, -2)$stride()` respectively."},{"path":"https://torch.mlverse.org/docs/reference/torch_solve.html","id":"solve-input-a-gt-tensor-tensor-","dir":"Reference","previous_headings":"","what":"solve(input, A) -> (Tensor, Tensor)","title":"Solve — torch_solve","text":"function returns solution system linear equations represented \\(AX = B\\) LU factorization , order namedtuple solution, LU. LU contains L U factors LU factorization . torch_solve(B, ) can take 2D inputs B, inputs batches 2D matrices. inputs batches, returns batched outputs solution, LU.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_solve.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Solve — torch_solve","text":"","code":"if (torch_is_installed()) {  A = torch_tensor(rbind(c(6.80, -2.11,  5.66,  5.97,  8.23),                       c(-6.05, -3.30,  5.36, -4.44,  1.08),                       c(-0.45,  2.58, -2.70,  0.27,  9.04),                       c(8.32,  2.71,  4.35,  -7.17,  2.14),                       c(-9.67, -5.14, -7.26,  6.08, -6.87)))$t() B = torch_tensor(rbind(c(4.02,  6.19, -8.22, -7.57, -3.03),                       c(-1.56,  4.00, -8.67,  1.75,  2.86),                       c(9.81, -4.09, -4.57, -8.61,  8.99)))$t() out = torch_solve(B, A) X = out[[1]] LU = out[[2]] torch_dist(B, torch_mm(A, X)) # Batched solver example A = torch_randn(c(2, 3, 1, 4, 4)) B = torch_randn(c(2, 3, 1, 4, 6)) out = torch_solve(B, A) X = out[[1]] LU = out[[2]] torch_dist(B, A$matmul(X)) } #> torch_tensor #> 1.22982e-05 #> [ CPUFloatType{} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_sort.html","id":null,"dir":"Reference","previous_headings":"","what":"Sort — torch_sort","title":"Sort — torch_sort","text":"Sort","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sort.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sort — torch_sort","text":"self (Tensor) input tensor. dim (int, optional) dimension sort along descending (bool, optional) controls sorting order (ascending descending) stable (bool, optional) – makes sorting routine stable, guarantees order equivalent elements preserved.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sort.html","id":"sort-input-dim-descending-false-gt-tensor-longtensor-","dir":"Reference","previous_headings":"","what":"sort(input, dim=-1, descending=FALSE) -> (Tensor, LongTensor)","title":"Sort — torch_sort","text":"Sorts elements input tensor along given dimension ascending order value. dim given, last dimension input chosen. descending TRUE elements sorted descending order value. namedtuple (values, indices) returned, values sorted values indices indices elements original input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sort.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sort — torch_sort","text":"","code":"if (torch_is_installed()) {  x = torch_randn(c(3, 4)) out = torch_sort(x) out out = torch_sort(x, 1) out } #> [[1]] #> torch_tensor #> -1.9596  0.3040 -0.9921  0.1079 #> -1.9441  1.0724 -0.9182  1.1927 #> -0.5018  1.5238  1.6355  2.0199 #> [ CPUFloatType{3,4} ] #>  #> [[2]] #> torch_tensor #>  3  3  3  2 #>  2  2  2  3 #>  1  1  1  1 #> [ CPULongType{3,4} ] #>"},{"path":"https://torch.mlverse.org/docs/reference/torch_sparse_coo_tensor.html","id":null,"dir":"Reference","previous_headings":"","what":"Sparse_coo_tensor — torch_sparse_coo_tensor","title":"Sparse_coo_tensor — torch_sparse_coo_tensor","text":"Sparse_coo_tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sparse_coo_tensor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sparse_coo_tensor — torch_sparse_coo_tensor","text":"","code":"torch_sparse_coo_tensor(   indices,   values,   size = NULL,   dtype = NULL,   device = NULL,   requires_grad = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/torch_sparse_coo_tensor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sparse_coo_tensor — torch_sparse_coo_tensor","text":"indices (array_like) Initial data tensor. Can list, tuple,        NumPy ndarray, scalar, types. cast torch_LongTensor        internally. indices coordinates non-zero values matrix, thus        two-dimensional first dimension number tensor dimensions        second dimension number non-zero values. values (array_like) Initial values tensor. Can list, tuple,        NumPy ndarray, scalar, types. size (list, tuple, torch.Size, optional) Size sparse tensor.        provided size inferred minimum size big enough hold non-zero        elements. dtype (torch.dtype, optional) desired data type returned tensor.        Default: NULL, infers data type values. device (torch.device, optional) desired device returned tensor.        Default: NULL, uses current device default tensor type        (see torch_set_default_tensor_type). device CPU        CPU tensor types current CUDA device CUDA tensor types. requires_grad (bool, optional) autograd record operations        returned tensor. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sparse_coo_tensor.html","id":"sparse-coo-tensor-indices-values-size-null-dtype-null-device-null-requires-grad-false-gt-tensor-","dir":"Reference","previous_headings":"","what":"sparse_coo_tensor(indices, values, size=NULL, dtype=NULL, device=NULL, requires_grad=False) -> Tensor","title":"Sparse_coo_tensor — torch_sparse_coo_tensor","text":"Constructs sparse tensors COO(rdinate) format non-zero elements given indices given values. sparse tensor can uncoalesced, case, duplicate coordinates indices, value index sum duplicate value entries: torch_sparse_.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sparse_coo_tensor.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sparse_coo_tensor — torch_sparse_coo_tensor","text":"","code":"if (torch_is_installed()) {  i = torch_tensor(matrix(c(1, 2, 2, 3, 1, 3), ncol = 3, byrow = TRUE), dtype=torch_int64()) v = torch_tensor(c(3, 4, 5), dtype=torch_float32()) torch_sparse_coo_tensor(i, v) torch_sparse_coo_tensor(i, v, c(2, 4))  # create empty sparse tensors S = torch_sparse_coo_tensor(   torch_empty(c(1, 0), dtype = torch_int64()),    torch_tensor(numeric(), dtype = torch_float32()),    c(1) ) S = torch_sparse_coo_tensor(   torch_empty(c(1, 0), dtype = torch_int64()),    torch_empty(c(0, 2)),    c(1, 2) ) }"},{"path":"https://torch.mlverse.org/docs/reference/torch_split.html","id":null,"dir":"Reference","previous_headings":"","what":"Split — torch_split","title":"Split — torch_split","text":"Splits tensor chunks. chunk view original tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_split.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Split — torch_split","text":"","code":"torch_split(self, split_size, dim = 1L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_split.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Split — torch_split","text":"self (Tensor) tensor split. split_size (int) size single chunk list sizes chunk dim (int) dimension along split tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_split.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Split — torch_split","text":"split_size integer type, tensor split equally sized chunks (possible). Last chunk smaller tensor size along given dimension dim divisible split_size. split_size list, tensor split length(split_size) chunks sizes dim according split_size_or_sections.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sqrt.html","id":null,"dir":"Reference","previous_headings":"","what":"Sqrt — torch_sqrt","title":"Sqrt — torch_sqrt","text":"Sqrt","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sqrt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sqrt — torch_sqrt","text":"","code":"torch_sqrt(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_sqrt.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sqrt — torch_sqrt","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sqrt.html","id":"sqrt-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"sqrt(input, out=NULL) -> Tensor","title":"Sqrt — torch_sqrt","text":"Returns new tensor square-root elements input. $$     \\mbox{}_{} = \\sqrt{\\mbox{input}_{}} $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sqrt.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sqrt — torch_sqrt","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(4)) a torch_sqrt(a) } #> torch_tensor #>     nan #>     nan #>     nan #>  0.7801 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_square.html","id":null,"dir":"Reference","previous_headings":"","what":"Square — torch_square","title":"Square — torch_square","text":"Square","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_square.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Square — torch_square","text":"","code":"torch_square(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_square.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Square — torch_square","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_square.html","id":"square-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"square(input, out=NULL) -> Tensor","title":"Square — torch_square","text":"Returns new tensor square elements input.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_square.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Square — torch_square","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(4)) a torch_square(a) } #> torch_tensor #>  2.0539 #>  0.2019 #>  3.3780 #>  0.0784 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_squeeze.html","id":null,"dir":"Reference","previous_headings":"","what":"Squeeze — torch_squeeze","title":"Squeeze — torch_squeeze","text":"Squeeze","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_squeeze.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Squeeze — torch_squeeze","text":"","code":"torch_squeeze(self, dim)"},{"path":"https://torch.mlverse.org/docs/reference/torch_squeeze.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Squeeze — torch_squeeze","text":"self (Tensor) input tensor. dim (int, optional) given, input squeezed           dimension","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_squeeze.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Squeeze — torch_squeeze","text":"returned tensor shares storage input tensor, changing contents one change contents .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_squeeze.html","id":"squeeze-input-dim-null-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"squeeze(input, dim=NULL, out=NULL) -> Tensor","title":"Squeeze — torch_squeeze","text":"Returns tensor dimensions input size 1 removed. example, input shape: \\((\\times 1 \\times B \\times C \\times 1 \\times D)\\) tensor shape: \\((\\times B \\times C \\times D)\\). dim given, squeeze operation done given dimension. input shape: \\((\\times 1 \\times B)\\), squeeze(input, 0) leaves tensor unchanged, squeeze(input, 1) squeeze tensor shape \\((\\times B)\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_squeeze.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Squeeze — torch_squeeze","text":"","code":"if (torch_is_installed()) {  x = torch_zeros(c(2, 1, 2, 1, 2)) x y = torch_squeeze(x) y y = torch_squeeze(x, 1) y y = torch_squeeze(x, 2) y } #> torch_tensor #> (1,1,.,.) =  #>   0  0 #>  #> (2,1,.,.) =  #>   0  0 #>  #> (1,2,.,.) =  #>   0  0 #>  #> (2,2,.,.) =  #>   0  0 #> [ CPUFloatType{2,2,1,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_stack.html","id":null,"dir":"Reference","previous_headings":"","what":"Stack — torch_stack","title":"Stack — torch_stack","text":"Stack","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_stack.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Stack — torch_stack","text":"","code":"torch_stack(tensors, dim = 1L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_stack.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Stack — torch_stack","text":"tensors (sequence Tensors) sequence tensors concatenate dim (int) dimension insert. 0 number        dimensions concatenated tensors (inclusive)","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_stack.html","id":"stack-tensors-dim-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"stack(tensors, dim=0, out=NULL) -> Tensor","title":"Stack — torch_stack","text":"Concatenates sequence tensors along new dimension. tensors need size.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_std.html","id":null,"dir":"Reference","previous_headings":"","what":"Std — torch_std","title":"Std — torch_std","text":"Std","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_std.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Std — torch_std","text":"","code":"torch_std(self, dim, correction, unbiased = TRUE, keepdim = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_std.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Std — torch_std","text":"self (Tensor) input tensor. dim (int tuple ints) dimension dimensions reduce. correction type correction. unbiased (bool) whether use unbiased estimation keepdim (bool) whether output tensor dim retained .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_std.html","id":"std-input-unbiased-true-gt-tensor-","dir":"Reference","previous_headings":"","what":"std(input, unbiased=TRUE) -> Tensor","title":"Std — torch_std","text":"Returns standard-deviation elements input tensor. unbiased FALSE, standard-deviation calculated via biased estimator. Otherwise, Bessel's correction used.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_std.html","id":"std-input-dim-unbiased-true-keepdim-false-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"std(input, dim, unbiased=TRUE, keepdim=False, out=NULL) -> Tensor","title":"Std — torch_std","text":"Returns standard-deviation row input tensor dimension dim. dim list dimensions, reduce . keepdim TRUE, output tensor size input except dimension(s) dim size 1. Otherwise, dim squeezed (see torch_squeeze), resulting output tensor 1 (len(dim)) fewer dimension(s). unbiased FALSE, standard-deviation calculated via biased estimator. Otherwise, Bessel's correction used.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_std.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Std — torch_std","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(1, 3)) a torch_std(a)   a = torch_randn(c(4, 4)) a torch_std(a, dim=1) } #> torch_tensor #>  0.6745 #>  0.8308 #>  0.3051 #>  0.9916 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_std_mean.html","id":null,"dir":"Reference","previous_headings":"","what":"Std_mean — torch_std_mean","title":"Std_mean — torch_std_mean","text":"Std_mean","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_std_mean.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Std_mean — torch_std_mean","text":"","code":"torch_std_mean(self, dim, correction, unbiased = TRUE, keepdim = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_std_mean.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Std_mean — torch_std_mean","text":"self (Tensor) input tensor. dim (int tuple ints) dimension dimensions reduce. correction type correction. unbiased (bool) whether use unbiased estimation keepdim (bool) whether output tensor dim retained .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_std_mean.html","id":"std-mean-input-unbiased-true-gt-tensor-tensor-","dir":"Reference","previous_headings":"","what":"std_mean(input, unbiased=TRUE) -> (Tensor, Tensor)","title":"Std_mean — torch_std_mean","text":"Returns standard-deviation mean elements input tensor. unbiased FALSE, standard-deviation calculated via biased estimator. Otherwise, Bessel's correction used.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_std_mean.html","id":"std-mean-input-dim-unbiased-true-keepdim-false-gt-tensor-tensor-","dir":"Reference","previous_headings":"","what":"std_mean(input, dim, unbiased=TRUE, keepdim=False) -> (Tensor, Tensor)","title":"Std_mean — torch_std_mean","text":"Returns standard-deviation mean row input tensor dimension dim. dim list dimensions, reduce . keepdim TRUE, output tensor size input except dimension(s) dim size 1. Otherwise, dim squeezed (see torch_squeeze), resulting output tensor 1 (len(dim)) fewer dimension(s). unbiased FALSE, standard-deviation calculated via biased estimator. Otherwise, Bessel's correction used.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_std_mean.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Std_mean — torch_std_mean","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(1, 3)) a torch_std_mean(a)   a = torch_randn(c(4, 4)) a torch_std_mean(a, 1) } #> [[1]] #> torch_tensor #>  0.2808 #>  0.9155 #>  1.2249 #>  1.5778 #> [ CPUFloatType{4} ] #>  #> [[2]] #> torch_tensor #> -0.0317 #>  0.2710 #>  0.1895 #>  0.1946 #> [ CPUFloatType{4} ] #>"},{"path":"https://torch.mlverse.org/docs/reference/torch_stft.html","id":null,"dir":"Reference","previous_headings":"","what":"Stft — torch_stft","title":"Stft — torch_stft","text":"Stft","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_stft.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Stft — torch_stft","text":"","code":"torch_stft(   input,   n_fft,   hop_length = NULL,   win_length = NULL,   window = NULL,   center = TRUE,   pad_mode = \"reflect\",   normalized = FALSE,   onesided = TRUE,   return_complex = NULL )"},{"path":"https://torch.mlverse.org/docs/reference/torch_stft.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Stft — torch_stft","text":"input (Tensor) input tensor n_fft (int) size Fourier transform hop_length (int, optional) distance neighboring sliding window frames. Default: NULL (treated equal floor(n_fft / 4)) win_length (int, optional) size window frame STFT filter. Default: NULL  (treated equal n_fft) window (Tensor, optional) optional window function. Default: NULL (treated window \\(1\\) s) center (bool, optional) whether pad input sides \\(t\\)-th frame centered time \\(t \\times \\mbox{hop\\_length}\\). Default: TRUE pad_mode (string, optional) controls padding method used center TRUE. Default: \"reflect\" normalized (bool, optional) controls whether return normalized STFT results Default: FALSE onesided (bool, optional) controls whether return half results avoid redundancy Default: TRUE return_complex (bool, optional) controls whether return complex tensors .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_stft.html","id":"short-time-fourier-transform-stft-","dir":"Reference","previous_headings":"","what":"Short-time Fourier transform (STFT).","title":"Stft — torch_stft","text":"Short-time Fourier transform (STFT). $$         X[m, \\omega] = \\sum_{k = 0}^{\\mbox{win\\_length-1}}%                             \\mbox{window}[k]\\ \\mbox{input}[m \\times \\mbox{hop\\_length} + k]\\ %                             \\exp\\left(- j \\frac{2 \\pi \\cdot \\omega k}{\\mbox{win\\_length}}\\right), $$ \\(m\\) index sliding window, \\(\\omega\\) frequency \\(0 \\leq \\omega < \\mbox{n\\_fft}\\). onesided default value TRUE,","code":"Ignoring the optional batch dimension, this method computes the following expression: * `input` must be either a 1-D time sequence or a 2-D batch of time   sequences.  * If `hop_length` is `NULL` (default), it is treated as equal to   `floor(n_fft / 4)`.  * If `win_length` is `NULL` (default), it is treated as equal to   `n_fft`.  * `window` can be a 1-D tensor of size `win_length`, e.g., from   `torch_hann_window`. If `window` is `NULL` (default), it is   treated as if having \\eqn{1} everywhere in the window. If   \\eqn{\\mbox{win\\_length} < \\mbox{n\\_fft}}, `window` will be padded on   both sides to length `n_fft` before being applied.  * If `center` is `TRUE` (default), `input` will be padded on   both sides so that the \\eqn{t}-th frame is centered at time   \\eqn{t \\times \\mbox{hop\\_length}}. Otherwise, the \\eqn{t}-th frame   begins at time  \\eqn{t \\times \\mbox{hop\\_length}}.  * `pad_mode` determines the padding method used on `input` when   `center` is `TRUE`. See `torch_nn.functional.pad` for   all available options. Default is `\"reflect\"`.  * If `onesided` is `TRUE` (default), only values for \\eqn{\\omega}   in \\eqn{\\left[0, 1, 2, \\dots, \\left\\lfloor \\frac{\\mbox{n\\_fft}}{2} \\right\\rfloor + 1\\right]}   are returned because the real-to-complex Fourier transform satisfies the   conjugate symmetry, i.e., \\eqn{X[m, \\omega] = X[m, \\mbox{n\\_fft} - \\omega]^*}.  * If `normalized` is `TRUE` (default is `FALSE`), the function   returns the normalized STFT results, i.e., multiplied by \\eqn{(\\mbox{frame\\_length})^{-0.5}}.  Returns the real and the imaginary parts together as one tensor of size \\eqn{(* \\times N \\times T \\times 2)}, where \\eqn{*} is the optional batch size of `input`, \\eqn{N} is the number of frequencies where STFT is applied, \\eqn{T} is the total number of frames used, and each pair in the last dimension represents a complex number as the real part and the imaginary part."},{"path":"https://torch.mlverse.org/docs/reference/torch_stft.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Stft — torch_stft","text":"function changed signature version 0.4.1. Calling previous signature may cause error return incorrect result.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sub.html","id":null,"dir":"Reference","previous_headings":"","what":"Sub — torch_sub","title":"Sub — torch_sub","text":"Sub","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sub.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sub — torch_sub","text":"","code":"torch_sub(self, other, alpha = 1L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_sub.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sub — torch_sub","text":"self (Tensor) input tensor. (Tensor Scalar) tensor scalar subtract input alpha scalar multiplier ","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sub.html","id":"sub-input-other-alpha-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"sub(input, other, *, alpha=1, out=None) -> Tensor","title":"Sub — torch_sub","text":"Subtracts , scaled alpha, input. $$     \\mbox{{}}_i = \\mbox{{input}}_i - \\mbox{{alpha}} \\times \\mbox{{}}_i $$ Supports broadcasting common shape , type promotion , integer, float, complex inputs.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sub.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sub — torch_sub","text":"","code":"if (torch_is_installed()) {  a <- torch_tensor(c(1, 2)) b <- torch_tensor(c(0, 1)) torch_sub(a, b, alpha=2) } #> torch_tensor #>  1 #>  0 #> [ CPUFloatType{2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_subtract.html","id":null,"dir":"Reference","previous_headings":"","what":"Subtract — torch_subtract","title":"Subtract — torch_subtract","text":"Subtract","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_subtract.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Subtract — torch_subtract","text":"","code":"torch_subtract(self, other, alpha = 1L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_subtract.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Subtract — torch_subtract","text":"self (Tensor) input tensor. (Tensor Scalar) tensor scalar subtract input alpha scalar multiplier ","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_subtract.html","id":"subtract-input-other-alpha-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"subtract(input, other, *, alpha=1, out=None) -> Tensor","title":"Subtract — torch_subtract","text":"Alias torch_sub().","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sum.html","id":null,"dir":"Reference","previous_headings":"","what":"Sum — torch_sum","title":"Sum — torch_sum","text":"Sum","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sum.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sum — torch_sum","text":"","code":"torch_sum(self, dim, keepdim = FALSE, dtype = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/torch_sum.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sum — torch_sum","text":"self (Tensor) input tensor. dim (int tuple ints) dimension dimensions reduce. keepdim (bool) whether output tensor dim retained . dtype (torch.dtype, optional) desired data type returned tensor.        specified, input tensor casted dtype operation        performed. useful preventing data type overflows. Default: NULL.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sum.html","id":"sum-input-dtype-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"sum(input, dtype=NULL) -> Tensor","title":"Sum — torch_sum","text":"Returns sum elements input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sum.html","id":"sum-input-dim-keepdim-false-dtype-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"sum(input, dim, keepdim=False, dtype=NULL) -> Tensor","title":"Sum — torch_sum","text":"Returns sum row input tensor given dimension dim. dim list dimensions, reduce . keepdim TRUE, output tensor size input except dimension(s) dim size 1. Otherwise, dim squeezed (see torch_squeeze), resulting output tensor 1 (len(dim)) fewer dimension(s).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sum.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sum — torch_sum","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(1, 3)) a torch_sum(a)   a <- torch_randn(c(4, 4)) a torch_sum(a, 1) b <- torch_arange(1, 4 * 5 * 6)$view(c(4, 5, 6)) torch_sum(b, list(2, 1)) } #> torch_tensor #>  1160 #>  1180 #>  1200 #>  1220 #>  1240 #>  1260 #> [ CPUFloatType{6} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_svd.html","id":null,"dir":"Reference","previous_headings":"","what":"Svd — torch_svd","title":"Svd — torch_svd","text":"Svd","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_svd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Svd — torch_svd","text":"","code":"torch_svd(self, some = TRUE, compute_uv = TRUE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_svd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Svd — torch_svd","text":"self (Tensor) input tensor size \\((*, m, n)\\) * zero                    batch dimensions consisting \\(m \\times n\\) matrices. (bool, optional) controls shape returned U V compute_uv (bool, optional) option whether compute U V ","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_svd.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Svd — torch_svd","text":"singular values returned descending order. input batch matrices, singular values matrix batch returned descending order. implementation SVD CPU uses LAPACK routine ?gesdd (divide--conquer algorithm) instead ?gesvd speed. Analogously, SVD GPU uses MAGMA routine gesdd well. Irrespective original strides, returned matrix U transposed, .e. strides U.contiguous().transpose(-2, -1).stride() Extra care needs taken backward U V outputs. operation really stable input full rank distinct singular values. Otherwise, NaN can appear gradients properly defined. Also, notice double backward usually additional backward U V even original backward S. = FALSE, gradients U[..., :, min(m, n):] V[..., :, min(m, n):] ignored backward vectors can arbitrary bases subspaces. compute_uv = FALSE, backward performed since U V forward pass required backward operation.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_svd.html","id":"svd-input-some-true-compute-uv-true-gt-tensor-tensor-tensor-","dir":"Reference","previous_headings":"","what":"svd(input, some=TRUE, compute_uv=TRUE) -> (Tensor, Tensor, Tensor)","title":"Svd — torch_svd","text":"function returns namedtuple (U, S, V) singular value decomposition input real matrix batches real matrices input \\(input = U \\times diag(S) \\times V^T\\). TRUE (default), method returns reduced singular value decomposition .e., last two dimensions input m n, returned U V matrices contain \\(min(n, m)\\) orthonormal columns. compute_uv FALSE, returned U V matrices zero matrices shape \\((m \\times m)\\) \\((n \\times n)\\) respectively. ignored .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_svd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Svd — torch_svd","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(5, 3)) a out = torch_svd(a) u = out[[1]] s = out[[2]] v = out[[3]] torch_dist(a, torch_mm(torch_mm(u, torch_diag(s)), v$t())) a_big = torch_randn(c(7, 5, 3)) out = torch_svd(a_big) u = out[[1]] s = out[[2]] v = out[[3]] torch_dist(a_big, torch_matmul(torch_matmul(u, torch_diag_embed(s)), v$transpose(-2, -1))) } #> torch_tensor #> 2.9447e-06 #> [ CPUFloatType{} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_symeig.html","id":null,"dir":"Reference","previous_headings":"","what":"Symeig — torch_symeig","title":"Symeig — torch_symeig","text":"Symeig","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_symeig.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Symeig — torch_symeig","text":"","code":"torch_symeig(self, eigenvectors = FALSE, upper = TRUE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_symeig.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Symeig — torch_symeig","text":"self (Tensor) input tensor size \\((*, n, n)\\) * zero                    batch dimensions consisting symmetric matrices. eigenvectors (boolean, optional) controls whether eigenvectors computed upper (boolean, optional) controls whether consider upper-triangular lower-triangular region","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_symeig.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Symeig — torch_symeig","text":"eigenvalues returned ascending order. input batch matrices, eigenvalues matrix batch returned ascending order. Irrespective original strides, returned matrix V transposed, .e. strides V.contiguous().transpose(-1, -2).stride(). Extra care needs taken backward outputs. operation really stable eigenvalues distinct. Otherwise, NaN can appear gradients properly defined.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_symeig.html","id":"symeig-input-eigenvectors-false-upper-true-gt-tensor-tensor-","dir":"Reference","previous_headings":"","what":"symeig(input, eigenvectors=False, upper=TRUE) -> (Tensor, Tensor)","title":"Symeig — torch_symeig","text":"function returns eigenvalues eigenvectors real symmetric matrix input batch real symmetric matrices, represented namedtuple (eigenvalues, eigenvectors). function calculates eigenvalues (vectors) input \\(\\mbox{input} = V \\mbox{diag}(e) V^T\\). boolean argument eigenvectors defines computation eigenvectors eigenvalues eigenvalues . FALSE, eigenvalues computed. TRUE, eigenvalues eigenvectors computed. Since input matrix input supposed symmetric, upper triangular portion used default. upper FALSE, lower triangular portion used.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_symeig.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Symeig — torch_symeig","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(5, 5)) a = a + a$t()  # To make a symmetric a o = torch_symeig(a, eigenvectors=TRUE) e = o[[1]] v = o[[2]] e v a_big = torch_randn(c(5, 2, 2)) a_big = a_big + a_big$transpose(-2, -1)  # To make a_big symmetric o = a_big$symeig(eigenvectors=TRUE) e = o[[1]] v = o[[2]] torch_allclose(torch_matmul(v, torch_matmul(e$diag_embed(), v$transpose(-2, -1))), a_big) } #> [1] TRUE"},{"path":"https://torch.mlverse.org/docs/reference/torch_t.html","id":null,"dir":"Reference","previous_headings":"","what":"T — torch_t","title":"T — torch_t","text":"T","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_t.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"T — torch_t","text":"","code":"torch_t(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_t.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"T — torch_t","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_t.html","id":"t-input-gt-tensor-","dir":"Reference","previous_headings":"","what":"t(input) -> Tensor","title":"T — torch_t","text":"Expects input <= 2-D tensor transposes dimensions 0 1. 0-D 1-D tensors returned . input 2-D tensor equivalent transpose(input, 0, 1).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_t.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"T — torch_t","text":"","code":"if (torch_is_installed()) {  x = torch_randn(c(2,3)) x torch_t(x) x = torch_randn(c(3)) x torch_t(x) x = torch_randn(c(2, 3)) x torch_t(x) } #> torch_tensor #> -0.1711 -0.0716 #>  0.7940  0.5677 #> -0.7778 -0.0998 #> [ CPUFloatType{3,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_take.html","id":null,"dir":"Reference","previous_headings":"","what":"Take — torch_take","title":"Take — torch_take","text":"Take","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_take.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Take — torch_take","text":"","code":"torch_take(self, index)"},{"path":"https://torch.mlverse.org/docs/reference/torch_take.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Take — torch_take","text":"self (Tensor) input tensor. index (LongTensor) indices tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_take.html","id":"take-input-index-gt-tensor-","dir":"Reference","previous_headings":"","what":"take(input, index) -> Tensor","title":"Take — torch_take","text":"Returns new tensor elements input given indices. input tensor treated viewed 1-D tensor. result takes shape indices.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_take.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Take — torch_take","text":"","code":"if (torch_is_installed()) {  src = torch_tensor(matrix(c(4,3,5,6,7,8), ncol = 3, byrow = TRUE)) torch_take(src, torch_tensor(c(1, 2, 5), dtype = torch_int64())) } #> torch_tensor #>  4 #>  3 #>  7 #> [ CPUFloatType{3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_tan.html","id":null,"dir":"Reference","previous_headings":"","what":"Tan — torch_tan","title":"Tan — torch_tan","text":"Tan","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_tan.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tan — torch_tan","text":"","code":"torch_tan(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_tan.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tan — torch_tan","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_tan.html","id":"tan-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"tan(input, out=NULL) -> Tensor","title":"Tan — torch_tan","text":"Returns new tensor tangent elements input. $$     \\mbox{}_{} = \\tan(\\mbox{input}_{}) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_tan.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tan — torch_tan","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(4)) a torch_tan(a) } #> torch_tensor #> -0.0686 #>  0.0617 #>  0.5475 #> -0.8126 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_tanh.html","id":null,"dir":"Reference","previous_headings":"","what":"Tanh — torch_tanh","title":"Tanh — torch_tanh","text":"Tanh","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_tanh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tanh — torch_tanh","text":"","code":"torch_tanh(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_tanh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tanh — torch_tanh","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_tanh.html","id":"tanh-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"tanh(input, out=NULL) -> Tensor","title":"Tanh — torch_tanh","text":"Returns new tensor hyperbolic tangent elements input. $$     \\mbox{}_{} = \\tanh(\\mbox{input}_{}) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_tanh.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tanh — torch_tanh","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(4)) a torch_tanh(a) } #> torch_tensor #>  0.7301 #> -0.1744 #> -0.9210 #> -0.6822 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_tensor.html","id":null,"dir":"Reference","previous_headings":"","what":"Converts R objects to a torch tensor — torch_tensor","title":"Converts R objects to a torch tensor — torch_tensor","text":"Converts R objects torch tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_tensor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Converts R objects to a torch tensor — torch_tensor","text":"","code":"torch_tensor(   data,   dtype = NULL,   device = NULL,   requires_grad = FALSE,   pin_memory = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/torch_tensor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Converts R objects to a torch tensor — torch_tensor","text":"data R atomic vector, matrix array dtype torch_dtype instance device device creted torch_device() requires_grad autograd record operations returned tensor. pin_memory set, returned tensor allocated pinned memory.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_tensor.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Converts R objects to a torch tensor — torch_tensor","text":"","code":"if (torch_is_installed()) { torch_tensor(c(1, 2, 3, 4)) torch_tensor(c(1, 2, 3, 4), dtype = torch_int()) } #> torch_tensor #>  1 #>  2 #>  3 #>  4 #> [ CPUIntType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_tensordot.html","id":null,"dir":"Reference","previous_headings":"","what":"Tensordot — torch_tensordot","title":"Tensordot — torch_tensordot","text":"Returns contraction b multiple dimensions. tensordot implements generalized matrix product.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_tensordot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tensordot — torch_tensordot","text":"","code":"torch_tensordot(a, b, dims = 2)"},{"path":"https://torch.mlverse.org/docs/reference/torch_tensordot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tensordot — torch_tensordot","text":"(Tensor) Left tensor contract b (Tensor) Right tensor contract dims (int tuple two lists integers) number dimensions     contract explicit lists dimensions     b respectively","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_tensordot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tensordot — torch_tensordot","text":"","code":"if (torch_is_installed()) {  a <- torch_arange(start = 1, end = 60)$reshape(c(3, 4, 5)) b <- torch_arange(start = 1, end = 24)$reshape(c(4, 3, 2)) torch_tensordot(a, b, dims = list(c(2, 1), c(1, 2))) if (FALSE) { a = torch_randn(3, 4, 5, device='cuda') b = torch_randn(4, 5, 6, device='cuda') c = torch_tensordot(a, b, dims=2)$cpu() } }"},{"path":"https://torch.mlverse.org/docs/reference/torch_threshold_.html","id":null,"dir":"Reference","previous_headings":"","what":"Threshold_ — torch_threshold_","title":"Threshold_ — torch_threshold_","text":"Threshold_","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_threshold_.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Threshold_ — torch_threshold_","text":"","code":"torch_threshold_(self, threshold, value)"},{"path":"https://torch.mlverse.org/docs/reference/torch_threshold_.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Threshold_ — torch_threshold_","text":"self input tensor threshold value threshold value value replace ","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_threshold_.html","id":"threshold-input-threshold-value-gt-tensor-","dir":"Reference","previous_headings":"","what":"threshold_(input, threshold, value) -> Tensor","title":"Threshold_ — torch_threshold_","text":"-place version torch_threshold.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_topk.html","id":null,"dir":"Reference","previous_headings":"","what":"Topk — torch_topk","title":"Topk — torch_topk","text":"Topk","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_topk.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Topk — torch_topk","text":"","code":"torch_topk(self, k, dim = -1L, largest = TRUE, sorted = TRUE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_topk.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Topk — torch_topk","text":"self (Tensor) input tensor. k (int) k \"top-k\" dim (int, optional) dimension sort along largest (bool, optional) controls whether return largest           smallest elements sorted (bool, optional) controls whether return elements           sorted order","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_topk.html","id":"topk-input-k-dim-null-largest-true-sorted-true-gt-tensor-longtensor-","dir":"Reference","previous_headings":"","what":"topk(input, k, dim=NULL, largest=TRUE, sorted=TRUE) -> (Tensor, LongTensor)","title":"Topk — torch_topk","text":"Returns k largest elements given input tensor along given dimension. dim given, last dimension input chosen. largest FALSE k smallest elements returned. namedtuple (values, indices) returned, indices indices elements original input tensor. boolean option sorted TRUE, make sure returned k elements sorted","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_topk.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Topk — torch_topk","text":"","code":"if (torch_is_installed()) {  x = torch_arange(1., 6.) x torch_topk(x, 3) } #> [[1]] #> torch_tensor #>  6 #>  5 #>  4 #> [ CPUFloatType{3} ] #>  #> [[2]] #> torch_tensor #>  6 #>  5 #>  4 #> [ CPULongType{3} ] #>"},{"path":"https://torch.mlverse.org/docs/reference/torch_trace.html","id":null,"dir":"Reference","previous_headings":"","what":"Trace — torch_trace","title":"Trace — torch_trace","text":"Trace","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_trace.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Trace — torch_trace","text":"","code":"torch_trace(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_trace.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Trace — torch_trace","text":"self input tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_trace.html","id":"trace-input-gt-tensor-","dir":"Reference","previous_headings":"","what":"trace(input) -> Tensor","title":"Trace — torch_trace","text":"Returns sum elements diagonal input 2-D matrix.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_trace.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Trace — torch_trace","text":"","code":"if (torch_is_installed()) {  x <- torch_arange(1, 9)$view(c(3, 3)) x torch_trace(x) } #> torch_tensor #> 15 #> [ CPUFloatType{} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_transpose.html","id":null,"dir":"Reference","previous_headings":"","what":"Transpose — torch_transpose","title":"Transpose — torch_transpose","text":"Transpose","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_transpose.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transpose — torch_transpose","text":"","code":"torch_transpose(self, dim0, dim1)"},{"path":"https://torch.mlverse.org/docs/reference/torch_transpose.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Transpose — torch_transpose","text":"self (Tensor) input tensor. dim0 (int) first dimension transposed dim1 (int) second dimension transposed","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_transpose.html","id":"transpose-input-dim-dim-gt-tensor-","dir":"Reference","previous_headings":"","what":"transpose(input, dim0, dim1) -> Tensor","title":"Transpose — torch_transpose","text":"Returns tensor transposed version input. given dimensions dim0 dim1 swapped. resulting tensor shares underlying storage input tensor, changing content one change content .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_transpose.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Transpose — torch_transpose","text":"","code":"if (torch_is_installed()) {  x = torch_randn(c(2, 3)) x torch_transpose(x, 1, 2) } #> torch_tensor #>  1.3229  1.7494 #>  0.3605  0.4216 #> -1.0440  0.4398 #> [ CPUFloatType{3,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_trapz.html","id":null,"dir":"Reference","previous_headings":"","what":"Trapz — torch_trapz","title":"Trapz — torch_trapz","text":"Trapz","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_trapz.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Trapz — torch_trapz","text":"","code":"torch_trapz(y, dx = 1L, x, dim = -1L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_trapz.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Trapz — torch_trapz","text":"y (Tensor) values function integrate dx (float) distance points y sampled. x (Tensor) points function y sampled.        x ascending order, intervals decreasing        contribute negatively estimated integral (.e., convention        \\(\\int_a^b f = -\\int_b^f\\) followed). dim (int) dimension along integrate.        default, use last dimension.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_trapz.html","id":"trapz-y-x-dim-gt-tensor-","dir":"Reference","previous_headings":"","what":"trapz(y, x, *, dim=-1) -> Tensor","title":"Trapz — torch_trapz","text":"Estimate \\(\\int y\\,dx\\) along dim, using trapezoid rule.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_trapz.html","id":"trapz-y-dx-dim-gt-tensor-","dir":"Reference","previous_headings":"","what":"trapz(y, *, dx=1, dim=-1) -> Tensor","title":"Trapz — torch_trapz","text":", sample points spaced uniformly distance dx.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_trapz.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Trapz — torch_trapz","text":"","code":"if (torch_is_installed()) {  y = torch_randn(list(2, 3)) y x = torch_tensor(matrix(c(1, 3, 4, 1, 2, 3), ncol = 3, byrow=TRUE)) torch_trapz(y, x = x)  } #> torch_tensor #>  0.5575 #> -0.3415 #> [ CPUFloatType{2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_triangular_solve.html","id":null,"dir":"Reference","previous_headings":"","what":"Triangular_solve — torch_triangular_solve","title":"Triangular_solve — torch_triangular_solve","text":"Triangular_solve","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_triangular_solve.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Triangular_solve — torch_triangular_solve","text":"","code":"torch_triangular_solve(   self,   A,   upper = TRUE,   transpose = FALSE,   unitriangular = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/torch_triangular_solve.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Triangular_solve — torch_triangular_solve","text":"self (Tensor) multiple right-hand sides size \\((*, m, k)\\)                \\(*\\) zero batch dimensions (\\(b\\)) (Tensor) input triangular coefficient matrix size \\((*, m, m)\\)                \\(*\\) zero batch dimensions upper (bool, optional) whether solve upper-triangular system        equations (default) lower-triangular system equations. Default: TRUE. transpose (bool, optional) whether \\(\\) transposed        sent solver. Default: FALSE. unitriangular (bool, optional) whether \\(\\) unit triangular.        TRUE, diagonal elements \\(\\) assumed        1 referenced \\(\\). Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_triangular_solve.html","id":"triangular-solve-input-a-upper-true-transpose-false-unitriangular-false-gt-tensor-tensor-","dir":"Reference","previous_headings":"","what":"triangular_solve(input, A, upper=TRUE, transpose=False, unitriangular=False) -> (Tensor, Tensor)","title":"Triangular_solve — torch_triangular_solve","text":"Solves system equations triangular coefficient matrix \\(\\) multiple right-hand sides \\(b\\). particular, solves \\(AX = b\\) assumes \\(\\) upper-triangular default keyword arguments. torch_triangular_solve(b, ) can take 2D inputs b, inputs batches 2D matrices. inputs batches, returns batched outputs X","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_triangular_solve.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Triangular_solve — torch_triangular_solve","text":"","code":"if (torch_is_installed()) {  A = torch_randn(c(2, 2))$triu() A b = torch_randn(c(2, 3)) b torch_triangular_solve(b, A) } #> [[1]] #> torch_tensor #>  0.3981 -2.4839  0.2372 #> -0.6423  4.8577 -0.3119 #> [ CPUFloatType{2,3} ] #>  #> [[2]] #> torch_tensor #> -0.9897 -0.5224 #>  0.0000 -0.3678 #> [ CPUFloatType{2,2} ] #>"},{"path":"https://torch.mlverse.org/docs/reference/torch_tril.html","id":null,"dir":"Reference","previous_headings":"","what":"Tril — torch_tril","title":"Tril — torch_tril","text":"Tril","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_tril.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tril — torch_tril","text":"","code":"torch_tril(self, diagonal = 0L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_tril.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tril — torch_tril","text":"self (Tensor) input tensor. diagonal (int, optional) diagonal consider","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_tril.html","id":"tril-input-diagonal-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"tril(input, diagonal=0, out=NULL) -> Tensor","title":"Tril — torch_tril","text":"Returns lower triangular part matrix (2-D tensor) batch matrices input, elements result tensor set 0. lower triangular part matrix defined elements diagonal. argument diagonal controls diagonal consider. diagonal = 0, elements main diagonal retained. positive value includes just many diagonals main diagonal, similarly negative value excludes just many diagonals main diagonal. main diagonal set indices \\(\\lbrace (, ) \\rbrace\\) \\(\\[0, \\min\\{d_{1}, d_{2}\\} - 1]\\) \\(d_{1}, d_{2}\\) dimensions matrix.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_tril.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tril — torch_tril","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(3, 3)) a torch_tril(a) b = torch_randn(c(4, 6)) b torch_tril(b, diagonal=1) torch_tril(b, diagonal=-1) } #> torch_tensor #>  0.0000  0.0000  0.0000  0.0000  0.0000  0.0000 #>  1.3068  0.0000  0.0000  0.0000  0.0000  0.0000 #>  1.9373 -0.9743  0.0000  0.0000  0.0000  0.0000 #> -1.0832  0.4385 -0.7025  0.0000  0.0000  0.0000 #> [ CPUFloatType{4,6} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_tril_indices.html","id":null,"dir":"Reference","previous_headings":"","what":"Tril_indices — torch_tril_indices","title":"Tril_indices — torch_tril_indices","text":"Tril_indices","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_tril_indices.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tril_indices — torch_tril_indices","text":"","code":"torch_tril_indices(   row,   col,   offset = 0,   dtype = torch_long(),   device = \"cpu\",   layout = torch_strided() )"},{"path":"https://torch.mlverse.org/docs/reference/torch_tril_indices.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tril_indices — torch_tril_indices","text":"row (int) number rows 2-D matrix. col (int) number columns 2-D matrix. offset (int) diagonal offset main diagonal.        Default: provided, 0. dtype (torch.dtype, optional) desired data type returned tensor.        Default: NULL, torch_long. device (torch.device, optional) desired device returned tensor.        Default: NULL, uses current device default tensor type        (see torch_set_default_tensor_type). device CPU        CPU tensor types current CUDA device CUDA tensor types. layout (torch.layout, optional) currently support torch_strided.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_tril_indices.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Tril_indices — torch_tril_indices","text":"","code":"When running on CUDA, `row * col` must be less than \\eqn{2^{59}} to prevent overflow during calculation."},{"path":"https://torch.mlverse.org/docs/reference/torch_tril_indices.html","id":"tril-indices-row-col-offset-dtype-torch-long-device-cpu-layout-torch-strided-gt-tensor-","dir":"Reference","previous_headings":"","what":"tril_indices(row, col, offset=0, dtype=torch.long, device='cpu', layout=torch.strided) -> Tensor","title":"Tril_indices — torch_tril_indices","text":"Returns indices lower triangular part row-- col matrix 2--N Tensor, first row contains row coordinates indices second row contains column coordinates. Indices ordered based rows columns. lower triangular part matrix defined elements diagonal. argument offset controls diagonal consider. offset = 0, elements main diagonal retained. positive value includes just many diagonals main diagonal, similarly negative value excludes just many diagonals main diagonal. main diagonal set indices \\(\\lbrace (, ) \\rbrace\\) \\(\\[0, \\min\\{d_{1}, d_{2}\\} - 1]\\) \\(d_{1}, d_{2}\\) dimensions matrix.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_tril_indices.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tril_indices — torch_tril_indices","text":"","code":"if (torch_is_installed()) { if (FALSE) { a = torch_tril_indices(3, 3) a a = torch_tril_indices(4, 3, -1) a a = torch_tril_indices(4, 3, 1) a } }"},{"path":"https://torch.mlverse.org/docs/reference/torch_triu.html","id":null,"dir":"Reference","previous_headings":"","what":"Triu — torch_triu","title":"Triu — torch_triu","text":"Triu","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_triu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Triu — torch_triu","text":"","code":"torch_triu(self, diagonal = 0L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_triu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Triu — torch_triu","text":"self (Tensor) input tensor. diagonal (int, optional) diagonal consider","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_triu.html","id":"triu-input-diagonal-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"triu(input, diagonal=0, out=NULL) -> Tensor","title":"Triu — torch_triu","text":"Returns upper triangular part matrix (2-D tensor) batch matrices input, elements result tensor set 0. upper triangular part matrix defined elements diagonal. argument diagonal controls diagonal consider. diagonal = 0, elements main diagonal retained. positive value excludes just many diagonals main diagonal, similarly negative value includes just many diagonals main diagonal. main diagonal set indices \\(\\lbrace (, ) \\rbrace\\) \\(\\[0, \\min\\{d_{1}, d_{2}\\} - 1]\\) \\(d_{1}, d_{2}\\) dimensions matrix.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_triu.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Triu — torch_triu","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(3, 3)) a torch_triu(a) torch_triu(a, diagonal=1) torch_triu(a, diagonal=-1) b = torch_randn(c(4, 6)) b torch_triu(b, diagonal=1) torch_triu(b, diagonal=-1) } #> torch_tensor #>  0.5404  0.2572  0.2881  0.0915 -0.9819  0.8741 #>  0.1111  0.0094  1.0896  1.9976 -1.2385  0.2527 #>  0.0000 -0.5850  0.3817 -1.5193  1.4128  0.6110 #>  0.0000  0.0000  0.9613  0.5291 -0.7027  2.6263 #> [ CPUFloatType{4,6} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_triu_indices.html","id":null,"dir":"Reference","previous_headings":"","what":"Triu_indices — torch_triu_indices","title":"Triu_indices — torch_triu_indices","text":"Triu_indices","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_triu_indices.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Triu_indices — torch_triu_indices","text":"","code":"torch_triu_indices(   row,   col,   offset = 0,   dtype = torch_long(),   device = \"cpu\",   layout = torch_strided() )"},{"path":"https://torch.mlverse.org/docs/reference/torch_triu_indices.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Triu_indices — torch_triu_indices","text":"row (int) number rows 2-D matrix. col (int) number columns 2-D matrix. offset (int) diagonal offset main diagonal.        Default: provided, 0. dtype (torch.dtype, optional) desired data type returned tensor.        Default: NULL, torch_long. device (torch.device, optional) desired device returned tensor.        Default: NULL, uses current device default tensor type        (see torch_set_default_tensor_type). device CPU        CPU tensor types current CUDA device CUDA tensor types. layout (torch.layout, optional) currently support torch_strided.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_triu_indices.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Triu_indices — torch_triu_indices","text":"","code":"When running on CUDA, `row * col` must be less than \\eqn{2^{59}} to prevent overflow during calculation."},{"path":"https://torch.mlverse.org/docs/reference/torch_triu_indices.html","id":"triu-indices-row-col-offset-dtype-torch-long-device-cpu-layout-torch-strided-gt-tensor-","dir":"Reference","previous_headings":"","what":"triu_indices(row, col, offset=0, dtype=torch.long, device='cpu', layout=torch.strided) -> Tensor","title":"Triu_indices — torch_triu_indices","text":"Returns indices upper triangular part row col matrix 2--N Tensor, first row contains row coordinates indices second row contains column coordinates. Indices ordered based rows columns. upper triangular part matrix defined elements diagonal. argument offset controls diagonal consider. offset = 0, elements main diagonal retained. positive value excludes just many diagonals main diagonal, similarly negative value includes just many diagonals main diagonal. main diagonal set indices \\(\\lbrace (, ) \\rbrace\\) \\(\\[0, \\min\\{d_{1}, d_{2}\\} - 1]\\) \\(d_{1}, d_{2}\\) dimensions matrix.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_triu_indices.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Triu_indices — torch_triu_indices","text":"","code":"if (torch_is_installed()) { if (FALSE) { a = torch_triu_indices(3, 3) a a = torch_triu_indices(4, 3, -1) a a = torch_triu_indices(4, 3, 1) a } }"},{"path":"https://torch.mlverse.org/docs/reference/torch_true_divide.html","id":null,"dir":"Reference","previous_headings":"","what":"TRUE_divide — torch_true_divide","title":"TRUE_divide — torch_true_divide","text":"TRUE_divide","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_true_divide.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"TRUE_divide — torch_true_divide","text":"","code":"torch_true_divide(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_true_divide.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"TRUE_divide — torch_true_divide","text":"self (Tensor) dividend (Tensor Scalar) divisor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_true_divide.html","id":"true-divide-dividend-divisor-gt-tensor-","dir":"Reference","previous_headings":"","what":"true_divide(dividend, divisor) -> Tensor","title":"TRUE_divide — torch_true_divide","text":"Performs \"true division\" always computes division floating point. Analogous division Python 3 equivalent torch_div except inputs bool integer scalar types, case cast default (floating) scalar type division. $$     \\mbox{}_i = \\frac{\\mbox{dividend}_i}{\\mbox{divisor}} $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_true_divide.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"TRUE_divide — torch_true_divide","text":"","code":"if (torch_is_installed()) {  dividend = torch_tensor(c(5, 3), dtype=torch_int()) divisor = torch_tensor(c(3, 2), dtype=torch_int()) torch_true_divide(dividend, divisor) torch_true_divide(dividend, 2) } #> torch_tensor #>  2.5000 #>  1.5000 #> [ CPUFloatType{2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_trunc.html","id":null,"dir":"Reference","previous_headings":"","what":"Trunc — torch_trunc","title":"Trunc — torch_trunc","text":"Trunc","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_trunc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Trunc — torch_trunc","text":"","code":"torch_trunc(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_trunc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Trunc — torch_trunc","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_trunc.html","id":"trunc-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"trunc(input, out=NULL) -> Tensor","title":"Trunc — torch_trunc","text":"Returns new tensor truncated integer values elements input.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_trunc.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Trunc — torch_trunc","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(4)) a torch_trunc(a) } #> torch_tensor #> -0 #>  0 #> -1 #> -1 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_unbind.html","id":null,"dir":"Reference","previous_headings":"","what":"Unbind — torch_unbind","title":"Unbind — torch_unbind","text":"Unbind","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_unbind.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Unbind — torch_unbind","text":"","code":"torch_unbind(self, dim = 1L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_unbind.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Unbind — torch_unbind","text":"self (Tensor) tensor unbind dim (int) dimension remove","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_unbind.html","id":"unbind-input-dim-gt-seq-","dir":"Reference","previous_headings":"","what":"unbind(input, dim=0) -> seq","title":"Unbind — torch_unbind","text":"Removes tensor dimension. Returns tuple slices along given dimension, already without .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_unbind.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Unbind — torch_unbind","text":"","code":"if (torch_is_installed()) {  torch_unbind(torch_tensor(matrix(1:9, ncol = 3, byrow=TRUE))) } #> [[1]] #> torch_tensor #>  1 #>  2 #>  3 #> [ CPULongType{3} ] #>  #> [[2]] #> torch_tensor #>  4 #>  5 #>  6 #> [ CPULongType{3} ] #>  #> [[3]] #> torch_tensor #>  7 #>  8 #>  9 #> [ CPULongType{3} ] #>"},{"path":"https://torch.mlverse.org/docs/reference/torch_unique_consecutive.html","id":null,"dir":"Reference","previous_headings":"","what":"Unique_consecutive — torch_unique_consecutive","title":"Unique_consecutive — torch_unique_consecutive","text":"Unique_consecutive","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_unique_consecutive.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Unique_consecutive — torch_unique_consecutive","text":"","code":"torch_unique_consecutive(   self,   return_inverse = FALSE,   return_counts = FALSE,   dim = NULL )"},{"path":"https://torch.mlverse.org/docs/reference/torch_unique_consecutive.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Unique_consecutive — torch_unique_consecutive","text":"self (Tensor) input tensor return_inverse (bool) Whether also return indices        elements original input ended returned unique list. return_counts (bool) Whether also return counts unique        element. dim (int) dimension apply unique. NULL, unique        flattened input returned. default: NULL","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_unique_consecutive.html","id":"test-","dir":"Reference","previous_headings":"","what":"TEST","title":"Unique_consecutive — torch_unique_consecutive","text":"Eliminates first element every consecutive group equivalent elements.","code":".. note:: This function is different from [`torch_unique`] in the sense that this function     only eliminates consecutive duplicate values. This semantics is similar to `std::unique`     in C++."},{"path":"https://torch.mlverse.org/docs/reference/torch_unique_consecutive.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Unique_consecutive — torch_unique_consecutive","text":"","code":"if (torch_is_installed()) { x = torch_tensor(c(1, 1, 2, 2, 3, 1, 1, 2)) output = torch_unique_consecutive(x) output torch_unique_consecutive(x, return_inverse=TRUE) torch_unique_consecutive(x, return_counts=TRUE) } #> [[1]] #> torch_tensor #>  1 #>  2 #>  3 #>  1 #>  2 #> [ CPUFloatType{5} ] #>  #> [[2]] #> torch_tensor #> [ CPULongType{0} ] #>  #> [[3]] #> torch_tensor #>  2 #>  2 #>  1 #>  2 #>  1 #> [ CPULongType{5} ] #>"},{"path":"https://torch.mlverse.org/docs/reference/torch_unsafe_chunk.html","id":null,"dir":"Reference","previous_headings":"","what":"Unsafe_chunk — torch_unsafe_chunk","title":"Unsafe_chunk — torch_unsafe_chunk","text":"Unsafe_chunk","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_unsafe_chunk.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Unsafe_chunk — torch_unsafe_chunk","text":"","code":"torch_unsafe_chunk(self, chunks, dim = 1L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_unsafe_chunk.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Unsafe_chunk — torch_unsafe_chunk","text":"self (Tensor) tensor split chunks (int) number chunks return dim (int) dimension along split tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_unsafe_chunk.html","id":"unsafe-chunk-input-chunks-dim-gt-list-of-tensors-","dir":"Reference","previous_headings":"","what":"unsafe_chunk(input, chunks, dim=0) -> List of Tensors","title":"Unsafe_chunk — torch_unsafe_chunk","text":"Works like torch_chunk() without enforcing autograd restrictions inplace modification outputs.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_unsafe_chunk.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Unsafe_chunk — torch_unsafe_chunk","text":"function safe use long input, outputs modified inplace calling function. user's responsibility ensure case. input one outputs modified inplace, gradients computed autograd silently incorrect.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_unsafe_split.html","id":null,"dir":"Reference","previous_headings":"","what":"Unsafe_split — torch_unsafe_split","title":"Unsafe_split — torch_unsafe_split","text":"Unsafe_split","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_unsafe_split.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Unsafe_split — torch_unsafe_split","text":"","code":"torch_unsafe_split(self, split_size, dim = 1L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_unsafe_split.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Unsafe_split — torch_unsafe_split","text":"self (Tensor) tensor split. split_size (int) size single chunk list sizes chunk dim (int) dimension along split tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_unsafe_split.html","id":"unsafe-split-tensor-split-size-or-sections-dim-gt-list-of-tensors-","dir":"Reference","previous_headings":"","what":"unsafe_split(tensor, split_size_or_sections, dim=0) -> List of Tensors","title":"Unsafe_split — torch_unsafe_split","text":"Works like torch_split() without enforcing autograd restrictions inplace modification outputs.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_unsafe_split.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Unsafe_split — torch_unsafe_split","text":"function safe use long input, outputs modified inplace calling function. user's responsibility ensure case. input one outputs modified inplace, gradients computed autograd silently incorrect.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_unsqueeze.html","id":null,"dir":"Reference","previous_headings":"","what":"Unsqueeze — torch_unsqueeze","title":"Unsqueeze — torch_unsqueeze","text":"Unsqueeze","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_unsqueeze.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Unsqueeze — torch_unsqueeze","text":"","code":"torch_unsqueeze(self, dim)"},{"path":"https://torch.mlverse.org/docs/reference/torch_unsqueeze.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Unsqueeze — torch_unsqueeze","text":"self (Tensor) input tensor. dim (int) index insert singleton dimension","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_unsqueeze.html","id":"unsqueeze-input-dim-gt-tensor-","dir":"Reference","previous_headings":"","what":"unsqueeze(input, dim) -> Tensor","title":"Unsqueeze — torch_unsqueeze","text":"Returns new tensor dimension size one inserted specified position. returned tensor shares underlying data tensor. dim value within range [-input.dim() - 1, input.dim() + 1) can used. Negative dim correspond unsqueeze applied dim = dim + input.dim() + 1.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_unsqueeze.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Unsqueeze — torch_unsqueeze","text":"","code":"if (torch_is_installed()) {  x = torch_tensor(c(1, 2, 3, 4)) torch_unsqueeze(x, 1) torch_unsqueeze(x, 2) } #> torch_tensor #>  1 #>  2 #>  3 #>  4 #> [ CPUFloatType{4,1} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_vander.html","id":null,"dir":"Reference","previous_headings":"","what":"Vander — torch_vander","title":"Vander — torch_vander","text":"Vander","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_vander.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Vander — torch_vander","text":"","code":"torch_vander(x, N = NULL, increasing = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_vander.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Vander — torch_vander","text":"x (Tensor) 1-D input tensor. N (int, optional) Number columns output. N specified, square array returned \\((N = len(x))\\). increasing (bool, optional) Order powers columns. TRUE, powers increase left right, FALSE (default) reversed.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_vander.html","id":"vander-x-n-none-increasing-false-gt-tensor-","dir":"Reference","previous_headings":"","what":"vander(x, N=None, increasing=FALSE) -> Tensor","title":"Vander — torch_vander","text":"Generates Vandermonde matrix. columns output matrix elementwise powers input vector \\(x^{(N-1)}, x^{(N-2)}, ..., x^0\\). increasing TRUE, order columns reversed \\(x^0, x^1, ..., x^{(N-1)}\\). matrix geometric progression row named Alexandre-Theophile Vandermonde.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_vander.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Vander — torch_vander","text":"","code":"if (torch_is_installed()) {  x <- torch_tensor(c(1, 2, 3, 5)) torch_vander(x) torch_vander(x, N=3) torch_vander(x, N=3, increasing=TRUE) } #> torch_tensor #>   1   1   1 #>   1   2   4 #>   1   3   9 #>   1   5  25 #> [ CPUFloatType{4,3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_var.html","id":null,"dir":"Reference","previous_headings":"","what":"Var — torch_var","title":"Var — torch_var","text":"Var","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_var.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Var — torch_var","text":"","code":"torch_var(self, dim, correction, unbiased = TRUE, keepdim = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_var.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Var — torch_var","text":"self (Tensor) input tensor. dim (int tuple ints) dimension dimensions reduce. correction type correction. unbiased (bool) whether use unbiased estimation keepdim (bool) whether output tensor dim retained .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_var.html","id":"var-input-unbiased-true-gt-tensor-","dir":"Reference","previous_headings":"","what":"var(input, unbiased=TRUE) -> Tensor","title":"Var — torch_var","text":"Returns variance elements input tensor. unbiased FALSE, variance calculated via biased estimator. Otherwise, Bessel's correction used.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_var.html","id":"var-input-dim-keepdim-false-unbiased-true-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"var(input, dim, keepdim=False, unbiased=TRUE, out=NULL) -> Tensor","title":"Var — torch_var","text":"Returns variance row input tensor given dimension dim. keepdim TRUE, output tensor size input except dimension(s) dim size 1. Otherwise, dim squeezed (see torch_squeeze), resulting output tensor 1 (len(dim)) fewer dimension(s). unbiased FALSE, variance calculated via biased estimator. Otherwise, Bessel's correction used.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_var.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Var — torch_var","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(1, 3)) a torch_var(a)   a = torch_randn(c(4, 4)) a torch_var(a, 1) } #> torch_tensor #>  1.6636 #>  0.3532 #>  1.7643 #>  0.6631 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_var_mean.html","id":null,"dir":"Reference","previous_headings":"","what":"Var_mean — torch_var_mean","title":"Var_mean — torch_var_mean","text":"Var_mean","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_var_mean.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Var_mean — torch_var_mean","text":"","code":"torch_var_mean(self, dim, correction, unbiased = TRUE, keepdim = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_var_mean.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Var_mean — torch_var_mean","text":"self (Tensor) input tensor. dim (int tuple ints) dimension dimensions reduce. correction type correction. unbiased (bool) whether use unbiased estimation keepdim (bool) whether output tensor dim retained .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_var_mean.html","id":"var-mean-input-unbiased-true-gt-tensor-tensor-","dir":"Reference","previous_headings":"","what":"var_mean(input, unbiased=TRUE) -> (Tensor, Tensor)","title":"Var_mean — torch_var_mean","text":"Returns variance mean elements input tensor. unbiased FALSE, variance calculated via biased estimator. Otherwise, Bessel's correction used.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_var_mean.html","id":"var-mean-input-dim-keepdim-false-unbiased-true-gt-tensor-tensor-","dir":"Reference","previous_headings":"","what":"var_mean(input, dim, keepdim=False, unbiased=TRUE) -> (Tensor, Tensor)","title":"Var_mean — torch_var_mean","text":"Returns variance mean row input tensor given dimension dim. keepdim TRUE, output tensor size input except dimension(s) dim size 1. Otherwise, dim squeezed (see torch_squeeze), resulting output tensor 1 (len(dim)) fewer dimension(s). unbiased FALSE, variance calculated via biased estimator. Otherwise, Bessel's correction used.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_var_mean.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Var_mean — torch_var_mean","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(1, 3)) a torch_var_mean(a)   a = torch_randn(c(4, 4)) a torch_var_mean(a, 1) } #> [[1]] #> torch_tensor #>  0.9536 #>  0.6696 #>  0.4329 #>  1.9070 #> [ CPUFloatType{4} ] #>  #> [[2]] #> torch_tensor #>  0.8302 #> -0.1983 #> -0.2831 #>  0.6986 #> [ CPUFloatType{4} ] #>"},{"path":"https://torch.mlverse.org/docs/reference/torch_vdot.html","id":null,"dir":"Reference","previous_headings":"","what":"Vdot — torch_vdot","title":"Vdot — torch_vdot","text":"Vdot","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_vdot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Vdot — torch_vdot","text":"","code":"torch_vdot(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_vdot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Vdot — torch_vdot","text":"self (Tensor) first tensor dot product. conjugate used complex. (Tensor) second tensor dot product.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_vdot.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Vdot — torch_vdot","text":"function broadcast .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_vdot.html","id":"vdot-input-other-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"vdot(input, other, *, out=None) -> Tensor","title":"Vdot — torch_vdot","text":"Computes dot product (inner product) two tensors. vdot(, b) function handles complex numbers differently dot(, b). first argument complex, complex conjugate first argument used calculation dot product.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_vdot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Vdot — torch_vdot","text":"","code":"if (torch_is_installed()) {  torch_vdot(torch_tensor(c(2, 3)), torch_tensor(c(2, 1))) if (FALSE) { a <- torch_tensor(list(1 +2i, 3 - 1i)) b <- torch_tensor(list(2 +1i, 4 - 0i)) torch_vdot(a, b) torch_vdot(b, a) } }"},{"path":"https://torch.mlverse.org/docs/reference/torch_view_as_complex.html","id":null,"dir":"Reference","previous_headings":"","what":"View_as_complex — torch_view_as_complex","title":"View_as_complex — torch_view_as_complex","text":"View_as_complex","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_view_as_complex.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"View_as_complex — torch_view_as_complex","text":"","code":"torch_view_as_complex(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_view_as_complex.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"View_as_complex — torch_view_as_complex","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_view_as_complex.html","id":"view-as-complex-input-gt-tensor-","dir":"Reference","previous_headings":"","what":"view_as_complex(input) -> Tensor","title":"View_as_complex — torch_view_as_complex","text":"Returns view input complex tensor. input complex tensor size \\(m1, m2, \\dots, mi, 2\\), function returns new complex tensor size \\(m1, m2, \\dots, mi\\) last dimension input tensor expected represent real imaginary components complex numbers.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_view_as_complex.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"View_as_complex — torch_view_as_complex","text":"torch_view_as_complex supported tensors torch_dtype torch_float64() torch_float32().  input expected last dimension size 2. addition, tensor must stride 1 last dimension. strides dimensions must even numbers.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_view_as_complex.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"View_as_complex — torch_view_as_complex","text":"","code":"if (torch_is_installed()) { if (FALSE) { x=torch_randn(c(4, 2)) x torch_view_as_complex(x) } }"},{"path":"https://torch.mlverse.org/docs/reference/torch_view_as_real.html","id":null,"dir":"Reference","previous_headings":"","what":"View_as_real — torch_view_as_real","title":"View_as_real — torch_view_as_real","text":"View_as_real","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_view_as_real.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"View_as_real — torch_view_as_real","text":"","code":"torch_view_as_real(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_view_as_real.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"View_as_real — torch_view_as_real","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_view_as_real.html","id":"view-as-real-input-gt-tensor-","dir":"Reference","previous_headings":"","what":"view_as_real(input) -> Tensor","title":"View_as_real — torch_view_as_real","text":"Returns view input real tensor. input complex tensor size \\(m1, m2, \\dots, mi\\), function returns new real tensor size \\(m1, m2, \\dots, mi, 2\\), last dimension size 2 represents real imaginary components complex numbers.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_view_as_real.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"View_as_real — torch_view_as_real","text":"torch_view_as_real() supported tensors complex dtypes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_view_as_real.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"View_as_real — torch_view_as_real","text":"","code":"if (torch_is_installed()) {  if (FALSE) { x <- torch_randn(4, dtype=torch_cfloat()) x torch_view_as_real(x) } }"},{"path":"https://torch.mlverse.org/docs/reference/torch_vstack.html","id":null,"dir":"Reference","previous_headings":"","what":"Vstack — torch_vstack","title":"Vstack — torch_vstack","text":"Vstack","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_vstack.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Vstack — torch_vstack","text":"","code":"torch_vstack(tensors)"},{"path":"https://torch.mlverse.org/docs/reference/torch_vstack.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Vstack — torch_vstack","text":"tensors (sequence Tensors) sequence tensors concatenate","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_vstack.html","id":"vstack-tensors-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"vstack(tensors, *, out=None) -> Tensor","title":"Vstack — torch_vstack","text":"Stack tensors sequence vertically (row wise). equivalent concatenation along first axis 1-D tensors reshaped torch_atleast_2d().","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_vstack.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Vstack — torch_vstack","text":"","code":"if (torch_is_installed()) {  a <- torch_tensor(c(1, 2, 3)) b <- torch_tensor(c(4, 5, 6)) torch_vstack(list(a,b)) a <- torch_tensor(rbind(1,2,3)) b <- torch_tensor(rbind(4,5,6)) torch_vstack(list(a,b)) } #> torch_tensor #>  1 #>  2 #>  3 #>  4 #>  5 #>  6 #> [ CPUFloatType{6,1} ]"},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/torch_where.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Where — torch_where","text":"","code":"torch_where(condition, self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_where.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Where — torch_where","text":"condition (BoolTensor) TRUE (nonzero), yield x, otherwise yield y self (Tensor) values selected indices condition TRUE (Tensor) values selected indices condition FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_where.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Where — torch_where","text":"See also torch_nonzero().","code":"The tensors `condition`, `x`, `y` must be broadcastable ."},{"path":"https://torch.mlverse.org/docs/reference/torch_where.html","id":"where-condition-x-y-gt-tensor-","dir":"Reference","previous_headings":"","what":"where(condition, x, y) -> Tensor","title":"Where — torch_where","text":"Return tensor elements selected either x y, depending condition. operation defined : $$     \\mbox{}_i = \\left\\{ \\begin{array}{ll}         \\mbox{x}_i & \\mbox{} \\mbox{condition}_i \\\\         \\mbox{y}_i & \\mbox{otherwise} \\\\     \\end{array}     \\right. $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_where.html","id":"where-condition-gt-tuple-of-longtensor-","dir":"Reference","previous_headings":"","what":"where(condition) -> tuple of LongTensor","title":"Where — torch_where","text":"torch_where(condition) identical torch_nonzero(condition, as_tuple=TRUE).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_where.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Where — torch_where","text":"","code":"if (torch_is_installed()) {  if (FALSE) { x = torch_randn(c(3, 2)) y = torch_ones(c(3, 2)) x torch_where(x > 0, x, y) }    }"},{"path":"https://torch.mlverse.org/docs/reference/torch_zeros.html","id":null,"dir":"Reference","previous_headings":"","what":"Zeros — torch_zeros","title":"Zeros — torch_zeros","text":"Zeros","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_zeros.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Zeros — torch_zeros","text":"","code":"torch_zeros(   ...,   names = NULL,   dtype = NULL,   layout = torch_strided(),   device = NULL,   requires_grad = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/torch_zeros.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Zeros — torch_zeros","text":"... sequence integers defining shape output tensor.        Can variable number arguments collection like list tuple. names optional dimension names dtype (torch.dtype, optional) desired data type returned tensor.        Default: NULL, uses global default (see torch_set_default_tensor_type). layout (torch.layout, optional) desired layout returned Tensor.        Default: torch_strided. device (torch.device, optional) desired device returned tensor.        Default: NULL, uses current device default tensor type        (see torch_set_default_tensor_type). device CPU        CPU tensor types current CUDA device CUDA tensor types. requires_grad (bool, optional) autograd record operations        returned tensor. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_zeros.html","id":"zeros-size-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-","dir":"Reference","previous_headings":"","what":"zeros(*size, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor","title":"Zeros — torch_zeros","text":"Returns tensor filled scalar value 0, shape defined variable argument size.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_zeros.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Zeros — torch_zeros","text":"","code":"if (torch_is_installed()) {  torch_zeros(c(2, 3)) torch_zeros(c(5)) } #> torch_tensor #>  0 #>  0 #>  0 #>  0 #>  0 #> [ CPUFloatType{5} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_zeros_like.html","id":null,"dir":"Reference","previous_headings":"","what":"Zeros_like — torch_zeros_like","title":"Zeros_like — torch_zeros_like","text":"Zeros_like","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_zeros_like.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Zeros_like — torch_zeros_like","text":"","code":"torch_zeros_like(   input,   dtype = NULL,   layout = torch_strided(),   device = NULL,   requires_grad = FALSE,   memory_format = torch_preserve_format() )"},{"path":"https://torch.mlverse.org/docs/reference/torch_zeros_like.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Zeros_like — torch_zeros_like","text":"input (Tensor) size input determine size output tensor. dtype (torch.dtype, optional) desired data type returned Tensor.        Default: NULL, defaults dtype input. layout (torch.layout, optional) desired layout returned tensor.        Default: NULL, defaults layout input. device (torch.device, optional) desired device returned tensor.        Default: NULL, defaults device input. requires_grad (bool, optional) autograd record operations        returned tensor. Default: FALSE. memory_format (torch.memory_format, optional) desired memory format        returned Tensor. Default: torch_preserve_format.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_zeros_like.html","id":"zeros-like-input-dtype-null-layout-null-device-null-requires-grad-false-memory-format-torch-preserve-format-gt-tensor-","dir":"Reference","previous_headings":"","what":"zeros_like(input, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, memory_format=torch.preserve_format) -> Tensor","title":"Zeros_like — torch_zeros_like","text":"Returns tensor filled scalar value 0, size input. torch_zeros_like(input) equivalent torch_zeros(input.size(), dtype=input.dtype, layout=input.layout, device=input.device).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_zeros_like.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Zeros_like — torch_zeros_like","text":"0.4, function support keyword. alternative, old torch_zeros_like(input, =output) equivalent torch_zeros(input.size(), =output).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_zeros_like.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Zeros_like — torch_zeros_like","text":"","code":"if (torch_is_installed()) {  input = torch_empty(c(2, 3)) torch_zeros_like(input) } #> torch_tensor #>  0  0  0 #>  0  0  0 #> [ CPUFloatType{2,3} ]"},{"path":"https://torch.mlverse.org/docs/reference/with_detect_anomaly.html","id":null,"dir":"Reference","previous_headings":"","what":"Context-manager that enable anomaly detection for the autograd engine. — with_detect_anomaly","title":"Context-manager that enable anomaly detection for the autograd engine. — with_detect_anomaly","text":"two things:","code":""},{"path":"https://torch.mlverse.org/docs/reference/with_detect_anomaly.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Context-manager that enable anomaly detection for the autograd engine. — with_detect_anomaly","text":"","code":"with_detect_anomaly(code)"},{"path":"https://torch.mlverse.org/docs/reference/with_detect_anomaly.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Context-manager that enable anomaly detection for the autograd engine. — with_detect_anomaly","text":"code Cod execued detect anomaly context.","code":""},{"path":"https://torch.mlverse.org/docs/reference/with_detect_anomaly.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Context-manager that enable anomaly detection for the autograd engine. — with_detect_anomaly","text":"Running forward pass detection enabled allow backward pass print traceback forward operation created failing backward function. backward computation generate \"nan\" value raise error.","code":""},{"path":"https://torch.mlverse.org/docs/reference/with_detect_anomaly.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Context-manager that enable anomaly detection for the autograd engine. — with_detect_anomaly","text":"mode enabled debugging different tests slow program execution.","code":""},{"path":"https://torch.mlverse.org/docs/reference/with_detect_anomaly.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Context-manager that enable anomaly detection for the autograd engine. — with_detect_anomaly","text":"","code":"if (torch_is_installed()) { x <- torch_randn(2, requires_grad = TRUE) y <- torch_randn(1) b <- (x^y)$sum() y$add_(1)  try({   b$backward()    with_detect_anomaly({     b$backward()   }) }) } #> Error in (function (self, inputs, gradient, retain_graph, create_graph)  :  #>   one of the variables needed for gradient computation has been modified by an inplace operation: [CPUFloatType [1]] is at version 1; expected version 0 instead. Hint: enable anomaly detection to find the operation that failed to compute its gradient, with torch.autograd.set_detect_anomaly(True). #> Exception raised from unpack at ../torch/csrc/autograd/saved_variable.cpp:164 (most recent call first): #> frame #0: c10::Error::Error(c10::SourceLocation, std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char> >) + 98 (0x10bfce522 in libc10.dylib) #> frame #1: c10::detail::torchCheckFail(char const*, char const*, unsigned int, std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char> > const&) + 106 (0x10bfccc3a in libc10.dylib) #> frame #2: torch::autograd::SavedVariable::unpack(std::__1::shared_ptr<torch::autograd::Node>) const + 2546 (0x1135a7f82 in libtorch_cpu.dylib) #> frame #3: torch::autograd::generated::PowBackward1::apply(std::__1::vector<at::Tensor, std::__1::allocator<at::Tensor> >&&) + 237 (0x112c6475d in libtorch_cpu.dylib) #> frame #4: torch::autograd::Node::operator()(std::__1::vector<at::Tensor, std::__1::allocator<at::Tensor> >&&) + 407 (0x11356c237 in libtorch_cpu.dylib) #> frame #5: torch::autograd::Engine::evaluate_function(std::__1::shared_ptr<torch::autograd::GraphTask>&, torch::autograd::Node*, torch::autograd::InputBuffer&, std::__1::shared_ptr<torch::autograd::ReadyQueue> const&) + 1897 (0x113561b99 in libtorch_cpu.dylib) #> frame #6: torch::autograd::Engine::thread_main(std::__1::shared_ptr<torch::autograd::GraphTask> const&) + 1961 (0x113560dc9 in libtorch_cpu.dylib) #> frame #7: torch::autograd::Engine::execute_with_graph_task(std::__1::shared_ptr<torch::autograd::GraphTask> const&, std::__1::shared_ptr<torch::autograd::Node>, torch::autograd::InputBuffer&&) + 1139 (0x11356ac23 in libtorch_cpu.dylib) #> frame #8: torch::autograd::Engine::execute(std::__1::vector<torch::autograd::Edge, std::__1::allocator<torch::autograd::Edge> > const&, std::__1::vector<at::Tensor, std::__1::allocator<at::Tensor> > const&, bool, bool, bool, std::__1::vector<torch::autograd::Edge, std::__1::allocator<torch::autograd::Edge> > const&) + 2339 (0x1135687e3 in libtorch_cpu.dylib) #> frame #9: torch::autograd::run_backward(std::__1::vector<at::Tensor, std::__1::allocator<at::Tensor> > const&, std::__1::vector<at::Tensor, std::__1::allocator<at::Tensor> > const&, bool, bool, std::__1::vector<at::Tensor, std::__1::allocator<at::Tensor> > const&, bool, bool) + 2167 (0x1135533b7 in libtorch_cpu.dylib) #> frame #10: torch::autograd::backward(std::__1::vector<at::Tensor, std::__1::allocator<at::Tensor> > const&, std::__1::vector<at::Tensor, std::__1::allocator<at::Tensor> > const&, c10::optional<bool>, bool, std::__1::vector<at::Tensor, std::__1::allocator<at::Tensor> > const&) + 100 (0x113553914 in libtorch_cpu.dylib) #> frame #11: torch::autograd::VariableHooks::_backward(at::Tensor const&, c10::ArrayRef<at::Tensor>, c10::optional<at::Tensor> const&, c10::optional<bool>, bool) const + 455 (0x1135acab7 in libtorch_cpu.dylib) #> frame #12: at::Tensor::_backward(c10::ArrayRef<at::Tensor>, c10::optional<at::Tensor> const&, c10::optional<bool>, bool) const + 75 (0x110900f9b in libtorch_cpu.dylib) #> frame #13: _lantern_Tensor__backward_tensor_tensorlist_tensor_bool_bool + 286 (0x10b9ca90e in liblantern.dylib) #> frame #14: std::__1::__function::__func<cpp_torch_method__backward_self_Tensor_inputs_TensorList(XPtrTorchTensor, XPtrTorchTensorList, XPtrTorchOptionalTensor, XPtrTorchoptional_bool, XPtrTorchbool)::$_0, std::__1::allocator<cpp_torch_method__backward_self_Tensor_inputs_TensorList(XPtrTorchTensor, XPtrTorchTensorList, XPtrTorchOptionalTensor, XPtrTorchoptional_bool, XPtrTorchbool)::$_0>, void ()>::operator()() + 53 (0x10b1c7d85 in torchpkg.so) #> frame #15: std::__1::packaged_task<void ()>::operator()() + 72 (0x10b1c6628 in torchpkg.so) #> frame #16: void* std::__1::__thread_proxy<std::__1::tuple<std::__1::unique_ptr<std::__1::__thread_struct, std::__1::default_delete<std::__1::__thread_struct> >, (anonymous namespace)::schedule_backward_task(std::__1::packaged_task<void ()>&&)::$_7> >(void*) + 132 (0x10b1c64a4 in torchpkg.so) #> frame #17: _pthread_start + 224 (0x7fff2045b8fc in libsystem_pthread.dylib) #> frame #18: thread_start + 15 (0x7fff20457443 in libsystem_pthread.dylib) #>"},{"path":"https://torch.mlverse.org/docs/reference/with_enable_grad.html","id":null,"dir":"Reference","previous_headings":"","what":"Enable grad — with_enable_grad","title":"Enable grad — with_enable_grad","text":"Context-manager enables gradient calculation. Enables gradient calculation, disabled via with_no_grad.","code":""},{"path":"https://torch.mlverse.org/docs/reference/with_enable_grad.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Enable grad — with_enable_grad","text":"","code":"with_enable_grad(code)"},{"path":"https://torch.mlverse.org/docs/reference/with_enable_grad.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Enable grad — with_enable_grad","text":"code code executed gradient recording.","code":""},{"path":"https://torch.mlverse.org/docs/reference/with_enable_grad.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Enable grad — with_enable_grad","text":"context manager thread local; affect computation threads.","code":""},{"path":"https://torch.mlverse.org/docs/reference/with_enable_grad.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Enable grad — with_enable_grad","text":"","code":"if (torch_is_installed()) {  x <- torch_tensor(1, requires_grad = TRUE) with_no_grad({   with_enable_grad({     y <- x * 2   }) }) y$backward() x$grad } #> torch_tensor #>  2 #> [ CPUFloatType{1} ]"},{"path":"https://torch.mlverse.org/docs/reference/with_no_grad.html","id":null,"dir":"Reference","previous_headings":"","what":"Temporarily modify gradient recording. — with_no_grad","title":"Temporarily modify gradient recording. — with_no_grad","text":"Temporarily modify gradient recording.","code":""},{"path":"https://torch.mlverse.org/docs/reference/with_no_grad.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Temporarily modify gradient recording. — with_no_grad","text":"","code":"with_no_grad(code)"},{"path":"https://torch.mlverse.org/docs/reference/with_no_grad.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Temporarily modify gradient recording. — with_no_grad","text":"code code executed gradient recording.","code":""},{"path":"https://torch.mlverse.org/docs/reference/with_no_grad.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Temporarily modify gradient recording. — with_no_grad","text":"","code":"if (torch_is_installed()) { x <- torch_tensor(runif(5), requires_grad = TRUE) with_no_grad({   x$sub_(torch_tensor(as.numeric(1:5))) }) x x$grad } #> torch_tensor #> [ Tensor (undefined) ]"},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"torch-070","dir":"Changelog","previous_headings":"","what":"torch 0.7.0","title":"torch 0.7.0","text":"CRAN release: 2022-02-18","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"breaking-changes-0-7-0","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"torch 0.7.0","text":"torch_sort Tensor$sort now return 1-indexed results. (#709, @mohamed-180) Support LibTorch 1.10.2. See also release notes PyTorch v1.10. (#758, #763, #775, @hsbadr). Changed default dim 1 2 nnf_cosine_similarity. (#769) default value arguments various functions changed. bug code generation truncating default values specially float values needed 6 digit precision. (#770)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"new-features-0-7-0","dir":"Changelog","previous_headings":"","what":"New features","title":"torch 0.7.0","text":"jit_save_for_mobile allows save traced model bytecode form, loaded LiteModuleLoader. (#713) Exported is_torch_tensor check wether object tensor . (#730, @rdinnager) Adds cuda_get_device_properties(device) allows one query device capability properties. (#734, @rdinnager) Implemented call_torch_function() allow calling potentially unexported torch core functions. (#743, @rdinnager) Now installing torch LibTorch Lantern headers installed within inst directory. allow packages extending torch bind directly C++ library. (#718) dataset_subset use .getbatch method wrapped dataset one available. (#742, @egillax) Added nn_flatten nn_unflatten modules. (#773) Added cuda_memory_stats() cuda_memory_summary() verify amount memory torch using GPU. (#774) Added backends_cudnn_version() query CuDNN version found torch. (#774)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"bug-fixes-0-7-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"torch 0.7.0","text":"Fixed bug .validate_sample Distribution class incorrectly check tensors. (#739, @hsbadr) Fixed memory leak applying custom autograd_functions. (#750) Fixed bug caused autograd_grad deadlock used custom autograd functions. (#771) Fixed bug torch_max torch_min fail length=2 Tensors. (#772)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"documentation-0-7-0","dir":"Changelog","previous_headings":"","what":"Documentation","title":"torch 0.7.0","text":"Improved ‘Loading data’ vignette datasets documentation. (#780, @jnolis)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"internal-0-7-0","dir":"Changelog","previous_headings":"","what":"Internal","title":"torch 0.7.0","text":"Refactored internal Lantern types Rcpp types made clearer exported types can used C++ extensions. (#718) Simplified concurrency related constructs autograd. (#755, @yitao-li) R C++ code cleanup, styling, formatting. (#753, @hsbadr) Dataloaders slightly faster new transpose function. (#783) torch_tensor now C++ function slighly increasing performance situations. (#784)","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"new-features-0-6-1","dir":"Changelog","previous_headings":"","what":"New features","title":"torch 0.6.1","text":"Fixed valgrind errors CRAN requiring recent version knitr. Updated LibTorch version 1.9.1 (#725 @hsbadr) now check lantern DLL’s loaded calling lantern function. avoids segfaults Lantern installed. (#723).","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"torch-060","dir":"Changelog","previous_headings":"","what":"torch 0.6.0","title":"torch 0.6.0","text":"CRAN release: 2021-10-07","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"breaking-changes-0-6-0","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"torch 0.6.0","text":"nn_sequential now bare nn_module, allowing easily inherit . breaking change used name argument. name behavior can achieved subclassing; see tests PR. (#699)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"new-features-0-6-0","dir":"Changelog","previous_headings":"","what":"New features","title":"torch 0.6.0","text":"Additional info showed printing tensors like requires grad grad fn. (#668, #669, #673, @mohamed-180) can now subset nn_sequential modules using [. (#678, @mohamed-180) now allow padding='' padding='valid' using convolutions. (#679) nnf_cross_entropy now uses ATen cross_entropy operation directly instead logsoftmax + NLLLoss. (#680) Inherited classes now persisted subclasses. specially useful subclass nn_sequential still want specific S3 methods still work. (#701)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"bug-fixes-0-6-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"torch 0.6.0","text":"Fixed bug indexing numeric vectors. (#693, @mohamed-180) Fixed bug indexing tensors ellipsis tensor. (#696)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"documentation-0-6-0","dir":"Changelog","previous_headings":"","what":"Documentation","title":"torch 0.6.0","text":"Improved optimizer documentation adding ‘Warning’ regarding creation usage order. (#698)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"torch-050","dir":"Changelog","previous_headings":"","what":"torch 0.5.0","title":"torch 0.5.0","text":"CRAN release: 2021-08-17","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"breaking-changes-0-5-0","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"torch 0.5.0","text":"Droped support CUDA 10.1 (#610) torch_manual_seed() now matches PyTorch’s behavior can easily compare implementations. Since breaking change added torch.old_seed_behavior=TRUE option users can stick old behavior. (#639) Indexing vectors now behavior R indexing, making easier understand. Users can still use old behavior using torch_index torch_index_put. (#649)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"new-features-0-5-0","dir":"Changelog","previous_headings":"","what":"New features","title":"torch 0.5.0","text":"Added support ScriptModule. Loaded JIT modules now operate nn_modules. (#593) Added jit_compile function allows compiling arbitrary TorchScript code script function can serialized executed. (#601) Added jit_trace support nn_module created R. (#604) Updated LibTorch version 1.9.0 (#610) Added Linear Algebra functions (#612) Added contrib_sort_vertices efficiently sort vertices CUDA. (#619) Allows querying graph traced modules. (#623) Added with_detect_anomaly debug autograd errors. (#628) Implemented traced_module$graph_for() allow inspecting optimized jit graph. (#643) Added slc allow dynamically creating slices indexing tensors. (#648)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"bug-fixes-0-5-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"torch 0.5.0","text":"Fixed bug using .getbatch method didn’t return torch_tensor. (#615) Fixed warning using %/% caused call deprecated torch_floor_divide (#616) Improved CUDA version auto-detection (#644)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"internal-changes-0-5-0","dir":"Changelog","previous_headings":"","what":"Internal changes","title":"torch 0.5.0","text":"Improved R <-> JIT types conversion. (#593) Added Dockerfiles CUDA 11.1 (#597) warning raised incompatible dataset passed parallel dataloader. (#626) Additionally calling gc CUDA memory exhausted now call R_RunPendingFinalizers. improve memory usage, now delete tensors earlier. (#654) Fix rchk issues (#667)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"torch-040","dir":"Changelog","previous_headings":"","what":"torch 0.4.0","title":"torch 0.4.0","text":"CRAN release: 2021-06-10","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"breaking-changes-0-4-0","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"torch 0.4.0","text":"torch_multinomial now returns 1-based indexes comply 1-based indexing across torch. (#588)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"new-features-0-4-0","dir":"Changelog","previous_headings":"","what":"New features","title":"torch 0.4.0","text":"Added parameter multihead attention module allow output unaveraged attention weights. (@jonathanbratt #542) now allow jit_trace functions 1 argument. (#544) Added Multivariate normal distribution (#552) Export torch_diff function added docs . (#565) Added device argument torch_load() allowing one select device parameters loaded. (#578) Added distr_categorical() (#576) Added distr_mixture_same_family() (#576) Improve handling optimizers state implement load_state_dict() state_dict() optimizers. (#585) Added ability save R lists containing torch_tensors using torch_save. allows us save state optimizers modules using torch_save(). (#586)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"bug-fixes-0-4-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"torch 0.4.0","text":"Fixed bug nn_multihead_attention q,k,v inputs . (@jonathanbratt #540) Fixed $copy_ correctly respects src requires_grad() reloading saved models torch_load(). (#545) Fixed nn_init_xavier_normal_() nn_init_xavier_uniform_() standard deviation calculation. (#557) Fixed bug torch_tensordot() called infering dimensions. (#563) Dataset’s .getbatch now takes integer vector input instead list(). (#572) Fixed bug tensor$size() indexing negative numbers. (#570) Fixed bug log_prob distr_bernoulli() (#581)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"internal-changes-0-4-0","dir":"Changelog","previous_headings":"","what":"Internal changes","title":"torch 0.4.0","text":"Better handling optional Tensor arguments using explicit XPtrTorchOptionalTensor class. (#565) Tensors R side point C++ Tensor now guaranteed object. allows easily determine unique model parameters. (#582)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"torch-030","dir":"Changelog","previous_headings":"","what":"torch 0.3.0","title":"torch 0.3.0","text":"CRAN release: 2021-04-28","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"breaking-changes-0-3-0","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"torch 0.3.0","text":"torch_nonzero tensor$nonzero() now return 1-based indexes. (#432) Breaking change: torch_arange returns closed interval [start, end] instead half open [start, end). makes behave similar R’s seq. (#506)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"new-features-0-3-0","dir":"Changelog","previous_headings":"","what":"New features","title":"torch 0.3.0","text":"torch_split now accepts list sizes well fixed size. (#429) Added nn_layer_norm. (#435) Allow timeout=360 install_torch() parameter large file download (@cregouby #438) Added install_torch_from_file() get_install_libs_url()setup cases direct download possible (@cregouby #439) Added mean.torch_tensor (#448) New arguments worker_globals worker_packages allowing easily pass objects workers parallel dataloaders (#449). now call R garbage collector ’s memory available GPU, can help cases laziness garbage collector allows many tensors memory even though longer referenced R. (#456) Implemented nn_group_norm fixed bug nnf_group_norm (#474) Added backend functions allowing us query optimizations LibTorch compiled (#476) Added normal distribution (#462) Added bernoulli distribution (#484) .list nn_modules (#492) Enumerate support Bernoulli distribution (#490) Added Poisson Distriibution (#495) Allow optional .getbatch datasets/dataloaders (#498) nn_lstm, nn_gru nn_gru can now use cudnn accelerations available (#503). Added Gamma distribution (#489) now respect TORCH_HOME env var automatically install torch. (#522) Implement comparison operator != torch dtypes. (#524) Added Chi-square distribution. (#518) Added optimizer function allowing easily implement custom optimizers. (#527)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"bug-fixes-0-3-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"torch 0.3.0","text":"Fixed bug optim_lbfgs make model objects exponentially big. (#431) Correctly handle NaNs L-BFGS optimizer (#433) default collate function now respects data type converting tensor (dataset returns R object) (#434) Fixed torch_normal. (#450) Fixed backward compatibility issue loading models saved older versions torch. bug introduced #452 now fixed also added regression test. (#458) Fixed bug using RNN’s GPU (#460) Found fixed memory leaks, specially creating datatypes strings saving models torch_save. (#454) Fixed bug nnf_pad using mode='circular'. (#471) Bugfixes nn_multihead_attention (#496) Fixed bug using packed sequences nn_lstm (#500) Fixed bug method nn_module reset requires_grad attribute parameters. (#501) Added strong_wolfe option optim_lbfgs. (#517) Fixed default argument nn_init_trunc_normal_ initializer function. 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-- 
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